NECPAL tool prognostication in advanced chronic illness: a rapid review and expert consensus

The prognostic value of the NECPAL instrument, Palliative Prognostic Index, and PROFUND index in elderly residents of nursing homes with advanced chronic condition

BACKGROUND

It is essential to assess the need for palliative care and the life prognosis of elderly nursing home residents with an advanced chronic condition, and the NECPAL ICO-CCOMS©4.0 prognostic instrument may be adequate for both purposes. The objective of this study was to examine the predictive capacity of NECPAL, the Palliative Prognosis Index, and the PROFUND index in elderly residents with advanced chronic condition with and without dementia, comparing their results at different time points.

METHODS

This prospective observational study was undertaken in eight nursing homes, following the survival of 146 residents with advanced chronic condition (46.6% with dementia) at 3, 6, 12, and 24 months. The capacity of the three instruments to predict mortality was evaluated by calculating the area under the receiver operating characteristic curve (AUC), with 95% confidence interval, for the global population and separately for residents with and without dementia.

RESULTS

The mean age of residents was 84.63 years (± 8.989 yrs); 67.8% were female. The highest predictive capacity was found for PROFUND at 3 months (95%CI: 0.526-0.756; p = 0.016), for PROFUND and NECPAL at 12 months (non-significant; AUC > 0.5), and NECPAL at 24 months (close-to-significant (AUC = 0.624; 95% CI: 0.499-0.750; p = 0.053). The highest capacity at 12 months was obtained using PROFUND in residents with dementia (AUC = 0.698; 95%CI: 0.566-0.829; p = 0.003) and NECPAL in residents without dementia (non-significant; AUC = 0.649; 95%CI: 0.432-0.867; p = 0.178). Significant differences in AUC values were observed between PROFUND at 12 (p = 0.017) and 24 (p = 0.028) months.

CONCLUSIONS

PROFUND offers the most accurate prediction of survival in elderly care home residents with advanced chronic condition overall and in those with dementia, especially over the short term, whereas NECPAL ICO-CCOMS©4.0 appears to be the most useful to predict the long-term survival of residents without dementia. These results support early evaluation of the need for palliative care in elderly care home residents with advanced chronic condition.

Rural Palliative Care Telemedicine for Advanced Cancer Patients: A Systematic Review

Context: Telemedicine offers the opportunity to provide remote palliative care for patients to control symptoms and improve quality of life, even for patients with advanced diseases. Objectives: Establish a telemedicine model of rural palliative care for advanced cancer patients with difficulties in accessing standard care. Methods: This review comports with the minimum standards described in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and uses the palliative care literature review iterative method (PALETTE) proposed by Zwakman et al in 2018. Results: Three hundred, ninety-two articles were identified in PubMed and EMBASE databases and alternative search engines such as Google Scholar and OpenGrey. A telemedicine delivery model was developed for patients with limited access to standard care, which consists of identifying the candidate population, establishing the most convenient telemedicine modality, agreeing with patients and caregivers on palliative care needs, and evaluating the interventions effectiveness. Conclusion: Telemedicine is a revolutionary tool to provide palliative care to advanced cancer patients whose clinical condition or location prevent them from accessing conventional care.

Predicting Patient Mortality for Earlier Palliative Care Identification in Medicare Advantage Plans: Features of a Machine Learning Model

BACKGROUND

Machine learning (ML) can offer greater precision and sensitivity in predicting Medicare patient end of life and potential need for palliative services compared to provider recommendations alone. However, earlier ML research on older community dwelling Medicare beneficiaries has provided insufficient exploration of key model feature impacts and the role of the social determinants of health.

OBJECTIVE

This study describes the development of a binary classification ML model predicting 1-year mortality among Medicare Advantage plan members aged ≥65 years (N=318,774) and further examines the top features of the predictive model.

