A case series of diabetic ketoacidosis associated with SGLT2 inhibitors

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are increasingly initiated as treatment for type 2 diabetes due to favourable cardiorenal characteristics. However, studies have identified an increased risk of diabetic ketoacidosis (DKA). We carried out a retrospective, case-based study at East and North Herts NHS Trust between February 2018 and December 2020. Fifteen cases of SGLT2i associated DKA were identified in people with presumed type 2 diabetes;33.3% were classed as euglycaemic DKA with a blood glucose of <11mmol/L. All cases were associated with a significant precipitating factor including diarrhoea, vomiting, reduced oral intake and sepsis. One case was related to COVID-19. Two people were subsequently found to have raised islet autoantibodies suggesting type 1 diabetes or latent autoimmune diabetes in adults. It is important that awareness of SGLT2i associated DKA is raised among users and health care practitioners, including the recognition of euglycaemic DKA. Sick day rules should be emphasised and reiterated at clinical encounters. Non-specialists in primary care, oncology and in perioperative settings should be empowered to advocate for temporary withdrawal and there should be readier access to blood ketone monitoring when required. When SGLT2i associated DKA occurs, due consideration should be given to evaluate the diabetes classification and investigate the circumstances of the event. Copyright © 2023 John Wiley & Sons.Copyright © 2023 John Wiley & Sons, Ltd.

Current Healthcare Policy and Environmental Status of Digital Health Medical Devices in South Korea

Objectives: COVID 19 and increasing unmet needs of health technology had accelerated an adoption of digital health globally and the major categories are mobile-health, health information technology, telemedicine. Digital health interventions have various benefit on clinical efficacy, quality of care and reducing healthcare costs. The objective of the study is to identify new reimbursement policy trend of digital health medical devices in South Korea. Method(s): Official announcements published in national bodies and supplementary secondary research were used to capture policies, frameworks and currently approved products since 2019. Result(s): With policy development, several digital health devices and AI software have been introduced as non-reimbursement by utilizing new Health Technology Assessment (nHTA) pathway including grace period of nHTA and innovative medical devices integrated assessment pathway. AI based cardiac arrest risk management software (DeepCARS) and electroceutical device for major depressive disorders (MINDD STIM) have been approved as non-reimbursement use for about 3 years. Two digital therapeutics for insomnia and AI software for diagnosis of cerebral infarction were approved as the first innovative medical devices under new integrated assessment system, and they could be treated in the market. In addition, there is remote patient monitoring (RPM) reimbursement service fee. Continuous glucose monitoring devices have been reimbursed for type 1 diabetes patients by the National Health Insurance Service (NHIS) since January 2019. Homecare RPM service for peritoneal dialysis patients with cloud platform (Sharesource) has been reimbursed since December 2019, and long-term continuous ECG monitoring service fee for wearable ECG monitoring devices (ATpatch, MEMO) became reimbursement since January 2022. Conclusion(s): Although Korean government has been developed guidelines for digital health actively, only few products had been reimbursed. To introduce new technologies for improved patient centric treatment, novel value-based assessment and new pricing guideline of digital health medical devices are quite required.Copyright © 2023

Impact on glycaemic outcomes of transitioning from in-person to virtual FreeStyle Libre onboarding during the Covid-19 pandemic

Background and aims: Onboarding of the FreeStyle Libre, an intermittently scanned continuous glucose monitoring (isCGM) device, was pre-dominantly conducted in-person prior to the Covid-19 pandemic. However, onboarding rapidly become virtual due to enforced social distancing restrictions. This audit aimed to determine if onboarding method impacted on glycaemic outcomes and engagement statistics in people living with diabetes (pwD). Method(s): PwD who started FreeStyle Libre between January 2019 and March 2022, had their mode of onboarding recorded and had >=70% data were identified and included within the audit. Glycaemic indices and engagement statistics (previous 90 day averages) were obtained from LibreView (Abbott, USA) three months after the last person was onboarded, and compared using linear models, adjusting for FreeStyle Libre duration, %active (where appropriate), age and sex. Result(s): From 1007 eligible participants (in-person 44% [n = 445];virtual 56% [n = 562]), FreeStyle Libre usage duration was greater for those onboarded in-person vs. virtually (974[891,1101) vs. 420[280,564] days [p < 0.001]). There were no significant differences in glycaemic or engagement indices between in-person and virtual onboarding methods: average glucose (10[9,11]) vs. 10[9,11])mmol/l), %time very-low (<3.0mmol/l, 0[0,1]) vs. 0[0,1]%), %time low (3.0-3.8mmol/ l, 2[1,4] vs. 2[1,4]), %time in range (3.9-10.0mmol/ l, 54[+/-17] vs. 53[+/-19]%), %time high (10.1-13.9mmol/ l, 27[21,31]) vs. 26[21,31]%), %time very-high (>13.9mmol/l, 14[6,24] vs. 15[7,26]%), %active (96[90,100] vs. 94[87,99]%) or scans/day (11[8,15] vs. 10[7,14]). Conclusion(s): There were no differences in glycaemic outcomes or engagement indices between pwD between onboarding methods. Virtual onboarding using online videos for isCGM is as equally effective as face to face.

