Impact of diabetes on COVID-19-related in-hospital mortality: a retrospective study from Northern Italy

Mortality-related risk factors of inpatients with diabetes and COVID-19: A multicenter retrospective study in Belgium

BACKGROUND AND AIMS

We describe mortality-related risk factors of inpatients with diabetes and coronavirus disease 2019 (COVID-19) in Belgium.

METHODS

We conducted a multicenter retrospective study from March to May, 2020, in 8 Belgian centers. Data on admission of patients with diabetes and COVID-19 were collected. Survivors were compared to non-survivors to identify prognostic risk factors for in-hospital death using multivariate analysis in both the total population and in the subgroup of patients admitted in the intensive care unit (ICU).

RESULTS

The study included 375 patients. The mortality rate was 26.4% (99/375) in the total population and 40% (27/67) in the ICU. Multivariate analysis identified older age (HR 1.05 [CI 1.03-1.07], P<0.0001) and male gender (HR 2.01 [1.31-3.07], P=0.0013) as main independent risk factors for in-hospital death in the total population. Metformin (HR 0.51 [0.34-0.78], P=0.0018) and renin-angiotensin-aldosterone system blockers (HR 0.56 [0.36-0.86], P=0.0088) use before admission were independent protective factors. In the ICU, chronic kidney disease (CKD) was identified as an independent risk factor for death (HR 4.96 [2.14-11.5], P<0.001).

CONCLUSION

In-hospital mortality due to the first wave of COVID-19 pandemic in Belgium was high in patients with diabetes. We found that advanced age and male gender were independent risk factors for in-hospital death. We also showed that metformin use before admission was associated with a significant reduction of COVID-19-related in-hospital mortality. Finally, we showed that CKD is a COVID-19-related mortality risk factor in patients with diabetes admitted in the ICU.

The impact of type 2 diabetes and glycaemic control on mortality and clinical outcomes in hospitalized patients with COVID-19 in the capital region of Denmark

AIM

To explore the impact of type 2 diabetes (T2D), glycaemic control and use of glucose-lowering medication on clinical outcomes in hospitalized patients with COVID-19.

MATERIALS AND METHODS

For all patients admitted to a hospital in the Capital Region of Denmark (1 March 2020 to 1 December 2021) with confirmed COVID-19, we extracted data on mortality, admission to intensive care unit (ICU), demographics, comorbidities, medication use and laboratory tests from the electronic health record system. We compared patients with T2D to patients without diabetes using Cox proportional hazards models adjusted for available confounding variables. Outcomes were 30-day mortality and admission to an ICU. For patients with T2D, we also analysed the association of baseline haemoglobin A1c (HbA1c) levels and use of specific glucose-lowering medications with the outcomes.

RESULTS

In total, 4430 patients were analysed, 1236 with T2D and 2194 without diabetes. The overall 30-day mortality was 19% (n = 850) and 10% (n = 421) were admitted to an ICU. Crude analyses showed that patients with T2D both had increased mortality [hazard ratio (HR) 1.37; 95% CI 1.19-1.58] and increased risk of ICU admission (HR 1.28; 95% CI 1.04-1.57). When adjusted for available confounders, this discrepancy was attenuated for both mortality (adjusted HR 1.13; 95% CI 0.95-1.33) and risk of ICU admission (adjusted HR 1.01; 95% CI 0.79-1.29). Neither baseline haemoglobin A1c nor specific glucose-lowering medication use were significantly associated with the outcomes.

CONCLUSION

Among those hospitalized for COVID-19, patients with T2D did not have a higher risk of death and ICU admission, when adjusting for confounders.

A deep transfer learning approach for COVID-19 detection and exploring a sense of belonging with Diabetes

COVID-19 is an epidemic disease that results in death and significantly affects the older adult and those afflicted with chronic medical conditions. Diabetes medication and high blood glucose levels are significant predictors of COVID-19-related death or disease severity. Diabetic individuals, particularly those with preexisting comorbidities or geriatric patients, are at a higher risk of COVID-19 infection, including hospitalization, ICU admission, and death, than those without Diabetes. Everyone's lives have been significantly changed due to the COVID-19 outbreak. Identifying patients infected with COVID-19 in a timely manner is critical to overcoming this challenge. The Real-Time Polymerase Chain Reaction (RT-PCR) diagnostic assay is currently the gold standard for COVID-19 detection. However, RT-PCR is a time-consuming and costly technique requiring a lab kit that is difficult to get in crises and epidemics. This work suggests the CIDICXR-Net50 model, a ResNet-50-based Transfer Learning (TL) method for COVID-19 detection via Chest X-ray (CXR) image classification. The presented model is developed by substituting the final ResNet-50 classifier layer with a new classification head. The model is trained on 3,923 chest X-ray images comprising a substantial dataset of 1,360 viral pneumonia, 1,363 normal, and 1,200 COVID-19 CXR images. The proposed model's performance is evaluated in contrast to the results of six other innovative pre-trained models. The proposed CIDICXR-Net50 model attained 99.11% accuracy on the provided dataset while maintaining 99.15% precision and recall. This study also explores potential relationships between COVID-19 and Diabetes.

