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A nomogram for predicting the risk of postoperative delirium in individuals undergoing cardiovascular surgery. | LitMetric

AI Article Synopsis

  • Delirium is a common issue after cardiovascular surgery, affecting 15-23% of patients, and current treatments are often ineffective.
  • A study involving 729 patients developed a nomogram—a predictive tool using logistic regression—to estimate the chances of experiencing postoperative delirium, validated through various statistical analyses.
  • The model identified seven key predictors of delirium including age and certain medical indicators, achieving a satisfactory accuracy (AUROC = 0.754), indicating its potential clinical usefulness in predicting delirium in surgical patients.

Article Abstract

Background And Purpose: Delirium is a common mental disorder after adult cardiovascular surgery. Fifteen to 23% of patients undergoing cardiovascular surgery and cardiomyopathy experience delirium, and the efficacy of treatment interventions for delirium has been consistently unsatisfactory.

Methods: A total of 729 patients who underwent cardiovascular surgery were randomly allocated into a training set and a validation set. A nomogram was developed using a logistic regression model to predict the incidence of delirium following cardiovascular surgery. The validity of the model was assessed by determining the receiver operating characteristic (ROC) curve, calculating the area under the ROC curve (AUROC), performing a calibration plot, and executing a decision curve analysis. This model was internally validated using the bootstrap method.

Results: Postoperative delirium (POD) occurred in 165 cases (22.6%) among the 729 patients. Predictors included age, transient ischemic attack, length of preoperative stay, preoperative left ventricular injection fraction and N-terminal pro-B-type natriuretic peptide level, and intraoperative infusion of dexmedetomidine and human fibrinogen. The nomogram showed sufficient differentiation and calibration (AUROC = 0.754, 95% confidence interval = 0.703-0.804). The calibration graphs showed that the predictive values of the nomogram were in agreement with the actual values. The analysis of the training and validation sets suggested that the model possessed specific clinical significance.

Conclusions: In summary, the predictive model consists of seven factors that can roughly predict the occurrence of POD in patients who undergo cardiovascular surgery.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11555157PMC
http://dx.doi.org/10.1111/ene.16483DOI Listing

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