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Introduction: Drone-delivered automated external defibrillators (AEDs) may reduce delays to defibrillation for out-of-hospital cardiac arrests (OHCAs). We sought to determine how integration of drones and selection of drone bases between emergency service stations (i.e., paramedic, fire, police) would affect 9-1-1 call-to-arrival intervals., Methods: We identified all treated OHCAs in southern Vancouver Island, British Columbia, Canada from Jan. 2014 to Dec. 2020. We developed mathematical models to select 1-5 optimal drone base locations from each of: paramedic stations, fire stations, police stations, or an unrestricted grid-based set of points to minimize drone travel time to OHCAs. We evaluated models on the estimated first response interval assuming that drones were integrated with existing OHCA response. We compared median response intervals with historical response, as well as across drone base locations., Results: A total of 1610 OHCAs were included in the study with a historical median response interval of 6.4 minutes (IQR 5.0-8.6). All drone-integrated response systems significantly reduced the median response interval to 4.2-5.4 minutes (all P < 0.001), with grid-based stations using 5 drones resulting in the lowest response interval (4.2 minutes). Median response times between drone base location types differed by 6-16 seconds, all comparisons of which were statistically significant (all P < 0.02)., Conclusion: Integrating drone-delivered AEDs into OHCA response may reduce first response intervals, even with a small quantity of drones. Implementing drone response with only one emergency service resulted in similar response metrics regardless of the emergency service hosting the drone base and was competitive with unrestricted drone base locations., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: The optimal locations and cost-effectiveness of placing automated external defibrillators (AEDs) for out-of-hospital cardiac arrest (OHCAs) in urban residential neighbourhoods are unclear., Methods: We used prospectively collected data from 2016 to 2018 from the British Columbia OHCA Registry to examine the utilization and cost-effectiveness of hypothetical AED deployment in municipalities with a population of over 100 000. We geo-plotted OHCA events using seven hypothetical deployment models where AEDs were placed at the exteriors of public schools and community centers and fetched by bystanders. We calculated the "radius of effectiveness" around each AED within which it could be retrieved and applied to an individual prior to EMS arrival, comparing automobile and pedestrian-based retrieval modes. For each deployment model, we estimated the number of OHCAs within the "radius of effectiveness"., Results: We included 4017 OHCAs from ten urban municipalities. The estimated radius of effectiveness around each AED was 625 m for automobile and 240 m for pedestrian retrieval. With AEDs placed outside each school and community center, 2567 (64%) and 605 (15%) of OHCAs fell within the radii of effectiveness for automobile and pedestrian retrieval, respectively. For each AED, there was an average of 1.20-2.66 and 0.25-0.61 in-range OHCAs per year for automobile retrieval and pedestrian retrieval, respectively, depending on the deployment model. All of our proposed surpassed the cost-effectiveness threshold of 0.125 OHCA/AED/year provided > 5.3-11.6% in-range AEDs were brought-to-scene., Conclusions: The systematic deployment of AEDs at schools and community centers in urban neighbourhoods may result in increased application and be a cost-effective public health intervention., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Disability-related considerations have largely been absent from the COVID-19 response, despite evidence that people with disabilities are at elevated risk for acquiring COVID-19. We evaluated clinical outcomes in patients who were admitted to hospital with COVID-19 with a disability compared with patients without a disability., Methods: We conducted a retrospective cohort study that included adults with COVID-19 who were admitted to hospital and discharged between Jan. 1, 2020, and Nov. 30, 2020, at 7 hospitals in Ontario, Canada. We compared in-hospital death, admission to the intensive care unit (ICU), hospital length of stay and unplanned 30-day readmission among patients with and without a physical disability, hearing or vision impairment, traumatic brain injury, or intellectual or developmental disability, overall and stratified by age (≤ 64 and ≥ 65 yr) using multivariable regression, controlling for sex, residence in a long-term care facility and comorbidity., Results: Among 1279 admissions to hospital for COVID-19, 22.3% had a disability. We found that patients with a disability were more likely to die than those without a disability (28.1% v. 17.6%), had longer hospital stays (median 13.9 v. 7.8 d) and more readmissions (17.6% v. 7.9%), but had lower ICU admission rates (22.5% v. 28.3%). After adjustment, there were no statistically significant differences between those with and without disabilities for in-hospital death or admission to ICU. After adjustment, patients with a disability had longer hospital stays (rate ratio 1.36, 95% confidence interval [CI] 1.19-1.56) and greater risk of readmission (relative risk 1.77, 95% CI 1.14-2.75). In age-stratified analyses, we observed longer hospital stays among patients with a disability than in those without, in both younger and older subgroups; readmission risk was driven by younger patients with a disability., Interpretation: Patients with a disability who were admitted to hospital with COVID-19 had longer stays and elevated readmission risk than those without disabilities. Disability-related needs should be addressed to support these patients in hospital and after discharge., Competing Interests: Competing interests: Amol Verma has received personal fees from Ontario Health, and personal grant funding from AMS Healthcare (Compassion and AI Fellowship) and St. Michael’s Hospital. Michael Fralick is a consultant for Proof Diagnostics, a company that has created a point-of-care testing device using CRISPR for COVID-19. He has also received grants from the Canadian Institutes of Health Research (CIHR) and support from grants from the Canadian Military for clinical trials to identify treatments for COVID-19. Fahad Razak has received personal fees from Ontario Health and is a member of the Ontario COVID-19 Science Advisory Table. Yona Lunsky has received a grant from CIHR for research on disability and health during COVID-19. She is a member of the Board of Directors for Special Olympics Canada. Margaret Herridge has received grants from CIHR. No other competing interests were declared., (© 2022 CMA Impact Inc. or its licensors.)

Background: Although several Utstein variables are known to independently improve survival, how they moderate the effect of emergency medical service (EMS) response times on survival is unknown., Objectives: To quantify how public location, witnessed status, bystander CPR, and bystander AED shock individually and jointly moderate the effect of EMS response time delays on OHCA survival., Methods: This retrospective cohort study was a secondary analysis of the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database (December 2005 to June 2015). We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCAs from eleven sites across the US and Canada. We trained a logistic regression model with standard Utstein control variables and interaction terms between EMS response time and the four aforementioned OHCA characteristics., Results: 102,216 patients were included. Three of the four characteristics - witnessed OHCAs (OR = 0.962), bystander CPR (OR = 0.968) and public location (OR = 0.980) - increased the negative effect of a one-minute delay on the odds of survival. In contrast, a bystander AED shock decreased the negative effect of a one-minute response time delay on the odds of survival (OR = 1.064). The magnitude of the effect of a one-minute delay in EMS response time on the odds of survival ranged from 1.3% to 9.8% (average: 5.3%), depending on the underlying OHCA characteristics., Conclusions: Delays in EMS response time had the largest reduction in survival odds for OHCAs that did not receive a bystander AED shock but were witnessed, occurred in public, and/or received bystander CPR. A bystander AED shock appears to be protective against a delay in EMS response time., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Aim: Quantifying the ratio describing the difference between "true route" and "straight-line" distances from out-of-hospital cardiac arrests (OHCAs) to the closest accessible automated external defibrillator (AED) can help correct likely overestimations in AED coverage. Furthermore, we aimed to examine to what extent the closest AED based on true route distance differed from the closest AED using "straight-line"., Methods: OHCAs (1994-2016) and AEDs (2016) in Copenhagen, Denmark and in Toronto, Canada (2007-2015 and 2015, respectively) were identified. Three distances were calculated between OHCA and target AED: 1) the straight-line distance ("straight-line") to the closest AED, 2) the corresponding true route distance to the same AED ("true route"), and 3) the closest AED based only on true route distance ("shortest true route"). The ratio between "true route" and "straight-line" distance was calculated and differences in AED coverage (an OHCA ≤ 100 m of an accessible AED) were examined., Results: The "straight-line" AED coverage of 100 m was 24.2% (n = 2008/8295) in Copenhagen and 6.9% (n = 964/13916) in Toronto. The corresponding "true route" distance reduced coverage to 9.5% (n = 786) and 3.8% (n = 529), respectively. The median ratio between "true route" and "straight-line" distance was 1.6 in Copenhagen and 1.4 in Toronto. In 26.1% (n = 2167) and 22.9% (n = 3181) of all Copenhagen and Toronto OHCAs respectively, the closest AED in "shortest true route" was different than the closest AED initially found by "straight-line"., Conclusions: Straight-line distance is not an accurate measure of distance and overestimates the actual AED coverage compared to a more realistic true route distance by a factor 1.4-1.6., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Purpose: To advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research., Methods: We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three-dimensional (3D) dose distributions, and (b) dose-volume histogram (DVH) score, which evaluates a set DVH metrics. We used these scores to quantify the quality of the models based on their out-of-sample predictions. To develop and test their models, participants were given the data of 340 patients who were treated for head-and-neck cancer with radiation therapy. The data were partitioned into training ( n = 200 ), validation ( n = 40 ), and testing ( n = 100 ) datasets. All participants performed training and validation with the corresponding datasets during the first (validation) phase of the Challenge. In the second (testing) phase, the participants used their model on the testing data to quantify the out-of-sample performance, which was hidden from participants and used to determine the final competition ranking. Participants also responded to a survey to summarize their models., Results: The Challenge attracted 195 participants from 28 countries, and 73 of those participants formed 44 teams in the validation phase, which received a total of 1750 submissions. The testing phase garnered submissions from 28 of those teams, which represents 28 unique prediction methods. On average, over the course of the validation phase, participants improved the dose and DVH scores of their models by a factor of 2.7 and 5.7, respectively. In the testing phase one model achieved the best dose score (2.429) and DVH score (1.478), which were both significantly better than the dose score (2.564) and the DVH score (1.529) that was achieved by the runner-up models. Lastly, many of the top performing teams reported that they used generalizable techniques (e.g., ensembles) to achieve higher performance than their competition., Conclusion: OpenKBP is the first competition for knowledge-based planning research. The Challenge helped launch the first platform that enables researchers to compare KBP prediction methods fairly and consistently using a large open-source dataset and standardized metrics. OpenKBP has also democratized KBP research by making it accessible to everyone, which should help accelerate the progress of KBP research. The OpenKBP datasets are available publicly to help benchmark future KBP research., (© 2021 American Association of Physicists in Medicine.)

