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Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance.

ABSTRACT: BACKGROUND:Hospitals are subject to federal financial penalties for excessive 30-day hospital readmissions for acute myocardial infarction (AMI). Prospectively identifying patients hospitalized with AMI at high risk for readmission could help prevent 30-day readmissions by enabling targeted interventions. However, the performance of AMI-specific readmission risk prediction models is unknown. METHODS AND RESULTS:We systematically searched the published literature through March 2017 for studies of risk prediction models for 30-day hospital readmission among adults with AMI. We identified 11 studies of 18 unique risk prediction models across diverse settings primarily in the United States, of which 16 models were specific to AMI. The median overall observed all-cause 30-day readmission rate across studies was 16.3% (range, 10.6%-21.0%). Six models were based on administrative data; 4 on electronic health record data; 3 on clinical hospital data; and 5 on cardiac registry data. Models included 7 to 37 predictors, of which demographics, comorbidities, and utilization metrics were the most frequently included domains. Most models, including the Centers for Medicare and Medicaid Services AMI administrative model, had modest discrimination (median C statistic, 0.65; range, 0.53-0.79). Of the 16 reported AMI-specific models, only 8 models were assessed in a validation cohort, limiting generalizability. Observed risk-stratified readmission rates ranged from 3.0% among the lowest-risk individuals to 43.0% among the highest-risk individuals, suggesting good risk stratification across all models. CONCLUSIONS:Current AMI-specific readmission risk prediction models have modest predictive ability and uncertain generalizability given methodological limitations. No existing models provide actionable information in real time to enable early identification and risk-stratification of patients with AMI before hospital discharge, a functionality needed to optimize the potential effectiveness of readmission reduction interventions.

SUBMITTER: Smith LN

PROVIDER: S-EPMC5858710 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Project description:ImportanceIn the US, more than 600 000 adults will experience an acute myocardial infarction (AMI) each year, and up to 20% of the patients will be rehospitalized within 30 days. This study highlights the need for consideration of calibration in these risk models.ObjectiveTo compare multiple machine learning risk prediction models using an electronic health record (EHR)-derived data set standardized to a common data model.Design, setting, and participantsThis was a retrospective cohort study that developed risk prediction models for 30-day readmission among all inpatients discharged from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of AMI who were not transferred from another facility. The model was externally validated at Dartmouth-Hitchcock Medical Center from April 2, 2011, to December 31, 2016. Data analysis occurred between January 4, 2019, and November 15, 2020.ExposuresAcute myocardial infarction that required hospital admission.Main outcomes and measuresThe main outcome was thirty-day hospital readmission. A total of 141 candidate variables were considered from administrative codes, medication orders, and laboratory tests. Multiple risk prediction models were developed using parametric models (elastic net, least absolute shrinkage and selection operator, and ridge regression) and nonparametric models (random forest and gradient boosting). The models were assessed using holdout data with area under the receiver operating characteristic curve (AUROC), percentage of calibration, and calibration curve belts.ResultsThe final Vanderbilt University Medical Center cohort included 6163 unique patients, among whom the mean (SD) age was 67 (13) years, 4137 were male (67.1%), 1019 (16.5%) were Black or other race, and 933 (15.1%) were rehospitalized within 30 days. The final Dartmouth-Hitchcock Medical Center cohort included 4024 unique patients, with mean (SD) age of 68 (12) years; 2584 (64.2%) were male, 412 (10.2%) were rehospitalized within 30 days, and most of the cohort were non-Hispanic and White. The final test set AUROC performance was between 0.686 to 0.695 for the parametric models and 0.686 to 0.704 for the nonparametric models. In the validation cohort, AUROC performance was between 0.558 to 0.655 for parametric models and 0.606 to 0.608 for nonparametric models.Conclusions and relevanceIn this study, 5 machine learning models were developed and externally validated to predict 30-day readmission AMI hospitalization. These models can be deployed within an EHR using routinely collected data.

