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Recent event-related potential (ERP) studies have suggested that the N140cc component reflects target selection mechanisms in tactile search tasks in which the target is presented simultaneously with homogeneous distractors. To investigate if and how the attentional selection of the tactile target is affected by the presence of a singleton distractor, we presented a four-item search array (two stimuli to the middle and index fingers of the left and right hand) which included a singleton distractor in addition to the target and two other homogenous distractors. Participants had to localize the target (top or bottom finger on either hand), while ignoring all distractors. Behavioral results revealed that the target localization was hindered by the presence of the singleton distractor as demonstrated by distractor-presence costs for both speed and accuracy. ERP results confirmed that attention was directed to the singleton distractor when this was the only singleton item in the array as suggested by the presence of a distractor-related N140cc component on distractor only trials. Furthermore, when target and singleton distractor were presented to opposite hands (contralateral distractor trials) the target-related N140cc amplitude was reduced as compared to target only trials, suggesting reduced attentional resources to the target. However, when target and singleton distractor were presented to the same hand (ipsilateral distractor trials), the N140cc amplitude was comparable to that observed on target only trials. These findings suggest that the N140cc reflects the attentional selection of the target side rather than the competition between stimuli presented to the same hand., (© 2020 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)

Importance: Determining the association of contrast volume during percutaneous coronary intervention (PCI) with the risk of acute kidney injury (AKI) is important for optimizing PCI safety., Objective: To quantify how the risk of AKI is associated with contrast volume, accounting for the possibility of nonlinearity and heterogeneity among different baseline risks., Design, Setting, and Participants: This prognostic study used data from the American College of Cardiology National Cardiovascular Data Registry CathPCI Registry for 1694 US hospitals. Derivation analysis included 2 076 694 individuals who underwent PCI from July 1, 2011, to June 30, 2015. Validation analysis included 961 863 individuals who underwent PCI from July 1, 2015, to March 31, 2017. Data analysis took place from July 2018 to May 2019., Exposure: Contrast volume during PCI., Main Outcomes and Measures: Acute kidney injury was defined using 3 thresholds for preprocedure to postprocedure creatinine level increase (ie, ≥0.3 mg/dL, ≥0.5 mg/dL, and ≥1.0 mg/dL). A model quantifying the association of contrast volume with AKI was developed, and the existence of nonlinearity and heterogeneity were examined by likelihood ratio tests. The model was derived in the training set (a random 50% of the derivation cohort), and performance was evaluated in the test set (the remaining 50% of the derivation cohort) and an independent validation set by area under the receiver operating characteristic curve (AUC) and calibration slope of observed vs predicted risks., Results: The 2 076 694 patients in the derivation set had a mean (SD) age of 65.1 (12.1) years, and 662 525 (31.9%) were women; 133 306 (6.4%) had creatinine level increases of at least 0.3 mg/dL, 66 626 (3.2%) had creatinine level increases of at least 0.5 mg/dL, and 28 378 (1.4%) had creatinine level increases of at least 1.0 mg/dL. In the validation set of 961 843 patients (mean [SD] age, 65.7 [12.1] years; 305 577 [31.8%] women), these rates were 62 913 (6.5%), 34 229 (3.6%), and 15 555 (1.6%), respectively. The association of contrast volume and AKI risk was nonlinear (χ226 = 1436.2; P < .001) and varied by preprocedural risk (χ220 = 105.6; P < .001). In the test set, the model yielded an AUC of 0.777 (95% CI, 0.775-0.779) for predicting risk of a creatinine level increase of at least 0.3 mg/dL, 0.839 (95% CI, 0.837-0.841) for predicting risk of a creatinine level increase of at least 0.5 mg/dL, and 0.870 (95% CI, 0.867-0.873) for predicting risk of a creatinine level increase of at least 1.0 mg/dL; it achieved a calibration slope of 0.998 (95% CI, 0.989-1.007), 0.999 (95% CI, 0.989-1.008), and 0.986 (95% CI, 0.973-0.998), respectively, for the AKI severity levels. The model had similar performance in the validation set (creatinine level increase of ≥0.3 mg/dL: AUC, 0.794; 95% CI, 0.792-0.795; calibration slope, 1.039; 95% CI, 1.030-1.047; creatinine level increase of ≥0.5 mg/dL: AUC, 0.845; 95% CI, 0.843-0.848; calibration slope, 1.063; 95% CI, 1.054-1.074; creatinine level increase of ≥1.0 mg/dL: AUC, 0.872; 95% CI, 0.869-0.875; calibration slope, 1.103; 95% CI, 1.089-1.117)., Conclusions and Relevance: The association of contrast volume with AKI risk is complex, varies by baseline risk, and can be predicted by a model. Future research to evaluate the effect of the model on AKI is needed.

