Background: Early identification of palliative needs has proven benefits in quality of life, survival and decision-making. The NECesidades PALiativas (NECPAL) Centro Coordinador Organización Mundial de la Salud - Instituto Catalán de Oncología (CCOMS-ICO©) tool combines the physician’s insight with objective disease progression parameters and advanced chronic conditions. Some parameters have been independently associated with mortality risk in different populations. According to the concept of the ‘prognostic approach’ as a companion of the ‘palliative approach’, predictive models that identify individuals at high mortality risk are needed.
Objective: We aimed to identify prognostic factors of mortality in cancer in our cultural context.
Method: We assessed cancer patients with palliative needs until death using this validated predictive tool at three hospitals in Buenos Aires City. This multifactorial, quantitative and qualitative non-dichotomous assessment process combines subjective perception (the surprise question: Would you be surprised if this patient dies in the next year?) with other parameters, including the request (and need) for palliative care (PC), the assessment of disease severity, geriatric syndromes, psychosocial factors and comorbidities, as well as the use of healthcare resources.
Results: 2,104 cancer patients were identified, 681 were NECPAL+ (32.3%). During a 2-year follow-up period, 422 NECPAL+ patients died (61.9%). The mean overall survival was 8 months. A multivariate model was constructed with significant indicators in univariate analysis. The best predictors of mortality were: nutritional decline (p < 0.000), functional decline (p < 0.000), palliative performance scale (PPS) ≤ 50 (p < 0.000), persistent symptoms (p < 0.002), functional dependence (p < 0.000), poor treatment response (p < 0.000), primary cancer diagnosis (p = 0.024) and condition (in/outpatients) (p < 0.000). Only three variables remained as survival predictors: low response to treatment (p < 0.001), PPS ≤ 50 (p < 0.000) and condition (in/outpatients) (p < 0.000).
Conclusion: This prospective model aimed to improve cancer survival prediction and timely PC referral in Argentinian hospitals.