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Background: The characterization and influence of preoperative health care use on quality-of-care indicators (e.g., readmissions) has received limited attention in populations with musculoskeletal disorders. The purpose of this study was to characterize preoperative health care use and examine its effect on quality-of-care indicators among patients undergoing elective surgery for osteoarthritis. Methods: Data on health care use for 124,750 patients with elective surgery for osteoarthritis in Ontario, Canada, from April 1, 2015 to March 31, 2018 were linked across health administrative databases. Using total health care use one-year previous to surgery, patients were grouped from low to very high users. We used Poisson regression models to estimate rate ratios, while examining the relationship between preoperative health care use and quality-of-care indicators (e.g., extended length of stay, complications, and 90-day hospital readmissions). We controlled for covariates (age, sex, neighborhood income, rural/urban residence, comorbidities, and surgical anatomical site). Results: We found a statistically significant trend of increasing worse outcomes by health care use gradients that persisted after controlling for patient demographics and comorbidities. Findings were consistent across surgical anatomical sites. Moreover, very high users have relatively large numbers of visits to non-musculoskeletal specialists. Conclusions: Our findings highlight that information on patients' preoperative health care use, together with other risk factors (such as comorbidities), could help decision-making when benchmarking or reimbursing hospitals caring for complex patients undergoing surgery for osteoarthritis. [ABSTRACT FROM AUTHOR]

The increased availability of healthcare data has made predictive modeling popular in a clinical setting. If an expected patient-specific outcome can be estimated prior to a medical intervention the healthcare costs can be reduced for both patient and provider. The nature of data used to train such predictive models is frequently longitudinal, as interventions with convalescence times or chronic conditions contain outcome measures at intermediate follow-up points. Here we outline a predictive modeling approach that takes advantage of the longitudinal structure of the data by sequentially predicting the outcomes at intermediate time points and including them as predictors in models for later time points. This is done for continuous and threshold-dichotomized outcomes. The proposed method improves predictive accuracy as it takes advantage of the correlation in follow-up measures to distribute the estimation of coefficient effects over several models, making it advantageous for smaller datasets. This formulation also allows for effective screening of first-order interaction effects. The improved performance is illustrated using a simulation study and an applied example of predicting outcomes following surgery. The proposed approach is shown to be consistent for prediction, effective in modeling interactions and robust to presence of noise variables. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to investigate a profile of cardiovascular disease (CVD) risk factors by sex among individuals with and without osteoarthritis (OA) and to consider single‐site and multisite joint OA. Methods: Data were sourced from Cycle 1, Comprehensive Cohort, Canadian Longitudinal Study on Aging, a national sample of individuals ages 45 to 85 years. Systemic inflammatory/metabolic CVD risk factors collected were high‐sensitivity C‐reactive protein (hsCRP) level, high‐density lipoprotein, triglycerides, total cholesterol, body mass index (BMI), systolic blood pressure, and hemoglobin A1c. Smoking history was also collected. Respondents indicated doctor‐diagnosed OA in the knees, hips, and/or hands and were characterized as yes/no OA and single site/multisite OA. Individuals with OA were age‐ and sex‐matched to non‐OA controls. Covariates were age, sex, education, income, physical activity, timed up and go test findings, and comorbidities. A latent CVD risk variable was derived in women and men; standardized scores were categorized as follows: lowest, mid‐low, mid‐high, and highest risk. Associations with OA were quantified using ordinal logistic regressions. Results: A total of 6,098 respondents (3,049 with OA) had a median age of 63 years, and 55.8% were women. One‐third of OA respondents were in the highest risk category versus one‐fifth of non‐OA respondents. Apart from BMI (the largest contributor in both sexes), hsCRP level (an inflammation marker) was predominant in women, and metabolic factors and smoking were predominant in men. Overall, OA was associated with worse CVD risk quartiles compared with non‐OA. OA was increasingly associated with worse CVD risk quartiles with increasing risk thresholds among women with multisite OA, but not men. Conclusion: Findings suggest unique CVD risks by sex/multisite subgroups and point to a potentially important role for inflammation in OA over and above traditional CVD risk factors. [ABSTRACT FROM AUTHOR]