The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model

Heterogeneous treatment effects of sodium-glucose cotransporter 2 inhibitors on risk of dementia in people with type 2 diabetes: A population-based cohort study

INTRODUCTION

Sodium-glucose cotransporter 2 (SGLT2) inhibitors exhibit potential benefits in reducing dementia risk, yet the optimal beneficiary subgroups remain uncertain.

METHODS

Individuals with type 2 diabetes (T2D) initiating either SGLT2 inhibitor or sulfonylurea were identified from OneFlorida+ Clinical Research Network (2016-2022). A doubly robust learning was deployed to estimate risk difference (RD) and 95% confidence interval (CI) of all-cause dementia.

RESULTS

Among 35,458 individuals with T2D, 1.8% in the SGLT2 inhibitor group and 4.7% in the sulfonylurea group developed all-cause dementia over a 3.2-year follow-up, yielding a lower risk for SGLT2 inhibitors (RD, -2.5%; 95% CI, -3.0% to -2.1%). Hispanic ethnicity and chronic kidney disease were identified as the two important variables to define four subgroups in which RD ranged from -4.3% (-5.5 to -3.2) to -0.9% (-1.9 to 0.2).

DISCUSSION

Compared to sulfonylureas, SGLT2 inhibitors were associated with a reduced risk of all-cause dementia, but the association varied among different subgroups.

HIGHLIGHTS

New users of sodium-glucose cotransporter 2 (SGLT2) inhibitors were significantly associated with a lower risk of all-cause dementia as compared to those of sulfonylureas. The association varied among different subgroups defined by Hispanic ethnicity and chronic kidney disease. A significantly lower risk of Alzheimer's disease and vascular dementia was observed among new users of SGLT2 inhibitors compared to those of sulfonylureas.

Develop a bone mineral density T-score distribution nomograms based on osteoporosis risk factors for middle-aged and older adults

PURPOSE

This study aimed to understand how age, health status, and lifestyle impact bone mineral density (BMD) in middle-aged and older adults, focusing on predicting osteoporosis risk.

METHODS

This study included 2836 participants aged 50-88 from the Health Improvement Program of Bone (HOPE) conducted from 2021 to 2023. We used logistic regression to make a prediction tool. Then checked its accuracy and reliability using receiver operating characteristic (ROC) and calibration curves.

RESULTS

Factors like age, body weight, prior fractures, and smoking were independently found to affect BMD T-score distribution in men. In women, age and body weight were identified as independent factors influencing BMD T-score distribution. A nomogram was created to visually illustrate these predictive relationships.

CONCLUSIONS

The nomogram proved highly accurate in identifying men aged 50 and above and postmenopausal women based on their BMD T-score distribution, improving clinical decision-making and patient care in osteoporosis evaluation and treatment.

A machine learning model to predict therapeutic inertia in type 2 diabetes using electronic health record data

OBJECTIVE

To estimate the therapeutic inertia prevalence for patients with type 2 diabetes, develop and validate a machine learning model predicting therapeutic inertia, and determine the added predictive value of area-level social determinants of health (SDOH).

METHODS

This prognostic study with a retrospective cohort design used OneFlorida data (linked electronic health records (EHRs) from 1240 practices/clinics in Florida). The study cohort included adults (aged ≥ 18) with type 2 diabetes, HbA1C ≥ 7% (53 mmol/mol), ≥one ambulatory visit, and ≥one antihyperglycemic medication prescribed (excluded patients prescribed insulin before HbA1C). The outcome was therapeutic inertia, defined as absence of treatment intensification within six months after HbA1C ≥ 7% (53 mmol/mol). The predictors were patient, provider, and healthcare system factors. Machine learning methods included gradient boosting machines (GBM), random forests (RF), elastic net (EN), and least absolute shrinkage and selection operator (LASSO). The DeLong test compared the discriminative ability (represented by C-statistics) between models.

RESULTS

The cohort included 31,087 patients with type 2 diabetes (mean age = 58.89 (SD = 13.27) years, 50.50% male, 58.89% White). The therapeutic inertia prevalence was 39.80% among the 68,445 records. GBM outperformed (C-statistic from testing sample = 0.84, 95% CI = 0.83-0.84) RF (C-statistic = 0.80, 95% CI = 0.79-0.80), EN (C-statistic = 0.80, 95% CI = 0.80-0.81), and LASSO (C-statistic = 0.80, 95% CI = 0.80-0.81), p < 0.05. Area-level SDOH significantly increased the discriminative ability versus models without SDOH (C-statistic for GBM = 0.84, 95% CI = 0.84-0.85 vs. 0.84, 95% CI = 0.83-0.84), p < 0.05.

CONCLUSIONS

Using EHRs of patients with type 2 diabetes from a large state, machine learning predicted therapeutic inertia (prevalence = 40%). The model's ability to predict patients at high risk of therapeutic inertia is clinically applicable to diabetes care.

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

BACKGROUND

The growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management.

OBJECTIVE

This study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program.

METHODS

Registry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants' program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant's program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy.

RESULTS

The ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes.

CONCLUSIONS

This study explored the Livongo for Diabetes RDMP participants' temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant's diabetes management.

