The thrombodynamic ratio as a predictor of 28-day mortality in sepsis patients

Guo, Xi; Shuai, Xin-Yan; Cai, Tiantian; Wu, Zhiyang; Wu, Dawei; Ding, Shilei

doi:10.1016/j.cca.2022.04.994

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…Among these models, 25 studies built 64 machine learning models for predicting in-hospital death [ 22 , 27 , 31 , 33 , 35 , 43 , 45 , 46 , 48 , 49 , 52 – 55 , 58 , 60 , 61 , 63 , 66 , 68 , 69 , 71 , 74 , 75 ], 21 studies built 30 machine learning models for predicting death within 1 month [ 28 – 30 , 34 , 37 – 39 , 41 , 44 , 47 , 48 , 50 , 51 , 55 , 56 , 59 , 65 , 68 , 72 , 73 ], two studies built two machine learning models for predicting death within 3 months [ 32 , 71 ], other two studies built two machine learning models for predicting death within 1 year [ 62 , 70 ], and 4 studies built 6 machine learning models that did not specifically describe the time of sepsis death [ 40 , 42 , 64 , 67 ], these models are depicted in Fig. 2 .…”

Section: Resultsmentioning

confidence: 99%

Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis

Zhang,

Xu,

Yang

et al. 2023

BMC Med Inform Decis Mak

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Background and objectives Sepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended mortality risk assessment tools. However, these risk assessment tools seem to have limited predictive value. With the gradual integration of machine learning into clinical practice, some researchers have attempted to employ machine learning for early mortality risk prediction in sepsis patients. Nevertheless, there is a lack of comprehensive understanding regarding the construction of predictive variables using machine learning and the value of various machine learning methods. Thus, we carried out this systematic review and meta-analysis to explore the predictive value of machine learning for sepsis-related death at different time points. Methods PubMed, Embase, Cochrane, and Web of Science databases were searched until August 9th, 2022. The risk of bias in predictive models was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). We also performed subgroup analysis according to time of death and type of model and summarized current predictive variables used to construct models for sepsis death prediction. Results Fifty original studies were included, covering 104 models. The combined Concordance index (C-index), sensitivity, and specificity of machine learning models were 0.799, 0.81, and 0.80 in the training set, and 0.774, 0.71, and 0.68 in the validation set, respectively. Machine learning outperformed conventional clinical scoring tools and showed excellent C-index, sensitivity, and specificity in different subgroups. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) are the preferred machine learning models because they showed more favorable accuracy with similar modeling variables. This study found that lactate was the most frequent predictor but was seriously ignored by current clinical scoring tools. Conclusion Machine learning methods demonstrate relatively favorable accuracy in predicting the mortality risk in sepsis patients. Given the limitations in accuracy and applicability of existing prediction scoring systems, there is an opportunity to explore updates based on existing machine learning approaches. Specifically, it is essential to develop or update more suitable mortality risk assessment tools based on the specific contexts of use, such as emergency departments, general wards, and intensive care units.

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