Diagnosing gestational diabetes during the Covid-19 pandemic using HbA1c rather than oral glucose tolerance test (OGTT)

The Royal College of Obstetrics and Gynaecology advocated replacing OGTT with HbA1c for gestational diabetes (GDM) screening for women with risk factors during the Covid-19 pandemic. HbA1c >=48mmol/mol/random plasma glucose (RPG) >=11.1mmol/l at booking indicated diabetes, and 41-47mmol/ mol/9-11mmol/ l prediabetes or possible GDM. Testing was repeated at 26 weeks if normal previously, with HbA1c >=39mmol/mol, fasting PG >=5.6mmol/l, or RPG >=9mmol/l diagnostic for GDM. A) At her clinic booking visit at 10 weeks gestation, 36 year-old South Asian female had HbA1c 55mmol/mol/RPG 9.5mmol/l suggesting undiagnosed type 2 diabetes. Initially managed with dietary advice and home blood glucose monitoring, metformin was added when self-monitored glucose above pregnancy targets (fasting and pre-meal <5.3mmol/l or 1 h post meal <7.8mmol/l) but insulin was required later. Metformin and insulin were stopped after delivery at 38 weeks with HbA1c 50mmol/mol three months postpartum, supporting the earlier diagnosis of type 2 diabetes. B) 32 year-old White Caucasian female was screened for GDM on booking at 11 weeks as BMI 38 kg/m2. HbA1c 44mmol/mol and RPG 6.9mmol/l confirmed GDM which was managed by dietary/lifestyle changes with glucose and pregnancy targets achieved until 28 weeks when metformin added. Normal delivery at 40 weeks with HbA1c 40mmol/mol three months postpartum triggered advice on long-term dietary/lifestyle changes and annual HbA1c checks. HbA1c was useful during the pandemic but most centres reverted to OGTT for GDM screening due to a significant fall in diagnoses using HbA1c >=39mmol/mol at 26 weeks. But, HbA1c testing was advantageous at booking to diagnose type 2 diabetes earlier.

Managing diabetes remotely: Evaluation of the uptake and efficacy of the DBm app

Objectives: Insulin optimisation requires review of glucose monitoring;Covid-19 posed challenges to this. We evaluated DBm -a remote monitoring platform utilising a glucometer and smartphone app. Method(s): Evaluation was from January to November 2021. Inclusion criteria was insulin treated diabetes with HbA1c greater than 68mmol/mol. HbA1c, demographics, frequency of CBG uploads and interactions with clinicians were collected. Result(s): 97 patients were offered DBm. 48.5% used the app. There were no statistically significant differences in gender (p = 0.05), age (p = 0.36), type of diabetes (p = 0.13) or deprivation index (p = 0.96) between users and non-users. Patients of white ethnicity were less likely to use the platform (p = 0.01). Amongst users, 70% had a reduction of HbA1c of at least 5mmol/mol over six months, with a mean reduction of 25.6mmol/mol (p = 0.01). There was no difference in age (p = 0.64), gender (p = 0.4), and type of diabetes (p = 0.23) between responders and non-responders. There was also no difference in number of call back requests generated by patients (p = 0.32) or number of CBG uploads (p = 0.899) between responders and non-responders. Conclusion(s): Uptake of the remote monitoring solution was just under 50%, with no evidence of digital exclusion, although the finding that white ethnicity patients were less likely to use the system needs further exploration. Most users had improved glucose control, but there was no association with numbers of tests or call back requests. This study demonstrates that insulin optimisation can effectively be delivered using a remote glucose monitoring system. Future work will explore patient experience and patient satisfaction.

