An automated electronic predictive model using clinical and social factors outperformed existing models for predicting 30-day mortality (C statistic 0.86) and readmission (C statistic 0.72; P<0.05).
Observational (n=1,372)
No
Does an automated EMR-based predictive model accurately predict 30-day readmission or death in hospitalized heart failure patients compared to CMS and ADHERE models?
An automated EMR-based predictive model incorporating clinical and social factors accurately predicts 30-day mortality and readmission in hospitalized heart failure patients, outperforming existing CMS and ADHERE models.
p-value: p=<0.05
BACKGROUND: A real-time electronic predictive model that identifies hospitalized heart failure (HF) patients at high risk for readmission or death may be valuable to clinicians and hospitals who care for these patients. METHODS: An automated predictive model for 30-day readmission and death was derived and validated from clinical and nonclinical risk factors present on admission in 1372 HF hospitalizations to a major urban hospital between January 2007 and August 2008. Data were extracted from an electronic medical record. The performance of the electronic model was compared with mortality and readmission models developed by the Center for Medicaid and Medicare Services (CMS models) and a HF mortality model derived from the Acute Decompensated Heart Failure Registry (ADHERE model). RESULTS: The 30-day mortality and readmission rates were 3.1% and 24.1% respectively. The electronic model demonstrated good discrimination for 30 day mortality (C statistic 0.86) and readmission (C statistic 0.72) and performed as well, or better than, the ADHERE model and CMS models for both outcomes (C statistic ranges: 0.72-0.73 and 0.56-0.66 for mortality and readmissions respectively; P < 0.05 in all comparisons). Markers of social instability and lower socioeconomic status improved readmission prediction in the electronic model (C statistic 0.72 vs. 0.61, P < 0.05). CONCLUSIONS: Clinical and social factors available within hours of hospital presentation and extractable from an EMR predicted mortality and readmission at 30 days. Incorporating complex social factors increased the model's accuracy, suggesting that such factors could enhance risk adjustment models designed to compare hospital readmission rates.
Amarasingham et al. (Tue,) conducted a observational in Heart failure (n=1,372). Automated electronic predictive model vs. CMS models and ADHERE model was evaluated on 30-day readmission and death (p=<0.05). An automated electronic predictive model using clinical and social factors outperformed existing models for predicting 30-day mortality (C statistic 0.86) and readmission (C statistic 0.72; P<0.05).