Making Diabetes Electronic Medical Record Data Actionable: Promoting Benchmarking and Population Health Improvement Using the T1D Exchange Quality Improvement Portal.

This article describes how the T1D Exchange Quality Improvement Collaborative leverages an innovative web platform, the QI Portal, to gather and store electronic medical record (EMR) data to promote benchmarking and population health improvement in a type 1 diabetes learning health system. The authors explain the value of the QI Portal, the process for mapping center-level data from EMRs using standardized data specifications, and the QI Portal's unique features for advancing population health.

Real-world performance of hybrid closed loop in youth, young adults, adults and older adults with type 1 diabetes: Identifying a clinical target for hybrid closed-loop use.

AIM: To describe real-world hybrid closed loop (HCL) use and glycaemic outcomes across the lifespan and identify a clinical threshold for HCL use associated with meeting the internationally recommended target of 70% sensor glucose time in range (TIR; 70-180 mg/dL). MATERIALS AND METHODS: Mixed models examined MiniMed 670G HCL use and glycaemic outcomes in 276 people with type 1 diabetes from four age groups: youth (aged <18 years), young adults (18-25 years), adults (26-49 years) and older adults (≥50 years) for 1 year. ROC analysis identified the minimum percentage HCL use associated with meeting the TIR goal of 70%. RESULTS: HCL use at month 1 was 70.7% ± 2.9% for youth, 71.0% ± 3.8% for young adults, 78.9% ± 2.1% for adults and 84.7% ± 3.8% in older adults. HCL use declined significantly at 12 months to 49.3% ± 3.2% in youth (P < .001) and 55.7% ± 4.3% in young adults (P = .002). HCL use was sustained at 12 months in adults (76.4% ± 2.2%, P = .36) and older adults (80.4% ± 3.9%, P = .36). HCL use of 70.6% was associated with 70% TIR (sensitivity 58.3%, specificity 85%, AUC 0.77). Older age, 80% or higher continuous glucose monitor use and four or more blood glucose checks per day were associated with attaining the HCL-use threshold. CONCLUSIONS: HCL use of 70% or higher may be a useful target for clinicians to use to assist people with diabetes in attaining glycaemic goals. Youth may struggle with HCL use more than adults and require clinical intervention to help sustain HCL use across time.

Long-term real-life glycaemic outcomes with a hybrid closed-loop system compared with sensor-augmented pump therapy in patients with type 1 diabetes.

AIM: To compare glycaemic metrics at 3 and 6 months in patients with type 1 diabetes on a 670G hybrid closed-loop (HCL) system after using a sensor-augmented pump (SAP) for at least 3 months. MATERIALS AND METHODS: A retrospective study from a centre that has the largest number of 670G users in the United States was conducted. Data from 202 SAP users were reviewed. Sixty-one patients were excluded (two for steroid use, four for pregnancy, 27 for previous Enlite use, and 28 for non-continuous use of 670G). Out of 141 patients who met the inclusion criteria, 127 (aged 21-68 years) had complete data. RESULTS: HbA1c levels decreased by 0.4% at 3 months and were maintained at 6 months (7.6 ± 0.07 vs. 7.2 ± 0.08, P < 0.001) with no weight gain at 6 months. Time-in-range (70-180 mg/dL) increased from 59.5% ± 1.1% to 70.2% ± 1.2% and 70.1% ± 1.1% at 3 and 6 months (P < 0.001), respectively. At 6 months, time spent in hypoglycaemia (<70 mg/dL) and time spent in hyperglycaemia (>180 mg/dL) were reduced by 30% (2.2% ± 0.2% vs. 3.2% ± 0.2%; P < 0.05) and 26% (28.3% ± 1.2% vs. 38.1% ± 1.2%; P < 0.001), respectively. More time in auto-mode was associated with improved continuous glucose monitoring metrics, lower HbA1c and decreased glycaemic variability. Time in auto-mode declined in men after 3 months, while women maintained similar auto-mode use throughout the study. CONCLUSIONS: The HCL system improved HbA1c levels and time-in-range, and decreased time spent in hypoglycaemia and hyperglycaemia at 6 months. Auto-mode use was significantly correlated with continuous glucose monitoring metrics and glycaemic outcomes.

Diabetes mellitus and hypertension: a dual threat.

