Pyruvate dehydrogenase complex deficiency masked by septic shock-induced lactic acidosis: a case report.

Pyruvate dehydrogenase complex (PDHC) deficiency is a common genetic disorder leading to lactic acidosis, which can also result from several nongenetic conditions, such as septic shock. The present study reports a case of PDHC deficiency masked by septic shock-induced lactic acidosis. This case involved a 16-year-old adolescent with poor exercise tolerance compared with his peers, and no underlying diseases. The disease onset was characterized by cough, fever, and dyspnea, with hypotension and elevated lactate levels, which indicated septic shock. However, severe hypoglycemia and lactic acidosis persisted despite resolution of a pulmonary infection and correction of septic shock, requiring continuous intravenous infusion of 50% glucose. Although the patient did not experience acute kidney injury and had normal urine output, continuous renal replacement therapy was used to regulate the internal environment owing to the severity of the acidosis. The diagnosis of PDHC deficiency was considered on the basis of the persistent hypoglycemia and hyperlactatemia, before genetic mutation testing was completed. The clinical thinking process required a rich accumulation of pathophysiological knowledge. This article reports a case of PDHC deficiency masked by septic shock-induced lactic acidosis to raise awareness of the disease and avoid misdiagnosis and missed diagnosis.

Accuracy of continuous glucose monitoring during exercise-related hypoglycemia in individuals with type 1 diabetes.

Background: Hypoglycemia is common in individuals with type 1 diabetes, especially during exercise. We investigated the accuracy of two different continuous glucose monitoring systems during exercise-related hypoglycemia in an experimental setting. Materials and methods: Fifteen individuals with type 1 diabetes participated in two separate euglycemic-hypoglycemic clamp days (Clamp-exercise and Clamp-rest) including five phases: 1) baseline euglycemia, 2) plasma glucose (PG) decline ± exercise, 3) 15-minute hypoglycemia ± exercise, 4) 45-minute hypoglycemia, and 5) recovery euglycemia. Interstitial PG levels were measured every five minutes, using Dexcom G6 (DG6) and FreeStyle Libre 1 (FSL1). Yellow Springs Instruments 2900 was used as PG reference method, enabling mean absolute relative difference (MARD) assessment for each phase and Clarke error grid analysis for each day. Results: Exercise had a negative effect on FSL1 accuracy in phase 2 and 3 compared to rest (ΔMARD = +5.3 percentage points [(95% CI): 1.6, 9.1] and +13.5 percentage points [6.4, 20.5], respectively). In contrast, exercise had a positive effect on DG6 accuracy during phase 2 and 4 compared to rest (ΔMARD = -6.2 percentage points [-11.2, -1.2] and -8.4 percentage points [-12.4, -4.3], respectively). Clarke error grid analysis showed a decrease in clinically acceptable treatment decisions during Clamp-exercise for FSL1 while a contrary increase was observed for DG6. Conclusion: Physical exercise had clinically relevant impact on the accuracy of the investigated continuous glucose monitoring systems and their ability to accurately detect hypoglycemia.

A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study.

BACKGROUND: Older adults with diabetes are at high risk of severe hypoglycemia (SH). Many machine-learning (ML) models predict short-term hypoglycemia are not specific for older adults and show poor precision-recall. We aimed to develop a multidimensional, electronic health record (EHR)-based ML model to predict one-year risk of SH requiring hospitalization in older adults with diabetes. METHODS AND FINDINGS: We adopted a case-control design for a retrospective territory-wide cohort of 1,456,618 records from 364,863 unique older adults (age ≥65 years) with diabetes and at least 1 Hong Kong Hospital Authority attendance from 2013 to 2018. We used 258 predictors including demographics, admissions, diagnoses, medications, and routine laboratory tests in a one-year period to predict SH events requiring hospitalization in the following 12 months. The cohort was randomly split into training, testing, and internal validation sets in a 7:2:1 ratio. Six ML algorithms were evaluated including logistic-regression, random forest, gradient boost machine, deep neural network (DNN), XGBoost, and Rulefit. We tested our model in a temporal validation cohort in the Hong Kong Diabetes Register with predictors defined in 2018 and outcome events defined in 2019. Predictive performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) statistics, and positive predictive value (PPV). We identified 11,128 SH events requiring hospitalization during the observation periods. The XGBoost model yielded the best performance (AUROC = 0.978 [95% CI 0.972 to 0.984]; AUPRC = 0.670 [95% CI 0.652 to 0.688]; PPV = 0.721 [95% CI 0.703 to 0.739]). This was superior to an 11-variable conventional logistic-regression model comprised of age, sex, history of SH, hypertension, blood glucose, kidney function measurements, and use of oral glucose-lowering drugs (GLDs) (AUROC = 0.906; AUPRC = 0.085; PPV = 0.468). Top impactful predictors included non-use of lipid-regulating drugs, in-patient admission, urgent emergency triage, insulin use, and history of SH. External validation in the HKDR cohort yielded AUROC of 0.856 [95% CI 0.838 to 0.873]. Main limitations of this study included limited transportability of the model and lack of geographically independent validation. CONCLUSIONS: Our novel-ML model demonstrated good discrimination and high precision in predicting one-year risk of SH requiring hospitalization. This may be integrated into EHR decision support systems for preemptive intervention in older adults at highest risk.

