scholarly journals Incidence and Risk Factors for Prolonged Opioid Use after Arthroscopic Meniscal Surgery: An Analysis of 107,717 Cases

2020 ◽  
Vol 8 (7_suppl6) ◽  
pp. 2325967120S0046
Author(s):  
Jacqueline Baron ◽  
Alan Shamrock ◽  
Trevor Gulbrandsen ◽  
Brian Wolf ◽  
Kyle Duchman ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Logistic Regression ◽  
Chronic Obstructive ◽  
Multiple Logistic Regression ◽  
Opioid Use ◽  
Obstructive Pulmonary Disease ◽  
Factors Associated ◽  
Meniscal Surgery

Objectives: The current opioid epidemic in the United States is a significant cause of increasing morbidity and mortality. The purpose of this study was to determine rate of opioid use before and after arthroscopic meniscal surgery, and assess patient factors associated with prolonged opioid use following primary arthroscopic meniscal surgery. Methods: Patients undergoing primary arthroscopic meniscal surgery procedures from 2007-2016 were retrospectively accessed from the Humana Inc. administrative claims database. Patients were categorized as patients who filled opioid prescriptions within 3 months (OU), within 1 month (A-OU), between 1 to 3 months (C-OU), and never filled opioid prescriptions (N-OU) before surgery. Rates of opioid use were evaluated preoperatively and longitudinally tracked for OU and N-OU cohorts. Prolonged opioid use was defined as continued opioid prescription filling at ≥3 months after surgery. Multiple logistic regression analysis was used to control for various patient characteristics and identify factors associated with opioid use at 12 months after surgery, with significance defined as P<0.05 Results: There were 107,717 patients (54% female) that underwent arthroscopic meniscal surgery during the study period, of which 46.1% (n=49,630) were N-OU. One year after surgery, opioid fill rate was significantly higher in the OU group compared to the N-OU group with a relative risk of 6.98 (21.1% vs 3.02%; 95% CI: 6.61-7.36; p<0.0001). Multiple logistic regression model identified C-OU (OR:10.23, 95% CI: 9.74-10.76, p<0.0001) as the strongest predictor of opioid use at 12 months postoperatively. Furthermore, patients with acute preoperative opioid use (p<0.0001), preoperative diagnosis of diabetes mellitus (p<0.0001), hypertension (p<0.0001), chronic obstructive pulmonary disease (p<0.0001), anxiety or depression (p<0.0001), alcohol abuse (p= 0.0019), and tobacco use (p=0.0345) had a significantly increased odds of opioid use at 12 months postoperatively. However, males (p<0.0001) and patients <40 years (p<0.0001) had a significantly decreased odds of opioid use 12 months postoperatively. Conclusion: Preoperative opioid use is a significant risk factor for opioid use at 12 months following surgery. Diabetes mellitus, hypertension, chronic obstructive pulmonary disease, smoking status, and psychiatric diagnosis were independent risk factors for opioid use 1-year following surgery.

Abstract 237: Prevalence of Cardiovascular Risk Factors and Cardiovascular Disease in Transgender Persons in The United States

2020 ◽  
Vol 13 (Suppl_1) ◽  
Author(s):  
Aditi Malhotra ◽  
Hal A Skopicki ◽  
Smadar Kort ◽  
Noelle Mann ◽  
Puja Parikh
Keyword(s):  
Diabetes Mellitus ◽  
United States ◽  
Risk Factors ◽  
Cardiovascular Disease ◽  
Chronic Obstructive Pulmonary Disease ◽  
Chronic Kidney Disease ◽  
The United States ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Transgender Persons