PURPOSE OF REVIEW: The following is a review of the current concepts on the relationship between hypertension (HTN) and diabetes mellitus with a focus on the epidemiology and cardiovascular prognostic implications of coexistent HTN and diabetes mellitus, shared mechanisms underlying both conditions and pathophysiology of increased risk of cardiovascular disease, treatment of HTN in individuals with diabetes mellitus, and effects of anti-diabetic medications on blood pressure (BP). RECENT FINDINGS: Diabetes mellitus and HTN often coexist in the same individual. They share numerous risk factors and underlying pathophysiologic mechanisms, most important of which are insulin resistance and inappropriate activation of the rennin-angiotensin-aldosterone system. Recently updated guidelines recommend a BP goal of 140/90 mmHg in most individuals with diabetes mellitus. A new class of anti-diabetic medications, sodium-glucose co-transporter 2 inhibitors, has shown favorable effects on BP. SUMMARY: HTN affects the majority of individuals with diabetes mellitus. Coexistence of diabetes mellitus and HTN, especially if BP is not well controlled, dramatically increases the risk of morbidity and mortality from cardiovascular disease. BP control is an essential part of management of patients with diabetes mellitus, because it is one of the most effective ways to prevent vascular complications and death.

Limitations of 14-Day Continuous Glucose Monitoring Sampling for Assessment of Hypoglycemia and Glycemic Variability in Type 1 Diabetes.

Continuous glucose monitoring (CGM) has become the standard of care in diabetes management with the recent advances in technology and accessibility in the last decade. An International Consensus was established to define CGM metrics and its goals in diabetes care. The 2019 International Consensus suggested 14 days of CGM sampling for the assessment of CGM metrics stating the limitations that may occur for hypoglycemia and glycemic variability metrics. Since then, several studies assessed the correlation between CGM metrics and duration of the sampling period. This review summarized the studies that investigated the relationship between 14-day CGM sampling to 90-day CGM data in >70% CGM users for all CGM metrics and highlighted possible solutions for more accurate CGM sampling durations in type 1 diabetes (T1D). Accumulating evidence showed that 14-day CGM sampling correlates well with 90-day CGM data for mean glucose, time in 70-180 mg/dL, and hyperglycemia metrics; however, it correlates weakly for hypoglycemia and glycemic variability metrics. In the studies included in this review, in adults with T1D, minimum sampling duration was 14 days for mean glucose, time in 70-180 mg/dL, and time in hyperglycemia (>180 and >250 mg/dL); however, minimum sampling duration varied between 21 to 30 days for time <70 mg/dL, 30 to 35 days for time <54 mg/dL, and 28 to 35 days for coefficient of variation. Longer than 14 days of CGM, sampling was required to properly assess hypoglycemia and glycemic variability in T1D.

Advances in diabetes technology within the digital diabetes ecosystem.

Ongoing innovations in glucose monitoring, insulin delivery, and telehealth technologies have created a digital diabetes ecosystem populated by connected tools and technologies that have been shown to improve clinical outcomes, lower costs, and reduce the burden of diabetes. Advances in connected continuous glucose monitoring devices, insulin pumps, and insulin pens have led to the development of automated insulin delivery systems that modulate insulin infusion based on sensor glucose data. Similar integrations of continuous glucose monitoring and connected blood glucose meter data into "smart" pens have lessened the guesswork of intensive insulin management for individuals who prefer traditional injection therapy. A growing number of health apps that can be accessed through smartphones and wearable devices provide information and advice that support individuals in adopting healthier lifestyles. The differences in features and functionality give users the ability to select the devices that best meet their unique requirements and preferences. This article reviews the most current digital diabetes technologies and discusses how the connectivity of these tools can create an overarching architecture of feedback mechanisms that monitor an individual's health status, motivate and enhance adherence to self-management, and provide advice and decision-support tools to clinicians as well as other members of the health care team to make living with diabetes more manageable.

Emerging Technologies and Therapeutics for Type 1 Diabetes.

Recent years witnessed advancements in diabetes technologies and therapeutics. People with type 1 diabetes have more options to control their blood glucose, prevent hypoglycemia, and spend more time with their loved ones. Newer diabetes technologies and therapeutics improve the quality of life and boost the confidence of people with type 1 diabetes. In parallel to changes in the diabetes technology field, stem cell research has been evolving. Gene editing and production of ß cells from stem cells are ongoing. The current focus of cure studies is how to increase the survival of cells produced with stem cells. New adjunctive therapies are under development.

