Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)

Zhang, Luming; Xu, Fengshuo; Li, Shaojin; Zheng, Shuai; Lyu, Jun; Yin, Haiyan

doi:10.1186/s12873-022-00582-z

Cited by 42 publications

(25 citation statements)

References 48 publications

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“…30 A machine-learning research constructed a good predictive model for 30-day mortality in sepsis and RDW was one of top five important factors. 31 Our study mainly concentrated on the septic shock patients and found the close relationship between RDW and prognosis. Pathophysiologic mechanisms underlying the association of RDW with mortality could be speculated based on current researches.…”

Section: Discussionmentioning

confidence: 87%

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Red cell distribution width and in‐hospital mortality in septic shock: A public database research

Ding

et al. 2022

Int J Lab Hematology

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Objective: This study aimed to explore the relationship between red cell distribution width (RDW) and in-hospital mortality in septic shock based on a large-scale public database.Methods: All patients with septic shock in MIMIC-IV were enrolled. Based on RDW values, the general characteristics of different groups were compared. Different models were constructed for exploring the association of RDW and in-hospital mortality. To assess the predictive value of RDW, receiver operator characteristic (ROC) curve analysis was applied.Results: A total of 3006 patients with septic shock were included and in-hospital mortality was 32.27% (n = 970). The results of the fully adjusted model demonstrated that RDW was positively associated with in-hospital mortality in septic shock patients after adjusting all confounders (OR = 1.12, 95% CI:1.08-1.17, p < .001). A linear relationship between RDW and in-hospital mortality was found. For predicting in-hospital mortality, the area under the ROC curve (AUC) of RDW was .602 and the best threshold of RDW was 17.25%. Conclusion:RDW was associated with in-hospital mortality in septic shock. It could be a useful marker for predicting clinical outcomes in septic shock.

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Section: Discussionmentioning

confidence: 87%

“…Another retrospective analysis based on a public database illuminated that septic patients in the elevated level of RDW group had both higher rate of ICU mortality and hospital mortality 30 . A machine‐learning research constructed a good predictive model for 30‐day mortality in sepsis and RDW was one of top five important factors 31 …”

Section: Discussionmentioning

confidence: 99%

Red cell distribution width and in‐hospital mortality in septic shock: A public database research

Ding

et al. 2022

Int J Lab Hematology

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“…Nevertheless, if applying the Global Burden of Diseases, Injuries, and Risk Factors Study as the reference to compare with sepsis incidence, the incidence of sepsis can be as high as 677.5 cases per 100,000 persons worldwide ( 45 ). Previous research shows that RDW is a useful prediction of mortality in adult sepsis patients ( 46 , 47 ). In a meta-analysis from 2020, the result of synthesis of 11 included studies indicated that high-level RDW was associated with mortality (HR = 1.14, 95% CI 1.09–1.20, Z=5.78, P <.001).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of RDW for the prediction of mortality in adult sepsis patients: A systematic review and meta-analysis

Liao

Cao

et al. 2022

Front. Immunol.

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BackgroundRed blood cell distribution width (RDW) is a common biomarker of bacterial infections, and it can be easily obtained from a routine blood test. We investigate the diagnostic value of RDW for the prediction of mortality in adult sepsis patients through a review and meta-analysis. We registered this review in PROSPERO (Registration Number: CRD42022357712), and the details of the registration are included in Appendix 1.MethodsWe searched PubMed, Cochrane Library, Springer, and Embase between Jan. 1, 2000, and May 30, 2022, for primary studies about this research. We collected articles that investigated RDW for varying degrees of sepsis patients—those who suffered from sepsis, severe sepsis, or sepsis shock. Studies of healthy people and sepsis of children and neonates were excluded from our research. The definition of study characteristics and data extraction were finished by two independent researchers and discrepancies resolved by consensus. The combined sensitivities and specificities were calculated by meta-analysis using STATA14.0. The sensitivity of the included studies was analyzed by excluding studies that had potential heterogeneity. A summary operating characteristic curve was made to evaluate the diagnostic value for the prediction of mortality in adult sepsis patients. The Fagan test was used to explore likelihood ratios and posttest probabilities. Finally, we investigated the source of heterogeneity using meta-regression.ResultsTwenty-four studies, including 40,763 cases altogether, were included in this analysis. Bivariate analysis indicated a combined sensitivity of 0.81 (95% CI 0.73–0.86) and specificity of 0.65 (95% CI 0.54–0.75). The area under the summary receiver operating characteristic curve was 0.81 (95% CI 0.77–0.84). Substantial heterogeneity resided in the studies (I2 =96.68, 95% CI 95.95–97.4). Meta-regression showed that the reference description, prospective design, and blinded interpretation of the included studies could be responsible for the heterogeneity.ConclusionsRWD is an available and valuable biomarker for prediction of mortality in adult sepsis patients.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022357712.

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“…The APS-III scale has been widely used in the medical community as an important tool for predicting the risk of death prediction in ICU patients. In a recent study on prognosis prediction of ICU patients ( Zhang et al, 2022 ), the results showed that the independent receiver operating characteristic curve (ROC) curve results of the APS-III scale were superior compared to those of the SAPS-II, LODS, OASIS, and SOFA scales, indicating that the former has a more promising accuracy in the prognosis prediction of critically ill patients. Thus, the results of the APS-III scale were used to evaluate the prognosis of patients in this study Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction

Zhou

Li²,

Han³

et al. 2022

Front. Physiol.

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Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.

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Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)

Cited by 42 publications

References 48 publications

Red cell distribution width and in‐hospital mortality in septic shock: A public database research

Red cell distribution width and in‐hospital mortality in septic shock: A public database research

Diagnostic value of RDW for the prediction of mortality in adult sepsis patients: A systematic review and meta-analysis

Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction

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