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The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged25 years (-2.9 to 14.1). Among persons aged65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates.

Objective Although timely administration of antibiotics has an established benefit in serious bacterial infection, the majority of studies evaluating antibiotic delay focus only on the first dose. Recent evidence suggests that delays in redosing may also be associated with worse clinical outcome. In light of the increasing burden of boarding in Emergency Departments (ED) and subsequent need to redose antibiotic in the ED, we examined the association between delayed second antibiotic dose administration and mortality among patients admitted from the ED with a broad array of infections and characterized risk factors associated with delayed second dose administration. Methods We performed a retrospective cohort study of patients admitted through five EDs in a single healthcare system from 1/2018 through 12/2018. Our study included all patients, aged 18 years or older, who received two intravenous antibiotic doses within a 30-h period, with the first dose administered in the ED. Patients with end stage renal disease, cirrhosis and extremes of weight were excluded due to a lack of consensus on antibiotic dosing intervals for these populations. Delay was defined as administration of the second dose at a time-point greater than 125% of the recommended interval. The primary outcome was in-hospital mortality. Results A total of 5605 second antibiotic doses, occurring during 4904 visits, met study criteria. Delayed administration of the second dose occurred during 21.1% of visits. After adjustment for patient characteristics, delayed second dose administration was associated with increased odds of in-hospital mortality (OR 1.50, 95%CI 1.05–2.13). Regarding risk factors for delay, every one-hour increase in allowable compliance time was associated with a 18% decrease in odds of delay (OR 0.82 95%CI 0.75–0.88). Other risk factors for delay included ED boarding more than 4 h (OR 1.47, 95%CI 1.27–1.71) or a high acuity presentation as defined by emergency severity index (ESI) (OR 1.54, 95%CI 1.30–1.81 for ESI 1–2 versus 3–5). Conclusions Delays in second antibiotic dose administration were frequent in the ED and early hospital course, and were associated with increased odds of in-hospital mortality. Several risk factors associated with delays in second dose administration, including ED boarding, were identified.

Background To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia. Methods The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays. Results These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia. Conclusions This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

This cohort study examines mortality data from Texas, a racially and ethnically diverse state, to better understand excess mortality among adults aged 25 to 44 years during early months of the COVID-19 pandemic.

BackgroundAll-cause excess mortality (the number of deaths that exceed projections in any period) has been widely reported during the Covid-19 pandemic. Whether excess mortality has occurred during the Delta wave is less well understood.MethodsWe performed an observational study using data from the Massachusetts Department of Health. Five years of US Census population data and CDC mortality statistics were applied to a seasonal autoregressive integrated moving average (sARIMA) model to project the number of expected deaths for each week of the pandemic period, including the Delta period (starting in June 2021, extending through August 28th 2021, for which mortality data are >99% complete). Weekly Covid-19 cases, Covid-19-attributed deaths, and all-cause deaths are reported. County-level excess mortality during the vaccine campaign are also reported, with weekly rates of vaccination in each county that reported 100 or more all-cause deaths during any week included in the study period.ResultsAll-cause mortality was not observed after March 2021, by which time over 75% of persons over 65 years of age in Massachusetts had received a vaccination. Fewer deaths than expected (which we term ‘deficit mortality’) occurred both during the summer of 2020, the spring of 2021 and during the Delta wave (beginning June 13, 2021 when Delta isolates represented >10% of sequenced cases). After the initial wave in the spring of 2020, more Covid-19-attributed deaths were recorded that all-cause excess deaths, implying that Covid-19 was misattributed as the underlying cause, rather than a contributing cause of death in some cases.ConclusionIn a state with high vaccination rates, excess mortality has not been recorded during the Delta period. Deficit mortality has been recorded during this period.

This study uses national death certificate data to characterize trends in death and excess mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicide during the first 6 months of the pandemic in the US.

Introduction The COVID-19 pandemic has been associated with substantial rates of all-cause excess mortality. The contribution of external causes of death to excess mortality including drug overdose, homicide, suicide, and unintentional injuries during the initial outbreak in the United States is less well documented. Methods Using public data published by the National Center for Health Statistics on February 10, 2021, we measured monthly excess mortality (the gap between observed and expected deaths) from five external causes using national-level data published by National Center for Health Statistics; assault (homicide); intentional self-harm (suicide); accidents (unintentional injuries); and motor vehicle accidents. We used seasonal autoregressive integrated moving average (sARIMA) models developed with cause-specific monthly mortality counts and US population data from 2015-2019 and estimated the contribution of individual cause-specific mortality to all-cause excess mortality from March-July 2020. Results From March-July, 2020, 212,825 (95% CI 136,236-290,776) all-cause excess deaths occurred in the US). There were 8,540 excess drug overdoses (all intents) (95% CI 5,106 to 11,975), accounting for 4% of all excess mortality; 1,455 excess homicide deaths (95% CI 708 to 2202, accounting for 0.7% of excess mortality; 5,492 excess deaths due to unintentional accidents occurred (95% CI 85 to 10,899, accounting for 2.6% of excess mortality. Though a non-significantly 135 (95% CI -1361 to 1,630) more MVA deaths were recorded during the study period, a significant decrease in April (525; 95% CI -817 to -233) and significant increases in June-July (965; 95% CI 348 to 1,587) were observed. Suicide deaths were statistically lower than projected by 2,067 (95% CI 941-3,193 fewer deaths). Meaning Excess deaths from drug overdoses, homicide, and addicents occurred during the pandemic but represented a small fraction of all-cause excess mortality. The excess external causes of death, however, still represent thousands of lives lost. Notably, deaths from suicide were lower than expected and therefore did not contribute to excess mortality.

ObjectivesWe identify a correction for excess mortality that takes the sudden unexpected changes in the size of the United States population into account.DesignThis is a weekly cross-sectional analysis of all-cause mortality since week 5, 2020. We describe and apply a simple correction that takes population changes into account in order to provide corrected weekly estimates of expected deaths for 2020 and 2021.SettingThe United States.ParticipantsAll United States residents.InterventionsThe covid-19 pandemic.Main outcome measuresExpected and excess mortality for the United States during the covid-19 period.ResultsAs of week 53, 2020 (ending January 2, 2021), approximately >10,200 more excess deaths have occurred in the United States than could be detected if expected deaths projections were not amended to reflect population decreases during 2020. The figure is projected to rise to >12,600 (>600 weekly) by week 5, 2021. Assuming recent excess mortality and pandemic-associated visa reductions continue until the earliest time herd immunity could be approached resulting from a combination of infections and vaccinations (week 17, 2021), if point estimates of expected deaths are not corrected, expected deaths will be overestimated (and therefore potential excess mortality underestimated) by ∼43,000 during 2021, or >53,300 since the outbreak of the pandemic measurement period (beginning week 5, 2020). By late December 2021, weekly expected death differences are projected to approach 1,000 per week.ConclusionsCurrent models measuring excess mortality should be revised immediately so that public health officials do not lose the ability to detect ongoing excess mortality as the population changes continue to compound, lowering the number of weekly expected deaths. A similar approach should be used in the middle and late phases of all future pandemics.

