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AIMS - Clopidogrel is prescribed for the prevention of atherothrombotic events. While investigations have identified genetic determinants of inter-individual variability in on-treatment platelet inhibition (e.g. CYP2C19*2), evidence that these variants have clinical utility to predict major adverse cardiovascular events (CVEs) remains controversial.
METHODS AND RESULTS - We assessed the impact of 31 candidate gene polymorphisms on adenosine diphosphate (ADP)-stimulated platelet reactivity in 3391 clopidogrel-treated coronary artery disease patients of the International Clopidogrel Pharmacogenomics Consortium (ICPC). The influence of these polymorphisms on CVEs was tested in 2134 ICPC patients (N = 129 events) in whom clinical event data were available. Several variants were associated with on-treatment ADP-stimulated platelet reactivity (CYP2C19*2, P = 8.8 × 10-54; CES1 G143E, P = 1.3 × 10-16; CYP2C19*17, P = 9.5 × 10-10; CYP2B6 1294 + 53 C > T, P = 3.0 × 10-4; CYP2B6 516 G > T, P = 1.0 × 10-3; CYP2C9*2, P = 1.2 × 10-3; and CYP2C9*3, P = 1.5 × 10-3). While no individual variant was associated with CVEs, generation of a pharmacogenomic polygenic response score (PgxRS) revealed that patients who carried a greater number of alleles that associated with increased on-treatment platelet reactivity were more likely to experience CVEs (β = 0.17, SE 0.06, P = 0.01) and cardiovascular-related death (β = 0.43, SE 0.16, P = 0.007). Patients who carried eight or more risk alleles were significantly more likely to experience CVEs [odds ratio (OR) = 1.78, 95% confidence interval (CI) 1.14-2.76, P = 0.01] and cardiovascular death (OR = 4.39, 95% CI 1.35-14.27, P = 0.01) compared to patients who carried six or fewer of these alleles.
CONCLUSION - Several polymorphisms impact clopidogrel response and PgxRS is a predictor of cardiovascular outcomes. Additional investigations that identify novel determinants of clopidogrel response and validating polygenic models may facilitate future precision medicine strategies.
Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: email@example.com.
Extent of response to neoadjuvant chemotherapy, tumor size, and patient age are important prognostic variables for patients with osteosarcoma, but applying information from these continuous variables in survival models is difficult. Dichotomization is usually inappropriate and alternative statistical techniques should be considered instead. Nonlinear multivariable regression methods (restricted cubic splines and fractional polynomials) were applied to data from the National Cancer Database to model continuous prognostic factors for overall survival from localized, high-grade osteosarcoma of the appendicular and nonspinal skeleton following neoadjuvant chemotherapy and surgical resection (N=2493). The assumption that log hazard ratios were linear in relation to these continuous prognostic factors was tested using likelihood ratio tests of model deviance and Wald tests of spline coefficients. Log hazard ratios for increasing patient age were linear over the range of 4 to 80 years, but showed evidence for variation in the coefficient over elapsed follow-up time. Tumor size also showed a linear relationship with log hazard over the range of 1 to 30 cm. Hazard ratios for chemotherapy effect profoundly deviated from log-linear (P<0.004), with significantly decreased hazard for death from baseline for patients with ≥90% tumor necrosis (hazard ratio, 0.32; 95% confidence interval, 0.20-0.52; P<0.0001). Important implications of these results include: (1) ≥90% tumor necrosis defines good chemotherapy response in a clinically useful manner; (2) staging osteosarcoma by dichotomizing tumor size is inappropriate; and (3) patient age can be modeled as a linear effect on the log hazard ratio in prognostic models with the caveat that risk may change over duration of the analysis.
BACKGROUND - The objective of this study is to evaluate use of the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) online risk calculator for estimating common outcomes after operations for gallbladder cancer and extrahepatic cholangiocarcinoma.
METHODS - Subjects from the United States Extrahepatic Biliary Malignancy Consortium (USE-BMC) who underwent operation between January 1, 2000 and December 31, 2014 at 10 academic medical centers were included in this study. Calculator estimates of risk were compared to actual outcomes.
