Discussion
death, SCA, critically ill, more multisystem derangements, SHAP dependency, wasting, LTVA
WASTING, CRITICALLY ILL
The primary finding of this study demonstrates that ML algorithms significantly enhance the prognosis prediction performance for patients with LTVA. Compared with traditional scoring systems, including SAPS-II and LODS, the ML-based models enabled better prediction of in-hospital mortality in critically ill patients with LTVA.LTVAs are the main cause of SCA and are highly related to an increased risk of in-hospital mortality. Several prediction models have been developed to predict SCA. O’Mahony et al [There are several severity assessment systems that could be used to predict outcomes of patients who are critically ill. The application values of SAPS-II and LODS in predicting the prognosis in patients who received EMS have been demonstrated in previous studies [Of the 40 selected variables, we found that GCS score, RBC_max, and LOS in hospital were the most important predictors. The GCS score includes assessments of motor, verbal, and eye responses, which could reflect the level of consciousness. A previous study reported that the GCS score could be used to predict mortality in patients who are critically ill [In addition, the ML-based models in this study had certain interpretability. Many of the features used to perform mortality risk prediction in this study were tangible, and some of them had been proven intimately correlated with mortality in patients who are critically ill. The SHAP method, which could provide a visual interpretation of the ML-based models at the global and local levels, was introduced in this study. Specifically, the SHAP summary plot, which interpreted and ranked the significance of input features, and the SHAP dependency plot, which explained how a single feature affected the output of the ML-based models, were presented in this study.The prediction models in this study enable early and accurate selection of patients at high risk of in-hospital death, which may be conducive to risk stratification, clinical decision-making, and the improvement of prognosis in patients with LTVA. On the other hand, our prediction models were developed based on critical care cohorts. Essentially, sicker patients with more multisystem derangements are anticipated to have poorer prognosis. Considering their satisfactory specificities, our prediction models may help to identify subgroups of patients with LTVA treated with EMSs who are not anticipated to have an adverse prognosis. It is useful to avoid wasting medical services, such as through unnecessary intensive monitoring and aggressive therapies in the ICU.Although we demonstrated the predictive value of ML algorithms in predicting mortality in critical patients with LTVA, some limitations should be acknowledged. First, all data involved in this study were retrospectively collected from public databases, and further prospective studies are needed to confirm the findings. Second, some important variables, such as cardiac troponin and ejection fraction, were excluded from this study due to unacceptably high rates of missing values, which may be a potential source of bias. Third, the prediction models were developed based on critical care cohorts, which may affect application to the general population.In summary, the presented ML-based models exhibited better predictive values than did traditional severity assessment systems, such as SAPS-II and LODS, in predicting the mortality of critical patients with LTVA. Our findings indicate that ML algorithms could be used to improve model performance for predicting outcomes of patients with LTVA. Future prospective studies are needed to confirm the findings.This study was supported by the High-level Hospital Project of Fuwai Hospital, Chinese Academy of Medical Sciences (grant 2022-GSP-GG-25).Authors' Contributions: This study was designed by LL and LD. Zhuxin Zhang, LZ, YX, Zhenhao Zhang, and ZH were responsible for data collation and statistical analysis. LL wrote the first draft. YY reviewed and checked the manuscript. All authors read and approved the final manuscript.Conflicts of Interest: None declared.Baseline characteristics.All variables used in the machine learning models.Variables excluded after feature selection by least absolute shrinkage and selection operator (LASSO).Feature selection by least absolute shrinkage and selection operator (LASSO) analysis. (A) Plots for LASSO analysis coefficients. (B) Cross-validation plot for the penalty term.Characteristics of the included machine learning algorithms.Comparisons of the area under the receiver operating characteristic curve of different models using the Delong test.External validation of different models.