METHODS

A light gradient-boosted trees model configuration was selected based on 5-fold cross-validation. The model was trained with 80% of cases (n=255,020) using randomized feature generation periods, with 20% (n=63,754) reserved as a holdout for validation. The final algorithm used 907 feature inputs extracted primarily from claims and administrative data capturing patient diagnoses, service utilization, demographics, and census tract-based social determinants index measures.

RESULTS

The total sample had an actual mortality prevalence of 3.9% in the 2018 outcome period. The final model correctly predicted 44.2% of patient expirations among the top 1% of highest risk members (AUC=0.84; 95% CI 0.83-0.85) versus 24.0% predicted by the model iteration using only age, gender, and select high-risk utilization features (AUC=0.74; 95% CI 0.73-0.74). The most important algorithm features included patient demographics, diagnoses, pharmacy utilization, mean costs, and certain social determinants of health.

CONCLUSIONS

The final ML model better predicts Medicare Advantage member end of life using a variety of routinely collected data and supports earlier patient identification for palliative care.

The Experiences and Views on Palliative Care of Older People with Multimorbidities, Their Family Caregivers and Professionals in a Spanish Hospital

The increasing prevalence of complex chronic diseases in the population over 65 years of age is causing a major impact on health systems. This study aims to explore the needs and preferences of the multimorbid patient and carers to improve the palliative care received. The perspective of professionals who work with this profile of patients was also taken into account. A qualitative study was conducted using semi-structured interviews with open-ended questions. Separate topic guides were developed for patients, careers and health professionals. We included 12 patients, 11 caregivers and 16 health professionals in Spain. The results showed multiple unmet needs of patients and families/caregivers, including feelings of uncertainty, a sense of fear, low awareness and knowledge about palliative care in non-malignant settings, and a desire to improve physical, psychosocial and financial status. A consistent lack of specialized psychosocial care for both patients and caregivers was expressed and professionals highlighted the need for holistic needs assessment and effective and early referral pathways to palliative care. There is a lack of institutional support for multimorbid older patients in need of palliative care and important barriers need to be addressed by health systems to face the significant increase in these patients.

Palliative Care Needs and Clinical Features Related to Short-Term Mortality in Patients Enrolled in a Heart Failure Unit

(1) Background: Heart failure (HF) is a chronic and complex pathology requiring continuous patient management due to clinical instability, associated comorbidity, and extensive pharmacological treatment. Its unpredictable course makes the advanced stages challenging to recognize and raises the need for palliative care. This study aims to identify palliative care needs in HF patients and describe clinical features related to short-term mortality. (2) Methods: A descriptive, observational, cross-sectional, and retrospective study was carried out in an HF unit of a Spanish tertiary hospital. Patients’ socio-demographic and clinical data were collected from clinical records, and different instruments were used to establish mortality risks and patients’ needs for palliative care. Subsequently, univariate and bivariate descriptive analyses were performed. A binary logistic regression model helped to determine variables that could influence mortality 12 months after admission to the Unit. (3) Results: The studied population, sixty-five percent women, had an average age of 83.27 years. Among other clinical characteristics predominated preserved ejection fraction (pEF) and dyspnea NYHA (New York Heart Association) class II. The most prevalent comorbidities were hypertension and coronary heart disease. Forty-nine percent had a low–intermediate mortality risk in the following year, according to the PROFUND index. The NECPAL CCOMS-ICO© instrument identified subjects who meet the criteria for palliative care. This predictive model identified NECPAL CCOMS-ICO© results, using beta-blockers (BB) or AIIRA (Angiotensin II receptor antagonists) and low glomerular filtration rate (GFR) as explanatory variables of patients’ mortality in the following year. (4) Conclusions: The analysis of the characteristics of the population with HF allows us to identify patients in need of palliative care. The NECPAL CCOMS-ICO© instrument and the PROFUND have helped identify the characteristics of people with HF who would benefit from palliative management.

