Predictive factors of death in primary lung cancer patients on admission to the intensive care unit

Intensive Care Med. 2000 Dec;26(12):1811-6. doi: 10.1007/s001340000701.

Abstract

Objective: To assess the lung cancer patient's prognosis in the intensive care unit with early predictive factors of death.

Design: Retrospective study from July 1986 to February 1996.

Setting: Medical intensive care unit at a university hospital.

Patients: Fifty-seven patients with primary lung cancer admitted to our medical intensive care unit (MICU).

Measurements and results: Data collection included demographic data (age, sex, underlying diseases, MICU admitting diagnosis) and evaluation of tumor (pathologic subtypes, metastases, lung cancer staging, treatment options). Three indexes were calculated for each patient: Karnofsky performance status, Simplified Acute Physiology Score (SAPS) II, and multisystem organ failure score (ODIN score). Mortality was high in the MICU: 66% of patients died during their MICU stay, and hospital mortality reached 75%. In multivariate analysis, acute pulmonary disease and Karnofsky performance status < 70 were associated with a poor MICU and post-MICU prognosis. For the survivors, long-term survival after MICU discharge depended exclusively on the severity of the lung cancer.

Conclusions: We confirmed the high mortality rate of lung cancer patients admitted to the MICU. Two predictive factors of death in MICU were identified: performance status < 70 and acute pulmonary disease.

Publication types

  • Validation Study

MeSH terms

  • APACHE
  • Activities of Daily Living
  • Acute Disease
  • Aged
  • Hospital Mortality*
  • Humans
  • Intensive Care Units / statistics & numerical data*
  • Lung Neoplasms / classification
  • Lung Neoplasms / complications
  • Lung Neoplasms / mortality*
  • Middle Aged
  • Multiple Organ Failure / etiology
  • Multivariate Analysis
  • Neoplasm Staging
  • Patient Admission / statistics & numerical data*
  • Predictive Value of Tests
  • Prognosis
  • Proportional Hazards Models
  • Retrospective Studies
  • Risk Factors
  • Severity of Illness Index
  • Survival Analysis