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Study Design: Retrospective cohort., Objective: The aim of this study was to design a risk-stratified benchmarking tool for adolescent idiopathic scoliosis (AIS) surgeries., Summary of Background Data: Machine learning (ML) is an emerging method for prediction modeling in orthopedic surgery. Benchmarking is an established method of process improvement and is an area of opportunity for ML methods. Current surgical benchmark tools often use ranks and no "gold standards" for comparisons exist., Materials and Methods: Data from 6076 AIS surgeries were collected from a multicenter registry and divided into three datasets: encompassing surgeries performed (1) during the entire registry, (2) the past 10 years, and (3) during the last 5 years of the registry. We trained three ML regression models (baseline linear regression, gradient boosting, and eXtreme gradient boosted) on each data subset to predict each of the five outcome variables, length of stay (LOS), estimated blood loss (EBL), operative time, Scoliosis Research Society (SRS)-Pain and SRS-Self-Image. Performance was categorized as "below expected" if performing worse than one standard deviation of the mean, "as expected" if within 1 SD, and "better than expected" if better than 1 SD of the mean., Results: Ensemble ML methods classified performance better than traditional regression techniques for LOS, EBL, and operative time. The best performing models for predicting LOS and EBL were trained on data collected in the last 5 years, while operative time used the entire 10-year dataset. No models were able to predict SRS-Pain or SRS-Self-Image in any useful manner. Point-precise estimates for continuous variables were subject to high average errors., Conclusions: Classification of benchmark outcomes is improved with ensemble ML techniques and may provide much needed case-adjustment for a surgeon performance program. Precise estimates of health-related quality of life scores and continuous variables were not possible, suggesting that performance classification is a better method of performance evaluation., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Abstract: Because of a paucity of published data, we compared the 2-year major clinical outcomes between pre-percutaneous coronary intervention (pre-PCI) thrombolysis in myocardial infarction (TIMI) flow grade 0/1 (pre-TIMI flow grade [pre-TIMI] 0/1) group and pre-PCI TIMI flow grade 2/3 (pre-TIMI 2/3) group in patients with non-ST-segment elevation myocardial infarction (NSTEMI) who underwent successful implantation of newer-generation drug-eluting stent.A total of 7506 NSTEMI patients were divided into 2 groups: pre-TIMI 0/1 group (n = 3157) and pre-TIMI 2/3 group (n = 4349). The primary outcome was major adverse cardiac events defined as all-cause death, recurrent myocardial infarction, or any repeat revascularization. The secondary outcome was stent thrombosis (ST).After propensity score-matched (PSM) analysis, 2 PSM groups (2473 pairs, n = 4946, C-statistic = 0.684) were generated. Major adverse cardiac events (hazard ration [HR], 1.294; 95% confidence interval [CI]: 1.065-1.572; P = .009), all-cause death (HR, 1.559, P = .003), cardiac death (HR: 1.641, P = .005), and all-cause death or MI (HR: 1.531, P = .001) rates were significantly higher in the pre-TIMI 0/1 group than in the pre-TIMI 2/3 group. Moreover, these differences were more prominent during the first 1 month after the index PCI. However, the cumulative incidences of recurrent myocardial infarction, any revascularization, and ST were similar between the 2 groups.Among a contemporary cohort of NSTEMI, these data suggest that the presence of a pre-PCI patency of the infarct-related artery showed better mortality reduction capacity than those with a lack of patency., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Objective: The comparative long-term clinical outcomes between patients with prediabetes and diabetes in patients with non-ST-elevation myocardial infarction (NSTEMI) and multivessel disease (MVD) who were undergoing percutaneous coronary intervention (PCI) are not well known. We therefore compared the 2-year clinical outcomes in such patients., Methods: A total of 2963 patients with NSTEMI and MVD [normoglycemia (group A, n = 629), prediabetes (group B, n = 802), and diabetes (group C, n = 1532)] were evaluated. The primary outcomes were the occurrence of major adverse cardiac events (MACE) defined as all-cause death, recurrent myocardial infarction (Re-MI), and any repeat revascularization. The secondary outcome was stent thrombosis., Results: The cumulative incidence of MACE as well as for death or MI in group B and C were similar when compared to each other. However, they (P = 0.048 and P = 0.017, respectively and P = 0.022 and P = 0.001, respectively) were significantly higher than in group A. The cumulative incidence of all-cause death in group B (P = 0.042) and all-cause death and cardiac death in group C (P = 0.001 and P = 0.028, respectively) were significantly higher than in group A. However, those of all-cause death, cardiac death, Re-MI, any repeat revascularization, and ST were not significantly different between groups B and C., Conclusion: In this study, patients with NSTEMI and MVD who underwent successful implantation of newer-generation DES and were prediabetic had worse outcomes compared to normoglycemics and comparable to those with diabetes., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Abstract: Studies comparing long-term clinical outcomes of statin treatment between acute myocardial infarction (AMI) patients with prediabetes and those with type 2 diabetes mellitus (T2DM) after successful percutaneous coronary intervention (PCI) with the newer-generation drug-eluting stents (DESs) are limited. We compared 2-year clinical outcomes between these patients.A total of 11,962 AMI patients were classified as statin users (n = 10,243) and statin nonusers (n = 1719). Thereafter, statin users and nonusers were further divided into the normoglycemia, prediabetes, and T2DM groups. The major outcome was the occurrence of major adverse cardiac event (MACE) defined as all-cause death, recurrent myocardial infarction (Re-MI), or any repeat coronary revascularization.After statin treatment, the cumulative incidences of MACE (P = .314), all-cause death, cardiac death (CD), Re-MI, and any repeat revascularization were similar between the prediabetes and T2DM groups. However, the cumulative incidences of MACE (P = .025) and all-cause death (P = .038) in the prediabetes group and those of MACE (P = .001), all-cause death (P = .009), and CD (P = .048) in the T2DM group were significantly higher than those in the normoglycemia group. Moreover, in all the 3 glycemic groups, the cumulative incidences of MACE, all-cause death, and CD were significantly higher among statin nonusers than among statin users.This study revealed that AMI patients with prediabetes had worse clinical outcomes than those with normoglycemia and comparable to those with T2DM after 2-year statin treatment. However, further studies are warranted to confirm the current findings., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