Prognostication in palliative radiotherapy—ProPaRT: Accuracy of prognostic scores

Background

Prognostication can be used within a tailored decision-making process to achieve a more personalized approach to the care of patients with cancer. This prospective observational study evaluated the accuracy of the Palliative Prognostic score (PaP score) to predict survival in patients identified by oncologists as candidates for palliative radiotherapy (PRT). We also studied interrater variability for the clinical prediction of survival and PaP scores and assessed the accuracy of the Survival Prediction Score (SPS) and TEACHH score.

Materials and methods

Consecutive patients were enrolled at first access to our Radiotherapy and Palliative Care Outpatient Clinic. The discriminating ability of the prognostic models was assessed using Harrell’s C index, and the corresponding 95% confidence intervals (95% CI) were obtained by bootstrapping.

Results

In total, 255 patients with metastatic cancer were evaluated, and 123 (48.2%) were selected for PRT, all of whom completed treatment without interruption. Then, 10.6% of the irradiated patients who died underwent treatment within the last 30 days of life. The PaP score showed an accuracy of 74.8 (95% CI, 69.5–80.1) for radiation oncologist (RO) and 80.7 (95% CI, 75.9–85.5) for palliative care physician (PCP) in predicting 30-day survival. The accuracy of TEACHH was 76.1 (95% CI, 70.9–81.3) and 64.7 (95% CI, 58.8–70.6) for RO and PCP, respectively, and the accuracy of SPS was 70 (95% CI, 64.4–75.6) and 72.8 (95% CI, 67.3–78.3).

Conclusion

Accurate prognostication can identify candidates for low-fraction PRT during the last days of life who are more likely to complete the planned treatment without interruption.

All the scores showed good discriminating capacity; the PaP had the higher accuracy, especially when used in a multidisciplinary way.

Nosocomial COVID-19 Infection in a Long-Term Hospital in Spain: Retrospective Observational Study

Background and Objectives. The aim of this study is to compare clinical and epidemiological characteristics and outcomes in patients with versus without nosocomial COVID-19 after exposure to SARS-CoV-2 and to analyze the risk factors for severe outcomes of COVID-19 in a long-term hospital in Spain. Materials and methods. This retrospective, single-center observational study included all inpatients in a long-term hospital during a COVID-19 outbreak from 21 January to 15 March 2021. Results. Of 108 admitted patients, 65 (60.2%) were diagnosed with nosocomial COVID-19 disease (n = 34 women (52.3%), median age 77 years). In the univariable analysis, risk factors associated with nosocomial COVID-19 were dementia (OR 4.98 95% CI 1.58–15.75), dyspnea (OR 5.34 95% CI 1.69–16.82), asthenia (OR 5.10, 95% CI 1.40–18.60) and NECesidades PALiativas (NECPAL) (OR 1.28 95% CI 1.10–1.48). In the multivariable analysis, risk factors independently associated with nosocomial COVID-19 infection were dyspnea (aOR 7.39; 95% CI 1.27–43.11) and NECPAL (aOR 1.25; 95% CI 1.03–1.52). Of the 65 patients diagnosed with nosocomial COVID-19, 29 (44.6%) died, compared to 7/43 (16.2%) non-infected patients (OR 4.14, 95% CI 1.61–10.67). Factors associated with mortality in nosocomial COVID-19 were confusion (aOR 3.83; 95% CI 1.03–14.27) and dyspnea (aOR 7.47; 95% CI 1.87–29.82). The NECPAL tool played an important predictive role in both nosocomial COVID-19 infection and mortality (aOR 1.19, 95% CI: 1.00–1.41). Conclusions. In a long-term hospital, nosocomial COVID-19 main clinical characteristics associated with infection were dyspnea and NECPAL. Mortality was higher in the group with nosocomial COVID-19; risk factors were confusion and dyspnea. The NECPAL tool may help to predict progression and death in COVID-19.

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

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.

Identifying older adults with frailty approaching end-of-life: A systematic review

BACKGROUND

People with frailty may have specific needs for end-of-life care, but there is no consensus on how to identify these people in a timely way, or whether they will benefit from intervention.