RESULTS - The majority of patients underwent partial or major hepatectomy, Whipple procedures or extrahepatic bile duct resection. For the entire cohort, c-statistics for surgical site infection (0.635), reoperation (0.680) and readmission (0.565) were less than 0.7. The c-statistic for death was 0.740. For all outcomes the actual proportion of patients experiencing an event was much higher than the median predicted risk of that event. Similarly, the group of patients who experienced an outcome did have higher median predicted risk than those who did not.
CONCLUSIONS - The ACS NSQIP risk calculator is easy to use but requires further modifications to more accurately estimate outcomes for some patient populations and operations for which validation studies show suboptimal performance.
Copyright © 2017 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.
Objective - Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population.
Materials and Methods - Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years.
Results - Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods.
Conclusions - Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.
© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: firstname.lastname@example.org
Multiple different schemes are used to assess surgical resection margins in orthopedic pathology, but which is optimal for reporting resection margin status of osteosarcoma is uncertain. Moreover, the minimum tumor clearance (metric width of resection margin) necessary for local control is not well defined. In this investigation, the American Joint Committee on Cancer (AJCC) R system, Musculoskeletal Tumor Society (MSTS) system, and margin distance method for reporting resection margin status were compared in a series of 186 high-grade osteosarcomas. Hazard ratios for local recurrence for each resection margin category were compared with other categories within each margin assessment scheme to assess discriminatory ability. Cross-model comparisons of regression coefficients from parametric survival and logistic regression models were also performed. Predictive accuracy of each margin assessment scheme for determining 2-year local recurrence-free survival was evaluated by comparing the areas under receiver-operating characteristic curves generated from logistic regression analyses. Concordance with clinical outcomes was also calculated. Both the MSTS and margin distance schemes showed significantly greater predictive accuracy and concordance with observed outcomes than the AJCC R system. A margin distance of ≥2 mm significantly decreased the risk of local recurrence. Results were similar after adjustment for confounding prognostic factors (anatomic site, macroscopic lymphovascular invasion, and chemotherapy status). Therefore, surgical resection margins for osteosarcoma should be reported using either the MSTS or margin distance method instead of the AJCC R system.
RATIONALE - Screening for lung cancer using low-dose computed tomography (CT) reduces lung cancer mortality. However, in addition to a high rate of benign nodules, lung cancer screening detects a large number of indolent cancers that generally belong to the adenocarcinoma spectrum. Individualized management of screen-detected adenocarcinomas would be facilitated by noninvasive risk stratification.
OBJECTIVES - To validate that Computer-Aided Nodule Assessment and Risk Yield (CANARY), a novel image analysis software, successfully risk stratifies screen-detected lung adenocarcinomas based on clinical disease outcomes.
METHODS - We identified retrospective 294 eligible patients diagnosed with lung adenocarcinoma spectrum lesions in the low-dose CT arm of the National Lung Screening Trial. The last low-dose CT scan before the diagnosis of lung adenocarcinoma was analyzed using CANARY blinded to clinical data. Based on their parametric CANARY signatures, all the lung adenocarcinoma nodules were risk stratified into three groups. CANARY risk groups were compared using survival analysis for progression-free survival.
MEASUREMENTS AND MAIN RESULTS - A total of 294 patients were included in the analysis. Kaplan-Meier analysis of all the 294 adenocarcinoma nodules stratified into the Good, Intermediate, and Poor CANARY risk groups yielded distinct progression-free survival curves (P < 0.0001). This observation was confirmed in the unadjusted and adjusted (age, sex, race, and smoking status) progression-free survival analysis of all stage I cases.
CONCLUSIONS - CANARY allows the noninvasive risk stratification of lung adenocarcinomas into three groups with distinct post-treatment progression-free survival. Our results suggest that CANARY could ultimately facilitate individualized management of incidentally or screen-detected lung adenocarcinomas.
