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Research progress of prediction model for prognosis of critically ill elderly patients |
Received:January 17, 2017 Revised:February 26, 2017 |
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DOI:10.11915/j.issn.1671-5403.2017.07.127 |
Key words:frailty syndrome frailty index clinical frailty scale prediction model |
Author Name | Affiliation | E-mail | DONG Jia-Hui | Department of Geriatric Intensive Care Medicine, Guangdong Provincial Key Laboratory of Geriatric Infection and Organ Function Support Guangzhou Key Laboratory of Geriatric Infection and Organ Function Support, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, China | | SUN Jie | Department of Geriatric Intensive Care Medicine, Guangdong Provincial Key Laboratory of Geriatric Infection and Organ Function Support Guangzhou Key Laboratory of Geriatric Infection and Organ Function Support, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, China | | ZENG An | School of Computers, Guangdong University of Technology, Guangzhou 510000, China | | GUO Zhen-Hui | Department of Geriatric Intensive Care Medicine, Guangdong Provincial Key Laboratory of Geriatric Infection and Organ Function Support Guangzhou Key Laboratory of Geriatric Infection and Organ Function Support, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou 510010, China | micugzh@126.com |
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Abstract: |
Although Acute Physiology And Chronic Health Evaluation (APACHE) and Simplified Acute Physiology Score (SAPS) show good predictive effect on critically ill patients, they may have predictive data migration or poor population calibration when being used in the elderly critically ill patients. However, the established special prediction models for elderly are not of enough accuracy and reliability. So, frailty syndrome has become the key point in geriatric comprehensive assessment. Current studies showed that frailty index (FI) and clinical frailty scale (CFS) can be used as the objective models to assess frailty syndrome, and have become hotspot in the studies on predictive assessment for critically ill elderly. This article reviewed the development process and research progress of these predictive models, with a view of clinicians for better application. |
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