Association of government effectiveness, logistics performance, IT systems and income with COVID-19 mortality

The COVID-19 pandemic has unprecedentedly shaken the public health system worldwide. It has been one of the greatest humanitarian crises faced by all countries, regardless of their economic prosperity. However, some countries have been able to minimize the deaths caused by the coronavirus even in the face of a large number of cases, while others have failed to control the death rate even in a comparatively small number of cases. This study explores possible causes of this disparity using cross-sectional data from 126 countries associated with demography, governance, income level, the extent of ICT maturity and the geographical divide. The results of this study suggest that while government effectiveness is negatively associated with the COVID-19 death rate, the logistics performance of governments is positively linked to the COVID-19 mortality rate. The ICT maturity proxied through online service delivery did not confirm its association with the COVID-19 mortality rate. This study informs that poverty and the location of countries do not necessarily influence COVID-19 deaths. Hence, it behoves governments to focus on improving government effectiveness and putting in place more effective and efficient mobility systems, healthcare supply chains and digital administration to address the global health crisis posed by the COVID-19 pandemic and mitigate its harsh effects, including mortality.

Identification of Clinical Features Associated with Mortality in COVID-19 Patients

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings.

Prognostic factors for mortality, intensive care unit and hospital admission due to SARS-CoV-2: a systematic review and meta-analysis of cohort studies in Europe

BACKGROUND

As mortality from coronavirus disease 2019 (COVID-19) is strongly age-dependent, we aimed to identify population subgroups at an elevated risk for adverse outcomes from COVID-19 using age-/gender-adjusted data from European cohort studies with the aim to identify populations that could potentially benefit from booster vaccinations.

METHODS

We performed a systematic literature review and meta-analysis to investigate the role of underlying medical conditions as prognostic factors for adverse outcomes due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), including death, hospitalisation, intensive care unit (ICU) admission and mechanical ventilation within three separate settings (community, hospital and ICU). Cohort studies that reported at least age and gender-adjusted data from Europe were identified through a search of peer-reviewed articles published until 11 June 2021 in Ovid Medline and Embase. Results are presented as odds ratios with 95% confidence intervals and absolute risk differences in deaths per 1000 COVID-19 patients.

FINDINGS

We included 88 cohort studies with age-/gender-adjusted data from 6 653 207 SARS-CoV-2 patients from Europe. Hospital-based mortality was associated with high and moderate certainty evidence for solid organ tumours, diabetes mellitus, renal disease, arrhythmia, ischemic heart disease, liver disease and obesity, while a higher risk, albeit with low certainty, was noted for chronic obstructive pulmonary disease and heart failure. Community-based mortality was associated with a history of heart failure, stroke, diabetes and end-stage renal disease. Evidence of high/moderate certainty revealed a strong association between hospitalisation for COVID-19 and solid organ transplant recipients, sleep apnoea, diabetes, stroke and liver disease.

INTERPRETATION

The results confirmed the strong association between specific prognostic factors and mortality and hospital admission. Prioritisation of booster vaccinations and the implementation of nonpharmaceutical protective measures for these populations may contribute to a reduction in COVID-19 mortality, ICU and hospital admissions.

Clinical and laboratory profile and outcomes of hospitalized COVID-19 patients with type 2 diabetes mellitus in Ghana - A single-center study

BACKGROUND

In sub-Saharan Africa and particularly in Ghana, there is scarcity of published literature specifically on the impact of DM on outcomes in COVID-19 patients. Based on the difference in genetic makeup and demographic patterns in Africans compared to the Western world and with the rising burden of DM and other non-communicable diseases in Ghana there is a need to define the impact DM has on persons with COVID-19. This would ensure adequate risk stratification and surveillance for such patients as well as appropriate scale up of therapeutic management if needed.

AIMS

This single-center study describes the clinical and laboratory profile and outcomes of COVID-19 in-patients with type 2 diabetes mellitus (DM) in Ghana.

MATERIALS AND METHODS

Retrospective analysis was undertaken of the medical records of adults with COVID-19 hospitalized at a facility in Ghana from March to October 2020. Clinical, laboratory and radiological data and outcomes were analysed. Comparisons between COVID-19 patients with DM and non-diabetics were done with an independent t-test or a Mann-Whitney test when normality was not attained. Odds ratios (95% CI) were calculated using univariate logistic regression.

RESULTS

Out of 175 COVID-19 patients, 64 (36.6%) had DM. Overall mean age was 55.9 ± 18.3 years; DM patients were older compared to non-diabetics (61.1 ± 12.8 vs. 53.0 ± 20.2 years, p = .049). Compared to non-diabetics, diabetics were more likely to have higher blood glucose at presentation, have hypertension, be on angiotensin 2 receptor blockers [OR, 95% CI 3.3 (1.6-6.7)] and angiotensin converting enzyme inhibitors [OR, 95% CI 3.1 (1.3-7.4)]; and be HIV negative (p < .05). Although the values were normal, diabetics had a higher platelet count but decreased lymphocytes, aspartate transaminase and alkaline phosphatase compared to non-diabetics (p < .05). There was no difference in clinical symptoms, severity or mortality between the two groups.

DISCUSSION

The clinical profile of patients studied are similar to prior studies. However the outcome of this study showed that DM was not associated with worse clinical severity and in-hospital mortality. This could have been due to majority of DM patients in this study having relatively good blood glucose control on admission. Secondly, DM alone may not be a risk factor for mortality. Rather its concurrent existence with multiple co-morbidities (especially cardiovascular co-morbidities which may predispose to pro-inflammatory and pro-thrombotic states) may be driving the rise in severity and mortality risks reported in other studies. Furthermore, this study was conducted among an African population and Africa has been shown to be generally less severely hit by the COVID-19 pandemic compared to other regions outside the continent. This has been postulated to be due, among other factors, to inherent protective mechanisms in Africans due to early and repeated exposure to parasitic and other organisms resulting in a robust innate immunity.

