What is the clinical evidence from this study?

Study design: Observational. Population: Heart failure (n=1145). Intervention: Addition of clinical data to HDR-based models vs. HDR-only models. Primary outcome: Discrimination of 30-day mortality risk-adjustment model (Area under the ROC curve) (p=<0.001).

September 6, 2016Open Access

Risk-adjustment models for heart failure patients’ 30-day mortality and readmission rates: the incremental value of clinical data abstracted from medical charts beyond hospital discharge record

Q: What are the key findings of this study?

Adding clinical data from medical charts to hospital discharge records significantly improved the discrimination of the 30-day mortality risk-adjustment model for heart failure patients (AUC 0.84 vs. 0.73, p < 0.001).

Key Result

Adding clinical data from medical charts to hospital discharge records significantly improved the discrimination of the 30-day mortality risk-adjustment model for heart failure patients (AUC 0.84 vs. 0.73, p < 0.001).

Study Design

Type

Observational (n=1,145)

Multicenter

Yes

Structured PICO

Does adding clinical data to HDR-based risk-adjustment models improve the prediction of 30-day mortality and readmission in patients with heart failure?

Population

1,145 adult patients (mean age 81.3 years, 54.3% female) hospitalized for heart failure in Italy, evaluated for 30-day mortality and readmission risk.

Exposure

Risk-adjustment model incorporating both Hospital Discharge Records (HDR) and clinical data abstracted from medical charts

Comparator

Risk-adjustment model based on Hospital Discharge Records (HDR) only

Outcome

Model discrimination (Area Under the ROC Curve - AUC) for 30-day mortality and 30-day readmissionsurrogate

Adding clinical variables from medical charts to administrative hospital discharge records significantly improves the prediction of 30-day mortality, but not 30-day readmission, in patients hospitalized for heart failure.

Main Result

Absolute Event Rate: 0.84% vs 0.73%

p-value: p=<0.001

Limitations

Potential lack of generalizability to other settings
Data recording and accuracy might have differed among the three hospitals
Did not consider some variables predictive of mortality and readmission (e.g., body mass index, left ventricular ejection fraction, brain natriuretic peptide) because they were scarcely reported in medical charts

Abstract

BACKGROUND: Hospital discharge records (HDRs) are routinely used to assess outcomes of care and to compare hospital performance for heart failure. The advantages of using clinical data from medical charts to improve risk-adjustment models remain controversial. The aim of the present study was to evaluate the additional contribution of clinical variables to HDR-based 30-day mortality and readmission models in patients with heart failure. METHODS: This retrospective observational study included all patients residing in the Local Healthcare Authority of Bologna (about 1 million inhabitants) who were discharged in 2012 from one of three hospitals in the area with a diagnosis of heart failure. For each study outcome, we compared the discrimination of the two risk-adjustment models (i.e., HDR-only model and HDR-clinical model) through the area under the ROC curve (AUC). RESULTS: A total of 1145 and 1025 patients were included in the mortality and readmission analyses, respectively. Adding clinical data significantly improved the discrimination of the mortality model (AUC = 0.84 vs. 0.73, p < 0.001), but not the discrimination of the readmission model (AUC = 0.65 vs. 0.63, p = 0.08). CONCLUSIONS: We identified clinical variables that significantly improved the discrimination of the HDR-only model for 30-day mortality following heart failure. By contrast, clinical variables made little contribution to the discrimination of the HDR-only model for 30-day readmission.

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