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Concussion-recovered athletes have a higher risk of injury following return to sport. This study investigated the effect of history of concussion on the pattern and variability of inter-segmental coordination in athletes during squat jumps and timed squat and hinge tasks. A human pose estimation algorithm was applied to videos of 111 athletes (72 with no history of concussion (NOHX), 9 within 1 year of concussion (CONC1), 30 more than one-year post-concussion (CONC2) performing a series of movement tasks. Continuous relative phase metrics, calculated from phase angles of two contiguous segments, were used to evaluate inter-segmental coordination. Linear models were used to evaluate the causal effect of concussion group on hip, knee, and ankle coordination and repetition duration for each task. CONC1 affected repetition duration and knee and hip coordination and variability, while CONC2 influenced knee coordination. The findings suggest that concussion may have long-term persisting effects on lower-limb inter-segmental coordination in athletes.

Background: Multidisciplinary care is paramount in patient-specific decision making, especially as pertaining to oral cavity squamous cell cancer (OCSCC) treatment. Protracted surgery-postoperative-radiation (S-PORT) has a detrimental impact on OCSCC patients' outcomes. This study examined the impact of surgeon-radiation oncologist dyads on the treatment of OCSCC, focusing on S-PORT interval and disease specific outcomes., Methods: All OCSCC patients treated in a tertiary cancer center between 2009 to 2017 were included. Patients were categorized into "dyad" and "nondyad" groups defined as whether they were treated by a paired surgeon-radiation oncology team with joint multidisciplinary clinic or shared >30% patient volumes. Univariate and multivariate logistic regression were performed to identify factors associated with a prolonged S-PORT time interval (≥8 weeks). Overall survival and locoregional recurrence were estimated and compared., Results: A total of 444 OCSCC were eligible. Treatment by a dyad was significantly less likely associated with S-PORT ≥ 8 weeks (odds ratio [OR] unadjusted : 0.65; 95% confidence interval [CI] 0.44-0.96; p = 0.03). Obtaining pre-operative radiation oncology consultation also decreased the S-PORT interval. Advanced T-category and the need for free tissue flap reconstruction increased the likelihood of prolonged S-PORT on univariate but not multivariate analysis. No significant differences were observed in overall survival or locoregional recurrence by dyad status nor S-PORT (p > 0.05)., Conclusions: Surgeon-radiation oncology dyads significantly minimized time from surgery to postoperative radiation in OCSCC. While improvement in overall survival or locoregional recurrence was not observed, these findings support close knit collaborative multidisciplinary treatment care models, including dyad-based care., (© 2024. Society of Surgical Oncology.)

