Background There is growing evidence that patients recovering after a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may have a variety of acute sequelae including newly diagnosed diabetes. However, the risk of diabetes in the post-acute phase is unclear. To solve this question, we aimed to determine if there was any association between status post-coronavirus disease (COVID-19) infection and a new diagnosis of diabetes. Methods We performed a systematic review and meta-analysis of cohort studies assessing new-onset diabetes after COVID-19. PubMed, Embase, Web of Science, and Cochrane databases were all searched from inception to June 10, 2022. Three evaluators independently extracted individual study data and assessed the risk of bias. Random-effects models estimated the pooled incidence and relative risk (RR) of diabetes compared to non-COVID-19 after COVID-19. Results Nine studies with nearly 40 million participants were included. Overall, the incidence of diabetes after COVID-19 was 15.53 (7.91–25.64) per 1000 person-years, and the relative risk of diabetes after COVID-19 infection was elevated (RR 1.62 [1.45–1.80]). The relative risk of type 1 diabetes was RR=1.48 (1.26–1.75) and type 2 diabetes was RR=1.70 (1.32–2.19), compared to non-COVID-19 patients. At all ages, there was a statistically significant positive association between infection with COVID-19 and the risk of diabetes: <18 years: RR=1.72 (1.19–2.49), ≥18 years: RR=1.63 (1.26–2.11), and >65 years: RR=1.68 (1.22–2.30). The relative risk of diabetes in different gender groups was about 2 (males: RR=2.08 [1.27–3.40]; females: RR=1.99 [1.47–2.80]). The risk of diabetes increased 1.17-fold (1.02–1.34) after COVID-19 infection compared to patients with general upper respiratory tract infections. Patients with severe COVID-19 were at higher risk (RR=1.67 [1.25–2.23]) of diabetes after COVID-19. The risk (RR=1.95 [1.85–2.06]) of diabetes was highest in the first 3 months after COVID-19. These results remained after taking confounding factors into account. Conclusions After COVID-19, patients of all ages and genders had an elevated incidence and relative risk for a new diagnosis of diabetes. Particular attention should be paid during the first 3 months of follow-up after COVID-19 for new-onset diabetes.
idiopathic hypereosinophilia (iHe) and hypereosinophilic syndrome (HeS) are benign haematological disorders. Studies have suggested that venous thromboembolism (Vte) is a rare but sometimes fatal complication of hypereosinophilia; however, data are limited. We retrospectively analysed clinical features and short-term outcomes of 63 consecutive patients (82.5% men; mean age, 40.92 ± 10.89 years) with IHE or HES with concurrent VTE from January 1998 through December 2018. Risk factors for pulmonary embolism (PE) were explored by multivariate logistic analysis. DVT and/or PE was detected by imaging in all patients. independent risk factors for pe were a body mass index of >24.1 kg/m 2 (odds ratio [OR]: 5.62, 95% confidence interval [CI]: 1.21-26.13, P = 0.028), peak absolute eosinophil count of >6.3 × 10 9 /L (OR: 5.55, 95% CI: 1.292-23.875, P = 0.021), and >13.9-month duration of hypereosinophilia (OR: 4.51, 95% CI: 1.123-18.09, P = 0.034). All patients were treated with corticosteroids and anticoagulants. The short-term hypereosinophilia remission rate was 100%; no recurrent Vte or major bleeding was observed. Hypereosinophilia is a potential risk factor for Vte. pe in patients with IHE/HES and DVT is associated with a higher body mass index, higher peak absolute eosinophil count, and longer duration of hypereosinophilia. corticosteroids and anticoagulants provided effective short-term control of hypereosinophilia and VTE. Hypereosinophilia (HE) encompasses a diverse group of disorders characterised by peripheral blood eosinophilia (>1.5 × 10 9 /L) with or without a primary cause (such as haematologic, autoimmune, parasitic, or allergic disease) 1. Idiopathic HE (IHE) is HE without a primary aetiology or organ damage. When HE without a primary aetiology induces organ damage, it is termed hypereosinophilic syndrome (HES). Damage to various organs has been reported in patients with IHE and HES, most commonly dermal, lung, cardiovascular, and gastrointestinal system damage. Venous thromboembolism (VTE) is a rare complication of HE 1,2. Previous studies have focused on thrombus formation in patients with IHE/HES 3. A few studies have suggested that approximately 25% of patients with HES develop thromboembolic complications, with an associated mortality rate of 5% to 10% 2. Many types of thrombosis have been reported, including mural thrombus of the heart, inferior vena cava (IVC) thrombosis, superficial venous thrombosis, portal thrombosis, deep venous thrombosis (DVT) with or without pulmonary embolism (PE), cerebral arteriolar and venous thrombosis, and intracardiac thrombi 2,4-10. Among these, DVT with PE is a rare but often fatal complication of HES 11. Because most reported cases lacked primary aetiologies and traditional risk factors for VTE, we hypothesised that HE may cause development of VTE.
Identifying critically ill patients is a key challenge in emergency department (ED) triage. Mis-triage errors are still widespread in triage systems around the world. Here, we present a machine learning system (MLS) to assist ED triage officers better recognize critically ill patients and provide a text-based explanation of the MLS recommendation. To derive the MLS, an existing dataset of 22,272 patient encounters from 2012 to 2019 from our institution’s electronic emergency triage system (EETS) was used for algorithm training and validation. The area under the receiver operating characteristic curve (AUC) was 0.875 ± 0.006 (CI:95%) in retrospective dataset using fivefold cross validation, higher than that of reference model (0.843 ± 0.005 (CI:95%)). In the prospective cohort study, compared to the traditional triage system’s 1.2% mis-triage rate, the mis-triage rate in the MLS-assisted group was 0.9%. This MLS method with a real-time explanation for triage officers was able to lower the mis-triage rate of critically ill ED patients.
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