Impact of neutropenia duration on short-term mortality in neutropenic critically ill cancer patients

被引:95
作者
Darmon, M
Azoulay, E
Alberti, C
Fieux, F
Moureau, D
Le Gall, JR
Schlemmer, B
机构
[1] St Louis Univ Hosp, Med Intens Care Unit, F-75010 Paris, France
[2] St Louis Univ Hosp, Dept Biostat, Paris, France
[3] Univ Paris 07, Paris, France
关键词
intensive care; cancer patients; neutropenia; organ failure; neutropenia recovery; cox models;
D O I
10.1007/s00134-002-1528-7
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Objective: To identify predictors of 30-day mortality and to assess the impact of neutropenia recovery (NR) on 30-day mortality in critically ill cancer patients (CICPs). Design and setting: Retrospective review of the medical records of the 102 neutropenic CICPs admitted to a medical intensive care unit (ICU) over a 10-year period. Intervention: None. Measurements and results: Malignancies consisted of acute leukemia (n=42), lymphoma (n=23), myeloma (n=28), and solid tumors (n=9). Reasons for ICU admission were acute respiratory failure (n=81), shock (n=58), acute renal failure (n=33), and coma (n=13). Seventy patients needed conventional mechanical ventilation (MV) and 21 noninvasive MV, 67 vasopressor agents, and 28 dialysis. Sixty-two patients experienced NR during their ICU stay. In a multivariate logistic regression model, 30-day mortality was higher in patients with acute respiratory or renal failure and lower in patients with NR (OR, 0.09 [0.01-0.86]). This model assumed that patients who experienced NR in the ICU were merely these who did not die early in the ICU. To take into account the effect of time to occurrence of NR on time to death we secondarily used a Cox model including neutropenia duration and NR as time-dependent variables. In this second model, the only significant predictors of 30-day mortality were age, respiratory failure, renal failure, and coma. Conclusion: Organ failure but not disease progression or neutropenia duration affect 30-day mortality in neutropenic CICPs. ICU-acquired events might be modeled as time-dependent variables in a Cox model, rather than standard covariates in logistic regression models.
引用
收藏
页码:1775 / 1780
页数:6
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