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BackgroundProtease-activated receptor-1 antagonism by vorapaxar could facilitate arteriovenous fistula maturation but may increase bleeding risk.ObjectiveThe primary objective of the Vorapaxar Study for Maturation of arteriovenous fistula for Hemodialysis Access (VorapAccess) was to determine if vorapaxar improves arteriovenous fistula functional maturation in patients with end-stage renal disease.MethodsVorapAccess was a randomized, placebo-controlled, double-blind pilot trial comparing 2.5 mg vorapaxar per day with placebo for twelve weeks starting on day two after arteriovenous fistula creation. The primary outcome was time to functional maturation defined as successful cannulation for six hemodialysis sessions within three weeks. The planned sample size was 50 participants. The study was terminated early after withdrawal of planned financial support. Given the small number of randomized patients, we performed descriptive analyses without inference testing.ResultsA total of 13 participants were randomly allocated study drug (six vorapaxar and seven placebo). The median age was 56 years and seven participants (54%) were female. The median (minimum-maximum) days to functional maturation were 169 (77-287) days in the vorapaxar group and 145 (48-198) days in the placebo group. Six of the 13 (46%) participants had arteriovenous fistula functional maturation within 180 days; two of six (33%) in the vorapaxar group and four of seven (57%) in the placebo group. There was one bleeding event in the placebo group.ConclusionFewer than half of participants had functional maturation within 180 days after surgery, suggesting a major need for agents or strategies that enhance arteriovenous fistula maturation.

PurposeThe aim of this study was to compare coronary and carotid artery imaging and determine which one shows the strongest association with atherosclerotic cardiovascular disease (ASCVD) score.Patients and methodsTwo separate series patients who underwent either coronary computed tomography angiography (CTA) or carotid CTA were included. We recorded the ASCVD scores and assessed the CTA imaging. Two thirds were used to build predictive models, and the remaining one third generated predicted ASCVD scores. The Bland-Altman analysis analyzed the concordance.ResultsA total of 110 patients were included in each group. There was no significant difference between clinical characteristics. Three imaging variables were included in the carotid model. Two coronary models (presence of calcium or Agatston score) were created. The bias between true and predicted ASCVD scores was 0.37 ± 5.72% on the carotid model, and 2.07 ± 7.18% and 2.47 ± 7.82% on coronary artery models, respectively.ConclusionsBoth carotid and coronary artery imaging features can predict ASCVD score. The carotid artery was more associated to the ASCVD score than the coronary artery.

PURPOSE:To investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores. METHODS:One hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model. RESULTS:Nine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively. CONCLUSIONS:Selected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.

ObjectiveThe aim of this study was to characterize the relationship between computed tomography angiography imaging characteristics of coronary artery and atherosclerotic cardiovascular disease (ASCVD) score.MethodsWe retrospectively identified all patients who underwent a coronary computed tomography angiography at our institution from December 2013 to July 2016, then we calculated the 10-year ASCVD score. We characterized the relationship between coronary artery imaging findings and ASCVD risk score.ResultsOne hundred fifty-one patients met our inclusion criteria. Patients with a 10-year ASCVD score of 7.5% or greater had significantly more arterial segments showing stenosis (46.4%, P = 0.008) and significantly higher maximal plaque thickness (1.25 vs 0.53, P = 0.001). However, among 56 patients with a 10-year ASCVD score of 7.5% or greater, 30 (53.6%) had no arterial stenosis. Furthermore, among the patients with a 10-year ASCVD score of less than 7.5%, 24 (25.3%) had some arterial stenosis.ConclusionsThere is some concordance but not a perfect overlap between 10-year ASCVD risk scores and coronary artery imaging findings.

Introduction: Diabetic ketoacidosis (DKA) at diagnosis is associated with short- and long-term complications. We assessed the relationship between DKA status and hemoglobin A1c (A1c) levels in the first year following type 1 diabetes (T1D) diagnosis. Research Design and Methods: The Pilot Teamwork, Targets, Technology, and Tight Control (4T) study offered continuous glucose monitoring to youth with T1D within 1 month of diagnosis. A1c levels were compared between historical (n = 271) and Pilot 4T (n = 135) cohorts stratified by DKA status at diagnosis (DKA: historical = 94, 4T = 67 versus without DKA: historical = 177, 4T = 68). A1c was evaluated using locally estimated scatter plot smoothing. Change in A1c from 4 to 12 months postdiagnosis was evaluated using a linear mixed model. Results: Median age was 9.7 (interquartile range [IQR]: 6.6, 12.7) versus 9.7 (IQR: 6.8, 12.7) years, 49% versus 47% female, 44% versus 39% non-Hispanic White in historical versus Pilot 4T. In historical and 4T cohorts, DKA at diagnosis demonstrated higher A1c at 6 (0.5% [95% confidence interval (CI): 0.21–0.79; P < 0.01] and 0.38% [95% CI: 0.02–0.74; P = 0.04], respectively), and 12 months (0.62% [95% CI: −0.06 to 1.29; P = 0.07] and 0.39% [95% CI: −0.32 to 1.10; P = 0.29], respectively). The highest % time in range (TIR; 70–180 mg/dL) was seen between weeks 15–20 (69%) versus 25–30 (75%) postdiagnosis for youth with versus without DKA in Pilot 4T, respectively. Conclusions: Pilot 4T improved A1c outcomes versus the historical cohort, but those with DKA at diagnosis had persistently elevated A1c throughout the study and intensive diabetes management did not mitigate this difference. DKA prevention at diagnosis may translate into better glycemic outcomes in the first-year postdiagnosis. Clinical Trial Registration: clinicaltrials.gov: NCT04336969. [ABSTRACT FROM AUTHOR]

