Factors Associated With Discordant A1C-Estimated and Measured Average Glucose Among Hospitalized Patients With Diabetes.

In this retrospective analysis, we explored the correlation between measured average glucose (mAG) and A1C-estimated average glucose (eAG) in hospitalized patients with diabetes and identified factors associated with discordant mAG and eAG at the transition from home to hospital. Having mAG lower than eAG was associated with Black race, other race, increasing length of stay, community hospital setting, surgery, fever, metformin use, certain inpatient diets, home antihyperglycemic treatment, and coded type 1 or type 2 diabetes. Having mAG higher than eAG was associated with certain discharge services (e.g., intensive care unit), higher BMI, hypertension, tachycardia, higher albumin, higher potassium, anemia, inpatient glucocorticoid use, and treatment with home insulin, secretagogues, and glucocorticoids. These factors should be considered when using patients' A1C as an indicator of outpatient glycemic control to determine the inpatient antihyperglycemic regimens.

Validation of Diagnostic Coding for Diabetes Mellitus in Hospitalized Patients.

OBJECTIVE: Some studies have shown that there is an undercoding of diabetes mellitus among hospitalized patients, which can have adverse clinical and financial implications for health systems. We aimed to validate the discharge diagnostic coding of diabetes mellitus in hospitalized patients using clinical and laboratory-based diagnostic indicators as the reference. METHODS: This was a retrospective cohort study of 83 690 discharges of 48 615 unique adult patients who were hospitalized in an academic medical center over 4.5 years and had at least 4 blood glucose measurements during admission. A missing diabetes code (MDC) was defined using 2 criteria. MDC1 was defined as the presence of any of the following: blood glucose ≥200 (x2), A1C ≥6.5%, home antihyperglycemic medication, or preadmission code for diabetes, whereas MDC2 was defined as preadmission diabetes or at least 2 other criteria in MDC1. Multivariable logistic regression was used to identify factors associated with MDC compared to the present diabetes code. RESULTS: MDC1 and MDC2 were present in 12 186 (14.6%) and 3542 (4.7%) discharges, respectively. Factors associated with both MDC1 and MDC2 were medium-dose steroid use [adjusted odds ratio (aOR) 2.11, 2.01], high-dose steroid use (aOR 4.70, 2.50), intermediate medical care service (aOR 1.65, 1.55), infection (aOR 1.21, 1.34), and hepatic disease (aOR 1.93, 1.92). CONCLUSION: In this retrospective study, MDC ranged from 5% to 15% and was associated with various clinical factors. Further prospective studies are needed to validate these findings, explore the mechanisms behind these associations, and understand the clinical and financial implications.

Glycemic Outcomes of Hospitalized Patients on Ambulatory Humulin-R U-500 Insulin.

OBJECTIVE: Managing hospitalized patients on ambulatory U-500 insulin is challenging because of limited guidance on how to safely adjust insulin doses during admission. We sought to evaluate glycemic outcomes in relation to inpatient insulin doses in patients receiving U-500 prior to hospitalization. METHODS: Retrospective study of hospitalized patients on ambulatory U-500 seen consecutively from January 2015 to December 2019. Primary outcomes were inpatient hypoglycemia, hyperglycemia, and normoglycemia at different insulin dosages expressed as weight-based (unit/kg/d) inpatient total daily dose (TDD) and ratio of inpatient to outpatient TDD. RESULTS: We identified 66 admissions of 46 unique patients. The median (interquartile range) body mass index was 41.0 kg/m2 (35.1, 46.8), home TDD 212 units (120, 300), and home insulin dose 1.6 units/kg/d (1.1, 2.2). The median (interquartile range) inpatient insulin dose was 0.7 unit/kg/d (0.3, 1.0) and the ratio of inpatient to outpatient TDD was 0.4 (0.2, 0.8). Hyperglycemia persisted throughout the hospitalization. For the outcomes of hyperglycemia and normoglycemia, we found no association between increased levels of insulin dosages. For the outcome of hypoglycemia, significantly higher odds were observed when non-fasting patients received an inpatient TDD that was either > 40% of their home TDD or > 0.6 unit/kg/d of insulin. CONCLUSION: Patients on ambulatory U-500 have significant hyperglycemia during admission. Inpatient insulin doses of 40% of home TDD or ≤ 0.6 unit/kg were not associated with increased hypoglycemia risk. Further prospective studies are needed to determine effective doses in these high-risk patients.