CONCLUSIONS

This study suggested that DM was not associated with more severe clinical symptoms or worse outcomes among hospitalized COVID-19 patients. Despite this, it is important that DM patients adhere to their therapy, observe the COVID-19 containment protocols and are prioritized in the administration of the COVID-19 vaccines.

STUDY HIGHLIGHTS

In this retrospective, single-centre study on the clinical and laboratory profile and outcome of hospitalized DM patients with COVID-19, patients with DM did not have a more severe clinical profile or worse outcomes. They were, however, significantly older, more likely to have higher admission blood glucose, have hypertension, be on angiotensin 2 receptor blockers and angiotensin converting enzyme inhibitors; and be HIV negative compared to the cohort without DM. DM patients should be a priority group for the COVID-19 vaccines.

Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay

People with diabetes mellitus (DM) are at elevated risk of in-hospital mortality from coronavirus disease-2019 (COVID-19). This vulnerability has spurred efforts to pinpoint distinctive characteristics of COVID-19 patients with DM. In this context, the present article develops ML models equipped with interpretation modules for inpatient mortality risk assessments of COVID-19 patients with DM. To this end, a cohort of 156 hospitalised COVID-19 patients with pre-existing DM is studied. For creating risk assessment platforms, this work explores a pool of historical, on-admission, and during-admission data that are DM-related or, according to preliminary investigations, are exclusively attributed to the COVID-19 susceptibility of DM patients. First, a set of careful pre-modelling steps are executed on the clinical data, including cleaning, pre-processing, subdivision, and feature elimination. Subsequently, standard machine learning (ML) modelling analysis is performed on the cured data. Initially, a classifier is tasked with forecasting COVID-19 fatality from selected features. The model undergoes thorough evaluation analysis. The results achieved substantiate the efficacy of the undertaken data curation and modelling steps. Afterwards, SHapley Additive exPlanations (SHAP) technique is assigned to interpret the generated mortality risk prediction model by rating the predictors' global and local influence on the model's outputs. These interpretations advance the comprehensibility of the analysis by explaining the formation of outcomes and, in this way, foster the adoption of the proposed methodologies. Next, a clustering algorithm demarcates patients into four separate groups based on their SHAP values, providing a practical risk stratification method. Finally, a re-evaluation analysis is performed to verify the robustness of the proposed framework.

Concordance between indirect fibrosis and steatosis indices and their predictors in subjects with overweight/obesity

BACKGROUND

The non-invasive assessment of steatosis/fibrosis tried to overcome some of peri-procedural risk of liver biopsy; for this, several indices of steatosis and fibrosis in liver have been proposed.

AIM

To evaluate concordance of non-invasive fibrosis and steatosis indices in a large population of adult subjects at risk of NAFLD, and how obesity and its physio-pathological features may interact with steatosis/fibrosis indexes and related biomarkers of cardio-metabolic risk.

METHODS

Indices of steatosis (fatty liver index-FLI), NAFLD liver fat score-NLFS)) and fibrosis (Fibrosis 4 (FIB-4), BARD, BAAT and FORN) were calculated in 1145 outpatients with overweight or obesity at risk for T2D and NAFLD. Indices were correlated with clinical variables.

RESULTS

Concordance between tests occurred in 81% of the overall values between FLI and NLFS, but was lower when comparing the other fibrosis scores (FIB-4 vs FORN 72%, FIB-4 vs BARD 36%, BARD vs FORN 46%, BARD vs BAAT 58%, FIB-4 vs BAAT 46%, BAAT vs FORN 62%). Each index was differently correlated with anthropometric, clinical and laboratory variables.

CONCLUSION

Indices evaluated retain low concordance, clinicians should be aware of these differences between steatosis/fibrosis scores when expressing a differential liver disease diagnosis or assessing the progression of a known liver disease.

LEVEL OF EVIDENCE

Level V, descriptive research.

COVID-19 and arrhythmia: The factors associated and the role of myocardial electrical impulse propagation. An observational study based on cardiac telemetric monitoring

Background

The heart is commonly involved in COVID-19, and rhythm disorders have been largely reported.

Objective

To evaluate the association of some non-cardiac and cardiac comorbidities and QT dispersion with arrhythmias and their impact on outcomes in hospitalized patients with COVID-19.

Methods

Each patient underwent cardiac telemetry monitoring through the entire hospitalization period, laboratory analyses, 12-lead ECG, and lung imaging examination. Patients with arrhythmia were divided into three groups (bradyarrhythmias, tachyarrhythmias, and tachy- and bradyarrhythmias).

Results

Two-hundred patients completed the study (males, 123; mean age, 70.1 years); of these, 80 patients (40%) exhibited rhythm disorders on telemetry. Patients with arrhythmia were older (p &lt; 0.0001), had a greater number of comorbidities (p &lt; 0.0001), higher values of creatinine (p = 0.007), B-type natriuretic peptide (p &lt; 0.0001), troponin (p &lt; 0.0001), C-reactive protein (p = 0.01), ferritin (p = 0.001), D-dimer (p &lt; 0.0001), procalcitonin (p = 0.0008), QT interval (p = 0.002), QTc interval (p = 0.04), and QTc dispersion (p = 0.01), and lower values of sodium (p = 0.03), magnesium (p = 0.04), glomerular filtration rate (p &lt; 0.0001), and hemoglobin (p = 0.008) as compared to patients without arrhythmia. By comparing the three subgroups of patients, no significant differences were found. At multivariate analysis, age [odds ratio (OR) = 1.14 (95% CI: 1.07–1.22); p = 0.0004], coronary artery disease [OR = 12.7 (95% CI: 2.38–68.01); p = 0.005], and circulating troponin [OR = 1.05 (95% CI: 1.003–1.10); p = 0.04] represented risk factors independently associated with arrhythmia. All-cause in-hospital mortality was ∼40-fold higher among patients with arrhythmia [OR = 39.66 (95% CI: 5.20–302.51); p = 0.0004].

