Development and Assessment of a 28-Day Mortality Risk Prediction Model Utilizing a Column Chart for Patients with Pneumocystis Disease in the Intensive Care Unit

Purpose To develop and assess a predictive model utilizing column charts for evaluating the 28-day mortality risk in patients diagnosed with Pneumocystis who have been admitted to the intensive care unit (ICU). Methods From 2008 to 2022, clinical data on patients with Pneumocystis were collected using the American Critical Care Medical Information Database IV (MIMIC-IV). Initially, 63 significant predictive indicators were included, with ICU admission as the time node and all-cause mortality within 28 days as the outcome. Using complete data modeling, the variable selection approach combines two methods: Lasso regression (glmnet package) and collinearity screening (car package). Use the bootstrap method 1000 times for internal validation. Calculate the AUC, mean sensitivity, and specificity of 1000 resamplings, as well as the 95% confidence interval, and then plot the ROC, calibration, and DCA curves. Results The patients were split into two groups based on their 28-day survival status: 83 cases (67.48%) in the survival group and 40 instances (32.52%) in the death group. Five variables—the history of malignant tumors, Lods scores, Oasis scores, and complications of shock and severe renal injury—were eventually included in the entire sample after screening. According to Receiver Operating Characteristic (ROC) analysis, the model's sensitivity was 0.600 (95% CI 0.448-0.752), specificity was 0.904 (95% CI 0.840-0.967), and AUC was 0.814 (95% CI 0.732-0.897). The DCA curve indicates that the model application has high accuracy, which leads to a net benefit for population prediction, and the model calibration curve demonstrates good calibration accuracy. The bootstrap model's 1000 internal validation results demonstrate that the model's calibration performance is good and that its accuracy is highest when the probability of patient outcome events falls between 35% and 60%, which yields the highest net benefit for population prediction. Conclusions With five factors and good discrimination and calibration, a column chart model for predicting the 28-day mortality risk of Pneumocystis patients based on MIMIC-IV clinical big data offers a useful new tool for assessing the prognosis of such patients.