scholarly journals Risk prediction models for delirium in the intensive care unit after cardiac surgery: a systematic review and independent external validation

2017 ◽  
Vol 118 (3) ◽  
pp. 391-399 ◽  
Author(s):  
A Lee ◽  
J.L. Mu ◽  
G.M. Joynt ◽  
C.H. Chiu ◽  
V.K.W. Lai ◽  
...  
Keyword(s):  
Systematic Review ◽  
Intensive Care Unit ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Risk Prediction Models

scholarly journals Risk prediction models for intensive care unit-acquired weakness in intensive care unit patients: A systematic review

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257768
Author(s):  
Wei Zhang ◽  
Yun Tang ◽  
Huan Liu ◽  
Li ping Yuan ◽  
Chu chu Wang ◽  
...  
Keyword(s):  
Systematic Review ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Groups ◽  
Icu Patients ◽  
Risk Prediction Models ◽  
Verification Methods

Background and objectives Intensive care unit-acquired weakness (ICU-AW) commonly occurs among intensive care unit (ICU) patients and seriously affects the survival rate and long-term quality of life for patients. In this systematic review, we synthesized the findings of previous studies in order to analyze predictors of ICU-AW and evaluate the discrimination and validity of ICU-AW risk prediction models for ICU patients. Methods We searched seven databases published in English and Chinese language to identify studies regarding ICU-AW risk prediction models. Two reviewers independently screened the literature, evaluated the quality of the included literature, extracted data, and performed a systematic review. Results Ultimately, 11 studies were considered for this review. For the verification of prediction models, internal verification methods had been used in three studies, and a combination of internal and external verification had been used in one study. The value for the area under the ROC curve for eight models was 0.7–0.923. The predictor most commonly included in the models were age and the administration of corticosteroids. All the models have good applicability, but most of the models are biased due to the lack of blindness, lack of reporting, insufficient sample size, missing data, and lack of performance evaluation and calibration of the models. Conclusions The efficacy of most models for the risk prediction of ICU-AW among high-risk groups is good, but there was a certain bias in the development and verification of the models. Thus, ICU medical staff should select existing models based on actual clinical conditions and verify them before applying them in clinical practice. In order to provide a reliable basis for the risk prediction of ICU-AW, it is necessary that large-sample, multi-center studies be conducted in the future, in which ICU-AW risk prediction models are verified.

scholarly journals Development and Evaluation of Models to Predict Readmission or Death After Discharge from Intensive Care 

2021 ◽  
Author(s):  
Jamie M Boyd ◽  
Matthew T James ◽  
Danny J Zuege ◽  
Henry Thomas Stelfox
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Intensive Care ◽  
Risk Prediction ◽  
Prediction Models ◽  
Meta Analysis ◽  
Local Data ◽  
Predictive Values ◽  
Risk Prediction Models ◽  
Icu Discharge

Abstract Background Patients being discharged from the intensive care unit (ICU) have variable risks of subsequent readmission or death; however, there is limited understanding of how to predict individual patient risk. We sought to derive risk prediction models for ICU readmission or death after ICU discharge to guide clinician decision-making. Methods Systematic review and meta-analysis to identify risk factors. Development and validation of risk prediction models using two retrospective cohorts of patients discharged alive from medical-surgical ICUs (n = 3 ICUs, n = 11,291 patients; n = 14 ICUs, n = 11,400 patients). Models were developed using literature and data-derived weighted coefficients. Results Sixteen variables identified from the systematic review were used to develop four risk prediction models. In the validation cohort there were 795 (7%) patients who were re-admitted to ICU and 703 (7%) patients who died after ICU discharge. The area under the curve (AUROC) for ICU readmission for the literature (0.615 [95%CI: 0.593, 0.637]) and data (0.652 [95%CI: 0.631, 0.674]) weighted models showed poor discrimination. The AUROC for death after ICU discharge for the literature (0.708 [95%CI: 0.687, 0.728]) and local data weighted (0.752 [95%CI: 0.733, 0.770]) models showed good discrimination. The negative predictive values for ICU readmission and death after ICU discharge ranged from 94%-98%. Conclusions Identifying risk factors and weighting coefficients using systematic review and meta-analysis to develop prediction models is feasible and can identify patients at low risk of ICU readmission or death after ICU discharge.