Conclusion

Arrhythmias are associated with aging, coronary artery disease, subtle myocardial injury, hyperinflammatory status, coagulative unbalance, and prolonged QTc dispersion in patients with COVID-19, and confer a worse in-hospital prognosis. Given its usefulness, routinary use of cardiac telemetry should be encouraged in COVID wards.

J-shaped associations and joint effects of fasting glucose with inflammation and cytokines on COVID-19 mortality

OBJECTIVES

The aim of this study was to investigate the dose-response relationship of admission fasting glucose (FBG) with corona virus disease 2019 (COVID-19) mortality and to further evaluate potential interactions of hyperglycemia with inflammation and hypercoagulation on COVID-19 outcomes.

METHODS

This retrospective study included 2555 consecutively hospitalized patients with COVID-19, until death or discharge, in Wuhan Union hospital between January 1 and April 9, 2020. The poor early outcomes included admission to intensive care unit, intubation, and deaths occurring within 28 days. We used splines nested in Cox regression to visualize dose-response associations and generalized additive models to fit three-dimensional (3D) trend plots for joint effects of FBG with markers of inflammation and coagulation.

RESULTS

J-shaped associations existed between hospitalized mortality or poor early outcomes and FBG with a nadir at 5 mmol/L, which were more evident in women. 3D plots demonstrated significant joint effect trends, and patients with hyperglycemia and high neutrophil-lymphocyte ratio, C-reactive protein, lactate dehydrogenase, procalcitonin, d-dimer, and interleukin-6 had 7.4-25.3-fold risks; the proportions of joint associations attributed to additive interactions reached 30% to 54%.

CONCLUSIONS

FBG was associated with hospitalized mortality and poor early outcomes in a J-shaped manner, and a combination of hyperglycemia, inflammation, hypercoagulation, and cytokines conferred a dramatically higher risk.

Post-acute cardiac complications following SARS-CoV-2 infection in partial lipodystrophy due to LMNA gene p.R349W mutation

PURPOSE

SARS-CoV-2 infection may cause varying degrees of cardiac injury and the presence of underlying cardiovascular morbidities contributes to the frequency and severity of occurrence of this complication. Lipodystrophy syndromes are frequently characterized by severe metabolic derangements that represent relevant cardiovascular risk factors. Besides causing lipodystrophy, mutations in the lamin A/C (LMNA) gene can lead to a wide spectrum of tissue-specific disorders including cardiac involvement.

METHODS AND RESULTS

We herein examine the case of two patients affected by atypical progeroid syndrome and partial lipodystrophy due to a heterozygous missense LMNA mutation c.1045 C > T (p.R349W) who presented initially with mild COVID-19 and developed severe cardiovascular complications within few weeks of SARS-CoV-2 infection. Before being infected with SARS-CoV-2, our patients had cardiovascular morbidities (mild mitral regurgitation in one patient, ischemic heart disease with bifascicular block in the other patient) in adjunct to cardiovascular risk factors, but the SARS-CoV-2 infection contributed to quickly and significantly decompensate their balance.

CONCLUSION

These findings warn that patients affected by LMNA p.R349W mutation and likely other LMNA mutations associated with cardiovascular morbidity should be considered at extremely elevated risk of post-acute cardiological manifestations and should therefore undergo a vigilant follow-up after SARS-CoV-2 infection. Both patients developed COVID-19 before the specific vaccination was available to them and this unfortunate situation should remark the importance of vaccination coverage against SARS-CoV-2 infection for all patients affected by lipodystrophy, especially those with underlying comorbidities.

Diabetes and SARS-CoV-2–Is There a Mutual Connection?

SARS-CoV-2, a newly emerged virus described for the first time in late 2019, affects multiple organs in humans, including the pancreas. Here, we present the bilateral link between the pathophysiology of diabetes and COVID-19, with diabetes being COVID-19 comorbidity, and a complication of SARS-CoV-2 infection. Analysis of clinical data indicates that patients with chronic conditions like diabetes are at increased risk of severe COVID-19, hospitalization, ICU admission, and death compared to the healthy subjects. Further, we show that SARS-CoV-2 infection might be also associated with the development of new-onset diabetes and diabetic ketoacidosis. We then discuss the options for studying SARS-CoV-2 infection in pancreatic settings, including the use of human pluripotent stem cell-derived pancreatic organoids. Further, we review the presence of SARS-CoV-2 receptors in different pancreatic cell types and the infection efficiency based on pancreatic sections from COVID-19 patients and primary human islet in vitro studies. Finally, we discuss the impact of SARS-CoV-2 infection on human pancreatic cell homeostasis, focusing on β-cells.

COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework

This research develops machine learning models equipped with interpretation modules for mortality risk prediction and stratification in cohorts of hospitalised coronavirus disease-2019 (COVID-19) patients with and without diabetes mellitus (DM). To this end, routinely collected clinical data from 156 COVID-19 patients with DM and 349 COVID-19 patients without DM were scrutinised. First, a random forest classifier forecasted in-hospital COVID-19 fatality utilising admission data for each cohort. For the DM cohort, the model predicted mortality risk with the accuracy of 82%, area under the receiver operating characteristic curve (AUC) of 80%, sensitivity of 80%, and specificity of 56%. For the non-DM cohort, the achieved accuracy, AUC, sensitivity, and specificity were 80%, 84%, 91%, and 56%, respectively. The models were then interpreted using SHapley Additive exPlanations (SHAP), which explained predictors’ global and local influences on model outputs. Finally, the k-means algorithm was applied to cluster patients on their SHAP values. The algorithm demarcated patients into three clusters. Average mortality rates within the generated clusters were 8%, 20%, and 76% for the DM cohort, 2.7%, 28%, and 41.9% for the non-DM cohort, providing a functional method of risk stratification.