Prognostic nomogram for uncontrolled type 2 diabetes using Thailand nation-wide cross-sectional studies

BACKGROUND

Uncontrolled type 2 diabetes (T2DM) and limited hemoglobin A1c (HbA1c) levels examination are a burden in community hospitals in Thailand. The nomogram from the patients' information might be a practical solution to identify a high-risk group of diabetic complications. Thus, this study aimed to establish an effective prognostic nomogram for patients with uncontrolled T2DM.

METHODS

Sequential nationwide cross-sectional studies of T2DM patients in 2018 and 2015 were utilized for development and validation groups, respectively, with this chronological order aiming to capture recent trends during development and assess the nomogram's robustness across diverse timeframes. The predictive outcome was uncontrolled T2DM, defined as HbA1c ≥9%. The model was determined by multivariable regression analysis and established an effective prognostic nomogram. The receiver operating characteristic curve, Hosmer-Lemeshow goodness of fit test, and decision curve analysis (DCA) was applied to evaluate the performance of the nomogram.

RESULTS

In 2018, 24% of the 38,568 participants in the development group had uncontrolled T2DM (defined as Hba1c ≥9%). The predictive nomogram of uncontrolled diabetes consisted of demographic characteristics, prescription medications, history of diabetic complications, and laboratory results (C-statistic of 0.77). The goodness of fit test and DCA showed good agreement between the result and clinical application for T2DM.

CONCLUSION

The predictive nomogram demonstrates simplicity, accuracy, and valuable prediction to enhance diabetic care in resource-limited countries, including Thailand.

Diabetes-related complications, glycemic levels, and healthcare utilization outcomes after therapeutic inertia in type 2 diabetes mellitus

AIMS

To assess diabetes-related complications, glycemic levels, and healthcare utilization 12 months after exposure to therapeutic inertia among patients with type 2 diabetes mellitus (T2D).

METHODS

This retrospective cohort study analyzed data from the OneFlorida Clinical Research Consortium (electronic health records from Florida practices/clinics). The cohort included adult patients (≥18 years old) with T2D who had an HbA1c≥7.0% (53 mmol/mol) recorded from January 1, 2014-September 30, 2019. Therapeutic inertia (exposed vs. not exposed) was evaluated during the six months following HbA1c≥7.0% (53 mmol/mol). The outcomes assessed during the 12-month follow-up period included diabetes-related complications (continuous Diabetes Complications and Severity Index (DCSI)), glycemic levels (continuous follow-up HbA1c lab), and healthcare utilization counts. We analyzed data using multivariable regression models, adjusting for covariates.

RESULTS

The cohort included 26,881 patients with T2D (58.94% White race, 49.72% female, and mean age of 58.82 (SD=13.09)). After adjusting for covariates, therapeutic inertia exposure was associated with lower DCSI (estimate=-0.14 (SE=0.03), p < 0.001), higher follow-up HbA1c (estimate=0.14 (SE=0.04), p < 0.001), and lower rates of ambulatory visits (rate ratio=0.79, 95% CI=0.75-0.82).

CONCLUSIONS

Findings communicate the clinical practice implications and public health implications for combating therapeutic inertia in diabetes care.

Treatment approaches and costs associated with diabetes clinical metrics as measured by Healthcare Effectiveness Data and Information Set (HEDIS)

BACKGROUND

The clinical outcomes of diabetes can be influenced by primary care providers' (PCP) treatment approaches. This study explores the association between PCP approaches to management and performance measured by established diabetes metrics and related costs.

METHODS

In phase one, Electronic Medical Records were used to extract diabetes related metrics using Healthcare Effectiveness Data and Information Set (HEDIS), for patients with diabetes who had office visits to 44 PCP practices from April 2019 to March 2020. Using those metrics and scoring system, PCP practices were ranked and then categorized into high- and low-performing groups (top and bottom 25%, n = 11 each), with a total of 19,059 clinic visits by patients with a diagnosis of diabetes. Then extensive analysis was performed to evaluate a correlation between treatment approaches and diabetes outcomes across the top and bottom performing practices. In phase 2, patients with diabetes who were attributed to the aforementioned PCP practices were identified in a local health plan claims data base (a total of 3,221 patients), and the allowed amounts from their claims were used to evaluate differences in total and diabetes-related healthcare costs by providers' performance.

RESULTS

Comparing 10,834 visits in high-performing practices to 8,235 visits in low-performing practices, referrals to certified diabetes care and education specialists and provider-to-provider electronic consults (e-consults) were higher in high-performing practices (Z = 6.06, p < .0001), while traditional referrals were higher in low-performing practices (Z = -6.94, p < .0001). The patient-to-provider ratio was higher in the low-performing group (M = 235.23) than in the high-performing group (M = 153.26) (Z = -2.82, p = .0048). Claims data analysis included 1,825 and 1,396 patients from high- and low-performing providers, respectively. The patient-to-provider ratio was again higher in the low-performing group (p = .009, V = 0.62). Patients receiving care from lower-performing practices were more likely to have had a diabetes-related hospital observation (5.7% vs. 3.9%, p = .02; V = 0.04) and higher diabetes-related care costs (p = .002; d = - 0.07); these differences by performance status persisted when controlling for differences in patient and physician characteristics. Patients seeing low-performing providers had higher Charlson Comorbidity Index scores (Mdn = 3) than those seeing high-performing providers (Mdn = 2).

