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DELirium Prediction Based on Hospital Information (Delphi) in General Surgery Patients

View Article: PubMed Central - PubMed

ABSTRACT

To develop a simple and accurate delirium prediction score that would allow identification of individuals with a high probability of postoperative delirium on the basis of preoperative and immediate postoperative data.

Postoperative delirium, although transient, is associated with adverse outcomes after surgery. However, there has been no appropriate tool to predict postoperative delirium.

This was a prospective observational single-center study, which consisted of the development of the DELirium Prediction based on Hospital Information (Delphi) score (n = 561) and its validation (n = 533). We collected potential risk factors for postoperative delirium, which were identified by conducting a comprehensive review of the literatures.

Age, low physical activity, hearing impairment, heavy alcoholism, history of prior delirium, intensive care unit (ICU) admission, emergency surgery, open surgery, and increased preoperative C-reactive protein were identified as independent predictors of postoperative delirium. The Delphi score was generated using logistic regression coefficients. The maximum Delphi score was 15 and the optimal cut-off point identified with the Youden index was 6.5. Generated area under the (AUC) of the receiver operating characteristic (ROC) curve was 0.911 (95% CI: 0.88–0.94). In the validation study, the calculated AUC of the ROC curve based on the Delphi score was 0.938 (95% Cl: 0.91–0.97). We divided the validation cohort into the low-risk group (Delphi score 0–6) and high-risk group (7–15). Sensitivity of Delphi score was 80.8% and specificity 92.5%.

Our proposed Delphi score could help health-care provider to predict the development of delirium and make possible targeted intervention to prevent delirium in high-risk surgery patients.

No MeSH data available.


Patient enrollment in the development study (A) and validation study (B).
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Figure 2: Patient enrollment in the development study (A) and validation study (B).

Mentions: Sample sizes for the development and validation of the Delphi scoring system were determined as 10 patients per risk factor according to Nunnally's rule.20 As 48 risk factors were identified, at least 480 patients were needed. We expected a drop-out rate of 20% and planned to enroll 600 patients in each part of the study. Of the 600 eligible patients, 39 patients were dropped and 561 patients (93.5%) were enrolled in the development study (Figure 2A) and 47 patients were dropped and 553 patients (92.2%) were enrolled in the validation study (Figure 2B). All variables were collected from all enrolled patients.


DELirium Prediction Based on Hospital Information (Delphi) in General Surgery Patients
Patient enrollment in the development study (A) and validation study (B).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4998372&req=5

Figure 2: Patient enrollment in the development study (A) and validation study (B).
Mentions: Sample sizes for the development and validation of the Delphi scoring system were determined as 10 patients per risk factor according to Nunnally's rule.20 As 48 risk factors were identified, at least 480 patients were needed. We expected a drop-out rate of 20% and planned to enroll 600 patients in each part of the study. Of the 600 eligible patients, 39 patients were dropped and 561 patients (93.5%) were enrolled in the development study (Figure 2A) and 47 patients were dropped and 553 patients (92.2%) were enrolled in the validation study (Figure 2B). All variables were collected from all enrolled patients.

View Article: PubMed Central - PubMed

ABSTRACT

To develop a simple and accurate delirium prediction score that would allow identification of individuals with a high probability of postoperative delirium on the basis of preoperative and immediate postoperative data.

Postoperative delirium, although transient, is associated with adverse outcomes after surgery. However, there has been no appropriate tool to predict postoperative delirium.

This was a prospective observational single-center study, which consisted of the development of the DELirium Prediction based on Hospital Information (Delphi) score (n = 561) and its validation (n = 533). We collected potential risk factors for postoperative delirium, which were identified by conducting a comprehensive review of the literatures.

Age, low physical activity, hearing impairment, heavy alcoholism, history of prior delirium, intensive care unit (ICU) admission, emergency surgery, open surgery, and increased preoperative C-reactive protein were identified as independent predictors of postoperative delirium. The Delphi score was generated using logistic regression coefficients. The maximum Delphi score was 15 and the optimal cut-off point identified with the Youden index was 6.5. Generated area under the (AUC) of the receiver operating characteristic (ROC) curve was 0.911 (95% CI: 0.88–0.94). In the validation study, the calculated AUC of the ROC curve based on the Delphi score was 0.938 (95% Cl: 0.91–0.97). We divided the validation cohort into the low-risk group (Delphi score 0–6) and high-risk group (7–15). Sensitivity of Delphi score was 80.8% and specificity 92.5%.

Our proposed Delphi score could help health-care provider to predict the development of delirium and make possible targeted intervention to prevent delirium in high-risk surgery patients.

No MeSH data available.