Alex Milinovich

Clinical characteristics, complications, comorbidities and treatment patterns among patients with type 2 diabetes mellitus in a large integrated health system

Purpose To compare the prevalence of diabetes-related complications and comorbidities, clinical characteristics, glycemic control, and treatment patterns in patients with type 2 diabetes (T2D) within a large integrated healthcare system in 2008 vs 2013. Methods An electronic health record system was used to create a cross-sectional summary of all patients with T2D as on 1 July 2008 and 1 July 2013. Differences between the two data sets were assessed after adjusting for age, gender, race, and household income. Results In 2008 and 2013, 24 493 and 41 582 patients with T2D were identified, respectively, of which the majority were male (52.3% and 50.1%) and Caucasian (79% and 75.2%). The mean ages (years) were 64.8 and 64.3. The percentages of patients across the defined A1C categories were 64.3 and 66.7 for <7%, 21.1 and 18.8 for 7–7.9%, 7.8 and 7.5 for 8–8.9%, and 6.8 and 7.0 for ≥9% in 2008 and 2013, respectively. The most prevalent T2D-related comorbidities were hypertension (82.5% and 87.2%) and cardiovascular disease (26.9% and 22.3%) in 2008 and 2013, respectively. Thiazolidinedione and sulfonylurea use decreased, whereas metformin and dipeptidyl peptidase-4 inhibitor use increased in the 5-year period. Conclusions Patients with T2D are characterized by a high number of comorbidities. Over 85% of the patients had an A1C<8% within our integrated health delivery system in 2008 and 2013. In 2008 and 2013, metformin therapy was the most commonly utilized antidiabetic agent, and sulfonylureas were the most commonly utilized oral antidiabetic agent in combination with metformin. As integrated health systems assume greater shared financial risk in newer payment models, achieving glycemic targets (A1C) and the management of comorbidities will become ever-more important, for preventing diabetes-related complications, as well as to ensure reimbursement for the medical care that is rendered to patients with diabetes.

Intensification of Diabetes Therapy and Time Until A1C Goal Attainment Among Patients With Newly Diagnosed Type 2 Diabetes Who Fail Metformin Monotherapy Within a Large Integrated Health System

OBJECTIVE “Clinical inertia” has been used to describe the delay in the intensification of type 2 diabetes treatment among patients with poor glycemic control. Previous studies may have exaggerated the prevalence of clinical inertia by failing to adequately monitor drug dose changes and nonmedication interventions. This project evaluated the intensification of diabetes therapy and hemoglobin A1c (A1C) goal attainment among patients with newly diagnosed type 2 diabetes when metformin monotherapy failed. RESEARCH DESIGN AND METHODS The electronic health record at Cleveland Clinic was used to identify patients with newly diagnosed type 2 diabetes between 2005 and 2013 who failed to reach the A1C goal after 3 months of metformin monotherapy. A time-dependent survival analysis was used to compare the time until A1C goal attainment in patients who received early intensification of therapy (within 6 months of metformin failure) or late intensification. The analysis was performed for A1C goals of 7% (n = 1,168), 7.5% (n = 679), and 8% (n = 429). RESULTS Treatment was intensified early in 62%, 69%, and 72% of patients when poor glycemic control was defined as an A1C >7%, >7.5%, and >8%, respectively. The probability of undergoing an early intensification was greater the higher the A1C category. Time until A1C goal attainment was shorter among patients who received early intensification regardless of the A1C goal (all P < 0.05). CONCLUSIONS A substantial number of patients with newly diagnosed type 2 diabetes fail to undergo intensification of therapy within 6 months of metformin monotherapy failure. Early intervention in patients when metformin monotherapy failed resulted in more rapid attainment of A1C goals.

The Impact of Socioeconomic Status on the Utilization of Spinal Imaging.

BACKGROUND Few studies have examined the general correlation between socioeconomic status and imaging. This study is the first to analyze this relationship in the spine patient population. OBJECTIVE To assess the effect of socioeconomic status on the frequency with which imaging studies of the lumbar spine are ordered and completed. METHODS Patients that were diagnosed with lumbar radiculopathy and/or myelopathy and had at least 1 subsequent lumbar magnetic resonance imaging (MRI), computed tomography (CT), or X-ray ordered were retrospectively identified. Demographic information and the number of ordered and completed imaging studies were among the data collected. Patient insurance status and income level (estimated based on zip code) served as representations of socioeconomic status. RESULTS A total of 24,105 patients met the inclusion criteria for this study. Regression analyses demonstrated that uninsured patients were significantly less likely to have an MRI, CT, or X-ray study ordered (P < .001 for all modalities) and completed (P < .001 for MRI and X-ray, P = .03 for CT). Patients with lower income had higher rates of MRI, CT, and X-ray (P < .001 for all) imaging ordered but were less likely to have an ordered X-ray be completed (P = .009). There was no significant difference in the completion rate of ordered MRIs or CTs. CONCLUSION Disparities in image utilization based on socioeconomic characteristics such as insurance status and income level highlight a critical gap in access to health care. Physicians should work to mitigate the influence of such factors when deciding whether to order imaging studies, especially in light of the ongoing shift in health policy in the United States.

