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Purpose: To develop a machine learning algorithm, using patient-reported data from early pregnancy, to predict later onset of first time moderate-to-severe depression. Methods: A sample of 944 U.S. patient participants from a larger longitudinal observational cohortused a prenatal support mobile app from September 2019 to April 2022. Participants self-reported clinical and social risk factors during first trimester initiation of app use and completed voluntary depression screenings in each trimester. Several machine learning algorithms were applied to self-reported data, including a novel algorithm for causal discovery. Training and test datasets were built from a randomized 80/20 data split. Models were evaluated on their predictive accuracy and their simplicity (i.e., fewest variables required for prediction). Results: Among participants, 78% identified as white with an average age of 30 [IQR 26–34]; 61% had income ≥ $50,000; 70% had a college degree or higher; and 49% were nulliparous. All models accurately predicted first time moderate-severe depression using first trimester baseline data (AUC 0.74–0.89, sensitivity 0.35–0.81, specificity 0.78–0.95). Several predictors were common across models, including anxiety history, partnered status, psychosocial factors, and pregnancy-specific stressors. The optimal model used only 14 (26%) of the possible variables and had excellent accuracy (AUC = 0.89, sensitivity = 0.81, specificity = 0.83). When food insecurity reports were included among a subset of participants, demographics, including race and income, dropped out and the model became more accurate (AUC = 0.93) and simpler (9 variables). Conclusion: A relatively small amount of self-report data produced a highly predictive model of first time depression among pregnant individuals. [ABSTRACT FROM AUTHOR]

Many individuals with serious mental illness are at high risk for hospitalization or death due to inadequate treatment of medical conditions or unhealthy behaviors. The authors describe demographic and clinical characteristics associated with increased risk in this population. Electronic data were obtained for individuals in treatment at a large Veterans' healthcare system who were at high risk according to a validated model. A random sample of these individuals was assessed in person. Multivariable regressions estimated the effect of numerous demographic, health, and clinical characteristics on risk. Emergency visits and hospitalizations were common. Greater risk was associated with being male, not married, and having more diagnoses. While risk varied by race, this effect was no longer significant after controlling for other factors. Health-related quality of life worsened with increasing risk. Routine data identify a large population of high-risk individuals who may benefit from outreach to provide healthcare services. [ABSTRACT FROM AUTHOR]

Objective Maternal risk stratification systems are increasingly employed in predicting and preventing obstetric complications. These systems focus primarily on maternal morbidity, and few tools exist to stratify neonatal risk. We sought to determine if a maternal risk stratification score was associated with neonatal morbidity. Study Design Retrospective cohort study of patients with liveborn infants born at ≥24 weeks at four hospitals in one health system between January 1, 2020, and December 31, 2020. The Expanded Obstetric Comorbidity Score (EOCS) is used as the maternal risk score. The primary neonatal outcome was 5-minute Apgar <7. Logistic regression models determined associations between EOCS and neonatal morbidity. Secondary analyses were performed, including stratifying outcomes by gestational age and limiting analysis to "low-risk" term singletons. Model discrimination assessed using the area under the receiver operating characteristic curves (AUC) and calibration via calibration plots. Results A total of 14,497 maternal–neonatal pairs were included; 236 (1.6%) had 5-minute Apgar <7; EOCS was higher in 5-minute Apgar <7 group (median 41 vs. 11, p < 0.001). AUC for EOCS in predicting Apgar <7 was 0.72 (95% Confidence Interval (CI) 0.68, 0.75), demonstrating relatively good discrimination. Calibration plot revealed that those in the highest EOCS decile had higher risk of neonatal morbidity (7.6 vs. 1.7%, p < 0.001). When stratified by gestational age, discrimination weakened with advancing gestational age: AUC 0.70 for <28 weeks, 0.63 for 28 to 31 weeks, 0.64 for 32 to 36 weeks, and 0.61 for ≥37 weeks. When limited to term low-risk singletons, EOCS had lower discrimination for predicting neonatal morbidity and was not well calibrated. Conclusion A maternal morbidity risk stratification system does not perform well in most patients giving birth, at low risk for neonatal complications. The findings suggest that the association between EOCS and 5-minute Apgar <7 likely reflects a relationship with prematurity. This study cautions against intentional or unintentional extrapolation of maternal morbidity risk for neonatal risk, especially for term deliveries. Key Points EOCS had moderate discrimination for Apgar <7. Predictive performance declined when limited to low-risk term singletons. Relationship between EOCS and Apgar <7 was likely driven by prematurity. [ABSTRACT FROM AUTHOR]

Background: The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR‐based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. Methods: This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. Results: A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83–0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98–12.87). Conclusions: This simple EHR‐based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well‐being of older adults at risk of critical illness and its consequences. See related article by Haimovich and Deardorff. [ABSTRACT FROM AUTHOR]

Background: Elucidation of the interaction of biological and psychosocial/environmental factors on opioid dependence (OD) risk can inform our understanding of the etiology of OD. We examined the role of psychosocial/environmental factors in moderating polygenic risk for opioid use disorder (OUD). Methods: Data from 1958 European ancestry adults who participated in the Yale-Penn 3 study were analyzed. Polygenic risk scores (PRS) were based on a large-scale multi-trait analysis of genome-wide association studies (MTAG) of OUD. Results: A total of 420 (21.1%) individuals had a lifetime diagnosis of OD. OUD PRS were positively associated with OD (odds ratio [OR] 1.42, 95% confidence interval [CI] 1.21–1.66). Household income and education were the strongest correlates of OD. Among individuals with higher OUD PRS, those with higher education level had lower odds of OD (OR 0.92, 95% CI 0.85–0.98); and those with posttraumatic stress disorder (PTSD) were more likely to have OD relative to those without PTSD (OR 1.56, 95% CI 1.04–2.35). Conclusions: Results suggest an interplay between genetics and psychosocial environment in contributing to OD risk. While PRS alone do not yet have useful clinical predictive utility, psychosocial factors may help enhance prediction. These findings could inform more targeted clinical and policy interventions to help address this public health crisis. [ABSTRACT FROM AUTHOR]

Though currently considered a 'black box,' machine learning (ML) has a promising future to ameliorate the health-care burden of stroke which is the second leading cause of mortality worldwide. Through this study, we sought to review the most influential articles on the applications of ML in stroke. Web of Sciences database was searched, and a list of the top 50 most cited articles, assessing the application of ML in stroke, was prepared by 2 authors, independently. Subsequently, a detailed analysis was performed to characterize the most impactful studies. The total number of citations to the top 50 articles were 2959 (range 35–243 citations) with a median of 47 citations. Highest number of articles were published in the journal Stroke and the United States was the major contributing country. The majority of the studies focused on the utilization of ML to improve stroke risk prediction, diagnosis, and outcome prediction. Statistical analysis revealed an insignificant association between the total and mean number of citations and the impact factor of the journal (P = 0.516 and 0.987, respectively). Recent years have witnessed a surge in the application of ML in stroke, with an enhancement in interest and funding over the years. ML has revolutionized the management of stroke and continues to aid in the neurosurgical decision-making and care in stroke patients. [ABSTRACT FROM AUTHOR]

Severe coronavirus disease 2019 (COVID‐19) is a hyperinflammatory syndrome. The biomarkers of inflammation best suited to triage patients with COVID‐19 are unknown. We conducted a prospective multicenter observational study of adult patients hospitalized specifically for COVID‐19 from February 1, 2020 to October 19, 2022. Biomarkers measured included soluble urokinase plasminogen activator receptor (suPAR), C‐reactive protein, interleukin‐6, procalcitonin, ferritin, and D‐dimer. In‐hospital outcomes examined include death and the need for mechanical ventilation. Patients admitted in the United States (US, n = 1962) were used to compute area under the curves (AUCs) and identify biomarker cutoffs. The combined European cohorts (n = 1137) were used to validate the biomarker cutoffs. In the US cohort, 356 patients met the composite outcome of death (n = 197) or need for mechanical ventilation (n = 290). SuPAR was the most important predictor of the composite outcome and had the highest AUC (0.712) followed by CRP (0.642), ferritin (0.619), IL‐6 (0.614), D‐dimer (0.606), and lastly procalcitonin (0.596). Inclusion of other biomarkers did not improve discrimination. A suPAR cutoff of 4.0 ng/mL demonstrated a sensitivity of 95.4% (95% CI: 92.4%–98.0%) and negative predictive value (NPV) of 92.5% (95% CI: 87.5%–96.9%) for the composite outcome. Patients with suPAR < 4.0 ng/mL comprised 10.6% of the cohort and had a 0.8% probability of the composite outcome. Applying this cutoff to the validation cohort yielded a sensitivity of 93.8% (90.4%–96.7%) and NPV of 95.5% (93.1%–97.8%) for the composite outcome. Among commonly measured biomarkers, suPAR offered stronger discriminatory ability and may be useful in triaging low‐risk patients with COVID‐19. [ABSTRACT FROM AUTHOR]

