A Randomized Trial to Address Food Insecurity and Promote Smoking Cessation Among Low-Income Adults.

Many low-income adults who smoke also have unmet social needs, such as food insecurity, which can serve as a barrier to smoking cessation. We developed a novel intervention to jointly address smoking cessation and food insecurity and assessed its feasibility, acceptability, and preliminary outcomes. We enrolled participants who screened for food insecurity, reported smoking daily, and were ready to quit. All participants received 3 months of resources navigation from a community health worker through monthly telephone calls for referrals and check-ins for smoking cessation and food access resources. Participants randomized to the intervention group received an economic intervention equivalent to the cost of 1 week of groceries/month for 3 months. We randomized 55 participants who were smoking on average 13 cigarettes/day. The trial was feasible and acceptable based on 3-month retention rates (80%) and end-of-study qualitative feedback (91% would recommend the study to others). At 3 months, participants in the intervention versus control group reported a longer length of abstinence from smoking and had a higher proportion of serious quit attempts. Results from this pilot study suggest the importance of attending to social needs, particularly food insecurity, as a strategy to promote smoking cessation among low-income adults who smoke.

Food insecurity, social needs, and smoking status among patients in a county hospital system.

Tobacco use in the U.S. is increasingly concentrated among populations with socioeconomic disadvantages such as food insecurity. Building on prior studies showing that food insecurity increases odds of cigarette smoking, the current study sought to examine how food insecurity and other social needs, particularly financial strain, transportation barriers, and housing/utility insecurity, were associated with smoking status among adult patients seen in a county hospital system. We analyzed data from the electronic health record of patients from The MetroHealth System (Cleveland, Ohio, USA), covering a two-year period since implementation of social determinants of health assessments (2019-2021; N = 45,151 patients). Logistic regression analyses were used to examine associations with smoking status. Compared to the overall smoking prevalence (21 %), smoking was higher among patients screening for transportation barriers (41 %), financial strain (39 %), food insecurity (34 %), and housing/utility insecurity (27 %). Each of these social needs was independently associated with increased odds of current smoking (all p < 0.05). Smoking prevalence increased sequentially as the number of social needs increased; with each addition of a social need, there was a dose-response association with higher odds of current smoking (adjusted ORs ranged from 1.56 to 3.76, all p < 0.001), and current smoking specifically among ever smoking patients (adjusted ORs ranged from 1.39 to 3.01, all p < 0.001). There was substantial overlap among several social needs and smoking status. Alongside improving access to evidence-based cessation treatments and services, the findings raise the possibility that addressing social needs might reduce barriers to quitting and thereby reduce tobacco use disparities.

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.

Ten-Year Outcomes and Risk Factors After Anterior Cruciate Ligament Reconstruction: A MOON Longitudinal Prospective Cohort Study.

BACKGROUND: The long-term prognosis and risk factors for quality of life and disability after anterior cruciate ligament (ACL) reconstruction remain unknown. Hypothesis/Purpose: Our objective was to identify patient-reported outcomes and patient-specific risk factors from a large prospective cohort at a minimum 10-year follow-up after ACL reconstruction. We hypothesized that meniscus and articular cartilage injuries, revision ACL reconstruction, subsequent knee surgery, and certain demographic characteristics would be significant risk factors for inferior outcomes at 10 years. STUDY DESIGN: Therapeutic study; Level of evidence, 2. METHODS: Unilateral ACL reconstruction procedures were identified and prospectively enrolled between 2002 and 2004 from 7 sites in the Multicenter Orthopaedic Outcomes Network (MOON). Patients preoperatively completed a series of validated outcome instruments, including the International Knee Documentation Committee (IKDC), Knee injury and Osteoarthritis Outcome Score (KOOS), and Marx activity rating scale. At the time of surgery, physicians documented all intra-articular abnormalities, treatment, and surgical techniques utilized. Patients were followed at 2, 6, and 10 years postoperatively and asked to complete the same outcome instruments that they completed at baseline. The incidence and details of any subsequent knee surgeries were also obtained. Multivariable regression analysis was used to identify significant predictors of the outcome. RESULTS: A total of 1592 patients were enrolled (57% male; median age, 24 years). Ten-year follow-up was obtained on 83% (n = 1320) of the cohort. Both IKDC and KOOS scores significantly improved at 2 years and were maintained at 6 and 10 years. Conversely, Marx scores dropped markedly over time, from a median score of 12 points at baseline to 9 points at 2 years, 7 points at 6 years, and 6 points at 10 years. The patient-specific risk factors for inferior 10-year outcomes were lower baseline scores; higher body mass index; being a smoker at baseline; having a medial or lateral meniscus procedure performed before index ACL reconstruction; undergoing revision ACL reconstruction; undergoing lateral meniscectomy; grade 3 to 4 articular cartilage lesions in the medial, lateral, or patellofemoral compartments; and undergoing any subsequent ipsilateral knee surgery after index ACL reconstruction. CONCLUSION: Patients were able to perform sports-related functions and maintain a relatively high knee-related quality of life 10 years after ACL reconstruction, although activity levels significantly declined over time. Multivariable analysis identified several key modifiable risk factors that significantly influence the outcome.