AIM

To synthesise evidence on identification of older people with frailty approaching end-of-life, and whether associated intervention improves outcomes.

DESIGN

Systematic review (PROSPERO: CRD42020462624).

DATA SOURCES

Six databases were searched, with no date restrictions, for articles reporting prognostic or intervention studies. Key inclusion criteria were adults aged 65 and over, identified as frail via an established measure. End-of-life was defined as the final 12 months. Key exclusion criteria were proxy definitions of frailty, or studies involving people with cancer, even if also frail.

RESULTS

Three articles met the inclusion criteria. Strongest evidence came from one study in English primary care, which showed distinct trajectories in electronic Frailty Index scores in the last 12 months of life, associated with increased risk of death. We found no studies evaluating established clinical tools (e.g. Gold Standards Framework) with existing frail populations. We found no intervention studies; the literature on advance care planning with people with frailty has relied on proxy definitions of frailty.

CONCLUSION

Clear implications for policy and practice are hindered by the lack of studies using an established approach to assessing frailty. Future end-of-life research needs to use explicit approaches to the measurement and reporting of frailty, and address the evidence gap on interventions. A focus on models of care that incorporate a palliative approach is essential.

Characteristics and Service Utilization by Complex Chronic and Advanced Chronic Patients in Catalonia: A Retrospective Seven-Year Cohort-Based Study of an Implemented Chronic Care Program

The Chronic Care Program introduced in Catalonia in 2011 focuses on improving the identification and management of complex chronic (CCPs) and advanced chronic patients (ACPs) by implementing an individualized care model. Its first stage is their identification based on chronicity, difficult clinical management (i.e., complexity), and, in ACPs, limited life prognosis. Subsequent stages are individual evaluation and implementation of a shared personalized care plan. This retrospective study, including all CCPs and ACPs identified in Catalonia between 2013 and 2019, was aimed at describing the characteristics and healthcare service utilization among these patients. Data were obtained from an administrative database and included sociodemographic, clinical, and service utilization variables and morbidity-associated risk according to the Adjusted Morbidity Groups (GMA) stratification. During the study period, CCPs’ and ACPs’ prevalence increased and was higher in lower-income populations; most cases were women. CCPs and ACPs had all comorbidities at higher frequencies, higher utilization of healthcare services, and were more frequently at high risk (63% and 71%, respectively) than age-, sex-, and income level-adjusted non-CCP (23%) and non-ACP populations (30%). These results show effective identification of the program’s target population and demonstrate that CCPs and ACPs have a higher burden of multimorbidity and healthcare needs.

Frailty degree and illness trajectories in older people towards the end-of-life: a prospective observational study

OBJECTIVES

To assess the degree of frailty in older people with different advanced diseases and its relationship with end-of-life illness trajectories and survival.

METHODS

Prospective, observational study, including all patients admitted to the Acute Geriatric Unit of the University Hospital of Vic (Spain) during 12 consecutive months (2014-2015), followed for up to 2 years. Participants were identified as end-of-life people (EOLp) using the NECPAL (NECesidades PALiativas, palliative care needs) tool and were classified according to their dominant illness trajectory. The Frail-VIG index (Valoración Integral Geriátrica, Comprehensive Geriatric Assessment) was used to quantify frailty degree, to calculate the relationship between frailty and mortality (Receiver Operating Characteristic (ROC) curves), and to assess the combined effect of frailty degree and illness trajectories on survival (Cox proportional hazards model). Survival curves were plotted using the Kaplan-Meier estimator with participants classified into four groups (ie, no frailty, mild frailty, moderate frailty and advanced frailty) and were compared using the log-rank test.