Backgro und: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases. Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics. Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov–Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types. Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible. [ABSTRACT FROM AUTHOR]

Background: Existing models to predict fall‐related injuries (FRI) in nursing homes (NH) focus on hip fractures, yet hip fractures comprise less than half of all FRIs. We developed and validated a series of models to predict the absolute risk of FRIs in NH residents. Methods: Retrospective cohort study of long‐stay US NH residents (≥100 days in the same facility) between January 1, 2016 and December 31, 2017 (n = 733,427) using Medicare claims and Minimum Data Set v3.0 clinical assessments. Predictors of FRIs were selected through LASSO logistic regression in a 2/3 random derivation sample and tested in a 1/3 validation sample. Sub‐distribution hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated for 6‐month and 2‐year follow‐up. Discrimination was evaluated via C‐statistic, and calibration compared the predicted rate of FRI to the observed rate. To develop a parsimonious clinical tool, we calculated a score using the five strongest predictors in the Fine‐Gray model. Model performance was repeated in the validation sample. Results: Mean (Q1, Q3) age was 85.0 (77.5, 90.6) years and 69.6% were women. Within 2 years of follow‐up, 43,976 (6.0%) residents experienced ≥1 FRI. Seventy predictors were included in the model. The discrimination of the 2‐year prediction model was good (C‐index = 0.70), and the calibration was excellent. Calibration and discrimination of the 6‐month model were similar (C‐index = 0.71). In the clinical tool to predict 2‐year risk, the five characteristics included independence in activities of daily living (ADLs) (HR 2.27; 95% CI 2.14–2.41) and a history of non‐hip fracture (HR 2.02; 95% CI 1.94–2.12). Performance results were similar in the validation sample. Conclusions: We developed and validated a series of risk prediction models that can identify NH residents at greatest risk for FRI. In NH, these models should help target preventive strategies. [ABSTRACT FROM AUTHOR]

This study examined the relationship between the systemic inflammation response index (SIRI) and kidney stone occurrence in adults in the United States. It also evaluated its potential as a predictor of kidney stones. A total of 24,833 adult participants were included in the study using cross-sectional data from the 2007-2018 National Health and Nutrition Examination Survey (NHANES) database. A history of kidney stones was ascertained through the administration of a questionnaire, and SIRI values (calculated based on neutrophil, monocyte, and lymphocyte counts) were determined for each participant. Logistic regression models were employed to examine the relationship between SIRI and kidney stones while accounting for potential confounding variables such as gender, age, race, lifestyle, and history of chronic disease. Subgroup analyses were also conducted. A significant positive correlation was observed between SIRI and kidney stones. In the unadjusted model, elevated SIRI was significantly and positively associated with an increased risk of kidney stones (OR = 1.17). Analysis of SIRI quartiles demonstrated a gradual increase in the risk ratio of kidney stones with increasing SIRI levels, indicating a clear dose-response relationship. In particular, in the model adjusted for multiple confounding variables, the risk of developing kidney stones in the highest SIRI quartile was increased by 20% compared to the lowest SIRI quartile (OR = 1.20, P = 0.007). There is a significant positive correlation between SIRI and kidney stones. SIRI may predict kidney stone risk and highlight the systemic inflammatory state's substantial contribution to kidney stones' pathogenesis., Competing Interests: Declarations. Ethical approval: The studies involving humans were approved by the National Center for Health Statistics Ethics Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Informed consent: Informed consent was obtained from all subjects involved in the study. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Describe the rate of death over 4 consecutive quarters and determine optimal categorization of residents into risk-of-death categories, expanding the Changes in Health, Endstage Disease, Signs and Symptoms (CHESS) scale. Using secondary analysis design with Minimum Data Set (MDS) data, the CHESS scale provided the base upon which the DeathRisk-NH scale was developed. Baseline and 4 quarterly follow-up analyses of Canadian (n = 109,145) and US (n = 1,075,611) nursing home resident data were completed. Logistic regression analyses identified predictors of death, additive to CHESS, to form the DeathRisk-NH scale. The independent variable set used MDS items, focusing on clinical complexity indicators, diagnostic conditions, and measures of severe clinical distress. Country cohorts had similar percentages of residents with mean activities of daily living hierarchy scores, dependence in mobility, continence, memory, and overall CHESS scores. The percentage of individuals who died increased from 10.5% (3 months) to 30.7% (12 months). The average annual death rate for this cohort was 5.5 times higher than the national annual death rate of approximately 5.6%. The DeathRisk-NH is an effective prediction model to identify residents at risk of death within the first 12 months after admission to the nursing home. The tool may be helpful in patient care planning, resource allocation, and excess death monitoring. [ABSTRACT FROM AUTHOR]

Despite declines in total cardiovascular mortality rates in the United States, heart failure (HF) mortality rates as well as hospitalizations and readmissions have increased in the past decade. Increases have been relatively higher among young and middle‐aged adults (<65 years). Therefore, identification of individuals HF at‐risk (Stage A) or with pre‐HF (Stage B) before the onset of overt clinical signs and symptoms (Stage C) is urgently needed. Multivariate risk models (e.g., Pooled Cohort Equations to Prevent Heart Failure [PCP‐HF]) have been externally validated in diverse populations and endorsed by the 2022 HF Guidelines to apply a risk‐based framework for the prevention of HF. However, traditional risk factors included in the PCP‐HF model only account for half of an individual's lifetime risk of HF; novel risk factors (e.g., adverse pregnancy outcomes, impaired lung health, COVID‐19) are emerging as important risk‐enhancing factors that need to be accounted for in personalized approaches to prevention. In addition to determining the role of novel risk‐enhancing factors, integration of social determinants of health (SDoH) in identifying and addressing HF risk is needed to transform the current clinical paradigm for the prevention of HF. Comprehensive strategies to prevent the progression of HF must incorporate pharmacotherapies (e.g., sodium glucose co‐transporter‐2 inhibitors that have also been termed the "statins" of HF prevention), intensive blood pressure lowering, and heart‐healthy behaviors. Future directions include investigation of novel prediction models leveraging machine learning, integration of risk‐enhancing factors and SDoH, and equitable approaches to interventions for risk‐based prevention of HF. [ABSTRACT FROM AUTHOR]

