Prediction models for postoperative delirium after cardiac surgery: Systematic review and critical appraisal

Cai, Shining; LI, Jingjing; Gao, Jian; Wang, Pan; ZHANG, Yuxia

doi:10.1016/j.ijnurstu.2022.104340

Cited by 19 publications

(12 citation statements)

References 38 publications

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“…The observed prevalence of POD is consistent with the one reported in the literature, with a notably higher prevalence of POD among those undergoing cardiac surgery when compared to other types of surgeries. This difference has been associated to the complex interplay between predisposing factors such as the underlying cardiovascular disease, diabetes, low ejection fraction, elevated creatinine, postoperative atrial fibrillation, pneumonia, extracardiac arteriopathy [ 33 ] or environmental cofactors like ICU noise/light as well as the systemic inflammation with endothelial dysfunction and blood–brain barrier disruption reported after the use of cardiopulmonary bypass causing neuroinflammation [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Frailty index and its association with the onset of postoperative delirium in older adults undergoing elective surgery

et al. 2023

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Background The association of frailty based on the accumulation of deficits with postoperative delirium (POD) has been poorly examined. We aimed to analyze this association in older patients undergoing elective surgery. Methods Preoperative data was used to build a 30-item frailty index (FI) for participants of the PAWEL-study. Delirium was defined by a combination of I-CAM and chart review. Using logistic regressions models we analysed the association between frailty and POD adjusting for age, sex, smoking, alcohol consumption, education and type of surgery. Results Among 701 participants (mean age 77.1, 52.4% male) median FI was 0.27 (Q1 0.20| Q3 0.34), with 528 (75.3%) frail participants (FI ≥ 0.2). Higher median FI were seen in orthopedic than cardiac surgery patients (0.28 versus 0.23), and in women (0.28 versus 0.25 in men). Frail participants showed a higher POD incidence proportion (25.4% versus 17.9% in non-frail). An increased odds for POD was observed in frail versus non-frail participants (OR 2.14 [95% CI 1.33, 3.44], c-statistic 0.71). A 0.1 increment of FI was associated with OR 1.57 [95% CI 1.30, 1.90] (c-statistic 0.72) for POD. No interaction with sex or type of surgery was detected. Adding timed-up-and-go-test and handgrip strength to the FI did not improve discrimination. Conclusion Our data showed a significant association between frailty defined through a 30-item FI and POD among older adults undergoing elective surgery. Adding functional measures to the FI did not improve discrimination. Hence, our preoperative 30-item FI can help to identify patients with increased odds for POD. Trial registration PAWEL and PAWEL-R (sub-) study were registered on the German Clinical Trials Register (number DRKS00013311 and DRKS00012797).

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Section: Discussionmentioning

confidence: 99%

Frailty index and its association with the onset of postoperative delirium in older adults undergoing elective surgery

et al. 2023

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“…Compared to previous predictive models for postoperative delirium after cardiac surgery, this study explicitly focuses on the POD of VHD patients in the ICU, taking advantage of the large number of valve surgeries performed annually and the presence of the CSICU in our hospital. Furthermore, all participants underwent elective valve surgery with cardiopulmonary bypass [15]. We mainly set a period of seven days to assess delirium to increase the rigor, because POD is an acute postoperative syndrome that usually develops 2-5 days after surgery.…”

Section: Discussionmentioning

confidence: 99%

“…The characteristics of our participants were associated with the surgical procedure and the pathogenetic features of VHD. For example, our participants were predominantly female (59%), and the average age (55.7 years) was below 70 years, as VHD is more common in females and middle-aged individuals, which differs from other models that do not differentiate the primary disease [15]. The average cardiopulmonary bypass (CPB) duration (157.3 minutes versus 198.34 minutes), aortic cross-clamping duration (98.0 minutes versus 114.86 minutes), and anesthesia duration (259.0 minutes versus 476.91 minutes) were much shorter in this study compared with the previous POD study in patients with type A aortic dissection (AAD) [45].…”

