Background: Coronavirus disease 2019 (COVID-19), a highly infectious disease, has been rapidly spreading all over the world and remains a great threat to global public health. Patients diagnosed with severe or critical cases have a poor prognosis. Hence, it is crucial for us to identify potentially severe or critical cases early and give timely treatments for targeted patients. In the clinical practice of treating patients with COVID-19, we have observed that the neutrophil-to-lymphocyte ratio (NLR) of severe patients is higher than that in mild patients. We performed this systematic review and meta-analysis to evaluate the predictive values of NLR on disease severity and mortality in patients with COVID-19. Methods: We searched PubMed, EMBASE, China National Knowledge Infrastructure (CNKI) and Wanfang databases to identify eligible studies (up to August 11, 2020). Two authors independently screened studies and extracted data. The methodological quality of the included studies was assessed by Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Results: Thirteen studies involving 1579 patients reported the predictive value of NLR on disease severity. The pooled sensitivity (SEN), specificity (SPE) and area under curve (AUC) were 0.78 (95% CI 0.70–0.84), 0.78 (95% CI 0.73–0.83) and 0.85 (95% CI 0.81–0.88), respectively. Ten studies involving 2967 patients reported the predictive value of NLR on mortality. The pooled SEN, SPE and AUC were 0.83 (95% CI 0.75–0.89), 0.83 (95% CI 0.74–0.89) and 0.90 (95% CI 0.87–0.92), respectively. Conclusions: NLR has good predictive values on disease severity and mortality in patients with COVID-19 infection. Evaluating NLR can help clinicians identify potentially severe cases early, conduct early triage and initiate effective management in time, which may reduce the overall mortality of COVID-19. Trial registry: This meta-analysis was prospectively registered on PROSPERO database (Registration number: CRD42020203612).

Background: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patients with major trauma are limited. This study aimed to develop and validate an artificial intelligence (AI) platform for predicting the risk of sepsis among patients with major trauma. Patients and methods: This study involved 961 patients, with a prospective analysis of data from 244 patients with major trauma at our hospital and a retrospective analysis of data from 717 patients extracted from a database in the United States. The patients from our hospital constituted the model development cohort, and the patients from the database constituted the external validation cohort. The patients in the model development cohort were randomly divided into a training cohort and an internal validation cohort at a ratio of 8:2. The machine-learning algorithms used to train models included logistic regression, decision tree, extreme gradient boosting machine (eXGBM), neural network (NN), random forest, and light gradient boosting machine (LightGBM). Results: The incidence of sepsis for the model development cohort was 43.44%. Twelve predictors, including gender, abdominal trauma, open trauma, red blood cell count, heart rate, respiratory rate, injury severity score, sequential organ failure assessment score, Glasgow coma scale, smoking, total protein concentrations, and hematocrit, were used as features in the final model. Internal validation showed that the NN model had the highest area under the curve (AUC) of 0.932 (95% CI: 0.917–0.948), followed by the LightGBM and eXGBM models with AUCs of 0.913 (95% CI: 0.883–0.930) and 0.912 (95% CI: 0.880–0.935), respectively. In the external validation cohort, the eXGBM model (AUC: 0.891, 95% CI: 0.866–0.914) had the highest AUC value, followed by the LightGBM model (AUC: 0.886, 95% CI: 0.860–0.906), and the AUC value of the NN model was only 0.787 (95% CI: 0.751–0.829). Considering the predictive performance for both the internal and external validation cohorts, the LightGBM model had the highest score of 82, followed by the eXGBM (81) and NN (76) models. Thus, the LightGBM has emerged as the optimal model, and it was deployed online as an AI application. Conclusions: This study develops and validates an AI application to effectively assess the susceptibility of patients with major trauma to sepsis. The AI application equips healthcare professionals with a valuable tool to promptly identify individuals at high risk of developing sepsis. This will facilitate clinical decision-making and enable early intervention.