Efficacy and Safety of Tirzepatide in Adults With Type 1 Diabetes: A Proof of Concept Observational Study.

BACKGROUND: Tirzepatide is approved by the United States Food and Drug Administration (FDA) for the management of type 2 diabetes. The efficacy and safety of this drug have not been studied in people with type 1 diabetes (T1D). METHODS: In this single-center, retrospective, observational study, hemoglobin A1C (HbA1c), weight, body mass index (BMI), and continuous glucose monitoring (CGM) data were collected from electronic health records of adults with T1D at initiation of tirzepatide and at subsequent clinic visits over 8 months. Primary outcomes were reduction in HbA1c and percent change in body weight and secondary outcomes were change in CGM metrics and BMI over 8 months from baseline. RESULTS: The mean (±SD) age of the 26 adults (54% female) with T1D was 42 ± 8 years with a mean BMI of 36.7 ± 5.3 kg/m2. There was significant reduction in HbA1c by 0.45% at 3 months and 0.59% at 8 months, and a significant reduction in body weight by 3.4%, 10.5%, and 10.1% at 3, 6, and 8 months after starting tirzepatide. Time in target range (TIR = 70-180 mg/dL) and time in tight target range (TITR = 70-140 mg/dL) increased (+12.6%, P = .002; +10.7%, P = .0016, respectively) and time above range (TAR >180 mg/dL) decreased (-12.6%, P = .002) at 3 months, and these changes were sustained over 8 months. The drug was relatively safe and well tolerated with only 2 patients discontinuing the medication. CONCLUSIONS: Tirzepatide significantly reduced HbA1c and body weight in adults with T1D. A randomized controlled trial is needed to establish efficacy and safety of this drug in T1D.

Reduced Efficacy of Glucagon-Like Peptide-1 Receptor Agonists Therapy in People With Type 1 Diabetes and Genetic Forms of Obesity.

BACKGROUND: Once weekly Glucagon-Like Peptide-1 Receptor Agonists (GLP-1 RA) have been shown to improve glycemic outcomes and cause significant weight loss. However, 9% to 27% of individuals have little or no response to these drugs. In this article, we investigated the efficacy of GLP-1 RA therapy among adults with type 1 diabetes and obesity likely related to genetic mutations compared with obesity likely unrelated to genetic mutations. METHODS: In this retrospective study, we compared body weight and glycated hemoglobin (HbA1c) change with the use of GLP-1 RA therapy (including a dual agonist, Tirzepatide) over six months among adults with type 1 diabetes and obesity likely (n = 11, median age 39.5 years with a median BMI of 43.0 kg/m2) versus unlikely related to genetic mutation(s) (n = 15, median age 45.8 years with a median BMI of 38.7 kg/m2). RESULTS: Six months of GLP-1 RA treatment resulted in a numerically lower reduction of weight (-5.75 ± 9.46 kg vs -8.65 ± 9.36 kg, P = .44) and HbA1c (-0.28 ± 0.96% vs -0.43 ± 0.57%, P = .64) among individuals with obesity likely versus unlikely related to a genetic mutation(s), respectively. Fewer individuals with genetic obesity met goal weight loss ≥5% or HbA1c decrease ≥0.4% than did individuals with obesity unlikely related to a genetic cause (36.4% vs 80.0%, P = .04). CONCLUSIONS: The weight loss and glycemic lowering effects of GLP-1 RA therapy may be decreased in people with type 1 diabetes and obesity likely related to genetic causes. Further research is needed to understand GLP-1 RA mechanisms via energy regulating genes.

The Diabetes Technology Society Error Grid and Trend Accuracy Matrix for Glucose Monitors.

