Proportion and predictors of FVC decline in patients with interstitial lung disease.

RATIONALE: The proportion of patients who develop progressive pulmonary fibrosis (PPF), along with risk factors for progression remain poorly understood. OBJECTIVES: To examine factors associated with an increased risk of developing PPF among patients at a referral center. METHODS: We identified patients with a diagnosis of interstitial lung disease (ILD) seen within the Cleveland Clinic Health System. Utilizing a retrospective observational approach we estimated the risk of developing progression by diagnosis group and identified key clinical predictors using the FVC component of both the original progressive fibrotic interstitial lung disease (PFILD) and the proposed PPF (ATS) criteria. RESULTS: We identified 5934 patients with a diagnosis of ILD. The cumulative incidence of progression over the 24 months was similar when assessed with the PFILD and PPF criteria (33.1 % and 37.9 % respectively). Of those who met the ATS criteria, 9.5 % did not meet the PFILD criteria. Conversely, 4.3 % of patients who met PFILD thresholds did not achieve the 5 % absolute FVC decline criteria. Significant differences in the rate of progression were seen based on underlying diagnosis. Steroid therapy (HR 1.46, CI 1.31-1.62) was associated with an increased risk of progressive fibrosis by both PFILD and PPF criteria. CONCLUSION: Regardless of the definition used, the cumulative incidence of progressive disease is high in patients with ILD in the 24 months following diagnosis. Some differences are seen in the risk of progression when assessed by PFILD and PPF criteria. Further work is needed to identify modifiable risk factors for the development of progressive fibrosis.

Erratum: Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool. Diabetes Spectrum 2023;36:161-170 (https://doi.org/10.2337/ds22-0031).

[This corrects the article DOI: 10.2337/ds22-0031.].

Addressing Therapeutic Inertia: Development and Implementation of an Electronic Health Record-Based Diabetes Intensification Tool.

Objective: To assess whether an electronic health record (EHR)-based diabetes intensification tool can improve the rate of A1C goal attainment among patients with type 2 diabetes and an A1C ≥8%. Methods: An EHR-based tool was developed and sequentially implemented in a large, integrated health system using a four-phase, stepped-wedge design (single pilot site [phase 1] and then three practice site clusters [phases 2-4]; 3 months/phase), with full implementation during phase 4. A1C outcomes, tool usage, and treatment intensification metrics were compared retrospectively at implementation (IMP) sites versus nonimplementation (non-IMP) sites with sites matched on patient population characteristics using overlap propensity score weighting. Results: Overall, tool utilization was low among patient encounters at IMP sites (1,122 of 11,549 [9.7%]). During phases 1-3, the proportions of patients achieving the A1C goal (<8%) were not significantly improved between IMP and non-IMP sites at 6 months (range 42.9-46.5%) or 12 months (range 46.5-53.1%). In phase 3, fewer patients at IMP sites versus non-IMP sites achieved the goal at 12 months (46.7 vs. 52.3%, P = 0.02). In phases 1-3, mean changes in A1C from baseline to 6 and 12 months (range -0.88 to -1.08%) were not significantly different between IMP and non-IMP sites. Times to intensification were similar between IMP and non-IMP sites. Conclusion: Utilization of a diabetes intensification tool was low and did not influence rates of A1C goal attainment or time to treatment intensification. The low level of tool adoption is itself an important finding highlighting the problem of therapeutic inertia in clinical practice. Testing additional strategies to better incorporate, increase acceptance of, and improve proficiency with EHR-based intensification tools is warranted.

Unmet needs and opportunities for optimal management of patients with type 2 diabetes and chronic kidney disease.

This brief report utilizes EHR data from a large US health system to summarize unmet needs in patients with type 2 diabetes and chronic kidney disease and identifies areas of opportunity to optimize management within this patient population from treatment, screening and monitoring, and health care resource use perspectives.

Computable Phenotype of a Crohn's Disease Natural History Model.

