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Background: Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST‐Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition. Methods: This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID‐19 and had a length of stay of ≤ 30 days. Results: Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST‐Plus‐assisted RD evaluations. The lag between admission and diagnosis improved with MUST‐Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre‐/post‐implementation, the rate of both diagnoses and documentation of malnutrition showed improvement. Conclusion: MUST‐Plus, a machine learning‐based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning‐based processes to improve malnutrition screening and facilitate timely intervention. Key points/Highlights: Malnutrition is prevalent among hospitalised patients and frequently goes unrecognised, with the potential for severe sequelae. Accurate diagnosis, documentation and treatment of malnutrition have the potential of having a positive impact on morbidity rate, mortality rate, length of inpatient stay, readmission rate and hospital revenue. The tool's successful application highlights its potential to optimise malnutrition screening in healthcare systems, offering potential benefits for patient outcomes and hospital finances. [ABSTRACT FROM AUTHOR]

Background: Lung cancer has significantly higher incidence and mortality rates worldwide. In this study, we analyzed the metabolic profiles of non‐small cell lung cancer (NSCLC) patients and constructed prediction models for smokers and nonsmokers with internal validation. Methods: Plasma was collected from all patients enrolled for metabolic profiling by liquid chromatography–tandem mass spectrometry (LC–MS/MS). The total population was divided into two groups according to smoking or not. Statistical analysis of metabolites was performed separately for each group and prediction models were constructed. Results: A total of 1723 patients (1109 NSCLC patients and 614 healthy controls) were enrolled from the affiliated hospital during 2018 to 2021. After grouping by smoking history, each group was statistically analyzed and prediction models were constructed, which resulted in eight indicators (propionylcarnitine, arginine, citrulline, etc.) significantly associated with lung cancer risk for smokers and eight indicators (dodecanoylcarnitine, hydroxybutyrylcarnitine, asparagine, etc.) for nonsmokers (p < 0.05). The smoker model indicated an AUC of 0.860 in the training set and 0.850 in the validation set. The nonsmoker model showed an AUC of 0.783 in the training set and 0.762 in the validation set. Further calibration tests for both models indicated excellent goodness‐of‐fit results. Conclusions: In this study, we found a series of metabolites significantly associated with lung cancer incidence and constructed respectively prediction models for NSCLC risk in smokers and nonsmokers, with internal validation to confirm the efficiency to discriminate lung cancer risk in both smoking and nonsmoking states. [ABSTRACT FROM AUTHOR]

Background: Although 50 years represents middle age among uninfected individuals, studies have shown that persons living with HIV (PWH) begin to demonstrate elevated risk for serious falls and fragility fractures in the sixth decade; the proportions of these outcomes attributable to modifiable factors are unknown. Methods: We analyzed 21,041 older PWH on antiretroviral therapy (ART) from the Veterans Aging Cohort Study from 01/01/2010 through 09/30/2015. Serious falls were identified by Ecodes and a machine‐learning algorithm applied to radiology reports. Fragility fractures (hip, vertebral, and upper arm) were identified using ICD9 codes. Predictors for both models included a serious fall within the past 12 months, body mass index, physiologic frailty (VACS Index 2.0), illicit substance and alcohol use disorders, and measures of multimorbidity and polypharmacy. We separately fit multivariable logistic models to each outcome using generalized estimating equations. From these models, the longitudinal extensions of average attributable fraction (LE‐AAF) for modifiable risk factors were estimated. Results: Key risk factors for both outcomes included physiologic frailty (VACS Index 2.0) (serious falls [15%; 95% CI 14%–15%]; fractures [13%; 95% CI 12%–14%]), a serious fall in the past year (serious falls [7%; 95% CI 7%–7%]; fractures [5%; 95% CI 4%–5%]), polypharmacy (serious falls [5%; 95% CI 4%–5%]; fractures [5%; 95% CI 4%–5%]), an opioid prescription in the past month (serious falls [7%; 95% CI 6%–7%]; fractures [9%; 95% CI 8%–9%]), and diagnosis of alcohol use disorder (serious falls [4%; 95% CI 4%–5%]; fractures [8%; 95% CI 7%–8%]). Conclusions: This study confirms the contributions of risk factors important in the general population to both serious falls and fragility fractures among older PWH. Successful prevention programs for these outcomes should build on existing prevention efforts while including risk factors specific to PWH. [ABSTRACT FROM AUTHOR]