Background: Extracorporeal cardiopulmonary resuscitation (ECPR) can improve survival for refractory out-of-hospital cardiac arrest (OHCA). We sought to assess the feasibility of a proposed ECPR programme in Scotland, considering both in-hospital and pre-hospital implementation scenarios., Methods: We included treated OHCAs in Scotland aged 16-70 between August 2018 and March 2022. We defined those clinically eligible for ECPR as patients where the initial rhythm was ventricular fibrillation, ventricular tachycardia, or pulseless electrical activity, and where pre-hospital return of spontaneous circulation was not achieved. We computed the call-to-ECPR access time interval as the amount of time from emergency medical service (EMS) call reception to either arrival at an ECPR-ready hospital or arrival of a pre-hospital ECPR crew. We determined the number of patients that had access to ECPR within 45 min, and estimated the number of additional survivors as a result., Results: A total of 6,639 OHCAs were included in the geospatial modelling, 1,406 of which were eligible for ECPR. Depending on the implementation scenario, 52.9-112.6 (13.8-29.4%) OHCAs per year had a call-to-ECPR access time within 45 min, with pre-hospital implementation scenarios having greater and earlier access to ECPR for OHCA patients. We further estimated that an ECPR programme in Scotland would yield 11.8-28.2 additional survivors per year, with the pre-hospital implementation scenarios yielding higher numbers., Conclusion: An ECPR programme for OHCA in Scotland could provide access to ECPR to a modest number of eligible OHCA patients, with pre-hospital ECPR implementation scenarios yielding higher access to ECPR and higher numbers of additional survivors., 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 © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Defibrillation in the critical first minutes of out-of-hospital cardiac arrest (OHCA) can significantly improve survival. However, timely access to automated external defibrillators (AEDs) remains a barrier., Objectives: The authors estimated the impact of a statewide program for drone-delivered AEDs in North Carolina integrated into emergency medical service and first responder (FR) response for OHCA., Methods: Using Cardiac Arrest Registry to Enhance Survival registry data, we included 28,292 OHCA patients ≥18 years of age between 1 January 2013 and 31 December 2019 in 48 North Carolina counties. We estimated the improvement in response times (time from 9-1-1 call to AED arrival) achieved by 2 sequential interventions: 1) AEDs for all FRs; and 2) optimized placement of drones to maximize 5-minute AED arrival within each county. Interventions were evaluated with logistic regression models to estimate changes in initial shockable rhythm and survival., Results: Historical county-level median response times were 8.0 minutes (IQR: 7.0-9.0 minutes) with 16.5% of OHCAs having AED arrival times of <5 minutes (IQR: 11.2%-24.3%). Providing all FRs with AEDs improved median response to 7.0 minutes (IQR: 6.2-7.8 minutes) and increased OHCAs with <5-minute AED arrival to 22.3% (IQR: 16.4%-30.9%). Further incorporating optimized drone networks (326 drones across all 48 counties) improved median response to 4.8 minutes (IQR: 4.3-5.2 minutes) and OHCAs with <5-minute AED arrival to 56.3% (IQR: 46.9%-64.2%). Survival rates were estimated to increase by 34% for witnessed OHCAs with estimated drone arrival <5 minutes and ahead of FR and emergency medical service., Conclusions: Deployment of AEDs by FRs and optimized drone delivery can improve AED arrival times which may lead to improved clinical outcomes. Implementation studies are needed., Competing Interests: This study was funded by the NHLBI K23 Career Development Award (5K23HL153889-04). Dr Starks is funded the NHLBI K23 Career Development Award (1K23HL153889-04), Evaluation of the Requirements and Critical Features of a Drone-Deployed AED Network to Improve Community-Level Survival after OHCA; and is the principal investigator of AHA Health Equity Research Network (HERN) on Improving Access to Care and Other Health Inequities in Rural America (23HERNPRH1150361), Developing and Testing Drone-Delivered AEDs for Cardiac Arrests In Rural America. Dr Granger is PI of the Clinical Coordinating Center (CCC) of the 10.13039/100000050NHBLI funded Regional Approaches to Cardiovascular Emergencies (RACE-CARS) Trial (5UH3HL146935-04). Dr Mark is co-PI of the Data Coordinating Center (CCC) of the 10.13039/100000050NHBLI-funded Regional Approaches to Cardiovascular Emergencies (RACE-CARS) Trial (5U24HL146938-04). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)

Background: Current methods for Gamma Knife (GK) treatment planning utilizes either manual forward planning, where planners manually place shots in a tumor to achieve a desired dose distribution, or inverse planning, whereby the dose delivered to a tumor is optimized for multiple objectives based on established metrics. For other treatment modalities like IMRT and VMAT, there has been a recent push to develop knowledge-based planning (KBP) pipelines to address the limitations presented by forward and inverse planning. However, no complete KBP pipeline has been created for GK., Purpose: To develop a novel (KBP) pipeline, using inverse optimization (IO) with 3D dose predictions for GK., Methods: Data were obtained for 349 patients from Sunnybrook Health Sciences Centre. A 3D dose prediction model was trained using 322 patients, based on a previously published deep learning methodology, and dose predictions were generated for the remaining 27 out-of-sample patients. A generalized IO model was developed to learn objective function weights from dose predictions. These weights were then used in an inverse planning model to generate deliverable treatment plans. A dose mimicking (DM) model was also implemented for comparison. The quality of the resulting plans was compared to their clinical counterparts using standard GK quality metrics. The performance of the models was also characterized with respect to the dose predictions., Results: Across all quality metrics, plans generated using the IO pipeline performed at least as well as or better than the respective clinical plans. The average conformity and gradient indices of IO plans was 0.737 ± $\pm$ 0.158 and 3.356 ± $\pm$ 1.030 respectively, compared to 0.713 ± $\pm$ 0.124 and 3.452 ± $\pm$ 1.123 for the clinical plans. IO plans also performed better than DM plans for five of the six quality metrics. Plans generated using IO also have average treatment times comparable to that of clinical plans. With regards to the dose predictions, predictions with higher conformity tend to result in higher quality KBP plans., Conclusions: Plans resulting from an IO KBP pipeline are, on average, of equal or superior quality compared to those obtained through manual planning. The results demonstrate the potential for the use of KBP to generate GK treatment with minimal human intervention., (© 2024 American Association of Physicists in Medicine.)

