Dataset Information

Multinational development and validation of an early prediction model for delirium in ICU patients.

ABSTRACT: RATIONALE:Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention. PURPOSE:To develop and validate a model based on data available at ICU admission to predict delirium development during a patient's complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development. METHODS:Prospective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU. RESULTS:In total, 2914 patients were included. Delirium incidence was 23.6%. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 [95% confidence interval (CI) 0.73-0.77] in the development dataset and 0.75 (95% CI 0.71-0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95% CI 0.67-0.74), for delirium that developed <2 days, to 0.81 (95% CI 0.78-0.84), for delirium that developed >6 days. CONCLUSION:Patients' delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium.

SUBMITTER: Wassenaar A

PROVIDER: S-EPMC4477716 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Multinational development and validation of an early prediction model for delirium in ICU patients.

Wassenaar A A van den Boogaard M M van Achterberg T T Slooter A J C AJ Kuiper M A MA Hoogendoorn M E ME Simons K S KS Maseda E E Pinto N N Jones C C Luetz A A Schandl A A Verbrugghe W W Aitken L M LM van Haren F M P FM Donders A R T AR Schoonhoven L L Pickkers P P

Intensive care medicine 20150418 6

<h4>Rationale</h4>Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention.<h4>Purpose</h4>To develop and validate a model based on data available at ICU admission to predict delirium development during a patient's complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development.<h4>Methods</h4>Prospective cohort study in 13 ICUs from s ...[more]

PMID: 25894620

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Project description:BackgroundRisk prediction models allow clinicians to forecast which individuals are at a higher risk for developing a particular outcome. We developed and internally validated a delirium prediction model for incident delirium parameterized to patient ICU admission acuity.MethodsThis retrospective, observational, fourteen medical-surgical ICU cohort study evaluated consecutive delirium-free adults surviving hospital stay with ICU length of stay (LOS) greater than or equal to 24 hours with both an admission APACHE II score and an admission type (e.g., elective post-surgery, emergency post-surgery, non-surgical) in whom delirium was assessed using the Intensive Care Delirium Screening Checklist (ICDSC). Risk factors included in the model were readily available in electric medical records. Least absolute shrinkage and selection operator logistic (LASSO) regression was used for model development. Discrimination was determined using area under the receiver operating characteristic curve (AUC). Internal validation was performed by cross-validation. Predictive performance was determined using measures of accuracy and clinical utility was assessed by decision-curve analysis.ResultsA total of 8,878 patients were included. Delirium incidence was 49.9% (n = 4,431). The delirium prediction model was parameterized to seven patient cohorts, admission type (3 cohorts) or mean quartile APACHE II score (4 cohorts). All parameterized cohort models were well calibrated. The AUC ranged from 0.67 to 0.78 (95% confidence intervals [CI] ranged from 0.63 to 0.79). Model accuracy varied across admission types; sensitivity ranged from 53.2% to 63.9% while specificity ranged from 69.0% to 74.6%. Across mean quartile APACHE II scores, sensitivity ranged from 58.2% to 59.7% while specificity ranged from 70.1% to 73.6%. The clinical utility of the parameterized cohort prediction model to predict and prevent incident delirium was greater than preventing incident delirium by treating all or none of the patients.ConclusionsOur results support external validation of a prediction model parameterized to patient ICU admission acuity to predict a patients' risk for ICU delirium. Classification of patients' risk for ICU delirium by admission acuity may allow for efficient initiation of prevention measures based on individual risk profiles.

Project description:ObjectiveSepsis-induced coagulopathy (SIC) is extremely common in individuals with sepsis, significantly associated with poor outcomes. This study attempted to develop an interpretable and generalizable machine learning (ML) model for early predicting the risk of 28-day death in patients with SIC.MethodsIn this retrospective cohort study, we extracted SIC patients from the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV, and eICU-CRD database according to Toshiaki Iba's scale. And the overlapping in the MIMIC-IV was excluded for this study. Afterward, only the MIMIC-III cohort was randomly divided into the training set, and the internal validation set according to the ratio of 7:3, while the MIMIC-IV and eICU-CRD databases were considered the external validation sets. The predictive factors for 28-day mortality of SIC patients were determined using recursive feature elimination combined with tenfold cross-validation (RFECV). Then, we constructed models using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), accuracy, sensitivity, specificity, negative predictive value, positive predictive value, recall, and F1 score. Finally, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) were employed to provide a reasonable interpretation for the prediction results.ResultsA total of 3280, 2798, and 1668 SIC patients were screened from MIMIC-III, MIMIC-IV, and eICU-CRD databases, respectively. Seventeen features were selected to construct ML prediction models. XGBoost had the best performance in predicting the 28-day mortality of SIC patients, with AUC of 0.828, 0.913 and 0.923, the AUPRC of 0.807, 0.796 and 0.921, the accuracy of 0.785, 0.885 and 0.891, the F1 scores were 0.63, 0.69 and 0.70 in MIMIC-III (internal validation set), MIMIC-IV, and eICU-CRD databases. The importance ranking and SHAP analyses showed that initial SOFA score, red blood cell distribution width (RDW), and age were the top three critical features in the XGBoost model.ConclusionsWe developed an optimal and explainable ML model to predict the risk of 28-day death of SIC patients 28-day death risk. Compared with conventional scoring systems, the XGBoost model performed better. The model established will have the potential to improve the level of clinical practice for SIC patients.

Project description:BackgroundDelirium often occurs in children with congenital heart disease in the early postoperative period, which is not conducive to the rehabilitation and prognosis. There is little evidence to prove the effectiveness and safety of drug treatment of delirium in children, and the prevention has become an important topic. The purpose of this study is to analyze the early risk factors of delirium in children after congenital heart surgery, establish a nomogram prediction model, and explore the application efficiency of the model, so as to provide reference for early prevention of delirium.MethodsA total of 362 children treated in the cardiac intensive care unit (CICU) of Shanghai Children's Medical Center after congenital heart surgery from February 15 to April 15, 2021 were enrolled for the construction of the model. Bedside nurses who received unified training used the Cornell Assessment of Pediatric Delirium (CAPD) to evaluate delirium and recorded sixteen preoperative- and intraoperative-related influencing factors. A nomogram prediction model was created using multivariate logistic regression. The prediction effect of the model was evaluated by C-index and Brier value, and 96 children from April 16 to May 15, 2021 were included for effect verification. The model's effectiveness was validated by comparing the occurrence of delirium in children predicted by the model with the actual occurrence.ResultsMultivariate logistic regression analysis showed that male gender [odds ratio (OR) =1.786, 95% confidence interval (CI): 1.018-3.134, P=0.043], age <6.5 months (OR =0.224, 95% CI: 0.126-0.399, P=0.000), disease severity ≥4 points (OR =6.955, 95% CI: 3.564-13.576, P=0.003), and operation time ≥148 min (OR =2.401, 95%CI: 1.336-4.315, P=0.000) were independent risk factors for delirium in children after cardiac surgery. The C-index of the nomogram prediction model was 0.808, sensitivity was 76.1%, specificity was 70%, and the Brier value was 0.142. The validation of the model showed that the model predicted 20 cases and the actual occurrence was 20 cases, of which 8 cases were false negative and 8 cases were false positive, and the sensitivity, specificity, and accuracy of the model were 60%, 89.5%, and 83.3%, respectively.ConclusionsThe prediction model constructed in this study could provide early prediction of the occurrence of delirium in children after congenital heart surgery to a certain extent.

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Multinational development and validation of an early prediction model for delirium in ICU patients.

Publications

Multinational development and validation of an early prediction model for delirium in ICU patients.

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