Aim: The aim of this study was to describe what psychosocial factors associated with postoperative persistent pain can be found in electronic health records of patients with breast cancer, and which of these factors that may be used in the development of a decision‐support system algorithm to better support health professionals in their clinical work. Design: A qualitative descriptive study. Methods: A retrospective electronic health record review was done using manual semantic annotation. A set of 101 records of patients with breast cancer were selected by computerized random sampling. The data were analysed with deductive content analysis. Results: A total of 337 different expressions describing psychosocial factors associated with postoperative persistent pain were identified from the documentation done in the electronic health records. These regarded psychological strength and resilience, social factors, emotional factors, anxiety, sleep‐related factors and depression. No records were found dealing with pain catastrophizing. Although psychosocial factors associated with postoperative persistent pain were documented in the electronic health records, documentation about such factors was not found in all patient's records, nor was the documentation done in a systematic manner. Conclusions: The findings show that there is potential to use electronic health records as information source in the development of decision‐support system algorithms to better support nurses in the identification of patients at risk of developing postoperative persistent pain. However, the documentation quality needs to be acknowledged in the application of decision support systems, which are built on information extracted from electronic health records. Future work is needed to standardize documentation practices and assess the comprehensiveness of the documentation. [ABSTRACT FROM AUTHOR]

Objectives: To determine the real‐world outcomes associated with using the GetaKit.ca website to distribute COVID self‐tests to persons with risk factors, with a focus on facilitating testing for persons who are Black, Indigenous, or of Colour (BIPOC). Methods: GetaKit was an open cohort observational study to distribute free self‐tests, starting with HIV self‐testings and then adding the Lucira Check‐It® COVID self‐test. Participants would register on our website and complete a risk assessment, which would calculate their need for each type of testing. Results: Focusing on the COVID self‐tests, from September to December 2021 (with targeted outreach in winter 2022), we distributed 6474 COVID self‐tests to 3653 persons through 4161 unique orders, of which 47% came from BIPOC participants. Compared to white participants, BIPOC participants were more likely to have been a contact of COVID but less likely to be vaccinated. As well, 69% of results were reported back via the GetaKit.ca website, with 5.3% of these being positive. The positivity rate for our 3653 participants was 9.6%. Conclusions: Delivering COVID self‐tests via our website provided tests to BIPOC communities and yielded positivity rates that matched local COVID testing centres. This highlights the utility of such systems for delivering testing during future pandemics. [ABSTRACT FROM AUTHOR]

Aims: Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost‐effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. Design: A prediction model was developed for DSM‐IV AD onset by age 25 years using an ensemble machine‐learning algorithm known as 'Super Learner'. Shapley additive explanations (SHAP) assessed variable importance. Setting and Participants: Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. Measurements The primary outcome to be predicted was onset of life‐time DSM‐IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. Findings AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74–0.81] higher than any individual candidate risk model (0.73–0.77) and an area under the precision‐recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. Conclusions: A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models. [ABSTRACT FROM AUTHOR]

The article presents a study which examined the association between social isolation and incident dementia over nine years in large and nationally representative sample of Medicare beneficiaries in the U.S. Topics include criteria for participants to be classified as having probable dementia, distribution of baseline participant characteristics, and characteristics of baseline participants for the total sample and by social isolation status.