Background: Lung- and diaphragm-protective (LDP) ventilation may prevent diaphragm atrophy and patient self-inflicted lung injury in acute respiratory failure, but feasibility is uncertain. The objectives of this study were to estimate the proportion of patients achieving LDP targets in different modes of ventilation, and to identify predictors of need for extracorporeal carbon dioxide removal (ECCO 2 R) to achieve LDP targets., Methods: An in silico clinical trial was conducted using a previously published mathematical model of patient-ventilator interaction in a simulated patient population (n = 5000) with clinically relevant physiological characteristics. Ventilation and sedation were titrated according to a pre-defined algorithm in pressure support ventilation (PSV) and proportional assist ventilation (PAV+) modes, with or without adjunctive ECCO 2 R, and using ECCO 2 R alone (without ventilation or sedation). Random forest modelling was employed to identify patient-level factors associated with achieving targets., Results: After titration, the proportion of patients achieving targets was lower in PAV+ vs. PSV (37% vs. 43%, odds ratio 0.78, 95% CI 0.73-0.85). Adjunctive ECCO 2 R substantially increased the probability of achieving targets in both PSV and PAV+ (85% vs. 84%). ECCO 2 R alone without ventilation or sedation achieved LDP targets in 9%. The main determinants of success without ECCO 2 R were lung compliance, ventilatory ratio, and strong ion difference. In silico trial results corresponded closely with the results obtained in a clinical trial of the LDP titration algorithm (n = 30)., Conclusions: In this in silico trial, many patients required ECCO 2 R in combination with mechanical ventilation and sedation to achieve LDP targets. ECCO 2 R increased the probability of achieving LDP targets in patients with intermediate degrees of derangement in elastance and ventilatory ratio., (© 2024. The Author(s).)

Measuring soccer shooting skill is a challenging analytics problem due to the scarcity and highly contextual nature of scoring events. The introduction of more advanced data surrounding soccer shots has given rise to model-based metrics which better cope with these challenges. Specifically, metrics such as expected goals added, goals above expectation, and post-shot expected goals all use advanced data to offer an improvement over the classical conversion rate. However, all metrics developed to date assign a value of zero to off-target shots, which account for almost two-thirds of all shots, since these shots have no probability of scoring. We posit that there is non-negligible shooting skill signal contained in the trajectories of off-target shots and propose two shooting skill metrics that incorporate the signal contained in off-target shots. Specifically, we develop a player-specific generative model for shot trajectories based on a mixture of truncated bivariate Gaussian distributions. We use this generative model to compute metrics that allow us to attach non-zero value to off-target shots. We demonstrate that our proposed metrics are more stable than current state-of-the-art metrics and have increased predictive power., Competing Interests: Competing interests: The authors state no conflict of interest., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

Highlights: This tutorial provides a user-friendly guide to mathematically formulating constrained optimization problems and implementing them using Python.Two examples are presented to illustrate how constrained optimization is used in health applications, with accompanying Python code provided., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors received no financial support for the research, authorship, and/or publication of this article.

Purpose: To optimize artificial intelligence (AI) algorithms to integrate Scheimpflug-based corneal tomography and biomechanics to enhance ectasia detection., Design: Multicenter cross-sectional case-control retrospective study., Methods: A total of 3886 unoperated eyes from 3412 patients had Pentacam and Corvis ST (Oculus Optikgeräte GmbH) examinations. The database included 1 eye randomly selected from 1680 normal patients (N) and from 1181 "bilateral" keratoconus (KC) patients, along with 551 normal topography eyes from patients with very asymmetric ectasia (VAE-NT), and their 474 unoperated ectatic (VAE-E) eyes. The current TBIv1 (tomographic-biomechanical index) was tested, and an optimized AI algorithm was developed for augmenting accuracy., Results: The area under the receiver operating characteristic curve (AUC) of the TBIv1 for discriminating clinical ectasia (KC and VAE-E) was 0.999 (98.5% sensitivity; 98.6% specificity [cutoff: 0.5]), and for VAE-NT, 0.899 (76% sensitivity; 89.1% specificity [cutoff: 0.29]). A novel random forest algorithm (TBIv2), developed with 18 features in 156 trees using 10-fold cross-validation, had a significantly higher AUC (0.945; DeLong, P < .0001) for detecting VAE-NT (84.4% sensitivity and 90.1% specificity; cutoff: 0.43; DeLong, P < .0001) and a similar AUC for clinical ectasia (0.999; DeLong, P = .818; 98.7% sensitivity; 99.2% specificity [cutoff: 0.8]). Considering all cases, the TBIv2 had a higher AUC (0.985) than TBIv1 (0.974; DeLong, P < .0001)., Conclusions: AI optimization to integrate Scheimpflug-based corneal tomography and biomechanical assessments augments accuracy for ectasia detection, characterizing ectasia susceptibility in the diverse VAE-NT group. Some patients with VAE may have true unilateral ectasia. Machine learning considering additional data, including epithelial thickness or other parameters from multimodal refractive imaging, will continuously enhance accuracy. NOTE: Publication of this article is sponsored by the American Ophthalmological Society., (Copyright © 2022. Published by Elsevier Inc.)

