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BackgroundLumbar drainage is widely used in the clinic; however, forecasting lumbar drainage-related meningitis (LDRM) is limited. We aimed to establish prediction models using supervised machine learning (ML) algorithms.MethodsWe utilized a cohort of 273 eligible lumbar drainage cases. Data were preprocessed and split into training and testing sets. Optimal hyper-parameters were archived by 10-fold cross-validation and grid search. The support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were adopted for model training. The area under the operating characteristic curve (AUROC) and precision-recall curve (AUPRC), true positive ratio (TPR), true negative ratio (TNR), specificity, sensitivity, accuracy, and kappa coefficient were used for model evaluation. All trained models were internally validated. The importance of features was also analyzed.ResultsIn the training set, all the models had AUROC exceeding 0.8. SVM and the RF models had an AUPRC of more than 0.6, but the ANN model had an unexpectedly low AUPRC (0.380). The RF and ANN models revealed similar TPR, whereas the ANN model had a higher TNR and demonstrated better specificity, sensitivity, accuracy, and kappa efficiency. In the testing set, most performance indicators of established models decreased. However, the RF and AVM models maintained adequate AUROC (0.828 vs. 0.719) and AUPRC (0.413 vs. 0.520), and the RF model also had better TPR, specificity, sensitivity, accuracy, and kappa efficiency. Site leakage showed the most considerable mean decrease in accuracy.ConclusionsThe RF and SVM models could predict LDRM, in which the RF model owned the best performance, and site leakage was the most meaningful predictor.

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Background Short-term air pollution exposure and intracerebral hemorrhage (ICH) risk are related. However, the impact of the pollutant levels decline on this relationship, which attributes to clean air policy implementation and the COVID-19 pandemic lockdown, is unclear. In the present research, we explored the influence of different pollutant levels on ICH risk during eight years in a southwestern China megacity. Methods Our research used a time-stratified case-crossover design. We retrospectively analyzed ICH patients in a teaching hospital from January 1, 2014, to December 31, 2021, and divided 1571 eligible cases into two groups (1st group: 2014–2017; 2nd group: 2018–2021). We observed the trend of every pollutant in the entire study period and compared the pollution levels in each group, using air pollutants data (PM2.5, PM10, SO2, NO2, CO, and O3) documented by the local government. We further established a single pollutant model via conditional logistic regression to analyze the association between short-term air pollutants exposure and ICH risk. We also discussed the association of pollution levels and ICH risk in subpopulations according to individual factors and monthly mean temperature. Results We found that five air pollutants (PM2.5, PM10, SO2, NO2, CO) exhibited a continuous downward trend for the whole duration, and the daily concentration of all six pollutants decreased significantly in 2018–2021 compared with 2014–2017. Overall, the elevation of daily PM2.5, SO2, and CO was associated with increased ICH risk in the first group and was not positively associated with risk escalation in the second group. For patients in subgroups, the changes in the influence of lower pollutant levels on ICH risk were diverse. In the second group, for instance, PM2.5 and PM10 were associated with lower ICH risk in non-hypertension, smoking, and alcohol-drinking participants; however, SO2 had associations with increased ICH risk for smokers, and O3 had associations with raised risk in men, non-drinking, warm month population. Conclusions Our study suggests that decreased pollution levels diminish the adverse effects of short-term air pollutants exposure and ICH risk in general. Nevertheless, the influence of lower air pollutants on ICH risk in subgroups is heterogeneous, indicating unequal benefits among subpopulations.

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