An Administrative Claims Measure Suitable for Profiling Hospital Performance Based on 30-Day All-Cause Readmission Rates Among Patients With Acute Myocardial Infarction

Krumholz, Harlan M.; Lin, Zhenqiu; Drye, Elizabeth E.; Desai, Mayur M.; Han, Lein F.; Rapp, Michael; Mattera, Jennifer A.; Normand, Sharon‐Lise T.

doi:10.1161/circoutcomes.110.957498

Cited by 291 publications

(346 citation statements)

References 18 publications

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“…4 Such methods can be used as a fast screening tool to identify risk factors and subjects for further investigation. 36 For ischaemic heart disease, algorithms have been developed for surveillance of acute events 37,38 and drug safety surveillance 39 using the EMR. To date, there has been no comprehensive study of predicting readmissions in AMI patients using EMR data.…”

Section: Discussionmentioning

confidence: 99%

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Rana¹,

Tran²,

Luo³

et al. 2014

Aust. Health Review

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Objective. Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations.Methods. The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation.Results. The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78; 95% confidence interval (CI) 0.71-0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72; 95% CI 0.66-0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission.Conclusions. Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions.What is known about the topic? Many clinical and demographic risk factors are known for hospital readmissions following acute myocardial infarction, including multivessel disease, high baseline heart rate, hypertension, diabetes, obesity, chronic obstructive pulmonary disease and psychiatric morbidity. However, combining these risk factors into indices for predicting readmission had limited success. A recent study reported a C-statistic of 0.73 for predicting 30-day readmissions. In a recent American study, a simple seven-factor score was shown to predict hospital readmissions among medical patients. What does this paper add? This paper presents a way to predict readmissions following myocardial infarction using routinely collected administrative data. The model performed better than the recently described HOSPITAL score and a model derived from Elixhauser comorbidities. Moreover, the model uses only data generally available in most hospitals. What are the implications for practitioners? Routine hospital data available at discharges can be used to tailor preventative care for AMI patients, to improve institutional performance and to decrease the cost burden associated with AMI.

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Section: Discussionmentioning

confidence: 99%

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Rana¹,

Tran²,

Luo³

et al. 2014

Aust. Health Review

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“…Next, we used hierarchical generalized linear models to estimate RSRRs 17. This approach accounts for within‐hospital correlation of the observed readmissions and assumes that, after adjusting for patient risk and sampling variability, the remaining heterogeneity is attributable to hospital quality 18, 19…”

Section: Methodsmentioning

confidence: 99%

High Burden of 30‐Day Readmissions After Acute Venous Thromboembolism in the United States

Secemsky

Rosenfield

Kennedy

et al. 2018

JAHA

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BackgroundVenous thromboembolism (VTE) is the third leading cause of vascular disease and accounts for $10 billion in annual US healthcare costs. The nationwide burden of 30‐day readmissions after such events has not been comprehensively assessed.Methods and ResultsWe analyzed adults ≥18 years of age with hospitalizations associated with acute VTE between January 1, 2010, and December 31, 2014, in the Nationwide Readmissions Database. International Classification of Disease, Ninth Revision, Clinical Modification (ICD‐9‐ CM) codes were used to identify hospitalizations associated with acute pulmonary embolism or deep vein thrombosis. The primary outcome was the rate of unplanned 30‐day readmission. Hierarchical logistic regression was used to calculate hospital‐specific 30‐day risk‐standardized readmission rates, a marker of healthcare quality. Among 1 176 335 hospitalizations with acute VTE, in‐hospital death occurred in 6.2%. VTE was associated with malignancy in 19.7%, recent surgery in 19.3%, recent trauma in 4.6%, hypercoagulability in 3.3%, and pregnancy in 1.0%. Among survivors to discharge, the 30‐day readmission rate was 17.5%, with no significant difference in rates across study years (17.4%–17.7%; P=0.10 for trend). Major predictors of readmission were malignancy (relative risk, 1.49, 95% confidence interval 1.47‐1.50), Medicaid insurance (relative risk, 1.48, 95% confidence interval 1.46‐1.50), and nonelective index admission (relative risk, 1.31, 95% confidence interval 1.29‐1.33). Top causes of readmission included sepsis (9.6%) and procedural complications (8.1%). Median rehospitalization costs were $9781.7 (interquartile range, $5430.7–$18 784.1), and 8.1% died during readmission. The interquartile range in risk‐standardized readmission rates was 16.6% to 18.3%, suggesting modest interhospital heterogeneity in readmission risk.ConclusionsNearly 1 in 5 patients with acute VTE were readmitted within 30 days. Predictors and causes of readmission were primarily related to patient characteristics and complications from comorbid conditions, whereas healthcare quality had a moderate impact on readmission risk.

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“…Prior CMS models for hospital readmission risk considered only medical comorbidities and basic demographic factors, [63][64][65] with a relatively poor ability to predict hospital readmissions (c-statistics 0.60-0.63). The addition of functional measures can improve current CMS readmission risk models to more accurately identify patients at high risk of readmission and more fairly reimburse hospitals based on performance.…”

Section: Discussionmentioning

confidence: 99%

Functional Status Outperforms Comorbidities in Predicting Acute Care Readmissions in Medically Complex Patients

Shih

Gerrard²,

Goldstein

et al. 2015

J GEN INTERN MED

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OBJECTIVE:To examine functional status versus medical comorbidities as predictors of acute care readmissions in medically complex patients. DESIGN: Retrospective database study. SETTING: U.S. inpatient rehabilitation facilities. PARTICIPANTS: Subjects included 120,957 patients in the Uniform Data System for Medical Rehabilitation admitted to inpatient rehabilitation facilities under the medically complex impairment group code between 2002 and 2011. INTERVENTIONS: A Basic Model based on gender and functional status was developed using logistic regression to predict the odds of 3-, 7-, and 30-day readmission from inpatient rehabilitation facilities to acute care hospitals. Functional status was measured by the FIM ® motor score. The Basic Model was compared to six other predictive models-three Basic Plus Models that added a comorbidity measure to the Basic Model and three GenderComorbidity Models that included only gender and a comorbidity measure. The three comorbidity measures used were the Elixhauser index, Deyo-Charlson index, and Medicare comorbidity tier system. The c-statistic was the primary measure of model performance. MAIN OUTCOME MEASURES: We investigated 3-, 7-, and 30-day readmission to acute care hospitals from inpatient rehabilitation facilities. RESULTS: Basic Model c-statistics predicting 3-, 7-, and 30-day readmissions were 0.69, 0.64, and 0.65, respectively. The best-performing Basic Plus Model (Basic+Elixhauser) c-statistics were only 0.02 better than the Basic Model, and the best-performing GenderComorbidity Model (Gender+Elixhauser) c-statistics were more than 0.07 worse than the Basic Model. CONCLUSIONS:Readmission models based on functional status consistently outperform models based on medical comorbidities. There is opportunity to improve current national readmission risk models to more accurately predict readmissions by incorporating functional data.

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An Administrative Claims Measure Suitable for Profiling Hospital Performance Based on 30-Day All-Cause Readmission Rates Among Patients With Acute Myocardial Infarction

Cited by 291 publications

References 18 publications

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data

High Burden of 30‐Day Readmissions After Acute Venous Thromboembolism in the United States

Functional Status Outperforms Comorbidities in Predicting Acute Care Readmissions in Medically Complex Patients

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