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BackgroundDiagnostic neurovascular imaging data are important in stroke research, but obtaining these data typically requires laborious manual chart reviews. ObjectiveWe aimed to determine the accuracy of a natural language processing (NLP) approach to extract information on the presence and location of vascular occlusions as well as other stroke-related attributes based on free-text reports. MethodsFrom the full reports of 1320 consecutive computed tomography (CT), CT angiography, and CT perfusion scans of the head and neck performed at a tertiary stroke center between October 2017 and January 2019, we manually extracted data on the presence of proximal large vessel occlusion (primary outcome), as well as distal vessel occlusion, ischemia, hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status (secondary outcomes). Reports were randomly split into training (n=921) and validation (n=399) sets, and attributes were extracted using rule-based NLP. We reported the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the overall accuracy of the NLP approach relative to the manually extracted data. ResultsThe overall prevalence of large vessel occlusion was 12.2%. In the training sample, the NLP approach identified this attribute with an overall accuracy of 97.3% (95.5% sensitivity, 98.1% specificity, 84.1% PPV, and 99.4% NPV). In the validation set, the overall accuracy was 95.2% (90.0% sensitivity, 97.4% specificity, 76.3% PPV, and 98.5% NPV). The accuracy of identifying distal or basilar occlusion as well as hemorrhage was also high, but there were limitations in identifying cerebral ischemia, ASPECTS, and collateral status. ConclusionsNLP may improve the efficiency of large-scale imaging data collection for stroke surveillance and research.

BACKGROUND: Patient characteristics, clinical care, resource use and outcomes associated with admission to hospital for coronavirus disease 2019 (COVID-19) in Canada are not well described. METHODS: We described all adults with COVID-19 or influenza discharged from inpatient medical services and medical-surgical intensive care units (ICUs) between Nov. 1, 2019, and June 30, 2020, at 7 hospitals in Toronto and Mississauga, Ontario. We compared patient outcomes using multivariable regression models, controlling for patient sociodemographic factors and comorbidity level. We validated the accuracy of 7 externally developed risk scores to predict mortality among patients with COVID-19. RESULTS: There were 1027 hospital admissions with COVID-19 (median age 65 yr, 59.1% male) and 783 with influenza (median age 68 yr, 50.8% male). Patients younger than 50 years accounted for 21.2% of all admissions for COVID-19 and 24.0% of ICU admissions. Compared with influenza, patients with COVID-19 had significantly greater in-hospital mortality (unadjusted 19.9% v. 6.1%, adjusted relative risk [RR] 3.46, 95% confidence interval [CI] 2.56-4.68), ICU use (unadjusted 26.4% v. 18.0%, adjusted RR 1.50, 95% CI 1.25-1.80) and hospital length of stay (unadjusted median 8.7 d v. 4.8 d, adjusted rate ratio 1.45, 95% CI 1.25-1.69). Thirty-day readmission was not significantly different (unadjusted 9.3% v. 9.6%, adjusted RR 0.98, 95% CI 0.70-1.39). Three points-based risk scores for predicting in-hospital mortality showed good discrimination (area under the receiver operating characteristic curve [AUC] ranging from 0.72 to 0.81) and calibration. INTERPRETATION: During the first wave of the pandemic, admission to hospital for COVID-19 was associated with significantly greater mortality, ICU use and hospital length of stay than influenza. Simple risk scores can predict in-hospital mortality in patients with COVID-19 with good accuracy.

BackgroundHigh orphan drug prices have gained the attention of payers and policy makers. These prices may reflect the need to recoup the cost of drug development from a small patient pool. However, estimates of the cost of orphan drug development are sparse.MethodsUsing publicly available data, we estimated the differences in trial characteristics and clinical development costs with 100 orphan and 100 non-orphan drugs.ResultsWe found that the out-of-pocket clinical costs per approved orphan drug to be $166 million and $291 million (2013 USD) per non-orphan drug. The capitalized clinical costs per approved orphan drug and non-orphan drug were estimated to be $291 million and $412 million respectively. When focusing on new molecular entities only, we found that the capitalized clinical cost per approved orphan drug was half that of a non-orphan drug.ConclusionsMore discussion is needed to better align on which cost components should be included in research and development costs for pharmaceuticals.

