Patient and physician perspectives and experiences of basal insulin titration in type 2 diabetes in the United States: Cross-sectional surveys.

AIM: Patient- and physician-associated barriers impact the effectiveness of basal insulin (BI) titration in the management of type 2 diabetes (T2D). We evaluated the experiences of patients with T2D and physicians with BI titration education. MATERIALS AND METHODS: In this observational, cross-sectional study, patients with T2D and physicians treating patients with T2D were identified by claims in the Optum Research Database and were invited to complete a survey. Eligible patients had 12 months of continuous health-plan enrolment with medical and pharmacy benefits during the baseline period, and recent initiation of BI therapy. Eligible physicians had initiated BI for ≥1 eligible patient with T2D during the past 6 months. RESULTS: In total, 416 patients and 386 physicians completed the survey. Ninety per cent of physicians reported treating ≥50 patients with T2D; 66% treated ≥25% of patients with BI. Whereas 74% of patients reported that BI titration was explained to them by a physician, 96% of physicians reported doing so. Furthermore, 20% of patients stated they were offered educational materials whereas 56% of physicians reported having provided materials. Physicians had higher expectations of glycaemic target achievement than were seen in the patient survey; their main concern was the patients' ability to titrate accurately (79%). CONCLUSIONS: There is a marked difference in patients' and physicians' experiences of BI titration education. Novel tools and strategies are required to enable effective BI titration, with more educational resources at the outset, and ongoing access to tools that provide clear, simple direction for self-titration with less reliance on physicians/health care providers.

Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm.

AIM: To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup discovery algorithm. MATERIALS AND METHODS: Data from adults and children with type 1 diabetes and more than two diabetes-related visits were analysed from the Diabetes Prospective Follow-up Registry. Q-Finder, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH less than 7.3 during a hospitalization event. RESULTS: Data for 108 223 adults and children, of whom 5609 (5.2%) had DKA, were studied. Q-Finder analysis identified 11 profiles associated with an increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; an HbA1c of 8.87% or higher (≥ 73 mmol/mol); no fast-acting insulin intake; age younger than 15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycaemia; hypoglycaemic coma; and autoimmune thyroiditis. Risk of DKA increased with the number of risk profiles matching patients' characteristics. CONCLUSIONS: Q-Finder confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA.

Real-world persistence, adherence, health care resource utilization, and costs in people with type 2 diabetes switching from a first-generation basal insulin to a second-generation (insulin glargine 300 U/mL) vs an alternative first-generation basal insulin.