ImportanceCOVID-19 case fatality and hospitalization rates, calculated using the number of confirmed cases of COVID-19, have been described widely in the literature. However, the number of infections confirmed by testing underestimates the total infections as it is biased based on the availability of testing and because asymptomatic individuals may remain untested. The infection fatality rate (IFR) and infection hospitalization rate (IHR), calculated using the estimated total infections based on a representative sample of a population, is a better metric to assess the actual toll of the disease.ObjectiveTo determine the IHR and IFR for COVID-19 using the statewide SARS-CoV-2 seroprevalence estimates for the non-congregate population in Connecticut.DesignCross-sectional.SettingAdults residing in a non-congregate setting in Connecticut between March 1 and June 1, 2020.ParticipantsIndividuals aged 18 years or above.ExposureEstimated number of adults with SARS-CoV-2 antibodies.Main Outcome and MeasuresCOVID-19-related hospitalizations and deaths among adults residing in a non-congregate setting in Connecticut between March 1 and June 1, 2020.ResultsOf the 2.8 million individuals residing in the non-congregate settings in Connecticut through June 2020, 113,515 (90% CI 56,758–170,273) individuals had SARS-CoV-2 antibodies. There were a total of 9425 COVID-19-related hospitalizations and 4071 COVID-19-related deaths in Connecticut between March 1 and June 1, 2020, of which 7792 hospitalizations and 1079 deaths occurred among the non-congregate population. The overall COVID-19 IHR and IFR was 6.86% (90% CI, 4.58%–13.72%) and 0.95% (90% CI, 0.63%–1.90%) among the non-congregate population. Older individuals, men, non-Hispanic Black individuals and those belonging to New Haven and Litchfield counties had a higher burden of hospitalization and deaths, compared with younger individuals, women, non-Hispanic White or Hispanic individuals, and those belonging to New London county, respectively.Conclusion and RelevanceUsing representative seroprevalence estimates, the overall COVID-19 IHR and IFR were estimated to be 6.86% and 0.95% among the non-congregate population in Connecticut. Accurate estimation of IHR and IFR among community residents is important to guide public health strategies during an infectious disease outbreak.

Importance: A seroprevalence study can estimate the percentage of people with SARS-CoV-2 antibodies in the general population. Most existing reports have used a convenience sample, which may bias their estimates. Objective: To estimate the seroprevalence of antibodies against SARS-CoV-2 based on a random sample of adults living in Connecticut between March 1 and June 1, 2020. Design: Cross-sectional. Setting: We sought a representative sample of Connecticut residents who completed a survey between June 4 and June 23, 2020 and underwent serology testing for SARS-CoV-2-specific IgG antibodies between June 10 and July 6, 2020. Participants: 505 respondents, aged ≥18 years, residing in non-congregate settings who completed both the survey and the serology test. Main outcomes and measures: We estimated the seroprevalence of SARS-CoV-2-specific IgG antibodies among the overall population and across pre-specified subgroups. We also assessed the prevalence of symptomatic illness, risk factors for virus exposure, and self-reported adherence to risk mitigation behaviors among this population. Results: Of the 505 respondents (mean age 50 [±17] years; 54% women; 76% non-Hispanic White individuals) included, 32% reported having at least 1 symptom suggestive of COVID-19 since March 1, 2020. Overall, 18 respondents had SARS-CoV-2-specific antibodies, resulting in the state-level weighted seroprevalence of 3.1 (90% CI 1.4-4.8). Individuals who were asymptomatic had significantly lower seroprevalence (0.6% [90% CI 0.0-1.5]) compared with the overall state estimate, while those who reported having had ≥1 and ≥2 symptoms had a seroprevalence of 8.0% (90% CI 3.1-12.9) and 13.0% (90% CI 3.5-22.5), respectively. All 9 of the respondents who reported previously having a positive coronavirus test were positive for SARS-CoV-2-specific IgG antibodies. Nearly two-third of respondents reported having avoided public places (74%) and small gatherings of family or friends (75%), and 97% reported wearing a mask outside their home, at least part of the time. Conclusions and relevance: These estimates indicate that most people in Connecticut do not have detectable levels of antibodies against SARS-CoV-2. There is a need for continued adherence to risk mitigation behaviors among Connecticut residents, to prevent resurgence of COVID-19 in this region.

Background: Although increased age is associated with higher systolic blood pressure (SBP) in general, there may be variation across individuals in how SBP changes over time. The goal of this paper is to identify heterogeneity in SBP trajectories among young adults with similar initial values and identify personal characteristics associated with different trajectory patterns. This may have important implications for prevention and prognosis. Methods: A cohort of 12,468 individuals aged 18–35 years in the Qingdao Port Cardiovascular Health Study in China was followed yearly during 2000–2011. Individuals were categorized into three strata according to their baseline SBP: ≤110 mmHg, 111–130 mmHg, and >130 mmHg. Within each stratum, group-based trajectory analyses were conducted to identify distinct SBP trajectory patterns, and their association with sociodemographic and baseline health characteristics was assessed by ordinal logistic regression. Results: Five distinct groups of individuals exhibiting divergent patterns of increasing, stable or decreasing SBP trends were identified within each stratum. This is a first report to identify a subgroup with decreasing trend in SBP. Individuals with more advanced age, having less than high school education, family history of cardiovascular diseases, greater body mass index, greater waist circumference, and hyperlipidemia at baseline were more likely to experience trajectories of higher SBP within each stratum. Conclusions: The diverging trajectories among young adults with similar initial SBP highlight the need for prevention and feasibility of effective blood pressure control, while the identified risk factors may inform targeted interventions.

