Mortality Prediction for 14 Days of Mechanical Ventilation
Mortality Prediction for 14 Days of Mechanical Ventilation
A total of 491 patients were included in the development cohort. Patient characteristics and hospital outcomes are presented in Table 1. The mean age of patients was 54 ± 17 years old, and 40% were female. Median (interquartile range, IQR) duration of mechanical ventilation was 22 days (17–33 d). Hospital mortality was 29%, and 1-year mortality was 45%. Univariate analysis of associations between predictor variables and 1-year mortality is presented in Table 2. The AUC for the logistic regression model with all predictor variables was 0.80. Stepwise elimination of gender, glucose, WBC count, hemoglobin, and PEEP level resulted in a final model containing age, platelet count, vasopressors, hemodialysis, and nontrauma diagnosis (supplemental equation model presented in Supplemental Digital Content 1, http://links.lww.com/CCM/B369). Enrollment site was not an independent predictor when added to this model and did not affect the AUC. The AUC for the final parsimonious model was 0.80 (95% CI, 0.76–0.83), and the Hosmer and Lemeshow goodness-of-fit statistic was 3.32 with 8 df (p = 0.91). In contrast, the AUC using APACHE III to predict 1-year mortality in this cohort was 0.60 (95% CI, 0.55–0.64), which differed significantly from the ProVent 14 model (p < 0.0001).
Three hundred forty-two patients from FACTT received 14 or more days of mechanical ventilation, of whom 245 had known 1-year outcomes through EA-PAC and were included in the validation cohort. Table 1 compares patient characteristics and outcomes between the development and validation cohorts. Patients in the two cohorts were similar in age, gender, and race and had similar median days of mechanical ventilation. Patients in the validation cohort had higher acute illness severity at hospital admission as represented by APACHE III score, and they had higher hospital and 1-year mortality. The AUC for the ProVent 14 model in the validation cohort was 0.78 (95% CI, 0.72–0.83), and the Hosmer-Lemeshow statistic was 9.39 (p = 0.31). The AUC using APACHE III to predict 1-year mortality in the validation cohort was 0.62 (95% CI, 0.55–0.68), which differed significantly from the ProVent 14 model (p < 0.0001).
To develop the simplified prognostic scoring rule, age was cut at 50 and 65 years old as in the original ProVent model. Platelet count was cut at 100 × 10, which was associated with a higher risk of mortality when measured at day 14 than a cutpoint of 150 × 10 as in the original 21-day model. In the development cohort, a logistic regression model was fit with these categorized variables as independent variables and 1-year mortality as the dependent variable. Points assigned to each predictor according to the β coefficients from the categorical model are shown in Table 3.
Cumulative points based on the number of predictor variables present for a patient constitute the ProVent 14 Score. We combined the seven possible scores into five categories: 0, 1, 2, 3, and 4 or greater. Figure 1 shows the Kaplan-Meier plots with long-term survival by ProVent 14 Score in the development and validation cohorts. Table 4 presents observed mortality by ProVent 14 score. ProVent 14 Score models performed well both in the development cohort (AUC, 0.78; 95% CI, 0.74–82; Hosmer-Lemeshow statistic, 9.57; p = 0.02) and in the validation cohort (AUC, 0.76; 95% CI, 0.70–0.81; Hosmer-Lemeshow statistic, 1.45; p = 0.69).
(Enlarge Image)
Figure 1.
A, Kaplan-Meier plots of survival for development cohort by ProVent 14 Score. B, Kaplan-Meier plots of survival for validation cohort by ProVent 14 Score.
Results
Model Development
A total of 491 patients were included in the development cohort. Patient characteristics and hospital outcomes are presented in Table 1. The mean age of patients was 54 ± 17 years old, and 40% were female. Median (interquartile range, IQR) duration of mechanical ventilation was 22 days (17–33 d). Hospital mortality was 29%, and 1-year mortality was 45%. Univariate analysis of associations between predictor variables and 1-year mortality is presented in Table 2. The AUC for the logistic regression model with all predictor variables was 0.80. Stepwise elimination of gender, glucose, WBC count, hemoglobin, and PEEP level resulted in a final model containing age, platelet count, vasopressors, hemodialysis, and nontrauma diagnosis (supplemental equation model presented in Supplemental Digital Content 1, http://links.lww.com/CCM/B369). Enrollment site was not an independent predictor when added to this model and did not affect the AUC. The AUC for the final parsimonious model was 0.80 (95% CI, 0.76–0.83), and the Hosmer and Lemeshow goodness-of-fit statistic was 3.32 with 8 df (p = 0.91). In contrast, the AUC using APACHE III to predict 1-year mortality in this cohort was 0.60 (95% CI, 0.55–0.64), which differed significantly from the ProVent 14 model (p < 0.0001).
Model Validation
Three hundred forty-two patients from FACTT received 14 or more days of mechanical ventilation, of whom 245 had known 1-year outcomes through EA-PAC and were included in the validation cohort. Table 1 compares patient characteristics and outcomes between the development and validation cohorts. Patients in the two cohorts were similar in age, gender, and race and had similar median days of mechanical ventilation. Patients in the validation cohort had higher acute illness severity at hospital admission as represented by APACHE III score, and they had higher hospital and 1-year mortality. The AUC for the ProVent 14 model in the validation cohort was 0.78 (95% CI, 0.72–0.83), and the Hosmer-Lemeshow statistic was 9.39 (p = 0.31). The AUC using APACHE III to predict 1-year mortality in the validation cohort was 0.62 (95% CI, 0.55–0.68), which differed significantly from the ProVent 14 model (p < 0.0001).
ProVent 14 Score
To develop the simplified prognostic scoring rule, age was cut at 50 and 65 years old as in the original ProVent model. Platelet count was cut at 100 × 10, which was associated with a higher risk of mortality when measured at day 14 than a cutpoint of 150 × 10 as in the original 21-day model. In the development cohort, a logistic regression model was fit with these categorized variables as independent variables and 1-year mortality as the dependent variable. Points assigned to each predictor according to the β coefficients from the categorical model are shown in Table 3.
Cumulative points based on the number of predictor variables present for a patient constitute the ProVent 14 Score. We combined the seven possible scores into five categories: 0, 1, 2, 3, and 4 or greater. Figure 1 shows the Kaplan-Meier plots with long-term survival by ProVent 14 Score in the development and validation cohorts. Table 4 presents observed mortality by ProVent 14 score. ProVent 14 Score models performed well both in the development cohort (AUC, 0.78; 95% CI, 0.74–82; Hosmer-Lemeshow statistic, 9.57; p = 0.02) and in the validation cohort (AUC, 0.76; 95% CI, 0.70–0.81; Hosmer-Lemeshow statistic, 1.45; p = 0.69).
(Enlarge Image)
Figure 1.
A, Kaplan-Meier plots of survival for development cohort by ProVent 14 Score. B, Kaplan-Meier plots of survival for validation cohort by ProVent 14 Score.
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