BACKGROUND: Analytic tools to study important clinical issues in complex, chronic diseases such as Crohn's disease (CD) include randomized trials, claims database studies, or small longitudinal epidemiologic cohorts. Using natural language processing (NLP), we sought to define the computable phenotype health state of pediatric and adult CD and develop patient-level longitudinal histories for health outcomes. METHODS: We defined 6 health states for CD using a subjective symptom-based assessment (symptomatic/asymptomatic) and an objective disease state assessment (active/inactive/no testing). Gold standard for the 6 health states was derived using an iterative process during review by our CD experts. We calculated the transition probabilities to estimate the time to transitions between the various health states using nonparametric Kaplan-Meier estimation and a Markov model. Finally, we determined a standard utility measure from clinical patients assigned to different health states. RESULTS: The NLP computable phenotype health state model correctly ascertained the objective test results and symptoms 96% and 85% of the time, respectively, based on a blinded chart evaluation. In our model, >25% of patients who begin as asymptomatic/active transition to symptomatic/active over the following year. For both adult and pediatric CD health states, the utility assessments of a symptomatic/inactive health state closely resembled a symptomatic/active health state. CONCLUSIONS: Our methodology for a computable phenotype health state demonstrates the application of real-world data to define progression and optimal management of a chronic disease such as CD. The application of the model has the potential to lead to a better understanding of the true impact of a therapeutic intervention and can provide long-term cost-effectiveness analyses for a new therapy. HIGHLIGHTS: Using natural language processing, we defined the computable phenotype health state of Crohn's disease and developed patient-level longitudinal histories for health outcomes.Our methodology demonstrates the application of real-world data to define the progression of a chronic disease.The application of the model has the potential to provide better understanding of the true impact of a new therapy.

Implementation Experience with a 30-Day Hospital Readmission Risk Score in a Large, Integrated Health System: A Retrospective Study.

BACKGROUND: Driven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR). OBJECTIVE: We evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity. DESIGN: Retrospective cohort study. PARTICIPANTS: Adult patients discharged from a CCHS hospital April 2017-September 2020. MAIN MEASURES: Data was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic. RESULTS: The final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories. CONCLUSIONS: The CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

Associations of weight loss with obesity-related comorbidities in a large integrated health system.

AIMS: To determine the health outcomes associated with weight loss in individuals with obesity, and to better understand the relationship between disease burden (disease burden; ie, prior comorbidities, healthcare utilization) and weight loss in individuals with obesity by analysing electronic health records (EHRs). MATERIALS AND METHODS: We conducted a case-control study using deidentified EHR-derived information from 204 921 patients seen at the Cleveland Clinic between 2000 and 2018. Patients were aged ≥20 years with body mass index ≥30 kg/m2 and had ≥7 weight measurements, over ≥3 years. Thirty outcomes were investigated, including chronic and acute diseases, as well as psychological and metabolic disorders. Weight change was investigated 3, 5 and 10 years prior to an event. RESULTS: Weight loss was associated with reduced incidence of many outcomes (eg, type 2 diabetes, nonalcoholic steatohepatitis/nonalcoholic fatty liver disease, obstructive sleep apnoea, hypertension; P < 0.05). Weight loss >10% was associated with increased incidence of certain outcomes including stroke and substance abuse. However, many outcomes that increased with weight loss were attenuated by disease burden adjustments. CONCLUSIONS: This study provides the most comprehensive real-world evaluation of the health impacts of weight change to date. After comorbidity burden and healthcare utilization adjustments, weight loss was associated with an overall reduction in risk of many adverse outcomes.

Differences in Biologic Utilization and Surgery Rates in Pediatric and Adult Crohn's Disease: Results From a Large Electronic Medical Record-derived Cohort.

