Comparison of intuitive assessment and palliative care screening tool in the early identification of patients needing palliative care

Indian Society of Critical Care Medicine and Indian Association of Palliative Care Expert Consensus and Position Statements for End-of-life and Palliative Care in the Intensive Care Unit

End-of-life care (EOLC) exemplifies the joint mission of intensive and palliative care (PC) in their human-centeredness. The explosion of technological advances in medicine must be balanced with the culture of holistic care. Inevitably, it brings together the science and the art of medicine in their full expression. High-quality EOLC in the ICU is grounded in evidence, ethical principles, and professionalism within the framework of the Law. Expert professional statements over the last two decades in India were developed while the law was evolving. Recent landmark Supreme Court judgments have necessitated a review of the clinical pathway for EOLC outlined in the previous statements. Much empirical and interventional evidence has accumulated since the position statement in 2014. This iteration of the joint Indian Society of Critical Care Medicine-Indian Association of Palliative Care (ISCCM-IAPC) Position Statement for EOLC combines contemporary evidence, ethics, and law for decision support by the bedside in Indian ICUs.

How to cite this article

Mani RK, Bhatnagar S, Butola S, Gursahani R, Mehta D, Simha S, et al. Indian Society of Critical Care Medicine and Indian Association of Palliative Care Expert Consensus and Position Statements for End-of-life and Palliative Care in the Intensive Care Unit. Indian J Crit Care Med 2024;28(3):200-250.

Situational Analysis of Barriers to Continuity of End-of-Life Care in Urban Areas, Bangkok

This qualitative study was designed to describe the continuity of end-of-life care and identify barriers to continuity in urban Bangkok. Continuity of care is considered an essential part of palliative care to promote the quality of life of patients at the end of life. The majority of studies have been conducted exploring continuity of care in rural communities. However, few studies have focused on urban areas, particularly in big cities. Twelve healthcare providers were the participants, including nurses in inpatient units, and in the Health Community and Continuity of Care Unit, a palliative care physician, and social workers. The data collection consisted of individual interviews, field notes, and observations. Content analysis was used to analyze data and identify barriers. The continuity of end-of-life care in a selected setting was fragmented. Three main barriers to the continuity of end-of-life care consisted of misunderstandings about patients who required palliative care, staff workloads, and incomplete patient information. The development of a comprehensive patient information sheet for communication among a multidisciplinary team could promote continuity of end-of-life care from hospital to home. An interprofessional training course on continuity of end-of-life care is also recommended. Finally, the staff workload should be monitored and managed.

Screening instruments for early identification of unmet palliative care needs: a systematic review and meta-analysis

BACKGROUND

The early detection of individuals who require palliative care is essential for the timely initiation of palliative care services. This systematic review and meta-analysis aimed to (1) Identify the screening instruments used by health professionals to promote early identification of patients who may benefit from palliative care; and (2) Assess the psychometric properties and clinical performance of the instruments.

METHODS

A comprehensive literature search was conducted in PubMed, Embase, CINAHL, Scopus, CNKI and Wanfang from inception to May 2023. We used the COnsensus-based Standards for the Selection of Health Measurement INstruments to assess the methodological quality of the development process for the instruments. The clinical performance of the instruments was assessed by narrative summary or meta-analysis. Subgroup analyses were conducted where necessary. The quality of included studies was assessed using the Newcastle-Ottawa Scale and the Cochrane Collaboration's risk of bias assessment tool.

RESULTS

We included 31 studies that involved seven instruments. Thirteen studies reported the development and validation process of these instruments and 18 studies related to assessment of clinical performance of these instruments. The content validity of the instruments was doubtful or inadequate because of very low to moderate quality evidence. The pooled sensitivity (Se) ranged from 60.0% to 73.8%, with high heterogeneity (I2 of 88.15% to 99.36%). The pooled specificity (Sp) ranges from 70.4% to 90.2%, with high heterogeneity (I2 of 96.81% to 99.94%). The Supportive and Palliative Care Indicators Tool (SPICT) had better performance in hospitals than in general practice settings (Se=79.8% vs 45.3%, p=0.004; Sp=59.1% vs 97.0%, p=0.000).

CONCLUSION

The clinical performance of existing instruments in identifying patients with palliative care needs early ranged from poor to reasonable. The SPICT is used most commonly, has better clinical performance than other instruments but performs better in hospital settings than in general practice settings.

Identifying Nursing Home Residents with Unmet Palliative Care Needs: A Systematic Review of Screening Tool Measurement Properties

OBJECTIVES

Despite common use of palliative care screening tools in other settings, the performance of these tools in the nursing home has not been well established; therefore, the purpose of this review is to (1) identify palliative care screening tools validated for nursing home residents and (2) critically appraise, compare, and summarize the quality of measurement properties.

DESIGN

Systematic review of measurement properties consistent with Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines.

SETTINGS AND PARTICIPANTS

Embase (Ovid), MEDLINE (PubMed), CINAHL (EBSCO), and PsycINFO (Ovid) were searched from inception to May 2022. Studies that (1) reported the development or evaluation of a palliative care screening tool and (2) sampled older adults living in a nursing home were included.

METHODS

Two reviewers independently screened, selected, extracted data, and assessed risk of bias.

RESULTS

We identified only 1 palliative care screening tool meeting COSMIN criteria, the NECesidades Paliativas (NEC-PAL, equivalent to palliative needs in English), but evidence for use with nursing home residents was of low quality. The NEC-PAL lacked robust testing of measurement properties such as reliability, sensitivity, and specificity in the nursing home setting. Construct validity through hypothesis testing was adequate but only reported in 1 study. Consequently, there is insufficient evidence to guide practice. Broadening the criteria further, this review reports on 3 additional palliative care screening tools identified during the search and screening process but which were excluded during full-text review for various reasons.

CONCLUSION AND IMPLICATIONS

Given the unique care environment of nursing homes, we recommend future studies to validate available tools and develop new instruments specifically designed for nursing home use. In the meantime, we recommend that clinicians consider the evidence presented here and choose a screening instrument that best meets their needs.

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.