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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

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.