Background Measurement of cancer-related fatigue and nutrition in the same colorectal cancer patient group using fast-track surgery has never been examined previously. The association between fatigue and nutritional status in the same patient group is thus worthwhile to be investigated. Objective The aim of this study was to evaluate the relationship between fatigue and nutrition risk factors in colorectal cancer patients with fast-track surgery. Methods This is a single-arm, observational study. Seventy eligible postoperative patients with colorectal cancer fast-track surgery were enrolled in this study. Patients completed the Cancer Fatigue Scale and the Patient-Generated Subjective Global Assessment (PG-SGA) besides routine perioperative laboratory examination. Results In this study, all patients were found to have cancer-related fatigue; 20% of the patients had severe fatigue. Furthermore, 94.29% of the patients were malnourished according to the PG-SGA score; the average was 15.585.18. Fatigue severity was significantly, positively correlated with nutrition status. White blood cells and serum calcium were significantly, positively related to both Cancer Fatigue Scale and PG-SGA scores. Conclusion Fatigue and malnutrition commonly exist in patients with colorectal cancer experiencing fast-track surgery. Fatigue may reflect the nutritional status in this group of patients. Implications for practice Clinical nursing staff need to evaluate patients' fatigue status and nutritional status to provide the suitable clinical intervention when needed.

[Image: see text] CENTRAL MESSAGE Patient and procedure-specific risk models likely outperform ‘universal’ aggregate perioperative risk models and should be sought out whenever possible to accurately predict individual patient risks.

This comparative effectiveness research study assesses whether using present on admission codes and single, rather than grouped, diagnostic codes can enhance Centers for Medicare & Medicaid (CMS) models to predict payment for hospitalization for acute myocardial infarction, heart failure, and pneumonia., Key Points Question Does leveraging present on admission codes and using single, rather than grouped, diagnostic codes enhance risk models for acute myocardial infarction, heart failure, and pneumonia payment measures? Findings In this comparative effectiveness research study of risk models on 1 667 983 patients with 1 943 049 Medicare fee-for-service hospitalizations, use of present on admission codes and single diagnosis codes and separation of index admission codes from codes in the previous year improved models predicting payment that were compared with models based on Centers for Medicare & Medicaid Services grouped codes. The patient-level pseudo R2 improved from 0.077 to 0.129 for acute myocardial infarction, from 0.042 to 0.129 for heart failure, and from 0.114 to 0.237 for pneumonia. Meaning Changing candidate variables from the current standard improved models predicting payments, which has implications for research, benchmarking, public reporting, and calculations for population-based programs., Importance Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models. Objective To determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. Design, Setting, and Participants This comparative effectiveness research study used data from Medicare fee-for-service hospitalizations for AMI, HF, and pneumonia at acute care hospitals from July 1, 2013, through September 30, 2015. Payments across multiple care settings, services, and supplies were included and adjusted for geographic and policy variations, corrected for inflation, and winsorized. The same data source was used but varied for the candidate variables and their selection, and the method used by CMS for public reporting that used grouped codes was compared with variations that used POA codes and single diagnostic codes. Combinations of use of POA codes, separation of index admission diagnoses from those in the previous 12 months, and use of individual International Classification of Diseases, Ninth Revision, Clinical Modification codes instead of grouped diagnostic categories were tested. Data analysis was performed from December 4, 2017, to June 10, 2019. Main Outcomes and Measures The models’ goodness of fit was compared using root mean square error (RMSE) and the McFadden pseudo R2. Results Among the 1 943 049 total hospitalizations of the study participants, 343 116 admissions were for AMI (52.5% male; 37.4% aged ≤74 years), 677 044 for HF (45.5% male; 25.9% aged ≤74 years), and 922 889 for pneumonia (46.4% male; 28.2% aged ≤74 years). The mean (SD) 30-day payment was $23 103 ($18 221) for AMI, $16 365 ($12 527) for HF, and $17 097 ($12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0.077 to 0.129 for AMI, from 0.042 to 0.129 for HF, and from 0.114 to 0.237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions. Conclusions and Relevance Leveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.

Background The number of percutaneous coronary interventions (PCI) in China has increased more than 20-fold over the last decade. Consequently, there is a need for national-level information to characterize PCI indications and long-term patient outcomes, including health status, to understand and improve evolving practice patterns. Objectives This nationwide prospective study of patients receiving PCI is to: (1) measure long-term clinical outcomes (including death, acute myocardial infarction [AMI], and/or revascularization), patient-reported outcomes (PROs), cardiovascular risk factor control and adherence to medications for secondary prevention; (2) determine patient- and hospital-level factors associated with care process and outcomes; and (3) assess the appropriateness of PCI procedures. Methods The China Patient-centered Evaluative Assessment of Cardiac Events (PEACE) Prospective Study of PCI has enrolled 5,000 consecutive patients during 2012-2014 from 34 diverse hospitals across China undergoing PCI for any indication. We abstracted details of patient's medical history, treatments, and in-hospital outcomes from medical charts, and conducted baseline, 1-, 6-, and 12-month interviews to characterize patient demographics, risk factors, clinical presentation, healthcare utilization, and health status using validated PRO measures. The primary outcome, a composite measure of death, AMI and/or revascularization, as well as PROs, medication adherence and cardiovascular risk factor control, was assessed throughout the 12-month follow-up. Blood and urine samples were collected at baseline and 12 months and stored for future analyses. To validate reports of coronary anatomy, 2,000 angiograms are randomly selected and read by two independent core laboratories. Hospital characteristics regarding their facilities, processes and organizational characteristics are assessed by site surveys. Conclusion China PEACE Prospective Study of PCI will be the first study to generate novel, high-quality, comprehensive national data on patients' socio-demographic, clinical, treatment, and metabolic/genetic factors, and importantly, their long-term outcomes following PCI, including health status. This will build the foundation for PCI performance improvement efforts in China. © 2016 The Authors. Catheterization and Cardiovascular Interventions. Published by Wiley Periodicals, Inc.