Purpose: To evaluate the effects of different programmed optical zones (POZs) on achieved corneal refractive power (CRP) with myopic astigmatism after small incision lenticule extraction (SMILE)., Methods: In total, 113 patients (113 eyes) were included in this retrospective study. The eyes were divided into two groups according to POZ: group A (6.5, 6.6, and 6.7 mm, n = 59) and group B (6.8, 6.9, and 7.0 mm, n = 54). Fourier vector analysis was applied to evaluate the error values between the attempted and achieved corneal refractive power (CRP). Alpins vector analysis was used to calculate surgically induced astigmatism (SIA), difference vector (DV), magnitude of error (ME), and astigmatism correction index (ACI). Multivariate regression analysis was performed to assess potential factors associated with the error values., Results: The error values in the group with large POZ were closer to zero, and significantly associated with the POZ at 2 and 4 mm of the cornea (β =  - 0.50, 95% confidence interval [CI] [- 0.80, - 0.20]; β =  - 0.37, 95% CI [- 0.63, - 0.10], P < 0.05, respectively). For the correction of astigmatism, the values of SIA, ME, and ACI were lower in group B than in group A (P < 0.05). The fitting curves between TIA and SIA were y = 0.83x + 0.19 (R 2  = 0.84) and y = 1.05x + 0.04 (R 2  = 0.90), respectively., Conclusions: Smaller POZs resulted in higher error values between the achieved- and attempted-CRP in the SMILE procedure, which should be considered when performing surgery., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Background: Artificial intelligence (AI) implementation in primary care is limited. Those set to be most impacted by AI technology in this setting should guide it's application. We organized a national deliberative dialogue with primary care stakeholders from across Canada to explore how they thought AI should be applied in primary care., Methods: We conducted 12 virtual deliberative dialogues with participants from 8 Canadian provinces to identify shared priorities for applying AI in primary care. Dialogue data were thematically analyzed using interpretive description approaches., Results: Participants thought that AI should first be applied to documentation, practice operations, and triage tasks, in hopes of improving efficiency while maintaining person-centered delivery, relationships, and access. They viewed complex AI-driven clinical decision support and proactive care tools as impactful but recognized potential risks. Appropriate training and implementation support were the most important external enablers of safe, effective, and patient-centered use of AI in primary care settings., Interpretation: Our findings offer an agenda for the future application of AI in primary care grounded in the shared values of patients and providers. We propose that, from conception, AI developers work with primary care stakeholders as codesign partners, developing tools that respond to shared priorities., Competing Interests: Conflict of interest: None to declare., (© Copyright by the American Board of Family Medicine.)

