A Patient-Centered PaTH to Address Diabetes: Protocol for a Study on the Impact of Obesity Counseling

Bariatric surgery and COVID-19 outcomes: results from the PaTH to Health: Diabetes study

BACKGROUND

Obesity and type 2 diabetes mellitus (T2DM) are risk factors for severe COVID-19 infection. Bariatric surgery (BSG) is an effective treatment of obesity through weight loss and may reduce COVID-19 severity.

OBJECTIVES

We examined the effect of BSG on COVID-19 outcomes in patients with or at risk of T2DM.

SETTING

Electronic health record data from the PaTH Clinical Data Research Network, a partnership of 5 health systems reviewed from March 1, 2020, to December 31, 2020.

METHODS

Ambulatory and in-hospital patient encounters with COVID-19 diagnosis and obesity were identified. We constructed 2 patient groups: BSG and non-BSG (NBSG). The BSG group included patients with at least 1 encounter for the BSG procedure code and/or 1 BSG diagnosis code; the NBSG group included patients with no procedure or diagnosis code for BSG with body mass index (BMI) ≥40 or BMI ≥35 and at least 2 obesity-related co-morbidities. We matched 1 patient in the BSG group to 2 patients in the NBSG group based on age, gender (sex defined at birth), race and ethnicity, group (T2DM and at risk of T2DM), and site. The primary outcome was 30-day outcomes of COVID-19 severity.

RESULTS

After matching, we found that patients with BSG had lower odds of respiratory failure (41%) and ventilation/intensive care unit (ICU) admission/death (52%). Patients in the BSG group had lower odds of hospitalization, pneumonia, respiratory failure, and the most severe COVID-19 outcomes combined (ventilation/ICU admission/death). T2DM was identified as a risk factor for COVID-19 severity in the BSG group.

CONCLUSIONS

This retrospective, matched-cohort analysis found BSG to have a protective effect against severe COVID-19 outcomes.

Intensive behavioral Therapy for weight loss in patients with, or At-Risk of, type 2 Diabetes: Results from the PaTH to health diabetes study

Intensive behavioral therapy (IBT) is an important component of obesity treatment and can reduce the risk of type 2 diabetes (T2DM). Objective was to compare the effectiveness of IBT to usual care in achieving weight loss in two study cohorts within PaTH Network: T2DM and At-Risk of T2DM. The TD2M cohort was defined as age 18 years and older with an indication of T2DM in the EHR based on a validated algorithm and at least 2 outpatient primary care visits. The At-Risk of T2DM cohort was defined by a BMI ≥ 25 kg/m². The primary outcome was weight change within 1-year of index date. Mixed-effects models assessed the effectiveness of IBT by comparing the changes between study groups. Between 2009 and 2020, a total of 567,908 patients were identified in the T2DM cohort and2,054,256 patients in the At-Risk of T2DM cohort. Both IBT patients and matched non-IBT patients in the T2DM cohort had decreased mean weight (primary outcome) (-1.56 lbs, 95 %CI: -1.88, -1.24 vs -1.70 lbs, 95 %CI: -1.95, -1.44) in 1-year after index date. In the At-Risk of T2DM cohort, both IBT and non-IBT patients experienced weight gain and resultant increased BMI. Patients with more than one IBT visit gained less weight than those with only one visit (1.22 lbs, 95 %CI: 0.82, 1.62 vs 6.72 lbs, 95 %CI: 6.48, 6.97; p < 0.001). IBT was unlikely to result in clinically significant weight loss. Barriers to utilizing IBT require further research to ensure broader adoption of obesity management in primary care.

Hospitalization and mortality in patients with COVID-19 with or at risk of type 2 diabetes: data from five health systems in Pennsylvania and Maryland

OBJECTIVE

To identify the demographic and clinical characteristics associated with adverse COVID-19 outcomes across a 12-month period in 2020 and 2021.

RESEARCH DESIGN AND METHODS

We conducted a retrospective cohort study using electronic health records from five academic health systems in Pennsylvania and Maryland, including patients with COVID-19 with type 2 diabetes or at risk of type 2 diabetes. Patients were classified based on 30-day outcomes: (1) no hospitalization; (2) hospitalization only; or (3) a composite measure including admission to the intensive care unit (ICU), intubation, or death. Analyses were conducted in patients with type 2 diabetes and patients at risk of type 2 diabetes separately.

RESULTS

We included 15 725 patients with COVID-19 diagnoses between March 2020 and February 2021. Older age and higher Charlson Comorbidity Index scores were associated with higher odds of adverse outcomes, while COVID-19 diagnoses later in the study period were associated with lower odds of severe outcomes. In patients with type 2 diabetes, individuals on insulin treatment had higher odds for ICU/intubation/death (OR=1.59, 95% CI 1.27 to 1.99), whereas those on metformin had lower odds (OR=0.56, 95% CI 0.45 to 0.71). Compared with non-Hispanic White patients, Hispanic patients had higher odds of hospitalization in patients with type 2 diabetes (OR=1.73, 95% CI 1.36 to 2.19) or at risk of type 2 diabetes (OR=1.77, 95% CI 1.43 to 2.18.) CONCLUSIONS: Adults who were older, in racial minority groups, had multiple chronic conditions or were on insulin treatment had higher risks for severe COVID-19 outcomes. This study reinforced the urgency of preventing COVID-19 and its complications in vulnerable populations.

