Abstract 161: Adding Social and Functional Determinants of Health Improves Readmission Risk Prediction in Urban Underserved Adults Hospitalized for Acute Myocardial Infarction

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.

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GW28-e0408 Risk Prediction Model of Acute Myocardial Infarction in Patients with Type 2 Diabetes Mellitus

Journal of the American College of Cardiology ◽

10.1016/j.jacc.2017.07.307 ◽

2017 ◽

Vol 70 (16) ◽

pp. C87

Author(s):

Lina Han ◽

Shuli Guo ◽

Wang Haijun ◽

Li Zhang ◽

Qiming Chen

Keyword(s):

Diabetes Mellitus ◽

Myocardial Infarction ◽

Type 2 Diabetes ◽

Acute Myocardial Infarction ◽

Type 2 Diabetes Mellitus ◽

Prediction Model ◽

Risk Prediction ◽

Risk Prediction Model

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Abstract 186: Improving Identification and Assessment of Readmission Risk for Acute Myocardial Infarction and Heart Failure Patients Following Implementation of a National Quality Improvement Program

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.11.suppl_1.186 ◽

2018 ◽

Vol 11 (suppl_1) ◽

Author(s):

Ty J Gluckman ◽

Nancy M Albert ◽

Robert L McNamara ◽

Gregg C Fonarow ◽

Adnan Malik ◽

...

Keyword(s):

Myocardial Infarction ◽

Heart Failure ◽

Acute Myocardial Infarction ◽

Quality Improvement ◽

Improvement Program ◽

Patient Level ◽

Transition Care ◽

National Quality ◽

The Impact ◽

Readmission Risk

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.

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