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Staphylococcus aureus generates biofilms during many chronic human infections, which contributes to its growth and persistence in the host. Multiple genes and pathways necessary for S. aureus biofilm production have been identified, but knowledge is incomplete, and little is known about spontaneous mutations that increase biofilm formation as infection progresses.

Rationale: Staphylococcus aureus is the most common respiratory pathogen isolated from patients with cystic fibrosis (CF) in the United States. Although modes of acquisition and genetic adaptation ...

This article summarizes new scientific evidence on the pathogenesis of surgical site infection, including the roles of the patient microbiome and antimicrobial resistance, and reviews changes in guidelines and clinical practices for prevention.

BackgroundMicrosatellite instability (MSI) predicts oncological response to checkpoint blockade immunotherapies. Although microsatellite mutation is pathognomonic for the condition, loci have unequal diagnostic value for predicting MSI within and across cancer types.MethodsTo better inform molecular diagnosis of MSI, we examined 9438 tumor-normal exome pairs and 901 whole genome sequence pairs from 32 different cancer types and cataloged genome-wide microsatellite instability events. Using a statistical framework, we identified microsatellite mutations that were predictive of MSI within and across cancer types. The diagnostic accuracy of different subsets of maximally informative markers was estimated computationally using a dedicated validation set.ResultsTwenty-five cancer types exhibited hypermutated states consistent with MSI. Recurrently mutated microsatellites associated with MSI were identifiable in 15 cancer types, but were largely specific to individual cancer types. Cancer-specific microsatellite panels of 1 to 7 loci were needed to attain ≥95% diagnostic sensitivity and specificity for 11 cancer types, and in 8 of the cancer types, 100% sensitivity and specificity were achieved. Breast cancer required 800 loci to achieve comparable performance. We were unable to identify recurrent microsatellite mutations supporting reliable MSI diagnosis in ovarian tumors. Features associated with informative microsatellites were cataloged.ConclusionsMost microsatellites informative for MSI are specific to particular cancer types, requiring the use of tissue-specific loci for optimal diagnosis. Limited numbers of markers are needed to provide accurate MSI diagnosis in most tumor types, but it is challenging to diagnose breast and ovarian cancers using predefined microsatellite locus panels.

Diagnostically informative microbial cell-free DNA (cfDNA) can be detected from blood plasma during fulminant infections such as sepsis. However, the potential for DNA from airway pathogens to enter the circulation of cystic fibrosis (CF) patients during chronic infective states has not yet been evaluated. We assessed whether patient blood contained measurable quantities of cfDNA from CF respiratory microorganisms by sequencing plasma from 21 individuals with CF recruited from outpatient clinics and 12 healthy controls. To account for possible contamination with exogenous microbial nucleic acids, statistical significance of microbe-derived read counts from CF patients was determined relative to the healthy control population. In aggregate, relative abundance of microbial cfDNA was nearly an order of magnitude higher in CF patients than in healthy subjects (p = 8.0×10−3). 15 of 21 (71%) CF patients demonstrated cfDNA from one or more relevant organisms. In contrast, none of the healthy subjects evidenced significant microbial cfDNA for any of the organisms examined. Concordance of cfDNA with standard microbiological culture of contemporaneously collected patient sputum was variable. Our findings provide evidence that cfDNA from respiratory pathogens are present in the bloodstream of most CF patients, which could potentially be exploited for the purposes of noninvasive clinical diagnosis.

Since the first recognition of a cluster of novel respiratory viral infections in China in late December 2019, intensivists in the United States have watched with growing concern as infections with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus―now named Coronavirus Disease of 2019 (COVID-19)―have spread to hospitals in the United States. Because COVID-19 is extremely transmissible and can progress to a severe form of respiratory failure, the potential to overwhelm available critical care resources is high and critical care management of COVID-19 patients has been thrust into the spotlight. COVID-19 arrived in the United States in January and, as anticipated, has dramatically increased the usage of critical care resources. Three of the hardest-hit cities have been Seattle, New York City, and Chicago with a combined total of over 14,000 cases as of March 23, 2020. In this special article, we describe initial clinical impressions of critical care of COVID-19 in these areas, with attention to clinical presentation, laboratory values, organ system effects, treatment strategies, and resource management. We highlight clinical observations that align with or differ from already published reports. These impressions represent only the early empiric experience of the authors and are not intended to serve as recommendations or guidelines for practice, but rather as a starting point for intensivists preparing to address COVID-19 when it arrives in their community.