Effect of switch from flash glucose monitoring to flash glucose monitoring with real-time alarms on hypoglycaemia in people with type 1 diabetes mellitus.

We aimed to evaluate the utility of the FreeStyle Libre 2 device for reducing time below range level 1 and level 2 compared with the Freestyle Libre device (without alarms) in people with type 1 diabetes mellitus. We conducted longitudinal observational follow-up study of a cohort of 100 people with type 1 diabetes mellitus who had switched from FreeStyle Libre to FreeStyle Libre 2 as part of routine clinical practice. Three months after switching to FreeStyle Libre 2, compared with results with FreeStyle Libre, there were a significant improvements in time below range level 1 (p = 0.02) and level 2 (p <0.001), time in range (p <0.001), time above range level 1 (p = 0.002), glucose management indicator (p= 0.04) and mean glucose (p= 0.04) during follow-up. Furthermore there was a significant direct association between age and change in TIR with a coefficient of 0.23, and a significant inverse association between age and change in TAR-1 with a coefficient of 0.11. Switching to a flash glucose monitoring system with alarms improves time below range, time in range and coefficient of variation in people with type 1 diabetes mellitus.

Análisis retrospectivo de las hipoglucemias inducidas por venlafaxina en pacientes con sobredosis / Retrospective analysis of venlafaxine-induced hypoglycemia in patients with overdose

Introducción: Estudios recientes en intoxicaciones por venlafaxina (VLF) relacionan la presencia de hipoglucemia con la dosis. Nuestro objetivo fue analizar las características clínicas de los pacientes que presentaron hipoglucemia inducida por sobredosis de VLF.Pacientes y métodosEstudio retrospectivo realizado en las Islas Baleares (2020-2023). Como criterios de inclusión se tomaron en cuenta las concentraciones séricas de VLF + ortodesmetilvenlafaxina (O-VLF) > 800 ng/mL. Se compararon las características de los pacientes con y sin hipoglucemia.ResultadosSe incluyeron 21 pacientes, ocho (38,1%) con hipoglucemia. No se hallaron diferencias en las dosis referidas en ambos grupos. Las concentraciones máximas de VLF + O-VLF (ng/mL) fueron 9.783 (4.459-17.976) en sujetos con hipoglucemia y 1.413 (930-1.769) en aquellos sin esta enfermedad (p<0,0001). La presencia de hipoglucemia se asoció con: menor edad y nivel de conciencia; y mayor frecuencia de tentativas suicidas, convulsiones, midriasis, taquicardia y síndrome serotoninérgico, soporte respiratorio invasivo, sueroterapia e ingreso en la Unidad de Cuidados Intensivos (UCI) (p < 0,05).ConclusionesLa detección de hipoglucemia en individuos intoxicados por VLF es un marcador fácilmente disponible para sospechar la gravedad del paciente. En cualquier caso, las concentraciones séricas, cuando se disponen, permiten confirmar la intoxicación. (AU)