CONCLUSIONS

Referrals to the CDCES and e-Consult were associated with better measured diabetes outcomes, as were certain aspects of cost and types of hospital utilization. Higher patients to providers ratio and patients with more comorbidities were observed in low performing group.

Machine learning-based nomogram to predict poor response to overnight orthokeratology in Chinese myopic children: A multicentre, retrospective study

PURPOSE

To develop and validate an effective nomogram for predicting poor response to orthokeratology.

METHODS

Myopic children (aged 8-15 years) treated with orthokeratology between February 2018 and January 2022 were screened in four hospitals of different tiers (i.e. municipal and provincial) in China. Potential predictors included 32 baseline clinical variables. Nomogram for the outcome (1-year axial elongation ≥0.20 mm: poor response; <0.20 mm: good response) was computed from a logistic regression model with the least absolute shrinkage and selection operator. The data from the First Affiliated Hospital of Chengdu Medical College were randomly assigned (7:3) to the training and validation cohorts. An external cohort from three independent multicentre was used for the model test. Model performance was assessed by discrimination (the area under curve, AUC), calibration (calibration plots) and utility (decision curve analysis).

RESULTS

Between January 2022 and March 2023, 1183 eligible subjects were screened from the First Affiliated Hospital of Chengdu Medical College, then randomly divided into training (n = 831) and validation (n = 352) cohorts. A total of 405 eligible subjects were screened in the external cohort. Predictors included in the nomogram were baseline age, spherical equivalent, axial length, pupil diameter, surface asymmetry index and parental myopia (p < 0.05). This nomogram demonstrated excellent calibration, clinical net benefit and discrimination, with the AUC of 0.871 (95% CI 0.847-0.894), 0.863 (0.826-0.901) and 0.817 (0.777-0.857) in the training, validation and external cohorts, respectively. An online calculator was generated for free access (http://39.96.75.172:8182/#/nomogram).

CONCLUSION

The nomogram provides accurate individual prediction of poor response to overnight orthokeratology in Chinese myopic children.

Advancing type 2 diabetes therapy with iGlarLixi in older people: Pooled analysis of four randomized controlled trials

AIM

To assess the efficacy and safety of iGlarLixi in older people (≥65 years) with type 2 diabetes (T2D) advancing or switching from oral agents, a glucagon-like peptide-1 receptor agonist (GLP-1RA), or basal insulin.

MATERIALS AND METHODS

The data of participants aged <65 years and ≥65 years from four LixiLan trials (LixiLan-O, LixiLan-G, LixiLan-L, SoliMix) were evaluated over 26 or 30 weeks.

RESULTS

Participants aged <65/≥65 years (n = 1039/n = 497) had a mean baseline body mass index of 31.4 and 30.7 kg/m2 and glycated haemoglobin (HbA1c) concentration of 66 mmol/mol (8.2%) and 65 mmol/mol (8.1%), respectively. Least squares mean HbA1c change from baseline to end of treatment (EOT) was -14.32 mmol/mol (-1.31%) (95% confidence interval [CI] -14.97, -13.77 [-1.37%, -1.26%]) for those aged <65 years and -13.66 mmol/mol (-1.25%) (95% CI -14.54, -12.79 [-1.33%, -1.17%]) for those aged ≥65 years. At EOT, achievement of HbA1c targets was similar between the group aged <65 years and the group aged ≥65 years: <53 mmol/mol (<7%) (59.0% and 56.5%, respectively), <59 mmol/mol (<7.5%) (75.5% and 73.0%, respectively) and <64 mmol/mol (<8%) (83.8% and 84.1%, respectively). The incidence and event rate of American Diabetes Association Level 1 hypoglycaemia during the studies were also comparable between the two groups: 26.7% and 28.2% and 1.7 and 2.1 events per patient-year for the group aged <65 years and the group aged ≥65 years, respectively. A clinically relevant reduction in HbA1c (>1% from baseline for HbA1c ≥64 mmol/mol [≥8%] or ≥0.5% from baseline for HbA1c <64 mmol/mol [<8%]) without hypoglycaemia was attained by 50.0% and 47.6% of participants aged <65 years and ≥65 years, respectively. Adverse events were similar between the two age groups.

CONCLUSIONS

iGlarLixi is a simple, well-tolerated, once-daily alternative for treatment advancement in older people with T2D that provides significant improvements in glycaemic control without increasing hypoglycaemia risk, thus reducing the treatment burden.

BEYOND 2 years: durability of metabolic benefits by simplification of complex insulin regimens in type 2 diabetes

PURPOSE

To assess the magnitude and durability of the metabolic benefits by simplification of complex insulin treatments in patients with type 2 diabetes inadequately controlled by a full basal-bolus insulin regimen. Herein we report the results of the scheduled 2-year extension of the BEYOND trial.

METHODS

Originally, 305 participants with inadequate glycemic control (HbA1c > 7.5%) were randomly assigned to intensification of basal-bolus insulin regimen (n = 101), to a fixed-ratio combination (basal insulin + GLP-1RA, n = 102), or to an association of basal insulin plus an SGLT-2 inhibitor (gliflo-combo, n = 102). The primary efficacy outcome was change from baseline in HbA1c at 24 months assessed by an intention-to-treat analysis. A per-protocol analysis was also performed.