Data quality assessment framework to assess electronic medical record data for use in research.

INTRODUCTION The proliferation and use of electronic medical records (EMR) in the clinical setting now provide a rich source of clinical data that can be leveraged to support research on patient outcomes, comparative effectiveness, and health systems research. Once the large volume and variety of data that robust clinical EMRs provide is aggregated, the suitability of the data for research purposes must be addressed. Therefore, the purpose of this paper is two-fold. First, we present a stepwise framework capable of guiding initial data quality assessment when matching multiple data sources regardless of context or application. Then, we demonstrate a use case of initial analysis of a longitudinal data repository of electronic health record data that illustrates the first four steps of the framework, and report results. METHODS A six-step data quality assessment framework is proposed and described that includes the following data quality assessment steps: (1) preliminary analysis, (2) documentation-longitudinal concordance, (3) breadth, (4) data element presence, (5) density, and (6) prediction. The six-step framework was applied to the Transport Data Mart-a data repository that contains over 28,000 records for patients that underwent interhospital transfer that includes EMRs from the sending hospitalization, transport, and receiving hospitalization. RESULTS There were a total of 9557 log entries of which 8139 were successfully matched to corresponding hospital encounters. 2832 were successfully mapped to both the sending and receiving hospital encounters (resulting in a 93% automatic matching rate), with 590 including air medical transport EMR data representing a complete case for testing. Results from Step 2 indicate that once records are identified and matched, there appears to be relatively limited drop-off of additional records when the criteria for matching increases, indicating the a proportion of records consistently contain nearly complete data. Measures of central tendency used in Step 3 and 4 exhibit a right skewness suggesting that a small proportion of records contain the highest number of repeated measures for the measured variables. CONCLUSIONS The proposed six-step data quality assessment framework is useful in establishing the metadata for a longitudinal data repository that can be replicated by other studies. There are practical issues that need to be addressed including the data quality assessments-with the most prescient being the need to establish data quality metrics for benchmarking acceptable levels of EMR data inclusiveness through testing and application.

Patient Characteristics Associated With Severe Hypoglycemia in a Type 2 Diabetes Cohort in a Large, Integrated Health Care System From 2006 to 2015

OBJECTIVE To identify severe hypoglycemia events, defined as emergency department visits or hospitalizations for hypoglycemia, in patients with type 2 diabetes receiving care in a large health system and to identify patient characteristics associated with severe hypoglycemia events. RESEARCH DESIGN AND METHODS This was a retrospective cohort study from January 2006 to December 2015 using the electronic medical record in the Cleveland Clinic Health System (CCHS). Participants included 50,439 patients with type 2 diabetes receiving care in the CCHS. Number of severe hypoglycemia events and associated patient characteristics were identified. RESULTS The incidence proportion of severe hypoglycemia increased from 0.12% in 2006 to 0.31% in 2015 (P = 0.01). Compared with patients who did not experience severe hypoglycemia, those with severe hypoglycemia had similar median glycosylated hemoglobin (HbA1c) levels. More patients with severe hypoglycemia versus those without had a prior diagnosis of nonsevere hypoglycemia (9% vs. 2%, P < 0.001). Logistic regression confirmed an increased odds for severe hypoglycemia with insulin, sulfonylureas, increased number of diabetes medications, history of nonsevere hypoglycemia (odds ratio [OR] 3.01, P < 0.001), HbA1c <6% (42 mmol/mol) (OR 1.95, P < 0.001), black race, and increased Charlson comorbidity index. Lower odds of severe hypoglycemia were noted with higher BMI and use of metformin, dipeptidyl peptidase 4 inhibitors, and glucagon-like peptide 1 agonists. CONCLUSIONS In this retrospective study of patients with type 2 diabetes with severe hypoglycemia, patient characteristics were identified. Patients with severe hypoglycemia had previous nonsevere hypoglycemia diagnoses more frequently than those without. Identifying patients at high risk at the point of care can allow for change in modifiable risk factors and prevention of severe hypoglycemia events.

Clinical Inertia in Type 2 Diabetes Management: Evidence From a Large, Real-World Data Set

Despite clinical practice guidelines that recommend frequent monitoring of HbA1c (every 3 months) and aggressive escalation of antihyperglycemic therapies until glycemic targets are reached (1,2), the intensification of therapy in patients with uncontrolled type 2 diabetes (T2D) is often inappropriately delayed. The failure of clinicians to intensify therapy when clinically indicated has been termed “clinical inertia.” A recently published systematic review found that the median time to treatment intensification after an HbA1c measurement above target was longer than 1 year (range 0.3 to >7.2 years) (3). We have previously reported a rather high rate of clinical inertia in patients uncontrolled on metformin monotherapy (4). Treatment was not intensified early (within 6 months of metformin monotherapy failure) in 38%, 31%, and 28% of patients when poor glycemic control was defined as an HbA1c >7% (>53 mmol/mol), >7.5% (>58 mmol/mol), and >8% (>64 mmol/mol), respectively. Using the electronic health record system at Cleveland Clinic (2005–2016), we identified a cohort of 7,389 patients with T2D who had an HbA1c value ≥7% (≥53 mmol/mol) (“index HbA1c”) despite having been on a stable regimen of two oral antihyperglycemic drugs (OADs) for at least 6 months prior to the …