Heart failure (HF) is a major cause of mortality and morbidity in the United States that carries substantial healthcare costs. Multiple risk prediction models and strategies have been developed over the past 30 years with the aim of identifying those at high risk of developing HF and of implementing preventive therapies effectively. This review highlights recent developments in HF risk prediction tools including emerging risk factors, innovative risk prediction models, and novel screening strategies from artificial intelligence to biomarkers. These developments allow more accurate prediction, but their impact on clinical outcomes remains to be investigated. Implementation of these risk models in clinical practice is a considerable challenge, but HF risk prediction tools offer a promising opportunity to improve outcomes while maintaining value., Competing Interests: Declaration of competing interest Dr. Fonarow reports consulting for Abbott, Amgen, AstraZeneca, Bayer, Cytokinetics, Eli Lilly, Janssen, Medtronic, Merck, Novartis, and Pfizer. Dr. Gulati reports consulting for Esperion, Medtronic, and Novartis. The remaining authors have no competing interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Despite the high burden of diabetes and cardiovascular risk factors in American Indian communities in the United States, prospective studies of heart failure (HF) in this population group are scarce, and the generalizability of previous HF risk scales may be limited. We developed a parsimonious HF risk prediction equation that accounts for relevant risk factors affecting American Indian communities, focusing on diabetes and kidney damage., Methods and Results: A total of 3059 participants from the SHS (Strong Heart Study) (56±8 years of age, 58% women) were included. Five hundred seven developed HF. Progressively adjusted Cox proportional hazards models were used to identify risk factors for HF and HF subtypes. Predictors of risk at 5 and 10 years included older age (hazard ratio [HR], 1.79 [95% CI, 1.43-2.25]; HR, 1.68 [95% CI, 1.44-1.95]), smoking (HR, 2.26 [95% CI, 1.23-4.13]; HR, 2.08 [95% CI, 1.41-3.06]), macroalbuminuria (HR, 8.38 [95% CI, 4.44-15.83]; HR, 5.20 [95% CI, 3.42-7.9]), microalbuminuria (HR, 2.72 [95% CI, 1.51-4.90]; HR, 1.92 [95% CI, 1.33, 2.78]), and previous myocardial infarction (HR, 6.58 [95% CI, 2.54-17.03]; HR, 3.87 [95% CI, 2.29-6.54]), respectively. These predictors, together with diabetes diagnosis and glycated hemoglobin were significant at 10 and 28 years. High discrimination performance was achieved (C index, 0.81 [95% CI, 0.76-0.84]; C index, 0.78 [95% CI, 0.75-0.81]; and C index, 0.77 [95% CI, 0.74-0.78] at 5, 10, and up to 28 years of follow up, respectively). Some associations varied across HF subtypes, although diabetes, albuminuria, and previous myocardial infarction were associated with all subtypes., Conclusions: This prospective study of HF risk factors in American Indian communities identifies that smoking, body mass index, and indicators of diabetes control and kidney damage (glycated hemoglobin and albuminuria) are major determinants of HF. Our findings can improve HF risk assessment in populations with a high burden of diabetes.

Introduction: Patients with chronic limb-threatening ischemia (CLTI) have high mortality rates after revascularization. Risk stratification for short-term outcomes is challenging. We aimed to develop machine-learning models to rank predictive variables for 30-day and 90-day all-cause mortality after peripheral vascular intervention (PVI)., Methods: Patients undergoing PVI for CLTI in the Medicare-linked Vascular Quality Initiative were included. Sixty-six preprocedural variables were included. Random survival forest (RSF) models were constructed for 30-day and 90-day all-cause mortality in the training sample and evaluated in the testing sample. Predictive variables were ranked based on the frequency that they caused branch splitting nearest the root node by importance-weighted relative importance plots. Model performance was assessed by the Brier score, continuous ranked probability score, out-of-bag error rate, and Harrell's C-index., Results: A total of 10,114 patients were included. The crude mortality rate was 4.4% at 30 days and 10.6% at 90 days. RSF models commonly identified stage 5 chronic kidney disease (CKD), dementia, congestive heart failure (CHF), age, urgent procedures, and need for assisted care as the most predictive variables. For both models, eight of the top 10 variables were either medical comorbidities or functional status variables. Models showed good discrimination (C-statistic 0.72 and 0.73) and calibration (Brier score 0.03 and 0.10)., Conclusion: RSF models for 30-day and 90-day all-cause mortality commonly identified CKD, dementia, CHF, need for assisted care at home, urgent procedures, and age as the most predictive variables as critical factors in CLTI. Results may help guide individualized risk-benefit treatment conversations regarding PVI., Competing Interests: Declaration of conflicting interestsThe authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr Mena-Hurtado reports unrestricted research grants from Philips and Shockwave, and is a consultant for Abbott Vascular, Cook, Medtronic, and Optum Labs; Dr Smolderen reports unrestricted research grants from Philips, Merck, Shockwave, and Johnson & Johnson; she is a consultant for Optum Labs, Cook, Tegus, Twill Inc., and Abbott Vascular. The other authors report no competing interests.

Supplemental digital content is available in the text. Background: About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. Objectives: The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes). Methods: This study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation). Results: Best performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories. Discussion: This study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources. [ABSTRACT FROM AUTHOR]

Background: Early hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.Aims: We leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.Methods: A national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.Results: We analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.Conclusion: Our model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination. [ABSTRACT FROM AUTHOR]

Head and neck cancer (HNC) risk prediction models based on risk factor profiles have not yet been developed. We took advantage of the large database of the International Head and Neck Cancer Epidemiology (INHANCE) Consortium, including 14 US studies from 1981–2010, to develop HNC risk prediction models. Seventy percent of the data were used to develop the risk prediction models; the remaining 30% were used to validate the models. We used competing-risk models to calculate absolute risks. The predictors included age, sex, education, race/ethnicity, alcohol drinking intensity, cigarette smoking duration and intensity, and/or family history of HNC. The 20-year absolute risk of HNC was 7.61% for a 60-year-old woman who smoked more than 20 cigarettes per day for over 20 years, consumed 3 or more alcoholic drinks per day, was a high school graduate, had a family history of HNC, and was non-Hispanic white. The 20-year risk for men with a similar profile was 6.85%. The absolute risks of oropharyngeal and hypopharyngeal cancers were generally lower than those of oral cavity and laryngeal cancers. Statistics for the area under the receiver operating characteristic curve (AUC) were 0.70 or higher, except for oropharyngeal cancer in men. This HNC risk prediction model may be useful in promoting healthier behaviors such as smoking cessation or in aiding persons with a family history of HNC to evaluate their risks. [ABSTRACT FROM AUTHOR]

Objective: Identification of SCD risk is important in the general population from a public health perspective. The objective is to summarize and appraise the available prediction models for the risk of SCD among the general population. Methods: Data were obtained searching six electronic databases and reporting prediction models of SCD risk in the general population. Studies with duplicate cohorts and missing information were excluded from the meta-analysis. Results: Out of 8,407 studies identified, fifteen studies were included in the systematic review, while five studies were included in the meta-analysis. The Cox proportional hazards model was used in thirteen studies (96.67%). Study locations were limited to Europe and the United States. Our pooled meta-analyses included four predictors: diabetes mellitus (ES = 2.69, 95%CI: 1.93, 3.76), QRS duration (ES = 1.16, 95%CI: 1.06, 1.26), spatial QRS-T angle (ES = 1.46, 95%CI: 1.27, 1.69) and factional shortening (ES = 1.37, 95%CI: 1.15, 1.64). Conclusion: Risk prediction model may be useful as an adjunct for risk stratification strategies for SCD in the general population. Further studies among people except for white participants and more accessible factors are necessary to explore., Competing Interests: The authors declare that they do not have any conflicts of interest., (Copyright © 2024 Li, Liu, Liu, Li, Mei, Fan and Cao.)

Introduction: The prevalence of diabetes, a common chronic disease, has shown a gradual increase, posing substantial burdens on both society and individuals. In order to enhance the effectiveness of diabetes risk prediction questionnaires, optimize the selection of characteristic variables, and raise awareness of diabetes risk among residents, this study utilizes survey data obtained from the risk factor monitoring system of the Centers for Disease Control and Prevention in the United States., Methods: Following univariate analysis and meticulous screening, a more refined dataset was constructed. This dataset underwent preprocessing steps, including data distribution standardization, the application of the Synthetic Minority Oversampling Technique (SMOTE) in combination with the Round function for equilibration, and data standardization. Subsequently, machine learning (ML) techniques were employed, utilizing enumerated feature variables to evaluate the strength of the correlation among diabetes risk factors., Results: The research findings effectively delineated the ranking of characteristic variables that significantly influence the risk of diabetes. Obesity emerges as the most impactful factor, overshadowing other risk factors. Additionally, psychological factors, advanced age, high cholesterol, high blood pressure, alcohol abuse, coronary heart disease or myocardial infarction, mobility difficulties, and low family income exhibit correlations with diabetes risk to varying degrees., Discussion: The experimental data in this study illustrate that, while maintaining comparable accuracy, optimization of questionnaire variables and the number of questions can significantly enhance efficiency for subsequent follow-up and precise diabetes prevention. Moreover, the research methods employed in this study offer valuable insights into studying the risk correlation of other diseases, while the research results contribute to heightened societal awareness of populations at elevated risk of diabetes., Competing Interests: JW was employed by the company E-link Wisdom Co., Ltd., Shenzhen, China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Jiang, Yang, Liu, Xia, Yang, Gong, Wang and Wang.)

