Derivation and Validation of an Ensemble Model for the Prediction of Agitation in Mechanically Ventilated Patients Maintained Under Light Sedation

Zhang, Zhongheng; Liu, Jianwen; Xi, Jingjing; Gong, Yan; Zeng, Lin; Ma, Pan

doi:10.1097/ccm.0000000000004821

Cited by 31 publications

(28 citation statements)

References 39 publications

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“…Early assessment of the prognosis of patients with AFLP may play an important role in improving maternal and fetal survival ( 4 ), and predictive analytics for risk stratification can help to improve patients management in critical care setting and add some reference for this statement ( 5 – 7 ). Previous clinical studies on AFLP, largely based on a small number of patients owing to its low prevalence, have found significant differences in its epidemiology ( 1 , 8 ), symptoms ( 9 ), complications ( 9 ), and outcomes ( 1 , 10 , 11 ).…”

Section: Introductionmentioning

confidence: 99%

Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

et al. 2021

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Acute fatty liver of pregnancy (AFLP) is a rare but potentially life-threatening hepatic disorder that leads to considerable maternal and fetal mortality. To explore the risk factors for maternal and fetal mortality in AFLP and develop new predictive models, through this retrospective study, we analyzed the demographic characteristics, clinical symptoms, and laboratory findings of 106 patients with AFLP who were admitted to Shandong Provincial Hospital. Risk factors for maternal and fetal mortality were analyzed by univariate and multivariate logistic regression analysis. The new models based on the multivariate logistic regression analysis and the model for end-stage liver disease (MELD) were tested in AFLP. The receiver operating characteristic curve (ROC) was applied to compare the predictive efficiency, sensitivity, and specificity of the two models. Prenatal nausea (p = 0.037), prolonged prothrombin time (p = 0.003), and elevated serum creatinine (p = 0.003) were independent risk factors for maternal mortality. The ROC curve showed that the area under the curve (AUC) of the MELD was 0.948, with a sensitivity of 100% and a specificity of 83.3%. The AUC of the new model for maternal mortality was 0.926, with a sensitivity of 90% and a specificity of 94.8%. Hepatic encephalopathy (p = 0.016) and thrombocytopenia (p = 0.001) were independent risk factors for fetal mortality. Using the ROC curve, the AUC of the MELD was 0.694, yielding a sensitivity of 68.8% and a specificity of 64.4%. The AUC of the new model for fetal mortality was 0.893, yielding a sensitivity of 100% and a specificity of 73.3%. Both the new predictive model for maternal mortality and the MELD showed good predictive efficacy for maternal mortality in patients with AFLP (AUC = 0.926 and 0.948, respectively), and the new predictive model for fetal mortality was superior to the MELD in predicting fetal mortality (AUC = 0.893 and 0.694, respectively). The two new predictive models were more readily available, less expensive, and easier to implement clinically, especially in low-income countries.

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Section: Introductionmentioning

confidence: 99%

Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

et al. 2021

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“…David et al used machine learning algorithms to improve the cardiovascular risk prediction of patients with end-stage renal disease on hemodialysis ( de Gonzalo-Calvo et al, 2020 ). Michalis et al also found that, based on machine learning algorithms, using volatile organic compounds in exhaled gas as predictors distinguishes lung cancer from other lung diseases or healthy individuals well ( Zhang Z. et al, 2021 ). We used the four machine learning algorithms to develop the EAS predictive model.…”

Section: Discussionmentioning

confidence: 99%

“…However, the LG-based approach fails to consider the complex non-linear interactions between variables, which can be captured by more sophisticated model algorithms, thus improving the accuracy of risk prediction. Recently, machine learning has been widely applied to the development of clinical tools for disease diagnosis ( Rajkomar et al, 2019 ; de Gonzalo-Calvo et al, 2020 ; Zhang Z. et al, 2021 ). Unlike the traditional LG-based approach, machine learning can recognize hidden patterns and non-linear interactions in complex data, allowing for a better assessment of clinical outcomes ( Myszczynska et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning

Xie

et al. 2021

Front. Physiol.

