Adverse outcomes in COVID-19 and diabetes: a retrospective cohort study from three London teaching hospitals

Izzi‐Engbeaya, Chioma; Distaso, Walter; Amin, Anjali; Yang, Wei; Idowu, Oluwagbemiga; Kenkre, Julia; Shah, Ronak J; Woin, Evelina; Shi, Christine; Alavi, N; Bedri, Hala; Brady, Niamh; Blackburn, Sophie; Leczycka, Martina Eva; Patel, Suhani; Sokol, Elizaveta; Toke‐Bjolgerud, Edward; Qayum, A; Abdel-Malek, Mariana; Hope, David; Oliver, Nick; Bravis, Vassiliki; Misra, Shivani; Tan, Tricia; Hill, Neil; Salem, Victoria

doi:10.1136/bmjdrc-2020-001858

Cited by 54 publications

(44 citation statements)

References 32 publications

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“…A study in Korea reported neutrality for COVI-19 mortality (OR 0.84; 95% CI, 0.23–3.09) in SUs users vs. non-users [ 23 ]. A similar result was reported by Izzi-Engbeaya for deaths within 30 days (OR 0.66; 95% CI, 0.30–1.52) in univariate analysis [ 24 ] and by Silverii and colleagues [ 25 ]. The Coronavirus SARS-CoV-2 and Diabetes Outcomes (CORONADO) study also found a neutral effect of SUs/glinides on mortality (OR 0.83; 95% CI, 0.67–1.03) within 28 days [ 26 ].…”

Section: Sulphonylureassupporting

confidence: 86%

Managing diabetes in diabetic patients with COVID: where do we start from?

2021

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Aims COVID-19 has and still is sweeping away the national health systems worldwide. In this review, we sought to determine the evidence base proofs on the antidiabetic treatment capable to reduce the risk of COVID-19-related mortality. Methods We have performed a systematic search of published articles using PubMed, and EMBASE from March 2020 to March 31st, 2021. We excluded editorials, commentary, letters to the editor, reviews, and studies that did not have mortality as an outcome. For metformin and insulin only, we performed a meta-analysis using Cochrane RevMan 5.2. Results Among antidiabetic drugs, metformin was the only drug associated with a reduced risk of mortality. Conversely, insulin appears associated with an increased risk. The other classes of drugs were neutral. Conclusions The totality of articles reports retrospective data strongly affected by “channeling bias” so that most of the existing results on each class of drugs are driven by the phenotype of patients likely to receive that specific drug by prescription.

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Section: Sulphonylureassupporting

confidence: 86%

Managing diabetes in diabetic patients with COVID: where do we start from?

2021

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“…Seven studies (13.5%) reported the unadjusted effect of frailty based on the CFS with delirium (OR 2.91; 95% CI 2.00–4.25; I 2 = 56%; Figure S6). 29,40,43,48,58,64,69 Eight studies (15.4%) reported an unadjusted association of frailty based on the CFS with ICU admission 8,23,28,29,39,40,43,69 (OR 0.24, 95% CI 0.08–0.71; I 2 = 92%; Figure S7), and three studies (5.8%) reported the adjusted association 8,67,69 (OR 0.87, 95% CI 0.56–1.35; I 2 = 69%; Figure S8). Six (11.5%) studies reported unadjusted associations between frailty based on the CFS and receipt of mechanical ventilation (OR 0.32, 95% CI 0.08–1.30; I 2 = 89%; Figure S9).…”

Section: Resultsmentioning

confidence: 99%

Association of frailty with outcomes in individuals with COVID‐19: A living review and meta‐analysis