Key Points Question What can be learned from a crowdsourced challenge for the prediction of COVID-19 diagnosis and hospitalization? Findings This diagnostic and prognostic study used a model-to-data approach to implement a continuous benchmarking challenge that has enabled 482 participants to join in the effort to use regularly updated COVID-19 patient data to build machine learning models for COVID-19 diagnosis and hospitalization prediction. Machine learning models showed high accuracy in COVID-19 outcome prediction, but analysis of subgroups and prospective data revealed limitations and bias in the models. Meaning This study suggests that crowdsourced clinical algorithms can predict COVID-19 diagnosis and hospitalization, but evaluation of the submitted models using reserved data sets is necessary to avoid self-assessment traps., Importance Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models., This diagnostic and prognostic study describes the development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provides an assessment of model performance, and benchmarks model performance on subgroups.

Supplemental Digital Content is available in the text., OBJECTIVES: To evaluate factors predictive of clinical progression among coronavirus disease 2019 patients following admission, and whether continuous, automated assessments of patient status may contribute to optimal monitoring and management. DESIGN: Retrospective cohort for algorithm training, testing, and validation. SETTING: Eight hospitals across two geographically distinct regions. PATIENTS: Two-thousand fifteen hospitalized coronavirus disease 2019–positive patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Anticipating Respiratory failure in Coronavirus disease (ARC), a clinically interpretable, continuously monitoring prognostic model of acute respiratory failure in hospitalized coronavirus disease 2019 patients, was developed and validated. An analysis of the most important clinical predictors aligns with key risk factors identified by other investigators but contributes new insights regarding the time at which key factors first begin to exhibit aberrency and distinguishes features predictive of acute respiratory failure in coronavirus disease 2019 versus pneumonia caused by other types of infection. Departing from prior work, ARC was designed to update continuously over time as new observations (vitals and laboratory test results) are recorded in the electronic health record. Validation against data from two geographically distinct health systems showed that the proposed model achieved 75% specificity and 77% sensitivity and predicted acute respiratory failure at a median time of 32 hours prior to onset. Over 80% of true-positive alerts occurred in non-ICU settings. CONCLUSIONS: Patients admitted to non-ICU environments with coronavirus disease 2019 are at ongoing risk of clinical progression to severe disease, yet it is challenging to anticipate which patients will develop acute respiratory failure. A continuously monitoring prognostic model has potential to facilitate anticipatory rather than reactive approaches to escalation of care (e.g., earlier initiation of treatments for severe disease or structured monitoring and therapeutic interventions for high-risk patients).

BACKGROUND: Postoperative hypotension is associated with adverse outcomes, but intraoperative prediction of postanaesthesia care unit (PACU) hypotension is not routine in anaesthesiology workflow. Although machine learning models may support clinician prediction of PACU hypotension, clinician acceptance of prediction models is poorly understood. METHODS: We developed a clinically informed gradient boosting machine learning model using preoperative and intraoperative data from 88 446 surgical patients from 2015 to 2019. Nine anaesthesiologists each made 192 predictions of PACU hypotension using a web-based visualisation tool with and without input from the machine learning model. Questionnaires and interviews were analysed using thematic content analysis for model acceptance by anaesthesiologists. RESULTS: The model predicted PACU hypotension in 17 029 patients (area under the receiver operating characteristic [AUROC] 0.82 [95% confidence interval {CI}: 0.81–0.83] and average precision 0.40 [95% CI: 0.38–0.42]). On a random representative subset of 192 cases, anaesthesiologist performance improved from AUROC 0.67 (95% CI: 0.60–0.73) to AUROC 0.74 (95% CI: 0.68–0.79) with model predictions and information on risk factors. Anaesthesiologists perceived more value and expressed trust in the prediction model for prospective planning, informing PACU handoffs, and drawing attention to unexpected cases of PACU hypotension, but they doubted the model when predictions and associated features were not aligned with clinical judgement. Anaesthesiologists expressed interest in patient-specific thresholds for defining and treating postoperative hypotension. CONCLUSIONS: The ability of anaesthesiologists to predict PACU hypotension was improved by exposure to machine learning model predictions. Clinicians acknowledged value and trust in machine learning technology. Increasing familiarity with clinical use of model predictions is needed for effective integration into perioperative workflows.

