Critical Care Database Comprising Patients With Infection

Xu, Ping; Chen, Lin; Zhu, Yuanfang; Sheng, Yu; Chen, Rangui; Huang, Wenbin; Wu, Fuli; Zhang, Zhongheng

doi:10.3389/fpubh.2022.852410

Cited by 12 publications

(10 citation statements)

References 25 publications

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“…All the data in the current study were extracted from an online international database, Medical Information Mart for Intensive Care III (MIMIC III), which encompasses 53,243 different hospital admissions for adult patients in intensive care units between 2001 and 2012 [ 14 ]., and a dataset from a Chinese hospital which was conducted as external validation set [ 15 ]. All the patients in the database were deidentified for privacy protection purposes, and the need for informed consent was waived.…”

Section: Methodsmentioning

confidence: 99%

A Nomogram to Better Predict the In-Hospital Mortality of Trauma Patients with Sepsis in the Intensive Care Unit

Xie

et al. 2022

International Journal of Clinical Practice

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Background. Trauma has a high incidence and mortality worldwide, and sepsis is one of the main causes of mortality in trauma patients. Therefore, it is essential to identify the risk factors of in-hospital mortality for trauma patients with sepsis. Methods. Data were extracted from the Medical Information Mart for Intensive Care III database and divided into a training set and internal validation set, and another Chinese dataset was used as external validation set. Then, risk factors were estimated using univariate and multivariate logistic regression analyses in the training set. Finally, a nomogram was created to predict the probability of in-hospital mortality for trauma patients with sepsis. Results. A total of 503 patients were enrolled in our study (335 in the training set and 168 in the validation set). Multivariate logistic regression analysis revealed that age (1.047 [1.025–1.071]), respiratory rate (1.258 [1.135–1.394]), PTT (1.026 [1.008–1.044]), ventilation (6.703 [1.528–29.408]), and vasopressor use (3.682 [1.502–9.025]) were independent factors associated with in-hospital mortality. The nomogram for trauma-related sepsis predicted in-hospital mortality with AUC values of 0.8939 in the training set, 0.8200 in the internal validation set, and 0.7779 in the external validation set. Conclusions. The new nomogram has a well predicted value for in-hospital mortality for patients with trauma and sepsis in intensive care units.

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Section: Methodsmentioning

confidence: 99%

A Nomogram to Better Predict the In-Hospital Mortality of Trauma Patients with Sepsis in the Intensive Care Unit

Xie

et al. 2022

International Journal of Clinical Practice

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“…Future works should be used in elucidate the diversity of AMP resistance mechanisms in more realistic clinical settings. In future works, our model should be used in the multi-centric study or other clinical datasets such as MIMIC III ( 31 ) or a critical care database involving patients with infection ( 32 ). Secondly, our model was built based on the clinical data from patients with P. aeruginosa infection.…”

Section: Discussionmentioning

confidence: 99%

A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa

Tang

Lin

et al. 2022

Front. Med.

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Pseudomonas aeruginosa is a ubiquitous opportunistic bacterial pathogen, which is a leading cause of nosocomial pneumonia. Early identification of the risk factors is urgently needed for severe infection patients with P. aeruginosa. However, no detailed relevant investigation based on machine learning has been reported, and little research has focused on exploring relationships between key risk clinical variables and clinical outcome of patients. In this study, we collected 571 severe infections with P. aeruginosa patients admitted to the Xijing Hospital of the Fourth Military Medical University from January 2010 to July 2021. Basic clinical information, clinical signs and symptoms, laboratory indicators, bacterial culture, and drug related were recorded. Machine learning algorithm of XGBoost was applied to build a model for predicting mortality risk of P. aeruginosa infection in severe patients. The performance of XGBoost model (AUROC = 0.94 ± 0.01, AUPRC = 0.94 ± 0.03) was greater than the performance of support vector machine (AUROC = 0.90 ± 0.03, AUPRC = 0.91 ± 0.02) and random forest (AUROC = 0.93 ± 0.03, AUPRC = 0.89 ± 0.04). This study also aimed to interpret the model and to explore the impact of clinical variables. The interpretation analysis highlighted the effects of age, high-alert drugs, and the number of drug varieties. Further stratification clarified the necessity of different treatment for severe infection for different populations.

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“…Data were collected from two of the largest critical care databases in USA, the Medical Information Mart for Intensive Care (MIMIC-IV) database ( 9 , 10 ) and the eICU Collaborative Research Database (eICU-CRD, version 2.0) ( 11 ), and a large critical care database in China (named “dtChina”) ( 12 ). This study was approved by the Institutional Review Broad (IRB) of the Massachusetts Institute of Technology (MIT) online (Record ID: 38889441), and informed consent was waived.…”

Section: Methodsmentioning

confidence: 99%

Machine learning models to predict in-hospital mortality in septic patients with diabetes

Lei

et al. 2022

Front. Endocrinol.

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BackgroundSepsis is a leading cause of morbidity and mortality in hospitalized patients. Up to now, there are no well-established longitudinal networks from molecular mechanisms to clinical phenotypes in sepsis. Adding to the problem, about one of the five patients presented with diabetes. For this subgroup, management is difficult, and prognosis is difficult to evaluate.MethodsFrom the three databases, a total of 7,001 patients were enrolled on the basis of sepsis-3 standard and diabetes diagnosis. Input variable selection is based on the result of correlation analysis in a handpicking way, and 53 variables were left. A total of 5,727 records were collected from Medical Information Mart for Intensive Care database and randomly split into a training set and an internal validation set at a ratio of 7:3. Then, logistic regression with lasso regularization, Bayes logistic regression, decision tree, random forest, and XGBoost were conducted to build the predictive model by using training set. Then, the models were tested by the internal validation set. The data from eICU Collaborative Research Database (n = 815) and dtChina critical care database (n = 459) were used to test the model performance as the external validation set.ResultsIn the internal validation set, the accuracy values of logistic regression with lasso regularization, Bayes logistic regression, decision tree, random forest, and XGBoost were 0.878, 0.883, 0.865, 0.883, and 0.882, respectively. Likewise, in the external validation set 1, lasso regularization = 0.879, Bayes logistic regression = 0.877, decision tree = 0.865, random forest = 0.886, and XGBoost = 0.875. In the external validation set 2, lasso regularization = 0.715, Bayes logistic regression = 0.745, decision tree = 0.763, random forest = 0.760, and XGBoost = 0.699.ConclusionThe top three models for internal validation set were Bayes logistic regression, random forest, and XGBoost, whereas the top three models for external validation set 1 were random forest, logistic regression, and Bayes logistic regression. In addition, the top three models for the external validation set 2 were decision tree, random forest, and Bayes logistic regression. Random forest model performed well with the training and three validation sets. The most important features are age, albumin, and lactate.

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Critical Care Database Comprising Patients With Infection

Cited by 12 publications

References 25 publications

A Nomogram to Better Predict the In-Hospital Mortality of Trauma Patients with Sepsis in the Intensive Care Unit

A Nomogram to Better Predict the In-Hospital Mortality of Trauma Patients with Sepsis in the Intensive Care Unit

A prediction and interpretation machine learning framework of mortality risk among severe infection patients with pseudomonas aeruginosa

Machine learning models to predict in-hospital mortality in septic patients with diabetes

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