BACKGROUND: People with type 2 diabetes (T2D) who change their basal insulin (BI) may have variable persistence with therapy. Compared with first-generation (long-acting) BI analogs (insulin glargine 100U/mL [Gla-100]; insulin detemir [IDet]), second-generation (longer-acting) BI analogs (insulin glargine 300U/mL [Gla-300]; insulin degludec) have similar glycated hemoglobin (HbA1c) attainment and lowered hypoglycemia risk, which could impact treatment persistence. OBJECTIVE: To compare persistence, adherence, health care resource utilization (HRU), and costs for individuals switching from neutral protamine Hagedorn insulin or a first-generation BI analog with either the second-generation BI, Gla-300, or an alternative first-generation BI analog (Gla-100 or IDet). METHODS: We used Optum Clinformatics claims data from adults (aged ≥ 18 years) with T2D who had received BI (neutral protamine Hagedorn, Gla-100, IDet) in the 6-month baseline period, and switched to either Gla-300 or an alternative first-generation BI (Gla-100 or IDet; treatment switch = index date) between April 1, 2015, and August 31, 2019. Participants were followed for 12 months, until plan disenrollment, or until death, whichever occurred first. Cohorts were propensity score matched (PSM) on baseline characteristics. The primary outcome was the proportion who were persistent with therapy at 12 months. Secondary outcomes were adherence (proportion of days covered); change in HbA1c; and all-cause, diabetes-related, and hypoglycemia-related HRU and costs. RESULTS: PSM generated 3,077 participants/group (mean age: 68 years, 52% female). Cohorts were well balanced except for hospitalization, which was adjusted in models as a covariate. During the 12-month follow-up period, participants who received Gla-300 vs first-generation BI had greater persistence with (45.5% vs 42.1%; adjusted P = 0.0001), and adherence to (42.8% vs 38.2%; adjusted P = 0.0006), BI therapy and a statistically larger reduction in HbA1c at 12 months (-0.65% vs -0.45%; adjusted P = 0.0040). The proportion of participants achieving HbA1c less than 8% (47.2% vs 40.9%; P < 0.0001), but not less than 7% (21.2% vs 20.8%), was significantly higher for Gla-300 vs first-generation BI. All-cause (45.3 vs 65.9 per 100 patient-years [P100PY]) and diabetes-related (21.5 vs 29.1 P100PY), but not hypoglycemia-related, hospitalizations (1.0 vs 1.5 P100PY) were significantly (P < 0.0001) lower for Gla-300 vs first-generation BI. Similarly, all-cause (111.9 vs 148.8 P100PY), diabetes-related (54.8 vs 74.2 P100PY), and hypoglycemia-related (2.9 vs 5.7 P100PY) emergency department (ED) visits were significantly lower for Gla-300 (all P < 0.0001). Costs for all-cause hospitalizations and hypoglycemia-related ED visits were significantly lower for Gla-300 vs first-generation BI. Although pharmacy costs were significantly higher for Gla-300 vs first-generation BI, all-cause total health care costs were not significantly different: $41,255 vs $45,316 per person per year, respectively. CONCLUSIONS: In this claims-based analysis of people with T2D receiving BI, switching to Gla-300 was associated with significantly better persistence, adherence, and HbA1c reduction compared with switching to an alternative first-generation BI analog. All-cause HRU was significantly lower; despite significantly higher pharmacy costs, total health care costs were similar. DISCLOSURES: This study was funded by Sanofi US. Medical writing support was provided by Helen Jones, PhD, CMPP, of Evidence Scientific Solutions and funded by Sanofi US. Dr Wright is on the speakers' bureau and sits on the advisory boards for Abbot Diabetes, Bayer, Boehringer Ingelheim, Eli Lilly, and Sanofi; sits on the advisory board for Medtronic; and is a consultant for Abbot Diabetes, Bayer, Boehringer Ingelheim, and Eli Lilly. Dr Malone is on advisory boards for Novartis and Avalere and consults for Pear Therapeutics, Sarepta, and Strategic Therapeutics. Dr Trujillo sits on advisory boards for Novo Nordisk and Sanofi. Drs Gill, Zhou, and Preblick and Mr Li are employees and stockholders of Sanofi. Mr Huse is an employee of Evidera and a contractor for Sanofi. Dr Reid is a speaker and consultant for Novo Nordisk and Sanofi-Aventis and is a consultant for AstraZeneca and Intarcia.

Impact of My Dose Coach App Frequency of Use on Clinical Outcomes in Type 2 Diabetes.

INTRODUCTION: My Dose Coach (MDC) is a US Food and Drug Administration-approved digital smartphone application designed to help users with type 2 diabetes (T2D) titrate their basal insulin (BI) according to a clinician-prescribed individualized titration plan. The aim of this analysis was to assess the impact of the frequency of MDC use on clinical outcomes. METHODS: This retrospective observational analysis included people with T2D who were registered for MDC (August 1st, 2018-April 30th, 2020) and received BI. Users with an activated care plan and ≥2 fasting blood glucose (FBG) observations spanning ≥2 weeks were defined as active. Outcomes included percentage achieving their individual FBG target, time to FBG target, change in FBG, change in insulin dose and hypoglycemia. Users were stratified into high (>3 days per week), moderate (>1- ≤3 days per week), and low (≤1 day per week) MDC usage groups. RESULTS: The analysis included 2517 active MDC users. Approximately 49% of users had high MDC usage. Overall, 44% of users across all usage frequencies achieved their individual FBG target. High MDC use was associated with significantly better FBG target achievement and less time to FBG target versus moderate- and low-usage groups (p≤0.01 for all). Insulin dose change was significantly greater in the high- versus moderate-usage group (p=0.01). There was no significant difference in hypoglycemia incidence among MDC usage groups (12%-16% of users in any usage group). CONCLUSIONS: More frequent MDC usage was associated with better FBG outcomes without increased hypoglycemia risk.

Performance assessment of different machine learning approaches in predicting diabetic ketoacidosis in adults with type 1 diabetes using electronic health records data.