Insulin Dosing and Glycemic Outcomes Among Steroid-treated Hospitalized Patients.

OBJECTIVE: To determine the optimal insulin-to-steroid dose ratio for the attainment of glycemic control in hospitalized patients. METHODS: We retrospectively studied data collected from the electronic health records within an academic medical center from 18 599 patient-days where patients were treated concurrently with insulin and steroids. Multivariate logistic regression analyses, which included demographic and clinical variables, were performed to assess the relationships between the exposures of total and basal insulin-to-steroid ratios and the outcomes of glycemic control (all blood glucose readings on the following patient-day were >70 and ≤180 mg/dL) and hypoglycemia within 3 subgroups of steroid dosing: low (≤10-mg prednisone equivalent dose [PED]), medium (from >10-mg to ≤40-mg PED), and high (>40-mg PED). RESULTS: Increased insulin-to-steroid ratio was associated with increased odds of both glycemic control and hypoglycemia. The optimal total insulin-to-steroid ratio for attaining glycemic control was 0.294 U/kg/10-mg PED in the low-dose subgroup, 0.257 U/kg/10-mg PED in the medium-dose subgroup, and 0.085 U/kg/10-mg PED in the high-dose subgroup. The optimal basal insulin-to-steroid ratio was 0.215 U/kg/10-mg PED in the low-dose subgroup, 0.126 U/kg/10-mg PED in the medium-dose subgroup, and 0.036 U/kg/10-mg PED in the high-dose subgroup. CONCLUSION: Increasing insulin-to-steroid ratios are positively associated with glycemic control and hypoglycemia. Our study suggests that approximately 0.3 U/kg/10-mg PED is an optimal dose for low- and medium-dose steroids, whereas approximately 0.1 U/kg/10-mg PED is an optimal dose for high-dose steroids. Further prospective studies are needed to identify insulin regimens that will optimize glycemic control in steroid-treated patients while minimizing the risk of hypoglycemia.

Provider Response to Critical Action Values for Hypoglycemia in the Ambulatory Setting: a Retrospective Cohort Study.

BACKGROUND: The blood glucose level triggering a critical action value (CAV) for hypoglycemia is not standardized, and associated outcomes are unknown. OBJECTIVE: To evaluate the clinical consequences of, and provider responses to, CAVs for hypoglycemia. DESIGN: Retrospective cohort study at Johns Hopkins Hospital and Johns Hopkins Bayview Medical Center between April 1, 2013, and January 31, 2017. PARTICIPANTS: Patients with an ambulatory serum glucose < 50 mg/dL. Point-of-care capillary glucose and whole blood glucose samples were excluded. MAIN MEASURES: Electronic medical record (EMR) review for providers' documented response to CAV, associated patient symptoms, and serious adverse events. KEY RESULTS: We analyzed 209 CAVs for hypoglycemia from 154 patients. The median age (IQR) was 59 years (46, 69), 89 (57.8%) were male, and 96 (62.3%) were black. Provider-to-patient contact occurred in 128 of 209 (61.2%) episodes, among which no documented etiology was observed for 81 of 128 (63.3%), no recommendations were provided in 32 of 128 (25.0%), and no patient-reported hypoglycemic symptoms were documented in 103 of 128 (80.5%). Serious adverse events were documented in 4 of 128 episodes (3.1%), two required glucagon administration, and three required an ED visit. Provider-to-patient contact was associated with the patient having malignant neoplasm (adjusted OR 3.63, p = 0.045) or a hypoglycemic disorder (adjusted OR 7.70, p = 0.018) and inversely associated with a longer time from specimen collection to EMR result (adjusted OR 0.90 per hour, p = 0.016). CONCLUSIONS: There is inconsistent provider-to-patient contact following CAVs for hypoglycemia, and the etiology and symptoms of hypoglycemia were infrequently documented. There were few serious documented adverse events associated with hypoglycemia, although undocumented events may have occurred, and the incidence of serious adverse events in non-contacted patients remains unknown. These findings demonstrate a need to standardize provider response to CAVs for hypoglycemia. Decreasing the lag time between sample collection and laboratory result reporting may increase provider-to-patient contact.

Peroral endoscopic myotomy: anterior versus posterior approach: a randomized single-blinded clinical trial.