Background The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI. Methods and findings We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749–0.754) versus 0.711 (95% CI 0.708–0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615–0.0618) versus 0.0636 (95% CI 0.0634–0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988–1.028) versus 1.036 (95% CI 1.015–1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015–March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors. Conclusions Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., In a retrospective cohort study, Harlan M. Krumholz and colleagues use machine learning techniques in an effort to improve identification of PCI patients who may benefit from AKI risk-mitigation strategies., Author summary Why was this study done? Accurately estimating the risk of developing acute kidney injury (AKI) is important to determine revascularization strategies and inform peri- and postprocedural care. The current AKI risk prediction model was developed by employing prior clinical knowledge to choose and transform candidate variables and applying regression techniques. It is unknown whether and how machine learning techniques could improve predictive performance from the current AKI risk model. What did the researchers do and find? We used data from 947,091 percutaneous coronary intervention (PCI) procedures from the National Cardiovascular Data Registry (NCDR) CathPCI registry to develop AKI prediction models, the same data from which the current AKI risk model was derived. Temporal validation was performed on a more contemporary cohort of 970,869 PCIs from the NCDR CathPCI registry. We compared the performance of the current AKI risk model with a series of new AKI risk models, which were derived using different regression and machine learning techniques. An AKI risk model derived from machine learning techniques had significantly better discrimination, calibration, and risk stratification than the current AKI risk model in both the internal test set and temporal validation set. What do these findings mean? The demonstrated improvement in predictive power supports the potential of machine learning techniques for improving risk prediction modeling. The improved prediction for AKI suggests great potential of the model to improve targeting of risk-mitigation treatment and quality assessment of cardiovascular care. Future studies need to assess the feasibility of integrating the machine learning model into clinical care and evaluate the benefit of the model for patients at the point of care.

PURPOSE: To estimate medical device utilization needed to detect safety differences among implantable cardioverter defibrillators (ICDs) generator models and compare these estimates to utilization in practice. METHODS: We conducted repeated sample size estimates to calculate the medical device utilization needed, systematically varying device‐specific safety event rate ratios and significance levels while maintaining 80% power, testing 3 average adverse event rates (3.9, 6.1, and 12.6 events per 100 person‐years) estimated from the American College of Cardiology’s 2006 to 2010 National Cardiovascular Data Registry of ICDs. We then compared with actual medical device utilization. RESULTS: At significance level 0.05 and 80% power, 34% or fewer ICD models accrued sufficient utilization in practice to detect safety differences for rate ratios

This study sought to determine the use of intravenous fluids in the early care of patients with acute decompensated heart failure (HF) who are treated with loop diuretics.Intravenous fluids are routinely provided to many hospitalized patients.We conducted a retrospective cohort study of patients admitted with HF to 346 hospitals from 2009 to 2010. We assessed the use of intravenous fluids during the first 2 days of hospitalization. We determined the frequency of adverse in-hospital outcomes. We assessed variation in the use of intravenous fluids across hospitals and patient groups.Among 131,430 hospitalizations for HF, 13,806 (11%) were in patients treated with intravenous fluids during the first 2 days. The median volume of administered fluid was 1,000 ml (interquartile range: 1,000 to 2,000 ml), and the most commonly used fluids were normal saline (80%) and half-normal saline (12%). Demographic characteristics and comorbidities were similar in hospitalizations in which patients did and did not receive fluids. Patients who were treated with intravenous fluids had higher rates of subsequent critical care admission (5.7% vs. 3.8%; p 0.0001), intubation (1.4% vs. 1.0%; p = 0.0012), renal replacement therapy (0.6% vs. 0.3%; p 0.0001), and hospital death (3.3% vs. 1.8%; p 0.0001) compared with those who received only diuretics. The proportion of hospitalizations that used fluid treatment varied widely across hospitals (range: 0% to 71%; median: 12.5%).Many patients who are hospitalized with HF and receive diuretics also receive intravenous fluids during their early inpatient care, and the proportion varies among hospitals. Such practice is associated with worse outcomes and warrants further investigation.

Background SPRINT (Systolic Blood Pressure Intervention Trial) and the ACCORD (Action to Control Cardiovascular Risk in Diabetes) blood pressure trial used similar interventions but produced discordant results. We investigated whether differences in systolic blood pressure ( SBP ) response contributed to the discordant trial results. Methods and Results We evaluated the distributions of SBP response during the first year for the intensive and standard treatment groups of SPRINT and ACCORD using growth mixture models. We assessed whether significant differences existed between trials in the distributions of SBP achieved at 1 year and the treatment‐independent relationships of achieved SBP with risks of primary outcomes defined in each trial, heart failure, stroke, and all‐cause death. We examined whether visit‐to‐visit variability was associated with heterogeneous treatment effects. Among the included 9027 SPRINT and 4575 ACCORD participants, the difference in mean SBP achieved between treatment groups was 15.7 mm Hg in SPRINT and 14.2 mm Hg in ACCORD , but SPRINT had significantly less between‐group overlap in the achieved SBP (standard deviations of intensive and standard groups, respectively: 6.7 and 5.9 mm Hg in SPRINT versus 8.8 and 8.2 mm Hg in ACCORD ; P SBP and outcomes was consistent across trials except for stroke and all‐cause death. Higher visit‐to‐visit variability was more common in SPRINT but without treatment‐effect heterogeneity. Conclusions SPRINT and ACCORD had different degrees of separation in achieved SBP between treatment groups, even as they had similar mean differences. The greater between‐group overlap of achieved SBP may have contributed to the discordant trial results.

Joseph S Ross,1–4 Jonathan Bates,4 Craig S Parzynski,4 Joseph G Akar,4,5 Jeptha P Curtis,4,5 Nihar R Desai,4,5 James V Freeman,4,5 Ginger M Gamble,4 Richard Kuntz,6 Shu-Xia Li,4 Danica Marinac-Dabic,7 Frederick A Masoudi,8 Sharon-Lise T Normand,9,10 Isuru Ranasinghe,11 Richard E Shaw,12 Harlan M Krumholz2–5 1Section of General Medicine, Department of Medicine, 2Robert Wood Johnson Foundation Clinical Scholars Program, Yale School of Medicine, 3Department of Health Policy and Management, Yale School of Public Health, 4Center for Outcomes Research and Evaluation, Yale–New Haven Hospital, 5Section of Cardiovascular Medicine, Department of Medicine, Yale School of Medicine, New Haven, CT, 6Medtronic Inc, Minneapolis, MN, 7Division of Epidemiology, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, 8Division of Cardiology, Department of Medicine, University of Colorado, Aurora, CO, 9Department of Health Care Policy, Harvard Medical School, 10Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA; 11Discipline of Medicine, University of Adelaide, Adelaide, SA, Australia; 12Department of Clinical Informatics, California Pacific Medical Center, San Francisco, CA, USA Background: Machine learning methods may complement traditional analytic methods for medical device surveillance.Methods and results: Using data from the National Cardiovascular Data Registry for implantable cardioverter–defibrillators (ICDs) linked to Medicare administrative claims for longitudinal follow-up, we applied three statistical approaches to safety-signal detection for commonly used dual-chamber ICDs that used two propensity score (PS) models: one specified by subject-matter experts (PS-SME), and the other one by machine learning-based selection (PS-ML). The first approach used PS-SME and cumulative incidence (time-to-event), the second approach used PS-SME and cumulative risk (Data Extraction and Longitudinal Trend Analysis [DELTA]), and the third approach used PS-ML and cumulative risk (embedded feature selection). Safety-signal surveillance was conducted for eleven dual-chamber ICD models implanted at least 2,000 times over 3 years. Between 2006 and 2010, there were 71,948 Medicare fee-for-service beneficiaries who received dual-chamber ICDs. Cumulative device-specific unadjusted 3-year event rates varied for three surveyed safety signals: death from any cause, 12.8%–20.9%; nonfatal ICD-related adverse events, 19.3%–26.3%; and death from any cause or nonfatal ICD-related adverse event, 27.1%–37.6%. Agreement among safety signals detected/not detected between the time-to-event and DELTA approaches was 90.9% (360 of 396, k=0.068), between the time-to-event and embedded feature-selection approaches was 91.7% (363 of 396, k=–0.028), and between the DELTA and embedded feature selection approaches was 88.1% (349 of 396, k=–0.042).Conclusion: Three statistical approaches, including one machine learning method, identified important safety signals, but without exact agreement. Ensemble methods may be needed to detect all safety signals for further evaluation during medical device surveillance. Keywords: implanted cardioverter–defibrillator, methodology, surveillance

