Comparison of the Parsonnet score and hospital-specific models using cardiac surgery patients from Montreal

Rationale: Risk assessment tools are frequently used to help surgeons and patients decide whether the potential benefits of cardiac surgery outweigh the risks. One of the most popular risk estimation tools, the 2000 Parsonnet score, has shown varying levels of predictive ability in different regions throughout the world. This risk estimation tool is based on 37 variables. According to the literature, institutional quality, medical personnel and patient ethnicity also have predictive value when measuring the risk of in-hospital mortality yet these are not included as risk factors in the model. It is hypothesized that a regional score based on the 2000 Parsonnet score would have greater accuracy in its patient population. This is because the 2000 Parsonnet score was developed using data from only one region (New Jersey) and the literature has shown that it has varying levels of accuracy in different regions. Methods: Patient level data from two Montreal hospitals were used to create two hospital-specific models based on the 2000 Parsonnet score using multivariable logistic regression. There were 1,162 patients included from one hospital and 2,656 patients from the other hospital. Risk factor coefficients of the hospital-specific models were compared to the coefficients of the Parsonnet model using z-tests. Additionally, the predictive accuracies of the hospital-specific models were compared to the Parsonnet model using receiver operating characteristic (ROC) analyses. The number of preventable deaths at different risk thresholds was also calculated between models.Results: Between the Montreal-based institutions, 2 variables had different risk factor coefficients. One hospital-specific model had 4 different risk factor weights and the other hospital-specific model also had 4 different weights when compared to the Parsonnet model, (p≤0.05). Only one model was found to be marginally more accurate with an area under the ROC curve of 0.882 compared to 0.868 for the Parsonnet model (p=0.049). No difference was detected for the other model with an area under the ROC curve of 0.804 compared to 0.798 for the Parsonnet model (p>0.05). Additionally, in one hospital, for three out of four risk thresholds, the use of the hospital-specific model instead of the Parsonnet model would have prevented between 2 and 8 in-hospital mortalities. In the other hospital, for four out of four risk thresholds, the use of the hospital-specific model instead of the Parsonnet model would have prevented between 3 and 18 in-hospital mortalities. Conclusions: These results demonstrate that the Parsonnet model has approximately the same risk factor weights and predictive accuracy as the hospital-specific models. Also, the potential clinical benefit of prevented mortality was too small to justify using hospital-specific models over the Parsonnet model. Future research in other regions and in a larger number of hospitals is required to validate this conclusion.

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