Background: The implementation and clinical impact of machine learning–based early warning systems for patient deterioration in hospitals have not been well described. We sought to describe the implementation and evaluation of a multifaceted, real-time, machine learning–based early warning system for patient deterioration used in the general internal medicine (GIM) unit of an academic medical centre. Methods: In this nonrandomized, controlled study, we evaluated the association between the implementation of a machine learning–based early warning system and clinical outcomes. We used propensity score–based overlap weighting to compare patients in the GIM unit during the intervention period (Nov. 1, 2020, to June 1, 2022) to those admitted during the pre-intervention period (Nov. 1, 2016, to June 1, 2020). In a difference-indifferences analysis, we compared patients in the GIM unit with those in the cardiology, respirology, and nephrology units who did not receive the intervention. We retrospectively calculated system predictions for each patient in the control cohorts, although alerts were sent to clinicians only during the intervention period for patients in GIM. The primary outcome was non-palliative in-hospital death. Results: The study included 13 649 patient admissions in GIM and 8470 patient admissions in subspecialty units. Non-palliative deaths were significantly lower in the intervention period than the pre-intervention period among patients in GIM (1.6% v. 2.1%; adjusted relative risk [RR] 0.74, 95% confidence interval [CI] 0.55–1.00) but not in the subspecialty cohorts (1.9% v. 2.1%; adjusted RR 0.89, 95% CI 0.63–1.28). Among high-risk patients in GIM for whom the system triggered at least 1 alert, the proportion of non-palliative deaths was 7.1% in the intervention period, compared with 10.3% in the pre-intervention period (adjusted RR 0.69, 95% CI 0.46–1.02), with no meaningful difference in subspecialty cohorts (10.4% v. 10.6%; adjusted RR 0.98, 95% CI 0.60–1.59). In the difference-indifferences analysis, the adjusted relative risk reduction for non-palliative death in GIM was 0.79 (95% CI 0.50–1.24). Interpretation: Implementing a machine learning–based early warning system in the GIM unit was associated with lower risk of non-palliative death than in the pre-intervention period. Machine learning–based early warning systems are promising technologies for improving clinical outcomes. [ABSTRACT FROM AUTHOR]

IntroductionXylitol (or 'birch sugar') is a naturally occurring sugar with antibacterial properties that has been used as a natural non-sugar sweetener in chewing gums, confectionery, toothpaste and medicines. In this preventative randomised trial, xylitol will be tested for the prevention of acute otitis media (AOM), a common and costly condition in young children. The primary outcome will be the incidence of AOM. Secondary outcomes will include upper respiratory tract infections (URTIs) and dental caries.Methods and analysisThis study will be a pragmatic, blinded (participant and parents, practitioners and analyst), two-armed superiority, placebo-controlled randomised trial with 1:1 allocation, stratified by clinical site. The trial will be conducted in the 11 primary care group practices participating in the TARGet Kids! research network in Canada. Eligible participants between the ages of 2-4 years will be randomly assigned to the intervention arm of regular xylitol syrup use or the control arm of regular sorbitol use for 6 months. We expect to recruit 236 participants, per treatment arm, to detect a 20% relative risk reduction in AOM episodes. AOM will be identified through chart review. The secondary outcomes of URTIs and dental caries will be identified through monthly phone calls with specified questions.Ethics and disseminationEthics approval from the Research Ethics Boards at the Hospital for Sick Children and St. Michael's Hospital has been obtained for this study and also for the TARGet Kids! research network. Results will be submitted for publication to a peer-reviewed journal and will be discussed with decision makers.Trial registration numberNCT03055091; Pre-results.

BACKGROUND:Incremental cost-effectiveness ratios (ICERs) are used to assess the value for money of new drugs. Many believe that ICERs for drugs that treat rare diseases are much higher than those of common drugs. Our objective was to compare the proportion of ICERs that are cost effective for rare and common cancers. METHODS:We used the Tufts Medical Center Cost-Effectiveness Analysis (CEA) Registry to identify cost-effectiveness studies of pharmaceutical interventions for cancers. Studies that assessed FDA-approved 'orphan drugs' were categorized as assessing rare cancers. The proportion of common and rare cancer drugs that were cost effective at various ICER thresholds were compared along with study characteristics. Logistic regressions were conducted to assess important predictors of cost effectiveness. RESULTS:We identified 303 studies that reported 701 ICERs. Seventy nine percent (n = 240) of studies evaluated drugs for common cancers. At a threshold of US$50,000/QALY, 58% (n = 321) of ICERs for drugs treating common cancers and 64% (n = 94) of ICERs for drugs treating rare cancers were cost effective (p = 0.23). At US$100,000/QALY, 74% (n = 409) of ICERs for common cancers and 78% (n = 115) of ICERs for rare cancers were cost effective (p = 0.35). Results from the logistic regressions demonstrated that rarity was not a statistically significant predictor of cost effectiveness at both thresholds with publication year, study sponsorship, and cancer type as covariates. CONCLUSIONS:The proportion of ICERs that were cost effective at both thresholds does not appear to be significantly different between the two groups. Rarity is not statistically significantly associated with cost effectiveness, even when adjusted for important covariates.

