Machine learning algorithms for improved prediction of in-hospital outcomes after moderate-to-severe traumatic brain injury: a Chinese retrospective cohort study.

AIM: Controversy remains high over the superiority of advanced machine learning (ML) algorithms to conventional logistic regression (LR) in the prediction of prognosis after traumatic brain injury (TBI). This study aimed to compare the performance of ML and LR models in predicting in-hospital prognosis after TBI. METHOD: In a single-center retrospective cohort of adult patients hospitalized for moderate-to-severe TBI (Glasgow coma score ≤12) in our hospital from 2011 to 2020, LR and three ML algorithms (XGboost, lightGBM, and FT-transformer) were run to build prediction models for in-hospital mortality and the Glasgow Outcome Scale (GOS) functional outcomes using either all 19 clinical and laboratory features or the 10 non-laboratory ones collected at admission to the neurological intensive care unit. The Shapley (SHAP) value was used for model interpretation. RESULT: In total, 482 patients had an in-hospital mortality rate of 11.0%. A total of 23.0% of the patients had good functional scores (GOS ≥ 4) at discharge. All ML models performed better than the LR model in predicting in-hospital prognosis after TBI, among which the lightGBM model showed the best performance: When predicting mortality, the lightGBM model yielded an area under the curve (AUC) of 0.953 using all 19 features (the LR model: 0.813) and an AUC of 0.935 using 10 non-laboratory features (the LR model: 0.803); when predicting GOS functional outcomes, it yielded an AUC of 0.913 using all 19 features (the LR model: 0.832) and an AUC of 0.889 using non-laboratory data (the LR model: 0.818). The SHAP method identified key contributors to explain the lightGBM models. Finally, the integration of the lightGBM models with different prediction purposes was found to provide refined prognostic information, particularly for patients who survived moderate-to-severe TBI. CONCLUSION: The study supported the superiority of ML to LR in predicting prognosis after moderate-to-severe TBI and highlighted its potential use for clinical application.

Lovastatin derivative dehydrolovastatin ameliorates ulcerative colitis in mice by suppressing NF-κB and inflammatory cytokine expression.

Ulcerative colitis (UC) is associated with intestinal immune imbalance and inflammatory response. Because dehydrolovastatin (DLVT), a derivative of lovastatin, has been recently shown to inhibit inflammation and relieve immune arthritis induced by chemical stimuli, we studied its effect and possible mechanism on UC induced by dextran sulfate sodium. The BALB/c mice were classified into six groups: normal control group, model group, DLVT high dose group, DLVT low dose group, salazosulfapyridine (SASP) group and lovastatin (LVT) group. The disease activity indices of UC and pathological changes were investigated. The myeloperoxidase (MPO) activity in colon tissue and inflammatory factors such as IL-6, IL-10, IL-17, and TNF-α in the serum were analyzed by ELISA, while the expression of NF-κB p65 protein in colon tissue was detected by immunohistochemistry and western blot. DLVT relieved the disease activity indices and pathological damage of the UC mice. Furthermore, DLVT significantly decreased MPO activity and improved the imbalance of inflammatory cytokines through inhibiting the expression of NF-κB p65. Meanwhile, the positive drug of SASP has a similar effect to DLVT, but the effect of DLVT in both decreasing IL-17, TNF-α, and increasing IL-10 was significantly stronger than that of SASP. These results suggest that DLVT may ameliorates the symptoms of UC.