Background: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence (AI) and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality. Methods: A total of 52 707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application. Findings: The eXGBM model exhibited the best prediction performance, with an area under the curve (AUC) of 0.908 (95% CI: 0.881–0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/, based on the eXGBM model. The comparative testing revealed that the AI application’s predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P<0.001). Conclusions: The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

Objective: The causality between cardiometabolic disease (CMD) and sepsis has remained largely unknown. To elucidate this, we conducted a Mendelian randomization (MR) and population study. Methods: First, we used univariable and multivariable MR analyses to investigate causal associations between CMD and sepsis-related outcomes. We obtained genome-wide association study summary from both the MRC Integrative Epidemiology Unit and the FinnGen consortium. Subsequently, a two-step mediation MR analysis was performed to explore mediators. Afterward, we conducted an observational study using the Medical Information Mart for Intensive Care IV database, in which multivariable logistic regression models were utilized to examine the relationship between CMD and sepsis-related outcomes. Results: In the MR study, type 2 diabetes mellitus (OR = 1.058, 95% CI = 1.017–1.100, P = 0.005), obesity (OR = 1.113, 95% CI = 1.057–1.172, P < 0.001), and heart failure (HF) (OR = 1.178, 95% CI = 1.063–1.305, P = 0.002) were independently causally related to sepsis. Obesity (OR = 1.215, 95% CI = 1.027–1.437, P = 0.023) and HF (OR = 1.494, 95% CI = 1.080–2.065, P = 0.015) also showed independent causal associations with sepsis critical care admission. Mediation MR analysis identified 23 blood metabolites potentially causally linked to sepsis (P < 0.05), yet none mediated the relationship between CMD and sepsis. In the observational study, we found associations between sepsis and several conditions including type 2 diabetes mellitus, obesity, hypertension, stroke, HF, and hyperlipidemia after adjusting for confounding factors. Moreover, hypertension, stroke, HF, coronary artery disease, and hyperlipidemia were linked to sepsis critical care admission. Conclusion: This study has, for the first time, revealed indicative evidence of a causal relationship between CMD and sepsis through observational and genetic evidence. Taken together, clinical attention to sepsis may be warranted among patients with CMD.

Background: Stress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuation, which are associated with adverse outcomes in patients with some diseases. However, the relationship between combined assessment of SHR and GV and mortality risk in sepsis remains unclear. This study aims to investigate the associations of SHR, GV, and their combination with sepsis mortality among individuals with different glucose metabolic states, and to develop a mortality prediction model using machine learning (ML) models. Methods: Patients with sepsis were screened in the MIMIC-IV database, stratified into normal glucose regulation (NGR), prediabetes mellitus (Pre-DM), and diabetes mellitus (DM) groups based on glucose metabolic status. Associations with mortality were analyzed using Kaplan–Meier (KM) curves, Cox proportional hazards model, restricted cubic splines (RCS), and landmark analyses. Five ML algorithms were employed for prediction, with SHapley Additive explanations (SHAP) interpreting key predictors. Results: A total of 4838 patients were enrolled, with a median age of 68 years. Overall, 641 patients (13.2%) died in the ICU, and 936 patients (19.3%) died within 28 days after admission to the ICU. In NGR patients, combined high SHR (>1.23; highest tertile) and high GV (>28.56; highest tertile) – determined based on tertile distribution – conferred the highest 28-day mortality risk (HR = 2.06, 95% CI: 1.40–3.04). Pre-DM patients with low SHR/high GV (SHR < 1.23, GV > 28.56) showed the greatest 28-day mortality risk (HR = 2.45, 95% CI: 1.73–3.48). DM patients with high SHR/low GV (SHR > 1.23, GV < 28.56) had the highest 28-day mortality risk (HR = 1.46, 95% CI: 1.06–2.01). Machine learning models – particularly XGBoost (AUC: 0.746), Random Forest (AUC: 0.776), and Logistic Regression (AUC: 0.776) – demonstrated the strongest predictive performance for these endpoints. Conclusion: The combined assessment of SHR and GV may provide useful information for predicting mortality in sepsis patients – particularly among individuals with NGR and Pre-DM. This integrated approach highlights the potential need for personalized glycemic management strategies, which warrants further investigation in prospective studies.