IMPORTANCE Although metabolic surgery (defined as procedures that influence metabolism by inducing weight loss and altering gastrointestinal physiology) significantly improves cardiometabolic risk factors, the effect on cardiovascular outcomes has been less well characterized. OBJECTIVE To investigate the relationship between metabolic surgery and incident major adverse cardiovascular events (MACE) in patients with type 2 diabetes and obesity. DESIGN, SETTING, AND PARTICIPANTS Of 287 438 adult patients with diabetes in the Cleveland Clinic Health System in the United States between 1998 and 2017, 2287 patients underwent metabolic surgery. In this retrospective cohort study, these patients were matched 1:5 to nonsurgical patients with diabetes and obesity (body mass index [BMI] Ն30), resulting in 11 435 control patients, with follow-up through December 2018. EXPOSURES Metabolic gastrointestinal surgical procedures vs usual care for type 2 diabetes and obesity. MAIN OUTCOMES AND MEASURES The primary outcome was the incidence of extended MACE (composite of 6 outcomes), defined as first occurrence of all-cause mortality, coronary artery events, cerebrovascular events, heart failure, nephropathy, and atrial fibrillation. Secondary end points included 3-component MACE (myocardial infarction, ischemic stroke, and mortality) and the 6 individual components of the primary end point. RESULTS Among the 13 722 study participants, the distribution of baseline covariates was balanced between the surgical group and the nonsurgical group, including female sex (65.5% vs 64.2%), median age (52.5 vs 54.8 years), BMI (45.1 vs 42.6), and glycated hemoglobin level (7.1% vs 7.1%). The overall median follow-up duration was 3.9 years (interquartile range, 1.9-6.1 years). At the end of the study period, 385 patients in the surgical group and 3243 patients in the nonsurgical group experienced a primary end point (cumulative incidence at 8-years, 30.8% [95% CI, 27.6%-34.0%] in the surgical group and 47.7% [95% CI, 46.1%-49.2%] in the nonsurgical group [P < .001]; absolute 8-year risk difference [ARD], 16.9% [95% CI, 13.1%-20.4%]; adjusted hazard ratio [HR], 0.61 [95% CI, 0.55-0.69]). All 7 prespecified secondary outcomes showed statistically significant differences in favor of metabolic surgery, including mortality. All-cause mortality occurred in 112 patients in the metabolic surgery group and 1111 patients in the nonsurgical group (cumulative incidence at 8 years, 10.0% [95% CI, 7.8%-12.2%] and 17.8% [95% CI, 16.6%-19.0%]; ARD, 7.8% [95% CI, 5.1%-10.2%]; adjusted HR, 0.59 [95% CI, 0.48-0.72]). CONCLUSIONS AND RELEVANCE Among patients with type 2 diabetes and obesity, metabolic surgery, compared with nonsurgical management, was associated with a significantly lower risk of incident MACE. The findings from this observational study must be confirmed in randomized clinical trials.
Background: Physicians’ and patients’ decision-making process between bone–patellar tendon–bone (BTB) and hamstring tendon autografts for anterior cruciate ligament (ACL) reconstruction (ACLR) may be influenced by a variety of factors in the young, active athlete. Purpose: To determine the incidence of both ACL graft revisions and contralateral ACL tears resulting in subsequent ACLR in a cohort of high school– and college-aged athletes who initially underwent primary ACLR with either a BTB or a hamstring autograft. Study Design: Cohort study; Level of evidence, 2. Methods: Study inclusion criteria were patients aged 14 to 22 years who were injured in sports, had a contralateral normal knee, and were scheduled to undergo unilateral primary ACLR with either a BTB or a hamstring autograft. All patients were prospectively followed for 6 years to determine whether any subsequent ACLR was performed in either knee after their initial ACLR. Multivariable regression modeling controlled for age, sex, ethnicity/race, body mass index, sport and competition level, baseline activity level, knee laxity, and graft type. The 6-year outcomes were the incidence of subsequent ACLR in either knee. Results: A total of 839 patients were eligible, of which 770 (92%) had 6-year follow-up for the primary outcome measure of the incidence of subsequent ACLR. The median age was 17 years, with 48% female, and the distribution of BTB and hamstring grafts was 492 (64%) and 278 (36%), respectively. The incidence of subsequent ACLR at 6 years was 9.2% in the ipsilateral knee, 11.2% in the contralateral normal knee, and 19.7% for either knee. High-grade preoperative knee laxity (odds ratio [OR], 2.4 [95% confidence interval [CI], 1.4-3.9]; P = .001), autograft type (OR, 2.1 [95% CI, 1.3-3.5]; P = .004), and age (OR, 0.8 [95% CI, 0.7-1.0]; P = .009) were the 3 most influential predictors of ACL graft revision in the ipsilateral knee. The odds of ACL graft revision were 2.1 times higher for patients receiving a hamstring autograft than patients receiving a BTB autograft (95% CI, 1.3-3.5; P = .004). No significant differences were found between autograft choices when looking at the incidence of subsequent ACLR in the contralateral knee. Conclusion: There was a high incidence of both ACL graft revisions and contralateral normal ACL tears resulting in subsequent ACLR in this young athletic cohort. The incidence of ACL graft revision at 6 years after index surgery was 2.1 times higher with a hamstring autograft compared with a BTB autograft.
Aims The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC. Results Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193.
To construct and internally validate prediction models to estimate the risk of longterm end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and practitioners who are considering metabolic surgery. RESEARCH DESIGN AND METHODSA total of 2,287 patients with type 2 diabetes who underwent metabolic surgery between 1998 and 2017 in the Cleveland Clinic Health System were propensitymatched 1:5 to 11,435 nonsurgical patients with BMI ‡30 kg/m 2 and type 2 diabetes who received usual care with follow-up through December 2018. Multivariable time-to-event regression and random forest machine learning models were built and internally validated using fivefold cross-validation to predict the 10-year risk for four outcomes of interest. The prediction models were programmed to construct user-friendly web-based and smartphone applications of Individualized Diabetes Complications (IDC) Risk Scores for clinical use. RESULTSThe prediction tools demonstrated the following discrimination ability based on the area under the receiver operating characteristic curve (1 5 perfect discrimination and 0.5 5 chance) at 10 years in the surgical and nonsurgical groups, respectively: all-cause mortality (0.79 and 0.81), coronary artery events (0.66 and 0.67), heart failure (0.73 and 0.75), and nephropathy (0.73 and 0.76). When a patient's data are entered into the IDC application, it estimates the individualized 10-year morbidity and mortality risks with and without undergoing metabolic surgery. CONCLUSIONSThe IDC Risk Scores can provide personalized evidence-based risk information for patients with type 2 diabetes and obesity about future cardiovascular outcomes and mortality with and without metabolic surgery based on their current status of obesity, diabetes, and related cardiometabolic conditions.
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