Introduction: Recent publications relate the presence of hypoglycemia in venlafaxine (VLX) poisoning depending on the dose. Our aim was to analyze the clinical characteristics of patients who presented hypoglycemia induced by VLF overdose.Patients and methodsRetrospective study carried out in the Balearic Islands (2020–2023). Inclusion criteria: serum concentrations of VLX + O-desmethyl-venlafaxine (O-VLX)>800 ng/mL. The characteristics of patients with and without hypoglycemia were compared.ResultsTwenty-one patients were included, 8 (38.1%) with hypoglycemia. No differences were found in the doses referred to in both groups. Peak concentrations of VLX + O-VLX (ng/mL) were 9,783 [4,459–17,976] in patients with hypoglycemia and 1,413 [930–1,719] in patients without hypoglycemia (p<0.0001). The presence of hypoglycemia was associated with: lower age and level of consciousness; and higher frequency of suicide attempts, seizures, mydriasis, tachycardia and serotonin syndrome, invasive respiratory support, fluid therapy and ICU admission (p<0.05).ConclusionsThe detection of hypoglycemia in a VLX overdose case is a readily available marker to suspect the severity of the patient. In any case, serum concentrations when available allow us to confirm intoxication. (AU)

[Continuous glucose monitoring data: how can they be collected and used in practice?] / Les données de la mesure continue du glucose : comment les récupérer et pour quel usage dans la pratique ?

CONTINUOUS GLUCOSE MONITORING DATA: HOW CAN THEY BE COLLECTED AND USED IN PRACTICE? Continuous glucose monitoring (CGM) is becoming an essential part of diabetes management. The AGP report is obtained over a 14-day period, with at least 70% of captured data. The time spent in the 70-180 mg/dl targel range, withe a target of over 70% or 50% in frail patients, is a new parameter that is essential for assessing glycemic control via CGM. Complemented by estimated HBA1c, now called GMI (Glucose Management Indicator), the time spent in hypoglycemia (target inférieur 5% or even inférieur 1% for frail patients) and the coefficient of variation (target inférieur 36%), the CGM offers a very comprehensive analysis of blood glucose levels, with individualized treatment adjustments based on ambulatory blood glucose profiles.

One-step vs 2-step gestational diabetes mellitus screening and pregnancy outcomes: an updated systematic review and meta-analysis.

OBJECTIVE: This was a systematic review and meta-analysis comparing maternal and neonatal outcomes of patients screened with the 1-step or 2-step screening method for gestational diabetes mellitus. DATA SOURCES: PubMed, Scopus, Cochrane, ClinicalTrials.gov, and LILACS were searched from inception up to September 2022. STUDY ELIGIBILITY CRITERIA: Only randomized controlled trials were included. Studies that had overlapping populations were excluded (International Prospective Register of Systematic Review registration number: CRD42022358903). METHODS: Risk ratios were computed with 95% confidence intervals by 2 authors. Unpublished data were requested. Large for gestational age was the primary outcome. RESULTS: The search yielded 394 citations. Moreover, 7 randomized controlled trials met the inclusion criteria. A total of 54,650 participants were screened for gestational diabetes mellitus by either the 1-step screening method (n=27,163) or the 2-step screening method (n=27,487). For large for gestational age, there was no significant difference found between the groups (risk ratio, 0.99; 95% confidence interval, 0.93-1.05; I2=0%). Newborns of patients who underwent 1-step screening had higher rates of neonatal hypoglycemia (risk ratio, 1.24; 95% confidence interval, 1.14-1.34; I2=0%) and neonatal intensive care unit admissions (risk ratio, 1.13; 95% confidence interval, 1.04-1.21; I2=0%) than newborns of patients who underwent 2-step screening. Patients in the 1-step screening method group were more likely to be diagnosed with gestational diabetes mellitus (risk ratio, 1.73; 95% confidence interval, 1.44-2.09; I2=80%) than patients in the 2-step screening method group. In addition, among trials that tested all patients before randomization and excluded patients with pregestational diabetes mellitus, newborns were more likely to have macrosomia (risk ratio, 1.27; 95% confidence interval, 1.21-1.34; I2=0%). Overall risk of bias assessment was of low concern. CONCLUSION: Large for gestational age did not differ between patients screened using the 1-step screening method and those screened using the 2-step screening method. However, patients randomized to the 1-step screening method had higher rates of neonatal hypoglycemia and neonatal intensive care unit admission and maternal gestational diabetes mellitus diagnosis than the patients randomized to the 2-step screening method.

