TAI is present in many type 1 diabetes patients at the time of diagnosis and is associated with a high prevalence of thyroid dysfunction. The clinical presentation of diabetes and the evolution of metabolic control and insulin-secretory reserves are not influenced by the presence of TAI. Patients with type 1 diabetes should be screened for TAI at diagnosis.
Diabetic ketoacidosis is a medical emergency with somatic and psychological implications in patients with type 1 diabetes mellitus (DM1). Menstrual cycle-related glycemic change is rarely described as a precipitant of diabetic ketoacidosis. We present a case of a 23-year-old female with a history of DM1, on her second day of menstruation, who presents with diabetic ketoacidosis. In absence of other precipitating factors, examination of the time pattern of her recurrent diabetic ketoacidosis was consistent with catamenial diabetic ketoacidosis. To date, the reports of catamenial diabetic ketoacidosis are few and without consistent results. Moreover, most of the reports investigated only women with tight glycemic control. Systematic studies of catamenial hyperglycemia suggest variation in hyperglycemic patterns among individuals and even inconsistent glycemic variability patterns with consecutive cycles, making the management of catamenial hyperglycemia challenging. This case illustrates recurrent adverse outcomes (including diabetic ketoacidosis) due to complexities in DM1 management influenced by catamenial hyperglycemia. The complexity of diabetes management suggests the need of closed-loop glucose control systems that self-adjust basal insulin patterns to respond to the glucose management needs throughout menstrual cycles, which may ultimately prevent the risk for catamenial diabetic ketoacidosis, improve overall glycemic control, and reduce psychological burden of DM1.
Introduction: The coronavirus disease 2019 (COVID-19) led to a global pandemic. Comorbidities such as hypertension, diabetes mellitus, elevated cholesterol, cardiac/pulmonary diseases, and obesity were postulated as prognostic factors for a worse outcome. Hypothesis: Obese COVID-19 patients have a worse prognosis. Methods: From March to June 2020, we obtained data on all patients ≥18 y.o. who were admitted with a positive COVID-19 test at the Rush System, Chicago. Multivariable logistic regression analysis was performed between predictors and a composite outcome of intubation and in-hospital mortality. Results: Among the 1345 admitted patients, 69 (5%) were underweight (BMI<18.5kg/m2), 365 (27%) of normal weight (BMI 18.5-25kg/m2), 405 (30%) overweight (BMI 25-30kg/m2), 258 (19%) of obesity class I (BMI 30-35kg/m2), 119 (9%) of obesity class II (BMI 35-40kg/m2) and 129 (10%) of obesity class III (BMI >40kg/m2). In a multivariable model assessing the risk for the in-hospital death or intubation, underweight patients showed decreased risk (odds ratio (OR) 0.31) while obesity class III patients showed increased risk (OR 1.68, Figure 1) when compared to normal BMI. When accounting for obesity classes, male sex, atrial fibrillation and coronary artery disease were also independent predictors adverse outcomes. Conclusions: Consistent with previous research, morbidly obese patients had a higher risk for a worse outcome, even when accounting for numerous comorbidities. Underweight patients appeared to be protected. Higher body mass leads to inherent changes in lung function, increased risk of thrombosis, greater viral replication, higher release of adipokines and higher inflammation. Inversely, fewer adipocytes could possibly limit the risk for cytokine storm by reducing the amount of proinflammatory factors released. Figure: Odds ratios with 95% confidence intervals for the outcome of death or intubation in all COVID-19 positive admitted patients.
No abstract
Introduction: Early studies of coronavirus disease 2019 (COVID-19) patients suggested that heart failure (HF) may lead to poorer prognosis. We evaluated demographics and short-term clinical outcomes of patients with evidence of left ventricular systolic dysfunction (LVSD) in comparison to those with preserved LV systolic function (PSF). Methods: In this retrospective study of patients hospitalized for COVID-19 between March and June 2, 2020 at Rush Health Systems in Metro Chicago, demographics, comorbidities and clinical outcomes of patients who demonstrated LVSD (ejection fraction [EF] <50%) on transthoracic echocardiogram (TTE) were compared to that of controls with PSF. Results: Out of 1,312 hospitalized patients, 225 underwent TTE, and 44 patients showed LVSD. Demographics were similar between two groups, with exception of a higher prevalence of African American (AA) race (48 % vs. 29%; p=0.03) in the LVSD group. While 82% of patients in the LVSD cohort had history of chronic HF, only 26% of patients in the PSF had pre-existing HF (p<0.001). Underlying comorbidities were similar between groups: obesity (39% vs. 36%; p=0.86), diabetes (57% vs. 57%; p=1.0), hypertension (70% vs. 66%; p=0.72) and end-stage renal disease (20% vs. 19%; p=0.83). Coronary artery disease trended toward a higher frequency (50% vs. 34%; p=0.058) in the LVSD group. Troponin elevation (18% vs. 12%; p=0.43), vasopressor use (57% vs. 56%; p=1.0), endotracheal intubation (59% vs. 57%; p=0.87), myocardial infarction (30% vs. 23%; p=0.43), ICU admission (75% vs. 75%; p=1.0), hospital length of stay (median 11 days vs. 15 days; p=0.4), and death (25% vs. 23%; p=0.84) were similar between groups. Patients with LVSD had higher incidence of sustained ventricular tachycardia or fibrillation than those with PSF (18% vs. 6%; p=0.016). Conclusions: In our COVID-19 admissions, LVSD was more common in AA patients. Patients with LVSD had a higher risk of ventricular arrhythmias. However, there were no differences between need for ICU admission or intubation, vasopressor requirements, length of stay or death between patients with LVSD and those without. Longitudinal follow-up studies are needed to identify differences in long-term sequelae of COVID-19 infection with evidence for LVSD.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.