AI Article Synopsis
- - The study aimed to predict short and long-term mortality in patients who experienced unplanned hospitalizations in a medical oncology ward, based on data collected from May 2021 to May 2022 at Tekirdag Namik Kemal University Hospital in Turkey.
- - A total of 253 advanced cancer patients were included, revealing that 11.1% died within 10 days (short-term mortality) and 35.6% died eventually during the study period (long-term mortality), with various factors identified as significant predictors of short-term mortality.
- - The results emphasize the complexity of making treatment decisions for cancer patients in such situations, suggesting that new mortality prediction models could help guide the transition from curative to palliative care.
Article Abstract
Objective: To predict short and long-term mortality in patients who were admitted to the emergency department and then hospitalised unplanned in medical oncology-ward.
Study Design: An observational study. Place and Duration of the Study: Department of Medical Oncology, Tekirdag Namik Kemal University Hospital, Tekirdag, Turkiye, from May 2021 to May 2022.
Methodology: Consecutive patients admitted to the emergency department with unplanned hospitalisation in the oncology ward, were included. Patients receiving treatment with the curative intent, patients hospitalised for febrile neutropenia, and terminally ill patients requiring intensive care unit follow-up at admission were excluded from the study. Univariate and multivariate logistic regression analyses were used to identify predictive factors for short and long-term mortality-dependent variables.
Results: This study included 253 advanced cancer patients. The number of patients who died in the ward within 10 days (short-term mortality) was 28 (11.1%). Ninety patients (35.6%) died afterwards anytime in the ward during the study (long-term mortality). In the multivariate analysis established for short-term mortality, higher ALT (OR = 6.75, 95% CI: 2.09 - 21.85, p=0.001), rapid deterioration in performance status (OR = 5.49, 95% CI: 1.81-16.67, p=0.003), higher CRP (OR = 5.86, 95% CI: 1.20-28.53, p=0.029), higher procalcitonin (OR = 7.94, 95% CI: 0.99 - 63.82, p=0.051), and higher lactate (OR = 2.47, 95% CI: 0.94-6.51, p=0.067) showed significant predictive features.
Conclusion: The decision of whether to continue treatment or not is challenging in cancer patients who require unplanned hospitalisation while receiving palliative systemic therapy. New mortality estimation models can be used in making the transition from life-long to palliative treatments.
Key Word: Mortality prediction, Hospitalisation, Estimation of survival, Chemotherapy.
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Source |
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| http://dx.doi.org/10.29271/jcpsp.2023.05.548 | DOI Listing |
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