Optimal continuous glucose monitoring metrics for identification of women with gestational diabetes

Aims: Continuous glucose monitoring (CGM) is widely used in pregnant women with pre-gestational diabetes, but optimal targets have not been defined in gestational diabetes. Previous work identified mild hyperglycaemia in pregnant women without gestational diabetes, but with risk factors such as obesity. We aimed to examine CGM metrics and patterns of glycaemia in women with gestational diabetes compared to healthy pregnant women with comparable risk factors. Method(s): We recruited 73 healthy women with >1 risk factor (gestational diabetes excluded using Covid-19 criteria, OGTT) and 200 women with gestational diabetes (NICE and interim-Covid- 19 criteria) from antenatal clinics at 28 weeks' gestation. A Dexcom G6 CGM device was cited on the non-dominant upper arm. Result(s): Women with gestational diabetes had significantly higher weight (mean +/- SEM 95.7 kg +/- 1.3 Vs 85.4 kg +/- 2.2) and BMI (36.0 +/- 0.5 Vs 31.3 +/- 0.7) compared to healthy pregnant women (p < 0.01). Women with gestational diabetes had significantly higher mean CGM-glucose (mean +/- SEM 5.6 +/- 0.01 Vs 5.4 +/- 0.01mmol/l;p < 0.01), significantly altered time-below- range (median(IQR);1.0% (0.2-2.9) vs 2.5% (0.7-5.5);p < 0.05) and time-in- range (95.0% (91.1-97.9) vs 94.5% (87.9-96.2);p < 0.05) but comparable time-above- range to healthy women with risk factors. Diurnal glucose profiles in women with gestational diabetes were comparable to healthy women between 14:00 and 18:00, but demonstrated significant increases in glucose at all other time points during the 24-h cycle (p < 0.01). Conclusion(s): Mean CGM glucose is the most reliable CGM metric to distinguish women with gestational diabetes from healthy pregnant women with risk factors.

Change in glycaemic control immediately after Covid-19 vaccination in people with type 1 diabetes: What happens after the most recent vaccination?

Aims: We have shown previously in 93 individuals with type 1 diabetes using the FreeStyle Libre flash glucose monitor that the week after their first Covid-19 vaccination, the percent 'time in target range 3.9-10mmol/ l' (%TTR) average went from 55.2%-> 52.4% (effect size -5.1%) with 58% of people recording a fall. 47 (50%) people with HbA1c < 56mmol/mol %TTR went from 69.3-> 63.5 (-8.3%) and 24 (25%) people using insulin+oral treatment 56.7%-> 50.7% (-10.1%). We have now repeated the exercise after the most recent Covid-19 vaccination. Method(s): FreeStyle Libre data and medical records of the same patients from the previous study were examined for the week before and week after their most recent Covid-19 vaccination. () in the results section show change in %TTR as % of the prior value to show effect size. TTR% results from 2 weeks before and after were also considered. Result(s): Median time between vaccines was 38 weeks IQR (37-40). After the latest vaccination average %TTR average went from 51.1%-> 49.8% (-2.5%) with a reduction found in 54% of patients. Impact on the 39 patients with HbA1c < 56mmol/mol -% TTR from 66.2%-> 61.8% (-6.5%) and the 20 (25%) patients using insulin+oral %TTR from 48.2%-> 47.1% (-2.2%). 65% of the patients whose %TTR fell previously, fell again after this vaccination. Fortnight average %TTR 53.5%-> 52.1% (-2.7%) whereas in the previous study across fortnight %TTR 55.4%-> 54.0% (-2.4%). Conclusion(s): The perturbation effect on blood glucose with 1st Covid-19 vaccination was seen again in the latest vaccination but reduced in magnitude, confirming that a significant group of type 1 diabetes individuals' glycaemic control is still being impacted by the Covid-19 vaccination.

The intersection of technology, ethnicity, deprivation, and the covid-19 pandemic: Lessons from the National Diabetes Audit

Introduction: The aim was to investigate access to and the effect of intermittency scanned flash glucose monitoring (isCGM) on glycaemic control during the Covid-19 pandemic. Method(s): Data from the National Diabetes Audit from 2019 to 2021 was stratified into those who were already using isCGM on 1st April 2020 (A), those who started isCGM on or after 1st April 2020 (B), and those who did not receive isCGM (C). Logistic regression investigated the independent effects of ethnicity and deprivation on access to isCGM after adjustment for baseline covariates (age, gender, BMI, duration of diabetes, and baseline HbA1c). Ethnicity was categorized as White, Asian, Black, Mixed, and not reported. The Index of Multiple Deprivation (IMD) was divided into quintiles. Result(s): 251,620 people were identified with type 1 diabetes;88,910 (35%) had isCGM prescribed at 1st April 2020. The mean follow-up post-isCGM initiation was six months. Mean HbA1c at baseline was 67.4mmol/mol in (A), 73.6mmol/mol in (B) and 69.7mmol/mol in (C). Mean HbA1c at follow-up was 64.9mmol/mol (A) (p < 0.001), 65.5mmol/mol (p < 0.001) (B). After adjustment for age, sex, duration of diagnosis, baseline HbA1c, and BMI people with White ethnicity (OR = 1.79 p < 0.001) or in the least deprived quintile (OR = 1.54, p < 0.001) were more likely to be initiated on isCGM as compared to the black and most deprived groups. Conclusion(s): Initiating isCGM during the Covid-19 pandemic was associated with improved glycaemic control. Ethnic and socioeconomic disparities in access to isCGM were observed even during the pandemic. Ongoing work is investigating the effect of isCGM on diabetes-related hospital admissions during the pandemic.

