Prognostic studies for health care decision making

Abstract

A multicenter retrospective cohort study was conducted to develop and validate a prognostic model to predict 1-year mortality among adult patients receiving at least 14 uninterrupted days of mechanical ventilation. Likely prognostic variables were chosen, a priori, based on published literature and clinical judgment (10 variables). During the development phase of the study, the prognostic variables were included in a logistic regression model to evaluate how well each variable predicted 1-year mortality by calculating discrimination (ability to correctly classify patients into those who did and did not die) using ROC curves and area under the curve (AUC). The authors found that 5 of the 10 variables maximized the prognostic capability of the model for 1-year mortality (age, platelet count, vasopressor use, hemodialysis, no trauma diagnosis) showing very good discrimination (AUC = 0.80; 95% CI: 0.76-0.83). For the validation phase, the authors used the β-coefficient values estimated for each variable in the development cohort logistic regression model to predict 1-year mortality in a new cohort of patients and showed that discrimination was also very good (AUC = 0.78; 95% CI: 0.72-0.83), thus showing that the 5-variable model was valid. Then the authors created a clinical prediction rule, a point system used to easily calculate the probability of 1-year mortality for each patient, based on the strength of association of each variable (β-coefficient) with mortality in the development model. All β-coefficients were assigned 1 point except for the category "age ≥ 65 years," which was assigned 2 points. Lastly, the authors validated this point system by showing that, as the number of points increased, the probability of 1-year mortality increased.(1)