Background: With large volumes of longitudinal data in electronic medical records from diverse patients, primary care is primed for disruption by artificial intelligence (AI) technology. With AI applications in primary care still at an early stage in Canada and most countries, there is a unique opportunity to engage key stakeholders in exploring how AI would be used and what implementation would look like., Objective: To identify the barriers that patients, providers, and health leaders perceive in relation to implementing AI in primary care and strategies to overcome them., Design: 12 virtual deliberative dialogues. Dialogue data were thematically analyzed using a combination of rapid ethnographic assessment and interpretive description techniques., Setting: Virtual sessions., Participants: Participants from eight provinces in Canada, including 22 primary care service users, 21 interprofessional providers, and 5 health system leaders., Results: The barriers that emerged from the deliberative dialogue sessions were grouped into four themes: (1) system and data readiness, (2) the potential for bias and inequity, (3) the regulation of AI and big data, and (4) the importance of people as technology enablers. Strategies to overcome the barriers in each of these themes were highlighted, where participatory co-design and iterative implementation were voiced most strongly by participants., Limitations: Only five health system leaders were included in the study and no self-identifying Indigenous people. This is a limitation as both groups may have provided unique perspectives to the study objective., Conclusions: These findings provide insight into the barriers and facilitators associated with implementing AI in primary care settings from different perspectives. This will be vital as decisions regarding the future of AI in this space is shaped., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Darcel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: To develop a machine learning-based, 3D dose prediction methodology for Gamma Knife (GK) radiosurgery. The methodology accounts for cases involving targets of any number, size, and shape., Methods: Data from 322 GK treatment plans was modified by isolating and cropping the contoured MRI and clinical dose distributions based on tumor location, then scaling the resulting tumor spaces to a standard size. An accompanying 3D tensor was created for each instance to account for tumor size. The modified dataset for 272 patients was used to train both a generative adversarial network (GAN-GK) and a 3D U-Net model (U-Net-GK). Unmodified data was used to train equivalent baseline models. All models were used to predict the dose distribution of 50 out-of-sample patients. Prediction accuracy was evaluated using gamma, with criteria of 4 %/2mm, 3 %/3mm, 3 %/1mm and 1 %/1mm. Prediction quality was assessed using coverage, selectivity, and conformity indices., Results: The predictions resulting from GAN-GK and U-Net-GK were similar to their clinical counterparts, with average gamma (4 %/2mm) passing rates of 84.9 ± 15.3 % and 83.1 ± 17.2 %, respectively. In contrast, the gamma passing rate of baseline models were significantly worse than their respective GK-specific models (p < 0.001) at all criterion levels. The quality of GK-specific predictions was also similar to that of clinical plans., Conclusion: Deep learning models can use GK-specific data modification to predict 3D dose distributions for GKRS plans with a large range in size, shape, or number of targets. Standard deep learning models applied to unmodified GK data generated poorer predictions., 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 © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Clinical pathways are standardized processes that outline the steps required for managing a specific disease. However, patient pathways often deviate from clinical pathways. Measuring the concordance of patient pathways to clinical pathways is important for health system monitoring and informing quality improvement initiatives. In this paper, we develop an inverse optimization-based approach to measuring pathway concordance in breast cancer, a complex disease. We capture this complexity in a hierarchical network that models the patient's journey through the health system. A novel inverse shortest path model is formulated and solved on this hierarchical network to estimate arc costs, which are used to form a concordance metric to measure the distance between patient pathways and shortest paths (i.e., clinical pathways). Using real breast cancer patient data from Ontario, Canada, we demonstrate that our concordance metric has a statistically significant association with survival for all breast cancer patient subgroups. We also use it to quantify the extent of patient pathway discordances across all subgroups, finding that patients undertaking additional clinical activities constitute the primary driver of discordance in the population., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Systematic automated external defibrillator(AED) placement in schools may improve pediatric out-of-hospital cardiac arrest(OHCA) survival. To estimate their utility, we identified school-located pediatric and adult OHCAs to estimate the potential utilization of school-located AEDs. Further, we identified all OHCAs within an AED-retrievable distance of the school by walking, biking, and driving., Methods: We used prospectively collected data from the British Columbia(BC) Cardiac Arrest Registry(2013-2020), and geo-plotted all OHCAs and schools(n = 824) in BC. We identified adult and pediatric(age < 18 years) OHCAs occurring in schools, as well as nearby OHCAs for which a school-based externally-placed AED could be retrieved by a bystander prior to emergency medical system(EMS) arrival., Results: Of 16,409 OHCAs overall in the study period, 28.6 % occurred during school hours. There were 301 pediatric OHCAs. 5(1.7 %) occurred in schools, of whom 2(40 %) survived to hospital discharge. Among both children and adults, 28(0.17 %) occurred in schools(0.0042/school/year), of whom 21(75 %) received bystander resuscitation, 4(14 %) had a bystander AED applied, and 14(50 %) survived to hospital discharge. For each AED, an average of 0.29 OHCAs/year(95 % CI 0.21-0.37), 0.93 OHCAs/year(95 % CI 0.69-1.56) and 1.69 OHCAs/year(95 % CI 1.21-2.89) would be within the potential retrieval distance of a school-located AED by pedestrian, cyclist and automobile retrieval, respectively, using the median EMS response times., Conclusion: While school-located OHCAs were uncommon, outcomes were favourable. 11.1% to 60.9% of all OHCAs occur within an AED-retrievable distance to a school, depending on retrieval method. Accessible external school-located AEDs may improve OHCA outcomes of school children and in the surrounding community., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Introduction: To compare intrasession agreement and repeatability of wavefront aberration measurements from three different aberrometers obtained using Hartmann-Shack, ray tracing and automated retinoscopy methods, as well as their interdevice agreement., Methods: Three consecutive measurements were obtained using the Pentacam AXL Wave, the iTrace and the OPD-Scan III in 47 eyes of 47 patients. Wavefront refractions, root mean square of total aberrations (RMS total), RMS of higher-order aberrations (HOA) and second-, third- and fourth-order HOAs were exported for 4-mm pupils. Wavefront refractions were converted into vector components: M, J 0 and J 45 . Intrasession agreement and repeatability were evaluated using intraclass correlation coefficients (ICCs) and repeatability coefficients (RCs); interdevice agreement was assessed using the Bland-Altman method., Results: The intrasession agreement and repeatability of RMS HOA were comparable between the three devices; both the Pentacam AXL Wave and the OPD-Scan III had better intrasession agreement and repeatability for the RMS total than the iTrace (p ≤ 0.02). Intrasession repeatability for the majority of second- and third-order aberrations was better on the Pentacam AXL Wave than on the iTrace (p ≤ 0.01) and OPD-Scan III (p ≤ 0.04), although their agreement and repeatability in spherical aberration were comparable (p ≥ 0.24). Significant systematic differences and proportional bias were detected for almost all refraction power vectors and Zernike coefficients among the three devices., Conclusions: In this study, all three devices provided good-to-excellent agreement for aberration measurements. Most of the individual Zernike's components were not exchangeable between different aberrometers. Their relative intrasession performance in agreement and repeatability varied significantly across different ocular aberration parameters., (© 2022 College of Optometrists.)

