Genetics Navigator: protocol for a mixed methods randomized controlled trial evaluating a digital platform to deliver genomic services in Canadian pediatric and adult populations.

INTRODUCTION: Genetic testing is used across medical disciplines leading to unprecedented demand for genetic services. This has resulted in excessive waitlists and unsustainable pressure on the standard model of genetic healthcare. Alternative models are needed; e-health tools represent scalable and evidence-based solution. We aim to evaluate the effectiveness of the Genetics Navigator, an interactive patient-centred digital platform that supports the collection of medical and family history, provision of pregenetic and postgenetic counselling and return of genetic testing results across paediatric and adult settings. METHODS AND ANALYSIS: We will evaluate the effectiveness of the Genetics Navigator combined with usual care by a genetics clinician (physician or counsellor) to usual care alone in a randomised controlled trial. One hundred and thirty participants (adults patients or parents of paediatric patients) eligible for genetic testing through standard of care will be recruited across Ontario genetics clinics. Participants randomised into the intervention arm will use the Genetics Navigator for pretest and post-test genetic counselling and results disclosure in conjunction with their clinician. Participants randomised into the control arm will receive usual care, that is, clinician-delivered pretest and post-test genetic counselling, and results disclosure. The primary outcome is participant distress 2 weeks after test results disclosure. Secondary outcomes include knowledge, decisional conflict, anxiety, empowerment, quality of life, satisfaction, acceptability, digital health literacy and health resource use. Quantitative data will be analysed using statistical hypothesis tests and regression models. A subset of participants will be interviewed to explore user experience; data will be analysed using interpretive description. A cost-effectiveness analysis will examine the incremental cost of the Navigator compared with usual care per unit reduction in distress or unit improvement in quality of life from public payer and societal perspectives. ETHICS AND DISSEMINATION: This study was approved by Clinical Trials Ontario. Results will be shared through stakeholder workshops, national and international conferences and peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT06455384.

Seroprevalence of SARS-CoV-2 antibodies among children receiving primary care in Toronto, Ontario.

OBJECTIVE: Characterizing the seroprevalence of SARS-CoV-2 antibodies in children is needed to optimize the COVID-19 public health response. We quantified the seroprevalence of SARS-CoV-2 infection-acquired antibodies and vaccine-acquired antibodies among children receiving primary care in Toronto, Canada. METHODS: We conducted a longitudinal cohort study between January 2021 and November 2022 in healthy children aged 0-16 years receiving primary care in Toronto. The primary and secondary outcomes were seroprevalence of SARS-COV-2 infection-acquired antibodies and vaccine-acquired antibodies ascertained from finger-prick dried blood spots. Samples were tested using an enzyme-linked immunosorbent assay for antibodies to full-length spike trimer and nucleocapsid. We explored sociodemographic differences with Firth's penalized generalized estimating equations. RESULTS: Of the 475 participants, 50.1% were girls and mean age was 6.4 years (SD = 3.2). We identified 103 children seropositive for infection-acquired antibodies, with a crude seroprevalence that rose from 2.6% (95%CI 1.39-4.92) from January to July 2021 to 50.7% (95%CI 39.5-61.8) by July to November 2022. Seroprevalence of vaccine-acquired antibodies was 45.2% by July to November 2022 (95%CI 34.3-56.58). No differences in sociodemographic factors (age, sex, income, or ethnicity) were identified for infection-acquired antibodies; however, children with vaccine-acquired antibodies were more likely to be older, have mothers with university education, and have mothers who had also been vaccinated. CONCLUSION: Our results provide a benchmark for seroprevalence of SARS-CoV-2 antibodies in children in Toronto. Ongoing monitoring of the serological status of children is important, particularly with the emergence of new variants of concern, low vaccine coverage, and discontinuation of PCR testing.