My diabetes My Way: Intelligent delivery of diabetes education and support for self-management

Background: My Diabetes My Way (MDMW) is NHS Scotland's interactive website, offering education, structured eLearning and online records access for people living with diabetes. We aimed to analyse user activity during the last 12 months. Method(s): Data were collected during the period from November 2021 to October 2022. Registration and user audit logs were analysed, observing activity across all website content and features. Result(s): An average of 62,853 pages were accessed on the public website each month. Significant activity increases were observed in December 2021 (n = 81,237). There were increased views in September 2022 (n = 76,502) and October (n = 73,039) The top five pages accessed were;Coronavirus: advice for people living with diabetes (n = 12,478), FreeStyle Libre (n = 4325), Emergency advice (n = 1576), Blood pressure-reducing your risks of complications (n = 1559) and Blood glucose monitoring and HbA1c targets (n = 1485). eLearning: During this period, 382 individuals completed one of 11 QISMET-accredited structured eLearning courses. eLearning course usage increased in relation to patient awareness activity. Social Media: There are currently 3919 Facebook and 3600 Twitter followers. Records Access: 67,655 patients had registered to access their data and 35,157 had actively accessed their records by the end of October 2022. Patient feedback remains highly positive. Conclusion(s): MDMW is a consistent and reliable resource for people with diabetes and their families to access at any time online. User statistics continue to rise, while latest development plans include the addition of new Patient Reported Outcome Measures, risk prediction features, and enhanced sharing of data with the healthcare team.

Association between Covid-19 and Diabetes Mellitus

Aim: To determine the association between Covid-19 and diabetes mellitus. Study Design: Retrospective study. Place and Duration of Study: Department of Medicine & Respiratory Physiology, Independent Medical College Faisalabad from 1st July 2022 to 31st December 2022. Methodology: Fifty five patients received at outdoor patient department of Independent University Hospital with confirmed diagnosis for Covid-19 through naso-pharyngeal reverse transcription polymerase chain reaction (RT-PCR) and aged 13-65 years were included. The complete medical files of each confirmed Covid-19 case was completely studied in relevance to diabetes mellitus association and compared with normal matched controls that only visited the OPD against the suspicion of the disease and underwent complete biochemical profiling. The baseline levels of HbA1C and glucose monitoring in each patient and control was done and compared. Result(s): The mean age of the CoVid-19 cases was 39.5+/-5.3 years while of controls as 25.65+/-4.3 years. There was an obvious significant variance in the odds ratio of Covid-19 patients and those of controls in reference to diabetes mellitus. A significant increase was observed in Odds Ratio of Covid-19 cases within the age group of 51-65 years. The Elixhauser Comorbidity Index (ECI) categories also presented, ECI >5 to be higher in Covid-19 cases than controls. Conclusion(s): There is a higher risk of diabetes new onset in Covid-19 confirmed cases as compared to matched controls.Copyright © 2023 Lahore Medical And Dental College. All rights reserved.

Telemedicine for diabetes management during COVID-19: what we have learnt, what and how to implement.

The past two decades have witnessed telemedicine becoming a crucial part of health care as a method to facilitate doctor-patient interaction. Due to technological developments and the incremental acquisition of experience in its use, telemedicine's advantages and cost-effectiveness has led to it being recognised as specifically relevant to diabetology. However, the pandemic created new challenges for healthcare systems and the rate of development of digital services started to grow exponentially. It was soon discovered that COVID-19-infected patients with diabetes had an increased risk of both mortality and debilitating sequelae. In addition, it was observed that this higher risk could be attenuated primarily by maintaining optimal control of the patient's glucose metabolism. As opportunities for actual physical doctor-patient visits became restricted, telemedicine provided the most convenient opportunity to communicate with patients and maintain delivery of care. The wide range of experiences of health care provision during the pandemic has led to the development of several excellent strategies regarding the applicability of telemedicine across the whole spectrum of diabetes care. The continuation of these strategies is likely to benefit clinical practice even after the pandemic crisis is over.