Factors associated with mortality in hospitalized cardiovascular disease patients infected with COVID‐19

Introduction

To reduce mortality in hospitalized patients with COVID‐19 and cardiovascular disease (CVD), it is necessary to understand the relationship between patient's symptoms, risk factors, and comorbidities with their mortality rate. To the best of our knowledge, this paper is the first which take into account the determinants like risk factors, symptoms, and comorbidities leading to mortality in CVD patients who are hospitalized with COVID‐19.

Methods

This study was conducted on 660 hospitalized patients with CVD and COVID‐19 recruited between January 2020 and January 2021 in Iran. All patients were diagnosed with the previous history of CVD like angina, myocardial infarction, heart failure, cardiomyopathy, abnormal heart rhythms, and congenital heart disease before they were hospitalized for COVID‐19. We collected data on patient's signs and symptoms, clinical and paraclinical examinations, and any underlying comorbidities. t test was used to determine the significant difference between the two deceased and alive groups. In addition, the relation between pairs of symptoms and pairs of comorbidities has been determined via correlation computation.

Results

Our findings suggest that signs and symptoms such as fever, cough, myalgia, chest pain, chills, abdominal pain, nausea, vomiting, diarrhea, and anorexia had no impact on patients' mortality. There was a significant correlation between COVID‐19 cardiovascular patients' mortality rate and symptoms such as headache, loss of consciousness (LOC), oxygen saturation less than 93%, and need for mechanical ventilation.

Conclusions

Our results might help physicians identify early symptoms, comorbidities, and risk factors related to mortality in CVD patients hospitalized for COVID‐19.

Glucose control in home-isolated adults with type 1 diabetes affected by COVID-19 using continuous glucose monitoring

PURPOSE

This study is aimed at evaluating changes in metrics of glucose control in home-isolated patients with type 1 diabetes and COVID-19 using a continuous glucose monitoring (CGM) system.

METHODS

We included adults aged 18-45 years with type 1 diabetes, using CGM, followed by telemedicine at a Southern Italian University Hospital. Thirty-two home-quarantined subjects with SARS-CoV-2 positive swab constituted the COVID-19 group. Thirty age-matched diabetic individuals without COVID-19 formed the control group. The effects of COVID-19 on glycemic control in patients infected were assessed at different time points [2 weeks before-COVID-19 (Time 1), 2 weeks during-COVID-19 (Time 2) and 2 weeks after COVID-19 (Time 3)] and compared with those without infection.

RESULTS

A significant reduction of TIR (Time 1 vs Time 2, %, 60.1 ± 16.6 vs 55.4 ± 19.2, P = 0.03), associated with a significant increase of TAR level 2 (10.1 ± 7.3 vs 16.7 ± 12.9, P < 0.001), GMI (7.1 ± 0.6 vs 7.5 ± 0.8, P < 0.001), CV (37.3 ± 7.1 vs 39.6 ± 7.0, P = 0.04), mean glucose values (mg/dL, 160.2 ± 26.5 vs 175.5 ± 32.6, P = 0.001) and standard deviation (59.2 ± 13.1 vs 68.6 ± 17.7, P = 0.001) was observed in patients with COVID-19. No significant change of glycemic metrics was found in the NO COVID-19 group across the time.

CONCLUSION

Young home-isolated patients with type 1 diabetes and COVID-19 showed a worsening of glucose control during COVID-19, as compared with age-matched diabetic subjects without the infection.

Association of serum C-reactive protein (CRP) and D-dimer concentration on the severity of COVID-19 cases with or without diabetes: a systematic review and meta-analysis

INTRODUCTION

Coronavirus disease (COVID-19) is a highly contagious disease that poses major public health risks. Fewer studies link high CRP and D-dimer levels to severe COVID-19 infection. Therefore, this study investigates the association of serum CRP and D-dimer concentration with COVID-19 severity in diabetic and non-diabetic patients.

AREAS COVERED

Relevant published articles were identified using electronic search engines, such as Google Scholar, PubMed, Springer, Science Direct, and Researchgate. A total of 29 articles reporting on 15,282 patients (4,733 diabetes and 10,549 non-diabetes) were included in this systematic review and meta-analysis. RevMan V5.4, STATA V14 software, and SPSS V25 were used for the meta-analysis. Egger's regression and Begg-Mazumdar's test were used for assessing publication bias. The pooled result of all studies revealed that serum CRP (Standard mean difference (SMD) 0.41 mg/L; P < 0.0001; I2 93%) and D-dimer (SMD 0.32 mg/L; P < 0.0001; I2 83%) concentration was significantly higher in COVID-19 diabetic patients. The prevalence of COVID-19 infection was comparatively higher in male diabetic patients (OR 2.41; P < 0.00001; I2 88%). There was no publication bias. CRP and D-dimer rose with age in COVID-19 diabetic and non-diabetic patients.

EXPERT OPINION

Overall, the serum CRP and D-dimer concentration in COVID-19 diabetic patients was significantly higher than non-diabetic patients indicating severe illness.