RéSUMé: OBJECTIF: Caractériser la séroprévalence des anticorps du SRAS-CoV-2 chez les enfants est nécessaire pour optimiser la réponse de santé publique à COVID-19. Nous avons quantifié la séroprévalence des anticorps acquis par l'infection au SRAS-CoV-2 et des anticorps acquis par le vaccin chez les enfants recevant des soins primaires à Toronto, au Canada. MéTHODES: Nous avons mené une étude de cohorte longitudinale entre janvier 2021 et novembre 2022 auprès d'enfants en bonne santé âgés de 0 à 16 ans recevant des soins primaires à Toronto. Les résultats principaux et secondaires étaient la séroprévalence des anticorps acquis par l'infection du SRAS-CoV-2 et des anticorps acquis par le vaccin déterminés à partir de taches de sang séché par piqûre au doigt. Les échantillons ont été testés à l'aide d'un test immuno-enzymatique pour détecter les anticorps dirigés contre le trimère de pointe complet et la nucléocapside. Nous avons exploré les différences sociodémographiques à l'aide des équations d'estimation généralisées pénalisées de Firth. RéSULTATS: Sur les 475 participants, 50,1 % étaient des filles et l'âge moyen était de 6,4 ans (ET = 3,2). Nous avons identifié 103 enfants séropositifs aux anticorps acquis lors d'une infection, avec une séroprévalence non ajusté qui est passée de 2,6 % (IC 95% : 1,39­4,92) de janvier à juillet 2021 à 50,7 % (IC 95% : 39,5­61,8) de juillet à novembre 2022. La séroprévalence des anticorps acquis par le vaccin était de 45,2 % de juillet à novembre 2022 (IC à 95% : 34,3­56,58). Aucune différence dans les facteurs sociodémographiques (âge, sexe, revenu ou appartenance ethnique) n'a été identifiée pour les anticorps acquis lors d'une infection; cependant, les enfants avec des anticorps acquis par le vaccin étaient plus susceptibles d'être plus âgés, d'avoir des mères ayant fait des études universitaires et d'avoir des mères également vaccinées. CONCLUSION: Nos résultats fournissent une référence pour la séroprévalence des anticorps du SRAS-CoV-2 chez les enfants de Toronto. La surveillance continue du statut sérologique des enfants est importante, en particulier avec l'émergence de nouveaux variants préoccupants, la faible couverture vaccinale et l'arrêt des tests PCR.

Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

BACKGROUND: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS. METHODS AND RESULTS: The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10-fold cross-validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra- and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1-year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93-0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67). The extreme gradient boosting model maintained excellent performance at the intra- and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative). CONCLUSIONS: Machine learning can accurately predict 1-year stroke or death following TFCAS, performing better than logistic regression.

Brief Report: A population-based study of the impact of the COVID-19 pandemic on benzodiazepine use among children and young adults.

The COVID-19 pandemic was associated with increases in the prevalence of depression and anxiety among children and young adults. We studied whether the pandemic was associated with changes in prescription benzodiazepine use. We conducted a population-based study of benzodiazepine dispensing to children and young adults ≤ 24 years old between January 1, 2013, and June 30, 2022. We used structural break analyses to identify the pandemic month(s) when changes in prescription benzodiazepine dispensing occurred, and interrupted time series models to quantify changes in dispensing following the structural break and compare observed and expected benzodiazepine use. A structural break occurs where there is a sudden change in the trend of a time series. We observed an immediate decline in benzodiazepine dispensing of 23.6 per 100,000 (95% confidence interval [CI]: -33.6 to -21.2) associated with a structural break in April 2020, followed by a monthly decrease in the trend of 0.3 per 100,000 (95% CI: -0.74 to 0.14). Lower than expected benzodiazepine dispensing rates were observed each month of the pandemic from April 2020 onward, with relative percent differences ranging from - 7.4% (95% CI: -10.1% to - 4.7%) to -20.9% (95% CI: -23.2% to -18.6%). Results were generally similar in analyses stratified by sex, age, neighbourhood income quintile, and urban versus rural residence. Further research is required to understand the clinical implications of these findings and whether these trends were sustained with further follow-up.

Predicting inferior vena cava filter complications using machine learning.