BACKGROUND AND AIMS: Peroral endoscopic myotomy (POEM) has become the mainstay for the treatment of achalasia at many institutions around the world since its inception in 2008. POEM can be performed using either the anterior or posterior approach. The primary aim of this study was to compare the efficacy of the anterior and posterior approaches at 1 year after POEM. METHODS: This is a single-blinded, randomized, noninferiority international clinical trial. Eligible participants were adult patients with a confirmed diagnosis of achalasia via high-resolution esophageal manometry. Patients were randomly allocated with a 1:1 ratio to receive POEM with anterior or posterior approach. The primary aim was to compare the rate of clinical success (Eckardt score <3) of anterior and posterior approaches at 1 year. RESULTS: One hundred fifty patients were randomized to receive either anterior (n = 73) or posterior (n = 77) POEM. One hundred forty-eight patients received the POEM treatment, and 138 patients completed the 1-year follow-up and were included in the primary efficacy analysis. Technical success was achieved in 71 patients (97.3%) in the anterior group versus 77 patients (100%) in the posterior group (P = .23). The median (interquartile range) length of hospital stay after the procedure was 2 (1-3) days for both groups. Adverse events occurred in 15 patients (10%), 8 patients (11%) in the anterior group and 7 patients (9%) in the posterior group (P = .703). Clinical success was achieved in 90% of patients in the anterior group and 89% of patients in the posterior group. Abnormal esophageal acid exposure was detected in 29 of 59 patients (49%) and 25 of 60 patients (42%) in the anterior and posterior groups, respectively (P = .67). GERD questionnaire scores were also not significantly different between the study groups. In both groups, quality of life improved after POEM for all 36-Item Short-Form Health Survey measures and was similar between both groups. CONCLUSIONS: Posterior myotomy during POEM was not inferior to anterior myotomy in terms of efficacy and safety in the treatment of patients with achalasia. (Clinical trial registration number: NCT02454335.).

Glycemic Control in Diabetic Foot Ulcers: A Comparative Analysis of Wound and Wound-free Periods in Adults With Type 1 and Type 2 Diabetes.

OBJECTIVE: Our aim in this study was to determine whether there are differences in glycemia during wound and wound-free states among individuals with diabetes at a multidisciplinary diabetic foot and wound clinic from 2012 to 2019. METHODS: We conducted a retrospective analysis of prospectively collected data over 7.4 years from the Johns Hopkins Multidisciplinary Diabetic Foot and Wound Clinic. Participants with diabetic foot ulcers were observed during at least one wound period and one wound-free period and had at least one glycated hemoglobin (A1C) measurement in both a wound and wound-free period. The A1C measurements were aggregated and summarized across wound and wound-free periods, and compared using the Wilcoxon matched-pairs signed rank test. RESULTS: Two hundred six eligible participants with a total of 623 wounds were included in this analysis. Participants were followed for a median period of 2.4 years (876 days). There were no significant differences in mean, minimum, and maximum A1C between the aggregate wound and wound-free periods, with median values of 7.6% (interquartile range [IQR] 6.6% to 9.1%) and 7.5% (IQR 6.6% to 9.1%) for mean A1C (p=0.43), 6.9% (IQR 6.0% to 8.0%) and 6.8% (6.0% to 8.1%) for minimum A1C (p=0.78), and 8.6% (IQR 7.1% to 10.9%) and 8.5% (IQR 7.0% to 10.7%) for maximum A1C (p=0.06) in the wound and wound-free periods, respectively. CONCLUSIONS: This retrospective study shows similar levels of A1C during wound and wound-free periods; however, given the limitations of missing A1C and small sample size, further studies leveraging continuous glucose monitoring are needed to determine whether glycemia worsens in the setting of a DFU.

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.