Background: Mathematical optimization can be used to place automated external defibrillators (AEDs) in locations that maximize coverage of out-of-hospital cardiac arrests (OHCAs). We sought to determine whether optimization can improve alignment between AED locations and OHCA counts across levels of socioeconomic deprivation., Methods: All suspected OHCAs and registered AEDs in Scotland between Jan. 2011 and Sept. 2017 were included and mapped to a corresponding socioeconomic deprivation level using the Scottish Index of Multiple Deprivation (SIMD). We used mathematical optimization to determine optimal locations for placing 10%, 25%, 50%, and 100% additional AEDs, as well as locations for relocating existing AEDs. For each AED placement policy, we examined the impact on AED distribution and OHCA "coverage" (suspected OHCA occurring within 100 m of AED) with respect to SIMD quintiles., Results: We identified 49,432 suspected OHCAs and 1532 AEDs. The distribution of existing AED locations across SIMD quintiles significantly differed from the distribution of suspected OHCAs (P < 0.001). Optimization-guided AED placement increased coverage of suspected OHCAs compared to existing AED locations (all P < 0.001). Optimization resulted in more AED placements and increased OHCA coverage in areas of greater socioeconomic deprivation, such that resulting distributions across SIMD quintiles matched the shape of the OHCA count distribution. Optimally relocating existing AEDs achieved similar OHCA coverage levels to that of doubling the number of total AEDs., Conclusions: Mathematical optimization results in AED locations and suspected OHCA coverage that more closely resembles the suspected OHCA distribution and results in more equitable coverage across levels of socioeconomic deprivation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

To evaluate the repeatability and agreement of corneal and biometry measurements obtained with two swept-source optical coherence tomography (SSOCT) and a partial coherence interferometry-based device. This is a cross-sectional study. Forty-eight eyes of 48 patients had three consecutive measurements for ANTERION (Heidelberg Engineering, Germany), CASIAII (Tomey, Japan) and IOLMaster500 (Carl Zeiss Meditec, USA) on the same visit. Mean keratometry (Km), central corneal thickness (CCT), anterior chamber depth (ACD) and axial length (AL) were recorded. Corneal astigmatic measurements were converted into vector components-J0 and J45. Intra-device repeatability and agreements of measurements amongst the devices were evaluated using repeatability coefficients (RCs) and Bland-Altman plots, respectively. All devices demonstrated comparable repeatability for Km (p ≥ 0.138). ANTERION had the lowest RC for J0 amongst the devices (p ≤ 0.039). Systematic difference was found for the Km and J0 obtained with IOLMaster500 compared to either SSOCTs (p ≤ 0.010). The ACD and AL measured by IOLMaster500 showed a higher RC compared with either SSOCTs (p < 0.002). Systematic difference was found in CCT and ACD between the two SSOCTs (p < 0.001), and in AL between ANTERION and IOLMaster500 (p < 0.001), with a mean difference of 1.6 µm, 0.022 mm and 0.021 mm, respectively. Both SSOCTs demonstrated smaller test-retest variability for measuring ACD and AL compared with IOLMaster500. There were significant disagreement in keratometry and AL measurements between the SSOCTs and PCI-based device; their measurements should not be considered as interchangeable., (© 2021. The Author(s).)

Background: Donor milk is the standard of care for hospitalized very low birth weight (VLBW) infants when mother's milk is unavailable; however, growth of donor milk-fed infants is frequently suboptimal. Variability in nutrient composition of donated milk complicates the production of a uniform pooled product and, subsequently, the provision of adequate nutrition to promote optimal growth and development of VLBW infants. We reasoned a machine learning approach to construct batches using characteristics of the milk donation might be an effective strategy in reducing the variability in donor milk product composition., Objective: The objective of this study was to identify whether machine learning models can accurately predict donor milk macronutrient content. We focused on predicting fat and protein, given their well-established importance in VLBW infant growth outcomes., Methods: Samples of donor milk, consisting of 272 individual donations and 61 pool samples, were collected from the Rogers Hixon Ontario Human Milk Bank and analyzed for macronutrient content. Four different machine learning models were constructed using independent variable groups associated with donations, donors, and donor-pumping practices. A baseline model was established using lactation stage and infant gestational status. Predictions were made for individual donations and resultant pools., Results: Machine learning models predicted protein of individual donations and pools with a mean absolute error (MAE) of 0.16 g/dL and 0.10 g/dL, respectively. Individual donation and pooled fat predictions had an MAE of 0.91 g/dL and 0.42 g/dL, respectively. At both the individual donation and pool levels, protein predictions were significantly more accurate than baseline, whereas fat predictions were competitive with baseline., Conclusions: Machine learning models can provide accurate predictions of macronutrient content in donor milk. The macronutrient content of pooled milk had a lower prediction error, reinforcing the value of pooling practices. Future research should examine how macronutrient content predictions can be used to facilitate milk bank pooling strategies., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

We identified biologically relevant moderators of response to tumor necrosis factor (TNF)-α inhibitor, infliximab, among 60 individuals with bipolar depression. Data were derived from a 12-week, randomized, placebo-controlled clinical trial secondarily evaluating the efficacy of infliximab on a measure of anhedonia (i.e., Snaith-Hamilton Pleasure Scale). Three inflammatory biotypes were derived from peripheral cytokine measurements using an iterative, machine learning-based approach. Infliximab-randomized participants classified as biotype 3 exhibited lower baseline concentrations of pro- and anti-inflammatory cytokines and soluble TNF receptor-1 and reported greater pro-hedonic improvements, relative to those classified as biotype 1 or 2. Pretreatment biotypes also moderated changes in neuroinflammatory substrates relevant to infliximab's hypothesized mechanism of action. Neuronal origin-enriched extracellular vesicle (NEV) protein concentrations were reduced to two factors using principal axis factoring: phosphorylated nuclear factorκB (p-NFκB), Fas-associated death domain (p-FADD), and IκB kinase (p-IKKα/β) and TNF receptor-1 (TNFR1) comprised factor "NEV1," whereas phosphorylated insulin receptor substrate-1 (p-IRS1), p38 mitogen-activated protein kinase (p-p38), and c-Jun N-terminal kinase (p-JNK) constituted "NEV2". Among infliximab-randomized subjects classified as biotype 3, NEV1 scores were decreased at weeks 2 and 6 and increased at week 12, relative to baseline, and NEV2 scores increased over time. Decreases in NEV1 scores and increases in NEV2 scores were associated with greater reductions in anhedonic symptoms in our classification and regression tree model (r 2  = 0.22, RMSE = 0.08). Our findings provide preliminary evidence supporting the hypothesis that the pro-hedonic effects of infliximab require modulation of multiple TNF-α signaling pathways, including NF-κB, IRS1, and MAPK., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited part of Springer Nature.)

