Abstract 014: Evaluation of Readmission after Acute Myocardial Infarction for Patients Included in Medicare’s Hospital Readmissions Reduction Program

2017 ◽  
Vol 10 (suppl_3) ◽  
Author(s):  
Lila M Martin ◽  
Ryan W Thompson ◽  
Timothy G Ferris ◽  
Jagmeet P Singh ◽  
Elizabeth Laikhter ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Hospital Readmissions ◽  
Index Hospitalization ◽  
Readmission Rates ◽  
Troponin Elevation ◽  
Reduction Program ◽  
Improve Accuracy ◽  
Episodes Of Care ◽  
Index Hospital

Introduction: Medicare’s Hospital Readmissions Reduction Program assesses financial penalties for hospitals based on risk-standardized readmission rates after specific episodes of care, including acute myocardial infarction (AMI). Whether the algorithm accurately identifies patients with AMI who have preventable readmission is unknown. Methods: Using administrative data from Medicare, we conducted physician-adjudicated chart reviews of all patients considered 30 day readmissions after AMI attributed to one hospital from July 2012-June 2015. We extracted information about revascularization during index hospitalization. For patients readmitted to the index hospital or an affiliate, we also extracted reason for readmission. Results: Of 199 admissions, 66 (33.2%) received PCI and 19 (9.6%) underwent CABG on index hospitalization. The remainder of patients did not receive any intervention, i.e. 39 patients (19.6%) were declined due to procedural risk, 15 (7.5%) because of goals of care and 14 (7.0%) refused revascularization. Forty-six patients (23.1%) had troponin elevation in the absence of an MI and did not have an indication for revascularization. The most common diagnoses of the 161 (80.9%) patients readmitted to the index hospital or an affiliate were infections and cardiac and non-cardiac chest discomfort (Table 1). Conclusions: Our results demonstrate that many AMI patients who count towards the Medicare penalty do not receive revascularization during the index hospitalization because of high procedural risk or patient preference. Focusing on these patients may improve readmission metric performance. Furthermore, adding administrative codes for prohibitive procedural risk may improve accuracy of the metric as a measure of quality.

Abstract 148: The Timing of 30-Day Readmissions after Hospitalization for Heart Failure and Acute Myocardial Infarction

2012 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
Kumar Dharmarajan ◽  
Fu-Chi Hsieh ◽  
Zhenqiu Lin ◽  
Joseph S Ross ◽  
Nancy Kim ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Acute Myocardial Infarction ◽  
Coronary Revascularization ◽  
Hospital Readmissions ◽  
Cumulative Number ◽  
Readmission Rates ◽  
Time Period ◽  
Equal Rate ◽  
Readmission Risk

Background: Readmissions are frequent and costly outcomes in patients hospitalized for heart failure (HF) and acute myocardial infarction (AMI). Knowledge of the exact timing of 30-day readmissions after hospitalization for HF and AMI can help identify time periods during which patients are at the highest readmission risk and guide the development of interventions designed to prevent early readmissions. Methods: Using Medicare Standard Analytic and Denominator files, we identified all HF and AMI hospitalizations in 2007-2009. We excluded hospitalizations for patients aged<65, transferred out, discharged against medical advice, or with an inpatient death. For both HF and AMI cohorts, we identified all readmissions to short-stay acute care hospitals due to any cause occurring within 30 days of hospital discharge except for planned coronary revascularization. Our primary outcome was the number of observed readmissions occurring during each day (0-30) after discharge. We also calculated the cumulative number of observed readmissions occurring during the 1 st 3 days, 1 st week, and 1 st 15 days after discharge. We used a one-tailed two-proportion z test to evaluate if the proportion of readmissions during the 1 st 3 days, 1 st week, and 1 st 15 days was higher than what would be expected had readmissions occurred at an equal rate during the 30 days (alpha=0.05). Results: We identified 329,308 readmissions within 30 days after 1,330,157 hospitalizations for HF (4,633 hospitals) and 108,992 readmissions within 30 days after 548,834 hospitalizations for AMI (3,895 hospitals). Readmission frequency by day is described for both HF and AMI in the accompanying figure. Following hospitalization for HF, 13.4% of 30-day readmissions occur during the 1 st 3 days after discharge, 31.7% occur during the 1 st week, and 61.0% occur during the 1 st 15 days. Following hospitalization for AMI, 19.1% of 30-day readmissions occur during the 1 st 3 days after discharge, 40.1% occur during the 1 st week, and 67.6% occur during the 1 st 15 days. For both HF and AMI cohorts, readmissions after 3, 7, and 15 days were higher than what would be predicted had readmission rates remained constant (p<0.0001 for all). Conclusion: For patients hospitalized with HF and AMI, a disproportionately high percentage of 30-day readmissions occur soon after discharge. Interventions designed to reduce hospital readmissions may therefore generate substantive benefits when applied to the time period shortly after hospitalization.

scholarly journals Impact of the Medicare hospital readmissions reduction program on vulnerable populations

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yunwei Gai ◽  
Dessislava Pachamanova
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
High Risk ◽  
Vulnerable Populations ◽  
Low Income ◽  
Hospital Readmissions ◽  
High Risk Patients ◽  
Reduction Program ◽  
Risk Patients ◽  
Linear Probability