BACKGROUND: Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established effective intervention for diabetes prevention. However, participation in this 12-month lifestyle change program has historically been low. Digital DPPs have emerged as a scalable alternative, accessible asynchronously and recognized by the Centers for Disease Control and Prevention (CDC). Yet, most digital programs still incorporate human coaching, potentially limiting scalability. Furthermore, existing effectiveness results of digital DPPs are primarily derived from per protocol, longitudinal non-randomized studies, or comparisons to control groups that do not represent the standard of care DPP. The potential of an AI-powered DPP as an alternative to the DPP is yet to be investigated. We propose a randomized controlled trial (RCT) to directly compare these two approaches. METHODS: This open-label, multicenter, non-inferiority RCT will compare the effectiveness of a fully automated AI-powered digital DPP (ai-DPP) with a standard of care human coach-based DPP (h-DPP). A total of 368 participants with elevated body mass index (BMI) and prediabetes will be randomized equally to the ai-DPP (smartphone app and Bluetooth-enabled body weight scale) or h-DPP (referral to a CDC recognized DPP). The primary endpoint, assessed at 12 months, is the achievement of the CDC's benchmark for type 2 diabetes risk reduction, defined as any of the following: at least 5% weight loss, at least 4% weight loss and at least 150 min per week on average of physical activity, or at least a 0.2-point reduction in hemoglobin A1C. Physical activity will be objectively measured using serial actigraphy at baseline and at 1-month intervals throughout the trial. Secondary endpoints, evaluated at 6 and 12 months, will include changes in A1C, weight, physical activity measures, program engagement, and cost-effectiveness. Participants include adults aged 18-75 years with laboratory confirmed prediabetes, a BMI of ≥ 25 kg/m2 (≥ 23 kg/m2 for Asians), English proficiency, and smartphone users. This U.S. study is conducted at Johns Hopkins Medicine in Baltimore, MD, and Reading Hospital (Tower Health) in Reading, PA. DISCUSSION: Prediabetes is a significant public health issue, necessitating scalable interventions for the millions affected. Our pragmatic clinical trial is unique in directly comparing a fully automated AI-powered approach without direct human coach interaction. If proven effective, it could be a scalable, cost-effective strategy. This trial will offer vital insights into both AI and human coach-based behavioral change strategies in real-world clinical settings. TRIAL REGISTRATION: ClinicalTrials.gov NCT05056376. Registered on September 24, 2021, https://clinicaltrials.gov/study/NCT05056376.

Use of Diabetes Technologies and Retinopathy in Adults With Type 1 Diabetes.

Importance: Diabetic retinopathy (DR) is a complication of diabetes that can lead to vision loss. Outcomes of continuous glucose monitoring (CGM) and insulin pump use in DR are not well understood. Objective: To assess the use of CGM, insulin pump, or both, and DR and proliferative diabetic retinopathy (PDR) in adults with type 1 diabetes (T1D). Design, Setting, and Participants: A retrospective cohort study of adults with T1D in a tertiary diabetes center and ophthalmology center was conducted from 2013 to 2021, with data analysis performed from June 2022 to April 2023. Exposure: Use of diabetes technologies, including insulin pump, CGM, and both CGM and insulin pump. Main Outcomes and Measures: The primary outcome was development of DR or PDR. A secondary outcome was the progression of DR for patients in the longitudinal cohort. Multivariable logistic regression models assessed for development of DR and PDR and association with CGM and insulin pump use. Results: A total of 550 adults with T1D were included (median age, 40 [IQR, 28-54] years; 54.4% female; 24.5% Black or African American; and 68.4% White), with a median duration of diabetes of 20 (IQR, 10-30) years, and median hemoglobin A1c (HbA1c) of 7.8% (IQR, 7.0%-8.9%). Overall, 62.7% patients used CGM, 58.2% used an insulin pump, and 47.5% used both; 44% (244 of 550) of the participants had DR at any point during the study. On univariate analysis, CGM use was associated with lower odds of DR and PDR, and CGM with pump was associated with lower odds of PDR (all P < .05), compared with no CGM use. Multivariable logistic regression adjusting for age, sex, race and ethnicity, diabetes duration, microvascular and macrovascular complications, insurance type, and mean HbA1c, showed that CGM was associated with lower odds of DR (odds ratio [OR], 0.52; 95% CI, 0.32-0.84; P = .008) and PDR (OR, 0.42; 95% CI, 0.23-0.75; P = .004), compared with no CGM use. In the longitudinal analysis of participants without baseline PDR, 79 of 363 patients (21.8%) had progression of DR during the study. Conclusions and Relevance: In this cohort study of adults with T1D, CGM use was associated with lower odds of developing DR and PDR, even after adjusting for HbA1c. These findings suggest that CGM may be useful for diabetes management to mitigate risk for DR and PDR.

Prediction of Next Glucose Measurement in Hospitalized Patients by Comparing Various Regression Methods: Retrospective Cohort Study.