External validation of the TIMI risk score for secondary cardiovascular events among patients with recent myocardial infarction.

BACKGROUND AND AIMS: Risk stratification of patients with recent myocardial infarction (MI) for subsequent cardiovascular (CV) events helps identify patients most likely to benefit from secondary prevention therapies. This study externally validated a new risk score (TRS2˚P) for secondary events derived from the TRA2°P-TIMI 50 trial among post-MI patients from two large health care systems. METHODS: This retrospective cohort study included 9618 patients treated for acute MI at either the Cleveland Clinic (CC) or Geisinger Health System (GHS) between 2008 and 2013. Patients with a clinic visit within 2-52 weeks of MI were included and followed for CV death, repeat MI, and ischemic stroke through electronic medical records (EMR). The TRS2˚P is based on nine factors determined through EMR documentation. Discrimination and calibration of the TRS2˚P were quantified in both patient populations. RESULTS: MI patients at CC and GHS were older, had more comorbidities, received fewer medications, and had higher 3-year event rates compared to subjects in the TRA2°P trial: 31% (CC), 33% (GHS), and 10% (TRA2°P-TIMI 50). The proposed risk score had similar discrimination across the three cohorts with c-statistics of 0.66 (CC), 0.66 (GHS), and 0.67 (TRA2°P-TIMI 50). A strong graded relationship between the risk score and event rates was observed in all cohorts, though 3-year event rates were consistently higher within TRS2°P strata in the CC and GHS cohorts relative to TRA2˚P-TIMI 50. CONCLUSIONS: The TRS2˚P demonstrated consistent risk discrimination across trial and non-trial patients with recent MI, but event rates were consistently higher in the non-trial cohorts.

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.

Association of glucagon-like peptide-1 receptor agonist use and rates of acute myocardial infarction, stroke and overall mortality in patients with type 2 diabetes mellitus in a large integrated health system.

AIMS: To assess the potential impact of glucagon-like peptide-1 receptor agonist (GLP-1RA) exposure on cardiovascular disease (CVD) and mortality outcomes in patients with type 2 diabetes (T2D), using a large retrospective cohort. RESEARCH DESIGN AND METHODS: Patients who had T2D between 2005 and 2014 (N = 105 862) were identified from the electronic health record system at Cleveland Clinic using a validated electronic phenotype. A time-dependent, Cox, multiple regression analysis was used to assess the association between GLP-1RA exposure and risk of acute myocardial infarction (AMI), stroke/cerebrovascular accident (CVA), and overall mortality, as well as the composite of all three outcomes. The findings were further evaluated by assessing the effect of GLP-1RAs on the same variables in patients with and without prior CVD. The model adjusted for differences in demographic information, hypertension, laboratory/vital signs, history of outcomes, and T2D medications. RESULTS: There were significantly lower rates of AMI (hazard ratio [HR] 0.80, 95% confidence interval [CI] 0.65 to 0.99; P = .045), CVA (HR 0.82, 95% CI 0.74 to 0.91, P < .001), overall mortality (HR 0.48, 95% CI 0.41 to 0.57; P < .001), and the composite outcome (HR 0.82, 95% CI 0.74 to 0.91; P < .002) during the consolidated time that patients were exposed to GLP-1RAs compared to corresponding rates during intervals without GLP-1RA exposure. GLP-1RA treatment was associated with a significant decrease in CVA, mortality, and the composite outcome in patients with and without established CVD, not significantly affecting AMI in these subgroups. CONCLUSIONS: GLP-1RA exposure was found to be associated with a reduction in the risk of cardiovascular events observed and overall mortality among patients with T2D with and without established CVD, after adjusting for potential confounders.