scholarly journals 702 Evaluation of Risk Prediction Models for Postoperative Pulmonary Complications in Adults Undergoing Major Abdominal Surgery: A Systematic Review and External Validation Study in The Respiratory Complications After Abdominal Surgery (RECON) Cohort

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Introduction Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to identify and externally validate PPC risk prediction models in an international, prospective cohort. Method A systematic review was conducted to identify risk prediction models for PPC following abdominal surgery. External validation was performed using data from a prospective dataset of adult patients undergoing major abdominal surgery from January to April 2019 in the UK, Ireland, and Australia. The primary outcome was identification of PPC within 30-days (StEP-COMPAC criteria definition). Model discrimination and diagnostic accuracy were compared. Results Six unique risk prediction models were eligible from 2819 records (112 full texts). These were validated across 11,591 patients, with an overall PPC rate of 7.8% (n = 903). The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score provided the best discrimination (AUROC: 0.709 (95% CI: 0.692-0.727), yet no risk prediction model demonstrated good discrimination (AUROC >0.7). Conclusions The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

scholarly journals V2 Evaluation of risk prediction models for postoperative pulmonary complications in adults undergoing major abdominal surgery: A systematic review and external validation study of the REspiratory COmplications after abdomiNal surgery (RECON) cohort

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
◽  
Omar Kouli
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Background Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to identify and externally validate PPC risk prediction models in an international, prospective cohort. Methods A systematic review was conducted to identify risk prediction models for PPC following abdominal surgery. External validation was performed using data from a prospective dataset of adult patients undergoing major abdominal surgery from January to April 2019 in the UK, Ireland and Australia. The primary outcome was identification of PPC within 30-days (StEP-COMPAC criteria definition). Model discrimination and diagnostic accuracy were compared. Results Six unique risk prediction models were eligible from 2819 records. These were validated across 11,591 patients, with an overall PPC rate of 7.8% (n = 903). The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score provided the best discrimination (AUC: 0.709 (95% CI: 0.692-0.727), yet no risk prediction model demonstrated good discrimination (AUC >0.7). Conclusion The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

scholarly journals Validation of Three Postoperative Risk Prediction Models for Intensive Care Unit Mortality after Cardiac Surgery

2018 ◽  
Vol 66 (08) ◽  
pp. 651-660 ◽  
Author(s):  
Camila Caiado ◽  
Charles McCollum ◽  
Michael Goldstein ◽  
Ignacio Malagon ◽  
Rajamiyer Venkateswaran ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Operating Characteristic ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Icu Mortality ◽  
Risk Prediction Models ◽  
Postoperative Risk ◽  
Operating Characteristic Curve

Background Several cardiac surgery risk prediction models based on postoperative data have been developed. However, unlike preoperative cardiac surgery risk prediction models, postoperative models are rarely externally validated or utilized by clinicians. The objective of this study was to externally validate three postoperative risk prediction models for intensive care unit (ICU) mortality after cardiac surgery. Methods The logistic Cardiac Surgery Scores (logCASUS), Rapid Clinical Evaluation (RACE), and Sequential Organ Failure Assessment (SOFA) scores were calculated over the first 7 postoperative days for consecutive adult cardiac surgery patients between January 2013 and May 2015. Model discrimination was assessed using receiver operating characteristic curve analyses. Calibration was assessed using the Hosmer–Lemeshow (HL) test, calibration plots, and observed to expected ratios. Recalibration of the models was performed. Results A total of 2255 patients were included with an ICU mortality rate of 1.8%. Discrimination for all three models on each postoperative day was good with areas under the receiver operating characteristic curve of >0.8. Generally, RACE and logCASUS had better discrimination than SOFA. Calibration of the RACE score was better than logCASUS, but ratios of observed to expected mortality for both were generally <0.65. Locally recalibrated SOFA, logCASUS and RACE models all performed well. Conclusion All three models demonstrated good discrimination for the first 7 days after cardiac surgery. After recalibration, logCASUS and RACE scores appear to be most useful for daily risk prediction after cardiac surgery. If appropriately calibrated, postoperative cardiac surgery risk prediction models have the potential to be useful tools after cardiac surgery.

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