Purpose: �
�� We propose methodology for developing and validating decision rules which recommend one of two or more competing treatments on the basis of minimizing expected risk of an outcome of interest.
Methods:
�� We present our methodology in the setting of recommending either coronary artery bypass graft (CABG) or percutaneous coronary angioplasty (PCA) for individual patients on the basis of minimizing risk of in-hospital mortality.
�� Data on 406,456 inpatient discharges for which the primary procedure was either CABG or PCA were extracted from seven AHRQ State Inpatient Databases (2009-2010).� Generally, our strategy was to simultaneously develop two models for the probability of mortality under each respective treatment using age, gender, and the set of (hierarchically-aggregated, based on cell size) present-on-admission diagnosis codes, and then define the decision rule as: Recommend CABG if the probability under the CABG model is lower than the probability under the PCA model, and recommend PCA otherwise.
�� Data were randomly partitioned into separate training and model calibration datasets for each treatment (50% and 25% of each treatment's samples, respectively) as well as a combined test dataset (25% of each treatment's sample) for evaluating the decision rule against existing practice (see Figure).
�� Elastic net regression was used to fit parsimonious models to their respective training datasets; each model was then recalibrated using the respective model calibration datasets so that predicted probabilities better represented observed outcome incidences in the test dataset.� The decision rule was then defined according to the recalibrated models' probability estimates for a given set of comorbidity values.
�� The test dataset was then used to recommend either treatment on the basis of the decision rule.� We then compared the odds of mortality between discharges conforming and not conforming to the rule's recommendation.
Results:
�� In the test dataset, mortality was observed for 365/22,664 (1.6%) CABG discharges and 793/79,079 (1.0%) PCA discharges.� Modeling resulted in 18 and 81 predictors for the CABG and PCA models, respectively.� Overall, there were 27,572/101,743 (27%) non-conforming discharges: 6,025/7,142 (84.4%) recommended for CABG and 21,547/94,601 (22.8%) recommended for PCA.� Odds of mortality were 4.51 times greater among non-conforming discharges than among conforming discharges (95% CI: 4.01-5.09).
Conclusion:
�� This approach may be effective for informing common decisions.� Future research will refine the methods, include preferences, and define limitations.