Palliative Care and Rapid Emergency Screening Tool and the Palliative Performance Scale to Predict Survival of Older Adults Admitted to the Hospital From the Emergency Department

A comparison of palliative care and rapid emergency screening (P-CaRES) tool, broad and narrow criteria, and surprise questions to predict survival of older emergency department patients

BACKGROUND

Palliative care is a form of medical care designed to enhance the quality of life of patients with life-threatening conditions. This study was conducted to compare the accuracy of predicted survival the 1 and 3-month survival rate of Broad and narrow criteria, Surprise questions (SQ), and Palliative Care and Rapid Emergency Screening (P-CaRES) after admission to the emergency department (ED).

METHODS

This prospective cohort study was conducted at an urban teaching hospital in Thailand. Patients aged ≥ 65 years admitted to the ED were classified according to their emergency severity index (ESI) (Level: 1-3). We collected data on SQ, P-CaRES, and broad and narrow criteria. A survival data of participants were collected at 1 and 3 months after admission to the ED. The survival rate was calculated using the Kaplan-Meier and log-rank tests.

RESULTS

A total of 269 patients completed the study. P-CaRES positive and P-CaRES negative patients had 1-month survival rates of 81% and 94.8%, respectively (P = 0.37), and at 3-month survival rates of 70.7% and 90.1%, respectively (P < 0.001). SQ (not surprised) had a 1-month survival rate of 79.3%, while SQ (surprised) had a 97% survival rate (P = 0.01), and SQ (not surprised) had a 75.4% survival rate at 3-months, while SQ (surprised) had a 96.3% survival rate (P = 0.01). Broad and narrow criteria that were positive and negative had 1-month survival rates of 88.1% and 92.5%, respectively (P = 0.71), while those that were positive and negative had 3-month survival rates of 78.6% and 87.2%, respectively (P = 0.19). The hazard ratio (HR) of SQ (not surprised) at 1 month was 3.22( 95%CI:1.16-8.89). The HR at 3 months of P-CaRES (positive) was 3.31 with a 95% confidence interval (CI): 1.74 - 6.27, while the HR for SQ (not surprise) was 7.33, 95% CI: 3.03-19.79; however, broad and narrow criteria had an HR of 1.78, 95% CI:0.84-3.77.

CONCLUSIONS

Among older adults who visited the ED, the SQ were good prognosis tools for predicting 1 and 3-month survival, and P-CaRES were good prognostic tools for predicting 3-month survival.

Screening tools to identify patients with unmet palliative care needs in the emergency department: A systematic review

OBJECTIVES

This systematic review identified and assessed psychometric properties of the available screening tools to identify patients with unmet palliative care (PC) needs in the emergency department (ED).

METHODS

A comprehensive search of electronic databases and the gray literature was conducted. Two independent reviewers completed study screening and inclusion, data extraction, and quality assessment. A descriptive summary of the results was reported using median of medians and interquartile ranges (IQRs).

RESULTS

A total of 35 studies were included, involving the assessment of 14 unique screening tools. The most commonly used screening tool was the surprise question (SQ; n = 12 studies), followed by the Palliative Care and Rapid Emergency Screening (P-CaRES) tool (n = 8), and the screening for palliative and end-of-life care needs in the emergency department (SPEED) instrument (n = 4). Twelve of the included studies reported on the psychometric properties of the screening tools, of which eight of these studies assessed the performance of the SQ to predict patient mortality. Overall, the median sensitivity (63%, IQR 38%-78%) and specificity (75%, IQR 57%-84%) of the SQ to predict mortality at 1 or 12 months was moderate. While the median positive predictive value of the SQ was low (32%, IQR 16%-40%), the median negative predictive value was high (91%, IQR 88%-95%). Across the studies, the proportion of patients identified as having unmet PC based on the criteria of the screening tools ranged from 5% to 83%.

CONCLUSIONS

This review identified 14 unique screening tools used to identify adult patients with unmet PC needs in the ED. One screening tool, the SQ, was found to have moderate sensitivity and specificity to accurately predict future patient mortality. Additional research is needed to better understand the clinical value of this and the other available tools prior to their widespread implementation.

A Systematic Review of the Development and Implementation of Needs-Based Palliative Care Tools in Heart Failure and Chronic Respiratory Disease

Background

The impetus to develop and implement tools for non-malignant patient groups is reflected in the increasing number of instruments being developed for heart failure and chronic respiratory diseases. Evidence syntheses of psychometric quality and clinical utility of these tools is required to inform research and clinical practice.

Aims

This systematic review examined palliative care needs tools for people diagnosed with advanced heart failure or chronic respiratory diseases, to determine their: (1) psychometric quality; and (2) acceptability, feasibility and clinical utility when implemented in clinical practice.

Methods

Systematic searches of MEDLINE, CINAHL, Embase, Cochrane and PsycINFO from database inception until June 2021 were undertaken. Additionally, the reference lists of included studies were searched for relevant articles. Psychometric properties of identified measures were evaluated against pre-determined and standard criteria.

Results

Eighteen tools met inclusion criteria: 11 were developed to assess unmet patient palliative care needs. Of those, 6 were generic, 4 were developed for heart failure and 1 was developed for interstitial lung disease. Seven tools identified those who may benefit from palliative care and include general and disease-specific indicators. The psychometric qualities of the tools varied. None met all of the accepted criteria for psychometric rigor in heart failure or respiratory disease populations. There is limited implementation of needs assessment tools in practice.

Conclusion

Several tools were identified, however further validation studies in heart failure and respiratory disease populations are required. Rigorous evaluation to determine the impact of adopting a systematic needs-based approach for heart failure and lung disease on the physical and psychosocial outcomes of patients and carers, as well as the economic costs and benefits to the healthcare system, is required.