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., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: SSD is supported by the Department of Veterans Affairs. WLS is a consultant for Hugo, a personal health information platform. CIM is a consultant for Cook, Bard, Medtronic, Abbott and Cardinal Health. FPW is supported by the National Science Foundation grant R01DK113191. JSR is the Chief Innovation Officer for the American College of Cardiology. HMK is a recipient of research agreements from Medtronic and from Johnson & Johnson (Janssen), through Yale University, to develop methods of clinical trial data sharing; was the recipient of a grant from the Food and Drug Administration and Medtronic to develop methods for postmarket surveillance of medical devices; works under contract with the Centers for Medicare and Medicaid Services to develop and maintain performance measures; chairs a cardiac scientific advisory board for UnitedHealth; is a member of the Advisory Board for Element Science and the Physician Advisory Board for Aetna; is a participant/participant representative of the IBM Watson Health Life Sciences Board; and is the founder of Hugo, a personal health information platform. JAS is supported by grants from Gilead, Genentech, Lilly, Amorcyte, and EvaHeart and has a patent for the Seattle Angina Questionnaire with royalties paid. He also owns the copyright to the Seattle Angina Questionnaire. He is the PI of an Analytic Center for the American College of Cardiology Foundation and has an equity interest in Health Outcomes Sciences. FAM has a contract (through his primary institution) for his role as Chief Science Officer of the NCDR. BJM is an associate editor for PLOS ONE, which is involved in this special issue. He has a relationship with the American College of Cardiology in selecting and pursuing innovative research based upon their registry data (unrelated to this paper). He has a pending patent application for an EHR-based prediction tool in Yale New Haven Health, as well as two funded studies, one by the DoD-Advanced Research Projects Agency and one with the NSF to support student travel to conferences in the body sensor networks field. American College of Cardiology may incorporate this work, or future iterations, into its registry. No other organisation named above has a competing interest in relation to this work. The other authors report no potential competing interests.

Objective: The aim of our study was to study the impact of glycoprotein IIb/IIIa inhibitors (GPI) on in-hospital outcomes., Background: There is paucity of data regarding the impact of GPI on the outcomes following peripheral endovascular interventions., Methods: The study cohort was derived from Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database between the years 2006 and 2011. Peripheral endovascular interventions and GPI utilization were identified using appropriate ICD-9 Diagnostic and procedural codes. Two-level hierarchical multivariate mixed models were created. The study outcomes were: primary (in-hospital mortality and amputation studied separately) and secondary (composite of in-hospital mortality and postprocedural complications). Hospitalization costs were also assessed., Results: GPI utilization (OR, 95% CI, P-value) was independently predictive of lower amputation rates (0.36, 0.27-0.49, <0.001). There was no significant difference in terms of in-hospital mortality (0.59, 0.31-1.14, P 0.117), although GPI use predicted worse secondary outcomes (1.23, 1.03-1.47, 0.023). Following propensity matching, the amputation rate was lower (3.2% vs. 8%, P < 0.001), while hospitalization costs were higher in the cohort that received GPI ($21,091 ± 404 vs. 19,407 ± 133, P < 0.001)., Conclusions: Multivariate analysis revealed GPI use in peripheral endovascular interventions to be suggestive of an increase in composite end-point of in-hospital mortality and postprocedural complications, no impact on in-hospital mortality alone, significantly lower rate of amputation, and increase in hospitalization costs. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)

Purpose: To examine the impact of intravascular ultrasound (IVUS) utilization during lower limb endovascular interventions as regards postprocedural complications and amputation., Methods: The study cohort was derived from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database between the years 2006 and 2011. Peripheral endovascular interventions were identified using appropriate ICD-9 procedure codes. Two-level hierarchical multivariate mixed models were created. The co-primary outcomes were in-hospital mortality and amputation; the secondary outcome was postprocedural complications. Model results are given as the odds ratio (OR) and 95% confidence interval (CI). Hospitalization costs were also assessed., Results: Overall, among the 92,714 patients extracted from the database during the observation period, IVUS was used in 1299 (1.4%) patients. IVUS utilization during lower extremity peripheral vascular procedures was independently predictive of a lower rate of postprocedural complications (OR 0.80, 95% CI 0.66 to 0.99, p=0.037) as well as lower amputation rates (OR 0.59, 95% CI 0.45 to 0.77, p<0.001) without any significant impact on in-hospital mortality. Multivariate analysis also revealed IVUS utilization to be predictive of a nonsignificant increase in hospitalization costs ($1333, 95% CI -$167 to +$2833, p=0.082)., Conclusion: IVUS use during lower limb endovascular interventions is predictive of lower postprocedural complication and amputation rates with a nonsignificant increase in hospitalization costs., (© The Author(s) 2015.)

The comparative data for angioplasty and stenting for treatment of peripheral arterial disease are largely limited to technical factors such as patency rates with sparse data on clinical outcomes like mortality, postprocedural complications, and amputation. The study cohort was derived from the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database from 2006 to 2011. Peripheral endovascular interventions were identified using appropriate International Classification of Diseases, Ninth Revision (ICD-9) Diagnostic and procedural codes. Two-level hierarchical multivariate mixed models were created. The primary outcome includes inhospital mortality, and secondary outcome was a composite of inhospital mortality and postprocedural complications. Amputation was a separate outcome. Hospitalization costs were also assessed. Endovascular stenting (odds ratio, 95% confidence interval, p value) was independently predictive of lower composite end point of inhospital mortality and postprocedural complications compared with angioplasty alone (0.96, 0.91 to 0.99, 0.025) and lower amputation rates (0.56, 0.53 to 0.60, <0.001) with no significant difference in terms of inhospital mortality alone. Multivariate analysis also revealed stenting to be predictive of higher hospitalization costs ($1,516, 95% confidence interval 1,082 to 1,950, p <0.001) compared with angioplasty. In conclusion, endovascular stenting is associated with a lower rate of postprocedural complications, lower amputation rates, and only minimal increase in hospitalization costs compared with angioplasty alone., (Copyright © 2015 Elsevier Inc. All rights reserved.)