OBJECTIVE: Despite improvements in the care of critically ill patients, hospital mortality rate for acute lung injury remains high at approximately 40%. We developed a classification rule to stratify mechanically ventilated patients with acute lung injury according to hospital mortality and compared this rule with the Acute Physiology and Chronic Health Evaluation III prediction. PATIENTS: We used data of 2,022 participants in Acute Respiratory Distress Syndrome Network trials to build a classification rule based on 54 variables collected before randomization. DESIGN: We used a classification tree approach to stratify patients according to hospital mortality using a training subset of 1800 participants and estimated expected prediction errors using tenfold crossvalidation. We validated our classification tree using a subset of 222 participants not included in model building and calculated areas under the receiver operating characteristic curves. MEASUREMENTS AND MAIN RESULTS: We identified combinations of age (>63 yrs), blood urea nitrogen (>15 mg/dL), shock, respiratory rate (>21 breaths/min), and minute ventilation (>13.9 L/min) as important predictors of hospital mortality at 90 days. The classification tree had a similar expected prediction error in the training set (28% vs. 26%; p = .18) and areas under the receiver operating characteristic curve in the validation set (0.71 vs. 0.73; p = .71) as did a model based on Acute Physiology and Chronic Health Evaluation III. CONCLUSIONS: Our tree-based classification rule performed similarly to Acute Physiology and Chronic Health Evaluation III in stratifying patients according to hospital mortality, is simpler to use, contains risk factors that may be specific to acute lung injury, and identified minute ventilation as a potential novel predictor of death in patients with acute lung injury.
OBJECTIVE: Despite improvements in the care of critically illpatients, hospital mortality rate for acute lung injury remains high at approximately 40%. We developed a classification rule to stratify mechanically ventilated patients with acute lung injury according to hospital mortality and compared this rule with the Acute Physiology and Chronic Health Evaluation III prediction. PATIENTS: We used data of 2,022 participants in Acute Respiratory Distress Syndrome Network trials to build a classification rule based on 54 variables collected before randomization. DESIGN: We used a classification tree approach to stratify patients according to hospital mortality using a training subset of 1800 participants and estimated expected prediction errors using tenfold crossvalidation. We validated our classification tree using a subset of 222 participants not included in model building and calculated areas under the receiver operating characteristic curves. MEASUREMENTS AND MAIN RESULTS: We identified combinations of age (>63 yrs), blood ureanitrogen (>15 mg/dL), shock, respiratory rate (>21 breaths/min), and minute ventilation (>13.9 L/min) as important predictors of hospital mortality at 90 days. The classification tree had a similar expected prediction error in the training set (28% vs. 26%; p = .18) and areas under the receiver operating characteristic curve in the validation set (0.71 vs. 0.73; p = .71) as did a model based on Acute Physiology and Chronic Health Evaluation III. CONCLUSIONS: Our tree-based classification rule performed similarly to Acute Physiology and Chronic Health Evaluation III in stratifying patients according to hospital mortality, is simpler to use, contains risk factors that may be specific to acute lung injury, and identified minute ventilation as a potential novel predictor of death in patients with acute lung injury.
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