A Rules-Based Algorithm to Prioritize Poor Prognosis Cancer Patients in Need of Advance Care Planning
| Autor: | Bobby Daly, Blase N. Polite, Brittany Beach, Selina Chow, Kristen Wroblewski, Michael T. Huber, Andrew Hantel, Monica Malec, Christine M. Bestvina |
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| Rok vydání: | 2018 |
| Předmět: |
Male
Advance care planning Poor prognosis medicine.medical_treatment Decision Making Guidelines as Topic Advance Care Planning 03 medical and health sciences 0302 clinical medicine Neoplasms Overall survival medicine Humans 030212 general & internal medicine General Nursing Aged Proportional Hazards Models Retrospective Studies Aged 80 and over Chicago Chemotherapy Clinical events business.industry Cancer General Medicine Emergency department Middle Aged Prognosis medicine.disease Survival Analysis Advanced cancer Anesthesiology and Pain Medicine 030220 oncology & carcinogenesis Female business Algorithm Algorithms |
| Zdroj: | Journal of Palliative Medicine. 21:846-849 |
| ISSN: | 1557-7740 1096-6218 |
| DOI: | 10.1089/jpm.2017.0408 |
| Popis: | Accurate understanding of the prognosis of an advanced cancer patient can lead to decreased aggressive care at the end of life and earlier hospice enrollment.Our goal was to determine the association between high-risk clinical events identified by a simple, rules-based algorithm and decreased overall survival, to target poor prognosis cancer patients who would urgently benefit from advanced care planning.A retrospective analysis was performed on outpatient oncology patients with an index visit from April 1, 2015, through June 30, 2015. We examined a three-month window for "high-risk events," defined as (1) change in chemotherapy, (2) emergency department (ED) visit, and (3) hospitalization. Patients were followed until January 31, 2017.A total of 219 patients receiving palliative chemotherapy at the University of Chicago Medicine with a prognosis of ≤12 months were included.The main outcome was overall survival, and each "high-risk event" was treated as a time-varying covariate in a Cox proportional hazards regression model to calculate a hazard ratio (HR) of death.A change in chemotherapy regimen, ED visit, hospitalization, and at least one high-risk event occurred in 54% (118/219), 10% (22/219), 26% (57/219), and 67% (146/219) of patients, respectively. The adjusted HR of death for patients with a high-risk event was 1.72 (95% confidence interval [CI] 1.19-2.46, p = 0.003), with hospitalization reaching significance (HR 2.74, 95% CI 1.84-4.09, p 0.001).The rules-based algorithm identified those with the greatest risk of death among a poor prognosis patient group. Implementation of this algorithm in the electronic health record can identify patients with increased urgency to address goals of care. |
| Databáze: | OpenAIRE |
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