Background and objective: Variations in the risk factors for sarcopenia can lead to differences in the likelihood of developing sarcopenia among older adults; however, few studies have explored the interactions among the risk factors. This study examined the interactions among risk factors and identified a discriminative pathway for groups at risk of sarcopenia in community‐dwelling older adults. Methods: A cross‐sectional study was conducted between July and August 2019 to recruit 200 older adults from an outpatient department of a hospital providing care for older people. Data on various risk factors, namely demographics (age, gender, education, comorbidities, and body mass index [BMI]), dietary habits (weekly consumption of milk, coffee, and meat), lifestyle behaviours (vitamin D supplementation, smoking, drinking, and physical activity), and depression symptoms were collected. Sarcopenia was defined according to the Asian Working Group for Sarcopenia criteria. A classification and regression tree (CART) model was used to examine interactions among these factors and identify groups at risk of sarcopenia. Findings: The prevalence of sarcopenia was 38.5%. The CART model identified two end groups at differential risks of sarcopenia, with a minimum of one and a maximum of three risk factors. In the first group, low BMI (<18.5 kg/m2) was a predominant risk factor for sarcopenia among older people. In the second group, older adults with a normal BMI, aged ≥68 years, and without a regular walking habit had a higher probability of developing sarcopenia than did their counterparts. Conclusions: The interactive effects among older age, BMI, and walking may cause different probabilities of developing sarcopenia in the older population. Implications for practice: Older adults with a low or normal BMI but without a regular walking habit could be a predominant risk group for sarcopenia. The appropriate maintenance of body weight and regular walking activity is suggested to prevent sarcopenia in community‐dwelling older adults. [ABSTRACT FROM AUTHOR]

Introduction: The Respiratory Disturbance Index (RDI) computed by an implantable cardioverter defibrillator (ICD) algorithm accurately identifies severe sleep apnea (SA). In the present analysis, we tested the hypothesis that RDI could also predict atrial fibrillation (AF) burden. Methods: Patients with ejection fraction ≤35% implanted with an ICD were enrolled and followed up for 24 months. One month after implantation, patients underwent a polysomnographic study. The weekly mean RDI value was considered, as calculated during the entire follow‐up period and over a 1‐week period preceding the sleep study. The endpoints were as follows: daily AF burden of ≥5 min, ≥6 h, ≥23 h. Results: Here, 164 patients had usable RDI values during the entire follow‐up period. Severe SA (RDI ≥ 30 episodes/h) was diagnosed in 92 (56%) patients at the time of the sleep study. During follow‐up, AF burden ≥ 5 min/day was documented in 70 (43%), ≥6 h/day in 48 (29%), and ≥23 h/day in 33 (20%) patients. Device‐detected RDI ≥ 30 episodes/h at the time of the polygraphy, as well as the polygraphy‐measured apnea hypopnea index ≥ 30 episodes/h, were not associated with the occurrence of the endpoints, using a Cox regression model. However, using a time‐dependent model, continuously measured weekly mean RDI ≥ 30 episodes/h was independently associated with AF burden ≥ 5 min/day (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: 1.24–3.65, p =.006), ≥6 h/day (HR: 2.75, 95% CI: 1.37–5.49, p =.004), and ≥23 h/day (HR: 2.26, 95% CI: 1.05–4.86, p =.037). Conclusions: In heart failure patients, ICD‐diagnosed severe SA on follow‐up data review identifies patients who are from two‐ to three‐fold more likely to experience an AF episode, according to various thresholds of daily AF burden. [ABSTRACT FROM AUTHOR]