BACKGROUND: Continuous glucose monitors have shown great promise in improving outpatient blood glucose (BG) control; however, continuous glucose monitors are not routinely used in hospitals, and glucose management is driven by point-of-care (finger stick) and serum glucose measurements in most patients. OBJECTIVE: This study aimed to evaluate times series approaches for prediction of inpatient BG using only point-of-care and serum glucose observations. METHODS: Our data set included electronic health record data from 184,320 admissions, from patients who received at least one unit of subcutaneous insulin, had at least 4 BG measurements, and were discharged between January 1, 2015, and May 31, 2019, from 5 Johns Hopkins Health System hospitals. A total of 2,436,228 BG observations were included after excluding measurements obtained in quick succession, from patients who received intravenous insulin, or from critically ill patients. After exclusion criteria, 2.85% (3253/113,976), 32.5% (37,045/113,976), and 1.06% (1207/113,976) of admissions had a coded diagnosis of type 1, type 2, and other diabetes, respectively. The outcome of interest was the predicted value of the next BG measurement (mg/dL). Multiple time series predictors were created and analyzed by comparing those predictors and the index BG measurement (sample-and-hold technique) with next BG measurement. The population was classified by glycemic variability based on the coefficient of variation. To compare the performance of different time series predictors among one another, R2, root mean squared error, and Clarke Error Grid were calculated and compared with the next BG measurement. All these time series predictors were then used together in Cubist, linear, random forest, partial least squares, and k-nearest neighbor methods. RESULTS: The median number of BG measurements from 113,976 admissions was 12 (IQR 5-24). The R2 values for the sample-and-hold, 2-hour, 4-hour, 16-hour, and 24-hour moving average were 0.529, 0.504, 0.481, 0.467, and 0.459, respectively. The R2 values for 4-hour moving average based on glycemic variability were 0.680, 0.480, 0.290, and 0.205 for low, medium, high, and very high glucose variability, respectively. The proportion of BG predictions in zone A of the Clarke Error Grid analysis was 61%, 59%, 27%, and 53% for 4-hour moving average, 24-hour moving average, 3 observation rolling regression, and recursive regression predictors, respectively. In a fully adjusted Cubist, linear, random forest, partial least squares, and k-nearest neighbor model, the R2 values were 0.563, 0.526, 0.538, and 0.472, respectively. CONCLUSIONS: When analyzing time series predictors independently, increasing variability in a patient's BG decreased predictive accuracy. Similarly, inclusion of older BG measurements decreased predictive accuracy. These relationships become weaker as glucose variability increases. Machine learning techniques marginally augmented the performance of time series predictors for predicting a patient's next BG measurement. Further studies should determine the potential of using time series analyses for prediction of inpatient dysglycemia.

Racial Disparities in Access and Use of Diabetes Technology Among Adult Patients With Type 1 Diabetes in a U.S. Academic Medical Center.

OBJECTIVE: Recent studies highlight racial disparities in insulin pump (PUMP) and continuous glucose monitor (CGM) use in children and adolescents with type 1 diabetes (T1D). This study explored racial disparities in diabetes technology among adult patients with T1D. RESEARCH DESIGN AND METHODS: This was a retrospective clinic-based cohort study of adult patients with T1D seen consecutively from April 2013 to January 2020. Race was categorized into non-Black (reference group) and Black. The primary outcomes were baseline and prevalent technology use, rates of diabetes technology discussions (CGMdiscn, PUMPdiscn), and prescribing (CGMrx, PUMPrx). Multivariable logistic regression analysis evaluated the association of technology discussions and prescribing with race, adjusting for social determinants of health and diabetes outcomes. RESULTS: Among 1,258 adults with T1D, baseline technology use was significantly lower for Black compared with non-Black patients (7.9% vs. 30.3% for CGM; 18.7% vs. 49.6% for PUMP), as was prevalent use (43.6% vs. 72.1% for CGM; 30.7% vs. 64.2% for PUMP). Black patients had adjusted odds ratios (aORs) of 0.51 (95% CI 0.29, 0.90) for CGMdiscn and 0.61 (95% CI 0.41, 0.93) for CGMrx. Black patients had aORs of 0.74 (95% CI 0.44, 1.25) for PUMPdiscn and 0.40 (95% CI, 0.22, 0.70) for PUMPrx. Neighborhood context, insurance, marital and employment status, and number of clinic visits were also associated with the outcomes. CONCLUSIONS: Significant racial disparities were observed in discussions, prescribing, and use of diabetes technology. Further research is needed to identify the causes behind these disparities and develop and evaluate strategies to reduce them.

Association between dysglycemia and the Charlson Comorbidity Index among hospitalized patients with diabetes.

