scholarly journals IMPACT OF TREATMENT STRATEGY FOLLOWING ACUTE MYOCARDIAL INFARCTION ON READMISSION RISK

2020 ◽  
Vol 75 (11) ◽  
pp. 241
Author(s):  
Raunak M. Nair ◽  
Mouin S. Abdallah ◽  
Michael Joseph Johnson ◽  
Kathleen Kravitz ◽  
Moses Anabila ◽  
...  
Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001442
Author(s):  
John A Dodson ◽  
Alexandra M Hajduk ◽  
Terrence E Murphy ◽  
Mary Geda ◽  
Harlan M Krumholz ◽  
...  

ObjectiveTo develop a 180-day readmission risk model for older adults with acute myocardial infarction (AMI) that considered a broad range of clinical, demographic and age-related functional domains.MethodsWe used data from ComprehenSIVe Evaluation of Risk in Older Adults with AMI (SILVER-AMI), a prospective cohort study that enrolled participants aged ≥75 years with AMI from 94 US hospitals. Participants underwent an in-hospital assessment of functional impairments, including cognition, vision, hearing and mobility. Clinical variables previously shown to be associated with readmission risk were also evaluated. The outcome was 180-day readmission. From an initial list of 72 variables, we used backward selection and Bayesian model averaging to derive a risk model (N=2004) that was subsequently internally validated (N=1002).ResultsOf the 3006 SILVER-AMI participants discharged alive, mean age was 81.5 years, 44.4% were women and 10.5% were non-white. Within 180 days, 1222 participants (40.7%) were readmitted. The final risk model included 10 variables: history of chronic obstructive pulmonary disease, history of heart failure, initial heart rate, first diastolic blood pressure, ischaemic ECG changes, initial haemoglobin, ejection fraction, length of stay, self-reported health status and functional mobility. Model discrimination was moderate (0.68 derivation cohort, 0.65 validation cohort), with good calibration. The predicted readmission rate (derivation cohort) was 23.0% in the lowest quintile and 65.4% in the highest quintile.ConclusionsOver 40% of participants in our sample experienced hospital readmission within 180 days of AMI. Our final readmission risk model included a broad range of characteristics, including functional mobility and self-reported health status, neither of which have been previously considered in 180-day risk models.


Author(s):  
Ty J Gluckman ◽  
Nancy M Albert ◽  
Robert L McNamara ◽  
Gregg C Fonarow ◽  
Adnan Malik ◽  
...  

Background: Optimal transition care represents an important step in mitigating the risk of early hospital readmission. For many hospitals, however, resources are not available to support transition care processes, and hospitals may not be able to identify patients in greatest need. It remains unknown whether a coordinated quality improvement campaign could help to increase a) identification of at-risk patients and b) use of a readmission risk score to identify patients needing extra services/resources. Methods: The American College of Cardiology Patient Navigator Program was designed as a 2-year (2015-2017) quality improvement campaign to assess the impact of transition-care interventions on transition care performance metrics for patients with acute myocardial infarction (AMI) and heart failure (HF) at 35 acute care hospitals. All sites were active participants in the NCDR ACTION Registry. Facilities were free to choose their transition care priorities, with at least 3 goals established at baseline. Pre-discharge identification of AMI and HF patients and assessment of their respective readmission risk were 4 of the 36 metrics tracked quarterly. Performance reports were provided regularly to the individual institutions. Sharing of best practices was actively encouraged through webinars, a listserv, and an online dashboard with display of blinded performance for all 35 hospitals. Results: At baseline, 31% (11/35) and 23% (8/35) of facilities did not have a process for prospectively identifying AMI and HF patients, respectively. At 2 years, the rate of not having processes decreased to 8% (3/35) and 3% (1/35), respectively. Among hospitals able to identify AMI and HF patients, there was high patient-level identification performance from the outset (91% for AMI and 86% for HF at baseline), with added improvement over 2 years (+2.2% for AMI and +9.3% for HF). At baseline, processes to assess readmission risk for AMI and HF patients were only completed by 26% (9/35) and 31% (11/35) of facilities, respectively. At 2 years, AMI and HF readmission risk assessment rose to 80% (28/35) and 86% (30/35), respectively. Similar improvements were noted at the patient-level, with 34% (52% --> 86%) and 16% (75% --> 91%) absolute 2-year increases in the percentage of AMI and HF patients undergoing assessment of readmission risk, respectively. Conclusions: Implementation of a quality improvement campaign focused on care transition can substantially improve prospective identification of AMI and HF patients and assessment of their readmission risk. It remains to be determined whether process improvement lead to reduction in 30-day readmission and/or improvement in other clinically important outcome measures.


Author(s):  
John A. Dodson ◽  
Alexandra M. Hajduk ◽  
Terrence E. Murphy ◽  
Mary Geda ◽  
Harlan M. Krumholz ◽  
...  

2018 ◽  
Vol 71 (11) ◽  
pp. A23
Author(s):  
Vinay Kini ◽  
Pamela Peterson ◽  
John Spertus ◽  
Kevin Kennedy ◽  
Jason Wasfy ◽  
...  

Author(s):  
Kumar Dharmarajan ◽  
Fu-Chi Hsieh ◽  
Zhenqiu Lin ◽  
Joseph S Ross ◽  
Nancy Kim ◽  
...  

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.


Author(s):  
Lauren N. Smith ◽  
Anil N. Makam ◽  
Douglas Darden ◽  
Helen Mayo ◽  
Sandeep R. Das ◽  
...  

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