Journal of the Formosan Medical Association最新文献

Background: Central nervous system (CNS) ependymoma is a heterogeneous tumor with highly variable clinical behavior. Most existing prognostic models primarily focus on overall survival, whereas robust tools specifically predicting cancer-specific survival (CSS) in CNS ependymoma are lacking. We aimed to develop a Surveillance, Epidemiology, and End Results (SEER)-based nomogram for individualized prediction of CSS in patients with CNS ependymoma.

Methods: We identified patients with primary CNS ependymoma (2010-2022) from the Surveillance, Epidemiology, and End Results (SEER) database. The cohort was randomly split in a 7:3 ratio into training and validation cohorts; patients were assigned using a computer-generated random number sequence. Multivariable Cox proportional hazards regression was used to identify independent prognostic factors. A nomogram was constructed to predict 3-, 5-, and 8-year CSS. Model performance was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), and was compared with a SEER summary stage-only model.

Results: The study included 1744 patients with CNS ependymoma. During a median follow-up of 63 months, the 3-, 5-, and 8-year CSS rates were 92.4%, 90.5%, and 86.7%, respectively. Multivariable analysis identified age, sex, histologic subtype, primary tumor site, and SEER summary stage as independent predictors of CSS. The nomogram demonstrated superior discrimination, with C-indices of 0.818 in the training cohort and 0.827 in the validation cohort, significantly outperforming the stage-only model (0.588 and 0.547, respectively). Similarly, the nomogram yielded higher areas under the curve (AUCs) at all time points (e.g., 5-year AUC: 0.850 vs. 0.600 in the training cohort). Calibration plots showed excellent agreement between predicted and observed CSS, and DCA indicated a higher net clinical benefit for the nomogram than for the stage-only model across a broad range of threshold probabilities.

Conclusions: We developed and internally validated a robust, CSS-oriented nomogram for CNS ependymoma using a contemporary, population-based cohort. This tool outperformed conventional staging systems and may provide a practical approach for individualized risk stratification and clinical decision-making in patients with CNS ependymoma.

Background: Although palliative care consultation is the predominant delivery model, the essential elements for effective interprofessional communication remain poorly defined. This study aimed to identify key information components necessary for effective palliative care consultations.

Method: We established Palliative Care consultation Information (INFORMATION) items through literature review and expert consensus. Three experienced palliative care specialists retrospectively examined palliative care consultation documents from National Taiwan University Hospital (April 1-30, 2023) in two phases: documentation effectiveness and INFORMATION clarity. Document effectiveness was rated on a 5-point scale to assess how well the documents assist palliative care consultation teams. INFORMATION clarity was rated as clear, vague, or omitted. The correlation between INFORMATION clarity and document effectiveness was analyzed using multivariate logistic regression.

Results: In total, 189 palliative care consultation documents were evaluated. Description quality was suboptimal across most INFORMATION items. Diagnosis was clearly presented in 92.06% of documents and advance decision in 62.96%. Information regarding place of death preferences, prognosis, and decision-maker was omitted in >50% of documents, whereas that for symptom and disease insight was predominantly vague at 83.59% and 59.79%, respectively. Multivariate analysis identified five INFORMATION items that significantly predicted document effectiveness: decision-maker (AOR [adjusted odds ratio] = 51.5, p < 0.001), place of death preferences (AOR = 28.0, p < 0.007), disease insight (AOR = 13.1, p < 0.038), symptoms (AOR = 38.1, p < 0.049), and consultation objectives (AOR = 4.65, p < 0.048).

Conclusion: Most INFORMATION items lacked clarity. Improving documentation of decision-maker, place of death preferences, disease insight, symptoms, and consultation objectives can enhance consultation communication effectiveness.