INTRODUCTION: An error grid compares measured versus reference glucose concentrations to assign clinical risk values to observed errors. Widely used error grids for blood glucose monitors (BGMs) have limited value because they do not also reflect clinical accuracy of continuous glucose monitors (CGMs). METHODS: Diabetes Technology Society (DTS) convened 89 international experts in glucose monitoring to (1) smooth the borders of the Surveillance Error Grid (SEG) zones and create a user-friendly tool-the DTS Error Grid; (2) define five risk zones of clinical point accuracy (A-E) to be identical for BGMs and CGMs; (3) determine a relationship between DTS Error Grid percent in Zone A and mean absolute relative difference (MARD) from analyzing 22 BGM and nine CGM accuracy studies; and (4) create trend risk categories (1-5) for CGM trend accuracy. RESULTS: The DTS Error Grid for point accuracy contains five risk zones (A-E) with straight-line borders that can be applied to both BGM and CGM accuracy data. In a data set combining point accuracy data from 18 BGMs, 2.6% of total data pairs equally moved from Zones A to B and vice versa (SEG compared with DTS Error Grid). For every 1% increase in percent data in Zone A, the MARD decreased by approximately 0.33%. We also created a DTS Trend Accuracy Matrix with five trend risk categories (1-5) for CGM-reported trend indicators compared with reference trends calculated from reference glucose. CONCLUSION: The DTS Error Grid combines contemporary clinician input regarding clinical point accuracy for BGMs and CGMs. The DTS Trend Accuracy Matrix assesses accuracy of CGM trend indicators.

Health Care Professionals' Perspectives on Use of Diabetes Technologies for Managing Visually Impaired Patients With Diabetes.

BACKGROUND: Despite well-established safety and efficacy of diabetes technologies in management of diabetes, their utility in visually impaired patients is currently unknown. We aimed to identify providers' preferences and challenges to prescribing diabetes technologies for visually impaired patients. METHODS: A survey-based study among health care providers in the United States seeing visually impaired patients with diabetes and using diabetes technologies was conducted. The quantitative and qualitative responses were analyzed using Student t test and χ2 tests. RESULTS: Of 79 providers, 66 completed the survey and were included in the analysis. Insulin pens and hybrid closed-loop systems were preferred insulin delivery systems for managing visually impaired patients with diabetes. Despite 96% of the providers recommending continuous glucose monitoring (CGM) for their visually impaired patients with diabetes, only 55% were aware of the voice-activation feature of Dexcom G6. Voice activation to announce glucose values and audible glucose alerts were two major incentives for selecting CGM. System initialization was seen as a prevalent challenge of using CGM in visually impaired patients with diabetes. Providers who are using CGM for visually impaired patients reported benefits for patients using the voice-activation feature for six months included HbA1c reductions of >0.5% (by 87% of providers) and less hypoglycemia (by 45% of providers). CONCLUSIONS: The CGM is the preferred glucose monitoring method for managing visually impaired patients with diabetes. Providers face many challenges of initiating CGM in these high-risk patients. Voice-activation feature of G6 CGM is underutilized and providers who used the system reported positive outcomes in visually impaired patients.

Increased Technology Use Associated With Lower A1C in a Large Pediatric Clinical Population.

OBJECTIVE: While continuous glucose monitors (CGMs), insulin pumps, and hybrid closed-loop (HCL) systems each improve glycemic control in type 1 diabetes, it is unclear how the use of these technologies impacts real-world pediatric care. RESEARCH DESIGN AND METHODS: We found 1,455 patients aged <22 years, with type 1 diabetes duration >3 months, and who had data from a single center in between both 2016-2017 (n = 2,827) and 2020-2021 (n = 2,731). Patients were grouped by multiple daily injections or insulin pump, with or without an HCL system, and using a blood glucose monitor or CGM. Glycemic control was compared using linear mixed-effects models adjusting for age, diabetes duration, and race/ethnicity. RESULTS: CGM use increased from 32.9 to 75.3%, and HCL use increased from 0.3 to 27.9%. Overall A1C decreased from 8.9 to 8.6% (P < 0.0001). CONCLUSIONS: Adoption of CGM and HCL was associated with decreased A1C, suggesting promotion of these technologies may yield glycemic benefits.

Prevalence of fear of hypoglycemia in adults with type 1 diabetes using a newly developed screener and clinician's perspective on its implementation.