Comparing Glucose Outcomes Following Face-to-Face and Remote Initiation of Flash Glucose Monitoring in People Living With Diabetes.

BACKGROUND: When launched, FreeStyle Libre (FSL; a flash glucose monitor) onboarding was mainly conducted face-to-face. The COVID-19 pandemic accelerated a change to online starts with patients directed to online videos such as Diabetes Technology Network UK for education. We conducted an audit to evaluate glycemic outcomes in people who were onboarded face-to-face versus those who were onboarded remotely and to determine the impact of ethnicity and deprivation on those outcomes. METHODS: People living with diabetes who started using FSL between January 2019 and April 2022, had their mode of onboarding recorded and had at least 90 days of data in LibreView with >70% data completion were included in the audit. Glucose metrics (percent time in ranges) and engagement statistics (previous 90-day averages) were obtained from LibreView. Differences between glucose variables and onboarding methods were compared using linear models, adjusting for ethnicity, deprivation, sex, age, percent active (where appropriate), and duration of FSL use. RESULTS: In total, 935 participants (face-to-face 44% [n = 413]; online 56% [n = 522]) were included. There were no significant differences in glycemic or engagement indices between onboarding methods and ethnicities, but the most deprived quintile had significantly lower percent active time (b = -9.20, P = .002) than the least deprived quintile. CONCLUSIONS: Online videos as an onboarding method can be used without significant differences in glucose and engagement metrics. The most deprived group within the audit population had lower engagement metrics, but this did not translate into differences in glucose metrics.

Hyperglycemia Associated With Raynaud Phenomenon and Liver Dysfunction After COVID-19 Vaccination in Type 1 Diabetes Mellitus.

Background/Objective: The association of COVID-19 vaccinations and the changes in glycemic control remains debatable. We report a case of a patient with type 1 diabetes mellitus (DM) with previously well-controlled glucose on a hybrid closed-loop insulin pump who developed significant glucose variation, new onset Raynaud phenomenon, and liver dysfunction after the vaccination. Case Report: A 33-year-old man with type 1 DM since the age of 5 years was on an insulin pump for 17 years. He had a reasonable controlled glucose level with a hemoglobin A1c level of 6.8% (51 mmol/mol). Three days after he received the COVID-19 vaccination, his glucose level started to fluctuate in the range of 46 to 378 mg/dL with 3.5 times higher total daily insulin requirement. The patient developed white-pale cold hands, weight gain, fatigue, and liver dysfunction. Computed tomography of the abdomen revealed mild hepatomegaly, and laboratory workup was negative for hepatitis. One month later, his glucose level became better controlled, and his liver function improved. Continuous glucose monitoring revealed that his glucose profile returned to baseline after 6 weeks. Discussion: COVID-19 vaccination resulted in significant glucose variation and fluctuations in this patient. It could be explained by the vaccine-induced immune response causing an increase in insulin resistance, such as in adipose tissue and muscle cells. Immune stimulation could have also caused the abnormal liver function and explain his new onset Raynaud phenomenon. Conclusion: We described, for the first time, the long-term continuous glucose monitoring glucose profile with a hybrid closed-loop system in type 1 DM after COVID-19 vaccination. Clinicians need to keep alert to glycemic excursion and side effects after immunization in type 1 DM.

The Optimal Method to Predict Hemoglobin A1c from Flash Glucose Monitor Data

In situations where it is difficult for patients to visit hospitals, such as the coronavirus disease pandemic, it is important to more detailly predict hemoglobin A1C (HbA1c) from flash glucose monitor (FGM) data. CGM data over 14 days can be obtained from a FGM sensor;therefore, there are many options for extracting the duration from which glucose levels are derived. Thus, the extracted durations were closely studied to determine which mean glucose levels can predict HbA1c more accurately. Seventy-three outpatients with type 2 diabetes mellitus underwent HbA1c testing, wore a FGM (FreeStyle Libre Pro), and did not change diabetic treatments, on a hospital visit. FGM data over 24 h 13 days (from 00:00 on day 2 to 24:00 on day 14 [FGM attachment: day 1]) were analyzed. The mean glucose levels were calculated corresponding to the following durations: 1 day: day 2 ~ day 14 (n=13), 2 days: days 2-3 ~ days 13-14 (n=12) 12 days: days 2-13 ~ days 3-14 (n=2), 13 days: days 2-14 (n=1) [total 91 durations] (extracted mean glucose levels). Data were analyzed in all patients (n=73), in patients with hypoglycemia in the 13 days (Hypo) group (n=40), and in patients without hypoglycemia in the 13 days (Nonhypo) group (n=33). In all patients, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.76-0.86, p<0.001). HbA1c was the most significantly correlated to the mean glucose levels over 13 days (days 2-14). "Correlation coefficients between HbA1c and extracted mean glucose levels" ("r, HbA1c, EMGL") were also correlated to number of extracted days for the extracted mean glucose levels (r=0.80, p<0.001 [n=91]). In the Hypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.55-0.73, p<0.001). The mean glucose levels over 13 days (days 2-14) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.68, p<0.001;Fig. 2). In the Nonhypo group, HbA1c was correlated to all 91 extracted mean glucose levels (r=0.73-0.87, p<0.001). The mean glucose levels over 12 days (days 2-13) were the most significantly correlated to HbA1c. "r, HbA1c, EMGL" correlated to the number of extracted days for the extracted mean glucose levels (r=0.61, p<0.001). The results of the present study are consistent with that of a previous study reporting that the minimum duration needed to estimate time in range over 90 days is 14 days. In the prediction of HbA1c using data from one FGM sensor, prolonged measurement can make the glucose management indicator more accurate. Especially for patients with hypoglycemia, the importance of prolonged measurement may be applicable.