PURPOSE: To assess the performance of different machine learning (ML) approaches in identifying risk factors for diabetic ketoacidosis (DKA) and predicting DKA. METHODS: This study applied flexible ML (XGBoost, distributed random forest [DRF] and feedforward network) and conventional ML approaches (logistic regression and least absolute shrinkage and selection operator [LASSO]) to 3400 DKA cases and 11 780 controls nested in adults with type 1 diabetes identified from Optum® de-identified Electronic Health Record dataset (2007-2018). Area under the curve (AUC), accuracy, sensitivity and specificity were computed using fivefold cross validation, and their 95% confidence intervals (CI) were established using 1000 bootstrap samples. The importance of predictors was compared across these models. RESULTS: In the training set, XGBoost and feedforward network yielded higher AUC values (0.89 and 0.86, respectively) than logistic regression (0.83), LASSO (0.83) and DRF (0.81). However, the AUC values were similar (0.82) among these approaches in the test set (95% CI range, 0.80-0.84). While the accuracy values >0.8 and the specificity values >0.9 for all models, the sensitivity values were only 0.4. The differences in these metrics across these models were minimal in the test set. All approaches selected some known risk factors for DKA as the top 10 features. XGBoost and DRF included more laboratory measurements or vital signs compared with conventional ML approaches, while feedforward network included more social demographics. CONCLUSIONS: In our empirical study, all ML approaches demonstrated similar performance, and identified overlapping, but different, top 10 predictors. The difference in selected top predictors needs further research.

Estimation of Annual Health Care Costs for Adults with Type 1 Diabetes in the United States.

BACKGROUND: Diabetes health care resource utilization (HCRU) studies tend to focus on patients with type 2 diabetes (T2D) or pool patients with T2D and type 1 diabetes (T1D). There is a paucity of recent data on the cost of treating patients with T1D in the United States. OBJECTIVES: To (a) estimate the per-patient per-year (PPPY) HCRU and costs, from a payer perspective, associated with treating U.S. adults with T1D and (b) compare these with the HCRU and costs for patients with T2D. METHODS: This retrospective cohort study used claims data from the Optum Clinformatics database between January 2015 and December 2017. Adults (aged ≥ 18 years) with a diagnosis of T1D were propensity score-matched to adults with T2D. Overall and nondiabetes-related HCRU and costs were assessed for T1D and T2D and compared between the 2 groups. RESULTS: Propensity scores were used to match 10,103 patient pairs from T1D and T2D cohorts (mean ages 54.4 and 56.9 years, respectively). In the T1D cohort, inpatient, emergency department (ED), outpatient, and prescription claims occurred in 14.0%, 17.3%, 85.5%, and 100% of patients, respectively, resulting in a mean total cost of U.S. $18,817 PPPY (diabetes-related = $11,002; nondiabetes-related = $7,816). The T1D cohort had significantly higher mean total costs than the T2D cohort ($18,817 vs. $14,148 PPPY; P < 0.001). When extrapolating these findings to a commercial health plan with 1 million covered lives, the estimated total direct medical costs of T1D would be $103.4 million. CONCLUSIONS: This study showed that the total annual cost of managing an adult with T1D is significantly higher than that of an adult with T2D. Nondiabetes costs accounted for 40% of the total per-patient cost, similar to patients with T2D, confirming that as patients with T1D live longer lives, they may also be at greater risk for cardiometabolic complications. DISCLOSURES: This study was funded by Sanofi U.S. and Lexicon Pharmaceuticals as part of a business partnership in a diabetes program at the time this study was conducted. Joish and Davies are employees and stockholders of Lexicon Pharmaceuticals. Zhou, Preblick, and Paranjape are employees and stockholders of Sanofi. Lin was a postdoctoral fellow at Sanofi through Rutgers University during this project. Deshpande provided consulting services through Communication Symmetry. Verma is an employee of Evidera, which was contracted by Sanofi for work on this study. Pettus is a consultant for Diasome, Insulet, Lexicon, Lilly, Mannkind, Novo Nordisk, Sanofi, and Senseonics.

Burden of Cardiovascular Disease in Adult Patients with Type 1 Diabetes in the US.