RESULTS

Fifty-five percent of patients completed the study in the two comparison arms. Compared with patients randomized to basal-bolus, patients of the other groups experienced non statistically different reductions in HbA1c level according to either an intention-to-treat analysis (-0.8 ± 1.1%, -0.7 ± 1.1%, and -1.3 ± 1.1%, mean ± SD, fixed-ratio, gliflo-combo and basal bolus, respectively) or per-protocol analysis (-1.2 ± 1.0%, -1.2 ± 1.1%, and -1.3 ± 1.0%, respectively). The final HbA1c level (per protocol) was 7.2 ± 0.8%, 7.3 ± 0.9%, and 7.5 ± 0.9%, respectively (P = NS). Treatment satisfaction (DTSQ) increased in both exchange groups, whereas the proportion of patients with hypoglycemia was lower.

CONCLUSION

Simplification of complex insulin regimen may be a durable option in at least one-half of patients with type 2 diabetes.

CLINICAL TRIAL REGISTRATION

Clinical trial registration no. NCT04196231, clinicaltrials.gov.

Predictors of lack of glycemic control in persons with type 2 diabetes

BACKGROUND

Professional guidelines recommend an HbA1c < 7% for most people with diabetes and < 8.5% for those with relaxed glycemic goals. However, many people with type 2 diabetes mellitus (T2DM) are unable to achieve the desired HbA1c goal. This study evaluated factors associated with lack of improvement in HbA1c over 3 years.

METHODS

All patients with T2DM treated within a major academic healthcare system during 2015-2020, who had at least one HbA1c value > 8.5% within 3 years from their last HbA1c were included in analysis. Patients were grouped as improved glycemic control (last HbA1c ≤ 8.5%) or lack of improvement (last HbA1c > 8.5%). Multivariate logistic regression analysis was performed to assess independent predictors of lack of improvement in glycemic control.

RESULTS

Out of 2,232 patients who met the inclusion criteria, 1,383 had an improvement in HbA1c while 849 did not. In the fully adjusted model, independent predictors of lack of improvement included: younger age (odds ratio, 0.89 per 1-SD [12 years]; 95% CI, 0.79-1.00), female gender (1.30, 1.08-1.56), presence of hypertension (1.29, 1.08-1.55), belonging to Black race (1.32, 1.04-1.68, White as reference), living in low income area (1.86,1.28-2.68, high income area as reference), and insurance coverage other than Medicare (1.32, 1.05-1.66). Presence of current smoking was associated with a paradoxical improvement in HbA1c (0.69, 0.47-0.99). In a subgroup analysis, comparing those with all subsequent HbA1c values > 8.5% (N = 444) to those with all subsequent HbA1c values < 8.5% (N = 341), similar factors were associated with lack of improvement, but smoking was no longer significant.

CONCLUSION

We conclude that socioeconomic factors like race, type of insurance coverage and living in low-income areas are associated with lack of improvement in HbA1c over a period of 3-years in people with T2DM. Intervention strategies focused on low-income neighborhoods need to be designed to improve diabetes management.

Examining Health Inequities in A1C Control over Time across Individual, Geospatial, and Geopolitical Factors among Adults with Type 2 Diabetes: Analyses of a Sample from One Commercial Insurer in a Southern State

INTRODUCTION

Type 2 diabetes impacts millions and poor maintenance of diabetes can lead to preventable complications, which is why achieving and maintaining target A1C levels is critical. Thus, we aimed to examine inequities in A1C over time, place, and individual characteristics, given known inequities across these indicators and the need to provide continued surveillance.

METHODS

Secondary de-identified data from medical claims from a single payer in Texas was merged with population health data. Generalized Estimating Equations were utilized to assess multiple years of data examining the likelihood of having non-target (>7% and ≥7%, two slightly different cut points based on different sources) and separately uncontrolled (>9%) A1C. Adults in Texas, with a Type 2 Diabetes (T2D) flag and with A1C reported in first quarter of the year using data from 2016 and 2019 were included in analyses.

RESULTS

Approximately 50% had A1Cs within target ranges (<7% and ≤7%), with 50% considered having non-target (>7% and ≥7%) A1Cs; with 83% within the controlled ranges (≤9%) as compared to approximately 17% having uncontrolled (>9%) A1Cs. The likelihood of non-target A1C was higher among those individuals residing in rural (vs urban) areas (P < .0001); similar for the likelihood of reporting uncontrolled A1C, where those in rural areas were more likely to report uncontrolled A1C (P < .0001). In adjusted analysis, ACA enrollees in 2016 were approx. 5% more likely (OR = 1.049, 95% CI = 1.002-1.099) to have non-target A1C (≥7%) compared to 2019; in contrast non-ACA enrollees were approx. 4% more likely to have non-target A1C (≥7%) in 2019 compared to 2016 (OR = 1.039, 95% CI = 1.001-1.079). In adjusted analysis, ACA enrollees in 2016 were 9% more likely (OR = 1.093, 95% CI = 1.025-1.164) to have uncontrolled A1C compared to 2019; whereas there was no significant change among non-ACA enrollees.

CONCLUSIONS

This study can inform health care interactions in diabetes care settings and help health policy makers explore strategies to reduce health inequities among patients with diabetes. Key partners should consider interventions to aid those enrolled in ACA plans, those in rural and border areas, and who may have coexisting health inequities.