ImportanceEpidemiological studies support a link between low 25-hydroxyvitamin D levels and a higher risk of viral upper respiratory tract infections. However, whether winter supplementation of vitamin D reduces the risk among children is unknown.ObjectiveTo determine whether high-dose vs standard-dose vitamin D supplementation reduces the incidence of wintertime upper respiratory tract infections in young children.Design, setting, and participantsA randomized clinical trial was conducted during the winter months between September 13, 2011, and June 30, 2015, among children aged 1 through 5 years enrolled in TARGet Kids!, a multisite primary care practice-based research network in Toronto, Ontario, Canada.InterventionsThree hundred forty-nine participants were randomized to receive 2000 IU/d of vitamin D oral supplementation (high-dose group) vs 354 participants who were randomized to receive 400 IU/d (standard-dose group) for a minimum of 4 months between September and May.Main outcome measuresThe primary outcome was the number of laboratory-confirmed viral upper respiratory tract infections based on parent-collected nasal swabs over the winter months. Secondary outcomes included the number of influenza infections, noninfluenza infections, parent-reported upper respiratory tract illnesses, time to first upper respiratory tract infection, and serum 25-hydroxyvitamin D levels at study termination.ResultsAmong 703 participants who were randomized (mean age, 2.7 years, 57.7% boys), 699 (99.4%) completed the trial. The mean number of laboratory-confirmed upper respiratory tract infections per child was 1.05 (95% CI, 0.91-1.19) for the high-dose group and 1.03 (95% CI, 0.90-1.16) for the standard-dose group, for a between-group difference of 0.02 (95% CI, -0.17 to 0.21) per child. There was no statistically significant difference in number of laboratory-confirmed infections between groups (incidence rate ratio [RR], 0.97; 95% CI, 0.80-1.16). There was also no significant difference in the median time to the first laboratory-confirmed infection: 3.95 months (95% CI, 3.02-5.95 months) for the high-dose group vs 3.29 months (95% CI, 2.66-4.14 months) for the standard-dose group, or number of parent-reported upper respiratory tract illnesses between groups (625 for high-dose vs 600 for standard-dose groups, incidence RR, 1.01; 95% CI, 0.88-1.16). At study termination, serum 25-hydroxyvitamin D levels were 48.7 ng/mL (95% CI, 46.9-50.5 ng/mL) in the high-dose group and 36.8 ng/mL (95% CI, 35.4-38.2 ng/mL) in the standard-dose group.Conclusions and relevanceAmong healthy children aged 1 to 5 years, daily administration of 2000 IU compared with 400 IU of vitamin D supplementation did not reduce overall wintertime upper respiratory tract infections. These findings do not support the routine use of high-dose vitamin D supplementation in children for the prevention of viral upper respiratory tract infections.Trial registrationclinicaltrials.gov Identifier: NCT01419262.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background and purposeProlonged ambulatory ECG monitoring after cryptogenic stroke improves detection of covert atrial fibrillation, but its long-term cost-effectiveness is uncertain.MethodsWe estimated the cost-effectiveness of noninvasive ECG monitoring in patients aged ≥55 years after a recent cryptogenic stroke and negative 24-hour ECG. A Markov model used observed rates of atrial fibrillation detection and anticoagulation from a randomized controlled trial (EMBRACE) and the published literature to predict lifetime costs and effectiveness (ischemic strokes, hemorrhages, life-years, and quality-adjusted life-years [QALYs]) for 30-day ECG (primary analysis) and 7-day or 14-day ECG (secondary analysis), when compared with a repeat 24-hour ECG.ResultsProlonged ECG monitoring (7, 14, or 30 days) was predicted to prevent more ischemic strokes, decrease mortality, and improve QALYs. If anticoagulation reduced stroke risk by 50%, 30-day ECG (at a cost of USD $447) would be highly cost-effective ($2000 per QALY gained) for patients with a 4.5% annual ischemic stroke recurrence risk. Cost-effectiveness was sensitive to stroke recurrence risk and anticoagulant effectiveness, which remain uncertain, especially at higher costs of monitoring. Shorter duration (7 or 14 days) monitoring was cost saving and more effective than an additional 24-hour ECG; its cost-effectiveness was less sensitive to changes in ischemic stroke risk and treatment effect.ConclusionsAfter a cryptogenic stroke, 30-day ECG monitoring is likely cost-effective for preventing recurrent strokes; 14-day monitoring is an attractive value alternative, especially for lower risk patients. These results strengthen emerging recommendations for prolonged ECG monitoring in secondary stroke prevention. Cost-effectiveness in practice will depend on careful patient selection.