Objectives: Hearing loss is the most common sensory loss in humans and carries an enhanced risk of depression. No prior studies have attempted a contemporary machine learning approach to predict depression using subjective and objective hearing loss predictors. The objective was to deploy supervised machine learning to predict scores on a validated depression scale using subjective and objective audiometric variables and other health determinant predictors., Design: A large predictor set of health determinants from the National Health and Nutrition Examination Survey 2015-2016 database was used to predict adults' scores on a validated instrument to screen for the presence and severity of depression (Patient Health Questionnaire-9 [PHQ-9]). After model training, the relative influence of individual predictors on depression scores was stratified and analyzed. Model prediction performance was determined by prediction error metrics., Results: The test set mean absolute error was 3.03 (95% confidence interval: 2.91 to 3.14) and 2.55 (95% confidence interval: 2.48 to 2.62) on datasets with audiology-only predictors and all predictors, respectively, on the PHQ-9's 27-point scale. Participants' self-reported frustration when talking to members of family or friends due to hearing loss was the fifth-most influential of all predictors. Of the top 10 most influential audiometric predictors, five were related to social contexts, two for significant noise exposure, two objective audiometric parameters, and one presence of bothersome tinnitus., Conclusions: Machine learning algorithms can accurately predict PHQ-9 depression scale scores from National Health and Nutrition Examination Survey data. The most influential audiometric predictors of higher scores on a validated depression scale were social dynamics of hearing loss and not objective audiometric testing. Such models could be useful in predicting depression scale scores at the point-of-care in conjunction with a standard audiologic assessment., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Competing Interests: Intérêts concurrents: Amol Verma et Fahad Razak déclarent des honoraires personnels de Santé Ontario. Michael Fralick déclare des honoraires personnels de Pine Trees, en dehors du travail soumis. Aucun autre intérêt concurrent n’a été déclaré.

Background: Drone-delivered defibrillators have the potential to significantly reduce response time for out-of-hospital cardiac arrest (OHCA). However, optimal policies for the dispatch of such drones are not yet known. We sought to develop dispatch rules for a network of defibrillator-carrying drones., Methods: We identified all suspected OHCAs in Peel Region, Ontario, Canada from Jan. 2015 to Dec. 2019. We developed drone dispatch rules based on the difference between a predicted ambulance response time to a calculated drone response time for each OHCA. Ambulance response times were predicted using linear regression and neural network models, while drone response times were calculated using drone specifications from recent pilot studies and the literature. We evaluated the dispatch rules based on response time performance and dispatch decisions, comparing them to two baseline policies of never dispatching and always dispatching drones., Results: A total of 3573 suspected OHCAs were included in the study with median and mean historical ambulance response times of 5.8 and 6.2 min. All machine learning-based dispatch rules significantly reduced the median response time to 3.9 min and mean response time to 4.1-4.2 min (all P < 0.001) and were non-inferior to universally dispatching drones (all P < 0.001) while reducing the number of drone flights by up to 30%. Dispatch rules with more drone flights achieved higher sensitivity but lower specificity and accuracy., Conclusion: Machine learning-based dispatch rules for drone-delivered defibrillators can achieve similar response time reductions as universal drone dispatch while substantially reducing the number of trips., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Introduction: The COVID-19 pandemic has led to closures of non-essential businesses and buildings. The impact of such closures on automated external defibrillator (AED) accessibility compared to changes in foot traffic levels is unknown., Methods: We identified all publicly available online AED registries in Canada last updated May 1, 2019 or later. We mapped AED locations to location types and classified each location type as completely inaccessible, partially inaccessible, or unaffected based on government-issued closure orders as of May 1, 2020. Using location and mobility data from Google's COVID-19 Community Mobility Reports, we identified the change in foot traffic levels between February 15-May 1, 2020 (excluding April 10-12) compared to the baseline of January 3-February 1, 2020, and determined the discrepancy between foot traffic levels and AED accessibility., Results: We identified four provincial and two municipal AED registries containing a total of 5848 AEDs. Of those, we estimated that 69.9% were completely inaccessible, 18.8% were partially inaccessible, and 11.3% were unaffected. Parks, retail and recreation locations, and workplaces experienced the greatest reduction in AED accessibility. The greatest discrepancies between foot traffic levels and AED accessibility occurred in parks, retail and recreation locations, and transit stations., Conclusion: A majority of AEDs became inaccessible during the COVID-19 pandemic due to government-mandated closures. In a substantial number of locations across Canada, the reduction in AED accessibility was far greater than the reduction in foot traffic., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid-base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patient's transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO 2 respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.

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., Competing Interests: Competing interests: Amol Verma and Fahad Razak report personal fees from Ontario Health. Michael Fralick reports personal fees from Pine Trees, outside the submitted work. No other competing interests were declared., (© 2021 Joule Inc. or its licensors.)

Background: The impending scale up of noncommunicable disease screening programs in low- and middle-income countries coupled with limited health resources require that such programs be as accurate as possible at identifying patients at high risk., Objective: The aim of this study was to develop machine learning-based risk stratification algorithms for diabetes and hypertension that are tailored for the at-risk population served by community-based screening programs in low-resource settings., Methods: We trained and tested our models by using data from 2278 patients collected by community health workers through door-to-door and camp-based screenings in the urban slums of Hyderabad, India between July 14, 2015 and April 21, 2018. We determined the best models for predicting short-term (2-month) risk of diabetes and hypertension (a model for diabetes and a model for hypertension) and compared these models to previously developed risk scores from the United States and the United Kingdom by using prediction accuracy as characterized by the area under the receiver operating characteristic curve (AUC) and the number of false negatives., Results: We found that models based on random forest had the highest prediction accuracy for both diseases and were able to outperform the US and UK risk scores in terms of AUC by 35.5% for diabetes (improvement of 0.239 from 0.671 to 0.910) and 13.5% for hypertension (improvement of 0.094 from 0.698 to 0.792). For a fixed screening specificity of 0.9, the random forest model was able to reduce the expected number of false negatives by 620 patients per 1000 screenings for diabetes and 220 patients per 1000 screenings for hypertension. This improvement reduces the cost of incorrect risk stratification by US $1.99 (or 35%) per screening for diabetes and US $1.60 (or 21%) per screening for hypertension., Conclusions: In the next decade, health systems in many countries are planning to spend significant resources on noncommunicable disease screening programs and our study demonstrates that machine learning models can be leveraged by these programs to effectively utilize limited resources by improving risk stratification., (©Justin J Boutilier, Timothy C Y Chan, Manish Ranjan, Sarang Deo. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 21.01.2021.)

Importance: Medical device performance and safety databases can be analyzed for patterns and novel opportunities for improving patient safety and/or device design. Objective: The objective of this analysis was to use supervised machine learning to explore patterns in reported adverse events involving cochlear implants. Design: Adverse event reports for the top three CI manufacturers were acquired for the analysis. Four supervised machine learning algorithms were used to predict which adverse event description pattern corresponded with a specific cochlear implant manufacturer and adverse event type. Setting: U.S. government public database. Participants: Adult and pediatric cochlear patients. Exposure: Surgical placement of a cochlear implant. Main Outcome Measure: Classification prediction accuracy (% correct predictions). Results: Most adverse events involved patient injury ( n  = 16,736), followed by device malfunction ( n  = 10,760), and death ( n  = 16). The random forest, linear SVC, naïve Bayes and logistic algorithms were able to predict the specific CI manufacturer based on the adverse event narrative with an average accuracy of 74.8%, 86.0%, 88.5% and 88.6%, respectively. Conclusions & relevance: Using supervised machine learning algorithms, our classification models were able to predict the CI manufacturer and event type with high accuracy based on patterns in adverse event text descriptions.