In the past 10 years, electronic health records (EHRs) have had growing impact in clinical care. EHRs efficiently capture and reuse clinical information, which can directly benefit patient care by guiding treatments and providing effective reminders for best practices. The increased adoption has also lead to more complex implementations, including robust, disease-specific tools, such as for rheumatoid arthritis (RA). In addition, the data collected through normal clinical care is also used in secondary research, helping to refine patient treatment for the future. Although few studies have directly demonstrated benefits for direct clinical care of RA, the opposite is true for EHR-based research - RA has been a particularly fertile ground for clinical and genomic research that have leveraged typically advanced informatics methods to accurately define RA populations. We discuss the clinical impact of EHRs in RA treatment and their impact on secondary research, and provide recommendations for improved utility in future EHR installations.
There is wide variation in the management of patients with atrial fibrillation (AF) in the emergency department (ED). We aimed to derive and internally validate the first prospective, ED-based clinical decision aid to identify patients with AF at low risk for 30-day adverse events. We performed a prospective cohort study at a university-affiliated tertiary-care ED. Patients were enrolled from June 9, 2010, to February 28, 2013, and followed for 30 days. We enrolled a convenience sample of patients in ED presenting with symptomatic AF. Candidate predictors were based on ED data available in the first 2 hours. The decision aid was derived using model approximation (preconditioning) followed by strong bootstrap internal validation. We used an ordinal outcome hierarchy defined as the incidence of the most severe adverse event within 30 days of the ED evaluation. Of 497 patients enrolled, stroke and AF-related death occurred in 13 (3%) and 4 (<1%) patients, respectively. The decision aid included the following: age, triage vitals (systolic blood pressure, temperature, respiratory rate, oxygen saturation, supplemental oxygen requirement), medical history (heart failure, home sotalol use, previous percutaneous coronary intervention, electrical cardioversion, cardiac ablation, frequency of AF symptoms), and ED data (2 hours heart rate, chest radiograph results, hemoglobin, creatinine, and brain natriuretic peptide). The decision aid's c-statistic in predicting any 30-day adverse event was 0.7 (95% confidence interval 0.65, 0.76). In conclusion, in patients with AF in the ED, Atrial Fibrillation and Flutter Outcome Risk Determination provides the first evidence-based decision aid for identifying patients who are at low risk for 30-day adverse events and candidates for safe discharge.
Copyright © 2015 Elsevier Inc. All rights reserved.
BACKGROUND - Texture analysis is a promising method of analyzing imaging data to potentially enhance diagnostic capability. This approach involves automated measurement of pixel intensity variation that may offer further insight into disease progression than do standard imaging techniques alone. We postulated that postoperative liver insufficiency, a major source of morbidity and mortality, correlates with preoperative heterogeneous parenchymal enhancement that can be quantified with texture analysis of cross-sectional imaging.
STUDY DESIGN - A retrospective case-matched study (waiver of informed consent and HIPAA authorization, approved by the Institutional Review Board) was performed comparing patients who underwent major hepatic resection and developed liver insufficiency (n = 12) with a matched group of patients with no postoperative liver insufficiency (n = 24) by procedure, remnant volume, and year of procedure. Texture analysis (with gray-level co-occurrence matrices) was used to quantify the heterogeneity of liver parenchyma on preoperative CT scans. Statistical significance was evaluated using Wilcoxon's signed rank and Pearson's chi-square tests.
RESULTS - No statistically significant differences were found between study groups for preoperative patient demographics and clinical characteristics, with the exception of sex (p < 0.05). Two texture features differed significantly between the groups: correlation (linear dependency of gray levels on neighboring pixels) and entropy (randomness of brightness variation) (p < 0.05).
CONCLUSIONS - In this preliminary study, the texture of liver parenchyma on preoperative CT was significantly more varied, less symmetric, and less homogeneous in patients with postoperative liver insufficiency. Therefore, texture analysis has the potential to provide an additional means of preoperative risk stratification.
Copyright © 2015 American College of Surgeons. Published by Elsevier Inc. All rights reserved.