PRe-Operative Prediction of postoperative DElirium by appropriate SCreening (PROPDESC) development and validation of a pragmatic POD risk screening score based on routine preoperative data

Menzenbach, Jan; Kirfel, Andrea; Guttenthaler, Vera; Feggeler, Johanna; Hilbert, Tobias; Ricchiuto, Arcangelo; Staerk, Christian; Mayr, Andreas; Coburn, Mark; Wittmann, Mária

doi:10.1016/j.jclinane.2022.110684

Cited by 14 publications

(19 citation statements)

References 39 publications

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“…The results presented here were obtained from the study “PRe-Operative Prediction of postoperative DElirium by appropriate SCreening (PROPDESC)” [ 18 , 19 ]. This has developed a preoperatively applicable risk score in a prospective observational setting that is easy and quick to apply in clinical routine and also has a good predictive power.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Perioperative and Predisposing Risk Factors on the Development of Postoperative Delirium and a Possible Gender Difference

et al. 2022

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(1) Background: Postoperative delirium (POD) is an undesirable event especially for older patients after surgery. Perioperative risks for POD development are multiple, but gender differences are still poorly considered. In this observational study, predisposing and precipitating risk factors of POD and the possible gender influence are distinguished. (2) Methods: This observational prospective trial enrolled 1097 patients in a tertiary hospital from September 2018 until October 2019. POD was considered positive, if one of the tests Confusion Assessment Method for ICU (CAM-ICU) or Confusion Assessment Method (CAM), 4 ‘A’s Test (4AT) or Delirium Observation Screening (DOS) scale was positive on one of five assessment days. (3) Results: POD incidence was 23.5% and the mean age of study population was 72.3 ± 7.3 years. The multiple logistic regression model showed a significant impact of age (Odds Ratio (OR) 1.74; 95% Confidence Interval (CI): 1.37–2.22), American Society of Anesthesiologists (ASA) (OR 1.67; 95% CI: 1.25–2.26), surgery risk (OR 2.10; 95% CI: 1.52–2.95) and surgery duration (OR 1.17; 95% CI: 1.07–1.28), ventilation time (OR 1.64; 95% CI: 1.27–2.24), as well as the male sex (OR 1.74; 95% CI: 1.37–2.22) on POD risk. (4) Conclusions: Perioperative and predisposing risk factors had an impact on the development of POD. The influence of male sex should be considered in future research.

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Section: Introductionmentioning

confidence: 99%

The Impact of Perioperative and Predisposing Risk Factors on the Development of Postoperative Delirium and a Possible Gender Difference

et al. 2022

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“…The search strategy identified a total of 921 records; after duplicate removal and title and abstract screening, 114 full-text studies were retrieved, of which 3916–54 met the selection criteria for inclusion in the final analysis (figure 2).…”

Section: Resultsmentioning

confidence: 99%

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

Strating

Hanjani

Tornvall

et al. 2023

BMJ Health Care Inform

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ObjectivesEarly identification of inpatients at risk of developing delirium and implementing preventive measures could avoid up to 40% of delirium cases. Machine learning (ML)-based prediction models may enable risk stratification and targeted intervention, but establishing their current evolutionary status requires a scoping review of recent literature.MethodsWe searched ten databases up to June 2022 for studies of ML-based delirium prediction models. Eligible criteria comprised: use of at least one ML prediction method in an adult hospital inpatient population; published in English; reporting at least one performance measure (area under receiver-operator curve (AUROC), sensitivity, specificity, positive or negative predictive value). Included models were categorised by their stage of maturation and assessed for performance, utility and user acceptance in clinical practice.ResultsAmong 921 screened studies, 39 met eligibility criteria. In-silico performance was consistently high (median AUROC: 0.85); however, only six articles (15.4%) reported external validation, revealing degraded performance (median AUROC: 0.75). Three studies (7.7%) of models deployed within clinical workflows reported high accuracy (median AUROC: 0.92) and high user acceptance.DiscussionML models have potential to identify inpatients at risk of developing delirium before symptom onset. However, few models were externally validated and even fewer underwent prospective evaluation in clinical settings.ConclusionThis review confirms a rapidly growing body of research into using ML for predicting delirium risk in hospital settings. Our findings offer insights for both developers and clinicians into strengths and limitations of current ML delirium prediction applications aiming to support but not usurp clinician decision-making.

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“…The PROPDESC trial was a prospective single centre, observational trial at the University Hospital Bonn from September 2018 to October 2019 [ 21 , 22 ]. It was registered in the German Registry for Clinical Studies under the number DRKS00015715.…”

Section: Methodsmentioning

confidence: 99%

“…The PROPDESC study developed an easily applicable preoperative risk score to detect patients at higher risk for POD [ 21 , 22 ]. The aim of this sub-analysis was to determine on which day a delirium has been identified in most patients and to compare the performance of different delirium tests used in this study.…”

Section: Introductionmentioning

confidence: 99%

Occurrence of Postoperative Delirium and the Use of Different Assessment Tools

et al. 2023

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(1) Background: Postoperative delirium (POD) poses a high risk of worsening outcomes for patients and is also a burden for hospitals. The leading guidelines recommend standardized POD assessment and prevention. The aim of this subgroup analysis of the PRe-Operative Prediction of Postoperative DElirium by Appropriate SCreening (PROPDESC) trial was to compare different delirium assessments and to analyse the frequency of POD on five postoperative days. (2) Methods: This prospective observational trial enrolled 1097 patients in a university hospital from 2018 until 2019. The following POD assessment tools were used for five consecutive days: Confusion Assessment Method for ICU (CAM-ICU) or Confusion Assessment Method for normal ward (CAM), 4 A’s Test (4AT) and Delirium Observation Screening (DOS) scale. (3) Results: In a 5-day visit interval, most new POD developments occurred on the first and second postoperative day. A clear recommendation for a specific POD assessment tool based on our results cannot be given. (4) Conclusions: According to guidelines, a POD assessment should take place on the first five postoperative days, but of these, the first two are those of highest POD occurrence. The POD assessment tool used should at best include direct patient questioning and aspects of patient observation.

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PRe-Operative Prediction of postoperative DElirium by appropriate SCreening (PROPDESC) development and validation of a pragmatic POD risk screening score based on routine preoperative data

Cited by 14 publications

References 39 publications

The Impact of Perioperative and Predisposing Risk Factors on the Development of Postoperative Delirium and a Possible Gender Difference

The Impact of Perioperative and Predisposing Risk Factors on the Development of Postoperative Delirium and a Possible Gender Difference

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

Occurrence of Postoperative Delirium and the Use of Different Assessment Tools

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