BACKGROUND AND AIMS: Crohn's disease (CD) is a chronic illness that affects both the pediatric and adult populations with an increasing worldwide prevalence. We aim to identify a large, single-center cohort of patients with CD using natural language processing (NLP) in combination with codified data and extract surgical rates and medication usage from the electronic medical record (EMR). METHODS: Patients with CD were identified from the entire Cleveland Clinic EMR using ICD codes and CD-specific terms identified by NLP to fit a logistic regression model. Cohorts were developed for pediatric-onset (younger than 18 years) and adult-onset (18 years and older) CD. Surgeries were identified using current procedural terminology (CPT) codes and NLP. Crohn's disease-related medications were extracted using physician orders in the EMR. RESULTS: Patients with pediatric-onset (n = 2060) and adult-onset (n = 4973) CD were identified from 2000 to 2017 with a positive predictive value of 98.5%. Rate of CD-related abdominal surgery over time was significantly higher in adult-onset compared with pediatric-onset CD (10-year surgery rate 49.9% vs 37.7%, respectively; P < 0.001). Treatment with biologics was significantly higher in pediatric vs adult-onset CD cohorts (63.6% vs 49.2%; P < 0.001). The overall rate of CD-related abdominal surgery was significantly higher in those who received <6 months of a biologic compared with ≥6 months of a biologic for both cohorts (pediatric 64.1% vs 39.1%, P ≤ 0.001; adult 69.3% vs 56.5%, P ≤ 0.001). Additionally, 60.9% in pediatric-onset CD and 43.5% in adult-onset CD treated with ≥6 months of biologic therapy have not required abdominal surgery. On multivariable analysis, perianal surgery was a significant risk factor for abdominal surgery in both cohorts. CONCLUSION: We used a combination of codified and NLP data to establish the largest, North American, single-center EMR cohort of pediatric- and adult-onset CD patients and determined that biologics are associated with lower rates of surgery over time, potentially altering the natural history of the disease.

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

OBJECTIVE: To assess patient characteristics and treatment factors associated with uncontrolled type 2 diabetes (T2D) and the probability of hemoglobin A1c (A1C) goal attainment. RESEARCH DESIGN AND METHODS: This was a retrospective cohort study using the electronic health record at Cleveland Clinic. Patients with uncontrolled T2D (A1C >9%) were identified on the index date of 31 December 2016 (n = 6,973) and grouped by attainment (n = 1,653 [23.7%]) or nonattainment (n = 5,320 [76.3%]) of A1C <8% by 31 December 2017, and subgroups were compared on a number of demographic and clinical variables. On the basis of these variables, a nomogram was created for predicting probability of A1C goal attainment. RESULTS: For the entire population, median age at index date was 57.7 years (53.3% male), and the majority were white (67.2%). Median A1C was 10.2%. Obesity (50.6%), cardiovascular disease (46.9%), and psychiatric disease (61.1%) were the most common comorbidities. Metformin (62.7%) and sulfonylureas (38.7%) were the most common antidiabetes medications. Only 1,653 (23.7%) patients achieved an A1C <8%. Predictors of increased probability of A1C goal attainment were older age, white/non-Hispanic race/ethnicity, Medicare health insurance, lower baseline A1C, higher frequency of endocrinology/primary care visits, dipeptidyl peptidase 4 inhibitor use, thiazolidinedione use, metformin use, glucagon-like peptide 1 receptor agonist use, and fewer classes of antidiabetes drugs. Factors associated with lower probability included insulin use and longer time in the T2D database (both presumed as likely surrogates for duration of T2D). CONCLUSIONS: A minority of patients with an A1C >9% achieved an A1C <8% at 1 year. While most identified predictive factors are nonmodifiable by the clinician, pursuit of frequent patient engagement and tailored drug regimens may help to improve A1C goal attainment.

Association between shared medical appointments and weight loss outcomes and anti-obesity medication use in patients with obesity.