OBJECTIVE: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent IVC filter placement between 2013 and 2024. We identified 77 preoperative demographic and clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or iliac vein thrombosis, new pulmonary embolism, access site thrombosis, or failed retrieval). The data were divided into training (70%) and test (30%) sets. Six ML models were trained using preoperative features with 10-fold cross-validation (Extreme Gradient Boosting, random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was assessed using calibration plot and Brier score. Performance was evaluated across subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, planned duration of filter, landing site of filter, and presence of prior IVC filter placement. RESULTS: Overall, 14,476 patients underwent IVC filter placement and 584 (4.0%) experienced 1-year filter-related complications. Patients with a primary outcome were younger (59.3 ± 16.7 years vs 63.8 ± 16.0 years; P < .001) and more likely to have thrombotic risk factors including thrombophilia, prior venous thromboembolism (VTE), and family history of VTE. The best prediction model was Extreme Gradient Boosting, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). In comparison, logistic regression had an AUROC of 0.63 (95% confidence interval, 0.61-0.65). Calibration plot showed good agreement between predicted/observed event probabilities with a Brier score of 0.07. The top 10 predictors of 1-year filter-related complications were (1) thrombophilia, (2) prior VTE, (3) antiphospholipid antibodies, (4) factor V Leiden mutation, (5) family history of VTE, (6) planned duration of IVC filter (temporary), (7) unable to maintain therapeutic anticoagulation, (8) malignancy, (9) recent or active bleeding, and (10) age. Model performance remained robust across all subgroups. CONCLUSIONS: We developed ML models that can accurately predict 1-year IVC filter complications, performing better than logistic regression. These algorithms have potential to guide patient selection for filter placement, counselling, perioperative management, and follow-up to mitigate filter-related complications and improve outcomes.

Predicting Major Adverse Carotid Cerebrovascular Events in Patients with Carotid Stenosis: Integrating a Panel of Plasma Protein Biomarkers and Clinical Features-A Pilot Study.

Background: Carotid stenosis (CS) is an atherosclerotic disease of the carotid artery that can lead to devastating cardiovascular outcomes such as stroke, disability, and death. The currently available treatment for CS is medical management through risk reduction, including control of hypertension, diabetes, and/or hypercholesterolemia. Surgical interventions are currently suggested for patients with symptomatic disease with stenosis >50%, where patients have suffered from a carotid-related event such as a cerebrovascular accident, or asymptomatic disease with stenosis >60% if the long-term risk of death is <3%. There is a lack of current plasma protein biomarkers available to predict patients at risk of such adverse events. Methods: In this study, we investigated several growth factors and biomarkers of inflammation as potential biomarkers for adverse CS events such as stroke, need for surgical intervention, myocardial infarction, and cardiovascular-related death. In this pilot study, we use a support vector machine (SVM), random forest models, and the following four significantly elevated biomarkers: C-X-C Motif Chemokine Ligand 6 (CXCL6); Interleukin-2 (IL-2); Galectin-9; and angiopoietin-like protein (ANGPTL4). Results: Our SVM model best predicted carotid cerebrovascular events with an area under the curve (AUC) of >0.8 and an accuracy of 0.88, demonstrating strong prognostic capability. Conclusions: Our SVM model may be used for risk stratification of patients with CS to determine those who may benefit from surgical intervention.

Current Prognostic Biomarkers for Abdominal Aortic Aneurysm: A Comprehensive Scoping Review of the Literature.

Abdominal aortic aneurysm (AAA) is a progressive dilatation of the aorta that can lead to aortic rupture. The pathophysiology of the disease is not well characterized but is known to be caused by the general breakdown of the extracellular matrix within the aortic wall. In this comprehensive literature review, all current research on proteins that have been investigated for their potential prognostic capabilities in patients with AAA was included. A total of 45 proteins were found to be potential prognostic biomarkers for AAA, predicting incidence of AAA, AAA rupture, AAA growth, endoleak, and post-surgical mortality. The 45 proteins fell into the following seven general categories based on their primary function: (1) cardiovascular health, (2) hemostasis, (3) transport proteins, (4) inflammation and immunity, (5) kidney function, (6) cellular structure, (7) and hormones and growth factors. This is the most up-to-date literature review on current prognostic markers for AAA and their functions. This review outlines the wide pathophysiological processes that are implicated in AAA disease progression.

Development and evaluation of a prediction model for peripheral artery disease-related major adverse limb events using novel biomarker data.