Randomized trials of hypertension have seldom examined heterogeneity in response to treatments over time and the implications for cardiovascular outcomes. Understanding this heterogeneity, however, is a necessary step toward personalizing antihypertensive therapy. We applied trajectory-based modeling to data on 39 763 study participants of the ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) to identify distinct patterns of systolic blood pressure (SBP) response to randomized medications during the first 6 months of the trial. Two trajectory patterns were identified: immediate responders (85.5%), on average, had a decreasing SBP, whereas nonimmediate responders (14.5%), on average, had an initially increasing SBP followed by a decrease. Compared with those randomized to chlorthalidone, participants randomized to amlodipine (odds ratio, 1.20; 95% confidence interval [CI], 1.10–1.31), lisinopril (odds ratio, 1.88; 95% CI, 1.73–2.03), and doxazosin (odds ratio, 1.65; 95% CI, 1.52–1.78) had higher adjusted odds ratios associated with being a nonimmediate responder (versus immediate responder). After multivariable adjustment, nonimmediate responders had a higher hazard ratio of stroke (hazard ratio, 1.49; 95% CI, 1.21–1.84), combined cardiovascular disease (hazard ratio, 1.21; 95% CI, 1.11–1.31), and heart failure (hazard ratio, 1.48; 95% CI, 1.24–1.78) during follow-up between 6 months and 2 years. The SBP response trajectories provided superior discrimination for predicting downstream adverse cardiovascular events than classification based on difference in SBP between the first 2 measurements, SBP at 6 months, and average SBP during the first 6 months. Our findings demonstrate heterogeneity in response to antihypertensive therapies and show that chlorthalidone is associated with more favorable initial response than the other medications.

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008–2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition’s seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes.

Background: Dynamic changes of cancer-related fatigue (CRF) among hospitalized patients with colorectal cancer were determined. Materials and Methods: This longitudinal, descriptive study involved 96 hospitalized patients with colorectal cancer, all recruited from a tertiary general hospital in Guangzhou, China. Patients completed questionnaires three times to assess the degree of fatigue, and measurement points were within one week of admission, at 2 to 3 days after surgery and 3 weeks after surgery. Results: Significant differences among the three measurement points (p

Key Points Question What is the association of contrast volume used during percutaneous coronary intervention with the risk of acute kidney injury? Findings In this prognostic study of nearly 3 million adults undergoing percutaneous coronary intervention, a predictive model for the association of contrast volume with the risk of acute kidney injury was developed and validated. The association was nonlinear along the spectrum of contrast volume and varied according to patient baseline risk. Meaning This model could support personalized approaches that consider the complex association between contrast volume and the risk of acute kidney injury when determining contrast dosing during interventional procedures., This prognostic study quantifies the association of contrast volume during percutaneous coronary intervention with the risk of acute kidney injury, accounting for the possibility of nonlinearity and heterogeneity among different baseline risks., Importance Determining the association of contrast volume during percutaneous coronary intervention (PCI) with the risk of acute kidney injury (AKI) is important for optimizing PCI safety. Objective To quantify how the risk of AKI is associated with contrast volume, accounting for the possibility of nonlinearity and heterogeneity among different baseline risks. Design, Setting, and Participants This prognostic study used data from the American College of Cardiology National Cardiovascular Data Registry CathPCI Registry for 1694 US hospitals. Derivation analysis included 2 076 694 individuals who underwent PCI from July 1, 2011, to June 30, 2015. Validation analysis included 961 863 individuals who underwent PCI from July 1, 2015, to March 31, 2017. Data analysis took place from July 2018 to May 2019. Exposure Contrast volume during PCI. Main Outcomes and Measures Acute kidney injury was defined using 3 thresholds for preprocedure to postprocedure creatinine level increase (ie, ≥0.3 mg/dL, ≥0.5 mg/dL, and ≥1.0 mg/dL). A model quantifying the association of contrast volume with AKI was developed, and the existence of nonlinearity and heterogeneity were examined by likelihood ratio tests. The model was derived in the training set (a random 50% of the derivation cohort), and performance was evaluated in the test set (the remaining 50% of the derivation cohort) and an independent validation set by area under the receiver operating characteristic curve (AUC) and calibration slope of observed vs predicted risks. Results The 2 076 694 patients in the derivation set had a mean (SD) age of 65.1 (12.1) years, and 662 525 (31.9%) were women; 133 306 (6.4%) had creatinine level increases of at least 0.3 mg/dL, 66 626 (3.2%) had creatinine level increases of at least 0.5 mg/dL, and 28 378 (1.4%) had creatinine level increases of at least 1.0 mg/dL. In the validation set of 961 843 patients (mean [SD] age, 65.7 [12.1] years; 305 577 [31.8%] women), these rates were 62 913 (6.5%), 34 229 (3.6%), and 15 555 (1.6%), respectively. The association of contrast volume and AKI risk was nonlinear (χ226 = 1436.2; P