The isolated effect of age on the risk of COVID-19 severe outcomes: a systematic review with meta-analysis

INTRODUCTION

Increased age has been reported to be a factor for COVID-19 severe outcomes. However, many studies do not consider the age dependency of comorbidities, which influence the course of disease. Protection strategies often target individuals after a certain age, which may not necessarily be evidence based. The aim of this review was to quantify the isolated effect of age on hospitalisation, admission to intensive care unit (ICU), mechanical ventilation and death.

METHODS

This review was based on an umbrella review, in which Pubmed, Embase and preprint databases were searched on 10 December 2020, for relevant reviews on COVID-19 disease severity. Two independent reviewers evaluated the primary studies using predefined inclusion and exclusion criteria. The results were extracted, and each study was assessed for risk of bias. The isolated effect of age was estimated by meta-analysis, and the quality of evidence was assessed using Grades of Recommendations, Assessment, Development, and Evaluation framework.

RESULTS

Seventy studies met our inclusion criteria (case mortality: n=14, in-hospital mortality: n=44, hospitalisation: n=16, admission to ICU: n=12, mechanical ventilation: n=7). The risk of in-hospital and case mortality increased per age year by 5.7% and 7.4%, respectively (effect size (ES) in-hospital mortality=1.057, 95% CI 1.038 to 1.054; ES case mortality=1.074, 95% CI 1.061 to 1.087), while the risk of hospitalisation increased by 3.4% per age year (ES=1.034, 95% CI 1.021 to 1.048). No increased risk was observed for ICU admission and intubation by age year. There was no evidence of a specific age threshold at which the risk accelerates considerably. The confidence of evidence was high for mortality and hospitalisation.

CONCLUSIONS

Our results show a best-possible quantification of the increase in COVID-19 disease severity due to age. Rather than implementing age thresholds, prevention programmes should consider the continuous increase in risk. There is a need for continuous, high-quality research and 'living' reviews to evaluate the evidence throughout the pandemic, as results may change due to varying circumstances.

The underlying mechanisms for severe COVID-19 progression in people with diabetes mellitus: a critical review

Diabetes mellitus (DM) has a high incidence of comorbidities among patients with severe coronavirus disease 2019 (COVID-19). The elevated prevalence of DM in the world population makes it a significant risk factor because diabetic individuals appear to be prone to clinical complications and have increased mortality rates. Here, we review the possible underlying mechanisms involved in DM that led to worse outcomes in COVID-19. The impacts of hyperglycemia side effects, secondary comorbidities, weakened innate and adaptive immunity, chronic inflammation, and poor nutritional status, commonly present in DM, are discussed. The role of the SARS-CoV-2 receptor and its polymorphic variations on higher binding affinity to facilitate viral uptake in people with DM were also considered. Clinical differences between individuals with type 1 DM and type 2 DM affected by COVID-19 and the potential diabetogenic effect of SARS-CoV-2 infection were addressed.

Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies

OBJECTIVES

We conducted a systematic literature review and meta-analysis of observational studies to investigate the association between diabetes, hypertension, body mass index (BMI) or smoking with the risk of death in patients with COVID-19 and to estimate the proportion of deaths attributable to these conditions.

METHODS

Relevant observational studies were identified by searches in the PubMed, Cochrane library and Embase databases through 14 November 2020. Random-effects models were used to estimate summary relative risks (SRRs) and 95% CIs. Certainty of evidence was assessed using the Cochrane methods and the Grading of Recommendations, Assessment, Development and Evaluations framework.

RESULTS

A total of 186 studies representing 210 447 deaths among 1 304 587 patients with COVID-19 were included in this analysis. The SRR for death in patients with COVID-19 was 1.54 (95% CI 1.44 to 1.64, I2=92%, n=145, low certainty) for diabetes and 1.42 (95% CI 1.30 to 1.54, I2=90%, n=127, low certainty) for hypertension compared with patients without each of these comorbidities. Regarding obesity, the SSR was 1.45 (95% CI 1.31 to 1.61, I2=91%, n=54, high certainty) for patients with BMI ≥30 kg/m2 compared with those with BMI <30 kg/m2 and 1.12 (95% CI 1.07 to 1.17, I2=68%, n=25) per 5 kg/m2 increase in BMI. There was evidence of a J-shaped non-linear dose-response relationship between BMI and mortality from COVID-19, with the nadir of the curve at a BMI of around 22-24, and a 1.5-2-fold increase in COVID-19 mortality with extreme obesity (BMI of 40-45). The SRR was 1.28 (95% CI 1.17 to 1.40, I2=74%, n=28, low certainty) for ever, 1.29 (95% CI 1.03 to 1.62, I2=84%, n=19) for current and 1.25 (95% CI 1.11 to 1.42, I2=75%, n=14) for former smokers compared with never smokers. The absolute risk of COVID-19 death was increased by 14%, 11%, 12% and 7% for diabetes, hypertension, obesity and smoking, respectively. The proportion of deaths attributable to diabetes, hypertension, obesity and smoking was 8%, 7%, 11% and 2%, respectively.

CONCLUSION

Our findings suggest that diabetes, hypertension, obesity and smoking were associated with higher COVID-19 mortality, contributing to nearly 30% of COVID-19 deaths.

TRIAL REGISTRATION NUMBER

CRD42020218115.

Pre-existing health conditions and severe COVID-19 outcomes: an umbrella review approach and meta-analysis of global evidence

BACKGROUND

This study applies an umbrella review approach to summarise the global evidence on the risk of severe COVID-19 outcomes in patients with pre-existing health conditions.

