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Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction.

ABSTRACT: BACKGROUND:Early 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 RESULTS:SILVER-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). CONCLUSIONS:In 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 REGISTRATION:URL: https://www.clinicaltrials.gov. Unique identifier: NCT01755052.

SUBMITTER: Dodson JA

PROVIDER: S-EPMC6481309 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction.

Dodson John A JA Hajduk Alexandra M AM Murphy Terrence E TE Geda Mary M Krumholz Harlan M HM Tsang Sui S Nanna Michael G MG Tinetti Mary E ME Goldstein David D Forman Daniel E DE Alexander Karen P KP Gill Thomas M TM Chaudhry Sarwat I SI

Circulation. Cardiovascular quality and outcomes 20190501 5

<h4>Background</h4>Early 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.<h4>Methods and results</h4>SILVER-AMI (Comprehensive Evaluati ...[more]

PMID: 31010300

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Project description:Rehospitalization is a quality-of-care indicator, yet little is known about its occurrence and predictors after myocardial infarction (MI) in the community.To examine 30-day rehospitalizations after incident MI.Retrospective cohort study.Population-based registry in Olmsted County, Minnesota.3010 patients who were hospitalized in Olmsted County with first-ever MI from 1987 to 2010 and survived to hospital discharge.Diagnoses, therapies, and complications during incident and subsequent hospitalizations were identified. Manual chart review was performed to determine the cause of all rehospitalizations. The hazard ratios and cumulative incidence of 30-day rehospitalizations were determined by using Cox proportional hazards regression models.Among 3010 patients (mean age, 67 years; 40.5% female) with incident MI (31.2% ST-segment elevation), 643 rehospitalizations occurred within 30 days in 561 (18.6%) patients. Overall, 30.2% of rehospitalizations were unrelated to the incident MI and 42.6% were related; the relationship was unclear in 27.2% of rehospitalizations. Angiography was performed in 153 (23.8%) rehospitalizations. Revascularization was performed in 103 (16.0%) rehospitalizations, of which 46 (44.7%) had no revascularization during the index hospitalization. After adjustment for potential confounders, diabetes, chronic obstructive pulmonary disease, anemia, higher Killip class, longer length of stay during the index hospitalization, and a complication of angiography or reperfusion or revascularization were associated with increased rehospitalization risk. The 30-day incidence of rehospitalization was 35.3% in patients who experienced a complication of angiography during the index MI hospitalization and 31.6% in those who experienced a complication of reperfusion or revascularization during the index MI hospitalization, compared with 16.8% in patients who had reperfusion or revascularization without complications.This study represents the experiences of a single community.Comorbid conditions, longer length of stay, and complications of angiography and revascularization or reperfusion are associated with increased 30-day rehospitalization risk after MI. Many rehospitalizations seem to be unrelated to the incident MI.National Institutes of Health.

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:Background:Older adults with acute myocardial infarction (AMI) have higher prevalence of functional impairments, including deficits in cognition, strength, and sensory domains, than their younger counterparts. Objective:To develop and evaluate the prognostic utility of a risk model for 6-month post-AMI mortality in older adults that incorporates information about functional impairments. Design:Prospective cohort study. (ClinicalTrials.gov: NCT01755052). Setting:94 hospitals throughout the United States. Participants:3006 persons aged 75 years or older who were hospitalized with AMI and discharged alive. Measurements:Functional impairments were assessed during hospitalization via direct measurement (cognition, mobility, muscle strength) or self-report (vision, hearing). Clinical variables associated with mortality in prior risk models were ascertained by chart review. Seventy-two candidate variables were selected for inclusion, and backward selection and Bayesian model averaging were used to derive (n = 2004 participants) and validate (n = 1002 participants) a model for 6-month mortality. Results:Participants' mean age was 81.5 years, 44.4% were women, and 10.5% were nonwhite. There were 266 deaths (8.8%) within 6 months. The final risk model included 15 variables, 4 of which were not included in prior risk models: hearing impairment, mobility impairment, weight loss, and lower patient-reported health status. The model was well calibrated (Hosmer-Lemeshow P > 0.05) and showed good discrimination (area under the curve for the validation cohort = 0.84). Adding functional impairments significantly improved model performance, as evidenced by category-free net reclassification improvement indices of 0.21 (P = 0.008) for hearing impairment and 0.26 (P < 0.001) for mobility impairment. Limitation:The model was not externally validated. Conclusion:A newly developed model for 6-month post-AMI mortality in older adults was well calibrated and had good discriminatory ability. This model may be useful in decision making at hospital discharge. Primary Funding Source:National Heart, Lung, and Blood Institute of the National Institutes of Health.

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Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction.

Publications

Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction.

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