RESULTS

Of the 590 persons with a mean (SD) age of 86.4 (5.6) years recruited, 260 (44.1%) were identified as EOLp, distributed into cancer (n=31, 11.9%), organ failure (n=79, 30.4%), dementia (n=86, 33.1%) and multimorbidity (n=64, 24.6%) trajectories. All 260 EOLp had some degree of frailty, mostly advanced frailty (n=184, 70.8%), regardless of the illness trajectory, and 220 (84.6%) died within 2 years. The area under the ROC curve (95% CI) after 2 years of follow-up for EOLp was 0.87 (0.84 to 0.92) with different patterns of survival decline in the different end-of-life trajectories (p<0.0001). Cox regression analyses showed that each additional deficit of the Frail-VIG index increased the risk of death by 61.5%, 30.1%, 29.6% and 12.9% in people with dementia, organ failure, multimorbidity and cancer, respectively (p<0.01 for all the coefficients).

CONCLUSIONS

All older people towards the end-of-life in this study were frail, mostly with advanced frailty. The degree of frailty is related to survival across the different illness trajectories despite the differing survival patterns among trajectories. Frailty indexes may be useful to assess end-of-life older people, regardless of their trajectory.

A New Approach to the Identification of Palliative Care Needs and Advanced Chronic Patients among Nursing Home Residents

Background: Proper planning of Palliative Care in nursing homes requires advanced knowledge of the care needs that residents show. The aim of the study was to evaluate Palliative Needs and other conditions such as fragility, complexity, and prognosis and also to suggest new indicators for the establishment of the resident’s advanced chronic condition. Methods: Cross-sectional study conducted in 149 nursing homes Complex Chronic residents evaluated by trained professionals. Palliative Care Needs, assessed by the NECPAL ICO-CCOMS© tool, and fragility, case and palliative complexity and prognosis were evaluate through a comprehensive assessment. Descriptive analyses and association measures were performed setting the statistical significance at 0.05. Results: More than 50% of the residents had positive Surprise Question and other Palliative Needs and were classified as Advanced Chronic Patients. Distress and/or Severe Adaptative Disorder was the most frequent need shown by the residents and significant differences in levels of frailty and other characteristics, were found between the Positive and the Negative Surprise Question Groups. Statistically significant correlations were also found between aspects of both groups. Conclusions: Nursing homes residents show Palliative Needs regardless of the response to the Surprise Question of the NECPAL tool. Other characteristics such as presence of an intermediate level of frailty are suggested as a new perspective to identify advanced chronic patients among nursing homes residents.

NECPAL prognostic tool: a palliative medicine retrospective cohort study

OBJECTIVE

To develop and validate a prognostic model to assess mortality risk at 24 months in patients with advanced chronic conditions.

METHODS

Retrospective design based on a previous population cohort study with 789 adults who were identified with the surprise question and NECPAL tool from primary and intermediate care centres, nursing homes and one acute hospital of Spain. A Cox regression model was used to derive a mortality predictive model based on patients' age and six previously selected NECPAL prognostic factors (palliative care need identified by healthcare professionals, functional decline, nutritional decline, multimorbidity, use of resources, disease-specific criteria of severity/progression). Patients were split into derivation/validation cohorts, and four steps were followed: descriptive analysis, predictors' assessment, model estimation and model assessment.

RESULTS

All predictive variables were independently associated with increased risk of mortality at 24 months. Performance model including age was good; discrimination power by area under the curve (AUC) was 0.72/0.67 in the derivation/validation cohorts, and correlation between expected and observed (E/O) mortality ratio was 0.74/0.70. The model showed similar performance across settings (AUC 0.65-0.74, E/O 1.00-1.01), the best performance in oncological patients (AUC 0.78, E/O 0.76) and the worst in dementia patients (AUC 0.58, E/O 0.85). Based on the number of factors affected, three prognostic stages with significant differences and a median survival of 38, 17.2 and 3.6 months (p<0.001) were defined.

CONCLUSION

The NECPAL prognostic tool is accurate and eventually useful at the clinical practice. Stratification in risk groups may enable early intervention and enhance policy-making and service planning.