Palliative and prognostic approach in cancer patients identified in the multicentre NECesidades PALiativas 2 study in Argentina

Abstract

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.

Machine Learning-Based Assessment Indicates Patient-Reported Outcomes to Be Less Reliable than Clinical Parameters for Predicting 1-Year Survival in Cancer Patients.

“…The variables used in this study were classified via ML regarding its relevance between important and unimportant and as positive or negative prognostic factors for the patient's prognosis. The variables weighed as positive prognostic factors supported by the expert consensus were mostly accurate and already documented in the literature: need of antiemetics [47], body max index [48] [49], level or care [50], number of emergency room attendances [51] [52], initial stage [53], decrease in appetite [50], ECOG/Performance status [49,54] male sex [54] or the oncological therapy was terminated [49] (see Table 3). The negative prognostic factors classified via ML supported by the expert consensus and already documented in the literature were: the oncological therapy was not terminated [49], number of inpatient admissions after the screening, subjective physical status and general well-being [49].…”

“…The variables weighed as positive prognostic factors supported by the expert consensus were mostly accurate and already documented in the literature: need of antiemetics [47], body max index [48] [49], level or care [50], number of emergency room attendances [51] [52], initial stage [53], decrease in appetite [50], ECOG/Performance status [49,54] male sex [54] or the oncological therapy was terminated [49] (see Table 3). The negative prognostic factors classified via ML supported by the expert consensus and already documented in the literature were: the oncological therapy was not terminated [49], number of inpatient admissions after the screening, subjective physical status and general well-being [49]. Other variables were weighted as important via ML, however, were excluded after the expert consensus and the literature research: not receiving antibody therapy, intensity of constipation, adjuvant chemotherapy, not receiving immuno-or antibody therapy, or strong opioids.…”

“…Other variables were weighted as important via ML, however, were excluded after the expert consensus and the literature research: not receiving antibody therapy, intensity of constipation, adjuvant chemotherapy, not receiving immuno-or antibody therapy, or strong opioids. The variables feeling of restlessness, anxiety or difficulties relaxing during the last 2 weeks are related to emotional distress, which has been shown to play a role in prognosis [49], however the limitation of the ePROM question "during the last 2 weeks'' prevent us further assessing this variable. The variable "not receiving immunotherapy" was assessed as a positive prognostic factor via ML but rejected from the expert panel, given that immunotherapy has been shown to contribute to prognostic improvement among glioblastoma patients [55].…”

Identification of palliative care needs and prognostic factors of survival in tailoring appropriate interventions in advanced oncological, renal and pulmonary diseases: a prospective observational protocol

“…Two systematic reviews of tools used to identify patients who potentially require an integrated PC approach are available 5 6. Several additional tools have been developed, such as the NECPAL and the PaP Scores in specific patient populations or settings, but research shows that these tools are rarely used in clinical practice 11 12 14 20. Analysing the appropriateness and timeliness of a progressive PC approach is the first step toward fulfilling the overall purpose of this project, which sees the introduction of a palliative, person-centred approach early in the trajectory of chronic incurable illnesses.…”

O uso de ferramenta prognóstica para estimar sobrevida e esta-belecer plano de cuidados no câncer avançado: revisão de litera-tura

“…Na cidade de Buenos Aires, Argentina, 2104 pacientes foram acompanhados para verificar a validação da ferramenta NECPAL CCOMS-ICO© tool. Esta serviu para identificar fatores prognósticos de mortalidade a fim de detectar indivíduos com doença crônica avançada e necessidades de cuidados paliativos 18,19 . (questionário de 7 questões, sendo cada uma delas representantes de um domínio da avaliação geriátrica) de triagem em pacientes idosos com câncer avançado, planejados para se submeterem a quimioterapia paliativa de primeira linha.…”