OBJECTIVE: In shared medical appointments (SMAs), multiple patients with a similar clinical diagnosis are seen by a multidisciplinary team for interactive group sessions. Very few studies have specifically studied SMAs and weight loss in patients with obesity. This study compared weight loss outcomes and anti-obesity medication (AOM) access between patients with obesity managed through (SMAs) versus individual appointments. METHODS: Retrospective study of adults seen for obesity between September 2014 and February 2017 at Cleveland Clinic Institute of Endocrinology and Metabolism. Percent weight loss from baseline was compared between two propensity score-matched populations: patients who attended ≥1 SMA and patients managed with individual medical appointments. RESULTS: From all eligible patients identified (n=310 SMA, n=1,993 non-SMA), 301 matched pairs were evaluated for weight loss. The SMA group (n=301) lost a mean of 4.2%, 5.2% and 3.8% of baseline weight over 6, 12 and 24 months; the non-SMA group (n=301) lost significantly less weight (1.5%, 1.8% and 1.6%, respectively) (paired t-test, P<.05). All patients were eligible for US Food and Drug Administration-approved AOMs based on obesity diagnosis; however, 49.8% (150/301) of matched SMA patients were prescribed an AOM versus 12.3% (37/301) of matched non-SMA patients. CONCLUSION: This study suggests that SMAs may offer a promising alterative for obesity management and one that may facilitate greater utilization of AOMs. In propensity score-matched cohorts, SMAs were associated with greater weight loss outcomes when compared to usual care facilitated through individual medical appointments alone.

Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

OBJECTIVE: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH). RESEARCH DESIGN AND METHODS: From 2005 to 2017, we identified NSH events by diagnosis codes and NLP. We then built an SH prediction model. RESULTS: There were 204,517 patients with type 2 diabetes and no diagnosis codes for NSH. Evidence of NSH was found in 7,035 (3.4%) of patients using NLP. We reviewed 1,200 of the NLP-detected NSH notes and confirmed 93% to have NSH. The SH prediction model (C-statistic 0.806) showed increased risk with NSH (hazard ratio 4.44; P < 0.001). However, the model with NLP did not improve SH prediction compared with diagnosis code-only NSH. CONCLUSIONS: Detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction.

Risk prediction for severe hypoglycemia in a type 2 diabetes population with previous non-severe hypoglycemia.

BACKGROUND/AIM: Episodes of non-severe hypoglycemia can be captured through diagnoses documented in the electronic medical record. We aimed to create a clinically useful prediction model for a severe hypoglycemia event, requiring an emergency department visit or hospitalization, in patients with Type 2 diabetes with a history of non-severe hypoglycemia. METHODS: Using electronic medical record data from 50,439 patients with Type 2 diabetes in one health system, number of severe hypoglycemia events and associated patient characteristics from 2006 to 2015 were previously defined. Using the landmarking method, a dynamic prediction model was built using the subset of 1876 patients who had a documented non-severe hypoglycemia diagnosis code, using logistic regression to obtain landmark-specific odds of severe hypoglycemia in this group. For model performance, the bootstrap procedure was employed for internal validation and area under the curve (AUC) and index of prediction accuracy (IPA) were calculated. RESULTS: Glycosylated hemoglobin (HbA1c) less than 7% (53 mmol/mol) was associated with increased odds ratio (OR) of severe hypoglycemia at 3 months (OR 1.92 95% Confidence Interval (CI) 1.19-3.10 at HbA1c 5% (31 mmol/mol) and OR 1.21, CI 1.03-1.41 at HbA1c 6%(42 mmol/mol).) History of non-severe hypoglycemia within the past 3 months increased odds for severe hypoglycemia (OR 2.58 95% CI 1.80-3.70) as did Black race, insulin use with the past 3 months, and comorbidities. Metformin and sulfonlylurea use in the past 3 months, increasing age and body mass index had lower odds of a future severe hypoglycemia event. For the prediction model for 3 month risk of severe hypoglycemia, the AUC was 0.890 (CI 0.843-0.907) and the IPA was 10.8% (CI 4.4% - 12.4%). CONCLUSION: In patients with a documented diagnosis of non-severe hypoglycemia, a dynamic prediction model identifies patients with Type 2 diabetes with 3-month increased risk of severe hypoglycemia, allowing for preventive efforts, such as medication changes, at the point of care.