Abstract Background The Hospital Readmissions Reduction Program (HRRP) was established by the 2010 Patient Protection and Affordable Care Act (ACA) in an effort to reduce excess hospital readmissions, lower health care costs, and improve patient safety and outcomes. Although studies have examined the policy’s overall impacts and differences by hospital types, research is limited on its effects for different types of vulnerable populations. The aim of this study was to analyze the impact of the HRRP on readmissions for three targeted conditions (acute myocardial infarction, heart failure, and pneumonia) among four types of vulnerable populations, including low-income patients, patients served by hospitals that serve a high percentage of low-income or Medicaid patients, and high-risk patients at the highest quartile of the Elixhauser comorbidity index score. Methods Data on patient and hospital information came from the Nationwide Readmission Database (NRD), which contained all discharges from community hospitals in 27 states during 2010–2014. Using difference-in-difference (DD) models, linear probability regressions were conducted for the entire sample and sub-samples of patients and hospitals in order to isolate the effect of the HRRP on vulnerable populations. Multiple combinations of treatment and control groups and triple difference (DDD) methods were used for testing the robustness of the results. All models controlled for the patient and hospital characteristics. Results There have been statistically significant reductions in readmission rates overall as well as for vulnerable populations, especially for acute myocardial infarction patients in hospitals serving the largest percentage of low-income patients and high-risk patients. There is also evidence of spillover effects for non-targeted conditions among Medicare patients compared to privately insured patients. Conclusions The HRRP appears to have created the right incentives for reducing readmissions not only overall but also for vulnerable populations, accruing societal benefits in addition to previously found reductions in costs. As the reduction in the rate of readmissions is not consistent across patient and hospital groups, there could be benefits to adjusting the policy according to the socioeconomic status of a hospital’s patients and neighborhood.

scholarly journals Clinical Profile of Acute Myocardial Infarction Patients Included in the Hospital Readmissions Reduction Program

2018 ◽  
Vol 7 (16) ◽  
Author(s):  
Lila M. Martin ◽  
James L. Januzzi ◽  
Ryan W. Thompson ◽  
Timothy G. Ferris ◽  
Jagmeet P. Singh ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Hospital Readmissions ◽  
Clinical Profile ◽  
Reduction Program

scholarly journals Hospital revisits within 30 days after discharge for medical conditions targeted by the Hospital Readmissions Reduction Program in the United States: national retrospective analysis

BMJ ◽  
2019 ◽  
pp. l4563 ◽  
Author(s):  
Rishi K Wadhera ◽  
Karen E Joynt Maddox ◽  
Dhruv S Kazi ◽  
Changyu Shen ◽  
Robert W Yeh
Keyword(s):  
Myocardial Infarction ◽  
Heart Failure ◽  
Acute Myocardial Infarction ◽  
Emergency Department ◽  
Hospital Readmissions ◽  
The United States ◽  
Medical Conditions ◽  
Total Hospital ◽  
Reduction Program ◽  
Hospital Stays

AbstractObjectiveTo determine any changes in total hospital revisits within 30 days of discharge after a hospital stay for medical conditions targeted by the Hospital Readmissions Reduction Program (HRRP).DesignRetrospective cohort study.SettingHospital stays among Medicare patients for heart failure, acute myocardial infarction, or pneumonia between 1 January 2012 and 1 October 2015.ParticipantsMedicare fee-for-service patients aged 65 or over.Main outcomesTotal hospital revisits within 30 days of discharge after hospital stays for medical conditions targeted by the HRRP, and by type of revisit: treat-and-discharge visit to an emergency department, observation stay (not leading to inpatient readmission), and inpatient readmission. Patient subgroups (age, sex, race) were also evaluated for each type of revisit.ResultsOur study cohort included 3 038 740 total index hospital stays from January 2012 to September 2015: 1 357 620 for heart failure, 634 795 for acute myocardial infarction, and 1 046 325 for pneumonia. Counting all revisits after discharge, the total number of hospital revisits per 100 patient discharges for target conditions increased across the study period (monthly increase 0.023 visits per 100 patient discharges (95% confidence interval 0.010 to 0.035)). This change was due to monthly increases in treat-and-discharge visits to an emergency department (0.023 (0.015 to 0.032) and observation stays (0.022 (0.020 to 0.025)), which were only partly offset by declines in readmissions (−0.023 (−0.035 to −0.012)). Increases in observation stay use were more pronounced among non-white patients than white patients. No significant change was seen in mortality within 30 days of discharge for target conditions (−0.0034 (−0.012 to 0.0054)).ConclusionsIn the United States, total hospital revisits within 30 days of discharge for conditions targeted by the HRRP increased across the study period. This increase was due to a rise in post-discharge emergency department visits and observation stays, which exceeded the decline in readmissions. Although reductions in readmissions have been attributed to improvements in discharge planning and care transitions, our findings suggest that these declines could instead be because hospitals and clinicians have intensified efforts to treat patients who return to a hospital within 30 days of discharge in emergency departments and as observation stays.

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

2014 ◽  
Vol 38 (4) ◽  
pp. 377 ◽  
Author(s):  
Santu Rana ◽  
Truyen Tran ◽  
Wei Luo ◽  
Dinh Phung ◽  
Richard L. Kennedy ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Heart Disease ◽  
High Risk ◽  
Positive Predictive Value ◽  
Hospital Readmissions ◽  
Hospital Data ◽  
Readmission Rates ◽  
Better Than

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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