Advancing prognostic precision in unplanned oncology admissions: the promise of the PROMISE–CTI score
Unplanned hospital admissions continue to pose a substantial and resource-intensive burden in oncology, particularly for patients with advanced malignancies.These episodes frequently signal impending clinical deterioration and are associated with high short-term mortality.Despite the critical nature of these events, validated tools to stratify risk and guide early intervention have remained limited in both scope and external applicability.In their timely and methodologically robust study, Uyar et al. deliver a significant advancement by externally validating the Prognostic Score for Hospitalised Cancer Patients (PROMISE) and proposing a novel composite metric-PROMISE-CTI-which integrates the CRP-Triglyceride-Glucose Index (CTI) to enhance the predictive performance for 90-day mortality in this vulnerable population. 1 The PROMISE score, originally developed by Mirallas and colleagues, incorporates ECOG performance status, serum albumin, lactate dehydrogenase (LDH), and absolute neutrophil count to estimate short-term mortality risk in hospitalised cancer patients. 2Within a large, retrospective Turkish cohort (n = 1109), the authors confirmed the discriminative validity of PROM-ISE, with the combined PROMISE-CTI model achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.884.Remarkably, individuals classified as low risk by the combined metric demonstrated a 0% 90-day mortality rate, indicating a high negative predictive value and strong potential utility for clinical triage. 1 This study exhibits several noteworthy strengths.Firstly, it expands the generalisability of the PROMISE score beyond its original Spanish development cohort.Secondly, the incorporation of CTI-a parameter reflecting inflammatory and metabolic dysregulationadds physiological depth to the model without undermining feasibility.All components of the combined score are routinely available at the time of admission, rendering it readily applicable at the point of care. 3Furthermore, the validation occurred in the context of unscheduled hospitalisations, rather than within a
Authors
- İrfan Çiçin (ORCID: https://orcid.org/0000-0002-7584-3868)
- Mustafa Atçı
- Emir Çelik (ORCID: https://orcid.org/0000-0001-8440-3082)
Institutions
- Istinye University (TR)
Publication Details
- Journal
- The Lancet Regional Health - Europe
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1016/j.lanepe.2025.101336
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00