A proposed prognostic 7-day survival formula for patients with terminal cancer
| Autor: | Jui-Kun Chiang, Yee-Hsin Kao, Ning-Sheng Lai, Shi-Chi Chen, Mei-Huang Wang |
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| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
Male
medicine.medical_specialty Pediatrics Multivariate analysis Taiwan ECOG Performance Status Logistic regression Sensitivity and Specificity Severity of Illness Index Cohort Studies Interviews as Topic Patient satisfaction Patient Admission Predictive Value of Tests Internal medicine Neoplasms Severity of illness medicine Humans Terminally Ill Survival analysis Aged Monitoring Physiologic Terminal Care business.industry Clinical Laboratory Techniques lcsh:Public aspects of medicine Public Health Environmental and Occupational Health lcsh:RA1-1270 Middle Aged Prognosis Survival Analysis Hospice Care Predictive value of tests Chronic Disease Multivariate Analysis Female business Cohort study Research Article |
| Zdroj: | BMC Public Health BMC Public Health, Vol 9, Iss 1, p 365 (2009) |
| ISSN: | 1471-2458 |
| Popis: | Background The ability to identify patients for hospice care results in better end-of-life care. To develop a validated prognostic scale for 7-day survival prediction, a prospective observational cohort study was made of patients with terminal cancer. Methods Patient data gathered within 24 hours of hospital admission included demographics, clinical signs and symptoms and their severity, laboratory test results, and subsequent survival data. Of 727 patients enrolled, data from 374 (training group) was used to develop a prognostic tool, with the other 353 serving as the validation group. Results Five predictors identified by multivariate logistic regression analysis included patient's cognitive status, edema, ECOG performance status, BUN and respiratory rate. A formula of the predictor model based on those five predictors was constructed. When probability was >0.2, death within 7 days was predicted in the training group and validation group, with sensitivity of 80.9% and 71.0%, specificity of 65.9% and 57.7%, positive predictive value of 42.6% and 26.8%, and negative predictive value (NPV) of 91.7% and 90.1%, respectively. Conclusion This predictor model showed a relatively high sensitivity and NPV for predicting 7-day survival among terminal cancer patients, and could increase patient satisfaction by improving end-of-life care. |
| Databáze: | OpenAIRE |
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