Objective: The HIV epidemic remains a significant public health issue in the United States. HIV risk prediction models could be beneficial for reducing HIV transmission by helping clinicians identify patients at high risk for infection and refer them for testing. This would facilitate initiation on treatment for those unaware of their status and pre-exposure prophylaxis for those uninfected but at high risk. Existing HIV risk prediction algorithms rely on manual construction of features and are limited in their application across diverse electronic health record systems. Furthermore, the accuracy of these models in predicting HIV in females has thus far been limited., Materials and Methods: We devised a pipeline for automatic construction of prediction models based on automatic feature engineering to predict HIV risk and tested our pipeline on a local electronic health records system and a national claims data. We also compared the performance of general models to female-specific models., Results: Our models obtain similarly good performance on both health record datasets despite difference in represented populations and data availability (AUC = 0.87). Furthermore, our general models obtain good performance on females but are also improved by constructing female-specific models (AUC between 0.81 and 0.86 across datasets)., Discussion and Conclusions: We demonstrated that flexible construction of prediction models performs well on HIV risk prediction across diverse health records systems and perform as well in predicting HIV risk in females, making deployment of such models into existing health care systems tangible., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: American College of Cardiology/American Heart Association guidelines recommend distinct risk classification systems for primary and secondary cardiovascular disease prevention. However, both systems rely on similar predictors (eg, age and diabetes), indicating the possibility of a universal risk prediction approach for major adverse cardiovascular events (MACEs)., Objectives: The authors examined the performance of predictors in persons with and without atherosclerotic cardiovascular disease (ASCVD) and developed and validated a universal risk prediction model., Methods: Among 9,138 ARIC (Atherosclerosis Risk In Communities) participants with (n = 609) and without (n = 8,529) ASCVD at baseline (1996-1998), we examined established predictors in the risk classification systems and other predictors, such as body mass index and cardiac biomarkers (troponin and natriuretic peptide), using Cox models with MACEs (myocardial infarction, stroke, and heart failure). We also evaluated model performance., Results: Over a follow-up of approximately 20 years, there were 3,209 MACEs (2,797 for no prior ASCVD). Most predictors showed similar associations with MACE regardless of baseline ASCVD status. A universal risk prediction model with the predictors (eg, established predictors, cardiac biomarkers) identified by least absolute shrinkage and selection operator regression and bootstrapping showed good discrimination for both groups (c-statistics of 0.747 and 0.691, respectively), and risk classification and showed excellent calibration, irrespective of ASCVD status. This universal prediction approach identified individuals without ASCVD who had a higher risk than some individuals with ASCVD and was validated externally in 5,322 participants in the MESA (Multi-Ethnic Study of Atherosclerosis)., Conclusions: A universal risk prediction approach performed well in persons with and without ASCVD. This approach could facilitate the transition from primary to secondary prevention by streamlining risk classification and discussion between clinicians and patients., Competing Interests: Funding Support and Author Disclosures The ARIC study has been funded in whole or in part with Federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract nos. (75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, 75N92022D00005). The MESA was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org. The authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Cancer survivors may have elevated atherosclerotic cardiovascular disease (ASCVD) risk. Therefore, we tested how accurately the American College of Cardiology/American Heart Association 2013 pooled cohort equations (PCEs) predict 10-year ASCVD risk in cancer survivors., Objectives: To estimate the calibration and discrimination of the PCEs in cancer survivors compared to non-cancer participants in the Atherosclerosis Risk in Communities (ARIC) study., Methods: We evaluated the PCEs' performance among 1244 cancer survivors and 3849 cancer-free participants who were free of ASCVD at the start of follow-up. Each cancer survivor was incidence-density matched with up to five controls by age, race, sex, and study center. Follow-up began at the first study visit at least 1 year after the diagnosis date of the cancer survivor and finished at the ASCVD event, death, or end of follow-up. Calibration and discrimination were assessed and compared between cancer survivors and cancer-free participants., Results: Cancer survivors had higher PCE-predicted risk, at 26.1%, compared with 23.1% for cancer-free participants. There were 110 ASCVD events in cancer survivors and 332 ASCVD events in cancer-free participants. The PCEs overestimated ASCVD risk in cancer survivors and cancer-free participants by 45.6% and 47.4%, respectively, with poor discrimination in both groups (C-statistic for cancer survivors = 0.623; for cancer-free participants, C = 0.671)., Conclusions: The PCEs overestimated ASCVD risk in all participants. The performance of the PCEs was similar in cancer survivors and cancer-free participants., Implications for Cancer Survivors: Our findings suggest that ASCVD risk prediction tools tailored to survivors of adult cancers may not be needed., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

With the rising incidence of heart failure (HF) and increasing burden of morbidity, mortality, and healthcare expenditures, primary prevention of HF targeting individuals in at-risk HF (Stage A) and pre-HF (Stage B) Stages has become increasingly important with the goal to decrease progression to symptomatic (Stage C) HF. Identification of risk based on traditional risk factors (e.g., cardiovascular health which can be assessed with the American Heart Association's Life's Essential 8 framework), adverse social determinants of health, inherited risk of cardiomyopathies, and identification of risk-enhancing factors, such as patients with viral disease, exposure to cardiotoxic chemotherapy, and history of adverse pregnancy outcomes should be the first step in evaluation for HF risk. Next, use of guideline-endorsed risk prediction tools such as Pooled Cohort Equations to Prevent Heart Failure provide quantification of absolute risk of HF based in traditional risk factors. Risk reduction through counseling on traditional risk factors is a core focus of implementation of prevention and may include the use of novel therapeutics that target specific pathways to reduce risk of HF, such as mineralocorticoid receptor agonists (e.g., fineronone), angiotensin-receptor/neprolysin inhibitors, and sodium glucose co-transporter-2 inhibitors. These interventions may be limited in at-risk populations who experience adverse social determinants and/or individuals who reside in rural areas. Thus, strategies like telemedicine may improve access to preventive care. Gaps in the current knowledge base for risk-based prevention of HF are highlighted to outline future research that may target approaches for risk assessment and risk-based prevention with the use of artificial intelligence, genomics-enhanced strategies, and pragmatic trials to develop a guideline-directed medical therapy approach to reduce risk among individuals with Stage A and Stage B HF., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Liver transplantation is a life-saving procedure for patients with end-stage liver disease. There are two main challenges in liver transplant: finding the best matching patient for a donor and ensuring transplant equity among different subpopulations. The current MELD scoring system evaluates a patient's mortality risk if not receiving an organ within 90 days. However, the donor-patient matching should also consider post-transplant risk factors, such as cardiovascular disease, chronic rejection, etc., which are all common complications after transplant. Accurate prediction of these risk scores remains a significant challenge. In this study, we used predictive models to solve the above challenges. Specifically, we proposed a deep learning model to predict multiple risk factors after a liver transplant. By formulating it as a multi-task learning problem, the proposed deep neural network was trained to simultaneously predict the five post-transplant risks and achieve equal good performance by exploiting task-balancing techniques. We also proposed a novel fairness-achieving algorithm to ensure prediction fairness across different subpopulations. We used electronic health records of 160,360 liver transplant patients, including demographic information, clinical variables, and laboratory values, collected from the liver transplant records of the United States from 1987 to 2018. The model's performance was evaluated using various performance metrics such as AUROC and AUPRC. Our experiment results highlighted the success of our multi-task model in achieving task balance while maintaining accuracy. The model significantly reduced the task discrepancy by 39 %. Further application of the fairness-achieving algorithm substantially reduced fairness disparity among all sensitive attributes (gender, age group, and race/ethnicity) in each risk factor. It underlined the potency of integrating fairness considerations into the task-balancing framework, ensuring robust and fair predictions across multiple tasks and diverse demographic groups., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The new heart allocation system in the United States prioritizes patients supported by temporary mechanical circulatory support (TMCS) devices over those with uncomplicated durable continuous-flow left ventricular assist devices (CF-LVADs), which may increase the number of patients bridged to transplant with TMCS. Limited data are available in guiding post-transplant outcomes with various TMCS devices. We sought to describe post-transplant outcome and identify clinical variables associated with post-transplant outcome in patients bridged to transplant with TMCS. Using data from the International Society for Heart and Lung Transplantation Thoracic Transplant Registry, we included subjects who underwent transplantation between 2005 and 2016 with known use of mechanical circulatory support. Pre-transplant recipient, donor, and transplant-specific variables were abstracted. The primary outcome was patient survival at 1-year post-transplant. Outcomes of patients bridged to transplant with TMCS were compared with those of patients bridged with CF-LVADs. Cox regression analyses were performed to identify clinical variables associated with the outcomes. There were 6,528 patients bridged to transplant with the following types of mechanical circulatory support: durable CF-LVADs (n = 6,206), extracorporeal membrane oxygenation (ECMO, n = 134), percutaneous temporary CF-LVADs (n = 75), surgically implanted temporary CF-LVADs (n = 38) or surgically implanted temporary BiVAD (n = 75). Bridging with ECMO (hazard ratio 3.79, 95% confidence interval [CI] 2.69–5.34, p < 0.001) or percutaneous temporary CF-LVADs (hazard ratio 1.83, 95% CI 1.09–3.08, p = 0.02) was independently associated with higher risk of mortality. Additional risk factors included older donor age, female/male donor-recipient match, older recipient age, higher recipient body mass index, higher recipient creatinine, and prolonged ischemic time. This analysis of a large international cohort of patients bridged to transplant with mechanical circulatory support identified ECMO and percutaneous temporary CF-LVADs as predictors of mortality after transplant, along with additional donor and recipient clinical characteristics. These findings may provide guidance to clinicians in decisions on mechanical circulatory support device selection, transplant eligibility, and timing of transplant. [ABSTRACT FROM AUTHOR]