Background: There is a paucity of data regarding prevalence of cardiovascular disease (CVD) and corresponding cardiovascular (CV) risk factors in transgender individuals. We sought to assess the prevalence of CV risk factors and CVD in transgender persons in the United States. Methods: The 2018 Centers for Disease Control’s Behavioral Risk Factor Surveillance Survey was utilized to identify a cohort of 1,038 transgender individuals in the United States. Presence of CVD was noted with a single affirmative response to the following questions: “Has a health care professional ever told you that you had any of the following:” (1) a heart attack or myocardial infarction, (2) angina or coronary heart disease, (3) a stroke? Results: Among the 1,038 transgender individuals studied, a total of 145 (14.0%) had CVD while 893 (86.0%) did not. No differences in prevalence of CVD was noted in transgender individuals who transitioned from male-to-female (n=387), female-to-male (n=400), and gender nonconforming status (n=251) (15.0% vs 13.8% vs 12.7%, p=0.72). Transgender individuals with CVD were older, had lower annual income, higher rates of smoking (28.4% vs 18.1%, p=0.004), and higher rates of multiple co-morbidities including asthma (26.6% vs 17.4%, p = 0.009), skin cancer (21.8% vs 5.0%, p <0.001), non-skin cancers (16.8% vs 6.8%, p <0.001), chronic obstructive pulmonary disease (27.5% vs 7.0%, p <0.001), arthritis (65.3% vs 28.7%, p<0.001), depressive disorder (42.7% vs 31.0%, p= 0.006), chronic kidney disease (16.2% vs 3.3%, p< 0.001), and diabetes mellitus (42.0% vs 12.7%, p <0.001). No significant differences in race, health insurance status, or body mass index was noted between transgender individuals with CVD versus those without. In multivariable analysis, independent predictors of CVD in transgender individuals included older age, diabetes mellitus [odds ratio (OR) 2.82, 95% confidence interval (CI) 1.73 - 4.58], chronic kidney disease (OR 3.69, 95% CI 1.80 - 7.57), chronic obstructive pulmonary disease (OR 2.18, 95% CI 1.19 - 3.99), and depressive disorder (OR 1.82, 95% CI 1.09 - 3.03). Conclusions: In this observational contemporary study, CVD was prevalent in 14% of transgender individuals in the United States. Predictors of CVD in the transgender population exist and transgender persons should be appropriately screened for CV risk factors so as to minimize their risk of CVD.

scholarly journals Machine learning methods for diagnosis of chronic obstructive pulmonary disease in healthy subjects: Analysis of Risk factors To DEtect COPD (ARTDECO) (Preprint)

2020 ◽  
Author(s):  
Shigeo Muro ◽  
Masato Ishida ◽  
Yoshiharu Horie ◽  
Wataru Takeuchi ◽  
Shunki Nakagawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Logistic Regression ◽  
Pulmonary Disease ◽  
Vital Capacity ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Copd Diagnosis

BACKGROUND Airflow limitation is a critical physiological feature in chronic obstructive pulmonary disease (COPD), for which long-term exposure to noxious substances including tobacco smoke is an established risk. However, not all long-term smokers develop COPD, meaning that other risk factors exist. OBJECTIVE To predict risk factors for COPD diagnosis using machine learning in an annual medical check-up database. METHODS In this retrospective, observational cohort study (Analysis of Risk factors To DEtect COPD [ARTDECO]), annual medical check-up records for all Hitachi Ltd. employees in Japan collected from April 1998 to March 2019 were analyzed. Employees who provided informed consent via an opt-out model were screened and those aged 30–75 years, without prior diagnosis of COPD, asthma, or history of cancer were included. The database included clinical measurements (e.g., pulmonary function tests) and questionnaire responses. To predict risk factors for COPD diagnosis within a 3-year period, the Gradient Boosting Decision Tree machine learning method (XGBoost) was applied as a primary approach, with logistic regression as a secondary method. A diagnosis of COPD was made when the ratio of the pre-bronchodilator forced expiratory volume in 1 second (FEV1) to pre-bronchodilator forced vital capacity (FVC) was <0.7 during two consecutive examinations. RESULTS Of the 26,101 individuals screened, 1,213 met the exclusion criteria and thus 24,815 individuals were included in the analysis. The top 10 predictors for COPD diagnosis were FEV1/FVC, smoking status, allergic symptoms, cough, pack years, hemoglobin A1c, serum albumin, mean corpuscular volume, percent predicted vital capacity value, and percent predicted value of FEV1. The area under the receiver operating characteristic curves of the XGBoost model and the logistic regression model were 0.956 and 0.943, respectively. CONCLUSIONS Using a machine learning model in this longitudinal database, we identified a set multiple of parameters as risk factors other than smoking exposure or lung function to support general practitioners and occupational health physicians to predict the development of COPD. Further research to confirm our results is warranted, as our analysis involved a database used only in Japan. CLINICALTRIAL Not applicable.