TRIAL REGISTRATION NUMBER

NCT02788903.

Hospital utilization for hypoglycemia among patients with type 2 diabetes using pooled data from six health systems

INTRODUCTION

Hypoglycemia is the most common serious adverse effect of diabetes treatment and a major cause of medication-related hospitalization. This study aimed to identify trends and predictors of hospital utilization for hypoglycemia among patients with type 2 diabetes using electronic health record data pooled from six academic health systems.

RESEARCH DESIGN AND METHODS

This retrospective open cohort study included 549 041 adults with type 2 diabetes receiving regular care from the included health systems between 2009 and 2019. The primary outcome was the yearly event rate for hypoglycemia hospital utilization: emergency department visits, observation visits, or inpatient admissions for hypoglycemia identified using a validated International Classification of Diseases Ninth Revision (ICD-9) algorithm from 2009 to 2014. After the transition to ICD-10 in 2015, we used two ICD-10 code sets (limited and expanded) for hypoglycemia hospital utilization from prior studies. We identified independent predictors of hypoglycemia hospital utilization using multivariable logistic regression analysis with data from 2014.

RESULTS

Yearly rates of hypoglycemia hospital utilization decreased from 2.7 to 1.6 events per 1000 patients from 2009 to 2014 (p-trend=0.023). From 2016 to 2019, yearly event rates were stable ranging from 5.6 to 6.6, or 6.3 to 7.3, using the limited and expanded ICD-10 code sets, respectively. In 2014, the strongest independent risk factors for hypoglycemia hospital utilization were chronic kidney disease (OR 2.86, 95% CI 2.33 to 3.57), ages 18-39 years (OR 2.43 vs age 40-64 years, 95% CI 1.78 to 3.31), and insulin use (OR 2.13 vs no diabetes medications, 95% CI 1.67 to 2.73).

CONCLUSIONS

Rates of hypoglycemia hospital utilization decreased from 2009 to 2014 and varied considerably by clinical risk factors such that younger adults, insulin users, and those with chronic kidney disease were at especially high risk. There is a need to validate hypoglycemia ascertainment using ICD-10 codes, which detect a substantially higher number of events compared with ICD-9.

Utilization of intensive behavioural treatment for obesity in patients with diabetes

Obesity is a leading public health concern. The Centers for Medicare and Medicaid Services implemented a healthcare procedure code for intensive behavioural therapy (IBT) in 2012 to facilitate payment for addressing obesity in primary care settings, followed by universal coverage by insurers for all adults. Our objective was to understand utilization of IBT from 2009 to 2017 in patients with a diabetes diagnosis. Leveraging electronic health record data from the PaTH Clinical Data Research Network (CDRN), a partnership of six health systems, utilization of IBT was summarized at a yearly basis. The trend of IBT prevalence was examined for patients with diabetes by gender, race, age (>=65 vs <65) and rurality. A total of 205, 913 patients were included. While utilization of IBT is low (0.24% in 2017), use of IBT increased among patients with commercial insurance and Medicaid (codes S9449 and S9470) in 2011, and among patients with Medicare (code G0447) in 2012. IBT users tended to be less than 65 years of age, female, non-White (Black or Hispanic), and reside in urban areas. Overall, use of IBT in patients with diabetes remains low. Future work is necessary to understand the impact of IBT and, if effective, how to increase use within primary care.

Network Engagement in Action: Stakeholder Engagement Activities to Enhance Patient-centeredness of Research

BACKGROUND

Stakeholders (ie, patients, policymakers, clinicians, advocacy groups, health system leaders, payers, and others) offer critical input at various stages in the research continuum, and their contributions are increasingly recognized as an important component of effective translational research. Natural experiments, in particular, may benefit from stakeholder feedback in addressing real-world issues and providing insight into future policy decisions, though best practices for the engagement of stakeholders in observational studies are limited in the literature.

METHODS

The Natural Experiments for Translation in Diabetes 2.0 (NEXT-D2) network utilizes rigorous methods to evaluate natural experiments in health policy and program delivery with a focus on diabetes-related outcomes. Each of the 8 partnering institutions incorporates stakeholder engagement throughout multiple study phases to enhance the patient-centeredness of results. NEXT-D2 dedicates a committee to Engagement for resource sharing, enhancing engagement approaches, and advancing network-wide engagement activities. Key stakeholder engagement activities include Study Meetings, Proposal Development, Trainings & Educational Opportunities, Data Analysis, and Results Dissemination. Network-wide patient-centered resources and multimedia have also been developed through the broad expertise of each site's stakeholder group.

CONCLUSIONS

This collaboration has created a continuous feedback loop wherein site-level engagement approaches are informed via the network and network-level engagement efforts are shaped by individual sites. Emerging best practices include: incorporating stakeholders in multiple ways throughout the research, building on previous relationships with stakeholders, enhancing capacity through stakeholder and investigator training, involving stakeholders in refining outcome choices and understanding the meaning of variables, and recognizing the power of stakeholders in maximizing dissemination.