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BackgroundArterial stiffness assessed by pulse wave velocity is a major risk factor for cardiovascular diseases. The incidence of cardiovascular events remains high in diabetics. However, a clinical prediction model for elevated arterial stiffness using machine learning to identify subjects consequently at higher risk remains to be developed.MethodsLeast absolute shrinkage and selection operator and support vector machine-recursive feature elimination were used for feature selection. Four machine learning algorithms were used to construct a prediction model, and their performance was compared based on the area under the receiver operating characteristic curve metric in a discovery dataset (n = 760). The model with the best performance was selected and validated in an independent dataset (n = 912) from the Dryad Digital Repository (https://doi.org/10.5061/dryad.m484p). To apply our model to clinical practice, we built a free and user-friendly web online tool.ResultsThe predictive model includes the predictors: age, systolic blood pressure, diastolic blood pressure, and body mass index. In the discovery cohort, the gradient boosting-based model outperformed other methods in the elevated arterial stiffness prediction. In the validation cohort, the gradient boosting model showed a good discrimination capacity. A cutoff value of 0.46 for the elevated arterial stiffness risk score in the gradient boosting model resulted in a good specificity (0.813 in the discovery data and 0.761 in the validation data) and sensitivity (0.875 and 0.738, respectively) trade-off points.ConclusionThe gradient boosting-based prediction system presents a good classification in elevated arterial stiffness prediction. The web online tool makes our gradient boosting-based model easily accessible for further clinical studies and utilization.

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“…Machine learning algorithms can be applied to help understand large quantities of existing data and to make predictions about new data. Previous studies have used machine learning methods to diagnose or distinguish different types of diseases ( 19 , 20 ). Because of the extremely low incidence of candidaemia, the development of a prediction model requires a very large sample size and must overcome the imbalance between positive and negative results.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome

et al. 2021

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Background: Distinguishing ICU patients with candidaemia can help with the precise prescription of antifungal drugs to create personalized guidelines. Previous prediction models of candidaemia have primarily used traditional logistic models and had some limitations. In this study, we developed a machine learning algorithm trained to predict candidaemia in patients with new-onset systemic inflammatory response syndrome (SIRS).Methods: This retrospective, observational study used clinical information collected between January 2013 and December 2017 from three hospitals. The ICU patient data were used to train 4 machine learning algorithms–XGBoost, Support Vector Machine (SVM), Random Forest (RF), ExtraTrees (ET)–and a logistic regression (LR) model to predict patients with candidaemia.Results: Of the 8,002 cases of new-onset SIRS (in 7,932 patients) included in the analysis, 137 new-onset SIRS cases (in 137 patients) were blood culture positive for candidaemia. Risk factors, such as fungal colonization, diabetes, acute kidney injury, the total number of parenteral nutrition days and renal replacement therapy, were important predictors of candidaemia. The XGBoost machine learning model outperformed the other models in distinguishing patients with candidaemia [XGBoost vs. SVM vs. RF vs. ET vs. LR; area under the curve (AUC): 0.92 vs. 0.86 vs. 0.91 vs. 0.90 vs. 0.52, respectively]. The XGBoost model had a sensitivity of 84%, specificity of 89% and negative predictive value of 99.6% at the best cut-off value.Conclusions: Machine learning algorithms can potentially predict candidaemia in the ICU and have better efficiency than previous models. These prediction models can be used to guide antifungal treatment for ICU patients when SIRS occurs.

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Derivation and Validation of an Ensemble Model for the Prediction of Agitation in Mechanically Ventilated Patients Maintained Under Light Sedation

Cited by 31 publications

References 39 publications

Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients With Diabetes Using Machine Learning

Using Machine Learning Algorithms to Predict Candidaemia in ICU Patients With New-Onset Systemic Inflammatory Response Syndrome

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