scholarly journals Risk Factors and Outcomes Associated with Chronic Obstructive Pulmonary Disease Exacerbations Requiring Hospitalization

2009 ◽  
Vol 16 (4) ◽  
pp. e43-e49 ◽  
Author(s):  
Katayoun Bahadori ◽  
J Mark FitzGerald ◽  
Robert D Levy ◽  
Tharwat Fera ◽  
John Swiston
Keyword(s):  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Pulmonary Disease ◽  
Logistic Regression Analysis ◽  
Hospital Readmission ◽  
Chronic Obstructive ◽  
Readmission Rates ◽  
Obstructive Pulmonary Disease

BACKGROUND: Acute respiratory exacerbations are the most frequent cause of medical visits, hospitalization and death for chronic obstructive pulmonary disease (COPD) patients and, thus, exert a significant social and economic burden on society.OBJECTIVE: To identify the risk factors associated with hospital readmission(s) for acute exacerbation(s) of COPD (AECOPD).METHODS: A review of admission records from three large urban hospitals in Vancouver, British Columbia, identified 310 consecutive patients admitted for an AECOPD between April 1, 2001, and December 31, 2002. Logistic regression analysis was performed to identify risk factors for readmissions following an AECOPD.RESULTS: During the study period, 38% of subjects were readmitted at least once. The mean (± SD) duration from the index admission to the first readmission was 5±4.08 months. Comparative analysis among the three hospitals identified a significant difference in readmission rates (54%, 36% and 18%, respectively). Logistic regression analysis revealed that preadmission home oxygen use (OR 2.55; 95%CI 1.45 to 4.42; P=0.001), history of a lung infection within the previous year (OR 1.73; 95% CI 1.01 to 2.97; P=0.048), other chronic respiratory disease (OR 1.78; 95% CI 1.06 to 2.99; P=0.03) and shorter length of hospital stay (OR 0.97; 95% CI 0.945 to 0.995; P=0.021) were independently associated with frequent readmissions for an AECOPD.CONCLUSIONS: Hospital readmission rates for AECOPD were high. Only four clinical factors were found to be independently associated with COPD readmission. There was significant variability in the readmission rate among hospitals. This variability may be a result of differences in the patient populations that each hospital serves or may reflect variability in health care delivery at different institutions.

scholarly journals Factors Associated with Depression Score in People with Chronic Obstructive Pulmonary Disease at the North Sumatera University Hospital

2021 ◽  
Vol 9 (T3) ◽  
pp. 168-171
Author(s):  
Huzaipah Huzaipah ◽  
Elmeida Efffendy ◽  
Nazli Mahdinasari Nasution
Keyword(s):  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Marital Status ◽  
Pulmonary Disease ◽  
Employment Status ◽  
Depression Score ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Factors Associated ◽  
Duration Of Illness

BACKGROUND: Depression is major global public health problems. This disease is often associated with other chronic diseases, for example, depression in chronic obstructive pulmonary disease (COPD). The presence of depression in chronic disease exacerbates the underlying disease and leads to non-adherence to treatment, loss of disease control, lower quality of life, increased use of health resources, and increased morbidity and mortality and also depression in people with COPD can result in a high economic burden, therefore, screening, investigated the risk factors, and timely treatment of depressive symptoms in people with COPD are very important. AIM: This study aims to determine factors associated with depression score in people with COPD. METHODS: This study is a multivariate type of predictive study with a cross-sectional approach to determine the risk factors of depression score in people with COPD. Symptomatology of depression was assessed using the Beck depression inventory-II. RESULTS: Of the 119 people included, majority 76.5% of the participants were male. The mean ages were 61.09 ± 7.708 years. There was a significant association between depression and independent variables of the duration of illness (p < 0.001), gender (p = 0.006), employment status (p < 0.001), and marital status (p = 0.003) in people with COPD. CONCLUSIONS: Duration of illness, gender, employment status, and marital status were associated with depression in people with COPD.

