PREDICTING THE DURATION OF MECHANICAL VENTILATION - THE IMPORTANCE OFDISEASE AND PATIENT CHARACTERISTICS

Study objective: To analyze the determinants of an individual patient' s duration of mechanical ventilation and assess interhospital variatio ns for average durations of ventilation. Design: Prospective, multicen ter, inception, cohort study. Setting: Forty-two ICUs at 40 US hospita ls. Patients: A total of 5,915 patients undergoing mechanical ventilat ion on ICU day 1 selected from the acute physiology and chronic health evaluation (APACHE) III database of 17,440 admissions. Interventions: None. Measurements and results: Utilizing APACHE III data collected o n the 5,915 patients, multivariate regression analysis was performed o n selected patients and disease characteristics to determine which var iables were significantly associated with the duration of mechanical v entilation. An equation predicting duration of ventilation was then de veloped using the significant predictor variables and its accuracy was evaluated. Variables significantly associated with duration of ventil ation included primary reason for ICU admission, day 1 acute physiolog y score (APS) of APACHE III, age, prior patient location and hospital length of stay, activity limits due to respiratory disease, serum albu min, respiratory rate, and PaO2/FIo(2) measurements. Using an equation derived from these variables, predicted durations of ventilation were then calculated and compared with actual observed durations for each of the 42 ICUs. Average duration of ventilation for the 42 ICUs ranged from 2.6 to 7.9 days, but 60% of this variation was accounted for by differences in patient characteristics. Conclusions: For patients admi tted to the ICU and ventilated on day 1, total duration of ventilation is primarily determined by admitting diagnosis and degree of physiolo gic derangement as measured by APS. An equation developed using multiv ariate regression techniques can accurately predict average duration o f ventilation for groups of ICU patients, and we believe this equation will be useful for comparing ventilator practices between ICUs, contr olling for patient differences in clinical trials of new therapies or weaning techniques, and as a quality improvement mechanism.