Background Despite increased testing efforts and the deployment of vaccines, COVID-19 cases and death toll continue to rise at record rates. Health systems routinely collect clinical and non-clinical information in electronic health records (EHR), yet little is known about how the minimal or intermediate spectra of EHR data can be leveraged to characterize patient SARS-CoV-2 pretest probability in support of interventional strategies. Methods and findings We modeled patient pretest probability for SARS-CoV-2 test positivity and determined which features were contributing to the prediction and relative to patients triaged in inpatient, outpatient, and telehealth/drive-up visit-types. Data from the University of Washington (UW) Medicine Health System, which excluded UW Medicine care providers, included patients predominately residing in the Seattle Puget Sound area, were used to develop a gradient-boosting decision tree (GBDT) model. Patients were included if they had at least one visit prior to initial SARS-CoV-2 RT-PCR testing between January 01, 2020 through August 7, 2020. Model performance assessments used area-under-the-receiver-operating-characteristic (AUROC) and area-under-the-precision-recall (AUPR) curves. Feature performance assessments used SHapley Additive exPlanations (SHAP) values. The generalized pretest probability model using all available features achieved high overall discriminative performance (AUROC, 0.82). Performance among inpatients (AUROC, 0.86) was higher than telehealth/drive-up testing (AUROC, 0.81) or outpatient testing (AUROC, 0.76). The two-week test positivity rate in patient ZIP code was the most informative feature towards test positivity across visit-types. Geographic and sociodemographic factors were more important predictors of SARS-CoV-2 positivity than individual clinical characteristics. Conclusions Recent geographic and sociodemographic factors, routinely collected in EHR though not routinely considered in clinical care, are the strongest predictors of initial SARS-CoV-2 test result. These findings were consistent across visit types, informing our understanding of individual SARS-CoV-2 risk factors with implications for deployment of testing, outreach, and population-level prevention efforts.