Background: The Pediatric Emergency Care Applied Research Network (PECARN) head trauma clinical decision rules informed the development of algorithms that risk stratify the management of children based on their risk of clinically important traumatic brain injury (ciTBI). We aimed to determine the rate of ciTBI for each PECARN algorithm risk group in an external cohort of patients and that of ciTBI associated with different combinations of high‐ or intermediate‐risk predictors. Methods: This study was a secondary analysis of a large multicenter prospective data set, including patients with Glasgow Coma Scale scores of 14 or 15 conducted in Australia and New Zealand. We calculated ciTBI rates with 95% confidence intervals (CIs) for each PECARN risk category and combinations of related predictor variables. Results: Of the 15,163 included children, 4,011 (25.5%) were aged <2 years. The frequency of ciTBI was 8.5% (95% CI = 6.0%–11.6%), 0.2% (95% CI = 0.0%–0.6%), and 0.0% (95% CI = 0.0%–0.2%) in the high‐, intermediate‐, and very‐low‐risk groups, respectively, for children <2 years and 5.7% (95% CI = 4.4%–7.2%), 0.7% (95% CI = 0.5%–1.0%), and 0.0% (95% CI = 0.0%–0.1%) in older children. The isolated high‐risk predictor with the highest risk of ciTBI was "signs of palpable skull fracture" for younger children (11.4%, 95% CI = 5.3%–20.5%) and "signs of basilar skull fracture" in children ≥2 years (11.1%, 95% CI = 3.7%–24.1%). For older children in the intermediate‐risk category, the presence of all four predictors had the highest risk of ciTBI (25.0%, 95% CI = 0.6%–80.6%) followed by the combination of "severe mechanism of injury" and "severe headache" (7.7%, 95% CI = 0.2%–36.0%). The very few children <2 years at intermediate risk with ciTBI precluded further analysis. Conclusions: The risk estimates of ciTBI for each of the PECARN algorithms risk group were consistent with the original PECARN study. The risk estimates of ciTBI within the high‐ and intermediate‐risk predictors will help further refine clinical judgment and decision making on neuroimaging. [ABSTRACT FROM AUTHOR]

