Developing a Risk Prediction Model for Intensive Care Unit Mortality after Cardiac Surgery

2018 ◽  
Vol 66 (08) ◽  
pp. e1-e2
Author(s):  
Qing Liu ◽  
Fu-Shan Xue ◽  
Gui-Zhen Yang ◽  
Ya-Yang Liu
Keyword(s):  
Intensive Care Unit ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Intensive Care Unit Mortality ◽  
Risk Prediction Model

scholarly journals Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

10.2196/23128 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23128
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

PROPER: Development of an Early Pediatric Intensive Care Unit Readmission Risk Prediction Tool

2016 ◽  
Vol 33 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Harsheen Kaur ◽  
James M. Naessens ◽  
Andrew C. Hanson ◽  
Karen Fryer ◽  
Michael E. Nemergut ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Pediatric Intensive Care Unit ◽  
Risk Prediction ◽  
Pediatric Population ◽  
Cerebral Performance Category ◽  
Pediatric Intensive Care ◽  
Risk Prediction Model ◽  
Retrospective Case

Objective: No risk prediction model is currently available to measure patient’s probability for readmission to the pediatric intensive care unit (PICU). This retrospective case–control study was designed to assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer [SWIFT]) and to create a pediatric version (PRediction Of PICU Early Readmissions [PROPER]). Design: Eighty-six unplanned early (<48 hours) PICU readmissions from January 07, 2007, to June 30, 2014, were compared with 170 random controls. Patient- and disease-specific data and PICU workload factors were compared across the 2 groups. Factors statistically significant on multivariate analysis were included in the creation of the risk prediction model. The SWIFT scores were calculated for cases and controls and compared for validation. Results: Readmitted patients were younger, weighed less, and were more likely to be admitted from the emergency department. There were no differences in gender, race, or admission Pediatric Index of Mortality scores. A higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category in the moderate to severe disability category. Cases and controls did not differ with respect to staff workload at discharge or discharge day of the week; there was a much higher proportion of patients on supplemental oxygen in the readmission group. Only 2 of 5 categories in the SWIFT model were significantly different, and although the median SWIFT score was significantly higher in the readmissions group, the model discriminated poorly between cases and controls (area under the curve: 0.613). A 7-category PROPER score was created based on a multiple logistic regression model. Sensitivity of this model (score ≥12) for the detection of readmission was 81% with a positive predictive value of 0.50. Conclusion: We have created a preliminary model for predicting patients at risk of early readmissions to the PICU from the hospital floor. The SWIFT score is not applicable for predicting the risk for pediatric population.

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

BACKGROUND Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. OBJECTIVE The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. METHODS In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. RESULTS Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. CONCLUSIONS The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

scholarly journals Methods for updating a risk prediction model for cardiac surgery: a statistical primer

2019 ◽  
Vol 28 (3) ◽  
pp. 333-338 ◽  
Author(s):  
Sabrina Siregar ◽  
Daan Nieboer ◽  
Michel I M Versteegh ◽  
Ewout W Steyerberg ◽  
Johanna J M Takkenberg
Keyword(s):  
Cardiac Surgery ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model

scholarly journals A prospective study of acute kidney injury in the intensive care unit: development and validation of a risk prediction model

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Qi Wang ◽  
Yi Tang ◽  
Jiaojiao Zhou ◽  
Wei Qin
Keyword(s):  
Chronic Kidney Disease ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
High Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Validation Cohort ◽  
Kidney Injury ◽  
Risk Prediction Model ◽  
Risk Patients

Abstract Background Acute kidney injury (AKI) has high morbidity and mortality in intensive care units (ICU). It can also lead to chronic kidney disease (CKD), more costs and longer hospital stay. Early identification of AKI is important. Methods We conducted this monocenter prospective observational study at West China Hospital, Sichuan University, China. We recorded information of each patient in the ICU within 24 h after admission and updated every two days. Patients who reached the primary outcome were accepted into the AKI group. Of all patients, we randomly drew 70% as the development cohort and the remaining 30% as the validation cohort. Using binary logistic regression we got a risk prediction model of the development cohort. In the validation cohort, we validated its discrimination by the area under the receiver operator curve (AUROC) and calibration by a calibration curve. Results There were 656 patients in the development cohorts and 280 in the validation cohort. Independent predictors of AKI in the risk prediction model including hypertension, chronic kidney disease, acute pancreatitis, cardiac failure, shock, pH ≤ 7.30, CK > 1000 U/L, hypoproteinemia, nephrotoxin exposure, and male. In the validation cohort, the AUROC is 0.783 (95% CI 0.730–0.836) and the calibration curve shows good calibration of this prediction model. The optimal cut-off value to distinguish high-risk and low-risk patients is 4.5 points (sensitivity is 78.4%, specificity is 73.2% and Youden’s index is 0.516). Conclusions This risk prediction model can help to identify high-risk patients of AKI in ICU to prevent the development of AKI and treat it at the early stages. Trial registration TCTR, TCTR20170531001. Registered 30 May 2017, http://www.clinicaltrials.in.th/index.php?tp=regtrials&menu=trialsearch&smenu=fulltext&task=search&task2=view1&id=2573

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