Background: Recent therapeutic advances and screening technologies have improved survival among patients with lung cancer, who are now at high risk of developing second primary lung cancer (SPLC). Recently, an SPLC risk‐prediction model (called SPLC‐RAT) was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking. The predictive performance of SPLC‐RAT was evaluated in a hospital‐based cohort of lung cancer survivors. Methods: The authors analyzed data from 8448 ever‐smoking patients diagnosed with initial primary lung cancer (IPLC) in 1997–2006 at Mayo Clinic, with each patient followed for SPLC through 2018. The predictive performance of SPLC‐RAT and further explored the potential of improving SPLC detection through risk model‐based surveillance using SPLC‐RAT versus existing clinical surveillance guidelines. Results: Of 8448 IPLC patients, 483 (5.7%) developed SPLC over 26,470 person‐years. The application of SPLC‐RAT showed high discrimination area under the receiver operating characteristics curve: 0.81). When the cohort was stratified by a 10‐year risk threshold of ≥5.6% (i.e., 80th percentile from the SPLC‐RAT development cohort), the observed SPLC incidence was significantly elevated in the high‐risk versus low‐risk subgroup (13.1% vs. 1.1%, p < 1 × 10–6). The risk‐based surveillance through SPLC‐RAT (≥5.6% threshold) outperformed the National Comprehensive Cancer Network guidelines with higher sensitivity (86.4% vs. 79.4%) and specificity (38.9% vs. 30.4%) and required 20% fewer computed tomography follow‐ups needed to detect one SPLC (162 vs. 202). Conclusion: In a large, hospital‐based cohort, the authors validated the predictive performance of SPLC‐RAT in identifying high‐risk survivors of SPLC and showed its potential to improve SPLC detection through risk‐based surveillance. Plain Language Summary: Lung cancer survivors have a high risk of developing second primary lung cancer (SPLC).However, no evidence‐based guidelines for SPLC surveillance are available for lung cancer survivors.Recently, an SPLC risk‐prediction model was developed and validated using data from population‐based epidemiological cohorts and clinical trials, but real‐world validation has been lacking.Using a large, real‐world cohort of lung cancer survivors, we showed the high predictive accuracy and risk‐stratification ability of the SPLC risk‐prediction model.Furthermore, we demonstrated the potential to enhance efficiency in detecting SPLC using risk model‐based surveillance strategies compared to the existing consensus‐based clinical guidelines, including the National Comprehensive Cancer Network. Given the rapidly growing number of lung cancer survivors who are now at high risk of developing second primary lung cancer (SPLC), previous studies have identified SPLC risk factors and developed SPLC risk‐prediction models, but they lack insight into real‐world validation to help improve clinical decision‐making in SPLC surveillance for lung cancer survivors. Using a large, hospital‐based real‐world cohort of lung cancer survivors, the authors validated the predictive accuracy of an SPLC risk‐prediction model (area under the curve of 0.81), that can identify high‐risk lung cancer survivors for SPLC and can be incorporated into clinical decision‐making for SPLC surveillance to improve the systematic management of lung cancer survivors. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to use machine learning (ML) algorithms with clinical, lab, and imaging data as input to predict various outcomes in traumatic brain injury (TBI) patients. Methods: In this retrospective study, blood samples were analyzed for glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase L1 (UCH-L1). The non-contrast head CTs were reviewed by two neuroradiologists for TBI common data elements (CDE). Three outcomes were designed to predict: discharged or admitted for further management (prediction 1), deceased or not deceased (prediction 2), and admission only, prolonged stay, or neurosurgery performed (prediction 3). Five ML models were trained. SHapley Additive exPlanations (SHAP) analyses were used to assess the relative significance of variables. Results: Four hundred forty patients were used to predict predictions 1 and 2, while 271 patients were used in prediction 3. Due to Prediction 3's hospitalization requirement, deceased and discharged patients could not be utilized. The Random Forest model achieved an average accuracy of 1.00 for prediction 1 and an accuracy of 0.99 for prediction 2. The Random Forest model achieved a mean accuracy of 0.93 for prediction 3. Key features were extracranial injury, hemorrhage, UCH-L1 for prediction 1; The Glasgow Coma Scale, age, GFAP for prediction 2; and GFAP, subdural hemorrhage volume, and pneumocephalus for prediction 3, per SHAP analysis. Conclusion: Combining clinical and laboratory parameters with non-contrast CT CDEs allowed our ML models to accurately predict the designed outcomes of TBI patients. GFAP and UCH-L1 were among the significant predictor variables, demonstrating the importance of these biomarkers. [ABSTRACT FROM AUTHOR]

