Economic impact of postoperative delirium – Detection of risk factors for further prevention program
Bibliographic record
Abstract
Introduction: Postoperative delirium (POD) is an underdiagnosed and adverse complication in older adults. The aim of the PRe-Operative Prediction of postoperative DElirium by appropriate Screening (PROPDESC) study was to develop a pragmatic screening risk score for POD. Furthermore, the medico economic outcome was examined in the additional subgroup analysis. \nMethods: The prospective observational monocentric study enrolled 1097 patients from Sept. 2018 to Oct. 2019 in the University Hospital Bonn. Inclusion criteria were patient aged 60 years and older and a planned surgery duration of at least 60 minutes. The primary endpoint POD was considered positive if any of the following tests were positive on any of the five postoperative visit days: Confusion Assessment Method for ICU (CAM-ICU), CAM, 4'A's Test (4AT) and Delirium Observation Scale (DOS). The development and validation of the score is based on data-driven approaches to model generation, a boosting process. Multiple logistic regression model was performed for multivariate analysis. \nResults: The selected and simplified PROPDESC score with an AUC of 0.725 includes the following variables: age, ASA and NYHA classification, surgical risk as well as ´serial subtraction´ and ´sentence repetition´ of the Montreal Cognitive Assessment. The results of the logistic regression for patients aged 70 years and older showed POD as an independent predictor for a prolonged length of stay (LOS) in Intensive Care Unit (ICU) (36 %; 95 % CI 4–78 %; < 0.001) and in hospital (22 %; 95 % CI 4–43 %; < 0.001). Furthermore, in the cardiac surgery subgroup, the number of POD patients testing positive differed substantially from the coded POD diagnoses in Hospital and the Germany-wide average. \nConclusion: POD showed an independent effect on LOS in ICU and hospital and, moreover, it is highly underdiagnosed in clinical routine. The PROPDESC score, which can be collected in a short time, has good predictive accuracy regardless of surgical discipline.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".