The First Reported Case of Lorlatinib-Associated Diabetic Ketoacidosis

Introduction: Lorlatinib is a third-generation tyrosine kinase inhibitor that inhibits anaplastic lymphoma kinase (ALK) and c-ros oncogene 1 (ROS1). Although 2-10% of patients with non-small cell lung cancer developed hyperglycemia in phase 2 and 3 studies of lorlatinib, only one case has subsequently reported hyperglycemia >500 mg/dL, and no cases of diabetic ketoacidosis (DKA) have been previously reported. Phase 1 trials in neuroblastoma are ongoing. Case Description: A 34-year-old woman with ALK-mutated paraspinal neuroblastoma presented with DKA 14 months after initiation of lorlatinib. Prior to starting lorlatinib, her hemoglobin A1c had been 5.0% (n: < 5.7%). After 12 months of therapy, her A1c increased to 7.8%, prompting the initiation of metformin 500 mg daily. However, two months later she was admitted for DKA with a blood glucose of 591 mg/dL (n: 65-99 mg/dL), CO2 17 mmol/L (n: 20-30 mmol/L), anion gap 18 (n: 8-12), moderate serum ketones, and 3+ ketonuria. Her A1c was 14.8%, C-peptide was 1.2 ng/mL (n: 1.1-4.3 ng/mL), and her glutamic acid decarboxylase-65 and islet antigen-2 autoantibodies were negative. She was also found to be incidentally positive for COVID-19 but was asymptomatic without any oxygen requirement. The patient's DKA was successfully treated with IV insulin infusion, and she was discharged after 3 days with insulin glargine 27 units twice daily and insulin aspart 16 units with meals. One month later, her hemoglobin A1c had improved to 9.4%, and the patient's oncologist discontinued lorlatinib due to sustained remission of her neuroblastoma and her complication of DKA. After stopping lorlatinib, her blood glucose rapidly improved, and she self-discontinued all her insulin in the following 3 weeks. One month later, she was seen in endocrine clinic only taking metformin 500 mg twice daily with fasting and post-prandial blood glucose ranging 86-107 mg/dL. Discussion(s): This is the first reported case of DKA associated with lorlatinib. This case highlights the importance of close glucose monitoring and the risk of severe hyperglycemia and DKA while on lorlatinib therapy. Discontinuation of lorlatinib results in rapid improvement of glycemic control, and glucose-lowering treatments should be promptly deescalated to avoid hypoglycemia.Copyright © 2023

Medicines management of long-term conditions during the COVID19 pandemic: a narrative systematic review