Cardiovascular-kidney-metabolic health reflects the interplay among metabolic risk factors, chronic kidney disease, and the cardiovascular system and has profound impacts on morbidity and mortality. There are multisystem consequences of poor cardiovascular-kidney-metabolic health, with the most significant clinical impact being the high associated incidence of cardiovascular disease events and cardiovascular mortality. There is a high prevalence of poor cardiovascular-kidney-metabolic health in the population, with a disproportionate burden seen among those with adverse social determinants of health. However, there is also a growing number of therapeutic options that favorably affect metabolic risk factors, kidney function, or both that also have cardioprotective effects. To improve cardiovascular-kidney-metabolic health and related outcomes in the population, there is a critical need for (1) more clarity on the definition of cardiovascular-kidney-metabolic syndrome; (2) an approach to cardiovascular-kidney-metabolic staging that promotes prevention across the life course; (3) prediction algorithms that include the exposures and outcomes most relevant to cardiovascular-kidney-metabolic health; and (4) strategies for the prevention and management of cardiovascular disease in relation to cardiovascular-kidney-metabolic health that reflect harmonization across major subspecialty guidelines and emerging scientific evidence. It is also critical to incorporate considerations of social determinants of health into care models for cardiovascular-kidney-metabolic syndrome and to reduce care fragmentation by facilitating approaches for patient-centered interdisciplinary care. This presidential advisory provides guidance on the definition, staging, prediction paradigms, and holistic approaches to care for patients with cardiovascular-kidney-metabolic syndrome and details a multicomponent vision for effectively and equitably enhancing cardiovascular-kidney-metabolic health in the population.

Purpose: Mental illness (MI) and substance use disorders (SUD) are highly prevalent among people living with HIV (PLWH), and have been linked to poor HIV clinical outcomes. Innovative tools for early risk identification can facilitate timely interventions for PLWH and MI/SUD to improve their health outcomes, however, this is currently lacking in Texas, a state with the 4 th largest population of PLWH in the United States. To address this gap, we developed a predictive model to estimate the risk of suboptimal HIV clinical outcomes among PLWH and MI/SUD in Texas., Methods: The Texas Medical Monitoring Project data obtained from June 2015-May 2020 were used to develop and internally validate the predictive model. Univariate descriptive and bivariate inferential statistics were performed to describe the characteristics of the study population and unadjusted associations with HIV clinical outcomes. Multivariable logistic regression was used to develop the prediction model. Internal validation was performed using the bootstrap method., Results: A total of 518 respondents aged 18 years and above, representing 27,255 adults living with HIV and mental illness or substance use disorders in Texas were included. Most participants were male (77.0%), less than 50 years of age (60.0%), and had mild diagnosed mental illness and substance use disorder (54.8%). The risk predictive model contained eight predictors, which together yielded an area under the receiver operating characteristic (ROC) curve of 0.727. Non-retention in care appeared to be the strongest risk predictor for having suboptimal HIV clinical outcome (adjusted odds ratio (aOR) = 3.27; 95% confidence interval (CI) = 1.45, 7.42)., Conclusions: The predictive model had good discrimination between persons at risk of poor HIV clinical outcomes and those not at risk., Competing Interests: Declaration of Competing Interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2023 Elsevier Inc. All rights reserved.)

The utility of polygenic risk score (PRS) models has not been comprehensively evaluated for childhood acute lymphoblastic leukemia (ALL), the most common type of cancer in children. Previous PRS models for ALL were based on significant loci observed in genome-wide association studies (GWASs), even though genomic PRS models have been shown to improve prediction performance for a number of complex diseases. In the United States, Latino (LAT) children have the highest risk of ALL, but the transferability of PRS models to LAT children has not been studied. In this study, we constructed and evaluated genomic PRS models based on either non-Latino White (NLW) GWAS or a multi-ancestry GWAS. We found that the best PRS models performed similarly between held-out NLW and LAT samples (PseudoR 2  = 0.086 ± 0.023 in NLW vs. 0.060 ± 0.020 in LAT), and can be improved for LAT if we performed GWAS in LAT-only (PseudoR 2  = 0.116 ± 0.026) or multi-ancestry samples (PseudoR 2  = 0.131 ± 0.025). However, the best genomic models currently do not have better prediction accuracy than a conventional model using all known ALL-associated loci in the literature (PseudoR 2  = 0.166 ± 0.025), which includes loci from GWAS populations that we could not access to train genomic PRS models. Our results suggest that larger and more inclusive GWASs may be needed for genomic PRS to be useful for ALL. Moreover, the comparable performance between populations may suggest a more oligogenic architecture for ALL, where some large effect loci may be shared between populations. Future PRS models that move away from the infinite causal loci assumption may further improve PRS for ALL., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Our work involves assessing whether new biomarkers might be useful for cervical-cancer screening across populations with different disease prevalences and biomarker distributions. When comparing across populations, we show that standard diagnostic accuracy statistics (predictive values, risk-differences, Youden's index and Area Under the Curve (AUC)) can easily be misinterpreted. We introduce an intuitively simple statistic for a 2 × 2 table, Mean Risk Stratification (MRS): the average change in risk (pre-test vs. post-test) revealed for tested individuals. High MRS implies better risk separation achieved by testing. MRS has 3 key advantages for comparing test performance across populations with different disease prevalences and biomarker distributions. First, MRS demonstrates that conventional predictive values and the risk-difference do not measure risk-stratification because they do not account for test-positivity rates. Second, Youden's index and AUC measure only multiplicative relative gains in risk-stratification: AUC = 0.6 achieves only 20% of maximum risk-stratification (AUC = 0.9 achieves 80%). Third, large relative gains in risk-stratification might not imply large absolute gains if disease is rare, demonstrating a "high-bar" to justify population-based screening for rare diseases such as cancer. We illustrate MRS by our experience comparing the performance of cervical-cancer screening tests in China vs. the USA. The test with the worst AUC = 0.72 in China (visual inspection with acetic acid) provides twice the risk-stratification (i.e. MRS) of the test with best AUC = 0.83 in the USA (human papillomavirus and Pap cotesting) because China has three times more cervical precancer/cancer. MRS could be routinely calculated to better understand the clinical/public-health implications of standard diagnostic accuracy statistics. [ABSTRACT FROM AUTHOR]

Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States. [ABSTRACT FROM AUTHOR]