Purpose: To investigate the long-term stability of corneal astigmatism after combined femtosecond (fs)-assisted phacoemulsification and arcuate keratotomy., Design: Retrospective, interventional case series., Methods: Surgery was performed using a Victus (Bausch & Lomb) platform. A single, 450-μm-deep arcuate keratotomy was paired at the 8-mm zone with the main phacoemulsification incision in the opposite meridian. The keratotomy incisions were not opened. Corneal astigmatism measurements obtained preoperatively and at 2 and 5 years postoperatively were analyzed using vector analysis., Results: A total of 44 eyes of 44 patients (mean age 66.0 ± 10.1 years) were included. The mean preoperative corneal astigmatism was 1.40 ± 0.66 diopters (D). This was reduced to 0.74 ± 0.54 D at 2 years and 0.70 ± 0.50 at 5 years postoperatively (P < .001). There were no statistically significant differences between postoperative corneal astigmatism at 2 years and at 5 years (P = .609). Both magnitude of error and absolute angle of error were comparable between the 2 postoperative time points (P > .805). At the end of 5 years, 65% of the eyes were within 15 degrees of the preoperative astigmatic meridian. Comparative analysis showed significantly higher surgically induced astigmatism, lower differences in vector and absolute angles of error for the eyes with preoperative with-the-rule (WTR) astigmatism than eyes with against-the-rule (ATR) astigmatism at 5 years (P < .004)., Conclusions: Our study showed the stability of femtosecond (fs)-assisted arcuate keratotomy was well-maintained over 5 years. There was a tendency of increasing overcorrection of preoperative WTR astigmatism and undercorrection of ATR astigmatism over time., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objectives: To predict postoperative cochlear implant performance with heterogeneous text and numerical variables using supervised machine learning techniques., Study Design: A supervised machine learning approach comprising neural networks and decision tree-based ensemble algorithms were used to predict 1-year postoperative cochlear implant performance based on retrospective data., Setting: Tertiary referral center., Patients: One thousand six hundred four adults who received one cochlear implant from 1989 to 2019. Two hundred eighty two text and numerical objective demographic, audiometric, and patient-reported outcome survey instrument variables were included., Outcome Measures: Outcomes for postoperative cochlear implant performance were discrete Hearing in Noise Test (HINT; %) performance and binned HINT performance classification ("High," "Mid," and "Low" performers). Algorithm performance was assessed using hold-out validation datasets and were compared using root mean square error (RMSE) in the units of the target variable and classification accuracy., Results: The neural network 1-year HINT prediction RMSE and classification accuracy were 0.57 and 95.4%, respectively, with only numerical variable inputs. Using both text and numerical variables, neural networks predicted postoperative HINT with a RMSE of 25.0%, and classification accuracy of 73.3%. When applied to numerical variables only, the XGBoost algorithm produced a 1-year HINT score prediction performance RMSE of 25.3%. We identified over 20 influential variables including preoperative sentence-test performance, age at surgery, as well as specific tinnitus handicap inventory (THI), Short Form 36 (SF-36), and health utilities index (HUI) question responses as the highest influencers of postoperative HINT., Conclusion: Our results suggest that supervised machine learning can predict postoperative cochlear implant performance and identify preoperative factors that significantly influence that performance. These algorithms can help improve the understanding of the diverse factors that impact functional performance from heterogeneous data sources.

Background Mathematical optimization of automated external defibrillator (AED) placement may improve AED accessibility and out-of-hospital cardiac arrest (OHCA) outcomes compared with American Heart Association (AHA) and European Resuscitation Council (ERC) placement guidelines. We conducted an in silico trial (simulated prospective cohort study) comparing mathematically optimized placements with placements derived from current AHA and ERC guidelines, which recommend placement in locations where OHCAs are usually witnessed. Methods and Results We identified all public OHCAs of presumed cardiac cause from 2008 to 2016 in Copenhagen, Denmark. For the control, we computationally simulated placing 24/7-accessible AEDs at every unique, public, witnessed OHCA location at monthly intervals over the study period. The intervention consisted of an equal number of simulated AEDs placements, deployed monthly, at mathematically optimized locations, using a model that analyzed historical OHCAs before that month. For each approach, we calculated the number of OHCAs in the study period that occurred within a 100-m route distance based on Copenhagen's road network of an available AED after it was placed ("OHCA coverage"). Estimated impact on bystander defibrillation and 30-day survival was calculated by multivariate logistic regression. The control scenario involved 393 AEDs at historical, public, witnessed OHCA locations, covering 15.8% of the 653 public OHCAs from 2008 to 2016. The optimized locations provided significantly higher coverage (24.2%; P <0.001). Estimated bystander defibrillation and 30-day survival rates increased from 15.6% to 18.2% ( P <0.05) and from 32.6% to 34.0% ( P <0.05), respectively. As a baseline, the 1573 real AEDs in Copenhagen covered 14.4% of the OHCAs. Conclusions Mathematical optimization can significantly improve OHCA coverage and estimated clinical outcomes compared with a guidelines-based approach to AED placement.

Background: The coronavirus disease 2019 (COVID-19) outbreak increases the importance of strategies to enhance urgent medical care delivery in long-term care (LTC) facilities that could potentially reduce transfers to emergency departments. The study objective was to model resource requirements to deliver virtual urgent medical care in LTC facilities., Methods: We used data from all general medicine inpatient admissions at 7 hospitals in the Greater Toronto Area, Ontario, Canada, over a 7.5-year period (Apr. 1, 2010, to Oct. 31, 2017) to estimate historical patterns of hospital resource use by LTC residents. We estimated an upper bound of potentially avoidable transfers by combining data on short admissions (≤ 72 h) with historical data on the proportion of transfers from LTC facilities for which patients were discharged from the emergency department without admission. Regression models were used to extrapolate future resource requirements, and queuing models were used to estimate physician staffing requirements to perform virtual assessments., Results: There were 235 375 admissions to general medicine wards, and residents of LTC facilities (age 16 yr or older) accounted for 9.3% ( n = 21 948) of these admissions. Among the admissions of residents of LTC facilities, short admissions constituted 24.1% ( n = 5297), and for 99.8% ( n = 5284) of these admissions, the patient received laboratory testing, for 86.9% ( n = 4604) the patient received plain radiography, for 41.5% ( n = 2197) the patient received computed tomography and for 81.2% ( n = 4300) the patient received intravenous medications. If all patients who have short admissions and are transferred from the emergency department were diverted to outpatient care, the average weekly demand for outpatient imaging per hospital would be 2.6 ultrasounds, 11.9 computed tomographic scans and 23.9 radiographs per week. The average daily volume of urgent medical virtual assessments would range from 2.0 to 5.8 per hospital. A single centralized virtual assessment centre staffed by 2 or 3 physicians would provide services similar in efficiency (measured by waiting time for physician assessment) to 7 separate centres staffed by 1 physician each., Interpretation: The provision of acute medical care to LTC residents at their facility would probably require rapid access to outpatient diagnostic imaging, within-facility access to laboratory services and intravenous medication and virtual consultations with physicians. The results of this study can inform efforts to deliver urgent medical care in LTC facilities in light of a potential surge in COVID-19 cases., Competing Interests: Competing interests: None declared., (Copyright 2020, Joule Inc. or its licensors.)

Hearing loss is the leading human sensory system loss, and one of the leading causes for years lived with disability with significant effects on quality of life, social isolation, and overall health. Coupled with a forecast of increased hearing loss burden worldwide, national and international health organizations have urgently recommended that access to hearing evaluation be expanded to meet demand. The objective of this study was to develop 'AutoAudio' - a novel deep learning proof-of-concept model that accurately and quickly interprets diagnostic audiograms. Adult audiogram reports representing normal, conductive, mixed and sensorineural morphologies were used to train different neural network architectures. Image augmentation techniques were used to increase the training image set size. Classification accuracy on a separate test set was used to assess model performance. The architecture with the highest out-of-training set accuracy was ResNet-101 at 97.5%. Neural network training time varied between 2 to 7 h depending on the depth of the neural network architecture. Each neural network architecture produced misclassifications that arose from failures of the model to correctly label the audiogram with the appropriate hearing loss type. The most commonly misclassified hearing loss type were mixed losses. Re-engineering the process of hearing testing with a machine learning innovation may help enhance access to the growing worldwide population that is expected to require audiologist services. Our results suggest that deep learning may be a transformative technology that enables automatic and accurate audiogram interpretation.

Background Time to treatment is critical for survival from sudden cardiac arrest. Every minute delay in defibrillation results in a 7% to 10% reduction in survival. This is particularly problematic in rural and remote regions, where emergency medical service response is prolonged and automated external defibrillators (AEDs) are often not available. Our primary objective was to examine the feasibility of a novel AED drone delivery method for rural and remote sudden cardiac arrest. A secondary objective was to compare response times between AED drone delivery and ambulance to mock sudden cardiac arrest resuscitations. Methods and Results We conducted 6 simulations in 2 rural communities in southern Ontario, Canada. In the first 2 simulations, the drone and ambulance were dispatched from the same paramedic base. In simulations 3 and 4, the drone and ambulance were dispatched from separate paramedic bases; and in simulations 5 and 6, the drone was dispatched from an optimized location. During each simulation, a "mock" call was placed to 911 and a single AED drone and an ambulance were simultaneously dispatched to a predetermined destination. On scene, trained first responders retrieved the AED from the drone and initiated resuscitative efforts on a mannequin until paramedics arrived. No difficulties were encountered during drone activation by dispatch, ascent, landing, or bystander retrieval of the AED from the drone. During simulations 1 and 2, the distance to the scene was 6.6 km. For simulations 3 and 4, the ambulance response distance increased to 8.8 km while drone remained at 6.6 km; and in simulations 5 and 6, the ambulance response distance was 20 km compared with 9 km for the drone. During each flight, the AED drone arrived on scene before the ambulance, between 1.8 and 8.0 minutes faster. Conclusions This study suggests AED drone delivery is feasible, with the potential for improvements in response time during simulated sudden cardiac arrest scenarios. Further research is required to determine the appropriate system configuration for AED drone delivery in an integrated emergency medical service system as well as optimal strategies to simplify bystander application of a drone-delivered AED.

Aims: To determine if mathematical optimization of in-hospital defibrillator placements can reduce in-hospital cardiac arrest-to-defibrillator distance compared to existing defibrillators in a single hospital., Methods: We identified treated IHCAs and defibrillator placements in St. Michael's Hospital in Toronto, Canada from Jan. 2013 to Jun. 2017 and mapped them to a 3-D computer model of the hospital. An optimization model identified an equal number of optimal defibrillator locations that minimized the average distance between IHCAs and the closest defibrillator using a 10-fold cross-validation approach. The optimized and existing defibrillator locations were compared in terms of average distance to the out-of-sample IHCAs. We repeated the analysis excluding intensive care units (ICUs), operating theatres (OTs), and the emergency department (ED). We also re-solved the model using fewer defibrillators to determine when the average distance matched the performance of existing defibrillators., Results: We identified 433 treated IHCAs and 53 defibrillators. Of these, 167 IHCAs and 31 defibrillators were outside of ICUs, OTs, and the ED. Optimal defibrillator placements reduced the average IHCA-to-defibrillator distance from 16.1 m to 2.7 m (relative decrease of 83.0%; P = 0.002) compared to existing defibrillator placements. For non-ICU/OT/ED IHCAs, the average distance was reduced from 24.4 m to 11.9 m (relative decrease of 51.3%; P = 0.002. 8-9 optimized defibrillator locations were sufficient to match the average IHCA-to-defibrillator distance of existing defibrillator placements., Conclusions: Optimization-guided placement of in-hospital defibrillators can reduce the distance from an IHCA to the closest defibrillator. Equivalently, optimization can match existing defibrillator performance using far fewer defibrillators., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Synopsis: This 1-year prospective study demonstrated that the Tenon's layer reposition approach of trabeculectomy could achieve zero leakage rate, minimal rate of transient hypotony without compromising the surgical outcome., Purpose: The purpose of this study was to investigate the effectiveness and safety of a Tenon's layer reposition approach of trabeculectomy., Methods: A prospective, noncomparative case series of 30 eyes of 30 Chinese patients with mixed types of glaucoma who underwent fornix-based trabeculectomy combined with intraoperative mitomycin C application. During the conjunctival flap closure, the Tenons' layer was identified, separated, and anchored on to the sclera surface with 8/0 vicryl, followed by conjunctival closure with 10/0 nylon as a separate layer. All patients were followed up for 1 year. Assessment including intraocular pressure (IOP), vertical cup-disc ratio measurement, best-corrected visual acuity, and visual field examination were performed before and after the operation. Qualified and complete success was defined as IOP of ≤21 mm Hg in 2 consecutive visits with or without medication, respectively. Outcomes were evaluated using scattered plot and Kaplan-Meier survival curve., Results: Twenty-one eyes (70%) and 28 eyes (93.3%) achieved complete and partial success at 1 year, respectively. There was a significant reduction of IOP (28.5±9.6 to 15.5±2.6 mm Hg, P<0.001) and medication use (4.4±0.9 to 0.8±1.2 bottles/eye, P<0.001). There were no significant changes in best-corrected visual acuity, vertical cup-disc ratio, and visual field indices. No wound leak was identified throughout the study. The procedure did not induce significant astigmatic change. Other postoperative complications, including 2 eyes (6.7%) with transient hypotony and 1 eye (3.3%) required cataract surgery, were of relatively low rate., Conclusion: The Tenon's layer reposition approach of performing trabeculectomy is a safe and efficacious procedure for Chinese subjects with different types of glaucoma.

Purpose: To compare higher-order aberrations (HOAs) after small incision lenticule extraction (SMILE) in patients with and without intraoperative angle kappa adjustments., Methods: This is a retrospective case series. One hundred six eyes of 106 patients who underwent SMILE at Tianjin Eye Hospital (Tianjin Medical University, Tianjin, China) for correction of myopia and myopic astigmatism were divided into 2 groups. The first group consisted of eyes with intraoperative angle kappa adjustment and the second group consisted of eyes without adjustment. Preoperative and postoperative visual outcome, refraction, and HOA measurements at 1 and 3 months were compared., Results: At the pupil size of 6 mm, vertical coma at 1 and 3 months after SMILE for the angle kappa-adjusted group was 0.153 ± 0.107 and 0.157 ± 0.094 μm, which were significantly lower than those of the nonadjusted group (0.204 ± 0.117 and 0.203 ± 0.113 μm, respectively) (P = 0.026 at 1 mo, P = 0.047 at 3 mo). The change in vertical coma between preoperative and postoperative measurements was 0.011 ± 0.136 and 0.023 ± 0.129 μm at 1 and 3 months postoperatively for the angle kappa-adjusted group, which were lower than those of the nonadjusted group (0.082 ± 0.165 and 0.085 ± 0.150 μm, respectively) (P = 0.023 at 1 mo, P = 0.045 at 3 mo). Subgroup analysis for eyes with large angle kappa demonstrated that the vertical coma was significantly less in the angle kappa-adjusted group at both 1 and 3 months (P = 0.009, P = 0.043, respectively). No significant correlation was observed between angle kappa and HOAs in the angle kappa-adjusted group., Conclusions: Adjustment of angle kappa during SMILE resulted in less HOAs. It would provide more insight on how to optimize treatment centration in SMILE.

Background: In urbanized, low-income cities with high rates of congestion, delivery of antiepileptic drugs (AEDs) by unmanned aerial vehicles (drones) to people with epilepsy for both emergency and non-urgent distribution may prove beneficial., Methods: Conakry is the capital of the Republic of Guinea, a low-income sub-Saharan African country (2018 per capita gross national income US$830). We computed the number of drones and delivery times to distribute AEDs from a main urban hospital to 27 pre-identified gas stations, mosques and pharmacies and compared these to the delivery times of a personal vehicle., Results: We predict that a single drone could serve all pre-identified delivery locations in Conakry within a 20.4-h period. In an emergency case of status epilepticus, 8, 20 and 24 of the 27 pre-identified destinations can be reached from the hub within 5, 10 and 15 min, respectively. Compared with the use of a personal vehicle, the response time for a drone is reduced by an average of 78.8% across all times of the day., Conclusions: Drones can dramatically reduce the response time for both emergency and routine delivery of lifesaving medicines. We discuss the advantages and disadvantages of such a drone delivery model with relevance to epilepsy. However, the commissioning of a trial of drones for drug delivery in related diseases and geographies is justified., (© The Author(s) 2020. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

We determine how prediction methods combine with optimization methods in two-stage knowledge-based planning (KBP) pipelines to produce radiation therapy treatment plans. We trained two dose prediction methods, a generative adversarial network (GAN) and a random forest (RF) with the same 130 treatment plans. The models were applied to 87 out-of-sample patients to create two sets of predicted dose distributions that were used as input to two optimization models. The first optimization model, inverse planning (IP), estimates weights for dose-objectives from a predicted dose distribution and generates new plans using conventional inverse planning. The second optimization model, dose mimicking (DM), minimizes the sum of one-sided quadratic penalties between the predictions and the generated plans using several dose-objectives. Altogether, four KBP pipelines (GAN-IP, GAN-DM, RF-IP, and RF-DM) were constructed and benchmarked against the corresponding clinical plans using clinical criteria; the error of both prediction methods was also evaluated. The best performing plans were GAN-IP plans, which satisfied the same criteria as their corresponding clinical plans (78%) more often than any other KBP pipeline. However, GAN did not necessarily provide the best prediction for the second-stage optimization models. Specifically, both the RF-IP and RF-DM plans satisfied the same criteria as the clinical plans 25% and 15% more often than GAN-DM plans (the worst performing plans), respectively. GAN predictions also had a higher mean absolute error (3.9 Gy) than those from RF (3.6 Gy). We find that state-of-the-art prediction methods when paired with different optimization algorithms, produce treatment plans with considerable variation in quality., (Crown Copyright © 2020. Published by Elsevier Ltd. All rights reserved.)

Purpose: To investigate the postoperative straylight changes during the visual recovery phase after small incision lenticule extraction (SMILE) and their association., Methods: Seventy consecutive eyes from 37 patients with a mean age of 30.92 ± 7.26 years and a mean preoperative spherical equivalent of -5.24 ± 1.90 dioptres undergoing myopic or myopic astigmatism SMILE correction were included in this prospective study. Patients were followed up at days 1, 3, 7, 14, 21 and 28 after standard SMILE. Straylight was measured using the C-Quant straylight meter (Oculus GmbH, Germany) preoperatively and at each postoperative visit., Results: Preoperatively, the mean straylight measurement was 1.16 ± 0.16. After SMILE, the mean straylight values were 1.12 ± 0.14 and 1.13 ± 0.13 at days 7 and 14, which were significantly reduced compared to preoperative values (p ≤ 0.028). Straylight returned to baseline by week 3 (p = 0.160) and remained stable onwards (p = 0.651). A lower ablation ratio was associated with less straylight level at days 1, 3, 14 and 21 (p ≤ 0.0497) in the multivariable regression model. Likewise, better visual acuity was associated with lower straylight at days 7, 14 and 28 postoperatively (p ≤ 0.038). A small proportion of eyes (range: 0-12.86%) had ≥0.30 log(s) increase in postoperative straylight within the first month after SMILE., Conclusions: SMILE induced a temporary decrease in straylight. It gradually returned to the preoperative level, which could be related to a number of dynamic processes during corneal healing. In the small proportion of patients with an increase in straylight postoperatively, this can affect their visual recovery during the early postoperative period.

Purpose: To develop a knowledge-based automated planning pipeline that generates treatment plans without feature engineering, using deep neural network architectures for predicting three-dimensional (3D) dose., Methods: Our knowledge-based automated planning (KBAP) pipeline consisted of a knowledge-based planning (KBP) method that predicts dose for a contoured computed tomography (CT) image followed by two optimization models that learn objective function weights and generate fluence-based plans, respectively. We developed a novel generative adversarial network (GAN)-based KBP approach, a 3D GAN model, which predicts dose for the full 3D CT image at once and accounts for correlations between adjacent CT slices. Baseline comparisons were made against two state-of-the-art deep learning-based KBP methods from the literature. We also developed an additional benchmark, a two-dimensional (2D) GAN model which predicts dose to each axial slice independently. For all models, we investigated the impact of multiplicatively scaling the predictions before optimization, such that the predicted dose distributions achieved all target clinical criteria. Each KBP model was trained on 130 previously delivered oropharyngeal treatment plans. Performance was tested on 87 out-of-sample previously delivered treatment plans. All KBAP plans were evaluated using clinical planning criteria and compared to their corresponding clinical plans. KBP prediction quality was assessed using dose-volume histogram (DVH) differences from the corresponding clinical plans., Results: The best performing KBAP plans were generated using predictions from the 3D GAN model that were multiplicatively scaled. These plans satisfied 77% of all clinical criteria, compared to the clinical plans, which satisfied 67% of all criteria. In general, multiplicatively scaling predictions prior to optimization increased the fraction of clinical criteria satisfaction by 11% relative to the plans generated with nonscaled predictions. Additionally, these KBAP plans satisfied the same criteria as the clinical plans 84% and 8% more frequently as compared to the two benchmark methods, respectively., Conclusions: We developed the first knowledge-based automated planning framework using a 3D generative adversarial network for prediction. Our results, based on 217 oropharyngeal cancer treatment plans, demonstrated superior performance in satisfying clinical criteria and generated more realistic plans as compared to the previous state-of-the-art approaches., (© 2019 American Association of Physicists in Medicine.)

Objective: The use of machine learning technology to automate intellectual processes and boost clinical process efficiency in medicine has exploded in the past 5 years. Machine learning excels in automating pattern recognition and in adapting learned representations to new settings. Moreover, machine learning techniques have the advantage of incorporating complexity and are free from many of the limitations of traditional deterministic approaches. Cochlear implants (CI) are a unique fit for machine learning techniques given the need for optimization of signal processing to fit complex environmental scenarios and individual patients' CI MAPping. However, there are many other opportunities where machine learning may assist in CI beyond signal processing. The objective of this review was to synthesize past applications of machine learning technologies for pediatric and adult CI and describe novel opportunities for research and development., Data Sources: The PubMed/MEDLINE, EMBASE, Scopus, and ISI Web of Knowledge databases were mined using a directed search strategy to identify the nexus between CI and artificial intelligence/machine learning literature., Study Selection: Non-English language articles, articles without an available abstract or full-text, and nonrelevant articles were manually appraised and excluded. Included articles were evaluated for specific machine learning methodologies, content, and application success., Data Synthesis: The database search identified 298 articles. Two hundred fifty-nine articles (86.9%) were excluded based on the available abstract/full-text, language, and relevance. The remaining 39 articles were included in the review analysis. There was a marked increase in year-over-year publications from 2013 to 2018. Applications of machine learning technologies involved speech/signal processing optimization (17; 43.6% of articles), automated evoked potential measurement (6; 15.4%), postoperative performance/efficacy prediction (5; 12.8%), and surgical anatomy location prediction (3; 7.7%), and 2 (5.1%) in each of robotics, electrode placement performance, and biomaterials performance., Conclusion: The relationship between CI and artificial intelligence is strengthening with a recent increase in publications reporting successful applications. Considerable effort has been directed toward augmenting signal processing and automating postoperative MAPping using machine learning algorithms. Other promising applications include augmenting CI surgery mechanics and personalized medicine approaches for boosting CI patient performance. Future opportunities include addressing scalability and the research and clinical communities' acceptance of machine learning algorithms as effective techniques.

Objectives: To evaluate recent studies on available therapies for meibomian gland dysfunction (MGD)., Methods: A literature search on recent publications, within the last five years, concerning treatment options for MGD was performed., Results: A total of 35 articles were reviewed after curation by the authors for relevance. In general, all modalities of treatments were shown to have clinical efficacy in alleviating dry eye signs and symptoms, although the extent of improvement and persistency of outcomes varied between the different treatments. Evidence from published studies demonstrate that thermal pulsation produces the longest lasting effect per treatment, but it also incurs the highest per-treatment cost. Reusable methods for warm compress with lipid/semi-fluorinated alkane-containing eye drops are recommended as first-line treatment for mild-to-moderate dry eye patients, because this option is most technically feasible and cost-effective in clinical practice. Intense pulsed light (IPL) therapy and thermal pulsation may be suitable as second line for patients unresponsive to warm compress therapy; however, their respective limitations need to be considered. For refractory MGD with features of periductal fibrosis or severe blepharitis, supplementary treatment with meibomian gland probing or oral antibiotics may be used., Conclusions: All eight forms of treatments, including self-applied eyelid warming, thermal pulsation, IPL, MG probing, antibiotics, lipid-containing eye drops, and perfluorohexyloctane, were effective against MGD, although with varying extent of clinical improvements. A better understanding on the mechanisms of actions may guide physicians to make better treatment decisions targeting the root causes.