To determine whether location-linked anaesthesiology calculator mobile application (app) data can serve as a qualitative proxy for global surgical case volumes and therefore monitor the impact of the coronavirus disease 2019 (COVID-19) pandemic.We collected data provided by users of the mobile app "Anesthesiologist" during 1 October 2018-30 June 2020. We analysed these using RStudio and generated 7-day moving-average app use plots. We calculated country-level reductions in app use as a percentage of baseline. We obtained data on COVID-19 case counts from the European Centre for Disease Prevention and Control. We plotted changing app use and COVID-19 case counts for several countries and regions.A total of 100 099 app users within 214 countries and territories provided data. We observed that app use was reduced during holidays, weekends and at night, correlating with expected fluctuations in surgical volume. We observed that the onset of the pandemic prompted substantial reductions in app use. We noted strong cross-correlation between COVID-19 case count and reductions in app use in low- and middle-income countries, but not in high-income countries. Of the 112 countries and territories with non-zero app use during baseline and during the pandemic, we calculated a median reduction in app use to 73.6% of baseline.App data provide a proxy for surgical case volumes, and can therefore be used as a real-time monitor of the impact of COVID-19 on surgical capacity. We have created a dashboard for ongoing visualization of these data, allowing policy-makers to direct resources to areas of greatest need.Déterminer si les données provenant d'applications mobiles de calcul dédiées à l'anesthésie et munies d'une fonction de géolocalisation peuvent servir de substitut en vue d'évaluer le nombre d'interventions chirurgicales dans le monde et, par conséquent, de mesurer l'impact de la pandémie de maladie à coronavirus 2019 (COVID-19) sur ces interventions.Nous avons récolté les données fournies par les utilisateurs de l'application mobile «Anesthesiologist» entre le 1Au total, 100 099 utilisateurs originaires de 214 pays et régions nous ont communiqué leurs données. Nous avons observé une diminution dans l'utilisation de l'application durant les vacances, les week-ends et la nuit, ce qui correspond aux fluctuations prévues en matière de volume d'interventions. Nous avons également constaté que l'apparition de la pandémie avait entraîné une baisse considérable de l'utilisation de l'application. Nous avons noté une importante corrélation croisée entre le nombre de cas de COVID-19 et cette baisse d'utilisation dans les pays à faible et moyen revenu, mais pas dans les pays à haut revenu. Sur les 112 pays et régions affichant une utilisation non nulle de l'application pendant la période de référence et pendant la pandémie, nous avons calculé une réduction médiane de 73,6%.Les données provenant de l'application fournissent des informations indirectes qui servent à déterminer le nombre d'interventions chirurgicales, et peuvent donc être employées pour suivre en temps réel l'impact de la COVID-19 sur la capacité chirurgicale. Nous avons créé un tableau de bord pour visualiser ces données en continu, ce qui permet aux législateurs d'attribuer des ressources aux secteurs qui en ont le plus besoin.Determinar si los datos de las aplicaciones móviles para calcular la anestesia asociada a la localización pueden servir como un sustituto cualitativo para evaluar la cantidad de intervenciones quirúrgicas a nivel mundial y, por lo tanto, para medir el impacto de la pandemia de la enfermedad del coronavirus 2019 (COVID-19) en esas intervenciones.Se recopilaron los datos que suministraron los usuarios de la aplicación móvil «Anestesiólogo» durante el periodo comprendido entre el 1.Un total de 100 099 usuarios de la aplicación en 214 países y territorios suministraron datos. Se observó que el uso de la aplicación se redujo durante los días festivos, los fines de semana y por