Purpose: The purpose of this study was to construct and validate a simple model for the prediction of survival in patients with trauma‐related ARDS.Methods: This is a single‐center, retrospective cohort study using MIMIC‐III Clinical Database.Results: 842 patients were included in this study. 175 (20.8%) died in‐hospital, whereas 215 (25.5%) died within 90 days. The deceased group had higher Acute Physiology Score (APS III), Sequential Organ Failure Assessment (SOFA), and Simplified Acute Physiology Score II (SAPS II). In multivariate logistic regression model, independent risk factors for mortality in ARDS patients included age ([odds ratio] OR, 1.035; 95% confidence interval [CI], 1.020–1.049), body mass index (OR, 0.957; 95% CI, 0.926–0.989), red blood cell distribution width (OR, 1.283; 95% CI, 1.141–1.443), hematocrit (OR, 1.055; 95% CI, 1.017–1.095), lactate (OR, 1.226; 95% CI, 1.127–1.334), blood urea nitrogen (OR, 1.025; 95% CI, 1.007–1.044), acute kidney failure (OR, 1.875; 95% CI, 1.188–2.959), sepsis (OR, 1.917; 95% CI, 1.165–3.153), type of admission (emergency vs. elective [OR, 2.822; 95% CI, 1.647–4.837], and urgent vs. elective [OR, 5.156; 95% CI, 1.896–14.027]). The area under the curve (AUC) of the model was 0.826, which was superior than the SAPS II (0.776), APS III (0.718), and SOFA (0.692). In the cross‐validation model, the accuracy of the test set was 0.823, the precision was 0.643, and the AUC was 0.813.Conclusions: We established a prediction model using data commonly used in the clinic, which has high accuracy and precision and is worthy of use in clinical practice.

Background: This study addresses the critical need for timely and accurate diagnosis of early postoperative infection (EPI) following cardiac surgery. EPI significantly impacts patient outcomes and healthcare costs, making its early detection vital. Objectives: To develop, validate, and clinically implement a machine-learning-based model for diagnosing EPI post-cardiac surgery, enhancing postoperative care. Methods: In this multi-center cohort study spanning 2020 to 2022, data from four medical centers involved 2001 participants. Of these, 1400 were used for trainingand 601 for validation. Several machines-learning algorithms, including XGBoost, random forest, support vector machines, least absolute shrinkage and selection operator, and single-layer neural networks, were applied to develop predictive models. These were compared against a traditional logistic regression model. The model with the highest area under the receiver operating characteristic curve (AUROC) was deemed optimal. Implemented across four centers since 1 January 2023, a retrospective real-world study assessed its clinical applicability. Among 400 patients with an estimated EPI risk above 10%, identified by the optimal model, 55 followed its antibiotic upgrade recommendations (DEICS group). The remaining 345 patients upgraded antibiotics empirically, with 55 in the control group, matched 1:1 with the DEICS group. Clinical utility was evaluated through antibiotic use density (AUD), hospital costs, and ICU stay duration. Results: The XGBoost model achieved the highest performance with an AUROC of 0.96 (95% CI: 0.93–0.98). The calibration curve exhibited strong agreement with Brier scores of 0.02. According to the XGBoost model, the DEICS group significantly demonstrated reduced AUD (P < 0.01) in the matched cohort, along with decreased ICU stay time (median: 5 vs. 6 days, P = 0.01) and hospital costs (median: ¥150 000 vs. median: ¥200 000, P = 0.01) in the EPI cohort. Conclusion: The successful implementation of the XGBoost model facilitates accurate EPI diagnosis, improves postoperative recovery, and lowers hospital costs.

Background: Type A aortic dissection presents challenges with postoperative cerebral complications, and this study evaluates the predictive value of quantitative electroencephalography for perioperative brain function prognosis. Methods and Results: Amplitude‐integrated electroencephalography (aEEG) processes raw signals through filtering, amplitude integration, and time compression, displaying the data in a semilogarithmic format. Using this method, postoperative relative band power (post‐RBP) α% and dynamic aEEG (ΔaEEG) grade were significantly associated with neurological dysfunction in univariate and multivariable analyses, with area under the receiver operating characteristic curve of 0.876 (95% CI, 0.825–0.926) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with adverse outcomes, with area under the receiver operating characteristic curve of 0.903 (95% CI, 0.835–0.971) for the combined model. Postoperative relative band power α% and ΔaEEG were significantly associated with transient neurological dysfunction and stroke, with areas under the receiver operating characteristic curve of 0.818 (95% CI, 0.760–0.876) and 0.868 (95% CI, 0.810–0.926) for transient neurological dysfunction, and 0.815 (95% CI, 0.743–0.886) and 0.831 (95% CI, 0.746–0.916) for stroke. Among 56 patients, the Alberta Stroke Program Early Computed Tomography score was superior to ΔaEEG in predicting neurological outcomes (area under the receiver operating characteristic curve of 0.872 versus 0.708 [95% CI, 0.633–0.783]; P <0.05). Conclusions: Perioperative quantitative electroencephalography monitoring offers valuable insights into brain function changes in patients with type A aortic dissection. ∆aEEG grades can aid in early detection of adverse outcomes, while postoperative relative band power and ∆aEEG grades predict transient neurological dysfunction. Quantitative electroencephalography can assist cardiac surgeons in assessing brain function and improving outcomes in patients with type A aortic dissection. Registration: URL: https://www.chictr.org.cn ; Unique identifier: ChiCTR2200055980.

Background: Acute respiratory distress syndrome (ARDS) is a highly heterogeneous disease accompanied by high mortality. Our goal was to investigate the risk factors for 28‐day mortality and then establish a predictive online nomogram for ARDS originating from pulmonary disease (ARDSp).Methods: We examined 1087 patients diagnosed with ARDS from January 2010 to December 2019 at the Second Affiliated Hospital of Chongqing Medical University. A total of 185 ARDSp patients were finally enrolled in the training cohort. A total of 43 ARDSp patients from January 2020 to August 2021 in the Second Affiliated Hospital of Chongqing Medical University and the Traditional Chinese Medical Hospital of Jiangbei District were included in the external validation cohort. Fundamental, clinical and laboratory variables at admission were gathered from medical records, and the 28‐day prognosis was followed up.Results: In the training cohort, it was found that age, sex, C‐reactive protein, albumin and multiple organ dysfunction syndrome (MODS) were independent risk factors for 28‐day mortality via multivariate logistic regression. The online nomogram software for 28‐day mortality showed good discrimination, calibration and clinical utility in both the training cohort and external validation cohort.Conclusions: For ARDSp patients, older males, lower C‐reactive protein and albumin levels, and MODS were independent predictors of a poor 28‐day prognosis. The online nomogram based on five independent factors could act as a predictive appliance in clinical practice.