top of page
Md. Parvez

Improving Short-Term Mortality Care for High-Risk Older Adults

short-term-mortality

A cutting-edge screening tool designed to predict short-term mortality in older adults shows great potential for enhancing care in emergency departments (EDs) and other healthcare settings, including nursing homes. This tool, known as the Geriatric End-of-Life Screening Tool (GEST), offers significant advantages over traditional methods, potentially transforming how high-risk patients are identified and managed.


The Study Behind GEST's Effectiveness


A recent study published in the Journal of the American Medical Association (JAMA) highlighted the effectiveness of GEST in predicting 6-month mortality for older patients visiting emergency departments. The Beth Israel Deaconess Medical Center research in Boston, Massachusetts, included a cohort of 82,371 ED encounters by 40,505 patients aged 65 and older from 2017 to 2021. The primary outcome measured was the 6-month mortality rate following an ED visit.


Key Findings: GEST vs. Traditional Criteria


Researchers found that GEST is significantly more effective at identifying older adults at high risk of short-term mortality compared to severe traditional illness criteria. These criteria typically include conditions such as stroke, liver disease, cancer, lung disease, and advanced age. The study revealed that GEST, a logistic regression algorithm using readily available electronic health record (EHR) data, had a robust area under the receiver operating characteristic curve (AUROC) of 0.79, indicating high accuracy in predicting 6-month mortality.


"The findings of this study suggest that both serious illness criteria and GEST identified older ED patients at risk for 6-month mortality, but GEST offered more useful screening characteristics." "Future trials of serious illness interventions for high mortality risk in older adults may consider transitioning from diagnosis code criteria to GEST, an automatable EHR-based algorithm," researchers wrote.


Implications for Skilled Nursing Facilities


Implementing GEST could significantly enhance the accuracy of mortality risk predictions for skilled nursing operators, allowing for better-targeted end-of-life care interventions. GEST's ability to use readily available EHR data makes it a practical option for resource-constrained settings, such as EDs and nursing homes. This tool could improve the identification and care of high-risk patients, potentially leading to better outcomes and more efficient use of resources.


Sensitivity and Specificity of GEST


The study highlighted that 53.4% of the encounters involved patients with serious illnesses. GEST demonstrated a sensitivity of 77.4% and a specificity of 50.5%. The tool's flexibility was also noted, as adjusting the GEST cutoffs can balance sensitivity and specificity according to clinical needs. Notably, GEST reclassified 45.1% of patients with severe illnesses as ‘low risk’, with an observed mortality rate of 8.1%, and identified 2.6% of patients without severe illnesses as ‘high risk’, with an observed mortality rate of 34.3%.


The Path Forward: Integrating GEST into Care Protocols


Integrating GEST into care protocols could mark a significant step forward in geriatric care. By leveraging EHR data, GEST offers a scalable and efficient solution for identifying high-risk older adults, allowing healthcare providers to focus their efforts on those most in need. This can lead to more personalized care plans, better resource allocation, and improved patient outcomes.


In conclusion, the Geriatric End-of-Life Screening Tool (GEST) presents a promising advancement in the care of older adults. Its ability to accurately predict short-term mortality using EHR data makes it invaluable for emergency departments, nursing homes, and other healthcare settings. By adopting GEST, healthcare providers can enhance the quality of care for high-risk patients, ensuring that those in the final stages of life receive the compassionate and effective care they deserve.

Comments


bottom of page