BACKGROUND: Accurately estimating short-term survival in individuals receiving palliative care is crucial for guiding personalized clinical decisions. Existing tools often rely on subjective assessments and have limited applicability. This study aimed to develop a simple, interpretable nomogram integrating objective biomarkers and functional status, applicable to a diverse population of end-stage patients. METHODS: A total of 167 patients who began working with the palliative care team were retrospectively reviewed, and the cohort was randomly partitioned into a 7:3 training (n = 117) and an internal validation set (n = 50). From an initial pool of 31 variables, independent prognostic factors were identified using least absolute shrinkage and selection operator (LASSO) Cox regression, while optimal thresholds for continuous measures were determined via maximally selected rank statistics. A nomogram was subsequently developed, and its performance was evaluated through time-dependent receiver operating characteristic analysis, calibration curves, and decision curve analysis, as well as sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios (LR+ and LR-) in the validation set at 30, 60, and 90 days. RESULTS: = 0.01) were independent risk factors for mortality. The nomogram demonstrated 30-/60-/90-day AUCs of 0.764/0.716/0.705 in the training set and 0.770/0.748/0.788 in the validation set. Calibration plots showed excellent agreement between predicted and observed survival, and Decision curve analysis indicated clinical net benefit across commonly used risk thresholds. In the validation set, the nomogram showed sensitivity/specificity of 69.05%/75.00%, 83.33%/87.50%, and 88.10%/87.50% at 30, 60, and 90 days, respectively; the corresponding LR+ values were 2.762, 6.666, and 7.048, and LR- values were 0.413, 0.191, and 0.136. CONCLUSIONS: We successfully developed and internally validated a simple, interpretable nomogram integrating CRP, eGFR, and BADL scores, capable of rapidly predicting short-term survival in palliative care patients. In addition to favorable discrimination and calibration, the model demonstrated clinically informative classification performance at 30, 60, and 90 days. The model is applicable to a diverse patient population and may assist in clinical decision-making and resource optimization.
Tang et al. (Mon,) studied this question.