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Abstract and Introduction
Abstract
Objectives: The existing risk prediction model for patients requiring prolonged mechanical ventilation is not applicable until after 21 days of mechanical ventilation. We sought to develop and validate a mortality prediction model for patients earlier in the ICU course using data from day 14 of mechanical ventilation.
Design: Multicenter retrospective cohort study.
Setting: Forty medical centers across the United States.
Patients: Adult patients receiving at least 14 days of mechanical ventilation.
Interventions: None.
Measurements and Main Results: Predictor variables were measured on day 14 of mechanical ventilation in the development cohort and included in a logistic regression model with 1-year mortality as the outcome. Variables were sequentially eliminated to develop the ProVent 14 model. This model was then generated in the validation cohort. A simplified prognostic scoring rule (ProVent 14 Score) using categorical variables was created in the development cohort and then tested in the validation cohort. Model discrimination was assessed by the area under the receiver operator characteristic curve. Four hundred ninety-one patients and 245 patients were included in the development and validation cohorts, respectively. The most parsimonious model included age, platelet count, requirement for vasopressors, requirement for hemodialysis, and nontrauma admission. The area under the receiver operator characteristic curve for the ProVent 14 model using continuous variables was 0.80 (95% CI, 0.76–0.83) in the development cohort and 0.78 (95% CI, 0.72–0.83) in the validation cohort. The ProVent 14 Score categorized age at 50 and 65 years old and platelet count at 100 × 10/L and had similar discrimination as the ProVent 14 model in both cohorts.
Conclusion: Using clinical variables available on day 14 of mechanical ventilation, the ProVent 14 model can identify patients receiving prolonged mechanical ventilation with a high risk of mortality within 1 year. (Crit Care Med 2015; 43:2339–2345)
Introduction
For an increasing number of patients, critical illness or injury is neither self-limited nor imminently fatal. As survival from life-threatening illness improves, preexisting disease and newly acquired organ dysfunction may conspire against recovery, leaving patients dependent on life-supporting therapies for extended time periods. This syndrome, termed "chronic critical illness," is commonly typified by persistent respiratory failure that requires prolonged mechanical ventilation (PMV). Long-term mortality is high, approaching rates of 40–60% at 1 year in inclusive cohorts. Patients have a very high symptom burden during the weeks of prolonged ventilation, and chances of living at home with functional independence at the end of the year are as low as 10%.
Most PMV patients are unable to participate in their own clinical decision making and must therefore rely on surrogates to guide their goals of care. These surrogate decision makers report that physicians rarely discuss prognosis, and studies have shown that physician and family estimates of 1-year outcome are highly discordant. Traditional approaches to ICU mortality prediction, such as Acute Physiology and Chronic Health Evaluation (APACHE), are ill-suited to predicting longer term outcomes, particularly in this population. Our research group developed and validated the ProVent mortality prediction model, which estimates 1-year mortality for patients receiving at least 21 days of mechanical ventilation to address prognostic uncertainty for these unique patients.
However, many important decisions regarding the care of PMV patients occur before 21 days have elapsed, including electing to perform tracheotomy or transfer care to a long-term acute care hospital, both of which in are in some form decisions to continue life-sustaining therapies. If compelling prognostic data were available sooner for the patient whose goals of care are known, these decisions might be made more easily and earlier in the course of illness. Building on our previous work, we hypothesized that a simple mortality prediction model constructed from factors clinically available by day 14 in the course of mechanical ventilation could discriminate between PMV patients at high and low risk of death at 1 year. Using a heterogeneous multicenter cohort of PMV patients in addition to a contemporaneous cohort of patients with acute lung injury (ALI) who were enrolled in a randomized, controlled trial, we sought to develop and validate a new model using data collected on day 14 of mechanical ventilation rather than on day 21 as in our previous model.
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