Objective. To establish an open framework for developing plan optimization models for knowledge-based planning (KBP). Approach. Our framework includes radiotherapy treatment data (i.e. reference plans) for 100 patients with head-and-neck cancer who were treated with intensity-modulated radiotherapy. That data also includes high-quality dose predictions from 19 KBP models that were developed by different research groups using out-of-sample data during the OpenKBP Grand Challenge. The dose predictions were input to four fluence-based dose mimicking models to form 76 unique KBP pipelines that generated 7600 plans (76 pipelines × 100 patients). The predictions and KBP-generated plans were compared to the reference plans via: the dose score, which is the average mean absolute voxel-by-voxel difference in dose; the deviation in dose-volume histogram (DVH) points; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. Main results. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50-0.62, which indicates that the quality of the predictions was generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better ( P < 0.05; one-sided Wilcoxon test) on 18 of 23 DVH points. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans, which satisfied 3.5% more criteria than the set of all dose predictions. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for an inverse planning model. Significance. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. We found that the best performing models significantly outperformed the reference dose and dose predictions. In the interest of reproducibility, our data and code is freely available., (Creative Commons Attribution license.)

Background: Insufficient or excessive respiratory effort during acute hypoxemic respiratory failure (AHRF) increases the risk of lung and diaphragm injury. We sought to establish whether respiratory effort can be optimized to achieve lung- and diaphragm-protective (LDP) targets (esophageal pressure swing - 3 to - 8 cm H 2 O; dynamic transpulmonary driving pressure ≤ 15 cm H 2 O) during AHRF., Methods: In patients with early AHRF, spontaneous breathing was initiated as soon as passive ventilation was not deemed mandatory. Inspiratory pressure, sedation, positive end-expiratory pressure (PEEP), and sweep gas flow (in patients receiving veno-venous extracorporeal membrane oxygenation (VV-ECMO)) were systematically titrated to achieve LDP targets. Additionally, partial neuromuscular blockade (pNMBA) was administered in patients with refractory excessive respiratory effort., Results: Of 30 patients enrolled, most had severe AHRF; 16 required VV-ECMO. Respiratory effort was absent in all at enrolment. After initiating spontaneous breathing, most exhibited high respiratory effort and only 6/30 met LDP targets. After titrating ventilation, sedation, and sweep gas flow, LDP targets were achieved in 20/30. LDP targets were more likely to be achieved in patients on VV-ECMO (median OR 10, 95% CrI 2, 81) and at the PEEP level associated with improved dynamic compliance (median OR 33, 95% CrI 5, 898). Administration of pNMBA to patients with refractory excessive effort was well-tolerated and effectively achieved LDP targets., Conclusion: Respiratory effort is frequently absent  under deep sedation but becomes excessive when spontaneous breathing is permitted in patients with moderate or severe AHRF. Systematically titrating ventilation and sedation can optimize respiratory effort for lung and diaphragm protection in most patients. VV-ECMO can greatly facilitate the delivery of a LDP strategy., Trial Registration: This trial was registered in Clinicaltrials.gov in August 2018 (NCT03612583)., (© 2022. The Author(s).)