This study investigated the impacts of renin-angiotensin system inhibitors (RASIs) on 2-year clinical outcomes in diabetes and dyslipidemic acute myocardial infarction (AMI) patients after a successful percutaneous coronary intervention (PCI) using newer-generation drug-eluting stents (DESs).A total of 16,997 AMI patients were enrolled, and divided into four groups based on the presence or absence of diabetes and dyslipidemia as follows: diabetes -/dyslipidemia -(group A, 11,132 patients), diabetes +/dyslipidemia - (group B, 3,860 patients), diabetes -/dyslipidemia + (group C, 1,328 patients), and diabetes +/dyslipidemia + (group D, 677 patients). The clinical endpoint was the occurrence of major adverse cardiac events (MACEs), the composite of total death, recurrent myocardial infarction (re-MI), and any repeat revascularization, including target lesion revascularization (TLR), target vessel revascularization (TVR), and non-target vessel revascularization (non-TVR).After RASIs therapy, the cumulative incidences of MACEs (adjusted hazard ratio [aHR], 1.330; 95% confidence interval [CI], 1.022-1.732; P = .034), any repeat revascularization (aHR, 1.584; 95% CI, 1.092-2.298; P = .015), TLR, and TVR were significantly higher in group B than group C. However, the cumulative incidences of all-cause death, cardiac death, re-MI, and non-TVR were similar in groups B and C.In this study, under the newer-generation DESs era, repeat revascularization rate reduction benefit of RASIs therapy in diabetic AMI patients was lesser than that in dyslipidemic AMI patients. However, larger randomized controlled studies are needed to confirm these results in the future.

Introduction: General anesthesia induces unconsciousness along with functional changes in brain networks. Considering the essential role of hub structures for efficient information transmission, the authors hypothesized that anesthetics have an effect on the hub structure of functional brain networks., Methods: Graph theoretical network analysis was carried out to study the network properties of 21-channel electroencephalogram data from 10 human volunteers anesthetized on two occasions. The functional brain network was defined by Phase Lag Index, a coherence measure, for three states: wakefulness, loss of consciousness induced by the anesthetic propofol, and recovery of consciousness. The hub nodes were determined by the largest centralities. The correlation between the altered hub organization and the phase relationship between electroencephalographic channels was investigated., Results: Topology rather than connection strength of functional networks correlated with states of consciousness. The average path length, clustering coefficient, and modularity significantly increased after administration of propofol, which disrupted long-range connections. In particular, the strength of hub nodes significantly decreased. The primary hub location shifted from the parietal to frontal region, in association with propofol-induced unconsciousness. The phase lead of frontal to parietal regions in the α frequency band (8-13 Hz) observed during wakefulness reversed direction after propofol and returned during recovery., Conclusions: Propofol reconfigures network hub structure in the brain and reverses the phase relationship between frontal and parietal regions. Changes in network topology are more closely associated with states of consciousness than connectivity and may be the primary mechanism for the observed loss of frontal to parietal feedback during general anesthesia.

Rationale and Objectives: We sought to evaluate the potential benefits of a computer-aided detection (CAD) system for detecting lung nodules in multidetector row CT (MDCT) scans., Methods: A CAD system was developed for detecting lung nodules on MDCT scans and was applied to the data obtained from 15 patients. Two chest radiologists in consensus established the reference standard. The nodules were categorized according to their size and their relationship to the surrounding structures (nodule type). The differences in the sensitivities between an experienced chest radiologist and a CAD system without user interaction were evaluated using a chi2 analysis. The differences in the sensitivities also were compared in terms of the nodule size and the nodule type., Results: A total of 309 nodules were identified as the reference standard. The sensitivity of a CAD system (81%) was not significantly different from that of a radiologist (85%; P > 0.05). The sensitivities of the CAD system for detecting nodules < or = 5 mm in diameter as well as detecting isolated nodules were higher than those of a radiologist (83% vs. 75%, P > 0.05; 93% vs. 76%, P < 0.001). The sensitivities of a radiologist for detecting nodules >5 mm and the nodules attached to other structures were higher than those of a CAD system (98% vs. 79%, P < 0.001; 91% vs. 71%, P < 0.001). There were 28.8 false-positive results of CAD per CT study., Conclusion: The CAD system developed in this study performed the nodule detection task in different ways to that of a radiologist in terms of the nodule size and the nodule type, which suggests that the CAD system can play a complementary role to a radiologist in detecting nodules from large CT data sets.