Background: Data suggest that the prediction of adult cardiovascular disease using a model comprised entirely of adult nonlaboratory-based risk factors is equivalent to an approach that additionally incorporates adult lipid measures. We assessed and compared the utility of a risk model based solely on nonlaboratory risk factors in adolescence versus a lipid model based on nonlaboratory risk factors plus lipids for predicting high-risk carotid intima-media thickness (cIMT) in adulthood.Methods: The study comprised 2893 participants 12 to 18 years of age from 4 longitudinal cohort studies from the United States (Bogalusa Heart Study and the Insulin Study), Australia (Childhood Determinants of Adult Health Study), and Finland (The Cardiovascular Risk in Young Finns Study) and followed into adulthood when cIMT was measured (mean follow-up, 23.4 years). Overweight status was defined according to the Cole classification. Hypertension was defined according to the Fourth Report on High Blood Pressure in Children and Adolescents from the National High Blood Pressure Education Program. High-risk plasma lipid levels were defined according to the National Cholesterol Education Program Expert Panel on Cholesterol Levels in Children. High cIMT was defined as a study-specific value ≥90th percentile. Age and sex were included in each model.Results: In univariate models, all risk factors except for borderline high and high triglycerides in adolescence were associated with high cIMT in adulthood. In multivariable models (relative risk [95% confidence interval]), male sex (2.7 [2.0-2.6]), prehypertension (1.4 [1.0-1.9]), hypertension (1.9 [1.3-2.9]), overweight (2.0 [1.4-2.9]), obesity (3.7 [2.0-7.0]), borderline high low-density lipoprotein cholesterol (1.6 [1.2-2.2]), high low-density lipoprotein cholesterol (1.6 [1.1-2.1]), and borderline low high-density lipoprotein cholesterol (1.4 [1.0-1.8]) remained significant predictors of high cIMT (P<0.05). The addition of lipids into the nonlaboratory risk model slightly but significantly improved discrimination in predicting high cIMT compared with nonlaboratory-based risk factors only (C statistics for laboratory-based model 0.717 [95% confidence interval, 0.685-0.748] and for nonlaboratory 0.698 [95% confidence interval, 0.667-0.731]; P=0.02).Conclusions: Nonlaboratory-based risk factors and lipids measured in adolescence independently predicted preclinical atherosclerosis in young adulthood. The addition of lipid measurements to traditional clinic-based risk factor assessment provided a statistically significant but clinically modest improvement on adolescent prediction of high cIMT in adulthood. [ABSTRACT FROM AUTHOR]

Background: Interstitial lung disease (ILD) is an important cause of morbidity and mortality in patients with scleroderma (Scl). Risk prediction and prognostication in patients with Scl-ILD are challenging because of heterogeneity in the disease course.Methods: We aimed to develop a clinical mortality risk prediction model for Scl-ILD. Patients with Scl-ILD were identified from two ongoing longitudinal cohorts: 135 patients at the University of California, San Francisco (derivation cohort) and 90 patients at the Mayo Clinic (validation cohort). Using these two separate cohorts, a mortality risk prediction model was developed and validated by testing every potential candidate Cox model, each including three or four variables of a possible 19 clinical predictors, for time to death. Model discrimination was assessed using the C-index.Results: Three variables were included in the final risk prediction model (SADL): ever smoking history, age, and diffusing capacity of the lung for carbon monoxide (% predicted). This continuous model had similar performance in the derivation (C-index, 0.88) and validation (C-index, 0.84) cohorts. We created a point scoring system using the combined cohort (C-index, 0.82) and used it to identify a classification with low, moderate, and high mortality risk at 3 years.Conclusions: The SADL model uses simple, readily accessible clinical variables to predict all-cause mortality in Scl-ILD. [ABSTRACT FROM AUTHOR]

Aims: The need for a readily available, inexpensive, non-invasive method for improved risk stratification of heart failure (HF) patients is paramount. Prior studies have proposed that distinct fluctuation patterns underlying the variability of physiological signals have unique prognostic value. We tested this hypothesis in an extensively phenotyped cohort of HF patients using EntropyXQT, a novel non-linear measure of cardiac repolarization dynamics.Methods and Results: In a prospective, multicentre, observational study of 852 patients in sinus rhythm undergoing clinically indicated primary prevention implantable cardioverter-defibrillator (ICD) implantation (2003-10), exposures included demographics, history, physical examination, medications, laboratory results, serum biomarkers, ejection fraction, conventional electrocardiographic (ECG) analyses of heart rate and QT variability, and EntropyXQT. The primary outcome was first 'appropriate' ICD shock for ventricular arrhythmias. The secondary outcome was composite events (appropriate ICD shock and all-cause mortality). After exclusions, the cohort (n = 816) had a mean age of 60 ± 13 years, 28% women, 36% African Americans, 56% ischaemic cardiomyopathy, and 29 ± 16% Seattle HF risk score (SHFS) 5-year predicted mortality. Over 45 ± 24 months, there were 134 appropriate shocks and 166 deaths. After adjusting for 30 exposures, the hazard ratios (comparing the 5th to 1st quintile of EntropyXQT) for primary and secondary outcomes were 3.29 (95% CI 1.74-6.21) and 2.28 (1.53-3.41), respectively. Addition of EntropyXQT to a model comprised of the exposures or SHFS significantly increased net reclassification and the ROC curve area.Conclusions: EntropyXQT measured during ICD implantation strongly and independently predicts appropriate shock and all-cause mortality over follow-up. EntropyXQT complements conventional risk predictors and has the potential for broad clinical application. [ABSTRACT FROM AUTHOR]

Background: As a foundation for quality improvement, assessing clinical outcomes across hospitals requires appropriate risk adjustment to account for differences in patient case mix, including presentation after cardiac arrest.Objectives: The aim of this study was to develop and validate a parsimonious patient-level clinical risk model of in-hospital mortality for contemporary patients with acute myocardial infarction.Methods: Patient characteristics at the time of presentation in the ACTION (Acute Coronary Treatment and Intervention Outcomes Network) Registry-GWTG (Get With the Guidelines) database from January 2012 through December 2013 were used to develop a multivariate hierarchical logistic regression model predicting in-hospital mortality. The population (243,440 patients from 655 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. A simplified risk score was created to enable prospective risk stratification in clinical care.Results: The in-hospital mortality rate was 4.6%. Age, heart rate, systolic blood pressure, presentation after cardiac arrest, presentation in cardiogenic shock, presentation in heart failure, presentation with ST-segment elevation myocardial infarction, creatinine clearance, and troponin ratio were all independently associated with in-hospital mortality. The C statistic was 0.88, with good calibration. The model performed well in subgroups based on age; sex; race; transfer status; and the presence of diabetes mellitus, renal dysfunction, cardiac arrest, cardiogenic shock, and ST-segment elevation myocardial infarction. Observed mortality rates varied substantially across risk groups, ranging from 0.4% in the lowest risk group (score <30) to 49.5% in the highest risk group (score >59).Conclusions: This parsimonious risk model for in-hospital mortality is a valid instrument for risk adjustment and risk stratification in contemporary patients with acute myocardial infarction. [ABSTRACT FROM AUTHOR]

Purpose of Review: This review aims to better understand the utility of machine learning algorithms for predicting spatial patterns of contaminants in the United States (U.S.) drinking water., Recent Findings: We found 27 U.S. drinking water studies in the past ten years that used machine learning algorithms to predict water quality. Most studies (42%) developed random forest classification models for groundwater. Continuous models show low predictive power, suggesting that larger datasets and additional predictors are needed. Categorical/classification models for arsenic and nitrate that predict exceedances of pollution thresholds are most common in the literature because of good national scale data coverage and priority as environmental health concerns. Most groundwater data used to develop models were obtained from the United States Geological Survey (USGS) National Water Information System (NWIS). Predictors were similar across contaminants but challenges are posed by the lack of a standard methodology for imputation, pre-processing, and differing availability of data across regions. We reviewed 27 articles that focused on seven drinking water contaminants. Good performance metrics were reported for binary models that classified chemical concentrations above a threshold value by finding significant predictors. Classification models are especially useful for assisting in the design of sampling efforts by identifying high-risk areas. Only a few studies have developed continuous models and obtaining good predictive performance for such models is still challenging. Improving continuous models is important for potential future use in epidemiological studies to supplement data gaps in exposure assessments for drinking water contaminants. While significant progress has been made over the past decade, methodological advances are still needed for selecting appropriate model performance metrics and accounting for spatial autocorrelations in data. Finally, improved infrastructure for code and data sharing would spearhead more rapid advances in machine-learning models for drinking water quality., (© 2022. The Author(s).)