Prevalence and recognition of obesity and its associated comorbidities: cross-sectional analysis of electronic health record data from a large US integrated health system

Objective To determine the prevalence of obesity and its related comorbidities among patients being actively managed at a US academic medical centre, and to examine the frequency of a formal diagnosis of obesity, via International Classification of Diseases, Ninth Revision (ICD-9) documentation among patients with body mass index (BMI) ≥30 kg/m2. Design The electronic health record system at Cleveland Clinic was used to create a cross-sectional summary of actively managed patients meeting minimum primary care physician visit frequency requirements. Eligible patients were stratified by BMI categories, based on most recent weight and median of all recorded heights obtained on or before the index date of 1July 2015. Relationships between patient characteristics and BMI categories were tested. Setting A large US integrated health system. Results A total of 324 199 active patients with a recorded BMI were identified. There were 121 287 (37.4%) patients found to be overweight (BMI ≥25 and <29.9), 75 199 (23.2%) had BMI 30–34.9, 34 152 (10.5%) had BMI 35–39.9 and 25 137 (7.8%) had BMI ≥40. There was a higher prevalence of type 2 diabetes, pre-diabetes, hypertension and cardiovascular disease (P value<0.0001) within higher BMI compared with lower BMI categories. In patients with a BMI >30 (n=134 488), only 48% (64 056) had documentation of an obesity ICD-9 code. In those patients with a BMI >40, only 75% had an obesity ICD-9 code. Conclusions This cross-sectional summary from a large US integrated health system found that three out of every four patients had overweight or obesity based on BMI. Patients within higher BMI categories had a higher prevalence of comorbidities. Less than half of patients who were identified as having obesity according to BMI received a formal diagnosis via ICD-9 documentation. The disease of obesity is very prevalent yet underdiagnosed in our clinics. The under diagnosing of obesity may serve as an important barrier to treatment initiation.

Predicting 30-Day All-Cause Readmission Risk for Subjects Admitted With Pneumonia at the Point of Care

BACKGROUND: The pneumonia 30-d readmission rate has been endorsed by the National Quality Forum as a quality metric. Hospital readmissions can potentially be lowered by improving in-hospital care, transitions of care, and post-discharge disease management programs. The purpose of this study was to create an accurate prediction model for determining the risk of 30-d readmission at the point of discharge. METHODS: The model was created using a data set of 1,295 hospitalizations at the Cleveland Clinic Main Campus with pneumonia over 3 y. Candidate variables were limited to structured variables available in the electronic health record. The final model was compared with the Centers for Medicare and Medicaid Services (CMS) model among subjects 65 y of age and older (n = 628) and was externally validated. RESULTS: Three hundred thirty subjects (25%) were readmitted within 30 d. The final model contained 13 variables and had a bias-corrected C statistic of 0.74 (95% CI 0.71–0.77). Number of admissions in the prior 6 months, opioid prescription, serum albumin during the first 24 h, international normalized ratio and blood urea nitrogen during the last 24 h were the predictor variables with the greatest weight in the model. In terms of discriminative performance, the Cleveland Clinic model outperformed the CMS model on the validation cohort (C statistic 0.69 vs 0.60, P = .042). CONCLUSIONS: The proposed risk prediction model performed better than the CMS model. Accurate readmission risk prediction at the point of discharge is feasible and can potentially be used to focus post-acute care interventions in a high-risk group of patients.

Use of Visual Decision Aids in Physician–Patient Communication: A Pilot Investigation

Introduction: A risk calculator paired with a personalized decision aid (RC&DA) may foster shared decision-making in primary care. We assessed the feasibility of using an RC&DA with patients in a primary care outpatient clinic and patients’ experiences regarding communication and decision-making. Methods: This pilot study was conducted with 15 patients of 3 primary care physicians at a clinic within a tertiary medical center. An atherosclerotic cardiovascular disease (ASCVD) risk calculator was used to generate a personalized RC&DA that displayed absolute 10-year risk information as an icon array graphic. Patient perceptions of utility of the RC&DA, preferences for decision-making, and uncertainty with risk reduction decisions were measured with a semi-structured interview. Results: Patients reported that the RC&DA was easy to understand and knowledge gained was useful to modify their ASCVD risk. Patients used the RC&DA to make decisions and reported low uncertainty with those decisions. Conclusions: Our findings demonstrate the feasibility of, and positive patient experiences related to using, an RC&DA to facilitate shared decision-making between physicians and patients in an outpatient primary care setting.

Effect of glycemic control on the Diabetes Complications Severity Index score and development of complications in people with newly diagnosed type 2 diabetes: 在新诊断的2型糖尿病患者中血糖控制情况对糖尿病并发症严重程度指数评分以及并发症进展的影响

The aim of the present study was to assess the longitudinal accumulation of diabetes‐related complications and the effect of glycemic control on the Diabetes Complications Severity Index (DCSI) score in people with newly diagnosed type 2 diabetes (T2D).