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.

To evaluate the benefits and harmful effects of conservative versus liberal oxygen therapy in patients admitted to the Intensive Care Unit (ICU). A systematic review and meta-analysis was carried out. ICU. Adult patients (aged 18 years or older) were randomized to either a lower oxygenation target strategy (conservative oxygen therapy) or a higher oxygenation target strategy (liberal oxygen therapy) in the ICU. Patients received different oxygenation target strategies. Ten studies involving 5429 adult patients admitted to the ICU were included in the meta-analysis. The pooled results showed no decreased all-cause mortality at 28 days (RR 0.90; 95%CI 0.75-1.09; p = 0.28), 90 days (RR 1.02; 95%CI 0.92-1.13; p = 0.71) or longest follow-up (RR 0.97; 95%CI 0.88-1.08; p = 0.63) among patients administered conservative oxygen therapy. Secondary outcomes were comparable between the two groups. The results of sensitivity analyses and subgroup analyses were consistent with the main analyses. No beneficial or harmful effects of conservative oxygen therapy were found compared to liberal oxygen therapy in relation to all-cause mortality among adult patients in the ICU. Conservative oxygen therapy did not reduce all-cause mortality at 28 days, 90 days or longest follow-up. Other important clinical outcomes were also comparable between the two groups.