OBJECTIVE: Prognostic tools for individuals with peripheral artery disease (PAD) remain limited. We developed prediction models for 3-year PAD-related major adverse limb events (MALE) using demographic, clinical, and biomarker data previously validated by our group. METHODS: We performed a prognostic study using a prospectively recruited cohort of patients with PAD (n = 569). Demographic/clinical data were recorded including sex, age, comorbidities, previous procedures, and medications. Plasma concentrations of three biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP], fatty acid binding protein 3 [FABP3], and FABP4) were measured at baseline. The cohort was followed for 3 years. MALE was the primary outcome (composite of open/endovascular vascular intervention or major amputation). We trained three machine learning models with 10-fold cross-validation using demographic, clinical, and biomarker data (random forest, decision trees, and Extreme Gradient Boosting [XGBoost]) to predict 3-year MALE in patients. Area under the receiver operating characteristic curve (AUROC) was the primary model evaluation metric. RESULTS: Three-year MALE was observed in 162 patients (29%). XGBoost was the top-performing predictive model for 3-year MALE, achieving the following performance metrics: AUROC = 0.88 (95% confidence interval [CI], 0.84-0.94); sensitivity, 88%; specificity, 84%; positive predictive value, 83%; and negative predictive value, 91% on test set data. On an independent validation cohort of patients with PAD, XGBoost attained an AUROC of 0.87 (95% CI, 0.82-0.90). The 10 most important predictors of 3-year MALE consisted of: (1) FABP3; (2) FABP4; (3) age; (4) NT-proBNP; (5) active smoking; (6) diabetes; (7) hypertension; (8) dyslipidemia; (9) coronary artery disease; and (10) sex. CONCLUSIONS: We built robust machine learning algorithms that accurately predict 3-year MALE in patients with PAD using demographic, clinical, and novel biomarker data. Our algorithms can support risk stratification of patients with PAD for additional vascular evaluation and early aggressive medical management, thereby improving outcomes. Further validation of our models for clinical implementation is warranted.

Predicting Outcomes Following Lower Extremity Endovascular Revascularization Using Machine Learning.

BACKGROUND: Lower extremity endovascular revascularization for peripheral artery disease carries nonnegligible perioperative risks; however, outcome prediction tools remain limited. Using machine learning, we developed automated algorithms that predict 30-day outcomes following lower extremity endovascular revascularization. METHODS AND RESULTS: The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity endovascular revascularization (angioplasty, stent, or atherectomy) for peripheral artery disease between 2011 and 2021. Input features included 38 preoperative demographic/clinical variables. The primary outcome was 30-day postprocedural major adverse limb event (composite of major reintervention, untreated loss of patency, or major amputation) or death. Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 machine learning models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Overall, 21 886 patients were included, and 30-day major adverse limb event/death occurred in 1964 (9.0%) individuals. The best performing model for predicting 30-day major adverse limb event/death was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI, 0.92-0.94). In comparison, logistic regression had an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.70-0.74). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.09. The top 3 predictive features in our algorithm were (1) chronic limb-threatening ischemia, (2) tibial intervention, and (3) congestive heart failure. CONCLUSIONS: Our machine learning models accurately predict 30-day outcomes following lower extremity endovascular revascularization using preoperative data with good discrimination and calibration. Prospective validation is warranted to assess for generalizability and external validity.

Opioid-related deaths between 2019 and 2021 across 9 Canadian provinces and territories.

BACKGROUND: The drug toxicity crisis continues to accelerate across Canada, with rapid increases in opioid-related harms following the onset of the COVID-19 pandemic. We sought to describe trends in the burden of opioid-related deaths across Canada throughout the pandemic, comparing these trends by province or territory, age, and sex. METHODS: We conducted a repeated cross-sectional analysis of accidental opioid-related deaths between Jan. 1, 2019, and Dec. 31, 2021, across 9 Canadian provinces and territories using aggregated national data. Our primary measure was the burden of premature opioid-related death, measured by potential years of life lost. Our secondary measure was the proportion of all deaths attributable to opioids; we used the Cochrane-Armitage test for trend to compare proportions. RESULTS: Between 2019 and 2021, the annual number of opioid-related deaths increased from 3007 to 6222 and years of life lost increased from 126 115 to 256 336 (from 3.5 to 7.0 yr of life lost per 1000 population). In 2021, the highest number of years of life lost was among males (181 525 yr) and people aged 30-39 years (87 045 yr). In 2019, we found that 1.7% of all deaths among those younger than 85 years were related to opioids, rising to 3.2% in 2021. Significant increases in the proportion of deaths related to opioids were observed across all age groups (p < 0.001), representing 29.3% and 29.0% of deaths among people aged 20-29 and 30-39 years in 2021, respectively. INTERPRETATION: Across Canada, the burden of premature opioid-related deaths doubled between 2019 and 2021, representing more than one-quarter of deaths among younger adults. The disproportionate loss of life in this demographic group highlights the critical need for targeted prevention efforts.