Bayesian evaluation of sensitivity and specificity of blood culture media and hypoglycemia in sepsis-suspected calves.

BACKGROUND: Sepsis is a life-threatening condition for which critically important antimicrobials are often indicated. The value of blood culture for sepsis is indisputable, but appropriate guidelines on sampling and interpretation are currently lacking in cattle. OBJECTIVE: Compare the diagnostic accuracy of 2 blood culture media (pediatric plus [PP] and plus aerobic [PA]) and hypoglycemia for bacteremia detection. Estimate the contamination risk of blood cultures in critically ill calves. ANIMALS: One hundred twenty-six critically ill calves, 0 to 114 days. METHODS: Retrospective cross-sectional study in which the performance of PP, PA and hypoglycemia to diagnose sepsis was assessed using a Bayesian latent class model. A Cox proportional hazards model was used to compare time to positivity (TTP). Potential contamination was descriptively analyzed. Isolates were considered relevant when they were; member of the Enterobacterales, isolated from both blood cultures vials, or well-known, significant bovine pathogens. RESULTS: The sensitivities for PP, PA, and hypoglycemia were higher when excluding assumed contaminants; 68.7% (95% credibility interval = 30.5%-93.7%), 87.5% (47.0%-99.5%), and 61.3% (49.7%-72.4%), respectively. Specificity was estimated at 95.1% (82.2%-99.7%), 94.2% (80.7%-99.7%), and 72.4% (64.6%-79.6%), respectively. Out of 121 interpretable samples, 14.9% grew a presumed contaminant in PA, PP, or both. There was no significant difference in the TTP between PA and PP. CONCLUSIONS AND CLINICAL IMPORTANCE: PA and PP appear to outperform hypoglycemia as diagnostic tests for sepsis. PA seems most sensitive, but a larger sample size is required to verify this. Accuracy increased greatly after excluding assumed contaminants. The type of culture did not influence TTP or the contamination rate.

Hypoglycaemia following the 2-hour 75g OGTT in pregnancy - Investigating maternal and foetal outcomes.

AIMS: To investigate differences in maternal and foetal outcomes in pregnancy, where patients developed hypoglycaemia following the 2-hour 75g oral glucose tolerance test (OGTT). METHOD: A retrospective cohort study of 200 pregnancies attending the Antenatal Clinic at Tameside General Hospital between 2018 and 2022. Outcomes were compared between 4 groups: normal OGTT [G1; (n = 39, 20%), diagnosis of gestational diabetes mellitus (GDM) based on OGTT [G2; BG ≥ 5.6 mmol/L or 2-h OGTT ≥7.8 (n = 41, 21%)], hypoglycaemia [G3; 2 h OGTT 3.0-3.9 mmol/L (n = 93, 47%)], or clinically significant hypoglycaemia [G4; 2 h OGTT <3.0 mmol/L (n = 27, 14%)]. Maternal BMI, foetal birth weight (FBW), neonatal complications, neo-natal intensive care unit (NICU) stay and conversion to GDM were assessed. RESULTS: Maternal BMI was lower in G3 and G4 (27.3 kg/m2 and 28.1 kg/m2 respectively) compared to G1 (30.4 kg/m2) (p = 0.02). NICU stay was more frequent in G3 (12%, n = 11) and G4 (8%, n = 2) compared to G1 (5%, n = 2). Foetal complications occurred in 27% of G3 (n = 25) and 33% of G4 (n = 9) compared to 23% in G1 (n = 9) and 17% in G2 (n = 7). FBW was similar in G1 when compared to G3 and G4 (p = 0.34). Of the 120 patients in G3 and G4, 25 patients self-monitored blood glucose for two weeks; 28% (n = 7) subsequently developed GDM. CONCLUSION: Higher rates of NICU stay and foetal complications were seen in both hypoglycaemic groups. In patients with hypoglycaemia following OGTT there is evidence to support self-monitoring blood glucose as 28% were later diagnosed with GDM.

Solitary pleural fibroma causing IGF-2-mediated hypoglycaemia in a non-diabetic patient.