Dumitrascu

Branje

Hladkowicz

et al. 2021

J American Geriatrics Society

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Background and Objectives: Frailty leaves older adults vulnerable to adverse health outcomes. Frailty assessment is recommended by multiple COVID-19 guidelines to inform care and resource allocation. We aimed to identify, describe, and synthesize studies reporting the association of frailty with outcomes (informed by the Institute for Healthcare Improvement's Triple Aim [health, resource use, and experience]) in individuals with COVID-19.Design: Systematic review and meta-analysis. Setting: Studies reporting associations between frailty and outcomes in the setting of COVID-19 diagnosis. Participants: Adults with COVID-19.Measurements: Following review of titles, abstracts and full text, we included 52 studies that contained 118,373 participants with COVID-19. Risk of bias was assessed using the Quality in Prognostic studies tool. Our primary outcome was mortality, secondary outcomes included delirium, intensive care unit admission, need for ventilation and discharge location. Where appropriate, random-effects meta-analysis was used to pool adjusted and unadjusted effect measures by frailty instrument.Results: The Clinical Frailty Scale (CFS) was the most used frailty instrument. Mortality was reported in 37 studies. After confounder adjustment, frailty identified using the CFS was significantly associated with mortality in COVID-19 positive patients (odds ratio 1.79, 95% confidence interval [CI] 1.49-2.14; hazard ratio 1.87, 95% CI 1.33-2.61). On an unadjusted basis, frailty identified using the CFS was significantly associated with increased odds of delirium and reduced odds of intensive care unit admission. Results were generally consistent using other frailty instruments. Patient-reported, cost and experience outcomes were rarely reported. Conclusion:Frailty is associated with a substantial increase in mortality risk in COVID-19 patients, even after adjustment. Delirium risk is also increased. Frailty assessment may help to guide prognosis and individualized care

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“…Type-2 diabetes mellitus is more frequent in older patients, male gender, and is part of the metabolic syndrome with hypertension and obesity, which was previously demonstrated to have strong association with COVID-19 outcomes. (23) The association of diabetes and survival has been questioned by other studies, where the association was lost after controlling for other factors (4,24). Chronic therapy with ACE inhibitors and ARBs were associated with higher mortality in this analysis.…”

Section: Discussionmentioning

confidence: 61%

Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

Greco

Angelotti

Caruso

et al. 2021

Preprint

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Introduction: SARS-CoV-2 infection was first identified at the end of 2019 in China, and subsequently spread globally. COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making, considering that baseline comorbidities, age, and patient conditions at admission have been associated to poorer outcomes. Supervised machine learning techniques are increasingly diffuse in clinical medicine and can predict mortality and test associations reaching high predictive performance. We assessed performances of a machine learning approach to predict mortality in COVID-19 patients admitted to ICU using data from the Lombardy ICU Network.Methods: this is a secondary analysis of prospectively collected data from Lombardy ICU network. To predict survival at 7-,14- and 28 days we built two different models; model A included patient demographics, medications before admission and comorbidities, while model B also included the data of the first day since ICU admission. 10-fold cross validation was repeated 2500 times, to ensure optimal hyperparameter choice. The only constrain imposed to model optimization was the choice of logistic regression as final layer to increase clinical interpretability. Different imputation and over-sampling techniques were employed in model training.Results 1503 patients were included, with 766 deaths (51%). Exploratory analysis and Kaplan-Meier curves demonstrated mortality association with age and gender. Model A and B reached the greatest predictive performance at 28 days (AUC 0.77 and 0.79), with lower performance at 14 days (AUC 0.72 and 0.74) and 7 days (AUC 0.68 and 0.71). Male gender, age and number of comorbidities were strongly associated with mortality in both models. Among comorbidities, chronic kidney disease and chronic obstructive pulmonary disease demonstrated association. Mode of ventilatory assistance at ICU admission and Fraction of Inspired oxygen were associated with mortality in model B.Conclusions Supervised machine learning models demonstrated good performance in prediction of 28-day mortality. 7-days and 14-days predictions demonstrated lower performance. Machine learning techniques may be useful in emergency phases to reach higher predictive performance with reduced human supervision using complex data.

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Adverse outcomes in COVID-19 and diabetes: a retrospective cohort study from three London teaching hospitals

Cited by 54 publications

References 32 publications

Managing diabetes in diabetic patients with COVID: where do we start from?

Managing diabetes in diabetic patients with COVID: where do we start from?

Association of frailty with outcomes in individuals with COVID‐19: A living review and meta‐analysis

Artificial Intelligence to Predict Mortality in Critically ill COVID-19 Patients Using Data from the First 24h: A Case Study from Lombardy Outbreak

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