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.

Changes in Characteristics and Treatment Patterns of Patients with Newly Diagnosed Type 2 Diabetes in a Large United States Integrated Health System between 2008 and 2013.

To assess changes in the clinical characteristics and treatment patterns of patients with newly diagnosed type 2 diabetes (T2D), the electronic health record system at Cleveland Clinic was used to create cross-sectional summaries of all patients with new-onset T2D in 2008 and 2013. Differences between the 2008 and 2013 data sets were assessed after adjusting for age, gender, race, and income. Approximately one-third of patients with newly diagnosed T2D in 2008 and 2013 had an A1C ≥8%, suggesting the continued presence of a delayed recognition of the disease. Patients with newly diagnosed T2D in 2008 were older than those in 2013. Hypertension, cardiovascular disease, and neuropathy were highly prevalent among patients diagnosed with T2D. The prevalence of neuropathy, cerebrovascular disease, and peripheral vascular disease increased from 2008 to 2013. Metformin was the most commonly prescribed antidiabetic medication. Sulfonylurea usage remained unchanged, while use of thiazolidinediones decreased considerably.

Predicting Risk of Urinary Incontinence and Adverse Events After Midurethral Sling Surgery in Women.

OBJECTIVE: To construct and validate models that predict a patient's risk of developing stress and urgency urinary incontinence and adverse events 12 months after sling surgery. METHODS: This was a secondary analysis of four randomized trials. Twenty-five candidate predictors (patient characteristics and urodynamic variables) were identified from the National Institute of Diabetes and Digestive and Kidney Diseases Trial of Mid-Urethral Slings (N=597). Multiple logistic models were fit to predict four different outcomes: 1) bothersome stress urinary incontinence; 2) a positive stress test; 3) bothersome urgency urinary incontinence; and 4) any adverse event up to 12 months after sling surgery. Model discrimination was measured using a concordance index. Each model's concordance index was internally validated using 1,000 bootstrap samples and calibration curves were plotted. Final models were externally validated on a separate data set (n=902) from a combination of three different multicenter randomized trials. RESULTS: Four best models discriminated on internal validation between women with bothersome stress urinary incontinence (concordance index 0.728, 95% confidence interval [CI] 0.683-0.773), a positive stress test (concordance index 0.712, 95% CI 0.669-0.758), bothersome urgency urinary incontinence (concordance index 0.722, 95% CI 0.680-0.764), and any adverse event (concordance index 0.640, 95% CI 0.595-0.681) after sling surgery. Each model's concordance index was reduced as expected when important variables were removed for external validation, but model discrimination remained stable with bothersome stress urinary incontinence (concordance index 0.548), a positive stress test (concordance index 0.656), bothersome urgency urinary incontinence (concordance index 0.621), and any adverse event (concordance index 0.567). Predicted probabilities are closest to actual probabilities when predictions are less than 50%. CONCLUSION: Four best and modified models discriminate between women who will and will not develop urinary incontinence and adverse events 12 months after midurethral sling surgery 64-73% and 55-66% of the time, respectively.

Glycopeptides versus ß-lactams for the prevention of surgical site infections in cardiovascular and orthopedic surgery: a meta-analysis.