AIM: Inpatient dysglycemia has been linked to short-term mortality, but longer-term mortality data are lacking. Our aim was to evaluate the association between inpatient dysglycemia and one-year mortality risk. METHODS: Retrospective chart review of adults with diabetes hospitalized between 2015 and 2019. The Charlson Comorbidity Index (CCI) was used to estimate 1-year mortality risk, stratified into low (CCI ≤ 5) and high risk (CCI ≥6). Simple and multivariable logistic regression was used to evaluate the association between dysglycemic measures and high mortality risk. RESULTS: Among 22,639 unique admissions, BG ≥ 180, ≥300, ≤70, <54 and <40 mg/dL were associated with adjusted odds of 1.43 (95 % CI, 1.33, 1.54), 1.58 (95 % CI, 1.48, 1.68), 2.16 (95 % CI, 2.01, 2.32), 2.58 (95 % CI, 2.32, 2.86), and 2.56 (95 % CI, 2.19, 2.99) for high mortality risk, respectively. Older age and Black race were positively associated with hyperglycemia and hypoglycemia. Myocardial infarction, congestive heart failure (CHF), and moderate to severe liver disease were most strongly associated with hyperglycemia, while renal disease, CHF, peripheral vascular disease, and peptic ulcer disease were most strongly associated with hypoglycemia. CONCLUSIONS: Inpatient hypoglycemia and hyperglycemia were both positively associated with higher one-year mortality risk, with stronger magnitude of association observed for hypoglycemia. The association appears to be mediated mainly by presence of diabetes-related complications.

Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients.

BACKGROUND: Inpatient glucose management can be challenging due to evolving factors that influence a patient's blood glucose (BG) throughout hospital admission. The purpose of our study was to predict the category of a patient's next BG measurement based on electronic medical record (EMR) data. METHODS: EMR data from 184,361 admissions containing 4,538,418 BG measurements from five hospitals in the Johns Hopkins Health System were collected from patients who were discharged between January 1, 2015 and May 31, 2019. Index BGs used for prediction included the 5th to penultimate BG measurements (N = 2,740,539). The outcome was category of next BG measurement: hypoglycemic (BG  ≤  70 mg/dl), controlled (BG 71-180 mg/dl), or hyperglycemic (BG > 180 mg/dl). A random forest algorithm that included a broad range of clinical covariates predicted the outcome and was validated internally and externally. FINDINGS: In our internal validation test set, 72·8%, 25·7%, and 1·5% of BG measurements occurring after the index BG were controlled, hyperglycemic, and hypoglycemic respectively. The sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·77/0·81, 0·77/0·89, and 0·73/0·91, respectively. On external validation in four hospitals, the ranges of sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·64-0·70/0·80-0·87, 0·75-0·80/0·82-0·84, and 0·76-0·78/0·87-0·90, respectively. INTERPRETATION: A machine learning algorithm using EMR data can accurately predict the category of a hospitalized patient's next BG measurement. Further studies should determine the effectiveness of integration of this model into the EMR in reducing rates of hypoglycemia and hyperglycemia.

Cortisol values during the standard-dose cosyntropin stimulation test: Personal experience with Elecsys cortisol II assay.

Purpose: There has been debate regarding the appropriate cortisol cutoff during the cosyntropin stimulation test (CST) when newer cortisol assays are used. We aimed to evaluate the proper cortisol values during the standard dose CST in patients with normal hypothalamic-pituitary-adrenal (HPA) axis when the Elecsys® Cortisol II assay from Roche Diagnostics is used. Methods: We retrospectively reviewed the medical records of patients evaluated for possible adrenal insufficiency using the standard-dose (250 mcg) CST from January 2018 to December 2020 and eventually judged to have a normal HPA axis. All the CSTs were done in the outpatient setting. Evaluation by an endocrinologist, restrictive exclusion criteria including prior glucocorticoid and opioid use, and lack of glucocorticoid treatment for at least 6 months after the CST was used to define normal HPA axis. The results are reported in the median (range). Results: We identified 63 patients who met the inclusion criteria and were considered to have a normal HPA axis. The median age was 54.7 (27.6-89.1) years; 32 (51%) were female, and 27 (43%) were white. The duration of follow-up after the CST without any glucocorticoid replacement was 13.9 (6.3-43.9) months. Cortisol levels were 21.7 (15.7-29.1) µg/dl and 24.4 (17.9-35.8) µg/dl at 30- and 60-minutes after cosyntropin administration, respectively. The lowest cortisol levels at 30 and 60 minutes for patients with either normal TSH or gonadal axis (n=47) or in whom both axes were normal (n=18) were similar to the ones of the entire cohort. Conclusion: Our study supports using a lower than previously recommended cortisol cutoff value at 30 minutes after Cosyntropin using the Roche Elecsys® Cortisol II assay. The lowest cortisol levels in our cohort were 15.7 and 17.9 µg/dL at 30 and 60 minutes after the CST, respectively. Therefore, it is essential to consider the time of cortisol draw after cosyntropin administration.