A Cohort Study on the Outcome of Diabetic Foot Ulcers

BACKGROUND

Diabetic foot ulcers (DFUs) represent a significant and challenging complication of diabetes mellitus, often leading to serious morbidity and a substantial burden on healthcare systems. The study was conducted with the objectives of evaluating the outcomes of DFUs.

MATERIALS AND METHODS

A cohort study was conducted to evaluate the outcomes of DFUs from May 2019 to May 2020 at a tertiary care hospital located in Chennai. The study included patients aged 18 to 90 years who were diagnosed with DFUs. Individuals with diabetic foot lesions (skin lesions such as fissures, abscess, cellulites) other than ulcers or those without diabetes were excluded. The data was collected from a total of 100 diabetic patients using systematic random sampling technique.

RESULTS

The mean (SD) age of the study participants was 54.68 (6.72) years with males constituting 56% of the study population. Among 100 participants, 65% experienced healing while 35% did not. Logistic regression analysis showed that glycated haemoglobin (HbA1c) levels, age, and diabetes duration had significant effect on patient outcome. Logistic regression analysis showed that HbA1c levels, age, and diabetes duration had significant effect on patient outcome. Out of 12 patients with major amputation, seven (58.3%) survived, while out of 19 patients with minor amputations, 18 (94.7%) showed remarkably higher survival rate. Meanwhile, 100% survival rate was observed in patients with no amputation.

CONCLUSION

The study's comprehensive assessment of risk factors and their associations with healing outcomes provides essential knowledge for clinical practice. The study findings collectively support the optimization of interventions and strategies to prevent and manage DFUs, ultimately improving patient care and enhancing their quality of life. The study highlights the significance of glycemic control and limb preservation in DFU management.

Medication use and contextual factors associated with meeting guideline-based glycemic levels in diabetes among a nationally representative sample

Introduction

Based on the long-lasting diabetes management challenges in the United States, the objective was to examine glycemic levels among a nationally representative sample of people with diabetes stratified by prescribed antihyperglycemic treatment regimens and contextual factors.

Methods

This serial cross-sectional study used United States population-based data from the 2015 to March 2020 National Health and Nutrition Examination Surveys (NHANES). The study included non-pregnant adults (≥20 years old) with non-missing A1C and self-reported diabetes diagnosis from NHANES. Using A1C lab values, we dichotomized the outcome of glycemic levels into <7% versus ≥7% (meeting vs. not meeting guideline-based glycemic levels, respectively). We stratified the outcome by antihyperglycemic medication use and contextual factors (e.g., race/ethnicity, gender, chronic conditions, diet, healthcare utilization, insurance, etc.) and performed multivariable logistic regression analyses.

Results

The 2042 adults with diabetes had a mean age of 60.63 (SE = 0.50), 55.26% (95% CI = 51.39-59.09) were male, and 51.82% (95% CI = 47.11-56.51) met guideline-based glycemic levels. Contextual factors associated with meeting guideline-based glycemic levels included reporting an "excellent" versus "poor" diet (aOR = 4.21, 95% CI = 1.92-9.25) and having no family history of diabetes (aOR = 1.43, 95% CI = 1.03-1.98). Contextual factors associated with lower odds of meeting guideline-based glycemic levels included taking insulin (aOR = 0.16, 95% CI = 0.10-0.26), taking metformin (aOR = 0.66, 95% CI = 0.46-0.96), less frequent healthcare utilization [e.g., none vs. ≥4 times/year (aOR = 0.51, 95% CI = 0.27-0.96)], being uninsured (aOR = 0.51, 95% CI = 0.33-0.79), etc.

Discussion

Meeting guideline-based glycemic levels was associated with medication use (taking vs. not taking respective antihyperglycemic medication classes) and contextual factors. The timely, population-based estimates can inform national efforts to optimize diabetes management.

Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model

The effectiveness of using artificial intelligence (AI) systems to perform diabetic retinal exams ('screening') on preventing vision loss is not known. We designed the Care Process for Preventing Vision Loss from Diabetes (CAREVL), as a Markov model to compare the effectiveness of point-of-care autonomous AI-based screening with in-office clinical exam by an eye care provider (ECP), on preventing vision loss among patients with diabetes. The estimated incidence of vision loss at 5 years was 1535 per 100,000 in the AI-screened group compared to 1625 per 100,000 in the ECP group, leading to a modelled risk difference of 90 per 100,000. The base-case CAREVL model estimated that an autonomous AI-based screening strategy would result in 27,000 fewer Americans with vision loss at 5 years compared with ECP. Vision loss at 5 years remained lower in the AI-screened group compared to the ECP group, in a wide range of parameters including optimistic estimates biased toward ECP. Real-world modifiable factors associated with processes of care could further increase its effectiveness. Of these factors, increased adherence with treatment was estimated to have the greatest impact.

The state of diabetes care and obstacles to better care in Aceh, Indonesia: a mixed-methods study

BACKGROUND

Cardio-metabolic diseases are a major cause of death worldwide, including in Indonesia, where diabetes is one of the most critical diseases for the health system to manage.

METHODS

We describe the characteristics, levels of control, health behavior, and diabetes-related complications of diabetes patients in Aceh, Indonesia. We use baseline data and blood testing from a randomized-controlled trial. We conducted semi-structured interviews with eight health providers from Posbindu and Prolanis programs that target diabetes and other non-communicable diseases (NCDs). We also conducted three focus group discussions with 24 diabetes patients about their experiences of living with diabetes and the existing support programs.