OBJECTIVE: To compare the efficacy of glycopeptides and ß-lactams in preventing surgical site infections (SSIs) in cardiac, vascular, and orthopedic surgery. BACKGROUND: The cost-effectiveness of switching from ß-lactams to glycopeptides for preoperative antibiotic prophylaxis has been controversial. ß-Lactams are generally recommended in clean surgical procedures, but they are ineffective against resistant gram-positive bacteria. METHODS: PubMed, International Pharmaceuticals Abstracts, Scopus, and Cochrane were searched for randomized clinical trials comparing glycopeptides and ß-lactams for prophylaxis in adults undergoing cardiac, vascular, or orthopedic surgery. Abstracts and conference proceedings were included. Two independent reviewers performed study selection, data extraction, and assessment of risk of bias. RESULTS: Fourteen studies with a total of 8952 patients were analyzed. No difference was detected in overall SSIs between antibiotic types. However, compared with ß-lactams, glycopeptides reduced the risk of resistant staphylococcal SSIs by 48% (relative risk, 0.52; 95% confidence interval, 0.29-0.93; P = 0.03) and enterococcal SSIs by 64% (relative risk, 0.36; 95% confidence interval, 0.16-0.80; P = 0.01), but increased respiratory tract infections by 54% (relative risk, 1.54; 95% confidence interval, 1.19-2.01; P ≤ 0.01). Subgroup analysis of cardiac procedures showed superiority of ß-lactams in preventing superficial and deep chest SSIs, susceptible staphylococcal SSIs, and respiratory tract infections. CONCLUSIONS: Glycopeptides reduce the risk of resistant staphylococcal SSIs and enterococcal SSIs, but increase the risk of respiratory tract infections. Additional high-quality randomized clinical trials are needed as these results are limited by high risk of bias.

Using the landmark method for creating prediction models in large datasets derived from electronic health records.

With the integration of electronic health records (EHRs), health data has become easily accessible and abounded. The EHR has the potential to provide important healthcare information to researchers by creating study cohorts. However, accessing this information comes with three major issues: 1) Predictor variables often change over time, 2) Patients have various lengths of follow up within the EHR, and 3) the size of the EHR data can be computationally challenging. Landmark analyses provide a perfect complement to EHR data and help to alleviate these three issues. We present two examples that utilize patient birthdays as landmark times for creating dynamic datasets for predicting clinical outcomes. The use of landmark times help to solve these three issues by incorporating information that changes over time, by creating unbiased reference points that are not related to a patient's exposure within the EHR, and reducing the size of a dataset compared to true time-varying analysis. These techniques are shown using two example cohort studies from the Cleveland Clinic that utilized 4.5 million and 17,787 unique patients, respectively.

Strategies for handling missing data in electronic health record derived data.

Electronic health records (EHRs) present a wealth of data that are vital for improving patient-centered outcomes, although the data can present significant statistical challenges. In particular, EHR data contains substantial missing information that if left unaddressed could reduce the validity of conclusions drawn. Properly addressing the missing data issue in EHR data is complicated by the fact that it is sometimes difficult to differentiate between missing data and a negative value. For example, a patient without a documented history of heart failure may truly not have disease or the clinician may have simply not documented the condition. Approaches for reducing missing data in EHR systems come from multiple angles, including: increasing structured data documentation, reducing data input errors, and utilization of text parsing / natural language processing. This paper focuses on the analytical approaches for handling missing data, primarily multiple imputation. The broad range of variables available in typical EHR systems provide a wealth of information for mitigating potential biases caused by missing data. The probability of missing data may be linked to disease severity and healthcare utilization since unhealthier patients are more likely to have comorbidities and each interaction with the health care system provides an opportunity for documentation. Therefore, any imputation routine should include predictor variables that assess overall health status (e.g. Charlson Comorbidity Index) and healthcare utilization (e.g. number of encounters) even when these comorbidities and patient encounters are unrelated to the disease of interest. Linking the EHR data with other sources of information (e.g. National Death Index and census data) can also provide less biased variables for imputation. Additional methodological research with EHR data and improved epidemiological training of clinical investigators is warranted.