Prognostic and risk factor analysis of cancer patients after unplanned ICU admission: a real-world multicenter study

Authors:
Open Access
Publisher website
Google Scholar
Abstract
To investigate the occurrence and 90-day mortality of cancer patients following unplanned admission to the intensive care unit (ICU), as well as to develop a risk prediction model for their 90-day prognosis. We prospectively analyzed data from cancer patients who were admitted to the ICU without prior planning within the past 7 days, specifically between May 12, 2021, and July 12, 2021. The patients were grouped based on their 90-day survival status, and the aim was to identify the risk factors influencing their survival status. A total of 1488 cases were included in the study, with an average age of 63.2 ± 12.4 years. The most common reason for ICU admission was sepsis (n = 940, 63.2%). During their ICU stay, 29.7% of patients required vasoactive drug support (n = 442), 39.8% needed invasive mechanical ventilation support (n = 592), and 82 patients (5.5%) received renal replacement therapy. We conducted a multivariate COX proportional hazards model analysis, which revealed that BMI and a history of hypertension were protective factors. On the other hand, antitumor treatment within the 3 months prior to admission, transfer from the emergency department, general ward, or external hospital, high APACHE score, diagnosis of shock and respiratory failure, receiving invasive ventilation, and experiencing acute kidney injury (AKI) were identified as risk factors for poor prognosis within 90 days after ICU admission. The average length of stay in the ICU was 4 days, while the hospital stay duration was 18 days. A total of 415 patients died within 90 days after ICU admission, resulting in a mortality rate of 27.9%. We selected 8 indicators to construct the predictive model, which demonstrated good discrimination and calibration. The prognosis of cancer patients who are unplanned transferred to the ICU is generally poor. Assessing the risk factors and developing a risk prediction model for these patients can play a significant role in evaluating their prognosis.

This publication has 52 references indexed in Scilit: