Subacute Thyroiditis Due to COVID-19 Vaccine.

In this report, we present a case of a 90-year-old female who exhibited a sudden onset of confusion and severe generalized weakness after receiving her second dose of mRNA SARS-CoV-19 vaccination 19 days prior to her admission in 2021. Her thyroid-stimulating hormone (TSH) levels were low, while her thyroxine (T4), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) levels were elevated. Triiodothyronine (T3) level was not measured. Ultrasonography of the thyroid revealed multiple nodules with heterogeneous tissue, and a nuclear medicine thyroid uptake scan showed very low uptake. These findings indicated limited thyroid destruction in the form of subacute thyroiditis, likely triggered by the mRNA SARS-CoV-2 vaccine. This case illustrates an acute complication due to a novel vaccine. There are three key takeaways for physicians. First, there should be a discussion of the benefits and risks of Covid-19 vaccine. Second, patients who present with encephalopathy should have their thyroid function checked. Third, clinicians must be aware of the signs and symptoms of potentially life-threatening hyperthyroidism. Additional studies are needed to identify those patients at highest risk for Covid-19 vaccine complications.

Thyrotoxicosis in the Setting of Hydatidiform Mole with Subsequent Development of Takotsubo Cardiomyopathy Complicated by COVID-19.

We present a rare case of hydatidiform molar pregnancy, which led to the development of thyroid storm, followed by a rare complication of takotsubo cardiomyopathy in the setting of a COVID-19 infection. A 21-year-old female of 22 weeks gestational age presented with heavy vaginal blood loss, brown emesis, tachycardia, and lethargy. Through clinical presentation and ultrasound confirmation, a molar pregnancy was diagnosed. Laboratory data and clinical presentation of thyrotoxicosis supported a diagnosis of thyroid storm. Test for COVID-19 was positive. The patient was treated with dilation and curettage, antithyroid medication, and blood transfusions, resulting in symptom resolution. Thereafter, echocardiography confirmed takotsubo cardiomyopathy. It is suspected that the homology in structure between the human chorionic gonadotropin (hCG) and thyroid stimulating hormone subunits resulted in thyroid storm secondary to receptor cross-reactivity. We speculate that subsequent cardiovascular stress of b-hCG-induced thyroid storm with superimposed COVID-19 infection facilitated the development of Takotsubo cardiomyopathy.

Effects of the COVID-19 booster vaccine on glycemia and insulin resistance in people with type 1 diabetes: A prospective pilot study.

AIMS: Inflammation can trigger hyperglycemia in people with type 1 diabetes (T1D). Vaccines purposefully intend to cause an acute immunogenic response, and booster vaccines may cause even more potent immunologic responses. However, the effects of vaccines on glycemic control and insulin requirements in the days immediately post-vaccination remains poorly understood. The aim of this study was to examine the changes in glycemic control and insulin usage immediately preceding and following a COVID-19 booster vaccine among adults with T1D. METHODS: In this prospective cohort study of adults with T1D, participants wore blinded Dexcom G6 Pro continuous glucose monitors for 10 days. After a baseline period, participants received a COVID-19 booster vaccine, and subsequent changes in glycemic indices were evaluated. RESULTS: Among the 21 enrolled participants, 38% received a Moderna and 62% Pfizer-BioNTech booster. Compared to baseline (162.9 ± 44.1 mg/dL), mean glucose was significantly increased at Day 2 (172.8 ± 47.0 mg/dL; p = 0.04) and Day 3 (173.1 ± 45.0 mg/dL; p = 0.02) post-vaccination. Insulin resistance was also increased on Day 2 (p = 0.03). There were no differences in outcome metrics between booster vaccine manufacturers. CONCLUSIONS: These results suggest that adults with type 1 diabetes may experience transient mild glycemic elevations after receiving a COVID-19 booster vaccination. Studies examining the effects of other vaccines are warranted.

Increasing Frequency of Hemoglobin A1C Measurements in Hospitalized Patients With Diabetes: A Quality Improvement Project Using Lean Six Sigma.

BACKGROUND: The American Diabetes Association (ADA) recommends measuring A1C in all inpatients with diabetes if not performed in the prior three months. Our objective was to determine the impact of utilizing Lean Six Sigma to increase the frequency of A1C measurements in hospitalized patients. METHODS: We evaluated inpatients with diabetes mellitus consecutively admitted in a community hospital between January 2016 and June 2021, excluding those who had an A1C in the electronic health record (EHR) in the previous three months. Lean Six Sigma was utilized to define the extent of the problem and devise solutions. The intervention bundle delivered between November 2017 and February 2018 included (1) provider education on the utility of A1C, (2) more rapid turnaround of A1C results, and (3) an EHR glucose-management tab and insulin order set that included A1C. Hospital encounter and patient-level data were extracted from the EHR via bulk query. Frequency of A1C measurement was compared before (January 2016-November 2017) and after the intervention (March 2018-June 2021) using χ2 analysis. RESULTS: Demographics did not differ preintervention versus postintervention (mean age [range]: 70.9 [18-104] years, sex: 52.2% male, race: 57.0% white). A1C measurements significantly increased following implementation of the intervention bundle (61.2% vs 74.5%, P < .001). This level was sustained for more than two years following the initial intervention. Patients seen by the diabetes consult service (40.4% vs 51.7%, P < 0.001) and length of stay (mean: 135 hours vs 149 hours, P < 0.001) both increased postintervention. CONCLUSIONS: We demonstrate a novel approach in improving A1C in hospitalized patients. Lean Six Sigma may represent a valuable methodology for community hospitals to improve inpatient diabetes care.

Instituting a Successful Discharge Plan for Patients With Type 2 Diabetes: Challenges and Solutions.

Achieving target inpatient glycemic management outcomes has been shown to influence important clinical outcomes such as hospital length of stay and readmission rates. However, arguably the most profound, lasting impact of inpatient diabetes management is achieved at the time of discharge-namely reconciling and prescribing the right medications and making referrals for follow-up. Discharge planning offers a unique opportunity to break through therapeutic inertia, offer diabetes self-management education, and institute an individualized treatment plan that prepares the patient for discharge and promotes self-care and engagement. However, the path to a successful discharge plan can be fraught with potential pitfalls for clinicians, including lack of knowledge and experience with newer diabetes medications, costs, concerns over insurance coverage, and lack of time and resources. This article presents an algorithm to assist clinicians in selecting discharge regimens that maximize benefits and reduce barriers to self-care for patients and a framework for creating an interdisciplinary hospital diabetes discharge program.

Centers for Medicare & Medicaid Services' Hospital Harm Measures for Severe Hypoglycemia and Hyperglycemia: Is Your Hospital Ready?

Poor inpatient glycemic management is associated with increased lengths of stay and in-hospital morbidity and mortality. Improving inpatient glycemic outcomes can be difficult because there are no standardized benchmarks, and many hospitals lack the capacity to electronically extract and analyze glucose data. The Centers for Medicare & Medicaid Services recently proposed new electronic clinical quality measures to be incorporated into its mandatory Hospital Inpatient Quality Reporting Program. Among these measures is an assessment of hospital harm from severe hypoglycemia and severe hyperglycemia. Hospitals must be ready to collect the necessary data for these new measures by January 2023. The new measures could bring welcome attention to the need to implement guideline-based inpatient glycemic management. However, some hospitals that serve high-risk populations may be at risk for losing funding if they are unable to comply.

Primary adrenal insufficiency.

Primary adrenal insufficiency is a potentially life-threatening condition. We report a case of a 49-year-old female patient who presented to the hospital for evaluation of dizziness, nausea, and vomiting. Darkening of the palmar creases and tongue was noted. The adrenocorticotropic hormone stimulation test confirmed the diagnosis of adrenal insufficiency.

Diabetes-related Bias in Electronic Health Records and International Classification of Diseases.

Diabetes mellitus is a chronic health condition that affects the body's ability to convert food into energy. People living with diabetes, as well as doctors and hospitals, struggle to handle the challenge. Among these challenges is that the field of diabetology is filled with bias. People living with diabetes will say that "diabetes does not define them," yet they often refer to themselves as "diabetics." Doctors are frequently "trained" to call people "diabetics," and I am one of them. Psychological consequences associated with diabetes and obesity bias and stigma have been previously reported. People with diabetes may experience stigma and may blame themselves for causing their condition. They may have restricted opportunities in life and being subject to negative stereotyping. Importantly, obesity stigma has been recognized as a barrier to comprehensive and effective type 2 diabetes management. Electronic Health Records and the International Classification of Diseases are filled with diabetes-related bias. The word "diabetic" is frequently mentioned. Healthcare providers should recognize the person first, and not their medical condition. Changing behavior takes time, especially as this is a collective phenomenon. This commentary proposes the steps needed to be taken to overcome the challenge of behavior change and offers a personal reflection on the subject.

Expanding legal treatment options for medical marijuana in the State of Louisiana.

Background: The use of cannabis for 'medical' purposes has expanded throughout the USA. Despite the limited peer-reviewed medical research, medical marijuana therapy has been to treat chronic pain, stimulate appetite, treat nausea, and ameliorate muscle spasticity. Challenge: In the state of Louisiana, this potential treatment is strictly controlled. The ability of the individual patient to receive this therapy is limited since any prescribing provider had to be both licensed by the state medical board and registered with the board to prescribe medical marijuana. Medical cannabis could be used only for limited medical disorders. The 'Medical Marijuana' HB819 bill authorizes the recommendation of medical marijuana for additional conditions and allows any state-licensed physician to recommend/prescribe medical marijuana. Alternative options: The government may consider working with the state medical board to lessen its regulation allowing a collaborative effort to formalize protocols for safe prescribing of medical marijuana. A more liberal option would be to make it available to the consumer over the counter, while a state tracking mechanism is set in place to limit the amount purchased. Conclusions: Two stakeholders pertaining to this new legislation to focus on are the Louisiana State government and healthcare providers. This law probably has the biggest impact on healthcare providers and their relationship to patients. This legislation may allow providers to have more 'freedom in medical marijuana treatment plans'. These benefits would be monitored using such criteria as cost, access to care, as well as patient and healthcare provider satisfaction.

Haven Health is About to Disrupt the U.S. Healthcare System.

Background: Healthcare costs are increasing in the U.S. Healthcare market is fragmented and opaque. The Amazon, JP Morgan Chase, and Berkshire Hathaway partnered to form an independent healthcare company 'Haven', designed to support cost-containment in health care and to forge a better patient experience. Limited information is available in the public domain about Haven's strategy to deliver cost-effective healthcare for their employees. Objective: To describe the impact of Haven Health of modern healthcare. Methods: We reviewed literature in Pubmed (MEDLINE database of references and abstracts on life sciences and biomedical topics from the USA National Library of Medicine at the National Institutes of Health) and the public domain. Results: Amazon's expertise and abilities in e-commerce such as logistics, supply, and big data management will support Haven's quest to resolve inefficiencies in health care. Haven may attempt to directly negotiate with providers, bypassing insurance companies, to establish themselves as an independent payor. Additionally, Haven to establish themselves as a provider by building new low-cost primary care clinics, focusing on chronic disease prevention. The new healthcare system may leverage machine learning, artificial intelligence and big data analysis to support its initiatives and other 'big data' analytics to drive it all. Conclusions: Heaven Health may use its expertise to disrupt everything from the pharmaceutical supply chain to primary care and telehealth. More research is needed to evaluate the impact of Haven Healthcare on disease outcomes and healthcare costs.

Lower hair cortisol among patients with sickle cell disease may indicate decreased adrenal reserves.

INTRODUCTION: Sickle cell disease (SCD) is a chronic illness that presents with a wide range of phenotypic variation. Stress may be a contributing factor to differences that are found in this population. OBJECTIVES: Our objective is to determine the relationship between hair cortisol content (HCC), a biomarker of stress, and other clinical measures in individuals with SCD. METHODS: We collected hair samples and other clinical measures from 73 subjects with SCD (mean age: 39 ± 12 years, 63% female). RESULTS: HCC was lower among individuals who had greater than 30% hemoglobin S, compared with those who had less than 30% hemoglobin S (W=272.5, P=0.01). Lower HCC was also associated with report of not being on a chronic transfusion program (ß=48.34, SE=14.09, P=0.001) and higher ferritin levels (ß=-0.006, SE=0.002, P=0.02). Furthermore, HCC was significantly correlated with serum cortisol (rs=0.26, P=0.03) and corticosterone (rs=0.29, P=0.01). We also observed a consistent pattern of low steroid values among our population. CONCLUSION: Our findings suggest that individuals with higher hemoglobin S and ferritin, both markers of severe SCD, may have decreased cortisol levels. This is consistent with the relationship we observed between higher HCC among individuals who are on a chronic blood transfusion program, which typically increases quality of life. Our results suggest that hair cortisol may be an indicator in patients with SCD who could be at risk for developing adrenal insufficiency. We recommend that clinicians treating patients with SCD follow the Endocrine Society guidelines for testing for adrenal insufficiency and treat accordingly.

The Endocrine Hospitalist: Enhancing the Quality of Diabetes Care.

The endocrine hospitalist and inpatient diabetes management team increases access to endocrinology consultations and improves glycemic control and quality metrics such as length of stay and hospital readmission. Enhanced glycemic care is needed in both academic and community hospital settings. Endocrine fellowship programs should implement endocrine hospitalist rotations with emphasis on training endocrine fellows to deliver fast-paced inpatient endocrine care. Entrepreneurship, innovation, and a "start-up" culture within the field of Endocrinology should be encouraged and supported by healthcare systems.

Inpatient Insulin Pen Implementation, Waste, and Potential Cost Savings: A Community Hospital Experience.

BACKGROUND: Insulin pen injectors ("pens") are intended to facilitate a patient's self-administration of insulin and can be used in hospitalized patients as a learning opportunity. Unnecessary or duplicate dispensation of insulin pens is associated with increased healthcare costs. METHODS: Inpatient dispensation of insulin pens in a 240-bed community hospital between July 2018 and July 2019 was analyzed. We calculated the percentage of insulin pens unnecessarily dispensed for patients who had the same type of insulin pen assigned. The estimated cost of insulin pen waste was calculated. A pharmacist-led task force group implemented hospital-wide awareness and collaborated with hospital leadership to define goals and interventions. RESULTS: 9516 insulin pens were dispensed to 3121 patients. Of the pens dispensed, 6451 (68%) were insulin aspart and 3065 (32%) were glargine. Among patients on insulin aspart, an average of 2.2 aspart pens was dispensed per patient, but only an estimated 1.2 pens/patient were deemed necessary. Similarly, for inpatients prescribed glargine, an average of 2.1 pens/patient was dispensed, but only 1.3 pens/patient were necessary. A number of gaps were identified and interventions were undertaken to reduce insulin pen waste, which resulted in a significant decrease in both aspart (p = 0.0002) and glargine (p = 0.0005) pens/patient over time. Reductions in pen waste resulted in an estimated cost savings of $66 261 per year. CONCLUSIONS: In a community hospital setting, identification of causes leading to unnecessary insulin dispensation and implementation of hospital-wide staff education led to change in insulin pen dispensation practice. These changes translated into considerable cost savings and facilitated diabetes self-management education.

Retrospective Quality Improvement Study of Insulin-Induced Hypoglycemia and Implementation of Hospital-Wide Initiatives.

BACKGROUND: Hospitalized patients who are receiving antihyperglycemic agents are at increased risk for hypoglycemia. Inpatient hypoglycemia may lead to increased risk for morbidity, mortality, prolonged hospitalization, and readmission within 30 days of discharge, which in turn may lead to increased costs. Hospital-wide initiatives targeting hypoglycemia are known to be beneficial; however, their impact on patient care and economic measures in community nonteaching hospitals are unknown. METHODS: This retrospective quality improvement study examined the effects of hospital-wide hypoglycemia initiatives on the rates of insulin-induced hypoglycemia in a community hospital setting from January 1, 2016, until September 30, 2019. The potential cost of care savings has been calculated. RESULTS: Among 49 315 total patient days, 2682 days had an instance of hypoglycemia (5.4%). Mean ± SD hypoglycemic patient days/month was 59.6 ± 16.0. The frequency of hypoglycemia significantly decreased from 7.5% in January 2016 to 3.9% in September 2019 (P = .001). Patients with type 2 diabetes demonstrated a significant decrease in the frequency of hypoglycemia (7.4%-3.8%; P < .0001), while among patients with type 1 diabetes the frequency trended downwards but did not reach statistical significance (18.5%-18.0%; P = 0.08). Based on the reduction of hypoglycemia rates, the hospital had an estimated cost of care savings of $98 635 during the study period. CONCLUSIONS: In a community hospital setting, implementation of hospital-wide initiatives targeting hypoglycemia resulted in a significant and sustainable decrease in the rate of insulin-induced hypoglycemia. These high-leverage risk reduction strategies may be translated into considerable cost savings and could be implemented at other community hospitals.

Effects of a Dedicated Inpatient Diabetes Management Service on Glycemic Control in a Community Hospital Setting.

BACKGROUND: Community hospitals account for over 84% of all hospitals and over 94% of hospital admissions in the United States. In academic settings, implementation of an Inpatient Diabetes Management Service (IDMS) model of care has been shown to reduce rates of hyper- and hypoglycemia, hospital length of stay (LOS), and associated hospital costs. However, few studies to date have evaluated the implementation of a dedicated IDMS in a community hospital setting. METHODS: This retrospective study examined the effects of changing the model of inpatient diabetes consultations from a local, private endocrine practice to a full-time endocrine hospitalist on glycemic control, LOS, and 30-day readmission rates in a 267-bed community hospital. RESULTS: Overall diabetes patient days for the hospital were similar pre- and post-intervention (20,191 vs 20,262); however, the volume of patients seen by IDMS increased significantly after changing models. Rates of hyperglycemia decreased both among patients seen by IDMS (53.8% to 42.5%, P < .0001) and those not consulted on by IDMS (33.2% to 29.9%; P < .0001). When examined over time, rates of hypoglycemia steadily decreased in the 24 months after dedicated IDMS initiation (P = .02); no such time effect was seen prior to IDMS (P = .34). LOS and 30DRR were not significantly different between IDMS models. CONCLUSIONS: Implementation of an endocrine hospitalist-based IDMS at a community hospital was associated with significantly decreased hyperglycemia, while avoiding concurrent increases in hypoglycemia. Further studies are needed to investigate whether these effects are associated with improvements in clinical outcomes, patient or staff satisfaction scores, or total cost of care.

The Financial Impact of an Inpatient Diabetes Management Service.

CONTEXT: Diabetes is a leading metabolic disorder with a substantial cost burden, especially in inpatient settings. The complexity of inpatient glycemic management has led to the emergence of inpatient diabetes management service (IDMS), a multidisciplinary team approach to glycemic management. OBJECTIVE: To review recent literature on the financial and clinical impact of IDMS in hospital settings. METHODS: We searched PubMed using a combination of controlled vocabulary and keyword terms to describe the concept of IDMS and combined the search terms with a comparative effectiveness filter for costs and cost analysis developed by the National Library of Medicine. FINDINGS: In addition to several improved clinical endpoints such as glycemic management outcomes, IDMS implementation is associated with hospital cost savings through decreased length of stay, preventing hospital readmissions, hypoglycemia reduction, and optimizing resource allocation. There are other downstream potential cost savings in long-term patient health outcomes and avoidance of litigation related to suboptimal glycemic management. CONCLUSION: IDMS may play an important role in helping both academic and community hospitals to improve the quality of diabetes care and reduce costs. Clinicians and policymakers can utilize existing literature to build a compelling business case for IDMS to hospital administrations and state legislatures in the era of value-based healthcare.

Development and Validation of a Machine Learning Model to Predict Near-Term Risk of Iatrogenic Hypoglycemia in Hospitalized Patients.

Importance: Accurate clinical decision support tools are needed to identify patients at risk for iatrogenic hypoglycemia, a potentially serious adverse event, throughout hospitalization. Objective: To predict the risk of iatrogenic hypoglycemia within 24 hours after each blood glucose (BG) measurement during hospitalization using a machine learning model. Design, Setting, and Participants: This retrospective cohort study, conducted at 5 hospitals within the Johns Hopkins Health System, included 54 978 admissions of 35 147 inpatients who had at least 4 BG measurements and received at least 1 U of insulin during hospitalization between December 1, 2014, and July 31, 2018. Data from the largest hospital were split into a 70% training set and 30% test set. A stochastic gradient boosting machine learning model was developed using the training set and validated on internal and external validation. Exposures: A total of 43 clinical predictors of iatrogenic hypoglycemia were extracted from the electronic medical record, including demographic characteristics, diagnoses, procedures, laboratory data, medications, orders, anthropomorphometric data, and vital signs. Main Outcomes and Measures: Iatrogenic hypoglycemia was defined as a BG measurement less than or equal to 70 mg/dL occurring within the pharmacologic duration of action of administered insulin, sulfonylurea, or meglitinide. Results: This cohort study included 54 978 admissions (35 147 inpatients; median [interquartile range] age, 66.0 [56.0-75.0] years; 27 781 [50.5%] male; 30 429 [55.3%] White) from 5 hospitals. Of 1 612 425 index BG measurements, 50 354 (3.1%) were followed by iatrogenic hypoglycemia in the subsequent 24 hours. On internal validation, the model achieved a C statistic of 0.90 (95% CI, 0.89-0.90), a positive predictive value of 0.09 (95% CI, 0.08-0.09), a positive likelihood ratio of 4.67 (95% CI, 4.59-4.74), a negative predictive value of 1.00 (95% CI, 1.00-1.00), and a negative likelihood ratio of 0.22 (95% CI, 0.21-0.23). On external validation, the model achieved C statistics ranging from 0.86 to 0.88, positive predictive values ranging from 0.12 to 0.13, negative predictive values of 0.99, positive likelihood ratios ranging from 3.09 to 3.89, and negative likelihood ratios ranging from 0.23 to 0.25. Basal insulin dose, coefficient of variation of BG, and previous hypoglycemic episodes were the strongest predictors. Conclusions and Relevance: These findings suggest that iatrogenic hypoglycemia can be predicted in a short-term prediction horizon after each BG measurement during hospitalization. Further studies are needed to translate this model into a real-time informatics alert and evaluate its effectiveness in reducing the incidence of inpatient iatrogenic hypoglycemia.

Volumetric Modeling of Adrenal Gland Size in Primary Bilateral Macronodular Adrenocortical Hyperplasia.

CONTEXT: Radiological characterization of adrenal size in primary bilateral macronodular adrenocortical hyperplasia (PBMAH) has not been previously investigated. OBJECTIVE: We hypothesized that volumetric modeling of adrenal gland size may correlate with biochemical disease severity in patients with PBMAH. Secondary analysis of patients with concurrent primary aldosteronism (PA) was performed. DESIGN: A retrospective cross-sectional analysis of 44 patients with PBMAH was conducted from 2000 to 2019. SETTING: Tertiary care clinical research center. PATIENTS: Patients were diagnosed with PBMAH based upon clinical, genetic, radiographic and biochemical characteristics. INTERVENTION: Clinical, biochemical, and genetic data were obtained. Computed tomography scans were used to create volumetric models by manually contouring both adrenal glands in each slice using Vitrea Core Fx v6.3 software (Vital Images, Minnetonka, Minnesota). MAIN OUTCOME AND MEASURES: 17-hydroxycorticosteroids (17-OHS), ARMC5 genetics, and aldosterone-to-renin ratio (ARR) were retrospectively obtained. Pearson test was used for correlation analysis of biochemical data with adrenal volume. RESULTS: A cohort of 44 patients with PBMAH was evaluated, with a mean age (±SD) of 53 ±â€…11.53. Eight patients met the diagnostic criteria for PA, of whom 6 (75%) were Black. In the Black cohort, total adrenal volumes positively correlated with midnight cortisol (R = 0.76, P = 0.028), urinary free cortisol (R = 0.70, P = 0.035), and 17-OHS (R = 0.87, P = 0.0045), with a more pronounced correlation with left adrenal volume alone. 17-OHS concentration positively correlated with total, left, and right adrenal volume in patients harboring pathogenic variants in ARMC5 (R = 0.72, P = 0.018; R = 0.65, P = 0.042; and R = 0.73, P = 0.016, respectively). CONCLUSIONS: Volumetric modeling of adrenal gland size may associate with biochemical severity in patients with PBMAH, with particular utility in Black patients.