THE BENEFIT OF SHORT-TERM WEIGHT LOSS WITH ANTI-OBESITY MEDICATIONS IN REAL-WORLD CLINICAL PRACTICE.

Objective: The effectiveness of anti-obesity medications (AOMs) outside of clinical trials is unclear. The objective of this study was to compare the short-term effectiveness of AOMs in real-world practice. Methods: This retrospective study included adults aged ≥18 years, with body mass index ≥30 kg/m2 or ≥27 kg/m2 with at least one obesity-related comorbidity who were prescribed phentermine hydrochloride, phenterminetopiramate, bupropion-naltrexone, or lorcaserin for 12 consecutive weeks between 2006 and 2016 at a large tertiary healthcare system. Propensity score-matched cohorts were created for each pair of AOMs. The primary outcomes were percent and absolute weight loss from baseline after 12 weeks. A prediction model was constructed to estimate weight loss with different AOMs based on demographic and clinical data. Results: Of the 3,411 patients included in this study, patients lost an average of 3.45% of body weight from baseline. All AOMs were associated with a significant weight loss from baseline (P<.0001). Patients lost the highest percentage of body weight on phentermine hydrochloride (3.75 ± 5.66%), followed by phentermine-topiramate (3.63 ± 5.7%), bupropion-naltrexone (2.66 ± 5.03%), and lorcaserin (1.84 ± 6.69%). In propensity-matched cohorts, patients taking phentermine hydrochloride lost more weight than those taking lorcaserin or bupropion-naltrexone, and patients taking phentermine topiramate lost more weight than patients taking lorcaserin. Conclusion: In real-world practice, AOMs are associated with clinically meaningful weight loss of 2 to 4% after 12 weeks. In this study, phentermine hydrochloride and phentermine topiramate produced the most weight loss. AOMs should be seriously considered as part of the armamentarium to treat patients with obesity. Abbreviations: AOM = anti-obesity medication; BMI = body mass index; EMR = electronic medical record; FDA = Food and Drug Administration; T2D = type 2 diabetes.

Testosterone replacement therapy and the risk of adverse cardiovascular outcomes and mortality.

BACKGROUND: The risk of adverse cardiovascular events and mortality associated with testosterone replacement therapy is controversial. The purpose of this report was to evaluate the effect of testosterone replacement therapy (TRT) in men with secondary hypogonadism on the risk of myocardial infarction (MI), stroke (CVA) or all-cause mortality. METHODS: A retrospective cohort study was conducted using the Cleveland Clinic's electronic health record. Men ≥40 years of age, with at least two testosterone levels < 220 ng/dL, with one level obtained between 7 am and 10 am, were identified. Men with primary hypogonadism, secondary hypogonadism related to overt hypothalamic pituitary pathology, human immunodeficiency virus infection, metastatic cancer, and select contraindications to TRT, were excluded. Men exposed to TRT were matched to controls that were not exposed. A survival analysis was performed on the composite outcome of MI, CVA, or all-cause mortality. RESULTS: One hundred sixty-five patients exposed to TRT (treatment group) were matched with 210 not exposed to TRT (comparison group). The prevalence of established cardiovascular disease (CVD) was 20.0% in the treatment group vs. 17.1% in the comparison group (P = 0.478). The median [interquartile range (IQR)] age (years) and BMI (kg/m2) were 55 (49, 62) and 35.6 (32.1, 40.1) in the treatment group, and 55 (49, 61.7) and 36.3 (32.1, 40.8) in the comparison group, respectively. There were 12 (7.3%) events observed in the treatment group, and 16 (7.6%) in the comparison group. The median time (years) to the composite event was 2.1 (IQR 0.9, 4.6) and 1.8 (IQR 0.6, 3.4) for treatment and comparison groups, respectively. No difference in the risk of the combined cardiovascular endpoint was observed between the treatment group vs the comparison group, hazard ratio (HR) 0.81 (95% Confidence Interval [CI]: 0.38-1.71; P = 0.57). CONCLUSION: In hypogonadal men with a modest prevalence of established CVD, TRT was not observed to confer a protective or adverse effect on the risk of MI, CVA or all-cause mortality.