Background: A reliable risk prediction model is critically important for identifying individuals with high risk of developing lung cancer as candidates for low-dose chest computed tomography (LDCT) screening. Leveraging a cutting-edge machine learning technique that accommodates a wide list of questionnaire-based predictors, we sought to optimize and validate a lung cancer prediction model., Methods: We developed an Optimized early Warning model for Lung cancer risk (OWL) using the XGBoost algorithm with 323,344 participants from the England area in UK Biobank (training set), and independently validated it with 93,227 participants from UKB Scotland and Wales area (validation set 1), as well as 70,605 and 66,231 participants in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial (PLCO) control and intervention subpopulations, respectively (validation sets 2 & 3) and 23,138 and 18,669 participants in the United States National Lung Screening Trial (NLST) control and intervention subpopulations, respectively (validation sets 4 & 5). By comparing with three competitive prediction models, i.e., PLCO modified 2012 (PLCO m2012 ), PLCO modified 2014 (PLCO all2014 ), and the Liverpool Lung cancer Project risk model version 3 (LLPv3), we assessed the discrimination of OWL by the area under receiver operating characteristic curve (AUC) at the designed time point. We further evaluated the calibration using relative improvement in the ratio of expected to observed lung cancer cases (RI EO ), and illustrated the clinical utility by the decision curve analysis., Findings: For general population, with validation set 1, OWL (AUC = 0.855, 95% CI: 0.829-0.880) presented a better discriminative capability than PLCO all2014 (AUC = 0.821, 95% CI: 0.794-0.848) (p < 0.001); with validation sets 2 & 3, AUC of OWL was comparable to PLCO all2014 (AUC PLCOall2014 -AUC OWL < 1%). For ever-smokers, OWL outperformed PLCO m2012 and PLCO all2014 among ever-smokers in validation set 1 (AUC OWL  = 0.842, 95% CI: 0.814-0.871; AUC PLCOm2012  = 0.792, 95% CI: 0.760-0.823; AUC PLCOall2014  = 0.791, 95% CI: 0.760-0.822, all p < 0.001). OWL remained comparable to PLCO m2012 and PLCO all2014 in discrimination (AUC difference from -0.014 to 0.008) among the ever-smokers in validation sets 2 to 5. In all the validation sets, OWL outperformed LLPv3 among the general population and the ever-smokers. Of note, OWL showed significantly better calibration than PLCO m2012 , PLCO all2014 (RI EO from 43.1% to 92.3%, all p < 0.001), and LLPv3 (RI EO from 41.4% to 98.7%, all p < 0.001) in most cases. For clinical utility, OWL exhibited significant improvement in average net benefits (NB) over PLCO all2014 in validation set 1 (NB improvement: 32, p < 0.001); among ever smokers of validation set 1, OWL (average NB = 289) retained significant improvement over PLCO m2012 (average NB = 213) (p < 0.001). OWL had equivalent NBs with PLCO m2012 and PLCO all2014 in PLCO and NLST populations, while outperforming LLPv3 in the three populations., Interpretation: OWL, with a high degree of predictive accuracy and robustness, is a general framework with scientific justifications and clinical utility that can aid in screening individuals with high risks of lung cancer., Funding: National Natural Science Foundation of China, the US NIH., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Several studies have demonstrated the tremendous potential of using coronary artery calcium (CAC) in addition to traditional risk factors for coronary heart disease (CHD) risk prediction. However, to date, no risk score incorporating CAC has been developed.Objectives: The goal of this study was to derive and validate a novel risk score to estimate 10-year CHD risk using CAC and traditional risk factors.Methods: Algorithm development was conducted in the MESA (Multi-Ethnic Study of Atherosclerosis), a prospective community-based cohort study of 6,814 participants age 45 to 84 years, who were free of clinical heart disease at baseline and followed for 10 years. MESA is sex balanced and included 39% non-Hispanic whites, 12% Chinese Americans, 28% African Americans, and 22% Hispanic Americans. External validation was conducted in the HNR (Heinz Nixdorf Recall Study) and the DHS (Dallas Heart Study).Results: Inclusion of CAC in the MESA risk score offered significant improvements in risk prediction (C-statistic 0.80 vs. 0.75; p < 0.0001). External validation in both the HNR and DHS studies provided evidence of very good discrimination and calibration. Harrell's C-statistic was 0.779 in HNR and 0.816 in DHS. Additionally, the difference in estimated 10-year risk between events and nonevents was approximately 8% to 9%, indicating excellent discrimination. Mean calibration, or calibration-in-the-large, was excellent for both studies, with average predicted 10-year risk within one-half of a percent of the observed event rate.Conclusions: An accurate estimate of 10-year CHD risk can be obtained using traditional risk factors and CAC. The MESA risk score, which is available online on the MESA web site for easy use, can be used to aid clinicians when communicating risk to patients and when determining risk-based treatment strategies. [ABSTRACT FROM AUTHOR]

BACKGROUND: Approximately 20% of patients with stage II colorectal cancer will experience a relapse. Current clinical-pathologic stratification factors do not allow clear identification of these high-risk patients. ColoPrint (Agendia, Amsterdam, The Netherlands, http://www.agendia.com) is a gene expression classifier that distinguishes patients with low or high risk of disease relapse. METHODS: ColoPrint was developed using whole-genome expression data and validated in several independent validation cohorts. Stage II patients from these studies were pooled (n = 416), and ColoPrint was compared with clinical risk factors described in the National Comprehensive Cancer Network (NCCN) 2013 Guidelines for Colon Cancer. Median follow-up was 81 months. Most patients (70%) did not receive adjuvant chemotherapy. Risk of relapse (ROR) was defined as survival until first event of recurrence or death from cancer. RESULTS: In the pooled stage II data set, ColoPrint identified 63% of patients as low risk with a 5-year ROR of 10%, whereas high-risk patients (37%) had a 5-year ROR of 21%, with a hazard ratio (HR) of 2.16 (p = .004). This remained significant in a multivariate model that included number of lymph nodes retrieved and microsatellite instability. In the T3 microsatellite-stable subgroup (n = 301), ColoPrint classified 59% of patients as low risk with a 5-year ROR of 9.9%. High-risk patients (31%) had a 22.4% ROR (HR: 2.41; p = .005). In contrast, the NCCN clinical high-risk factors were unable to distinguish high- and low-risk patients (15% vs. 13% ROR; p = .55). CONCLUSION: ColoPrint significantly improved prognostic accuracy independent of microsatellite status or clinical variables, facilitating the identification of patients at higher risk who might be considered for additional treatment. [ABSTRACT FROM AUTHOR]

Increased height is a known independent risk factor for atrial fibrillation (AF). However, whether genetic determinants of height influence risk is uncertain. In this candidate gene study, we examined the association of 209 height-associated single-nucleotide polymorphisms (SNPs) with incident AF in 3,309 persons of European descent from the Cardiovascular Health Study, a prospective cohort study of older adults (aged ≥65 years) enrolled in 1989–1990. After a median follow-up period of 13.2 years, 879 participants developed incident AF. The height-associated SNPs together explained approximately 10% of the variation in height (P = 6.0 × 10−8). Using an unweighted genetic height score, we found a nonsignificant association with risk of AF (per allele, hazard ratio = 1.01, 95% confidence interval: 1.00, 1.02; P = 0.06). In weighted analyses, we found that genetically predicted height was strongly associated with AF risk (per 10 cm, hazard ratio = 1.30, 95% confidence interval: 1.03, 1.64; P = 0.03). Importantly, for all models, the inclusion of actual height completely attenuated the genetic height effect. Finally, we identified 1 nonsynonymous SNP (rs1046934) that was independently associated with AF and may warrant future study. In conclusion, we found that genetic determinants of height appear to increase the risk of AF, primarily via height itself. This approach of examining SNPs associated with an intermediate phenotype should be considered as a method for identifying novel genetic targets. [ABSTRACT FROM PUBLISHER]

The Level of Service Inventory-Revised (LSI-R) is a correctional assessment tool that identifies the individual risk and needs of criminal offenders. The predictive validity of the LSI-R has the support of numerous studies, large samples, and multiple meta-analyses. The dynamic nature of many of the items in the LSI-R suggests that offenders are capable of altering their likelihood for future reoffending. However, despite the hundreds of studies conducted on the LSI-R, there have been only two to investigate whether or not changes in LSI-R score were empirically related to recidivism (Raynor, 2007; Vose, Lowenkamp, Smith, & Cullen, 2009). The current study attempts to add to this literature by assessing the predictive and dynamic validity of the LSI-R. Furthermore, this study is the first to investigate the relationship between changes in LSI-R domain scores and recidivism. The results support the LSI-R as a valid instrument in predicting recidivism and suggest future research on risk/needs assessments should include examinations of the percentage change in risk scores. [ABSTRACT FROM PUBLISHER]

