Trial reportJournal of cardiothoracic surgery2022
Prediction model for delirium in patients with cardiovascular surgery: development and validation.
Trial report in Journal of cardiothoracic surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06685263 (Development of a Perioperative Delirium Prediction Model in Patients Undergoing Cardiovascular Surgery), which is not on this map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development of a Perioperative Delirium Prediction Model in Patients Undergoing Cardiovascular Surgery
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- The Predictive Value of Machine Learning for Postoperative Delirium in Cardiac Surgery: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- Neurological Complications After Thoracic Endovascular Repair (TEVAR): A Narrative Review of the Incidence, Mechanisms and Strategies for Prevention and Management.Journal of personalized medicine · 2026Review
- A machine learning-based prediction model for sepsis-associated delirium in intensive care unit patients with sepsis-associated acute kidney injury.Renal failure · 2025Observational
- Determining the ground truth for the prediction of delirium in adult patients in acute care: a scoping review.JAMIA open · 2025Review
- A nomogram for predicting delirium in the ICU among older patients with chronic obstructive pulmonary disease.BMC geriatrics · 2025Article
- Evaluation of postoperative delirium in cardiac surgery patients with the SDACS screening tool: a multicenter-multiphase study.Perioperative medicine (London, England) · 2025Article
- Prediction model for delirium in advanced cancer patients receiving palliative care: development and validation.BMC palliative care · 2025Observational
- Practical prognostic tools to predict the risk of postoperative delirium in older patients undergoing cardiac surgery: visual and dynamic nomograms.Journal of clinical monitoring and computing · 2025Article
- Development of an individualized model for predicting postoperative delirium in elderly patients with hepatocellular carcinoma.Scientific reports · 2024Article
- Effects of short-term preoperative intranasal dexmedetomidine plus conventional treatment on delirium following cardiac surgery in patients with sleep disorders.Perioperative medicine (London, England) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe aim of this study was to construct a nomogram model for discriminating the risk of delirium in patients undergoing cardiovascular surgery.
methodsFrom January 2017 to June 2020, we collected data from 838 patients who underwent cardiovascular surgery at the Affiliated Hospital of Nantong University. Patients were randomly divided into a training set and a validation set at a 5:5 ratio. A nomogram model was established based on logistic regression. Discrimination and calibration were used to evaluate the predictive performance of the model.
resultsThe incidence of delirium was 48.3%. A total of 389 patients were in the modelling group, and 449 patients were in the verification group. Logistic regression analysis showed that CPB duration (OR [Formula: see text] 1.004, 95% CI: 1.001-1.008, [Formula: see text] 0.018), postoperative serum sodium (OR [Formula: see text] 1.112, 95% CI: 1.049-1.178, [Formula: see text] 0.001), age (OR [Formula: see text] 1.027, 95% CI: 1.006-1.048, [Formula: see text] 0.011), and postoperative MV (OR [Formula: see text] 1.019, 95% CI: 1.008-1.030, [Formula: see text] 0.001) were independent risk factors. The results showed that AUC[Formula: see text] was 0.712 and that the 95% CI was 0.661-0.762. The Hosmer-Lemeshow goodness of fit test showed that the predicted results of the model were in good agreement with the actual situation ([Formula: see text] 6.200, [Formula: see text] 0.625). The results of verification showed that the AUC[Formula: see text] was 0.705, and the 95% CI was 0.657-0.752. The Hosmer-Lemeshow goodness of fit test results were [Formula: see text] 8.653 and [Formula: see text] 0.372, indicating that the predictive effect of the model is good.
conclusionsThe establishment of the model provides accurate and objective assessment tools for medical staff to start preventing postoperative delirium in a purposeful and focused manner when a patient enters the CSICU after surgery.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.