RESULTS

The blood tests revealed average HbA1c levels indicative of poor glycemic control in 75.8 percent of patients and only 20.3 percent were free from any symptoms. Our qualitative findings suggest that patients are diagnosed after diabetes-related symptoms manifest, and that they find it hard to comply with treatment recommendations and lifestyle advice. The existing programs related to NCDs are not tailored to their needs.

CONCLUSION

We identify the need to improve diabetes screening to enable earlier treatment and achieve better control of the disease. Among diagnosed patients, there are widespread beliefs about diabetes medication and alternative forms of treatment that need to be addressed in a respectful dialogue between healthcare professionals and patients. Current diabetes screening, treatment and management programs should be revised to meet the needs of the affected population and to better respond to the increasing burden of this disease.

Tirzepatide for the treatment of adults with type 2 diabetes: An endocrine perspective

Tirzepatide is a novel glucose-dependent insulinotropic polypeptide/glucagon-like peptide 1 (GLP-1) receptor agonist approved in the United States as an adjunct to diet and exercise to improve glycaemic control in adults with type 2 diabetes and under investigation for use in chronic weight management, major adverse cardiovascular events and the management of other conditions, including heart failure with preserved ejection fraction and obesity and non-cirrhotic non-alcoholic steatohepatitis. The Phase 3 SURPASS 1-5 clinical trial programme was designed to assess efficacy and safety of once-weekly subcutaneously injected tirzepatide (5, 10 and 15 mg), as monotherapy or combination therapy, across a broad spectrum of people with type 2 diabetes. Use of tirzepatide in clinical studies was associated with marked reductions of glycated haemoglobin (-1.87 to -2.59%, -20 to -28 mmol/mol) and body weight (-6.2 to -12.9 kg), as well as reductions in parameters commonly associated with heightened cardiometabolic risk such as blood pressure, visceral adiposity and circulating triglycerides. In SUPRASS-2, these reductions were greater than with the GLP-1 receptor agonist semaglutide 1 mg. Tirzepatide was well tolerated, with a low risk of hypoglycaemia when used without insulin or insulin secretagogues and showed a generally similar safety profile to the GLP-1 receptor agonist class. Accordingly, evidence from these clinical trials suggests that tirzepatide offers a new opportunity for the effective lowering of glycated haemoglobin and body weight in adults with type 2 diabetes.

Development and validation of a novel nomogram for predicting the occurrence of myopia in schoolchildren: A prospective cohort study

PURPOSE

Myopia is a major public health issue and occurs at young ages. Apart from its high prevalence, myopia results in high costs and irreversible blinding diseases. Accurate prediction of the risk of myopia onset is crucial for its precise prevention. We aimed to develop and validate an effective nomogram for predicting myopia onset in schoolchildren.

DESIGN

School-based prospective cohort study.

METHODS

A total of 1073 schoolchildren were enrolled from November 2014 to May 2019 in China, and were divided into the training and validation cohorts. Myopia was defined as a spherical equivalent refraction (SER) ≤-0.5 diopters. Predictors of myopia were determined through the least absolute shrinkage and selection operator regression and multivariable Cox proportional hazard model based on the training cohort. The predictive performance of the nomogram was validated internally through time-dependent receiver operating characteristic (ROC) curves, calibration plot, decision curve analysis, and Kaplan-Meier curves.

RESULTS

Independent predictors at baseline including gender, SER, axial length, corneal refractive power, and positive relative accommodation were included in the nomogram prediction model. This nomogram demonstrated excellent calibration, clinical net benefit, and discrimination, with all the area under the ROC curves (AUCs) between 0.74 and 0.86 in the training and validation cohorts. The Kaplan-Meier curves showed that 3 distinct risk groups stratified through X-tile analysis were well discriminated and robust among subgroups. The Harrell's C-index and net reclassification improvement demonstrated that the nomogram substantially improved compared with previous models. An online myopia risk calculator was generated for better individual prediction.

CONCLUTIONS

The nomogram provides accurate and individual prediction of myopia onset in schoolchildren. External validation is needed to verify the generalizability of this nomogram.

Association of musculoskeletal pain with the achievement of treatment targets for type 2 diabetes among primary care patients

AIMS

To assess the association of diagnosed musculoskeletal (MS) pain (low back, neck, shoulder, and knee pain; and the number of pain sites) with the achievement of targets for glycosylated haemoglobin A1c (HbA1c), low-density-lipoprotein cholesterol (LDL), and systolic blood pressure (SBP) among primary care patients with type 2 diabetes (T2D).

METHODS

The cross-sectional study population consisted of 3478 patients with a registry-based T2D diagnosis and available registry-based data on MS pain diagnoses, covariates, and outcomes between 2016 and 2019. Logistic regression analysis was used to evaluate the study aims.

RESULTS

Overall, 22% had at least one of the four types of MS pain, and 73%, 57%, and 51% achieved the treatment targets of HbA1c, LDL, and SBP, respectively. T2D patients with or without MS pain did not differ in their achievement of T2D treatment goals. Of pain locations, low back pain was associated with higher rates of achievement of the LDL target (OR 1.29, 95% CI 1.01-1.65), but the association was attenuated in the adjusted model.