A patient without a diagnosis of diabetes mellitus presented to the hospital due to a fall and hypoglycaemia on admission. The patient was found to have recurrent nocturnal fasting hypoglycaemia. CT revealed a large lung mass consistent with a solitary pleural fibroma, a rare tumour associated with insulin-like growth factor 2 (IGF-2) production. This case is an important reminder that potential causes of hypoglycaemia should be considered in non-diabetic patients.

Transitional Neonatal Hypoglycemia and Adverse Neurodevelopment in Midchildhood.

Importance: The circumstances under which neonatal hypoglycemia leads to brain damage remain unclear due to a lack of long-term data on the neurodevelopment of affected children. As a result, diagnostic strategies and treatment recommendations are inconsistent. Objective: To evaluate whether the occurrence of severe transitional neonatal hypoglycemia (defined as having at least 1 blood glucose measurement of 30 mg/dL or below) is associated with adverse neurodevelopment in midchildhood. Design, Setting, and Participants: This cohort study using neurodevelopmental testing of a retrospectively recruited cohort was conducted at a single-center tertiary hospital in Germany between March 2022 and February 2023. Children with neonatal blood glucose screening data were randomly selected from all births between 2010 and 2015. Frequency matching for sex, birth weight, gestational age, socioeconomic status, and primary risk factors for neonatal hypoglycemia was performed. Children with persistent hypoglycemia diseases or any risk factor for adverse neurodevelopment except hypoglycemia were excluded. Data were analyzed between February 2023 and March 2023. Exposure: At least 1 neonatal hypoglycemia measurement with blood glucose measuring 30 mg/dL or below vs all measured blood glucose levels above 30 mg/dL during postnatal blood glucose screening starting on the first day of life. Main Outcomes and Measures: Cognitive function measured by full-scale IQ test. Secondary outcomes included standardized scales of motor, visual, and executive functions, and child behavior, each measured at ages 7 to 11 years. Results: A total of 140 children (mean [SD] age 9.1 [1.3] years; 77 male [55.0%]) participated in the study. Children with severe neonatal hypoglycemia had a 4.8 points lower mean full-scale IQ than controls (107.0 [95% CI, 104.0-109.9] vs 111.8 [95% CI, 108.8-114.8]). They showed a 4.9-fold (95% CI, 1.5-15.5) increased odds of abnormal fine motor function and a 5.3-fold (95% CI, 2.1-13.3) increased odds of abnormal visual-motor integration. Significantly higher T scores for attention problems (58.2 [95% CI, 56.1-60.2] vs 54.6 [95% CI, 52.6-56.6]) and attention-deficit/hyperactivity disorder symptoms (58.2 [95% CI, 56.2-60.2] vs 54.7 [95% CI, 52.8-56.7]) were reported by parents. Conclusions and Relevance: Neonatal hypoglycemia with blood glucose levels of 30 mg/dL or below was associated with an increased risk for suboptimal neurodevelopmental outcomes in midchildhood. These findings imply that treatment strategies should aim to prevent episodes of hypoglycemia at these severely low levels.

Diagnostic Yield of Critical Sample and Elective Fast-Test in Children After a Hypoglycemic Event: Experience From a Single Center in Israel.

OBJECTIVE: To determine the diagnostic yield of the critical sample and fast-tests as dynamic function tests for the work-up of hypoglycemia in children. METHODS: A retrospective record review of children (0-18 years) with a diagnosis of hypoglycemia (glucose ≤ 50 mg/dL) was performed. A comparison of results of critical sample (obtained during an episode of hypoglycemia) and fast-test (performed to induce hypoglycemia in fasting state) was done. RESULTS: In 317 patients with hypoglycemia, data of 89 critical samples and 52 fast-tests were taken. Only 7 (7.8%) patients who underwent critical sample testing received an endocrine or metabolic diagnosis. No confirmatory diagnoses were made using the fast-tests. Idiopathic ketotic hypoglycemia was detected in 33/89 (37.1%) of critical samples and 21/52 (40.4%) of fast-tests. The completeness of workup including the hormonal and metabolic profile was <80% in both tests. CONCLUSION: The confirmatory yield of critical sample was better than fast-test. The processing of metabolic analytes was incomplete in a few, suggesting the need to rationalize the dynamic function testing.

Burden of impaired awareness of hypoglycemia in people with diabetes undergoing hemodialysis.