Machine Learning to Predict Outcomes of Endovascular Intervention for Patients With PAD.

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited. Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD. Design, Setting, and Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets. Exposures: A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified. Main Outcomes and Measures: Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data. Results: Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Conclusions and Relevance: In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Predicting outcomes following lower extremity open revascularization using machine learning.

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes.

Temporal trends in hemodialysis access creation during the fistula first era.

OBJECTIVE: Although forearm arteriovenous fistulas (AVFs) are the preferred initial vascular access for hemodialysis based on national guidelines, there are no population-level studies evaluating trends in creation of forearm vs upper arm AVFs and arteriovenous grafts (AVGs). The purpose of this study was to report temporal trends in first-time permanent hemodialysis access type, and to assess the effect of national initiatives on rates of AVF placement. METHODS: Retrospective cross-sectional study (2012-2022) utilizing the Vascular Quality Initiative database. All patients older than 18 years with creation of first-time upper extremity surgical hemodialysis access were included. Anatomic location of the AVF or AVG (forearm vs upper arm) was defined based on inflow artery, outflow vein, and presumed cannulation zone. Primary analysis examined temporal trends in rates of forearm vs upper arm AVFs and AVGs using time series analyses (modified Mann-Kendall test). Subgroup analyses examined rates of access configuration stratified by age, sex, race, dialysis, and socioeconomic status. Interrupted time series analysis was performed to assess the effect of the 2015 Fistula First Catheter Last initiative on rates of AVFs. RESULTS: Of the 52,170 accesses, 57.9% were upper arm AVFs, 25.2% were forearm AVFs, 15.4% were upper arm AVGs, and 1.5% were forearm AVGs. From 2012 to 2022, there was no significant change in overall rates of forearm or upper arm AVFs. There was a numerical increase in upper arm AVGs (13.9 to 18.2 per 100; P = .09), whereas forearm AVGs significantly declined (1.8 to 0.7 per 100; P = .02). In subgroup analyses, we observed a decrease in forearm AVFs among men (33.1 to 28.7 per 100; P = .04) and disadvantaged (Area Deprivation Index percentile ≥50) patients (29.0 to 20.7 per 100; P = .04), whereas female (17.2 to 23.1 per 100; P = .03), Black (15.6 to 24.5 per 100; P < .01), elderly (age ≥80 years) (18.7 to 32.5 per 100; P < .01), and disadvantaged (13.6 to 20.5 per 100; P < .01) patients had a significant increase in upper arm AVGs. The Fistula First Catheter Last initiative had no effect on the rate of AVF placement (83.2 to 83.7 per 100; P=.37). CONCLUSIONS: Despite national initiatives to promote autogenous vascular access, the rates of first-time AVFs have remained relatively constant, with forearm AVFs only representing one-quarter of all permanent surgical accesses. Furthermore, elderly, Black, female, and disadvantaged patients saw an increase in upper arm AVGs. Further efforts to elucidate factors associated with forearm AVF placement, as well as potential physician, center, and regional variation is warranted.

Impact of COVID-19 pandemic on prescription stimulant use among children and youth: a population-based study.

