A multicenter mortality prediction model for patients receiving prolonged mechanical ventilation

Shannon S. Carson, Jeremy M. Kahn, Catherine L. Hough, Eric J. Seeley, Douglas B. White, Ivor S. Douglas, Christopher E. Cox, Ellen Caldwell, Shrikant I. Bangdiwala, Joanne M. Garrett, Gordon D. Rubenfeld

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

Abstract

Objective: Significant deficiencies exist in the communication of prognosis for patients requiring prolonged mechanical ventilation after acute illness, in part because of clinician uncertainty about long-term outcomes. We sought to refine a mortality prediction model for patients requiring prolonged ventilation using a multicentered study design. Design: Cohort study. Setting: Five geographically diverse tertiary care medical centers in the United States (California, Colorado, North Carolina, Pennsylvania, and Washington). Patients: Two hundred sixty adult patients who received at least 21 days of mechanical ventilation after acute illness. Interventions: None. Measurements and Main Results: For the probability model, we included age, platelet count, and requirement for vasopressors and/or hemodialysis, each measured on day 21 of mechanical ventilation, in a logistic regression model with 1-yr mortality as the outcome variable. We subsequently modified a simplified prognostic scoring rule (ProVent score) by categorizing the risk variables (age 18-49, 50-64, and ≥65 yrs; platelet count 0-150 and >150; vasopressors; hemodialysis) in another logistic regression model and assigning points to variables according to β coefficient values. Overall mortality at 1 yr was 48%. The area under the curve of the receiver operator characteristic curve for the primary ProVent probability model was 0.79 (95% confidence interval 0.75-0.81), and the p value for the Hosmer-Lemeshow goodness-of-fit statistic was .89. The area under the curve for the categorical model was 0.77, and the p value for the goodness-of-fit statistic was .34. The area under the curve for the ProVent score was 0.76, and the p value for the Hosmer-Lemeshow goodness-of-fit statistic was .60. For the 50 patients with a ProVent score >2, only one patient was able to be discharged directly home, and 1-yr mortality was 86%. Conclusion: The ProVent probability model is a simple and reproducible model that can accurately identify patients requiring prolonged mechanical ventilation who are at high risk of 1-yr mortality.

Original languageEnglish (US)
Pages (from-to)1171-1176
Number of pages6
JournalCritical care medicine
Volume40
Issue number4
DOIs
StatePublished - Apr 2012
Externally publishedYes

Keywords

  • communication
  • critical care
  • mechanical ventilation
  • multiple organ failure
  • outcomes
  • prognosis

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine

Fingerprint Dive into the research topics of 'A multicenter mortality prediction model for patients receiving prolonged mechanical ventilation'. Together they form a unique fingerprint.

Cite this