CONTEXTE: Le risque d'événements cardiovasculaires indésirables et de mortalité associé au traitement de substitution de la testostérone est controversé. Le but du présent article est d'évaluer chez les hommes qui présentent un hypogonadisme secondaire l'effet du traitement de substitution de la testostérone (TST) sur le risque d'infarctus du myocarde (IM), d'accident cérébrovasculaire (ACV) ou de mortalité toutes causes confondues. PATIENTS ET MÉTHODES: Une étude de cohorte rétrospective a été menée en utilisant les dossiers de santé électroniques de la Clinique Cleveland. Ont été identifiés les hommes âgés de plus de 40 ans qui avaient au moins deux dosages de testostérone inférieurs à 220ng/dl, dont l'un obtenu le matin entre 7 et 10 heures. Ont été exclus les hommes qui présentaient un hypogonadisme primaire, un hypogonadisme secondaire lié à une pathologie hypothalamo-hypophysaire évidente, une infection par le virus de l'immunodéficience humaine, un cancer métastatique, et ceux qui présentaient des contrindications déterminées au TST. Les hommes exposés au TST ont été appariés à des témoins non exposés au TST. Une analyse de survie a été réalisée sur le paramètre composite incluant l'IM, l'AVC et la mortalité toutes causes confondues. RÉSULTATS: 165 patients exposés au TST (groupe traité) ont été appariés à 210 hommes non exposés au TST (groupe de référence). La prévalence de maladie cardiovasculaire (MCV) établie était de 20.0% dans le groupe traité versus 17.1% dans le groupe de référence (P=0.478). La médiane (écart interquartile (EI)) de l'âge et de l'indice de masse corporelle (IMC) était respectivement de 55 (49, 62) ans et de 35.6 (32.1, 40.1) kg/m2 dans le groupe traité, et respectivement de 55 (49, 61.7) ans et 36.3 (32.1, 40.8) kg/m2 dans le groupe de référence. Il y eut 12 événements indésirables (7.3%) dans le groupe traité, et 16 (7.6%) dans le groupe de référence. La médiane du temps (en années) des événements composites était de 2.1 (EI 0.9, 4.6) et de 1.8 (EI 0.6, 3.4) respectivement pour les groupes traité et de référence. Aucune différence n'a été observée en ce qui concerne le risque cardiovasculaire entre le groupe traité et le groupe de référence, rapport de risque (RR) 0.81 (Intervalle de Confiance à 95% [IC]: 0.38-1.71; P = 0.57). CONCLUSION: Chez les hommes qui présentent un hypogonadisme et une faible prévalence de maladie cardiovasculaire (MCV) établie, le TST n'apparait pas conférer un effet protecteur ou défavorable sur le risque de d'infarctus du myocarde (IM), d'accident cérébrovasculaire (ACV) ou de mortalité toutes causes confondues. MOTS-CLÉS: Traitement de substitution de la testostérone, Hypogonadisme masculin, Risque cardiovasculaire, Mortalité.

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.

Testing of a Tool for Prostate Cancer Screening Discussions in Primary Care.

BACKGROUND: As prostate cancer (PCa) screening decisions often occur in outpatient primary care, a brief tool to help the PCa screening conversation in busy clinic settings is needed. METHODS: A previously created 9-item tool to aid PCa screening discussions was tested in five diverse primary care clinics. Fifteen providers were recruited to use the tool for 4 weeks, and the tool was revised based upon feedback. The providers then used the tool with a convenience sample of patients during routine clinic visits. Pre- and post-visit surveys were administered to assess patients' knowledge of the option to be screened for PCa and of specific factors to consider in the decision. McNemar's and Stuart-Maxwell tests were used to compare pre-and post-survey responses. RESULTS: 14 of 15 providers completed feedback surveys and had positive responses to the tool. All 15 providers then tested the tool on 95 men aged 40-69 at the five clinics with 2-10 patients each. The proportion of patients who strongly agreed that they had the option to choose to screen for PCa increased from 57 to 72% (p = 0.018) from the pre- to post-survey, that there are factors in the personal or family history that may affect PCa risk from 34 to 47% (p = 0.012), and that their opinions about possible side effects of treatment for PCa should be considered in the decision from 47 to 61% (p = 0.009). CONCLUSION: A brief conversation tool for the PCa screening discussion was well received in busy primary-care settings and improved patients' knowledge about the screening decision.