METHODS

Systematic reviews (SRs) were identified in PubMed, Embase/Medline and seven pre-print servers until December 11, 2020. Due to the absence of age-adjusted risk effects stratified by geographical regions, a re-analysis of the evidence was conducted. Primary studies were extracted from SRs and evaluated for inclusion in the re-analysis. Studies were included if they reported risk estimates (odds ratio (OR), hazard ratio (HR), relative risk (RR)) for hospitalisation, intensive care unit admission, intubation or death. Estimated associations were extracted from the primary studies for reported pre-existing conditions. Meta-analyses were performed stratified for each outcome by regions of the World Health Organization. The evidence certainty was assessed using GRADE. Registration number CRD42020215846.

RESULTS

In total, 160 primary studies from 120 SRs contributed 464 estimates for 42 pre-existing conditions. Most studies were conducted in North America, European, and Western Pacific regions. Evidence from Africa, South/Latin America, and the Eastern Mediterranean region was scarce. No evidence was available from the South-East Asia region. Diabetes (HR range 1.2-2.0 (CI range 1.1-2.8)), obesity (OR range 1.5-1.75 (CI range 1.1-2.3)), heart failure (HR range 1.3-3.3 (CI range 0.9-8.2)), COPD (HR range 1.12-2.2 (CI range 1.1-3.2)) and dementia (HR range 1.4-7.7 (CI range 1.2-39.6)) were associated with fatal COVID-19 in different regions, although the estimates varied. Evidence from Europe and North America showed that liver cirrhosis (OR range 3.2-5.9 (CI range 0.9-27.7)) and active cancer (OR range 1.6-4.7 (CI range 0.5-14.9)) were also associated with increased risk of death. Association between HIV and undesirable COVID-19 outcomes showed regional heterogeneity, with an increased risk of death in Africa (HR 1.7 (CI 1.3-2.2)). GRADE certainty was moderate to high for most associations.

CONCLUSION

Risk of undesirable COVID-19 health outcomes is consistently increased in certain patient subgroups across geographical regions, showing high variability in others. The results can be used to inform COVID-19 vaccine prioritisation or other intervention strategies.

The effect of cardiovascular disease and acute cardiac injury on fatal COVID-19: a meta-analysis

Background

With the continuance of the global COVID-19 pandemic, cardiovascular disease (CVD) and cardiac injury have been suggested to be risk factors for severe COVID-19.

Objective

The aim is to evaluate the mortality risks associated with CVD and cardiac injury among hospitalized COVID-19 patients, especially in subgroups of populations in different countries.

Methods

A comprehensive systematic literature search was performed using 9 databases from November 1, 2019 to November 9, 2020. Meta-analyses were performed for CVD and cardiac injury between non-survivors and survivors of COVID-19.

Results

Although the prevalence of CVD in different populations was different, hospitalized COVID-19 patients with CVD were at a higher risk of fatal outcomes (OR = 2.72; 95% CI 2.35–3.16) than those without CVD. Separate meta-analyses of populations in four different countries also reached a similar conclusion that CVD was associated with an increase in mortality. Cardiac injury was common among hospitalized COVID-19 patients. Patients with cardiac injury had a significantly higher mortality risk than those without cardiac injury (OR = 13.25; 95% CI: 8.56–20.52).

Conclusions

Patients' CVD history and biomarkers of cardiac injury should be taken into consideration during the hospital stay and incorporated into the routine laboratory panel for COVID-19.

COPD and the risk of poor outcomes in COVID-19: A systematic review and meta-analysis

BACKGROUND

Patients with chronic obstructive pulmonary disease (COPD) are highly susceptible from respiratory exacerbations from viral respiratory tract infections. However, it is unclear whether they are at increased risk of COVID-19 pneumonia or COVID-19-related mortality. We aimed to determine whether COPD is a risk factor for adverse COVID-19 outcomes including hospitalization, severe COVID-19, or death.

METHODS

Following the PRISMA guidelines, we performed a systematic review of COVID-19 clinical studies published between November 1^st, 2019 and January 28^th, 2021 (PROSPERO ID: CRD42020191491). We included studies that quantified the number of COPD patients, and reported at least one of the following outcomes stratified by COPD status: hospitalization; severe COVID-19; ICU admission; mechanical ventilation; acute respiratory distress syndrome; or mortality. We meta-analyzed the results of individual studies to determine the odds ratio (OR) of these outcomes in patients with COPD compared to those without COPD.

FINDINGS

Fifty-nine studies met the inclusion criteria, and underwent data extraction. Most studies were retrospective cohort studies/case series of hospitalized patients. Only four studies examined the effects of COPD on COVID-19 outcomes as their primary endpoint. In aggregate, COPD was associated with increased odds of hospitalization (OR 4.23, 95% confidence interval [CI] 3.65-4.90), ICU admission (OR 1.35, 95% CI 1.02-1.78), and mortality (OR 2.47, 95% CI 2.18-2.79).

INTERPRETATION

Having a clinical diagnosis of COPD significantly increases the odds of poor clinical outcomes in patients with COVID-19. COPD patients should thus be considered a high-risk group, and targeted for preventative measures and aggressive treatment for COVID-19 including vaccination.

Digital Health Services among Patients with Diabetes during the COVID-19 Pandemic: A Scoping Review

BACKGROUND AND AIMS

COVID-19 pandemic causes massive disruption of the global health system. The diabetic patients are the vulnerable parts population who are predicted to have a significant issue during the pandemic regarding the conventional type of consultation by face-to-face which may result in the higher risk of COVID-19 exposure. This study aims to observe the use of digital health services for diabetes management during COVID-19 pandemic.

METHODS

A scoping review was conducted in PubMed, ScienceDirect, and Google Scholar during August until September 2020. The keywords that are used on the searching process are diabetes AND digital service OR telemedicine OR technology AND COVID-19. The criteria included the selection of scientific publication as an original research and reviews.