This comparative effectiveness study examines current US Centers for Medicare & Medicaid Services 30-day mortality risk models vs novel risk models for acute myocardial infarction, heart failure, and pneumonia hospitalizations., Key Points Question Could present on admission indicators and ungrouped diagnostic codes enhance risk models for acute myocardial infarction, heart failure, and pneumonia mortality measures and improve discrimination of hospital-level performance? Findings In this comparative effectiveness study including all Medicare fee-for-service beneficiaries hospitalized for acute myocardial infarction, heart failure, or pneumonia at acute care hospitals, incorporating present on admission coding and ungrouped historical and index admission International Classification of Diseases, Ninth Revision, Clinical Modification codes was associated with greater discrimination in patient-level and hospital-level 30-day mortality risk models. Meaning Changes incurring no additional cost could enhance the risk adjustment for mortality and increase discrimination of hospital-level performance., Importance Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement. Objective To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures. Design, Setting, and Participants This comparative effectiveness study used ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018. Main Outcomes and Measures The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped ICD-9-CM codes. Results There were 361 175 hospital admissions (mean [SD] age, 78.6 [8.4] years; 189 225 [52.4%] men) for AMI, 716 790 hospital admissions (mean [SD] age, 81.1 [8.4] years; 326 825 [45.6%] men) for HF, and 988 225 hospital admissions (mean [SD] age, 80.7 [8.6] years; 460 761 [46.6%] men) for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers. Conclusions and Relevance Incorporating present on admission coding and using ungrouped index and historical ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.

Importance Studies of public hospitals have reported increasing incidence of emergency department (ED) transfers of uninsured patients for hospitalization, which is perceived to be associated with financial incentives. Objective To examine the differences in risk-adjusted transfer and discharge rates by patient insurance status among hospitals capable of providing critical care. Design, Setting, and Participants A cross-sectional analysis of the 2015 National Emergency Department Sample was conducted, including visits between January 2015 and December 2015. Adult ED visits throughout 2015 (n = 215 028) for the 3 common medical conditions of pneumonia, chronic obstructive pulmonary disease, and asthma, at hospitals with intensive care capabilities were included. Only hospitals with advanced critical care capabilities for pulmonary care were included. Main Outcomes and Measures The primary outcomes were patient-level and hospital-level risk-adjusted ED discharges, ED transfers, and hospital admissions. Adjusted odds of discharge or transfer compared with admission among uninsured patients, Medicaid and Medicare beneficiaries, and privately insured patients are reported. Hospital ownership status was used for the secondary analysis. Results Of the 30 542 691 ED visits to 953 hospitals included in the 2015 National Emergency Department Sample, 215 028 visits (0.7%) were for acute pulmonary diseases to 160 intensive care–capable hospitals. These visits were made by patients with a median (interquartile range [IQR]) age of 55 (40-71) years and who were predominantly female (124 931 [58.1%]). Substantial variation in unadjusted and risk-standardized ED discharge, ED transfer, and hospital admission rates was found across EDs. Compared with privately insured patients, uninsured patients were more likely to be discharged (odds ratio [OR], 1.66; 95% CI, 1.57-1.76) and transferred (adjusted OR [aOR], 2.41; 95% CI, 2.08-2.79). Medicaid beneficiaries had comparable odds of discharge (aOR, 1.00; 95% CI, 0.97-1.04) but higher odds of transfer (aOR, 1.19; 95% CI, 1.05-1.33). Conclusions and Relevance After accounting for hospital critical care capability and patient case mix, the study found that uninsured patients and Medicaid beneficiaries with common medical conditions appeared to have higher odds of interhospital transfer.

Objectives: This study sought to determine hospital patterns of change in use of nesiritide over a 6-year period after publications of safety concerns in 2005 and to identify hospital characteristi...

This study aimed to investigate the factors associated with the health behavior of hypertensive elderly patients. This cross-sectional survey used a general data scale, a health conception scale, a scale for self-rated abilities for health practice, and health-promoting lifestyle profiles to investigate the health behavior of hypertensive elderly patients from clinical check-up to hospitalization. The health behavior score of hypertensive patients was 72.25 ± 16.66, and 23.8 % of the patients had high scores. The health-promotion score was positively correlated with those of health conception, self-efficacy, and education level, but negatively correlated with that of unhealthy habits (P

Background The availability of hospital cardiac services may vary between hospitals and influence care processes and outcomes. However, data on available cardiac services are restricted to a limited number of services collected by the American Hospital Association ( AHA ) annual survey. We developed an alternative method to identify hospital services using individual patient discharge data for acute myocardial infarction ( AMI ) in the Premier Healthcare Database. Methods and Results Thirty‐five inpatient cardiac services relevant for AMI care were identified using American Heart Association/American College of Cardiology guidelines. Thirty‐one of these services could be defined using patient‐level administrative data codes, such as International Classification of Diseases, Ninth Revision, Clinical Modification and Current Procedural Terminology codes. A hospital was classified as providing a service if it had ≥5 instances for the service in the Premier database from 2009 to 2011. Using this system, the availability of these services among 432 Premier hospitals ranged from 100% (services such as chest X‐ray) to 1.2% (heart transplant service). To measure the accuracy of this method using administrative data, we calculated agreement between the AHA survey and Premier for a subset of 16 services defined by both sources. There was a high percentage of agreement (≥80%) for 11 of 16 (68.8%) services, moderate agreement for 3 of 16 (18.8%) services, and low agreement (≤50%) for 2 of 16 services (12.5%). Conclusions The availability of cardiac services for AMI care varies widely among hospitals. Using individual patient discharge data is a feasible method to identify these cardiac services, particularly for those services pertaining to inpatient care.

Objectives To determine how often hospitalized older adults principally diagnosed with pneumonia, chronic obstructive pulmonary disease (COPD), or heart failure (HF) are concurrently treated for two or more of these acute cardiopulmonary conditions. Design Retrospective cohort study. Setting 368 U.S. hospitals in the Premier research database. Participants Individuals aged 65 and older principally hospitalized with pneumonia, COPD, or HF in 2009 or 2010. Measurements Proportion of diagnosed episodes of pneumonia, COPD, or HF concurrently treated for two or more of these acute cardiopulmonary conditions during the first 2 hospital days. Results Of 91,709 diagnosed pneumonia hospitalizations, 32% received treatment for two or more acute cardiopulmonary conditions (18% for HF, 18% for COPD, 4% for both). Of 41,052 diagnosed COPD hospitalizations, 19% received treatment for two or more acute cardiopulmonary conditions (all of which involved additional HF treatment). Of 118,061 diagnosed HF hospitalizations, 38% received treatment for two or more acute cardiopulmonary conditions (34% for pneumonia, 9% for COPD, 5% for both). Conclusion Hospitalized older adults diagnosed with pneumonia, COPD, or HF are frequently treated for two or more acute cardiopulmonary conditions, suggesting that clinical syndromes often fall between traditional diagnostic categories. Research is needed to evaluate the risks and benefits of real-world treatment for the many older adults whose presentations elicit diagnostic uncertainty or concern about coexisting acute conditions.