Introduction: Due to lockdown measures associated with the COVID 19 pandemic (1), there were substantial changes to healthcare delivery, including the suspension of face-to-face medical appointments, expansion of telehealth and changes to medication protocols.(2) It is important to learn from the successes and challenges of this period to ensure we adapt and improve how we support people to take medicines in the future. Aim(s): We sought to conduct a systematic review to explore the different approaches used to deliver medicines management services for people living with long term conditions (LTCs) during the pandemic and identify strategies that could be integrated into standard care. Method(s): We conducted a systematic review across 3 large databases: MEDLINE (OVID), EMBASE (OVID) and Cumulative Index to Nursing and Allied Health Literature (CINAHL). Our research question and search strategy was developed using the PICO framework (Population: adults with LTCs, Intervention: medicines management during the COVID 19 pandemic;no comparison group. Outcome(s): any aspect relating to medicines management. Search terms relating to 'long term conditions', 'medication management' and 'COVID-19' were used. One reviewer (LM) screened all titles, s, and full texts. We included studies discussing medication management of LTCs, in patients of all ages and healthcare settings, throughout the pandemic. Primary literature sources, feasibility studies and case studies, were included. We excluded studies solely focusing on disease monitoring, or the treatment of COVID/ 'long Covid'. One reviewer performed a thematic analysis, synthesising the findings into themes and sub-themes, which were discussed with a further reviewer (CT). A critical appraisal was performed using the Critical Appraisal Skills Programme checklists. Result(s): The search returned 2365 results. After deduplication, articles were removed at the title (n=1070) (n=813) and full text (n=232) stages. 31 studies were included. Studies were conducted in India (n=6), US (n=5), international (n=4), France (n=2), Italy (n=2), and one each from China, Japan, Jordan, Mexico, Morocco, Nigeria, Romania, Saudi Arabia, Spain, UK, UK and US, and location not specified. Most studies (n=17) employed subjective methods of data collection (surveys/ questionnaires). We identified 6 themes. These were: changes in consultation type, for instance using teleconsultations and smartphone apps to monitor glucose control and diabetic management. Studies described temporary changes to treatment protocols e.g., using oral chemotherapy to reduce the need for in-person appointments and reduce the infection risk associated with intravenous administration. Control of certain conditions for example epilepsy was reduced in some studies. Patients missed doses due to drug shortages associated with disruptions in the medication supply chain, particularly in low-income countries. Finally, we identified prescribing trend changes in certain classes of medicines (e.g. reduced biologic usage due to immunosuppression risk) and an increase in patients self-medicating conditions including anxiety and depression, with associated safety risks. Conclusion(s): This review suggested that certain medical conditions such as diabetes and hypertension were more suited to remote monitoring with technological interventions such as smartphone apps. While other conditions e.g., cancer and epilepsy, demonstrated a greater need for in-person care. Countries of lower socioeconomic status were disproportionately affected by the pandemic.

Clinical Experience of Using Telemedicine for the Management of Patients Using Continuous Subcutaneous Insulin Infusion in a Highly Complex Latin American Hospital.

INTRODUCTION: Continuous subcutaneous insulin infusion (CSII) has emerged as a potential solution for diabetes management during the pandemic, as it reduces the need for in-person visits and allows for remote monitoring of patients. Telemedicine has also become increasingly important in the management of diabetes during the pandemic, as it allows healthcare providers to provide remote consultations and support. Here, we discuss the implications of this approach for diabetes management beyond the pandemic, including the potential for increased access to care and improved patient outcomes. METHODS: We performed a longitudinal observational study between 1 March 2020 and 31 December 2020 to evaluate glycemic parameters in diabetic patients with CSII in a telehealth service. Glycemic parameters were time in range (TIR), time above range, time below range, mean daily glucose, glucose management indicator (GMI), and glycemic variability control. RESULTS: A total of 36 patients were included in the study, with 29 having type 1 diabetes and 6 having type 2 diabetes. The study found that the proportion of patients achieving target glucose variability and GMI remained unchanged during follow-up. However, in patients with type 2 diabetes, the time in target range increased from 70% to 80%, and the time in hyperglycemia decreased from 2% to 0%. CONCLUSIONS: The results of this study suggest that telemedicine is a strategy for maintaining glycemic control in patients using CSII. However, the lack of access to the internet and adequate telemonitoring devices make it difficult to use on a large scale in emerging countries like ours.

Accuracy and Glycemic Efficacy of Continuous Glucose Monitors in Critically Ill COVID-19 Patients: A Retrospective Study.

BACKGROUND: Continuous glucose monitoring (CGM) is approved for insulin dosing decisions in the ambulatory setting, but not currently for inpatients. CGM has the capacity to reduce patient-provider contact in inpatients with coronavirus disease 2019 (COVID-19), thus potentially reducing in hospital virus transmission. However, there are sparse data on the accuracy and efficacy of CGM to titrate insulin doses in inpatients. METHODS: Under an emergency use protocol, CGM (Dexcom G6) was used alongside standard point-of-care (POC) glucose measurements in patients critically ill from complications of COVID-19 requiring intravenous (IV) insulin. Glycemic control during IV insulin therapy was retrospectively assessed comparing periods with and without adjunctive CGM use. Accuracy metrics were computed and Clarke Error Grid analysis performed comparing CGM glucose values with POC measurements. RESULTS: Twenty-four critically ill patients who met criteria for emergency use of CGM resulted in 47 333 CGM and 5677 POC glucose values. During IV insulin therapy, individuals' glycemic control improved when CGM was used (mean difference -30.7 mg/dL). Among 2194 matched CGM: POC glucose pairs, a high degree of concordance was observed with a mean absolute relative difference of 14.8% and 99.5% of CGM: POC pairs falling in Zones A and B of the Clarke Error Grid. CONCLUSIONS: Continuous glucose monitoring use in critically ill COVID-19 patients improved glycemic control during IV insulin therapy. Continuous glucose monitoring glucose data were highly concordant with POC glucose during IV insulin therapy in critically ill patients suggesting that CGM could substitute for POC measurements in inpatients thus reducing patient-provider contact and mitigating infection transmission.

