Critical illness in patients with hematologic malignancy: a population-based cohort study
Ferreyro BL, Scales DC, Wunsch H, Cheung MC, Gupta V, Saskin R, Thyagu S, Munshi L. Intensive Care Med. 2021; Sep 14. [Epub ahead of print]. DOI: https://doi.org/10.1007/s00134-021-06502-2
Purpose — To describe the modern incidence and predictors of ICU admission for adult patients newly diagnosed with a hematologic malignancy.
Methods — We conducted a population-based cohort study of adults with a new diagnosis of hematologic malignancy (April 1, 2006-March 31, 2017) in Ontario, Canada. We described the baseline demographic, clinical and laboratory predictors of ICU admission and subsequent mortality. The primary outcome was the incidence of ICU admission within 1 year of hematologic malignancy diagnosis. We assessed the predictors of ICU admission using Cox-proportional models that accounted for the competing risk of death and reported as subdistribution hazard ratios (sHR) with 95% confidence intervals (CI).
Results — A total of 87,965 patients (mean [SD] age, 67.8 (15.7) years) were included. The 1-year incidence of ICU admission was 13.9% (median time 35 days), ranging from 7.3% (indolent lymphoma) to 22.5% (acute myeloid leukemia). After multivariable adjustment, compared to indolent lymphoma, acute myeloid leukemia (sHR, 3.09; 95% CI 2.84-3.35), aggressive non-Hodgkin lymphoma (sHR, 2.47; 95% CI 2.31-2.65) and acute lymphoblastic leukemia (sHR, 2.46; 95% CI 2.15-2.80) had the highest risk of ICU admission. Comorbidities such as cardiovascular disease (sHR, 2.09; 95% CI 2.01-2.19), chronic obstructive pulmonary disease (sHR, 1.33; 95% CI 1.26-1.39) and baseline laboratory abnormalities (anemia, thrombocytopenia and high creatinine) were also associated with ICU admission. Among ICU patients, 36.7% required invasive mechanical ventilation and in-hospital mortality was 31%.
Conclusion — Critical illness in patients with a newly diagnosed hematologic malignancy is frequent, occurring early after diagnosis. Certain baseline characteristics can help identify those patients at the highest risk.