CONCLUSIONS

MS pain was relatively prevalent among primary care patients with T2D, but did not influence the achievement of T2D treatment goals.

Expert Panel Guidance and Narrative Review of Treatment Simplification of Complex Insulin Regimens to Improve Outcomes in Type 2 Diabetes

Given the progressive nature of type 2 diabetes (T2D), most individuals with the disease will ultimately undergo treatment intensification. This usually involves the stepwise addition of a new glucose-lowering agent or switching to a more complex insulin regimen. However, complex treatment regimens can result in an increased risk of hypoglycaemia and high treatment burden, which may impact negatively on both therapeutic adherence and overall quality of life. Individuals with good glycaemic control may also be overtreated with unnecessarily complex regimens. Treatment simplification aims to reduce individual treatment burden, without compromising therapeutic effectiveness or safety. Despite data showing that simplifying therapy can achieve good glycaemic control without negatively impacting on treatment efficacy or safety, it is not always implemented in clinical practice. Current clinical guidelines focus on treatment intensification, rather than simplification. Where simplification is recommended, clear guidance is lacking and mostly focused on treatment of the elderly. An expert, multidisciplinary panel evaluated the current treatment landscape with respect to guidance, published evidence, recommendations and approaches regarding simplification of complex insulin regimens. This article outlines the benefits of treatment simplification and provides practical recommendations on simplifying complex insulin treatment strategies in people with T2D using illustrative cases.

Outreach Method Predicts Patient Re-engagement in Diabetes Care During Sustained Care Disruption

OBJECTIVE

During the COVID-19 pandemic, visits for diabetes care were abruptly canceled without predefined procedures to re-engage patients. This study was designed to determine how outreach influences patients to maintain diabetes care and identify factors that might impact the intervention's efficacy.

METHODS

A diabetes nursing team attempted outreach for patients who had a canceled appointment for diabetes between March 16, 2020, and June 19, 2020. Outreach status was defined as reached, message left, or no contact. Outcomes were defined as follows: (1) booking and (2) keeping a follow-up appointment.

RESULTS

Seven hundred eighty-seven patients were included (384 [49%] were reached, 152 (19%) were left a message, and 251 (32%) had no contact). Reached patients were more likely to book [odds ratio (OR) = 2.43, P < .001] and keep an appointment (OR = 2.39, P < .001) than no-contact patients. Leaving a message did not increase the odds of booking (OR = 1.05, P = .84) or keeping (OR = 1.17, P = .568) an appointment compared with no contact. Older age was a significant predictor of booking an appointment (OR = 1.014 for each year of age, P = .037). Patients on insulin were more likely to keep their appointment (OR = 1.70, P = .008). Patients with a higher hemoglobin A1C level were less likely to keep their appointment (OR = 0.87 for each 1.0% increase in the hemoglobin A1C level, P = .011).

CONCLUSION

These findings suggest that to optimize re-engagement during care disruption, 1-way communication is no better than no contact and that 2-way communication increases the likelihood that patients will maintain access to care. In addition, although higher-risk patients (eg, patients with older age or those on insulin) may be more incentivized to stay engaged, targeted outreach is needed for those with chronically poor glycemic control.

Prediction of overall survival in patients with Stage I esophageal cancer: A novel web-based calculator

BACKGROUND AND AIMS

In this study, we aimed to develop a convenient web-based calculator to predict the overall survival (OS) of patients with Stage I esophageal cancer (EC).

METHODS

Data of 1664 patients, between 2004 and 2015, were extracted from the Surveillance, Epidemiology, and End Results database. Least absolute shrinkage and selection operator regression was employed to sift variables; subsequently, Cox proportional hazards regression model was built. We applied the enhanced bootstrap validation to appraise the discrimination and calibration of the model. Clinical benefit was measured using decision curve analysis (DCA). Thereafter, a web-based calculator based on the model, which could be used to predict the 1-, 3-, and 5-year OS rates, was developed.

RESULTS

Race, age, histologic type, grade, N stage, and therapeutic methods were selected. C-indices of the prediction model in the training and validation groups were 0.726 (95% confidence interval [CI], 0.679-0.773) and 0.724 (95% CI, 0.679-0.769), respectively. Calibration curves showed good agreement between the groups. The DCA demonstrated that the prediction model is clinically useful.

CONCLUSIONS

The prediction model we developed showed a good performance in calculating the OS rates in patients with Stage I EC. The web-based calculator is available at https://championship.shinyapps.io/dynnomapp/.

Effect of Diabetes-Trained Nurse Practitioners on Glycemic Outcomes: Their Suggested Use in Busy Primary Care Practices

A Federally Qualified Health Center received ongoing external support for half-time salaries for two nurse practitioners to treat people with poorly controlled diabetes (A1C >9.0%) in the clinic's diabetes program using approved detailed treatment protocols. Patients were treated for 1 year and graduated from this program if their A1C fell to <7.5%. Ninety-one percent graduated, and treatment was deemed to have failed in 9% who did not achieve an A1C <7.5% by the end of the year of treatment. The suggestion is made to assign a specially trained diabetes nurse or physician assistant to serve many primary care providers at important clinical junctures to improve diabetes outcomes throughout busy primary care practices.