Risk Factors Associated with Development of Pulmonary Arterial Hypertension and Corpulmonale in Patients with Chronic Obstructive Pulmonary Disease

2020 ◽  
Vol 15 (4) ◽  
pp. 289-298
Author(s):  
Chaya Sindaghatta Krishnarao ◽  
Mahendra Maheshwarappa ◽  
Thippeswamy Thippeswamy ◽  
Jayaraj Biligere Siddaiah ◽  
Komarla Sundararaja Lokesh ◽  
...  
Keyword(s):  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Hypertension ◽  
Pulmonary Disease ◽  
Chronic Obstructive ◽  
Walk Test ◽  
Obstructive Pulmonary Disease ◽  
Factors Associated ◽  
Six Minute Walk Test ◽  
Minute Walk

Background: Chronic Obstructive Pulmonary Disease is an important cause of morbidity and mortality globally. The onset of pulmonary hypertension and corpulmonale is associated with decreased survival in patients with COPD. Objective: To assess risk factors associated with the development of pulmonary hypertension and corpulmonale and to identify high-risk phenotypes who may need early evaluation and intervention. Methods: Consecutive adult patients with COPD were evaluated for factors influencing the development of pulmonary hypertension and corpulmonale which included symptomatology, hospitalization in the previous year, MMRC dyspnea grade, SGRQ score, 6 minute walk test, ABG, CRP, spirometry and echocardiography. Results: We found Pulmonary Hypertension in 36(30%) patients and 27(22.5%) had corpulmonale. On multivariate analysis, we found PaO2 ≤75 mm Hg and six minute walk test <80% predicted to be significantly associated with the development of Pulmonary hypertension and we found hospitalization in the previous year to be significantly and independently associated with the development of corpulmonale. Conclusion: We observed hospitalization in the previous year was an independent risk factor for the development of corpulmonale and six-minute walk test <80% predicted, PaO2 <75 mm Hg were independent risk factors for the development of pulmonary hypertension.

CONTRIBUTION OF COMBINED RISK FACTORS INTO DEVELOPMENT OF CHRONIC NON-COMMUNICABLE DISEASES

2020 ◽  
Vol 66 (5) ◽  
pp. 1-1
Author(s):  
O.S. Kobyakova ◽  
◽  
E.A. Starovoitova ◽  
I.V. Tolmachev ◽  
K.S. Brazovsky ◽  
...  
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Risk Factors ◽  
Chronic Obstructive Pulmonary Disease ◽  
Body Mass Index ◽  
Arterial Hypertension ◽  
Communicable Diseases ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Non Communicable Diseases

Increased prevalence of chronic non-communicable diseases (NCD) and increased related mortality stimulate development of effective methods of their prevention. To date, there are little data on the combined effect of various risk factors on the development of a particular chronic disease, and how much the risk of developing chronic non-communicable diseases increases or decreases with a different combination of risk factors. Purpose. To assess contribution of the combined effect of risk factors into the development of chronic NCD using the method of neural network. Material and methods. Data on 9505 visitors seeking care at the Tomsk health centers were analyzed. To build a multidimensional decision-making model, the authors used the multi-layer perceptron algorithm implemented on the IBM Watson platform. Results. The highest accuracy of disease recognition in the test sample added up to 95.8% for diabetes mellitus. Chronic obstructive pulmonary disease (84.5%) and coronary heart disease (80.4%) rank second. Lower accuracy was registered for such diseases as asthma (73.6%) and arterial hypertension (73.3%). For the development of diabetes mellitus, such factors as patient’s age, level of systolic and diastolic blood pressure, and body mass index (BMI) are equally important. Smoking and gender are identified as the most significant factors for the development of chronic obstructive pulmonary disease. The most significant contribution to the development of arterial hypertension is made by body mass index only. Age and BMI turned out to be most significant for coronary heart disease and arterial hypertension. Conclusion. Use of the neural network method makes it possible to determine contribution of risk factors to the development of chronic ICD, to predict the risk of developing a disease depending on the combination of risk factors and to carry out preventive measures in a personalized manner, taking into account clinical situation of every person. Scope of application. The results of the study can be used by managers of medical organizations to optimize approaches to preventive activities. Keywords: risk factors; chronic non-communicable diseases; neural networks

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