title: Predictive models of prolonged mechanical ventilation yield moderate accuracy.
abstract: OBJECTIVE
  To develop a model to predict prolonged mechanical ventilation within 48 hours of its initiation.
  
  
  METHODS
  In 282 general intensive care unit patients, multiple variables from the first 2 days on mechanical ventilation and their total ventilation duration were prospectively collected. Three models accounting for early deaths were developed using different analyses: (a) multinomial logistic regression to predict duration > 7 days vs duration ≤ 7 days alive vs duration ≤ 7 days death; (b) binary logistic regression to predict duration > 7 days for the entire cohort and for survivors only, separately; and (c) Cox regression to predict time to being free of mechanical ventilation alive.
  
  
  RESULTS
  Positive end-expiratory pressure, postoperative state (negatively), and Sequential Organ Failure Assessment score were independently associated with prolonged mechanical ventilation. The multinomial regression model yielded an accuracy (95% confidence interval) of 60% (53%-64%). The binary regression models yielded accuracies of 67% (61%-72%) and 69% (63%-75%) for the entire cohort and for survivors, respectively. The Cox regression model showed an equivalent to area under the curve of 0.67 (0.62-0.71).
  
  
  CONCLUSIONS
  Different predictive models of prolonged mechanical ventilation in general intensive care unit patients achieve a moderate level of overall accuracy, likely insufficient to assist in clinical decisions.
authors: Division of Pulmonary and Critical Care Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX. Electronic address: Juan.Figueroa@ttuhsc.edu.
authors: Juan B
authors: JB
authors: Figueroa-Casas
authors: Division of Biostatistics and Epidemiology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX.
authors: Alok K
authors: AK
authors: Dwivedi
authors: Department of Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX.
authors: Sean M
authors: SM
authors: Connery
authors: Department of Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX.
authors: Raphael
authors: R
authors: Quansah
authors: Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX.
authors: Lowell
authors: L
authors: Ellerbrook
authors: Department of Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX.
authors: Juan
authors: J
authors: Galvis
mesh_terms: D000328:Adult; D000368:Aged; D015331:Cohort Studies; D003422:Critical Care; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D009104:Multiple Trauma / Q000628:therapy*; D063305:Organ Dysfunction Scores; D011175:Positive-Pressure Respiration; D011183:Postoperative Complications / Q000628:therapy*; D011446:Prospective Studies; D012121:Respiration, Artificial / Q000706:statistics & numerical data*; D012128:Respiratory Distress Syndrome / Q000628:therapy*; D013223:Statistics as Topic; D017741:Survivors; D013997:Time Factors; D015300:Ventilator Weaning / Q000706:statistics & numerical data
keywords: Epidemiological methods; Mechanical ventilation; Mechanical ventilator weaning; Projections and predictions; Regression analyses