COVID-19 associated public health measures and school closures exacerbated symptoms in some children and youth with attention-deficit hyperactivity disorder (ADHD). Less well understood is how the pandemic influenced patterns of prescription stimulant use. We conducted a population-based study of stimulant dispensing to children and youth ≤ 24 years old between January 1, 2013, and June 30, 2022. We used structural break analyses to identify the pandemic month(s) when changes in the dispensing of stimulants occurred. We used interrupted time series models to quantify changes in dispensing following the structural break and compare observed and expected stimulant use. Our main outcome was the change in the monthly rate of stimulant use per 100,000 children and youth. Following an initial immediate decline of 60.1 individuals per 100,000 (95% confidence interval [CI] - 99.0 to - 21.2), the monthly rate of stimulant dispensing increased by 11.8 individuals per 100,000 (95% CI 10.0-13.6), with the greatest increases in trend observed among females, individuals in the highest income neighbourhoods, and those aged 20 to 24. Observed rates were between 3.9% (95% CI 1.7-6.2%) and 36.9% (95% CI 34.3-39.5%) higher than predicted among females from June 2020 onward and between 7.1% (95% CI 4.2-10.0%) and 50.7% (95% CI 47.0-54.4%) higher than expected among individuals aged 20-24 from May 2020 onward. Additional research is needed to ascertain the appropriateness of stimulant use and to develop strategies supporting children and youth with ADHD during future periods of long-term stressors.

Vision Transformer-based Decision Support for Neurosurgical Intervention in Acute Traumatic Brain Injury: Automated Surgical Intervention Support Tool.

Purpose To develop an automated triage tool to predict neurosurgical intervention for patients with traumatic brain injury (TBI). Materials and Methods A provincial trauma registry was reviewed to retrospectively identify patients with TBI from 2005 to 2022 treated at a specialized Canadian trauma center. Model training, validation, and testing were performed using head CT scans with binary reference standard patient-level labels corresponding to whether the patient received neurosurgical intervention. Performance and accuracy of the model, the Automated Surgical Intervention Support Tool for TBI (ASIST-TBI), were also assessed using a held-out consecutive test set of all patients with TBI presenting to the center between March 2021 and September 2022. Results Head CT scans from 2806 patients with TBI (mean age, 57 years ± 22 [SD]; 1955 [70%] men) were acquired between 2005 and 2021 and used for training, validation, and testing. Consecutive scans from an additional 612 patients (mean age, 61 years ± 22; 443 [72%] men) were used to assess the performance of ASIST-TBI. There was accurate prediction of neurosurgical intervention with an area under the receiver operating characteristic curve (AUC) of 0.92 (95% CI: 0.88, 0.94), accuracy of 87% (491 of 562), sensitivity of 87% (196 of 225), and specificity of 88% (295 of 337) on the test dataset. Performance on the held-out test dataset remained robust with an AUC of 0.89 (95% CI: 0.85, 0.91), accuracy of 84% (517 of 612), sensitivity of 85% (199 of 235), and specificity of 84% (318 of 377). Conclusion A novel deep learning model was developed that could accurately predict the requirement for neurosurgical intervention using acute TBI CT scans. Keywords: CT, Brain/Brain Stem, Surgery, Trauma, Prognosis, Classification, Application Domain, Traumatic Brain Injury, Triage, Machine Learning, Decision Support Supplemental material is available for this article. © RSNA, 2024 See also commentary by Haller in this issue.

Xylitol for the prevention of acute otitis media episodes in children aged 1-5 years: a randomised controlled trial.

OBJECTIVE: To investigate the regular use of xylitol, compared with sorbitol, to prevent acute otitis media (AOM), upper respiratory tract infections (URTIs) and dental caries. DESIGN: Blinded randomised controlled trial with a 6-month study period. SETTING: Enrolment took place at 11 primary care practices in Ontario, Canada. PATIENTS: Children aged 1-5 years who did not use xylitol or sorbitol at enrolment. INTERVENTIONS: Children were randomly assigned to use a placebo syrup with sorbitol or xylitol syrup two times per day for 6 months. MAIN OUTCOME MEASURES: Primary outcome was the number of clinician-diagnosed AOM episodes over 6 months. Secondary outcomes were caregiver-reported URTIs and dental caries. RESULTS: Among the 250 randomised children, the mean (SD) age was 38±14 months and there were 124 girls (50%). There were three clinician-diagnosed AOM episodes in the 125 placebo group participants and six in the 125 xylitol group participants (OR 2.04; 95% CI 0.43, 12.92; p=0.50). There was no difference in number of caregiver-reported URTI episodes (rate ratio (RR) 0.88; 95% CI 0.70, 1.11) between the placebo (4.2 per participant over 6 months; 95% CI 3.6, 5.0) and xylitol (3.7; 95% CI 3.2, 4.4) groups. Dental caries were reported for four participants in the placebo group and two in the xylitol group (OR 0.42; 95% CI 0.04, 3.05; p=0.42). In a post-hoc analysis of URTIs during the COVID-19 pandemic, the rate among the 59 participants receiving placebo was 2.3 per participant over 6 months (95% CI 1.8, 3.0) and for the 55 receiving xylitol, 1.3 over 6 months (95% CI 0.92, 1.82; RR 0.56; 95% CI 0.36, 0.87). The most common adverse event was diarrhoea (28% with placebo; 34% with xylitol). CONCLUSIONS: Regular use of xylitol did not prevent AOM, URTIs or dental caries in a trial with limited statistical power. A post-hoc analysis indicated that URTIs were less common with xylitol exposure during the COVID-19 pandemic, but this finding could be spurious. TRIAL REGISTRATION NUMBER: NCT03055091.