We report a case of transient corneal ectasia developed after phacoemulsification in an eye previously treated with INTRACOR. There was a myopic refractive surprise after cataract surgery. Corneal tomography showed an increase in keratometry and elevation profile compared with preoperative examination. Soft contact lenses and intraocular pressure-lowering medications were prescribed as interim treatment. Clinical improvement was seen gradually, and the resolution of myopia and ectasia was achieved at 3 months. We believe that high intraocular pressure during phacoemulsification and the weakening effect of femtosecond intrastromal presbyopic treatment can be the culprits.

One of the key challenges with big data is leveraging the complex network of information to yield useful clinical insights. The confluence of massive amounts of health data and a desire to make inferences and insights on these data has produced a substantial amount of interest in machine-learning analytic methods. There has been a drastic increase in the otolaryngology literature volume describing novel applications of machine learning within the past 5 years. In this timely contemporary review, we provide an overview of popular machine-learning techniques, and review recent machine-learning applications in otolaryngology-head and neck surgery including neurotology, head and neck oncology, laryngology, and rhinology. Investigators have realized significant success in validated models with model sensitivities and specificities approaching 100%. Challenges remain in the implementation of machine-learning algorithms. This may be in part the unfamiliarity of these techniques to clinician leaders on the front lines of patient care. Spreading awareness and confidence in machine learning will follow with further validation and proof-of-value analyses that demonstrate model performance superiority over established methods. We are poised to see a greater influx of machine-learning applications to clinical problems in otolaryngology-head and neck surgery, and it is prudent for providers to understand the potential benefits and limitations of these technologies. Laryngoscope, 130:45-51, 2020., (© 2019 The American Laryngological, Rhinological and Otological Society, Inc.)

Purpose: To compare the repeatability and agreement between a swept-source biometer and a Scheimpflug biometer in cataract patients., Methods: Three consecutive measurements were obtained using a swept-source biometer (IOLMaster 700) and a Scheimpflug biometer (AL-Scan) in 52 eyes of 52 patients. Keratometry, central corneal thickness (CCT), anterior chamber depth (ACD), axial length, and white-to-white (WTW) distance were recorded. Astigmatism values were transformed into vector components of J0 and J45. Intraoperator repeatability was analyzed using intraclass correlation coefficients (ICCs) and reproducibility coefficients (RCs). Agreement of measurements between the two devices was evaluated using the Bland-Altman method., Results: The IOLMaster 700 showed higher ICCs and lower RCs for the mean keratometry (Km) (P≤0.018), CCT (P≤0.027), and ACD (P≤0.001) measurements, whereas the AL-Scan showed higher ICC and lower RC for the J45 vector component of astigmatism at the 2.4-mm zone (P≤0.034). Both the devices had excellent repeatability (ICC=0.999) in axial length measurement. Systematic differences were found in Km, CCT, ACD, and WTW (P≤0.018) between the devices. The mean difference for Km was -0.196 and -0.144 D measured at the 2.4-mm zone and 3.3-mm zone, respectively. The corresponding mean difference for CCT, ACD, and WTW distance was 14.92 μm, -0.017 mm, and 0.283 mm, respectively. These differences led to a statistically significant but clinically insignificant difference in the prediction of intraocular lens power., Conclusions: This study showed significant differences in anterior segment measurement repeatability and agreement between a swept-source biometer and a Scheimpflug biometer in eyes with cataract.

Diabetes mellitus is the most common cause of blindness in working age populations worldwide. While much of the focus for public health has been on secondary prevention in sight-threatening diabetic retinopathy, the cornea, including its epithelium and nerves, represents a major site of damage by chronic hyperglycemia. On injury, the diabetic cornea exhibits a delayed wound-healing response, as well as an altered ocular surface immune response. This suggests a potential association between the dysfunctional wound healing response and altered inflammation on the ocular surface. However, the presence of potential confounders makes this association difficult to investigate in human epidemiological studies. Thus, we turn to animal diabetic models for a better understanding. In this review, 20 original studies, published between 2008 and 2018, describe in vivo and in vitro models of diabetic cornea disease. We compared different models of diabetic cornea wound healing and discussed the relative strengths and drawbacks of each model. A number of molecular and cellular components involved in the corneal wound healing response that are altered in the presence of diabetes have been identified in the reviewed studies. Particularly, altered corneal epithelial protein concentrations of lumician and occludin were detected in diabetic eyes compared with controls. Additionally, the importance of IL-1β in modulating the inflammatory response after corneal injury in patients with diabetes and controls was further elucidated. Meanwhile, abnormal P2×7 receptor localization and decreased corneal sub-basal nerve density in diabetic eyes were shown to contribute to altered corneal nerve signaling after injury and thus affecting the wound healing response. Finally, the discovery of the therapeutic effects of topically administered aloe vera, Serpine 1, Resolvin D1 (RvD1), pigment epithelium-derived factor (PEDF) and Pro-His-Ser-Arg-Asn in diabetic animal models of cornea epithelial and nerve injury provide encouraging evidence for the future availability of effective treatment for diabetic keratopathy., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Sclerocornea is a rare congenital disorder characterized with the opacification of the cornea. Here, we report a nonconsanguineous Chinese family with multiple peripheral sclerocornea patients spanning across three generations inherited in an autosomal dominant manner., Methods: This is a retrospective case series of a peripheral sclerocornea pedigree. Comprehensive ophthalmic examinations were conducted and assessed on 14 pedigree members. Whole-exome sequencing was used to identify the genetic alterations in the affected pedigree members. Lymphoblastoid cell lines (LCLs) were established using blood samples from the family members. Functional tests were performed with these cell lines., Results: Six affected and eight unaffected members of a family with peripheral sclerocornea were examined. All affected individuals showed features of scleralization over the peripheral cornea of both eyes. Mean horizontal and vertical corneal diameter were found significantly decreased in the affected members. Significant differences were also observed on the mean apex pachymetry between affected and unaffected subjects. These ophthalmic parameters did not resemble that of cornea plana. A RAD21 C1348T variant was identified by whole-exome sequencing. Although this variant causes RAD21 R450C substitution at the separase cleavage site, cells from peripheral sclerocornea family members had no mitosis and ploidy defects., Conclusion: We report a family of peripheral sclerocornea with no association with cornea plana. A RAD21 variant was found cosegregating with peripheral sclerocornea. Our results suggest that RAD21 functions, other than its cell cycle and chromosome segregation regulations, could underline the pathogenesis of peripheral sclerocornea., Competing Interests: All authors declare no conflict of interest., (Copyright © 2019 Bi Ning Zhang et al.)

Background: Automated external defibrillators (AEDs) are often placed in areas of low risk and limited temporal availability. Mathematical optimization can improve AED accessibility but has not been compared with current practices., Objectives: This study sought to determine whether, compared with real AED locations, optimized AED locations improve coverage of out-of-hospital cardiac arrests (OHCAs)., Methods: The authors conducted the first retrospective in silico trial of an AED placement intervention. This study identified all public OHCAs of presumed cardiac cause and real AED deployed (control group) from 2007 to 2016 in Copenhagen, Denmark. Optimization models trained on historical OHCAs (1994 to 2007) were used to optimize an equal number of AEDs to the control group in locations with availabilities based on building hours (intervention #1) or 24/7 access (intervention #2). The 2 interventions and control scenario were compared using the number of OHCAs that occurred within 100 m of an accessible AED ("OHCA coverage") during the 2007 to 2016 period. Change in bystander defibrillation and 30-day survival were estimated using multivariate logistic regression., Results: There were 673 public OHCAs and 1,573 registered AEDs from 2007 to 2016. OHCA coverage of real AED placements was 22.0%. OHCA coverage of interventions #1 and #2 was significantly higher at 33.4% and 43.1%, respectively; relative gains of 52.0% to 95.9% (p < 0.001). Bystander defibrillation increased from 14.6% (control group) to 22.5% to 26.9% (intervention #1 to intervention #2); relative increase of 52.9% to 83.5% (p < 0.001). The 30-day survival rates increased from 31.3% (control group) to 34.7% to 35.4%, which is a relative increase of 11.0% to 13.3% (p < 0.001)., Conclusions: Optimized AED placements increased OHCA coverage by approximately 50% to 100% over real AED placements, leading to significant predicted increases in bystander defibrillation and 30-day survival., (Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

In recent years, there has been increasing scientific interest in the use of tear film biomarkers in the diagnosis and management of dry eye disease (DED), owing to their potential important roles in the pathogenesis of ocular surface damage, as well as the technical feasibility of tear sample collection techniques. An Entrez PubMed search was conducted on March 2, 2019, to include papers investigating the use of tear film biomarkers in DED, and the results were classified according to whether the DED is associated with systemic inflammatory disease or not and further classified within each section according to the molecular nature of the biomarker for further discussion. A total of 58 relevant articles were reviewed. Certain cytokines, including interleukin-6 (IL-6), tumor necrosis factor-alpha, IL-17, and IL-8, were found by a number of studies to consistently reflect disease severity well and had strong correlations with tear film metrics and tests for ocular surface damage in dry eye without systemic inflammatory disease. For dry eye with systemic inflammatory disease, IL-17, IL-8, and IL-1 receptor antagonists were shown to be consistently higher in affected eyes and correlated well with ocular surface disease severity in more than one type of inflammatory disease. With the advancement in technology and lowered costs in the future, tear film biomarker counts would allow better diagnosis and monitoring of DED, as well as facilitate personalized treatment strategies., Competing Interests: The authors declare that there are no conflicts of interests of this paper., (Copyright: © 2019 Taiwan J Ophthalmol.)

Purpose: To compare the accuracy and precision of the new Hill-RBF version 2.0 (Hill-RBF 2) formula with other formulas (Barrett Universal II, Haigis, Hoffer Q, Holladay 1, and SRK/T) in predicting residual refractive error after phacoemulsification in high axial myopic eyes., Design: Retrospective case series., Methods: 127 eyes of 127 patients with axial length (AL) ≥26 mm were included. The refractive prediction error (PE) was calculated as the difference between the postoperative refraction and the refraction predicted by each formula for the intraocular lens (IOL) power actually implanted. Standard deviation (SD) of PE, median absolute PE (MedAE), proportion of eyes within ±0.25, ±0.50, and ±1.00 diopter (D) of PE were compared. A generalized linear model was used to model the mean function and variance function of the PE (indicative of the accuracy and precision) with respect to biometric variables., Results: The MedAE and SD of Hill-RBF 2 were lower than that of Hoffer Q, Holladay 1, and SRK/T (P ≤ .036) and were comparable to Barrett Universal II and Haigis (P ≥ .077). Hill-RBF 2 had more eyes within ±0.25 D of the intended refraction (76 out of 127 eyes [59.84%]) compared to other formulas (P ≤ .034) except Barrett Universal II (P = .472). AL was associated with the mean function or variance function of the PE for all formulas except Hill-RBF 2., Conclusions: In this study, the precision of Hill-RBF 2 is comparable to Barret Universal II and Haigis. Unlike the other 5 formulas, its dispersion and the accuracy of the refractive prediction is independent of the AL., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Dry eyes is one of the most common complications after laser vision correction. Small incision lenticule extraction (SMILE) is a flapless procedure with a smaller corneal incision, less corneal nerves are transected during, making it theoretically less prone to dry eyes. Both SMILE and femtosecond-laser-assisted in situ keratomileusis (FS-LASIK) induce a transient worsening in dry eye parameters, but there is evidence showing that SMILE holds promises to have fewer negative impacts on the ocular surface parameters and allow an earlier recovery. SMILE-treated eyes may also have shown less corneal denervation and better corneal sensitivity compared with FS-LASIK eyes. This review summarizes the mechanisms of dry eyes after laser vision correction, the short-term (≤6 months) and long-term (>6 months) results in changes to dry eyes signs and symptoms, and corneal sensitivity of SMILE, as compared with FS-LASIK. Limitation of the studies and reasons accounting for their discrepancies will be discussed. Future randomized controlled trials with standardized postoperative regime are needed for better evaluation of dry eyes after SMILE.

Small incision lenticule extraction (SMILE) was introduced in the recent decade for the treatment of myopia and myopic astigmatism. This flap-free technique has a high efficacy and safety profile and also carries potential advantages over laser in situ keratomileusis such as a better corneal biomechanical stability, reduction in dry eyes rate, and the avoidance of flap complications. However, there have been concerns regarding the precision of astigmatism correction that undercorrection has been reported to be apparent. Various factors that affect astigmatism correction have been identified in the literature. The purpose of this review is to discuss the factors that affect astigmatism correction in SMILE and several techniques to improve the refractive outcomes.

Introduction: Although out-of-hospital cardiac arrest (OHCA) is thought of as a sudden event, recent studies suggest that many patients have symptoms or have sought medical attention prior to their arrest. Our objective was to evaluate patterns of healthcare utilization before OHCA., Methods: We conducted a population-based cohort study in Ontario, Canada, which included all patients ≥20 years, who suffered out-of-hospital cardiac arrest and transferred to an emergency department (ED) from 2007 to 2018. Measurements included emergency room assessments, hospitalizations and physician visits prior to arrest., Results: The cohort comprised 38,906 patients, their mean age was 66.5 years, and 32.7% were women. Rates of ED assessments and hospital admissions were relatively constant until 90 days prior to arrest where they markedly increased to the time before arrest. Within 90 days, rates of ED assessment, hospitalization, and primary care physician visit were 29.5%, 16.4%, and 70.1%, respectively. Cardiovascular conditions were diagnosed in 14.4% of ED visits, and 33.7% of hospitalizations in this time period. The largest age-difference was the mental and behavioural disorders within 90 days of OHCA in the ED, where rates were 12.2% among patients <65 years vs. 1.9% for patients ≥65 years., Conclusions: In contrast to the conventional wisdom that OHCA occurs without prior contacts to the health care system, we found that more than 1 in 4 patients were assessed in the ED prior within 90 days of their arrest. Identification of warning signs of OHCA may allow future development of prevention strategies., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Sclerocornea is a cornea opacification disorder. Disorganized corneal stroma fibrils are observed in patients' cornea. Previously we identified a RAD21 C1348T variant that is associated with a peripheral sclerocornea pedigree. To explore whether this RAD21 variant can induce sclerocornea-related phenotype, and to investigate the possible mechanisms of such phenotype, the orthologous rad21 wild-type and variant mRNAs were injected into Xenopus laevis embryos and the developed eyes were subjected for histological examination. Transmission electron microscopy was applied for corneal stroma organization check. rad21 is highly expressed in the eye region during X. laevis development. Disrupted eye development was observed in the rad21 variant injected embryos. Disorganized corneal stroma and decreased diameters of collagen fibrils were observed in the rad21 variant injected X. laevis eyes. These eye defects can be rescued by overexpression of the wild-type rad21. Histological examination found stroma attracting center, a key structure in X. laevis corneal development, was impaired in rad21 variant injected embryos. Our results suggest a key role of RAD21 during corneal development. Our data indicates the RAD21 R450C variant contributes to peripheral sclerocornea by disturbing collagen fibril organization in the corneal stroma., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Laser in situ keratomileusis (LASIK) is the most common refractive surgery in young patients, which aims at providing a clear distance vision without the use of spectacles. With time, these patients develop symptomatic cataract, which affects activities of daily living, and to improve visual acuity, intraocular lens (IOL) implantation can be considered. In post-myopic LASIK patients, to allow continuation of spectacle independence, the implantation of presbyopia-correcting IOLs is a suitable option. The purpose of this retrospective case series is to report the visual outcome and quality in post-myopic LASIK eyes after the implantation of AT LISA tri839MP IOL., Method: Twenty eyes of 13 patients with history of myopic LASIK within 20 years underwent phacoemulsification by one single surgeon. All eyes were implanted with AT LISA tri839PMP IOL, and their outcomes were evaluated at 6 months postoperation., Results: The mean postoperative uncorrected distance visual acuity (VA) is 0.28 ± 0.29, while the corrected distance VA is 0.06 ± 0.14. The mean postoperative uncorrected near VA is 0.02 ± 0.05, while the corrected near VA is 0.01 ± 0.02. The mean postoperative manifest refraction spherical equivalent (SE) is - 0.92 ± 0.76D. There is a statistically significant difference between the preoperative and postoperative refraction (p = 0.02), which shows a postoperative myopic shift. There is also a statistically significant difference between the mean targeted SE and postoperative manifest refraction SE (p = 0.00). Only one out of 20 eyes (5%) reported halo and glare symptoms. Ten out of 20 eyes (50%) are able to achieve spectacles independence., Conclusion: In conclusion, in post-myopic LASIK eyes, AT LISA tri839MP provides a good visual outcome at both near and distance, but is more predictable at near than at distance. There is a myopic shift in the postoperative SE. Visual quality is satisfactory and has not been exacerbated. Most patients can remain to be spectacles free at all distances.