INTRODUCTION: Impaired awareness of hypoglycemia (IAH) refers to a diminished capacity to detect hypoglycemia. IAH can result in severe and even life-threatening outcomes for individuals with diabetes, especially those in advanced stages of the disease. This study aimed to assess the prevalence of IAH in people with diabetes on hemodialysis. RESEARCH DESIGN AND METHODS: We conducted a single-center audit to assess the prevalence of IAH using the Clarke questionnaire. Simultaneously, we measured fear of hypoglycemia with an adapted version of the Hypoglycemia Survey and recorded the incidence of severe hypoglycemia. Data were presented as mean±SD or counts/percentages. Logistic regression was then employed to analyze the association between IAH and various sociodemographic and clinical factors. RESULTS: We included 56 participants with diabetes on hemodialysis, with a mean age of 67.2 years (±12.9), of whom 51.8% were male. The ethnic distribution was 23.2% white, 23.2% black, 19.6% Asian, and 33.9% unspecified. The mean HbA1c was 52 mmol/mol (±18.6). The majority (91.1%) had a diagnosis of type 2 diabetes, and 55.4% of those were treated with insulin. The use of diabetes technology was low, with 2.8% of the participants using a continuous glucose monitor. IAH prevalence was 23.2%, and among the 57 participants, 23.6% had a history of severe hypoglycemia, and 60.6% reported fear of hypoglycemia. There were no significant differences in sociodemographic and clinical characteristics between those with IAH and normal hypoglycemia awareness. CONCLUSIONS: We observed that 23.2% of individuals with diabetes undergoing hemodialysis had IAH. IAH was more prevalent in people who reported a fear of hypoglycemia and had a history of severe hypoglycemia episode. The study highlights the unmet needs and disparities in access to diabetes technology within this population.

The impact of prior exposure to hypoglycaemia on the inflammatory response to a subsequent hypoglycaemic episode.

BACKGROUND: Hypoglycaemia has been shown to induce a systemic pro-inflammatory response, which may be driven, in part, by the adrenaline response. Prior exposure to hypoglycaemia attenuates counterregulatory hormone responses to subsequent hypoglycaemia, but whether this effect can be extrapolated to the pro-inflammatory response is unclear. Therefore, we investigated the effect of antecedent hypoglycaemia on inflammatory responses to subsequent hypoglycaemia in humans. METHODS: Healthy participants (n = 32) were recruited and randomised to two 2-h episodes of either hypoglycaemia or normoglycaemia on day 1, followed by a hyperinsulinaemic hypoglycaemic (2.8 ± 0.1 mmol/L) glucose clamp on day 2. During normoglycaemia and hypoglycaemia, and after 24 h, 72 h and 1 week, blood was drawn to determine circulating immune cell composition, phenotype and function, and 93 circulating inflammatory proteins including hs-CRP. RESULTS: In the group undergoing antecedent hypoglycaemia, the adrenaline response to next-day hypoglycaemia was lower compared to the control group (1.45 ± 1.24 vs 2.68 ± 1.41 nmol/l). In both groups, day 2 hypoglycaemia increased absolute numbers of circulating immune cells, of which lymphocytes and monocytes remained elevated for the whole week. Also, the proportion of pro-inflammatory CD16+-monocytes increased during hypoglycaemia. After ex vivo stimulation, monocytes released more TNF-α and IL-1ß, and less IL-10 in response to hypoglycaemia, whereas levels of 19 circulating inflammatory proteins, including hs-CRP, increased for up to 1 week after the hypoglycaemic event. Most of the inflammatory responses were similar in the two groups, except the persistent pro-inflammatory protein changes were partly blunted in the group exposed to antecedent hypoglycaemia. We did not find a correlation between the adrenaline response and the inflammatory responses during hypoglycaemia. CONCLUSION: Hypoglycaemia induces an acute and persistent pro-inflammatory response at multiple levels that occurs largely, but not completely, independent of prior exposure to hypoglycaemia. Clinical Trial information Clinicaltrials.gov no. NCT03976271 (registered 5 June 2019).

Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study.

INTRODUCTION: Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures. RESEARCH DESIGN AND METHODS: Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics. RESULTS: Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score. CONCLUSIONS: Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.