Correspondence  |   October 2016
Prediction Model for In-hospital Mortality Should Accurately Predict the Risks of Patients Who Are Truly at Risk
Author Notes
  • Vanderbilt University Medical Center, Nashville, Tennessee (T.H.K). teus.kappen@vanderbilt.edu
  • (Accepted for publication June 16, 2016.)
    (Accepted for publication June 16, 2016.)×
Article Information
Correspondence
Correspondence   |   October 2016
Prediction Model for In-hospital Mortality Should Accurately Predict the Risks of Patients Who Are Truly at Risk
Anesthesiology 10 2016, Vol.125, 815-816. doi:10.1097/ALN.0000000000001269
Anesthesiology 10 2016, Vol.125, 815-816. doi:10.1097/ALN.0000000000001269
With great interest, we read the article by Le Manach et al.1  The article presents a prediction model for postoperative in-hospital mortality with very good discriminative abilities (C statistic of 0.93 in a validation cohort). However, we contend the conclusion that the predictive model is well calibrated.
A prediction model should first and foremost provide accurate predicted probabilities. When validating a prediction model, it is essential to answer the question whether predicted probabilities correspond to observed probabilities, especially for patients who may have a clinically relevant risk of the predicted outcome.
The reported calibration plot (fig. 2 in the article1 ) seems to show a well-calibrated model. However, the calibration plot is truncated at a predicted probability of 0.10, and the plot shows only 9 out of 10 deciles. Patients with the highest risks seem to have been omitted from the reported calibration plot. Figure 3 of the article1  shows the observed mortality in the validation cohort for a wider range of risk scores. Supplemental Digital Content 3 reports the predicted probabilities for all Preoperative Score to Predict Postoperative Mortality (POSPOM) scores. If we overlay the predicted probabilities for all POSPOM scores onto figure 3 of the article,1  we observe that the prediction model greatly overestimates the in-hospital mortality risk in the high-risk patients (fig. 1). Although the high-risk patients form only a small group, they are in fact the patients for whom the prediction model is most clinically relevant. We would not want to be the physician who communicates a 62% risk of death to a patient (POSPOM value of 40) while the actual risk is 23%.
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