Antidiabetic treatment patterns and specialty care utilization among patients with type 2 diabetes and cardiovascular disease.

BACKGROUND: To evaluate real-world patient characteristics, medication use, and health care utilization patterns in patients with type 2 diabetes with established cardiovascular disease (CVD). METHODS: Cross-sectional analysis of patients with type 2 diabetes seen at Cleveland Clinic from 2005 to 2016, divided into two cohorts: with-CVD and without-CVD. Patient demographics and antidiabetic medications were recorded in December 2016; department encounters included all visits from 1/1/2016 to 12/31/2016. Comorbidity burden was assessed by the diabetes complications severity index (DCSI) score. RESULTS: Of 95,569 patients with type 2 diabetes, 40,910 (42.8%) were identified as having established CVD. Patients with CVD vs. those without were older (median age 69.1 vs. 58.2 years), predominantly male (53.8% vs. 42.6%), and more likely to have Medicare insurance (69.4% vs. 35.3%). The with-CVD cohort had a higher proportion of patients with a DCSI score ≥ 3 than the without-CVD cohort (65.0% vs. 10.3%). Utilization rates of glucagon-like peptide-1 receptor agonists and sodium-glucose co-transporter-2 inhibitors were low in both with-CVD (4.1 and 2.5%) and without-CVD cohorts (5.4 and 4.1%), respectively. The majority of patient visits (75%) were seen by a primary care provider. During the 1-year observation period, 81.9 and 62.0% of patients with type 2 diabetes and CVD were not seen by endocrinology or cardiology, respectively. CONCLUSIONS: These data indicated underutilization of specialists and antidiabetic medications reported to confer CV benefit in patients with type 2 diabetes and CVD. The impact of recently updated guidelines and cardiovascular outcome trial results on management patterns in such patients remains to be seen.

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.

Effect of glycemic control on the Diabetes Complications Severity Index score and development of complications in people with newly diagnosed type 2 diabetes.

BACKGROUND: 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). METHODS: A retrospective cohort study was conducted using electronic health records from a large integrated healthcare system. People with newly diagnosed T2D were identified between 2005 and 2016 and stratified by initial HbA1c category (<7%, <8%, ≥8%). The DCSI scores were determined for each study year, and the cumulative incidence of diabetes-related complications was assessed. A Cox proportional hazard model was used to evaluate the effect of baseline HbA1c and worsening glycemic (HbA1c) control on longitudinal changes in DCSI scores. RESULTS: Of 32 174 people identified as having newly diagnosed T2D, 14 016 (44%), 21 657 (67%), and 9983 (31%) had an initial or baseline HbA1c <7%, <8%, and ≥8%, respectively. Ten years after diabetes diagnosis, retinopathy, chronic kidney disease, coronary heart disease, and neuropathy were diagnosed in 22%, 29%, 24%, and 36% of people. Baseline HbA1c did not affect the observed trend in longitudinal changes in DCSI scores throughout the 11-year period. For people in each of the initial HbA1c groups (<7%, <8%, ≥8%), worsening or persistently poor glycemic control was significantly associated with a 10%, 19%, or 16% increase in the risk of experiencing an increased DCSI score, respectively (all P < 0.01). CONCLUSIONS: Baseline glycemic control had no apparent effect on longitudinal changes in DCSI score. Worsening or persistently poor glycemic control was associated with an increased risk of an increase in the DCSI score.