INTRODUCTION: Fear of hypoglycemia (FoH) affects quality of life, emotional well-being, and diabetes management among people with type 1 diabetes (PwT1D). American Diabetes Association's (ADA) guidelines recommend assessing FoH in clinical practice. However, existing FoH measures are commonly used in research and not in clinical practice. In this study, prevalence of FoH was assessed in PwT1D using a newly developed FoH screener for clinical practice; its association with established measures and outcomes was also determined. In addition, healthcare providers' (HCPs) perspectives on implementing FoH screener into real-world practice were explored. RESEARCH DESIGN AND METHODS: This multiphase observational study used mixed methods in two phases. First, we collected a cross-sectional survey (including the screener) from PwT1D (≥18 years) from T1D Exchange Quality Improvement Collaborative adult clinics. Pearson correlations and regression analyses were performed on diabetes outcome measures using screener scores. Second, we conducted focus groups among HCPs who treat PwT1D and descriptive analysis to summarize results. RESULTS: We included 553 PwT1D. Participants had a mean±SD age of 38.9±14.2 years and 30% reported a high FoH total score. Regression analyses showed that higher A1c and higher number of comorbidities were significantly associated with high FoH (p<0.001). High FoH worry and behavior scores were significantly associated with 8-Item Patient Health Questionnaire and 7-Item Generalized Anxiety Disorder Scale scores. Participants with ≥1 severe hypoglycemia event(s) and impaired awareness of hypoglycemia had higher odds of high FoH. Eleven HCPs participated in focus group interviews; they expressed that the FoH screener is clinically necessary and relevant but poses implementation challenges that must be addressed. CONCLUSIONS: Our results demonstrate FoH is common in PwT1D and affects their psychosocial well-being and diabetes management. In alignment with ADA position statement, HCP focus group results emphasize importance of screening for FoH. Implementing this newly developed FoH screener may help HCPs identify FoH in PwT1D.

Racial Disparities in Diabetes Technology Adoption and Their Association with HbA1c and Diabetic Ketoacidosis.

Aim: Poorer glycemic control and higher diabetic ketoacidosis (DKA) rates are seen in racial/ethnic minorities with type 1 diabetes (T1D). Use of diabetes technologies such as continuous glucose monitors (CGM), continuous subcutaneous insulin infusion (CSII) and automated insulin delivery (AID) systems has been shown to improve glycemic control and reduce DKA risk. We examined race/ethnicity differences in diabetes technology use and their relationship with HbA1c and DKA. Methods: Data from patients aged ≥12 years with T1D for ≥1 year, receiving care from a single diabetes center, were examined. Patients were classified as Non-Hispanic White (n=3945), Non-Hispanic Black (Black, n=161), Hispanic (n=719), and Multiracial/Other (n=714). General linear models and logistic regression were used. Results: Black (OR=0.22, 0.15-0.32) and Hispanic (OR=0.37, 0.30-0.45) patients were less likely to use diabetes technology. This disparity was greater in the pediatric population (p-interaction=0.06). Technology use associated with lower HbA1c in each race/ethnic group. Among technology users, AID use associated with lower HbA1c compared to CGM and/or CSII (HbA1c of 8.4% vs 9.2%, respectively), with the greatest difference observed for Black adult AID users. CSII use associated with a lower odds of DKA in the past year (OR=0.73, 0.54-0.99), a relationship that did not vary by race (p-interaction =0.69); this inverse association with DKA was not observed for CGM or AID. Conclusion: Disparities in diabetes technology use, DKA, and glycemic control were apparent among Black and Hispanic patients with T1D. Differences in technology use ameliorated but did not fully account for disparities in HbA1c or DKA.

Efficacy and Safety of Tandem Control IQ Without User-Initiated Boluses in Adults with Uncontrolled Type 1 Diabetes.

Background: Adults with poorly controlled type 1 diabetes (T1D) who are missing meal boluses are typically excluded from clinical trials of diabetes technologies. We investigated the long-term real-life outcomes of the Tandem Control IQ automated insulin delivery (AID) system in adults with T1D who are missing meal boluses. Methods: In this single-center study with 30 adults with T1D, we evaluated efficacy (A1c and time in target range [TIR]) and safety (time below range [TBR]) in adults with T1D who initiated Tandem Control IQ with minimal or no user-initiated boluses (auto >90%) compared with age, gender, and diabetes duration matched adults with T1D with intermediate (auto 50%-90%) and high bolusing behaviors (auto 10%-49%). Results: Regardless of engagement with Control IQ system, there were significant improvements in A1c and TIR in all three groups over 3, 6, and 12 months. Compared with baseline, there was significant decrease in A1c by 1.6% ± 0.8% and increase in TIR by 19.3% ± 6.4% (P < 0.001 for both) over 12 months of Tandem Control IQ use in auto >90% use group without increasing TBR. Conclusions: Tandem Control IQ is effective in lowering A1c and improving TIR without increasing TBR regardless of users' engagement with the system. Therefore, adults with T1D with high A1c who are mostly missing meal boluses should not be considered as inappropriate candidates for Control IQ AID system.