BACKGROUND AND OBJECTIVES: The burden imposed by cardiovascular disease (CVD) on patients with type 1 diabetes (T1D) in the US has not been thoroughly addressed. In a retrospective observational analysis of the Optum® Clinformatics™ Data Mart database, the prevalence of CVD and cardiovascular risk factors (CVRF) as well as health economic outcomes were evaluated in adults with T1D. METHODS: Patients with at least one T1D medical claim between January 1, 2016, and December 31, 2016, were divided into cohorts based on the presence of CVD and/or CVRF. Descriptive and multivariate analyses enabled comparisons of healthcare resource utilization and costs between the cohorts. RESULTS: The analysis included 12,687 patients: CVD, 2871; CVRF, 5371; and no CVD/CVRF, 4445. The period prevalence of CVD and CVRF in the combined baseline and follow-up periods was 27% and 44%, respectively. Fewer patients in the no-CVD/CVRF cohort had a claim of a diabetes-related inpatient admission compared with the CVD cohort (8% vs. 26%, respectively; P < 0.001, standardized mean difference [SMD] > 0.1). Likewise, fewer patients with no CVD/CVRF visited the emergency department vs. those with CVRF or CVD (diabetes-related: 4% vs. 7% and 18%, respectively; P < 0.001, SMD > 0.1). Higher overall costs were observed for the CVD and CVRF vs. the no-CVD/CVRF cohort ($30,241 and $16,220, respectively, vs. $11,761; P < 0.05 and SMD ≥ 0.1 for both). CONCLUSIONS: Cardiovascular comorbidities are common among US adults with T1D. Considering their significant economic burden, optimal management is of the utmost importance to improve patient outcomes and reduce healthcare costs.

Differences between patients with type 1 diabetes with optimal and suboptimal glycaemic control: A real-world study of more than 30 000 patients in a US electronic health record database.

AIMS: To use electronic health record data from real-world clinical practice to assess demographics, clinical characteristics and disease burden of adults with type 1 diabetes (T1D) in the United States. MATERIALS AND METHODS: Retrospective observational study of adults with T1D for ≥24 months at their first visit with a T1D diagnosis code ("index date") between July 2014 and June 2016 in the Optum Humedica database. Demographic characteristics, acute complications (severe hypoglycaemia [SH], diabetic ketoacidosis [DKA]), microvascular complications, cardiovascular (CV) events and health care resource utilization during the 12 months before the index date ("baseline period") were compared between patients with optimal versus suboptimal glycaemic control (glycated haemoglobin [HbA1c] <7.0% vs. ≥7.0% [53 mmol/mol]) at the closest measurement to the index date. RESULTS: Of 31 430 adults with T1D, 79.9% had suboptimal glycaemic control (mean HbA1c 8.8% [73 mmol/mol]). These patients were more likely to be younger, African American, uninsured or on Medicaid, obese, smokers, have uncontrolled hypertension and have depression. Despite worse glycaemic control and increased CV risk factors of uncontrolled hypertension, obesity and smoking, rates of coronary heart disease and stroke were not higher in these patients. Patients with suboptimal glycaemic control also experienced more diabetes complications (including SH, DKA and microvascular disease) and utilized more emergency care, with more emergency department visits and inpatient stays. CONCLUSION: This real-world study of >30 000 adults with T1D showed that individuals with suboptimal versus optimal glycaemic control differed significantly in terms of health care coverage, comorbidities, diabetes-related complications, health care utilization and CV risk factors. However, suboptimal control was not associated with increased risk of CV outcomes.

Incidences of Severe Hypoglycemia and Diabetic Ketoacidosis and Prevalence of Microvascular Complications Stratified by Age and Glycemic Control in U.S. Adult Patients With Type 1 Diabetes: A Real-World Study.

OBJECTIVE: To assess the burden of disease for adults with type 1 diabetes in a U.S. electronic health record database by evaluating acute and microvascular complications stratified by age and glycemic control. RESEARCH DESIGN AND METHODS: This is a retrospective observational study of adults with type 1 diabetes (1 July 2014-30 June 2016) classified using a validated algorithm, with disease duration ≥24 months and, during a 12-month baseline period, not pregnant and having one or more insulin prescriptions and one or more HbA1c measurements. Demographic characteristics, acute complications (severe hypoglycemia [SH], diabetic ketoacidosis [DKA]), and microvascular complications (neuropathy, nephropathy, retinopathy) were stratified by age (18-25, 26-49, 50-64, ≥65 years) and glycemic control (HbA1c <7%, 7% to <9%, ≥9%). RESULTS: Of 31,430 patients, ∼20% had HbA1c <7%. Older patients had lower HbA1c values than younger patients (P < 0.001). Patients with poor glycemic control had the highest annual incidence of SH (4.2%, 4.0%, and 8.3%) and DKA (1.3%, 2.8%, and 15.8%) for HbA1c <7%, 7% to <9%, and ≥9% cohorts, respectively (both P < 0.001), and a higher prevalence of neuropathy and nephropathy (both P < 0.001). CONCLUSIONS: For adults with type 1 diabetes, glycemic control appears worse than previously estimated. Rates of all complications increased with increasing HbA1c. Compared with HbA1c <7%, HbA1c ≥9% was associated with twofold and 12-fold higher incidences of SH and DKA, respectively. Younger adults had more pronounced higher risks of SH and DKA associated with poor glycemic control than older adults.

Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer.

IMPORTANCE: Nomogram prognostic models can facilitate cancer patient treatment plans and patient enrollment in clinical trials. OBJECTIVE: The primary objective is to provide an updated and accurate prognostic model for predicting the survival of advanced non-small-cell lung cancer (NSCLC) patients, and the secondary objective is to validate a published nomogram prognostic model for NSCLC using an independent patient cohort. DESIGN: 1817 patients with advanced NSCLC from the control arms of 4 Phase III randomized clinical trials were included in this study. Data from 524 NSCLC patients from one of these trials were used to validate a previously published nomogram and then used to develop an updated nomogram. Patients from the other 3 trials were used as independent validation cohorts of the new nomogram. The prognostic performances were comprehensively evaluated using hazard ratios, integrated area under the curve (AUC), concordance index, and calibration plots. SETTING: General community. MAIN OUTCOME: A nomogram model was developed to predict overall survival in NSCLC patients. RESULTS: We demonstrated the prognostic power of the previously published model in an independent cohort. The updated prognostic model contains the following variables: sex, histology, performance status, liver metastasis, hemoglobin level, white blood cell counts, peritoneal metastasis, skin metastasis, and lymphocyte percentage. This model was validated using various evaluation criteria on the 3 independent cohorts with heterogeneous NSCLC populations. In the SUN1087 patient cohort, the continuous risk score output by the nomogram achieved an integrated area under the receiver operating characteristics (ROC) curve of 0.83, a log-rank P-value of 3.87e-11, and a concordance index of 0.717. In the SAVEONCO patient cohort, the integrated area under the ROC curve was 0.755, the log-rank P-value was 4.94e-6 and the concordance index was 0.678. In the VITAL patient cohort, the integrated area under the ROC curve was 0.723, the log-rank P-value was 1.36e-11, and the concordance index was 0.654. We implemented the proposed nomogram and several previously published prognostic models on an online Web server for easy user access. CONCLUSIONS: This nomogram model based on basic clinical features and routine lab testing predicts individual survival probabilities for advanced NSCLC and exhibits cross-study robustness.

Predictive Modeling of Hypoglycemia Risk with Basal Insulin Use in Type 2 Diabetes: Use of Machine Learning in the LIGHTNING Study.

INTRODUCTION: Hypoglycemia remains a global burden and a limiting factor in the glycemic management of people with diabetes using basal insulins or oral antihyperglycemic drugs. Hypoglycemia data gleaned from randomized controlled trials (RCTs) have limited generalizability, as the strict RCT methodology and inclusion criteria do not fully reflect the real-world clinical picture. Therefore, real-world evidence, gathered from sources including electronic health records (EHR), is increasingly recognized as an important adjunct to RCTs. AIMS AND METHODS: The LIGHTNING study applied advanced analytical methods, including machine learning (ML), to EHR data. The study aimed to predict hypoglycemic event rates in patients with type 2 diabetes (T2DM) receiving different basal insulin treatments to identify potential subgroups of patients who are at lower risk of hypoglycemia when treated with one basal insulin compared with another and to predict hypoglycemia-related cost savings in these subgroups. Here we provide an overview of the objectives, study design and methods, and validation approaches used in the LIGHTNING study. CONCLUSION: It is hoped that results of the LIGHTNING study will help facilitate real-world clinical decision-making in addition to providing a clinically relevant predictive model of hypoglycemia risk. FUNDING: Sanofi.

Comorbidity Type and Health Care Costs in Type 2 Diabetes: A Retrospective Claims Database Analysis.

