TY - JOUR
T1 - Acute Myocardial Infarction Readmission Risk Prediction Models
T2 - A Systematic Review of Model Performance
AU - Smith, Lauren N.
AU - Makam, Anil N.
AU - Darden, Douglas
AU - Mayo, Helen
AU - Das, Sandeep R.
AU - Halm, Ethan A.
AU - Nguyen, Oanh Kieu
N1 - Funding Information:
This study was supported by the Agency for Healthcare Research and Quality-funded UT Southwestern Center for Patient-Centered Outcomes Research (R24 HS022418-01). Dr Nguyen received funding support from the UT Southwestern KL2 Scholars Program (NIH/NCATS KL2 TR001103) and the National Heart, Lung, and Blood Institute (K23HL133441). Dr Makam received funding support from the National Institute on Aging (NIA K23 AG052603). Dr Halm was supported in part by the National Center for Advancing Translational Sciences at the National Institute of Health (U54 RFA-TR-12-006). The study sponsors had no role in the design and conduct of the study; collection, management, analysis or interpretation of the data; preparation, review, or approval of the article; or in the decision to submit the article for publication.
Publisher Copyright:
© 2018 American Heart Association, Inc.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Medicaid
KW - Medicare
KW - myocardial infarction
KW - patient readmission
KW - risk
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U2 - 10.1161/CIRCOUTCOMES.117.003885
DO - 10.1161/CIRCOUTCOMES.117.003885
M3 - Article
C2 - 29321135
AN - SCOPUS:85047115786
SN - 1941-7713
VL - 11
JO - Circulation: Cardiovascular Quality and Outcomes
JF - Circulation: Cardiovascular Quality and Outcomes
IS - 1
M1 - e003885
ER -