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Background The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI. Methods and findings We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors. Conclusions Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies., Author(s): Chenxi Huang 1, Karthik Murugiah 2, Shiwani Mahajan 1, Shu-Xia Li 1, Sanket S. Dhruva 3,4, Julian S. Haimovich 5, Yongfei Wang 1, Wade L. Schulz 1,6, Jeffrey M. [...]

Background: The Affordable Care Act of 2010 extended health insurance through expansion of Medicaid and subsidies for commercial insurance. Prior work has produced differing results in associating expanded insurance with improvements in health care processes and outcomes. Evaluating specific mechanisms of care processes and their association with insurance expansion may help reconcile those results.Methods and Results: We used inpatient hospitalization data in the Nationwide Inpatient Sample (NIS) Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality from 1/1/2008 to 9/30/2015. We included all hospitalizations for acute myocardial infarction (AMI). As a primary outcome, we defined percent rate of AMI hospitalizations receiving percutaneous coronary intervention (PCI) per month. In the non-Medicare (intervention) group, there was a relative decrease of 0.2% of the monthly trend before and after expansion (95% CI [-0.3%, -0.1%]). In the Medicare group, there was a relative decrease of 0.1% of the monthly trend before and after expansion (95% CI [-0.2%, 0%]).Conclusions: We did not detect a relative difference in PCI for AMI associated with insurance expansion under health reform. [ABSTRACT FROM AUTHOR]

Aims Physical activity may be an important modifiable risk factor for atrial fibrillation (AF), but associations have been variable and generally based on self-reported activity. Methods and results We analysed 93 669 participants of the UK Biobank prospective cohort study without prevalent AF who wore a wrist-based accelerometer for 1 week. We categorized whether measured activity met the standard recommendations of the European Society of Cardiology, American Heart Association, and World Health Organization [moderate-to-vigorous physical activity (MVPA) ≥150 min/week]. We tested associations between guideline-adherent activity and incident AF (primary) and stroke (secondary) using Cox proportional hazards models adjusted for age, sex, and each component of the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) risk score. We also assessed correlation between accelerometer-derived and self-reported activity. The mean age was 62 ± 8 years and 57% were women. Over a median of 5.2 years, 2338 incident AF events occurred. In multivariable adjusted models, guideline-adherent activity was associated with lower risks of AF [hazard ratio (HR) 0.82, 95% confidence interval (CI) 0.75–0.89; incidence 3.5/1000 person-years, 95% CI 3.3–3.8 vs. 6.5/1000 person-years, 95% CI 6.1–6.8] and stroke (HR 0.76, 95% CI 0.64–0.90; incidence 1.0/1000 person-years, 95% CI 0.9–1.1 vs. 1.8/1000 person-years, 95% CI 1.6–2.0). Correlation between accelerometer-derived and self-reported MVPA was weak (Spearman r  = 0.16, 95% CI 0.16–0.17). Self-reported activity was not associated with incident AF or stroke. Conclusions Greater accelerometer-derived physical activity is associated with lower risks of AF and stroke. Future preventive efforts to reduce AF risk may be most effective when targeting adherence to objective activity thresholds. [ABSTRACT FROM AUTHOR]

Identifying temporal variation in hospitalization rates may provide insights about disease patterns and thereby inform research, policy, and clinical care. However, the majority of medical conditions have not been studied for their potential seasonal variation. The objective of this study was to apply a data-driven approach to characterize temporal variation in condition-specific hospitalizations. Using a dataset of 34 million inpatient discharges gathered from hospitals in New York State from 2008–2011, we grouped all discharges into 263 clinical conditions based on the principal discharge diagnosis using Clinical Classification Software in order to mitigate the limitation that administrative claims data reflect clinical conditions to varying specificity. After applying Seasonal-Trend Decomposition by LOESS, we estimated the periodicity of the seasonal component using spectral analysis and applied harmonic regression to calculate the amplitude and phase of the condition’s seasonal utilization pattern. We also introduced four new indices of temporal variation: mean oscillation width, seasonal coefficient, trend coefficient, and linearity of the trend. Finally, K-means clustering was used to group conditions across these four indices to identify common temporal variation patterns. Of all 263 clinical conditions considered, 164 demonstrated statistically significant seasonality. Notably, we identified conditions for which seasonal variation has not been previously described such as ovarian cancer, tuberculosis, and schizophrenia. Clustering analysis yielded three distinct groups of conditions based on multiple measures of seasonal variation. Our study was limited to New York State and results may not directly apply to other regions with distinct climates and health burden. A substantial proportion of medical conditions, larger than previously described, exhibit seasonal variation in hospital utilization. Moreover, the application of clustering tools yields groups of clinically heterogeneous conditions with similar seasonal phenotypes. Further investigation is necessary to uncover common etiologies underlying these shared seasonal phenotypes. [ABSTRACT FROM AUTHOR]

BACKGROUND CONTEXT Risks of spinal fusion surgery include failed back surgery syndrome, nonunion, hardware failure, infection, bleeding, nerve root damage, pulmonary embolus, myocardial infarction, and stroke. It has been documented in several studies that these risks are amplified in the obese patient. Recent evidence suggests that rapid weight loss in the form of bariatric surgery may be associated with decreased bone mineral density and Vitamin D levels. It is unclear, however, whether metabolic changes in bone mineral density, in the setting of rapid weight loss, affect postoperative outcomes in patients undergoing lumbar fusion. PURPOSE The purpose of this study is to determine whether individuals undergoing a greater than 10% weight loss within 6 months prior to lumbar fusion will have lower postoperative complications. STUDY DESIGN/SETTING Retrospective database review. PATIENT SAMPLE A total of 129 patients undergoing lumbar fusion surgery with prior weightloss. OUTCOME MEASURES Post operative LOS, rates of: SSI, wound disruption, pneumonia, unplanned intubation events, acute renal failure, acute MI, transfusions, DVT, sepsis, PE. METHODS All available NSQIP datasets spanning 2005-2015 were included in the study. Patients who underwent lumbar fusion surgery during that time were selected based on CPT code. Patients were then further stratified into two groups based on 10% weight loss within the past 6 months prior to surgery. Patients with a history of malignancy or any chronic disease were excluded. Each patient in the weight loss(WL) group was matched with a randomized nonweight loss patient based on age, sex, smoking status, and BMI. Paired two-tailed T -tests were then used to compare surgical outcomes amongst the WL and non-WL lumbar fusion populations. RESULTS From 2005 to 2014, 4,609,299 surgical events were recorded in the NSQIP dataset. Of these patients, 39,742 patients underwent lumbar fusion surgery, and 129 (3.2%) of these lumbar fusion patients lost greater than 10% of their body weight in the 6 months prior to their surgery following exclusion of patients with malignancy or chronic disease. When compared the non-WL group, those in the WL group had a significantly longer total length of hospital stay (9.7 vs. 4.0 days; p<.05) and more days from operation to discharge (7.3 vs. 3.71 days; p<.05). The WL group experienced 8.0 total SSIs versus 3.0 among the non-WL group (p,0.05). The WL group also experienced more unplanned intubationand sepsis than did the non-WL group, though not significantly so (6.0 vs. 1.0 events for both; p=.051, p=.056). Finally, the number of bleeding transfusion occurrences and DVT were also significantly higher in the WL group compared to non-WL (40.0 and 5.0 vs. 20.0 and 0.00; p<.05, respectively). CONCLUSIONS On a nationwide scale, organic weight loss of greater than 10% of body weight within 6 months prior to lumbar spine fusion surgery is associated with worse post operative outcomes and longer length of stay. [ABSTRACT FROM AUTHOR]