INTRODUCTION: Previous studies suggest that the type and combination of comorbidities may impact diabetes care, but their cost implications are less clear. This study characterized how diabetes patients' health care utilization and costs may vary according to comorbidity type classified on the basis of the Piette and Kerr framework. METHODS: We conducted a retrospective observational study of privately insured US adults newly diagnosed with type 2 diabetes (n = 138,466) using the 2014-2016 Optum Clinformatics® Data Mart. Diabetes patients were classified into five mutually exclusive comorbidity groups: concordant only, discordant only, both concordant and discordant, any dominant, and none. We estimated average health care costs of each comorbidity group by using generalized linear models, adjusting for patient demographics, region, insurance type, and prior-year costs. RESULTS: Most type 2 diabetes patients had discordant conditions only (27%), dominant conditions (25%), or both concordant and discordant conditions (24%); 7% had concordant conditions only. In adjusted analyses, comorbidities were significantly associated with higher health care costs (p < 0.0001) and the magnitude of the association varied with comorbidity type. Diabetes patients with dominant comorbidities incurred substantially higher costs ($38,168) compared with individuals with both concordant and discordant conditions ($20,401), discordant conditions only ($9173), concordant conditions only ($9000), and no comorbidities ($3365). More than half of the total costs in our sample (53%) were attributable to 25% of diabetes patients who had dominant comorbidities. CONCLUSIONS: Diabetes patients with both concordant and discordant conditions and with clinically dominant conditions incurred substantially higher health costs than other diabetes patients. Our findings suggest that diabetes management programs must explicitly address concordant, discordant, and dominant conditions because patients may have distinctly different health care needs and utilization patterns depending on their comorbidity profiles. The Piette and Kerr framework may serve as a screening tool to identify high-need, high-cost diabetes patients and suggest targets for tailored interventions. FUNDING: Sanofi.

Clinical outcomes in real-world patients with type 2 diabetes switching from first- to second-generation basal insulin analogues: Comparative effectiveness of insulin glargine 300 units/mL and insulin degludec in the DELIVER D+ cohort study.

AIMS: To compare clinical outcomes in patients with type 2 diabetes (T2D) switching from insulin glargine 100 units/mL (Gla-100) or insulin detemir (IDet) to insulin glargine 300 units/mL (Gla-300) or insulin degludec (IDeg). MATERIALS AND METHODS: We conducted a retrospective, observational study of electronic medical records for Gla-300/IDeg adult switchers (March 1, 2015 to January 31, 2017) with active records for 12-month baseline (glycated haemoglobin [HbA1c] used a 6-month baseline period) and 6-month follow-up periods. Gla-300 and IDeg switchers were propensity score-matched using baseline demographic and clinical characteristics. Outcomes were HbA1c change and goal attainment (among patients with HbA1c captured at follow-up), and hypoglycaemia with fixed follow-up (intention-to-treat [ITT]; 6 months) and variable follow-up (on-treatment [OT]; to discontinuation or 6 months). RESULTS: Each matched cohort comprised 1592 patients. The mean decrease in HbA1c and HbA1c goal (<7.0% [53 mmol/mol] and <8.0% [64 mmol/mol]) attainment rates were similar for Gla-300 (n = 742) and IDeg (n = 727) switchers. Using fixed follow-up (ITT method), hypoglycaemia incidence decreased significantly from baseline with Gla-300 (all hypoglycaemia: 15.6% to 12.7%; P = .006; hypoglycaemia associated with inpatient/emergency department [ED] encounter: 5.3% to 3.5%; P = .007), but not with IDeg. After adjusting for baseline hypoglycaemia, no significant differences in hypoglycaemia incidence and event rate were found at follow-up (ITT) for Gla-300 vs IDeg. Using variable follow-up (OT), hypoglycaemia incidence was similar in both groups, but Gla-300 switchers had a lower inpatient/ED hypoglycaemia event rate at follow-up (adjusted rate ratio 0.56; P = .016). CONCLUSIONS: In a real-world setting, switching from Gla-100 or IDet to Gla-300 or IDeg was associated with similar improvements in glycaemic control and hypoglycaemia in adult patients with T2D.

Impact of a structured patient support program on adherence and persistence in basal insulin therapy for type 2 diabetes.

