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Outcome of patients with metastatic cancer or haematological malignancy in intensive care

Introduction

We studied our ICU database to estimate the effect of metastatic cancer and haematological malignancy on hospital mortality after intensive treatment.

Methods

The Reinier de Graaf Hospital is a 500-bed nonacademic teaching hospital with a 30-bed dedicated medical oncology ward supervised by a team of medical oncologists and a 10-bed mixed intensivist-led ICU. Data on all patients admitted to the ICU including illness severity scores, the presence of metastatic cancer or haematological malignancy and hospital mortality are prospectively collected in the ICU database. We studied the hospital mortality of all consecutive patients in the database admitted from 2004 until 2007 with respect to the presence or absence of metastatic cancer or haematological malignancy.

Results

A total of 2,951 patients were admitted in the study period. Patients with metastatic cancer or haematological malignancy were older and had higher illness severity scores on admission to the ICU (Table 1). Not surprisingly, hospital mortality was significantly higher in patients with metastatic cancer or haematological malignancy. We performed logistic regression analysis to see whether the higher mortality could be explained by the usual other confounders. After correction for APACHE II expected mortality, age, admission type, sepsis and mechanical ventilation, we found however that the presence of metastatic cancer or haematological malignancy independently increases the risk of dying (OR = 1.7 (95% CI = 1.1 to 2.8, P = 0.024)).

Table 1 (abstract P496)

Conclusion

Hospital mortality is higher in patients with metastatic cancer or haematological malignancy than in other patients, even after correction for age, APACHE II expected mortality, admission type, sepsis and mechanical ventilation.

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Meynaar, I., Visser, S.S., Dawson, L. et al. Outcome of patients with metastatic cancer or haematological malignancy in intensive care. Crit Care 13, P496 (2009). https://doi.org/10.1186/cc7660

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Keywords

  • Logistic Regression
  • Logistic Regression Analysis
  • Mechanical Ventilation
  • Emergency Medicine
  • Teaching Hospital