3.8 Article

Estimating patients' risk for postoperative delirium from preoperative routine data - Trial design of the PRe-Operative prediction of postoperative DElirium by appropriate SCreening (PROPDESC) study - A monocentre prospective observational trial

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ELSEVIER INC
DOI: 10.1016/j.conctc.2019.100501

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Postoperative delirium; Risk prediction; Risk score

资金

  1. Funding program Clinical Studies (FKS) of the Studienzentrum Bonn (SZB) at University Hospital Bonn (UKB) [O-417.0002, 2018-FKS-01]

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Background: Postoperative Delirium (POD) is the most common complication of elderly patients after surgery associated with increased postoperative morbidity, persistent care dependency and even mortality. Prevention of POD requires detection of patients at high risk prior to surgery. PROPDESC intends to provide an instrument for preoperative routine screening of patients' risk for POD. Methods: PROPDESC is a monocentric prospective observatory trial including 1000 patients older than 60 years from various disciplines of a university hospital planned for surgery of at least 60 min. To develop a score predicting the risk for POD, anesthesiological stratifications, laboratory values, medication and known risk factors as well as quality of life and cognitive performance are taken into account. POD assessment is performed daily on the first five days after the operation respectively the end of sedation in the intensive care units and normal wards. The score is evaluated from 600 data sets and subsequently validated internally. The most appropriate predictors are determined by a component-wise gradient boosting approach. Discussion: Based on retrospective investigations, etiology of POD is considered multifactorial. By a prospective analysis of various factors, PROPDESC intends to provide an applicable tool to predict the risk for POD from preoperative routine data and assessment of cognitive function. Objective is to establish an automatically generating score in preoperative routine to screen patients for increased risk of POD as starting point for POD reduction and management. Model compilation requires a high significance and enhancement within compound as well as regular availability of the selected predictors.

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