RESULTS

All published articles were gathered from 3 search engines; PubMed, Science Direct, and Google Scholar. The discussion explicates the practical considerations that are in accordance with the current condition of each country. In order to do so, the evidence is classified based on the level of global digital health framework in the developed and developing countries. It was found that the minimal level of digital health that connects diabetes patients with healthcare providers was at level 2, which is by using the video mode. The most advanced is at level 3B, which is applied by using the CGM devices, which provide active monitoring, diagnosis, and treatment based on timely clinical judgment.

CONCLUSION

It is feasible to utilize the digital health service during the COVID-19. This review is projected to be beneficial for the patient and health care providers to select the most feasible approach of digital health that merits the contextual resource.

Comorbidities in SARS-CoV-2 Patients: a Systematic Review and Meta-Analysis

COVID-19 has plagued the world since it was first identified in December 2019. Previous systematic reviews and meta-analysis were limited by various factors such as the usage of non-peer reviewed data and were also limited by the lack of clinical data on a global scale.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread across the globe at unprecedented speed and is showing no signs of slowing down. The outbreak of coronavirus disease 2019 (COVID-19) has led to significant health burden in infected patients especially in those with underlying comorbidities. The aim of this study was to evaluate the correlation between comorbidities and their role in the exacerbation of disease in COVID-19 patients leading to fatal outcomes. A systematic review was conducted using data from MEDLINE, Scopus, Web of Science, and EMBASE databases published from 1 December 2019 to 15 September 2020. Fifty-three articles were included in the systematic review. Of those 53 articles, 8 articles were eligible for meta-analysis. Hypertension, obesity, and diabetes mellitus were identified to be the most prevalent comorbidities in COVID-19 patients. Our meta-analysis showed that cancer, chronic kidney diseases, diabetes mellitus, and hypertension were independently associated with mortality in COVID-19 patients. Chronic kidney disease was statistically the most prominent comorbidity leading to death. However, despite having high prevalence, obesity was not associated with mortality in COVID-19 patients.

An investigation of risk factors of in-hospital death due to COVID-19: a case-control study in Rasht, Iran

Background

Identifying the non-survived patients’ characteristics compared to survived subjects and introducing the critical risk factors of COVID-19 mortality would help enhance patients’ prognosis and treatment.

Methods

In the current case-control study, medical records of 103 non-survived COVID-19 patients (cases) and 147 sex-matched survivors (controls) who admitted to Razi University Hospital in Rasht, Guilan, Northern Iran from April 21 to August 21, 2020, were explored. Data on demographic, anthropometric, clinical, and laboratory assessment was extracted from the electronic medical records. To estimate the association between variables of interest and mortality odds due to COVID-19 logistic regression was carried out.

Results

The patients who died (mean age = 62.87 years) were older than the discharged patients (57.33 years; P value = 0.009). According to the results of multivariable regression adjusted for potential confounders, elevated BMI (OR = 2.49; 95% CI = 1.15–5.41), higher CRP levels (OR = 2.28; 95% CI = 1.08–4.78), increased FBS levels (OR = 2.88; 95% CI = 1.35–6.17), higher levels of total cholestrol (OR = 2.55; 95% CI = 1.19–5.45) and LDL (OR = 2.27; 95% CI = 1.07–4.79), elevated triglyceride (OR = 5.14; 95% CI = 2.28–11.56), and raised levels of D-dimer (OR = 5.68; 95% CI = 2.22–14.49) were identified as independent risk factors of COVID-19 mortality. No significant association was detected regarding HDL level, QTc interval or heart size, and COVID-19 fatality odds.

Conclusion

The present findings demonstrated that obesity, higher levels of CRP, blood sugar, D-dimer, and lipid markers were likely to be predictive factors of COVID-19-related mortality odds.

The potential association between common comorbidities and severity and mortality of coronavirus disease 2019: A pooled analysis

BACKGROUD

The association between underlying comorbidities and cardiac injury and the prognosis in coronavirus disease 2019 (COVID-19) patients was assessed in this study.

HYPOTHESIS

The underlying comorbidities and cardiac injury may be associated with the prognosis in COVID-19 patients.

METHODS

A systematic search was conducted in PubMed, EMBASE, Web of science, and The Cochrane library from December 2019 to July 2020. The odds ratio (OR) and 95% confidence intervals (95% CI) were used to estimate the probability of comorbidities and cardiac injury in COVID-19 patients with or without severe type, or in survivors vs nonsurvivors of COVID-19 patients.

RESULTS

A total of 124 studies were included in this analysis. A higher risk for severity was observed in COVID-19 patients with comorbidities. The pooled result in patients with hypertension (OR 2.57, 95% CI: 2.12-3.11), diabetes (OR 2.54, 95% CI: 1.89-3.41), cardiovascular diseases (OR 3.86, 95% CI: 2.70-5.52), chronic obstractive pulmonary disease (OR 2.71, 95% CI: 1.98-3.70), chronic kidney disease (OR 2.20, 95% CI: 1.27-3.80), and cancer (OR 2.42, 95% CI: 1.81-3.22) respectively. All the comorbidities presented a higher risk of mortality. Moreover, the prevalence of acute cardiac injury is higher in severe group than in nonsevere group, and acute cardiac injury is associated with an increased risk for in-hospital mortality.

CONCLUSION

Comorbidities and acute cardiac injury are closely associated with poor prognosis in COVID-19 patients. It is necessary to continuously monitor related clinical indicators of organs injury and concern comorbidities in COVID-19 patients.