Using machine learning to predict outcomes following suprainguinal bypass.

OBJECTIVE: Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023. We identified 100 potential predictor variables from the index hospitalization (68 preoperative [demographic/clinical], 13 intraoperative [procedural], and 19 postoperative [in-hospital course/complications]). The primary outcomes were major adverse limb events (MALE; composite of untreated loss of patency, thrombectomy/thrombolysis, surgical revision, or major amputation) or death at 1 year following suprainguinal bypass. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The best performing algorithm was further trained using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, symptom status, procedure type, prior intervention for PAD, concurrent interventions, and urgency. RESULTS: Overall, 16,832 patients underwent suprainguinal bypass, and 3136 (18.6%) developed 1-year MALE or death. Patients with 1-year MALE or death were older (mean age, 64.9 vs 63.5 years; P < .001) with more comorbidities, had poorer functional status (65.7% vs 80.9% independent at baseline; P < .001), and were more likely to have chronic limb-threatening ischemia (67.4% vs 47.6%; P < .001) than those without an outcome. Despite being at higher cardiovascular risk, they were less likely to receive acetylsalicylic acid or statins preoperatively and at discharge. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.92 (95% confidence interval [CI], 0.91-0.93). In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). Our XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.93 (95% CI, 0.92-0.94) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.10 (postoperative). Of the top 10 predictors, nine were preoperative features including chronic limb-threatening ischemia, previous procedures, comorbidities, and functional status. Model performance remained robust on all subgroup analyses. CONCLUSIONS: We developed ML models that accurately predict outcomes following suprainguinal bypass, performing better than logistic regression. Our algorithms have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes following suprainguinal bypass.

Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives.

Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.

Predicting Outcomes Following Endovascular Abdominal Aortic Aneurysm Repair Using Machine Learning.

OBJECTIVE: To develop machine learning (ML) models that predict outcomes following endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA). BACKGROUND: EVAR carries non-negligible perioperative risks; however, there are no widely used outcome prediction tools. METHODS: The National Surgical Quality Improvement Program targeted database was used to identify patients who underwent EVAR for infrarenal AAA between 2011 and 2021. Input features included 36 preoperative variables. The primary outcome was 30-day major adverse cardiovascular event (composite of myocardial infarction, stroke, or death). Data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, 6 ML models were trained using preoperative features. The primary model evaluation metric was area under the receiver operating characteristic curve. Model robustness was evaluated with calibration plot and Brier score. Subgroup analysis was performed to assess model performance based on age, sex, race, ethnicity, and prior AAA repair. RESULTS: Overall, 16,282 patients were included. The primary outcome of 30-day major adverse cardiovascular event occurred in 390 (2.4%) patients. Our best-performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.95 (0.94-0.96) compared with logistic regression [0.72 [0.70-0.74)]. The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.06. Model performance remained robust on all subgroup analyses. CONCLUSIONS: Our newer ML models accurately predict 30-day outcomes following EVAR using preoperative data and perform better than logistic regression. Our automated algorithms can guide risk mitigation strategies for patients being considered for EVAR.

Using Machine Learning (XGBoost) to Predict Outcomes After Infrainguinal Bypass for Peripheral Artery Disease.

OBJECTIVE: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass. BACKGROUND: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited. METHODS: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores. RESULTS: Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative). CONCLUSIONS: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression.