Dong Chen

The stress hyperglycemia ratio (SHR) is a clinical index that quantifies acute stress-induced hyperglycemia by comparing immediate blood glucose levels with chronic glucose control (reflected by HbA1c). It is especially valuable in cardiovascular disease and surgical prognosis. However, there is still a lack of research on the relationship between SHR and new-onset atrial fibrillation (AF) in patients after coronary artery bypass grafting (CABG). This study investigates the impact of postoperative SHR on AF risk following CABG. This study is a retrospective cohort analysis conducted through the MIMIC-IV database, which included adult patients who underwent CABG and were admitted to the ICU. These patients were categorized into three distinct groups according to the tertiles of the baseline SHR level, and the primary outcome was the incidence of postoperative atrial fibrillation (POAF). We employed logistic regression models, restricted cubic splines (RCS), threshold effect analysis, ubgroup analysis, Boruta algorithm, lasso algorithm, and receiver operating characteristics (ROC) to analyze the relationship between SHR and POAF incidence comprehensively. 2112 patients undergoing CABG were included in this study, with a median age of 69 years (IQR: 62–76), of whom 1643 (77.79%) were male. Logistic regression results showed that the incidence of AF was significantly increased in patients in the highest third of the SHR group compared with the lowest third group (OR = 1.31, 95%CI = 1.03–1.67; P = 0.0275). SHR was an independent risk factor for the incidence of POAF (OR = 1.63, 95%CI = 1.19–2.23; P = 0.0023). At the same time, RCS analysis showed that SHR was positively and linearly correlated with the incidence of POAF in patients after cardiac surgery (P = 0.009, P for Nonliner = 0.848). Threshold effect analysis identified no significant threshold and further supported a linear relationship between SHR and POAF. In addition, SHR was double-screened by Boruta and Lasso algorithms, indicating that it was statistically and biologically significantly associated with AF after CABG. SHR is significantly related to AF after CABG. As SHR increases, the risk of POAF increases. Incorporating SHR into post-CABG risk assessment enhances AF prediction, offering a valuable reference for clinical decision-making. It may also be a potential biomarker for studying pathological mechanisms in patients after cardiac surgery. In the future, combining multi-omics data with clinical intervention trials is necessary to verify its clinical application value further.

Background: This meta‐analysis aimed to determine the incidence and overall risk of major adverse cardiovascular events (MACEs) related to immune checkpoint inhibitors (ICIs).Methods: We systematically searched all cohort studies, including the available MACE data in cancer patients receiving ICIs, in PubMed, Embase, and the Cochrane Library, from their inception to September 5, 2023. The primary outcome was the incidence of MACEs associated with ICI exposure, and the secondary outcome was the overall risk of MACEs associated with ICI exposure versus non‐ICI exposure controls. Risk ratios with 95% confidence intervals were used in the random‐ or fixed‐effects models.Results: Overall, 26 cohort studies met the inclusion criteria, involving 109,883 cancer patients. In the median follow‐up period ranging from 3.3 to 55.2 months, the incidence of MACEs associated with ICI exposure was 8.22%, ranging from 0.55% to 3.98%, among the nine MACEs, including myocarditis, tachyarrhythmia, pericarditis, pericardial effusions, cardiovascular death, myocardial infarction, heart failure, stroke, and conduction disorder. The incidence of MACE associated with non‐ICI exposure was 3.84%, ranging from 0.81% to 4.72%. The risks of all‐grade MACEs and pericardial effusions were significantly higher in the ICI group than in the non‐ICI controls. ICI treatment, age, male sex, and prior radiation therapy were significantly associated with MACEs.Conclusion: The risk of MACEs during ICI treatment in patients with cancer is more common than is currently recognized. ICI use is closely associated with an increased risk of MACEs. Patients at risk were older, male, and had a history of radiation therapy.