Comparisons of School-Day Glycemia in Different Settings for Children with Type 1 Diabetes Using Continuous Glucose Monitoring

Objective. Using continuous glucose monitoring (CGM), we examined patterns in glycemia during school hours for children with type 1 diabetes, exploring diferences between school and nonschool time. Methods. We conducted a retrospective analysis of CGM metrics in children 7-12 years (n = 217, diabetes duration 3.5 +/- 2.5 years, hemoglobin A1c 7.5 +/- 0.8%). Metrics were obtained for weekday school hours (8 AM to 3 PM) during four weeks in fall 2019. Two comparison settings included weekend (fall 2019) and weekday (spring 2020) data when children had transitioned to virtual school due to COVID-19. We used multilevel mixed models to examine factors associated with time in range (TIR) and compare glycemia between in-school, weekends, and virtual school. Results. Tough CGM metrics were clinically similar across settings, TIR was statistically higher, and time above range (TAR), mean glucose, and standard deviation (SD) were lower, for weekends and virtual school (p < 0.001). Hour and setting exhibited a signifcant interaction for several metrics (p < 0.001). TIR in-school improved from a mean of 40.9% at the start of the school day to 58.0% later in school, with a corresponding decrease in TAR. TIR decreased on weekends (60.8 to 50.7%) and virtual school (62.2 to 47.8%) during the same interval. Mean glucose exhibited a similar pattern, though there was little change in SD. Younger age (p = 0.006), lower hemoglobin A1c (p < 0.001), and insulin pump use (p = 0.02) were associated with higher TIR in-school. Conclusion. Although TIR was higher for weekends and virtual school, glycemic metrics improve while in-school, possibly related to benefcial school day routines.

Analysis of influencing factors of blood glucose levels in diabetic patients during epidemic of COVID-19.

Objective: To investigate the blood glucose control of diabetic patients during the Coronavirus disease 2019 (COVID-19) epidemic, and to explore the factors affecting blood glucose. Method(s): Three hundred and fifty patients with diabetes mellitus hospitalized in the Endocrinology Department of the Second Affiliated Hospital of Air Force Military Medical University from 2017 to 2019 were selected, and we send questionnaires (a self-made questionnaire containing 39 questions, Zung anxiety self-assessment scale, Zung depression self-assessment scale) to the patients through WeChat group. After the effective questionnaires were collected, the patients were divided into good blood glucose control group (fasting blood glucose <=7 mmol/L and 2 h postprandial blood glucose <=10 mmol/L) and poor blood glucose control group (fasting blood glucose>7 mmol/L and/or 2 hours postprandial blood glucose>10 mmol/L). Chi square test or Fisher exact probability method and t test were used to compare the differences between the two groups. In Multi-factor logistic regression, the backward regression method was performed. Result(s): A total of 310 questionnaires were collected, 4 of which did not meet the requirements were eliminated, and a total of 306 valid questionnaires were analyzed. There were 108 cases (35.3%) in the well-controlled group and 198 cases (64.7%) in the poorly controlled group. Compared with well-controlled group, there was a higher percentage of patients with aged >=45 years, diabetes course >=5 years, combined with chronic complications of diabetes, weekly exercise time during the epidemic period<150 min,weekly monitoring of blood glucose frequency <=1 to 2 times and sleep disorders during the epidemic, anxiety, and depression in poorly controlled group, and there were statistically significant differences (P<0.05).The above 8 factors with P<0.05 were included in the logistic regression model. Diabetes course >=5 years, weekly exercise time during the epidemic<150 min, sleep disturbance during the epidemic, weekly monitoring of blood glucose frequency <= 1 to 2 times, depression were risk factors for poor blood glucose control (P<0.05). Conclusion(s): During the epidemic period, the blood glucose level of diabetes patients was generally high. The factors that affected blood glucose control included a long course of diabetes, short exercise time, low monitoring frequency of blood glucose, sleep disorders, and depression.Copyright © 2020 by the Chinese Medical Association.