Project description:BackgroundEarly readmissions among older adults hospitalized for acute myocardial infarction (AMI) are costly and difficult to predict. Aging-related functional impairments may inform risk prediction but are unavailable in most studies. Our objective was to, therefore, develop and validate an AMI readmission risk model for older patients who considered functional impairments and was suitable for use before hospital discharge.Methods and resultsSILVER-AMI (Comprehensive Evaluation of Risk in Older Adults with AMI) is a prospective cohort study of 3006 patients of age ≥75 years hospitalized with AMI at 94 US hospitals. Participants underwent in-hospital assessment of functional impairments including cognition, vision, hearing, and mobility. Other variables plausibly associated with readmissions were also collected. The outcome was all-cause readmission at 30 days. We used backward selection and Bayesian model averaging to derive (N=2004) a risk model that was subsequently validated (N=1002). Mean age was 81.5 years, 44.4% were women, and 10.5% were nonwhite. Within 30 days, 547 participants (18.2%) were readmitted. Readmitted participants were older, had more comorbidities, and had a higher prevalence of functional impairments, including activities of daily living disability (17.0% versus 13.0%; P=0.013) and impaired functional mobility (72.5% versus 53.6%; P<0.001). The final risk model included 8 variables: functional mobility, ejection fraction, chronic obstructive pulmonary disease, arrhythmia, acute kidney injury, first diastolic blood pressure, P2Y12 inhibitor use, and general health status. Functional mobility was the only functional impairment variable retained but was the strongest predictor. The model was well calibrated (Hosmer-Lemeshow P value >0.05) with moderate discrimination (C statistics: 0.65 derivation cohort and 0.63 validation cohort). Functional mobility significantly improved performance of the risk model (net reclassification improvement index =20%; P<0.001).ConclusionsIn our final risk model, functional mobility, previously not included in readmission risk models, was the strongest predictor of 30-day readmission among older adults after AMI. The modest discrimination indicates that much of the variability in readmission risk among this population remains unexplained by patient-level factors.Clinical trial registrationURL: https://www.clinicaltrials.gov. Unique identifier: NCT01755052.

Project description:BackgroundReducing hospital readmission after acute myocardial infarction (AMI) has the potential to both improve quality and reduce costs. As such, readmission after AMI has been a target of financial penalties through Medicare. However, substantial concern exists about potential adverse effects and efficacious readmission-reduction strategies are not well validated.Methods and resultsWe started an AMI readmissions reduction program in November 2017. Between July 2016 and February 2019, hospital billing data were queried to detect all inpatient hospitalizations at the Massachusetts General Hospital for AMI. Thirty-day readmission was identified through hospital billing data, and mortality was extracted from our electronic health record. The data set was merged with claims data for patients in accountable care organizations to detect readmission at other hospitals. We performed segmented linear regression, adjusting for secular trend and case mix, to assess the independent association of our program on both outcome variables. After inclusion and exclusion criteria were applied, the study population included 2020 patients. The overall 30-day readmission rate was higher before the intervention than after the intervention (15.5% versus 10.7%, P=0.002). The overall 30-day mortality rate was similar in both time periods (1.8% versus 1.4%, P=0.457). The program was associated with initial reduction in 30-day readmission (-9.8%, P=0.0002) and 30-day mortality (-2.6%, P=0.041). The program did not change trend in 30-day readmission (+0.19% readmissions/mo, P=0.554) and trend in 30-day mortality (-0.21% deaths/mo, P=0.119).ConclusionsAn AMI readmissions reduction program that increases outpatient and emergency department (ED) access to cardiology care is associated with reduced 30-day readmission and 30-day mortality. Similar statistical techniques can be used to conduct a rigorous, mechanistic program evaluation of other quality improvement initiatives.

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Acute Myocardial Infarction Readmission Risk Prediction Models: A Systematic Review of Model Performance.

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