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Administrative versus clinical data for coronary artery bypass graft surgery report cards: the view from California
Joseph P Parker1, Zhongmin Li, Cheryl L Damberg
1Healthcare Outcomes Center, California Office of Statewide Health Planning and Development, 818 K Street, Sacramento, CA 95814, USA. jparker@oshpd.ca.gov
Insights
A new administrative data risk model for coronary artery bypass graft (CABG) surgery performs similarly to clinical models in predicting patient mortality. However, it identifies different hospital outliers, potentially indicating biased assessments.
Area of Science:
- Health Services Research
- Medical Informatics
- Cardiovascular Surgery Outcomes
Background:
- Accurate risk prediction is crucial for evaluating hospital performance in complex procedures like coronary artery bypass graft (CABG) surgery.
- Administrative data offers a potentially broader, more accessible data source compared to clinical data for risk modeling.
Purpose of the Study:
- To compare the predictive performance of a risk model derived from administrative data against a clinical risk model for isolated CABG surgery.
- To assess the ability of both models in identifying hospital performance outliers.
Main Methods:
- Linked California patient discharge data (PDD) with clinical data for 38,230 isolated CABG patients (2000-2001).
- Developed an administrative risk model using PDD indicators to differentiate preoperative conditions from surgical complications.
- Compared model discrimination (C-statistic) and hospital outlier identification using logistic regression.
Main Results:
- The clinical risk model demonstrated slightly better discrimination (C=0.824) than the administrative model (C=0.799).
- Both models identified a similar number of outliers (17 clinical, 16 administrative), with agreement on 12 hospitals.
- The administrative model's performance was robust to data exclusions.
Conclusions:
- An administrative risk model can predict mortality for isolated CABG surgery comparably to many clinical models.
- The administrative model identified distinct hospital outliers, suggesting potential biases in risk assessment compared to clinical models.
Objective:
The objective of this study was to compare the performance of a risk model for isolated coronary artery bypass graft (CABG) surgery based on administrative data with that of a clinical risk model in predicting mortality and identifying hospital performance outliers.
Methods:
Clinical data records from the California CABG Mortality Reporting Program for 38,230 isolated CABG patients undergoing surgery in 2000-2001 were linked to records in the California patient discharge data (PDD) abstract. Risk factors based on administrative data that mirrored clinical risk factors were developed using the condition present at admission indicator in the PDD to separate preoperative acute conditions from complications of surgery. Using logistic regression, risk model performance across data sources was compared along with hospital risk-adjusted mortality ranks and quality ratings.
Results:
The administrative data showed lower prevalence of risk factors when compared with the clinical data. The clinical risk model had somewhat better discrimination (C = 0.824) than the administrative model (C = 0.799). The clinical model yielded 17 outliers and the administrative model 16 with agreement on 12 hospitals' status. Performance of the administrative risk model was minimally affected by removal of information from prior admissions and removal of risk factors not confirmed in the clinical record.
Conclusions:
Unique properties of the California administrative data, including the ability to distinguish acute preoperative risk factors from complications of surgery, permitted construction of an administrative risk model that predicts mortality on par with most published clinical models. Despite this, the administrative model identified slightly different hospital outliers, which may indicate somewhat biased assessments of hospital patient risk.
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