Background: Competing risk models used for midpregnancy prediction of preterm pre‐eclampsia have shown detection rates (DR) of 85%, at fixed false‐positive rate (FPR) of 10%. The full algorithm used between 19+0 and 24+6 weeks includes maternal factors, mean arterial pressure (MAP), mean uterine artery pulsatility index (UtAPI), serum placental growth factor (PlGF) level in multiples of the median (MoM), and soluble Fms‐like tyrosine kinase‐1 (sFlt‐1) level in MoM. Aims: To assess performance of the Fetal Medicine Foundation (FMF) algorithm at midpregnancy to screen for preterm (<37 weeks) pre‐eclampsia. The outcome measured was preterm pre‐eclampsia. Materials and Methods: This is a prospective study including singleton pregnancies at 19–22 weeks gestation. Maternal bloods were collected and analysed using three different immunoassay platforms. Maternal characteristics, medical history, MAP, mean UtAPI, serum PlGF MoM and serum sFlt‐1 MoM were used for risk assessment. DR and FPR were calculated, and receiver operating characteristic curves produced. Results: Five hundred and twelve patients were included. Incidence of preterm pre‐eclampsia was 1.6%. Using predicted risk of pre‐eclampsia of one in 60 or more and one in 100 or higher, as given by the FMF predictive algorithm, the combination with the best predictive performance for preterm pre‐eclampsia included maternal factors, MAP, UtAPI and PlGF MoM, giving DRs of 100% and 100%, respectively, and FPRs of 9.3 for all platforms and 12.9–13.5, respectively. Addition of sFlt‐1 to the algorithm did not appear to improve performance. sFlt‐1 MoM and PlGF MoM values obtained on the different platforms performed very similarly. Conclusions: Second trimester combined screening for preterm pre‐eclampsia by maternal history, MAP, mean UtAPI and PlGF MoM using the FMF algorithm performed very well in this patient population. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives Hospital readmission within 30 days of discharge occurs in almost 20% of US Medicare patients and may be a marker of poor quality inpatient care, ineffective hospital to home transitions, or disease severity. Within a patient centered medical home, care transition interventions may only be practical from cost and staffing perspectives if targeted at patients with the greatest risk of readmission. Various scoring algorithms attempt to predict patients at risk for 30-day readmission, but head-to-head comparison of performance is lacking. Compare published scoring algorithms which use generally available electronic medical record data on the same set of hospitalized primary care patients. Methods The LACE index, the LACE+ index, the HOSPITAL score, and the readmission risk score were computed on a consecutive cohort of 26,278 hospital admissions. Classifier performance was assessed by plotting receiver operating characteristic curves comparing the computed score with the actual outcome of death or readmission within 30 days. Statistical significance of differences in performance was assessed using bootstrapping techniques. Results Correct readmission classification on this cohort was moderate with the following c-statistics: Readmission risk score 0.666; LACE 0.680; LACE+ 0.662; and HOSPITAL 0.675. There was no statistically significant difference in performance between classifiers. Conclusions Logistic regression based classifiers yield only moderate performance when utilized to predict 30-day readmissions. The task is difficult due to the variety of underlying causes for readmission, nonlinearity, and the arbitrary time period of concern. More sophisticated classification techniques may be necessary to increase performance and allow patient centered medical homes to effectively focus efforts to reduce readmissions. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives: Reducing the rate of hospital readmissions, particularly avoidable ones, has significant implications on patient outcomes, cost containment, and quality of care. Given that the reason of readmission may differ from the patient's main diagnosis in the index admission, this study aims to assess the influence of index comorbidities on the primary readmission diagnoses and explore the risk of deemed avoidable readmission because of prior comorbidities. Methods: A retrospective review of 3962 discharges was conducted at a 527-bed teaching hospital in Jordan, utilizing data related to 2025 internal medicine patients. Results: Among all discharges, 29% were followed by a 30-day readmission, of which 13% were identified as potentially avoidable. Of all readmissions, 36% of patients were readmitted because of one of the comorbidities that had been identified at index admission. In addition, 47% of the potentially avoidable readmissions had a main diagnosis that was one of the index comorbidities. The results also showed an association between readmission for one of the index stay's comorbidities and being avoidable, with an adjusted odds ratio of 2.12 (95% confidence interval, 1.65-2.72). Overall, the presence of certain diseases, being identified as one of the preceding comorbidities, was found to have a substantial influence on the risk of potentially avoidable readmission. These diseases included digestive, circulatory, respiratory, genitourinary systems, and infectious and parasitic diseases (adjusted relative risks = 1.57,1.49, 1.36,1.30, and 2.30, respectively). Conclusion: To help reduce the rates of readmission, potential gains seem available if hospitals adopt clinical practices that support the patient's care during the post-discharge transition. This implies that health care providers need to pay more attention to the comorbidities of high-risk patients to be closely monitored after discharge. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Methods Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management ( IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Results Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index ( RAMI). Conclusions Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. [ABSTRACT FROM AUTHOR]

Objectives To examine the extent to which receipt of recommended monitoring of diabetes mellitus (DM) differed for participants with and without comorbid dementia, as well as the effect of other comorbidities on monitoring of DM in individuals with comorbid dementia. Design Retrospective cohort study. Setting Secondary analysis of 2005/2006 claims and enrollment data for a 5% national random sample of Medicare beneficiaries. Participants Two hundred eighty-eight thousand eight hundred five Medicare fee-for-service beneficiaries with a diagnosis of DM before 2006, 44,717 (16%) of whom had evidence of comorbid dementia in claims. Measurements Established algorithms were used to determine whether patients received at least one glycosylated hemoglobin ( Hb A1c) test, one low-density lipoprotein cholesterol ( LDL- C) test, and one annual eye examination in 2006 and to construct variables representing comorbidities common in DM, sociodemographic characteristics, and patterns of healthcare utilization. Results In unadjusted and fully adjusted models, the presence of dementia reduced the likelihood of receiving Hb A1c tests, LDL- C tests, and eye examinations, with effects being smallest for Hb A1c tests. The effects of other comorbidities on DM monitoring in participants with dementia varied according to the nature of the comorbidity and the specific test. Conclusion Dementia reduces the likelihood that individuals with DM will receive recommended annual monitoring for DM. More research is needed to understand reasons for lower monitoring in this subgroup and how this affects functioning, adverse events, and quality of life. [ABSTRACT FROM AUTHOR]