BACKGROUND: Previous studies comparing percutaneous coronary intervention (PCI) with coronary artery bypass grafting (CABG) in patients with multivessel coronary disease not involving the left main have shown significantly lower rates of death, myocardial infarction (MI), or stroke after CABG. These studies did not routinely use current-generation drug-eluting stents or fractional flow reserve (FFR) to guide PCI. METHODS: FAME 3 (Fractional Flow Reserve versus Angiography for Multivessel Evaluation) is an investigator-initiated, multicenter, international, randomized trial involving patients with 3-vessel coronary artery disease (not involving the left main coronary artery) in 48 centers worldwide. Patients were randomly assigned to receive FFR-guided PCI using zotarolimus drug-eluting stents or CABG. The prespecified key secondary end point of the trial reported here is the 3-year incidence of the composite of death, MI, or stroke. RESULTS: A total of 1500 patients were randomized to FFR-guided PCI or CABG. Follow-up was achieved in >96% of patients in both groups. There was no difference in the incidence of the composite of death, MI, or stroke after FFRguided PCI compared with CABG (12.0% versus 9.2%; hazard ratio [HR], 1.3 [95% CI, 0.98-1.83]; P=0.07). The rates of death (4.1% versus 3.9%; HR, 1.0 [95% CI, 0.6-1.7]; P=0.88) and stroke (1.6% versus 2.0%; HR, 0.8 [95% CI, 0.4-1.7]; P=0.56) were not different. MI occurred more frequently after PCI (7.0% versus 4.2%; HR, 1.7 [95% CI, 1.1-2.7]; P=0.02). CONCLUSIONS: At 3-year follow-up, there was no difference in the incidence of the composite of death, MI, or stroke after FFR-guided PCI with current-generation drug-eluting stents compared with CABG. There was a higher incidence of MI after PCI compared with CABG, with no difference in death or stroke. These results provide contemporary data to allow improved shared decision-making between physicians and patients with 3-vessel coronary artery disease. [ABSTRACT FROM AUTHOR]

Background: Several types of head CT classification systems have been developed to prognosticate and stratify TBI patients. Objective: The purpose of our study was to compare the predictive value and accuracy of the different CT scoring systems, including the Marshall, Rotterdam, Stockholm, Helsinki, and NIRIS systems, to inform specific patient management actions, using the ProTECT III population of patients with moderate to severe acute traumatic brain injury (TBI). Methods: We used the data collected in the patients with moderate to severe (GCS score of 4–12) TBI enrolled in the ProTECT III clinical trial. ProTECT III was a NIH-funded, prospective, multicenter, randomized, double-blind, placebo-controlled clinical trial designed to determine the efficacy of early administration of IV progesterone. The CT scoring systems listed above were applied to the baseline CT scans obtained in the trial. We assessed the predictive accuracy of these scoring systems with respect to Glasgow Outcome Scale—Extended at 6 months, disability rating scale score, and mortality. Results: A total of 882 subjects were enrolled in ProTECT III. Worse scores for each head CT scoring systems were highly correlated with unfavorable outcome, disability outcome, and mortality. The NIRIS classification was more strongly correlated than the Stockholm and Rotterdam CT scores, followed by the Helsinki and Marshall CT classification. The highest correlation was observed between NIRIS and mortality (estimated odds ratios of 4.83). Conclusion: All scores were highly associated with 6-month unfavorable, disability and mortality outcomes. NIRIS was also accurate in predicting TBI patients' management and disposition. [ABSTRACT FROM AUTHOR]

Introduction: Traumatic brain injury (TBI) is a major public health concern in the U.S. Recommendations for patients admitted in the emergency department (ED) to receive head computed tomography (CT) scan are currently guided by various clinical decision rules. Objective: To compare how a blood biomarker approach compares with clinical decision rules in terms of predicting a positive head CT in adult patients suspected of TBI. Methods: We retrospectively identified patients transported to our emergency department and underwent a noncontrast head CT due to suspicion of TBI and who had blood samples available. Published thresholds for serum and plasma glial fibrillary acidic protein (GFAP), ubiquitin carboxyl-terminal hydrolase-L1 (UCH-L1), and serum S100β were used to make CT recommendations. These blood biomarker-based recommendations were compared to those achieved under widely used clinical head CT decision rules (Canadian, New Orleans, NEXUS II, and ACEP Clinical Policy). Results: Our study included 463 patients, of which 122 (26.3%) had one or more abnormalities presenting on head CT. Individual blood biomarkers achieved high negative predictive value (NPV) for abnormal head CT findings (88%–98%), although positive predictive value (PPV) was consistently low (25%–42%). A composite biomarker-based decision rule (GFAP+UCH-L1)'s NPV of 100% and PPV of 29% were comparable or better than those achieved under the clinical decision rules. Conclusion: Blood biomarkers perform at least as well as clinical rules in terms of selecting TBI patients for head CT and may be easier to implement in the clinical setting. A prospective study is necessary to validate this approach. [ABSTRACT FROM AUTHOR]