Pasireotide use for the treatment of endogenous hyperinsulinemic hypoglycemia refractory to conventional medical therapy: A case report and review of the literature.

Insulinomas are rare neuroendocrine pancreatic tumors that can be associated with severe episodes of hypoglycemia, leading to significant morbidity and mortality. These tumors are often difficult to localize, and hypoglycemia control can be challenging since glucose levels can be resistant to conventional therapies. Pasireotide is a novel somatostatin analog with a high affinity to multiple somatostatin receptors. It has up to 40 times higher affinity for somatostatin receptor subtype 5 in comparison with octreotide, leading to a higher inhibition of insulin release from beta cells. There are few case reports regarding the use of pasireotide in refractory hyperinsulinemic hypoglycemia. We describe a challenging case of endogenous hyperinsulinemic hypoglycemia refractory to standard medical treatment, in which pasireotide was used. In this case, imaging studies and calcium stimulation testing failed to localize an insulin-secreting tumor in an 83-year-old woman. Glucose levels remained low despite treatment with diazoxide, verapamil, and octreotide, necessitating the use of IV dextrose solutions. After starting subcutaneous (SC) pasireotide 0.9 mg twice a day, there was a significant improvement in the frequency and severity of hypoglycemic events, allowing the patient to be discharged from the hospital without needing IV glucose support.

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.

Stakeholder Perspectives on an Inpatient Hypoglycemia Informatics Alert: Mixed Methods Study.

BACKGROUND: Iatrogenic hypoglycemia is a common occurrence among hospitalized patients and is associated with poor clinical outcomes and increased mortality. Clinical decision support systems can be used to reduce the incidence of this potentially avoidable adverse event. OBJECTIVE: This study aims to determine the desired features and functionality of a real-time informatics alert to prevent iatrogenic hypoglycemia in a hospital setting. METHODS: Using the Agency for Healthcare Research and Quality Five Rights of Effective Clinical Decision Support Framework, we conducted a mixed methods study using an electronic survey and focus group sessions of hospital-based providers. The goal was to elicit stakeholder input to inform the future development of a real-time informatics alert to target iatrogenic hypoglycemia. In addition to perceptions about the importance of the problem and existing barriers, we sought input regarding the content, format, channel, timing, and recipient for the alert (ie, the Five Rights). Thematic analysis of focus group sessions was conducted using deductive and inductive approaches. RESULTS: A 21-item electronic survey was completed by 102 inpatient-based providers, followed by 2 focus group sessions (6 providers per session). Respondents universally agreed or strongly agreed that inpatient iatrogenic hypoglycemia is an important problem that can be addressed with an informatics alert. Stakeholders expressed a preference for an alert that is nonintrusive, accurate, communicated in near real time to the ordering provider, and provides actionable treatment recommendations. Several electronic medical record tools, including alert indicators in the patient header, glucose management report, and laboratory results section, were deemed acceptable formats for consideration. Concerns regarding alert fatigue were prevalent among both survey respondents and focus group participants. CONCLUSIONS: The design preferences identified in this study will provide the framework needed for an informatics team to develop a prototype alert for pilot testing and evaluation. This alert will help meet the needs of hospital-based clinicians caring for patients with diabetes who are at a high risk of treatment-related hypoglycemia.

Predictors of Time-to-Repeat Point-of-Care Glucose Following Hypoglycemic Events in Hospitalized Patients.