With the popularity of Coronavirus disease 2019 (COVID‐19) vaccine and the development of vaccination strategies, the impact of COVID‐19 vaccine on cancer patients receiving immune checkpoint inhibitors (ICIs) is still unclear. In the systematic review and meta‐analysis of patients with ICIs, we assessed the serological response of cancer patients receiving COVID‐19 vaccine, and explored the risk of immune related adverse events (irAEs). We searched PubMed, EMBASE and Cochrane Library as of 10 June 2023, and included cancer patients who received ICIs and COVID‐19 vaccine. The systematic review and meta‐analysis include cohort study, cross‐sectional study and case report. The outcome included the serological response, Spike‐specific T‐cell response, irAEs and rare adverse events. When possible, the data were analysed by random effect analysis, and the statistical heterogeneity was assessed by Q‐test and I² statistics. We explored the sources of heterogeneity through L’Abbe plots, Galbraith radial plots, and sensitivity analysis. The publication bias was evaluated by Egger's, Begg's linear regression test and funnel plot, and the impact of publication bias was further analysed by trim and fill method. 27 studies were eligible (19 cohort studies, 1 cross‐sectional study and 7 case reports), involving 8331 patients (with 4724 receiving ICIs). Most studies used mRNA vaccine (BNT162b2 or mRNA‐1273). Compared with cancer patients receiving chemotherapy, cancer patients receiving ICIs were significantly more likely to have seroconversion (RR = 1.05, 95%CI 1.01–1.10, P = 0.02). There were no statistically significant differences in seroconversion rates when comparing cancer patients receiving ICIs with controls without cancer (RR = 0.95, 95% CI 0.89–1.01, P = 0.09) or with cancer patients receiving targeted therapy (RR = 1.05, 95% CI 0.79–1.39, P = 0.75). The incidence of irAEs in patients receiving ICIs before and after COVID‐19 vaccination was (21.96%, 95%CI 16.66%–28.94%) and (14.88%, 95%CI 8.65%–25.57%), respectively. The most common irAEs were endocrine abnormalities, skin disorders, etc. The certainty of evidence was low in cancer patients with ICIs, compared with those receiving chemotherapy, and very low versus controls without cancer. Cancer patients treated with ICIs seem to be able to receive COVID‐19 vaccine safely without increasing the incidence of irAEs.

Coronavirus disease 2019 (COVID-19) and metabolic syndrome (MetS) are currently highly prevalent diseases worldwide. Studies on clinical outcomes of patients with Omicron and MetS, especially after vaccination with an inactivated vaccine are limited. Herein, we explored the relationship between MetS and the outcome of Omicron infection. Study Design: This was a retrospective observational study. Methods: This study recruited 316 individuals with Omicron infection. The inpatient data from between 8 January and 7 February 2022 were obtained from designated isolation hospitals in Tianjin, China. Hierarchical and multivariable analysis was conducted on age, gender, number of complications, and vaccination status. Results: Among the 316 study participants, 35.1% were diagnosed with MetS. The results showed that MetS was strongly associated with Intensive Unit Care (ICU) admission, Polymerase Chain Reaction (PCR) re-positivity, and severe COVID-19. The ICU admission rates of the unvaccinated individuals, those who received two-dose and full vaccination (3 doses), were 66.7%, 19.2%, and 0, respectively (p < 0.01). Two-dose and three-dose vaccinations significantly reduced PCR re-positivity. Conclusions: In summary, MetS increases the risk of ICU admission, PCR re-positivity, and severe COVID-19. MetS is a composite predictor of poor outcomes of Omicron infection. Two shots of inactivated vaccine, specifically three doses, effectively protect against Omicron even in the high-risk group.

Aims: To develop and validate a risk prediction model for Chinese patients with type 2 diabetes with the recurrence of diabetic foot ulcers (DFUs) based on a systematic review and meta‐analysis.Methods: A prospective analysis was performed with 1333 participants and followed up for 60 months. Three models were analysed using a derived cohort. The risk factors were screened using meta‐analysis and logistic regression, and the missing variables were interpolated by multiple imputation. The internal validation was performed using the bootstrap procedure, and the validation cohort was applied to the external validation. The performance of the model was evaluated in the area under the discrimination Receiver Operating Characteristic Curve (ROC). Calibration and discrimination methods were used for the validation cohort. The variables were selected according to their clinical and statistical importance to construct the nomograms.Results: Three models were developed and validated. Model 1 included seven social and clinical indicators like sex, diabetes mellitus duration, previous DFU, location of ulcer, smoking, history of amputation, and foot deformity. Model 2 included four more indicators besides those in Model 1, which were statin agents used, antiplatelet agents used, systolic blood pressure, and body mass index. Model 3 added further laboratory indicators to Model 2, such as LDL‐C, HbA1C, fibrinogen, and blood urea nitrogen. In the derivation cohort, 20.1% (206/1027) participants with DFU recurred as compared to the validation cohort, which was 38.2% (117/306). The areas under the curve in the derivation cohort for Models 1–3 were 0.781 (0.744–0.817), 0.843 (0.813–0.873), and 0.899 (0.876–0.922), respectively. The Youden indexes for Models 1–3 were 0.430, 0.559, and 0.653, respectively. Model 3 showed the highest sensitivity and specificity. All models performed well for both discrimination and calibration.Conclusions: Models 1–2 were non‐invasive, which indicate their role in general screening for patients at a high risk of recurrence of DFU. However, Model 3 offers a more specific screening due to its best performance in predicting the risk of DFU recurrence amongst the three models.