Background: Acute postpartum care utilization and readmissions are increasing in the United States and contribute significantly to maternal morbidity, mortality, and healthcare costs. Currently, there are limited data on the prediction of patients who will require acute postpartum care utilization., Objective: To develop and validate a risk prediction model for acute postpartum care utilization., Study Design: A retrospective cohort study of delivery hospitalizations with a linked birth certificate and discharge records in California from 2011 to 2015 was divided into a training and testing set for analysis and validation. Predictive models for acute postpartum care utilization using demographic, comorbidity, obstetrical complication, and other factors were developed using a backward stepwise logistic regression on training data. A risk score for acute postpartum care utilization was developed using beta coefficients from the factors remaining in the final multivariable model. Risk scores were validated using the testing dataset., Results: The final sample included 2,045,988 delivery hospitalizations with an acute postpartum care utilization rate of 7.6% in both training and testing cohorts. Twenty-two risk factors were identified for the final multivariable model, including several that were associated with two or more increased odds of acute care utilization (public insurance, postpartum hemorrhage, extremes of maternal age). The mean risk score was 2.45, conferring a 15 times higher risk of acute postpartum care utilization compared to those with a risk score <1 (RR 15.4, 95% CI: 11.0, 21.7). Demographics and test performance characteristics were comparably similar in predictive capability in both models (0.67 in both the training and testing cohorts)., Conclusion: Risk factors that are identifiable before discharge can be used to create a cumulative risk score to stratify patients at the lowest and highest risk of acute postpartum care utilization with satisfactory accuracy. External validation and the addition of other granular clinical variables are necessary to validate the feasibility of use.

Background: Despite growing acceptance of the role of context in shaping perinatal risk, data on how neighborhood factors may identify high-risk obstetric patients is limited. In this study, we evaluated the effect of neighborhood deprivation and neighborhood racial composition on severe maternal morbidity (SMM) among persons delivered in a large public health system in Atlanta, Georgia., Methods: We conducted a population cohort study using electronic medical record data on all deliveries at Grady Memorial Hospital during 2011-2020. Using residential zip codes, we calculated neighborhood deprivation index based on data from the US Census. We used log-binomial regression with generalized estimating equations to estimate crude and adjusted relative risks (aRR) and 95% confidence intervals (CI) for the association between tertile of neighborhood deprivation and SMM, adjusting for demographic, clinical, and neighborhood-level (racial composition, food desert, and transit access) covariates., Results: Among 25,257 deliveries, 6.2% (1566) experienced SMM. Approximately 24.0%, 32.0%, and 44.0% of women lived in the lowest, middle, and highest tertile of neighborhood deprivation, respectively and 64.9% lived in a neighborhood with majority non-Hispanic Black residents. After adjustment, there was no association between neighborhood deprivation and SMM (aRR: 1.0 (0.8, 1.1)) or residence in a majority Black neighborhood and SMM (aRR:1.0 (0.9, 1.2))., Conclusion: In this safety-net hospital, residence in a high deprivation or majority Black neighborhood did not predict SMM at or following delivery. Individual-level social determinants may better explain variation in risk, particularly in high-burden populations.

Background: Risk stratification in individuals with type 2 diabetes (T2D) remains an important priority in the management of associated morbidity and mortality, including from cardiovascular disease (CVD). The current investigation examined whether estimated glomerular filtration rate (eGFR) and urine albumin:creatinine ratio (UACR) were independent predictors of CVD-mortality in European Americans (EAs) with T2D after accounting for subclinical CVD. Methods: The family-based Diabetes Heart Study (DHS) cohort (n=1,220) had baseline measures of serum creatinine, eGFR, UACR and coronary artery calcified plaque (CAC) assessed by non-contrast computed tomography scan. Cox proportional hazards regression was performed to determine risk for all-cause mortality and CVD-mortality associated with indices of kidney disease after accounting for traditional CVD risk factors and CAC as a measure of subclinical CVD. Results: Participants were followed for 8.2±2.6 years (mean±SD) during which time 247 (20.9%) were deceased, 107 (9.1%) from CVD. Univariate analyses revealed positive associations between serum creatinine (HR:1.56; 95% CI: 1.37-1.80; p<0.0001) and UACR (1.59; 1.43-1.77; p>0.0001) and negative associations between serum albumin (0.74; 0.65-0.84; p<0.0001) and eGFR (0.66; 0.58-0.76; p<0.0001) with all-cause mortality. Associations remained significant after adjustment for traditional CVD risk factors, as well as for CAC. Similar trends were noted when predicting risk for CVD-mortality. Conclusions: The DHS reveals that kidney function and albuminuria are independent risk factors for all-cause mortality and CVD-mortality in EAs with T2D, even after accounting for CAC. [ABSTRACT FROM AUTHOR]

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research.

Objective: Hospitalized patients discharged to skilled nursing facilities (SNFs) for post-acute care are at high risk for adverse outcomes. Yet, absence of effective prognostic tools hinders optimal care planning and decision making. Our objective was to develop and validate a risk prediction model for 6-month all-cause death among hospitalized patients discharged to SNFs., Design: Retrospective cohort study., Setting and Participants: Patients discharged from 1 of 2 hospitals to 1 of 10 SNFs for post-acute care in an integrated health care delivery system between January 1, 2009, and December 31, 2016., Methods: Gradient-boosting machine modeling was used to predict all-cause death within 180 days of hospital discharge with use of patient demographic characteristics, comorbidities, pattern of prior health care use, and clinical parameters from the index hospitalization. Area under the receiver operating characteristic curve (AUC) was assessed for out-of-sample observations under 10-fold cross-validation., Results: We identified 9803 unique patients with 11,647 hospital-to-SNF discharges [mean (SD) age, 80.72 (9.71) years; female sex, 61.4%]. These discharges involved 9803 patients alive at 180 days and 1844 patients who died between day 1 and day 180 of discharge. Age, comorbid burden, health care use in prior 6 months, abnormal laboratory parameters, and mobility status during hospital stay were the most important predictors of 6-month death (model AUC, 0.82)., Conclusion and Implications: We derived a robust prediction model with parameters available at discharge to SNFs to calculate risk of death within 6 months. This work may be useful to guide other clinicians wishing to develop mortality prediction instruments specific to their post-acute SNF populations., (Copyright © 2022 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.)

Risk reclassification methods have become popular in the medical literature as a means of comparing risk prediction models. In this issue of the Journal, Pencina et al. (Am J Epidemiol. 2012;176(6):492–494) present further results for continuous measures of model discrimination and describe their characteristics in nested models with normally distributed variables. Measures include the change in the area under the receiver operating characteristic curve, the integrated discrimination improvement, and the continuous net reclassification improvement. Although theoretically interesting, these continuous measures may not be the most appropriate to assess clinical utility. The continuous net reclassification improvement, in particular, is a measure of effect rather than model improvement and can sometimes exhibit erratic behavior, as illustrated in 2 examples. Caution is needed before using this as a measure of improvement. Further, the test of the continuous net reclassification improvement and that for the integrated discrimination improvement are similar to the likelihood ratio test in nested models and may be overinterpreted. Reclassification in risk strata, while requiring thresholds, may be more relevant clinically with its ability to examine potential changes in treatment decisions. [ABSTRACT FROM PUBLISHER]

The article discusses a study on the external validity of the extant heart failure (HF) risk scores namely the Framingham Heart Study and Health ABC scores in the a large biracial cohort of middle-aged participants taken from 4 U.S. communities by the Athero-sclerosis Risk in Communities (ARIC) Study. Fifteen thousand seven hundred ninety two women and men aged 45 to 64 years were enrolled. It concludes that ARIC HF risk score can predict 10 year risk of hospitalized HF in community settings.

Cardiovascular risk assessment in patients without established disease allows to match the intensity of risk factor modification to the underlying hazard for cardiovascular events. The cardiologist needs to know the characteristics of current risk prediction algorithms, their advantages and limitations. [ABSTRACT FROM AUTHOR]

The article provides information on updated guidelines for reporting genetic research results from the National Heart, Lung and Blood Institue (NHLBI) in the U.S. It is stated that Clinical Laboratory Improvement Amndments (CLIA) certification is needed in the U.S. for all laboratories testing human samples for patient care. It notes that the Centers for Medicare and Medicaid Services (CMS) administers CLIA certification.