To characterize hospital phenotypes by their combined utilization pattern of percutaneous coronary interventions (PCI), coronary artery bypass grafting (CABG) procedures, and intensive care unit (ICU) admissions for patients hospitalized for acute myocardial infarction (AMI).Using the Premier Analytical Database, we identified 129,138 hospitalizations for AMI from 246 hospitals with the capacity for performing open-heart surgery during 2010-2013. We calculated year-specific, risk-standardized estimates of PCI procedure rates, CABG procedure rates, and ICU admission rates for each hospital, adjusting for patient clinical characteristics and within-hospital correlation of patients. We used a mixture modeling approach to identify groups of hospitals (ie, hospital phenotypes) that exhibit distinct longitudinal patterns of risk-standardized PCI, CABG, and ICU admission rates.We identified 3 distinct phenotypes among the 246 hospitals: (1) high PCI-low CABG-high ICU admission (39.2% of the hospitals), (2) high PCI-low CABG-low ICU admission (30.5%), and (3) low PCI-high CABG-moderate ICU admission (30.4%). Hospitals in the high PCI-low CABG-high ICU admission phenotype had significantly higher risk-standardized in-hospital costs and 30-day risk-standardized payment yet similar risk-standardized mortality and readmission rates compared with hospitals in the low PCI-high CABG-moderate ICU admission phenotype. Hospitals in these phenotypes differed by geographic region.Hospitals differ in how they manage patients hospitalized for AMI. Their distinctive practice patterns suggest that some hospital phenotypes may be more successful in producing good outcomes at lower cost.

Background— The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Methods and Results— Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Conclusions— Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates.

Objectives This study sought to determine hospital variation in the use of positive inotropic agents in patients with heart failure. Background Clinical guidelines recommend targeted use of positive inotropic agents in highly selected patients, but data are limited and the recommendations are not specific. Methods We analyzed data from 376 hospitals including 189,948 hospitalizations for heart failure from 2009 through 2010. We used hierarchical logistic regression models to estimate hospital-level risk-standardized rates of inotrope use and risk-standardized in-hospital mortality rates. Results The risk-standardized rates of inotrope use ranged across hospitals from 0.9% to 44.6% (median: 6.3%, interquartile range: 4.3% to 9.2%). We identified various hospital patterns based on the type of agents: dobutamine-predominant (29% of hospitals), dopamine-predominant (25%), milrinone-predominant (1%), mixed dobutamine and dopamine pattern (32%), and mixed pattern including all 3 agents (13%). When studying the factors associated with interhospital variation, the best model performance was with the hierarchical generalized linear models that adjusted for patient case mix and an individual hospital effect (receiver operating characteristic curves from 0.77 to 0.88). The intraclass correlation coefficients of the hierarchical generalized linear models (0.113 for any inotrope) indicated that a noteworthy proportion of the observed variation was related to an individual institutional effect. Hospital rates or patterns of use were not associated with differences in length of stay or risk-standardized mortality rates. Conclusions We found marked differences in the use of inotropic agents for heart failure patients among a diverse group of hospitals. This variability, occurring in the context of little clinical evidence, indicates an urgent need to define the appropriate use of these medications.

The objectives of this study were to examine the magnitude of, and 20-year trends in, age differences in short-term outcomes among men and women hospitalized with acute myocardial infarction (AMI) in central Massachusetts.The study population consisted of 5907 male and 4406 female residents of the Worcester, MA, metropolitan area hospitalized at all greater Worcester medical centers with AMI between 1986 and 2005.Overall, among both men and women, older patients were significantly more likely to have developed atrial fibrillation, heart failure, and to have died during hospitalization and within 30 days after admission compared with patients aged65 years. Among men, age differences in the risk of developing atrial fibrillation have widened over the past 2 decades, while differences in the risk of developing cardiogenic shock have narrowed for men 75 years and older as compared with those aged65 years. Among women, age differences in the risk of developing these major complications of AMI have not changed significantly over time. Age differences in short-term mortality have remained relatively unchanged over the past 20 years in both sexes, although individuals of all ages have experienced decreases in short-term death rates over this period.Elderly men and women are more likely to experience adverse short-term outcomes after AMI, and age differences in short-term mortality rates have remained relatively unchanged in both sexes over the past 20 years. More targeted treatment approaches during hospitalization for AMI and thereafter are needed for older patients to improve their prognosis.

Personalized cancer therapy is likely to be one of the next big advances in our search for a cure for cancer. To be able to treat people in an individualized manner, researchers need to know a great deal about their genetic constitution and the DNA repair status of their tumors. Specific knowledge is required regarding the polymorphisms individuals carry and how these polymorphisms influence responses to therapy. Researchers are actively engaged in biomarker discovery and validation for this purpose. In addition, the design of clinical trials must be reassessed to include new information on biomarkers and drug responses. In this review, we focus on personalized breast cancer therapy. The hypothesis we focus upon in this review is that there is connection between the DNA repair profile of individuals, their breast tumor subtypes, and their responses to cancer therapy. We first briefly review cellular DNA repair pathways that are likely to be impacted by breast cancer therapies. Next, we review the phenotypes of breast tumor subtypes with an emphasis on how a DNA repair deficiency might result in tumorigenesis itself and lead to the chemotherapeutic responses that are observed. Specific examples of breast tumor subtypes and their responses to cancer therapy are given, and we discuss possible DNA repair mechanisms that underlie the responses of tumors to various chemotherapeutic agents. Much is known about breast cancer subtypes and the way each of these subtypes responds to chemotherapy. In addition, we discuss novel design of clinical trials that incorporates rapidly emerging information on biomarkers.