BACKGROUND: Previous studies have shown low adherence to the recommendation to repeat point-of-care glucose (POCG) within 15 minutes following the treatment of inpatient hypoglycemia. We sought to evaluate whether patient and clinical factors may predict time-to-repeat (TTR) POCG following hypoglycemic events in hospitalized adult patients. METHODS: This was a retrospective cross-sectional analysis of 22 226 index hypoglycemic (≤70 mg/dL) readings (of 993 395 total POCG samples) from 6226 hospital admissions within the Johns Hopkins Health System over three years. Time-to-repeat was defined as the difference in time (minutes) between the index POCG and the next POCG sample. Multivariable logistic regression was used to evaluate the association of TTR with clinical, patient, and hospital factors. RESULTS: The median (IQR) TTR was 49 (25-119) minutes, and 14.1% of index POCGs had a TTR ≤15. Severity of hypoglycemia, intensive care unit (ICU), intermediate care (IMC) and pediatrics admissions, and dextrose or glucagon administration were associated with higher adjusted odds of TTR ≤15 minutes. Admission to community hospitals, procedural units, surgery, and labor and delivery was associated with lower adjusted odds of TTR ≤15 minutes. Age, sex, insulin on board, secretagogue use, diabetes type, nutritional status, previous POCG value, and glycemic variability were not significantly associated. CONCLUSION: There is low adherence to the recommendation to repeat a POCG within 15 minutes following the treatment of inpatient hypoglycemia, which may be mediated by both patient and hospital factors. Further studies are needed to understand the mediators and implications of this practice variability.

Patterns and predictors of antihyperglycemic intensification at hospital discharge for type 2 diabetic patients not on home insulin.

BACKGROUND: Diabetes mellitus is a prevalent condition among hospitalized patients and the inpatient setting presents an opportunity for providers to review and adjust antihyperglycemic medications. We sought to describe practice patterns and predictors of antihyperglycemic intensification (AHI) at hospital discharge for type 2 diabetes mellitus (T2DM) patients not on home insulin. METHODS: We conducted a retrospective study of adult patients with T2DM receiving either non-insulin antihyperglycemic (NIA) or no antihyperglycemic medications prior to admission who were hospitalized within two hospitals in the Johns Hopkins Health System from December 2015 to September 2016. Mean hospital glucose values and observed vs. individualized target hemoglobin A1C values (based on risk of mortality score) were used to define an indication for AHI. Multivariable logistic regression was used to identify predictors of AHI. RESULTS: A total of 554 discharges of 475 unique patients were included. An indication for AHI was present in 104 (18.8%) of discharges, and AHI occurred in 30 (28.8%) of these discharges. Higher mean admission BG values and A1C, fewer pre-admission antihyperglycemic agents, involvement of the diabetes service, and admitting service were associated with AHI, while no association was observed with age, sex, race, risk of mortality and severity of illness scores, or length of stay. AHI was not associated with 30-day readmission. CONCLUSION: An indication for AHI occurs relatively infrequently among hospitalized patients, but when present, AHI occurs in approximately 1 in 3 discharges. AHI appears to be related largely to the degree of hyperglycemia, and diabetes service involvement. Further studies are needed to understand the implications of AHI at hospital discharge on short and long-term outcomes in this population.

Hypoglycemic risk exposures in relation to low serum glucose values in ambulatory patients.

This study aimed to correlate hypoglycemic risk exposures (HREs) with low blood glucose value (BGV) in ambulatory patients to inform selection of a glucose critical action value (CAV).This was a retrospective study of ambulatory patients with at least 1 serum glucose ≤70 mg/dL obtained at 2 laboratories within the Johns Hopkins Health System over 3.8 years. Multivariable logistic regression was used to evaluate association of BGV cut-offs of <60, <54, <50, and <45 mg/dL with HREs. HREs were classified as "high hypoglycemic risk" (HHR), "moderate hypoglycemic risk" (MHR), "low hypoglycemic risk" (LHR), and "no hypoglycemic risk" (NHR).A total of 5404 patient samples of BG ≤70 mg/dL were analyzed, of which 30.3%, 23.2%, 28.5%, 18.0% occurred in NHR, LHR, MHR, and HHR groups, respectively. An inverse relationship was noted between BGV cut-offs and HHR, but no association was observed for LHR or MHR. After adjusting for age, sex, and race, there was an inverse association between BG thresholds and the odds of HHR. For classification of HHR, BGV cut-offs of <60, <54, <50, and <45 mg/dL correctly classified 71.2%, 69.8%, 68.8%, and 67.2% of BG samples, achieved false-positive rates of 13.6%, 4.7%, 1.7%, and 0.5% and positive likelihood ratios of 3.3, 6.0, 11.2, and 23.4, respectively.Nearly 70% of low BGVs occurred in patients with at least 1 HRE, but only ∼20% occurred in HHR patients. Given their high positive likelihood ratios, BGVs <54 or <50 mg/dL are reasonable candidates for CAVs that would allow sufficient clinician response time while minimizing false-positive alerts.