Inflammatory markers response to crocin supplementation in patients with type 2 diabetes mellitus: A randomized controlled trial

Inflammation and oxidative stress is a risk factor for the development of long-term consequences in patients with type 2 diabetes mellitus (T2DM). This study was designed to investigate the effects of crocin consumption on oxidative stress and inflammatory markers in patients with T2DM. In this clinical trial with a parallel-group design, 50 patients with T2DM were randomly assigned to either the crocin or the placebo group. The crocin group received 15 mg crocin twice daily, whereas the placebo group received corresponding placebos. At baseline and the end of week 12, serum high sensitive C-reactive protein (hs-CRP), interleukin-6 (IL-6), tumor necrosis factor-ɑ (TNF-ɑ), nuclear factor-κB (NF-κB), and malondialdehyde (MDA) were measured. Compared with placebo group, crocin reduced hs-CRP (-1.03 vs. 1.42, p = .007), TNF-ɑ (-0.8 vs. 0.28, p = .009), and NF-κB (-0.39 vs. 0.01, p = .047) after 12 weeks intervention; these improvements were also significant in comparison with the baseline values. Plasma IL-6 decreased significantly in the crocin group at the end of week 12 compared to baseline (p = .037), whereas no significant change was observed in the placebo group. Plasma concentration of MDA did not change within and between groups after intervention. This study indicates that daily administration of 30 mg crocin supplement to patients with T2DM reduces the concentrations of hs-CRP, TNF-ɑ, and NF-κB which are involved in the pathogenesis of complications of T2DM.

Ertugliflozin in the treatment of type 2 diabetes mellitus

More than 422 million people worldwide have diabetes, with 90–95% having type 2 diabetes (T2D). Glycemic control of T2D has demonstrated reductions in microvascular complications but recent data have demonstrated improvements in macrovascular outcomes with sodium–glucose cotransporter 2 (SGLT2) inhibitors. Ertugliflozin is the most recent SGLT2 inhibitor approved in the USA and Europe for the treatment of T2D. This narrative review aims to present and discuss the efficacy, safety, cardiovascular (CV), and renal outcomes related to the use of ertugliflozin in T2D. Ertugliflozin has been evaluated in eight clinical trials (n=5248) with a focus on glycemic control. These trials have demonstrated improvement in glycosylated hemoglobin (0.6–1%), fasting plasma glucose (30–50 mg/dL), 2-hour postprandial glucose (60–70 mg/dL), decreased body weight (2–3 kg), and lowering of blood pressure (3–5 mmHg) in patients with T2D when ertugliflozin is used as monotherapy or in addition to metformin, sitagliptin, insulin, and/or sulfonylureas. The findings from the VERTIS-CV trial (n=8246) were recently published and demonstrated that ertugliflozin use in patients with T2D and atherosclerotic CV disease is safe but did not demonstrate superiority in the lowering of major CV events compared to placebo. Other SGLT2 inhibitors, such as empagliflozin and canagliflozin, have demonstrated this benefit. The VERTIS-CV trial demonstrated that the use of ertugliflozin led to a decrease in the number of hospitalizations for heart failure and this lends further support that this benefit is a class effect of SGLT2 inhibitors.

Long-Acting Injectable GLP-1 Receptor Agonists for the Treatment of Adults with Type 2 Diabetes: Perspectives from Clinical Practice

Randomized controlled trials (RCTs) have consistently shown glycemic and extra-glycemic benefits of long-acting injectable glucagon-like-peptide-1 receptor agonists (GLP-1RAs, liraglutide, albiglutide, exenatide once-weekly, dulaglutide, and semaglutide) in terms of reduction in the rates of cardiovascular events and mortality among patients with type 2 diabetes. Recently, the analyses of large datasets collecting routinely-accumulated data from clinical practice (ie, real-world studies, RWS) have provided new opportunities to complement the information obtained from RCTs. In this narrative review, we addressed clinically relevant questions that might be answered by well-conducted RWS: are subjects treated with GLP-1RAs in the "real-world" similar to those included in RCTs? Is the performance of GLP-1RA observed in the RWS (effectiveness) similar to that described in RCTs (efficacy)? Is the effectiveness similar in population of patients generally under-represented in RCTs? Are the cardiovascular benefits of GLP-1RAs confirmed in RWS? We also describe a few comparisons currently un-explored by specific RCTs, such as direct comparison between different administration strategies (eg, fixed- versus flexible-combination with basal-insulin) or between GLP-1RAs versus dipeptidyl-peptidase-4 inhibitor (DDP4i) or versus sodium/glucose cotransporter-2 inhibitors (SGLT-2i) on hard cardio-renal outcomes. Altogether, RWS provide highly informative information on treatment with GLP-1RAs. On the one side, RWS showed different clinical characteristics between subjects enrolled in RCTs versus those attending real-world clinics and receiving a GLP-1RA. On the other hand, RWS showed that GLP-1RA effectiveness is overall consistent in subgroups of patients less represented in RCTs. In addition, RWS allowed the identification of modifiable factors (eg, titration or adherence) that might guide physicians towards better GLP-1RAs use. Finally, multiple RWS reported better cardio-renal outcomes with GLP-1RAs than with DPP-4i, while initial findings from RWS described a weaker cardiovascular protection compared to SGLT-2i. Therefore, there is the need for further RWS and RCTs comparing these different classes of glucose lowering medications.