Mitigation of Rebound Hyperglycemia With Real-Time Continuous Glucose Monitoring Data and Predictive Alerts.

BACKGROUND: Excess carbohydrate intake during hypoglycemia can lead to rebound hyperglycemia (RH). We investigated associations between RH and use of real-time continuous glucose monitoring (rtCGM) and an rtCGM system's predictive alert. METHODS: RH events were series of sensor glucose values (SGVs) >180 mg/dL starting within two hours of an antecedent SGV <70 mg/dL. Events were characterized by their frequency, duration (consecutive SGVs >180 mg/dL × five minutes), and severity (area under the glucose concentration-time curve). To assess the impact of rtCGM, data gathered during the four-week baseline phase (without rtCGM) and four-week follow-up phase (with rtCGM) from 75 participants in the HypoDE clinical trial (NCT02671968) of hypoglycemia-unaware individuals were compared. To assess the impact of predictive alerts, we identified a convenience sample of 24 518 users of an rtCGM system without predictive alerts who transitioned to a system whose predictive alert signals an SGV ≤55 mg/dL within 20 minutes (Dexcom G5 and G6, respectively). RH events from periods of blinded versus unblinded rtCGM wear and from periods of G5 and G6 wear were compared with paired t tests. RESULTS: Compared to RH events in the HypoDE baseline phase, the mean frequency, duration, and severity of events fell by 14%, 12%, and 23%, respectively, in the follow-up phase (all P < .05). Compared to RH events during G5 use, the mean frequency, duration, and severity of events fell by 7%, 8%, and 13%, respectively, during G6 use (all P < .001). CONCLUSIONS: Rebound hypreglycemia can be objectively quantified and mitigated with rtCGM and rtCGM-based predictive alerts.

Comparison of Cgmanalysis, a Free Open-Source Continuous Glucose Monitoring Data Management and Analysis Software, with Commercially Available CGM Platforms: Data Standardization for Diabetes Technology Research.

Background: Cgmanalysis is an open-source software based on the R programming language for data management and descriptive analysis of data from continuous glucose monitoring (CGM). We sought to validate the summary measures calculated by cgmanalysis against the results from proprietary software associated with four CGM commercially available models. Methods: Two weeks of data from 188 patients with type 1 diabetes using commercially available CGMs. Freestyle Libre Gen 1 (n = 53), Medtronic Guardian 3 (n = 52), Dexcom G6 reported by Dexcom Clarity (n = 48), and Dexcom G6 reported by Tandem (n = 35) were analyzed using proprietary software and cgmanalysis. Agreement was assessed using scatterplots, Bland-Altman plots, and equivalence tests. Results: Good agreement was obtained for all glycemic summary measures for all CGMs assessed. None of the differences between the cgmanalysis package and the manufacturers' software were outside the prespecified bounds of equivalence. Conclusions: Cgmanalysis is a validated open-source software to analyze commercially available CGM data and can be used to standardize diabetes technology research.

Differentiating Diabetic Ketoacidosis and Hyperglycemic Ketosis Due to Cannabis Hyperemesis Syndrome in Adults With Type 1 Diabetes.

OBJECTIVE: To differentiate diabetic ketoacidosis (DKA) from hyperglycemic ketosis due to cannabis hyperemesis syndrome (HK-CHS) in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS: Of 295 adults with type 1 diabetes who were seen with DKA-related ICD-10 codes, 68 patients with 172 DKA events meeting the inclusion criteria were analyzed. Cannabis use was defined as a positive urine test result for cannabis. Linear mixed models were used to define HK-CHS (pH ≥7.4 with bicarbonate ≥15 mmol/L [mEq/L]), and sensitivity and specificity were calculated using the receiver operating characteristic (ROC) curve. RESULTS: Cannabis users had significantly higher pH (7.42 ± 0.01 vs. 7.09 ± 0.02) and bicarbonate (19.2 ± 0.61 vs. 9.1 ± 0.71 mmol/L) (P < 0.0001) compared with nonusers. The area under the ROC curve for a positive cannabis urine test result predicting HK-CHS was 0.9892. CONCLUSIONS: In patients who present with DKA and higher pH, especially pH ≥7.4, cannabis use should be considered in the differential diagnosis.