An important biologically-relevant property of fullerenol. is its ability to quench free radicals Carbon tetrachloride (CCl(4))-induced hepatotoxicity and nephrotoxicity model was used to investigate the possible mechanisms of fullerenol protection in Sprague-Dawley rats in this study Rats were administrated with fullerenol (0, 1, 15 and 5 mg/kg d) by intravenous or intraperitoneal injection for 3 days before CCl(4) challenge, 24 h following the CCl(4) challenge, all the rats were assessed using serum and tissue homogenates biomarkers as well as the pathological evaluation All results showed that CCl(4) caused significant increasing serum activity of alanine aminotransferase and aspartate aminotransferase, as well as the concentration of blood urea nitrogen and creatinine Malondialdehyde level was also increased significantly, whereas the ratio of reduced glutathione to oxidized glutathione was decreased in tissue homogenates The pathological evaluation indicated the liver and kidney were damaged by CCl(4) Fullerenol-pretreatment alleviated biomarker changes as well as histological changes significantly, and fullerenol-pretreatment alone can increase the ratio of reduced glutathione to oxidized glutathione, which indicated fullerenol could protect tissues against CCl(4)-induced oxidative stress by improving the antioxidant ability (C) 2010 Elsevier Ltd All rights reserved

To investigate the clinicopathologic significance and predictive value of Bmi-1 expression in patients with colon cancer. Bmi-1 expression was assessed by immunohistochemistry, PCR, and western blotting in specimens from 203 patients and by immunohistochemistry in 66 specimens of lymph node metastasis (LNM). Positive staining of Bmi-1 occurred in 7.9% (16/203), 66.5% (135/203), and 86.4% (57/66) of specimens from normal tissue, colon cancer, and LNM, respectively. Staining was significantly correlated with clinical stage, depth of invasion, nodal involvement, distant metastasis, and Ki67 level. Bmi-1 was upregulated at the transcriptional and translational levels. Patients with Bmi-1-positive localized tumors had a much lower 5-year disease-free survival (relative risk 2.919, P

Insulin-like growth factor-II mRNA-binding protein 3 (IMP3) plays a vital role in carcinogenesis; however, its significance and prognostic value in colon cancer remain unclear.In this study, a tissue microarray (TMA) containing 203 samples of primary colon cancer was assessed for IMP3 expression by immunohistochemistry. The TMA included 66 lymph node metastasis (LNM) samples. The mRNA and protein expression levels of IMP3 were evaluated by reverse transcription-PCR and Western blot analysis, respectively.Cytoplasmic immunoreactivity of IMP3 was significantly higher in LNM (93%) than in primary colon cancer (65%) or normal mucosa (3.9%). Increased IMP3 levels were significantly correlated with higher clinical stage, T classification, LNM, presence of distant metastasis, and Ki-67 positivity. IMP3 was up-regulated in colon cancer compared with paired normal colonic mucosa. IMP3 expression was associated with an 11-fold increased risk of distant metastases (hazard ratio (HR) 10.7; 95% confidence interval (CI) 3.3-34.5; P0.0001). Patients with IMP3-positive localized tumors had lower 5-year disease-free survival (DFS) (HR 2.87; 95% CI 1.65-4.98; P0.0001) and overall survival (OS) (HR 4.2; 95% CI 2.51-10.17; P0.0001) than those with IMP3-negative tumors. Multivariate survival analysis showed that IMP3 was an independent prognostic marker for DFS (HR 1.92; 95% CI 1.06-3.47; P = 0.03) and OS (HR 2.37; 95% CI 1.2-4.7; P = 0.014).IMP3 may play an important role in colon cancer progression and could serve as a prognostic biomarker to identify patients at risk of developing metastasis or recurrence after colonectomy.

Background The treatment for patients with acute myocardial infarction (AMI) was transformed by the introduction of intensive care units (ICUs), yet we know little about how contemporary hospitals use this resource-intensive setting and whether higher use is associated with better outcomes. Methods We identified 114,136 adult hospitalizations for AMI from 307 hospitals in the 2009 to 2010 Premier database using codes from the International Classification of Diseases, Ninth Revision, Clinical Modification . Hospitals were stratified into quartiles by rates of ICU admission for AMI patients. Across quartiles, we examined in-hospital risk-standardized mortality rates and usage rates of critical care therapies for these patients. Results Rates of ICU admission for AMI patients varied markedly among hospitals (median 48%, Q1-Q4 20%-71%, range 0%-98%), and there was no association with in-hospital risk-standardized mortality rates (6% all quartiles, P = .7). However, hospitals admitting more AMI patients to the ICU were more likely to use critical care therapies overall (mechanical ventilation [from Q1 with lowest rate of ICU use to Q4 with highest rate 13%-16%], vasopressors/inotropes [17%-21%], intra-aortic balloon pumps [4%-7%], and pulmonary artery catheters [4%-5%]; P for trend Conclusions Rates of ICU admission for patients with AMI vary substantially across hospitals and were not associated with differences in mortality, but were associated with greater use of critical care therapies. These findings suggest uncertainty about the appropriate use of this resource-intensive setting and a need to optimize ICU triage for patients who will truly benefit.

Background During a myocardial infarction, no single best approach of systemic anticoagulation is recommended, likely due to a lack of comparative effectiveness studies and trade‐offs between treatments. Methods and Results We investigated the patterns of use and site‐level variability in anticoagulant strategies (unfractionated heparin [ UFH ] only, low‐molecular‐weight heparin [ LMWH ] only, UFH + LMWH , any bivalirudin) of 63 796 patients with a principal diagnosis of myocardial infarction treated with an early invasive strategy with percutaneous coronary intervention at 257 hospitals. About half (47%) of patients received UFH only, 6% UFH + LMWH , 7% LMWH only, and 40% bivalirudin. Compared with UFH , the median odds ratio was 2.90 for LMWH + UFH , 4.70 for LMWH only, and 3.09 for bivalirudin, indicating that 2 “identical” patients would have a 3‐ to 4‐fold greater likelihood of being treated with anticoagulants other than UFH at one hospital compared with another. We then categorized hospitals as low‐ or high‐users of LMWH and bivalirudin. Using hierarchical, multivariate regression models, we found that low bivalirudin‐using hospitals had higher unadjusted bleeding rates, but the risk‐adjusted and anticoagulant‐adjusted bleeding rates did not differ across the hospital anticoagulation phenotypes. Risk‐standardized mortality and risk‐standardized length of stay also did not differ across hospital phenotypes. Conclusions We found substantial site‐level variability in the choice of anticoagulants for invasively managed acute myocardial infarction patients, even after accounting for patient factors. No single hospital‐use pattern was found to be clinically superior. More studies are needed to determine which patients would derive the greatest benefit from various anticoagulants and to support consistent treatment of patients with the optimal anticoagulant strategy.