Introduction: Regional anesthesia is increasingly used in total joint arthroplasty (TJA). It has shown efficiency benefits as it allows parallel processing of patients in a dedicated block room (BR). However, granular quantification of these benefits to hospital operations is lacking. The goal of this study was to determine the financial effect of establishing a BR using comprehensive operational modeling., Methods: A discrete-event simulation model of daily operating room (OR) patient flow for TJA procedures at a mid-sized hospital was developed. Two scenarios were tested: (1) without and (2) with a BR. Scenarios were compared according to staffing requirements, hours/day, and labor costs. The number of ORs and cases varied from 2 to 6 ORs performing 3 to 5 cases. These results were used as the inputs of a discounted cash flow (CF) model. Discounted CF model outputs were CF, net present value, internal rate of return, and return on investment., Results: Mean time savings of incorporating a BR were 68 min/d (range: 30 to 80 min/d), reducing the OR closing time by 1 hour. Incremental labor costs/day from nurse overtime pay ranged from $2,025 to $10,125 with no BR and $1,595 to $9,045 with a BR, which resulted in an increase in profit/day from $360 to $1,605. The CF/annum was $54,363, the net present value was $213,082, the internal rate of return was 12%, and the return on investment was 43.61%., Discussion: This study demonstrates that under all scenarios, a BR is more profitable than no BR to a hospital performing TJA via a bundled care or private payer remuneration model. A BR was shown to be financially net positive even when considering the necessary financial investment to establish it. In addition, this study demonstrates the potential of combining discrete-event simulation with financial analyses to assess various operational models of care to improve hospital efficiency, such as dedicated trauma rooms and swing rooms., Level of Evidence: Level III., (Copyright © 2022 by the American Academy of Orthopaedic Surgeons.)

Purpose: To analyze the 5-year results of accelerated corneal collagen crosslinking (CXL) for progressive keratoconus and identify preoperative characteristics predictive of visual and topographic outcomes., Methods: A prospective interventional case series. Nineteen eyes of 19 patients receiving accelerated CXL with settings of 18 mW/cm 2 for 5 min were included. Clinical and topographic parameters were assessed. Linear regression and logistic regression were used to compare the R 2 and odds ratio (OR), respectively, between baseline characteristics and postoperative outcomes., Results: Corrected distance visual acuity (CDVA) remained stable from 0.28 ± 0.21 to 0.25 ± 0.18 logMAR (P = 0.486). The mean cylindrical refraction was stable (P = 0.119). The maximal keratometry (Kmax) decreased from 61.99 ± 10.37 to 59.25 ± 7.75 D (P < 0.001), flattening in the flattest and steepest meridians and mean keratometry were also observed (P ≤ 0.040). The mean anterior elevation at the apex reduced from 21.42 ± 16.69 to 18.53 ± 12.74 μm (P = 0.013) and changes in posterior elevation were non-significant (P = 0.629). Preoperative Kmax best predicted the postoperative change in Kmax (R 2 = 0.55, P < 0.001) compared to the other baseline characteristics (P ≤ 0.028), whereas preoperative CDVA was the only significant predictor of postoperative change in CDVA (R 2 = 0.41, P = 0.003). Accelerated CXL is less likely to fail in eyes with a steeper preoperative Kmax (OR = 0.74, P = 0.040) or greater posterior elevation at the apex (OR = 0.91, P = 0.042)., Conclusion: Kmax significantly decreased following accelerated CXL. Eyes with worse preoperative CDVA and higher Kmax were more likely to have an improvement in visual acuity and corneal flattening., Competing Interests: None

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.)