Section: Participants Characteristicsmentioning

confidence: 91%

“…This information can then be used to create better treatment plans and care protocols to help prevent the onset of delirium. However, few existing predictive models use machine learning algorithms, and many of these models have a high risk of bias [14,15,16,17]. We aim to develop and validate a prediction model using machine learning tools that adhere to the standards set by the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement: a guideline speci cally designed to guide the reporting of studies that create or validate multivariable prediction models [18].…”

Section: Introductionmentioning

confidence: 99%

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A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records

Li,

Chen

et al. 2023

Preprint

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Background: Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients. Methods: The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (logistic regression, support vector machine, K-nearest neighbors, Naïve Bayes classifier, perceptron, decision tree classifier, and random forest classifier) were used to develop delirium prediction models under full (n=32) and simple (n=20) feature sets, respectively. Result: The area under the receiver operating characteristic curve (AUC) was higher under the full feature set (ranging from 0.61 to 0.85) than under the simple feature set (ranging from 0.31 to 0.76). Among all machine learning methods, the random forest classifier showed excellent potential for predicting delirium in patients using the full or simple feature set. Conclusions: We established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.

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“…Although many high-quality studies have investigated the association between neuroinflammation and PNDs,14–16 and have reported the risk factors and the effective prediction models for the onset of PNDs,13 17 to the best of our knowledge, no accurate biomarker-based prediction models have been widely accepted and used in the routine clinical practice 18–20. This may be attributed to many factors such as different sample sizes, multiple mixed factors, only single predictive biomarker, and impractical biomarkers 16 18. Therefore, determining practical factors useful for the early identification of PNDs is of great importance in anaesthesia management and drug target designing.…”

Section: Introductionmentioning

confidence: 99%

Cerebrospinal fluid biomarkers of neuroinflammation and postoperative neurocognitive disorders in patients undergoing orthopaedic surgery: protocol for a systematic review and meta-analysis

et al. 2023

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IntroductionPostoperative neurocognitive disorders (PNDs) are characterised by gradual cognitive decline or change occurring after anaesthesia and surgery, and they are common in patients undergoing orthopaedic surgery. The onset of PNDs has been associated with dementia or other types of neurocognitive disorders in later life. Moreover, cerebrospinal fluid (CSF) biomarkers of neuroinflammation, including amyloid beta-40 peptide, amyloid beta-42 peptide, total tau protein, phosphorylated tau protein and neurofilament light chain, have been reported to be crucial in several high-quality clinical studies on PNDs. However, the role of these biomarkers in the onset of PNDs remains controversial. Therefore, this study aims to determine the association between CSF biomarkers of neuroinflammation and the onset of PNDs in patients undergoing orthopaedic surgery, which will provide novel insights for investigating PNDs and other types of dementia.Methods and analysisThis systematic review and meta-analysis will be conducted in accordance with the Preferred Reporting Items for Systematic Reviewd and Meta-Analyses 2020 statement. Moreover, we will search MEDLINE (via OVID), EMBASE and the Cochrane Library without any language and date restrictions. Observational studies will be included. Two reviewers will independently perform the entire procedure, and disagreements will be settled by discussion between them and consultation with a third reviewer. Standardised electronic forms will be generated to extract data. The risk of bias in the individual studies will be evaluated using the Newcastle-Ottawa scale. All statistical analyses will be performed using the RevMan software or the Stata software.Ethics and disseminationThis study will include peer-reviewed published articles; thus, no ethical issues will be involved. Further, the final manuscript will be published in a peer-reviewed journal.PROSPERO registration numberCRD42022380180.

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Prediction models for postoperative delirium after cardiac surgery: Systematic review and critical appraisal

Cited by 19 publications

References 38 publications

Frailty index and its association with the onset of postoperative delirium in older adults undergoing elective surgery

Frailty index and its association with the onset of postoperative delirium in older adults undergoing elective surgery

A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records

Cerebrospinal fluid biomarkers of neuroinflammation and postoperative neurocognitive disorders in patients undergoing orthopaedic surgery: protocol for a systematic review and meta-analysis

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