OBJECTIVE: Treatment adherence and persistence are essential to achieving therapeutic goals in diabetes and may be improved by patient support programs (PSPs). The COACH Program was launched in 2015 with the goal of supporting patients with diabetes who are prescribed insulin glargine 300 U/mL (Gla-300). The study objective was to assess the program's impact on persistence and adherence with therapy among patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: A retrospective 12-month analysis was conducted to compare treatment adherence and persistence in patients treated with Gla-300 who actively participated in the COACH PSP versus those who did not enroll using COACH engagement and claims data for the identification period from February 1, 2016 to July 31, 2016. COACH (n=544) and non-COACH (n=544) participants were matched on selected baseline characteristics. RESULTS: COACH participants were more likely to be adherent to (68.0% vs 61.4%, p= 0.0201; OR: 1.81, p=0.0002) and persistent (48.5% vs 42.1%, p= 0.0309; discontinuation HR: 0.60, p<0.0001) with Gla-300 than non-COACH patients during the 12-month follow-up after controlling for clinical confounders. Additionally, both insulin-naive and basal insulin switcher COACH participants, respectively, were more likely to be adherent (OR: 2.25, p=0.0082 and OR: 1.662, p=0.0364) and persistent (discontinuation HR: 0.53, p=0.0054 and HR: 0.67, p=0.0492) than non-COACH patients. Finally, COACH participants with greater level of engagement showed better persistence. CONCLUSION: These data demonstrate that participation and engagement with COACH PSPs are associated with improved persistence and adherence to Gla-300 among patients with type 2 diabetes.

Real-world evidence concerning clinical and economic outcomes of switching to insulin glargine 300 units/mL vs other basal insulins in patients with type 2 diabetes using basal insulin.

This retrospective cohort study compared real-world clinical and healthcare-resource utilization (HCRU) data in patients with type 2 diabetes using basal insulin (BI) who switched to insulin glargine 300 units/mL (Gla-300) or another BI. Data from the Predictive Health Intelligence Environment database 12 months before (baseline) and 6 months after (follow-up) the switch date (index date, March 1, 2015 to May 31, 2016) included glycated haemoglobin A1c (HbA1c), hypoglycaemia, HCRU and associated costs. Baseline characteristics were balanced using propensity score matching. Change in HbA1c from baseline was similar in both matched cohorts (n = 1819 in each). Hypoglycaemia incidence and adjusted event rate were significantly lower with Gla-300. Patients switching to Gla-300 had a significantly lower incidence of HCRU related to hypoglycaemia. All-cause and diabetes-related hospitalization and emergency-department HCRU were also favourable for Gla-300. Lower HCRU translated to lower costs in patients using Gla-300. In this real-world study, switching to Gla-300 reduced the risk of hypoglycaemia in patients with type 2 diabetes when compared with those switching to another BI, resulting in less HCRU and potential savings of associated costs.

Relationship between treatment persistence and A1C trends among patients with type 2 diabetes newly initiating basal insulin.

This study examines the relationship between glycated haemoglobin (A1C) levels and treatment persistence with, or time to discontinuation of, basal insulin in patients with type 2 diabetes (T2D) newly initiating insulin. Claims data were extracted from the Optum Clinformatics database from January 2010 to June 2015. Adult patients with T2D initiating insulin glargine 100 U/mL (Gla-100) or insulin detemir (DET) with ≥1 A1C measurement during 12-month baseline and 18-month follow-up periods were included. Patients with a refill gap of >90 days were considered non-persistent; otherwise, patients were considered persistent with insulin. The main outcome was A1C, measured closest to the end of each quarter during the follow-up period. A total of 3993 of 109 934 patients met the inclusion criteria (43.0% persistent; 57.0% non-persistent). Persistent patients were older (54.7 vs 52.7 years; P < .001), were more likely to be male (59.4% vs 54.4%; P = .002), and had significantly lower mean unadjusted A1C values at 18 months (8.26% vs 8.60%; P < .001) and quarterly. Only 43.0% of adults initiating basal insulin persisted with treatment for 18 months, with earlier discontinuation associated with higher A1C.

A DREAM Challenge to Build Prediction Models for Short-Term Discontinuation of Docetaxel in Metastatic Castration-Resistant Prostate Cancer.

PURPOSE: Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. PATIENTS AND METHODS: The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment. RESULTS: In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor ≤ 3) outperformed all other models. A postchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power. CONCLUSION: This work represents a successful collaboration between 34 international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design.

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

BACKGROUND: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. METHODS: Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. FINDINGS: 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. INTERPRETATION: Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. FUNDING: Sanofi US Services, Project Data Sphere.