las noches, en correlación con las fluctuaciones previstas de la cantidad de intervenciones quirúrgicas. Se observó que el inicio de la pandemia generó reducciones sustanciales en el uso de la aplicación. Se registró una fuerte correlación cruzada entre el recuento de los casos de la COVID-19 y las reducciones en el uso de la aplicación en los países de ingresos bajos y medios, pero no en los países de ingresos altos. De los 112 países y territorios que no usaron la aplicación durante el momento basal y durante la pandemia, se calculó una reducción mediana en el uso de la aplicación hasta el 73,6 % del valor basal.Los datos de la aplicación representan indicadores de la cantidad de intervenciones quirúrgicas y, por lo tanto, se pueden usar para medir en tiempo real el impacto de la COVID-19 en la capacidad quirúrgica. Se ha elaborado un tablero para visualizar estos datos de forma continua, lo que permite a los responsables de formular las políticas asignar recursos a las áreas de mayor necesidad.الغرض تحديد ما إذا كانت بيانات تطبيق الهاتف المحمول (التطبيق) للآلة الحاسبة للتخدير، والمرتبطة بالموقع، يمكنها أن تكون بمثابة مؤشر نوعي لأحجام الحالات الجراحية العالمية، وبالتالي مراقبة تأثير الإصابة بفيروس كورونا 2019 (كوفيد 19). الطريقة قمنا بجمع البيانات التي قدمها مستخدمو تطبيق الهاتف المحمول Anesthesiologist (طبيب التخدير) خلال الفترة من 1 أكتوبر/تشرين أول 2018 إلى 30 يونيو/حزيران 2020. كما قمنا بتحليل هذه البيانات باستخدام RStudio، وقمنا بإنشاء مخططات لاستخدام توضح متوسط ​​الحركة لمدة 7 أيام. قم باحتساب حالات الانخفاض في استخدام التطبيق على مستوى البلد كنسبة مئوية من خط الأساس. حصلنا على بيانات عن عدد حالات الإصابة بمرض كوفيد -19 من المركز الأوروبي للوقاية من الأمراض ومكافحتها. قمنا بتخطيط التغيير في استخدام التطبيق وأعداد حالات كوفيد-19 للعديد من البلدان والمناطق. النتائج إجمالي 100099 من مستخدمي التطبيق في 214 بلدًا وإقليمًا، قاموا بتقديم البيانات. لاحظنا أن استخدام التطبيق قد انخفض خلال الإجازات وعطلات نهاية الأسبوع وأثناء الليل، وهو ما يرتبط بالتقلبات المتوقعة في حجم العمليات الجراحية. لاحظنا أن تفشي الوباء أدى إلى حالات انخفاض ملموسة في استخدام التطبيق. لاحظنا وجود علاقة متبادلة قوية بين عدد حالات الإصابة بكوفيد 19، والانخفاضات في استخدام التطبيق في البلدان منخفضة ومتوسطة الدخل، ولكن ليس في البلدان مرتفعة الدخل. من بين 112 بلدًا وإقليمًا ينعدم فيها استخدام التطبيق أثناء خط الأساس وأثناء الوباء، قمنا باحتساب متوسط ​​الانخفاض في استخدام التطبيق بنسبة 73.6% من خط الأساس. الاستنتاج توفر بيانات تطبيق الاستنتاج مؤشرًا لأحجام الحالات الجراحية، وبالتالي يمكن استخدامها كمراقب في الوقت الفعلي لتأثير كوفيد 19 على القدرة الجراحية. لقد أنشأنا لوحة تحكم للتصور المستمر لهذه البيانات، مما يسمح لواضعي السياسات بتوجيه الموارد إلى المناطق الأكثر احتياجًا.确定定位麻醉学计算器移动应用程序（以下简称应用）数据是否可以作为全球外科手术量的定性替代指标，从而监测 2019 年全球流行病冠状病毒 (COVID-19) 产生的影响。.我们收集了 2018 年 10 月 1 日至 2020 年 6 月 30 日期间，移动应用程序麻醉师 (Anesthesiologist) 用户的使用数据。我们利用 Rstudio 分析这些数据，并绘制出 7 天内使用应用的移动平均曲线图。我们计算出国家或地区应用使用人数削减数量占基准值的百分比。我们从欧洲疾病预防控制中心了解到新型冠状病毒肺炎病例数。我们绘制了一些国家和地区应用使用人数和新型冠状病毒肺炎病例数量变化图。.214 个国家和地区共有 100,099 名应用用户提供了数据。我们发现，应用使用人数在节假日、周末和晚间有所减少，这与外科手术量的预期波动有关，我们还观察到，全球性流行病爆发导致应用使用人数大幅度下降。我们也注意到，在中低收入国家，新型冠状病毒肺炎病例与应用使用人数减少存在很强的互相关性，而在高收入国家则不存在。基准线和全球性流行病爆发期间，应用使用人数非零的 112 个国家和地区中，我们得出其中位数下降到基准线的 73.6%。.应用数据可以作为外科手术台次的替代指标，因此可用来实时监测新型冠状病毒肺炎对外科手术量的影响。我们设计了一个仪表盘，以便不间断地提供可视化数据，从而决策者可将资源集中于最需要的地区。.Определить, могут ли данные мобильного приложения калькулятора анестезиологии с привязкой к местоположению служить качественным показателем глобальных объемов хирургических операций и, следовательно, отслеживать влияние пандемии коронавирусного заболевания 2019 г. (COVID-19).Авторы собрали данные, предоставленные пользователями мобильного приложения «Анестезиолог» в период с 1 октября 2018 г. по 30 июня 2020 г. Эти данные были проанализированы с помощью программы RStudio, и по ним были построены графики использования приложения для скользящих средних показаний за 7 дней. Сокращение использования приложения на уровне страны было рассчитано в виде процентов от исходного уровня. Данные о количестве случаев COVID-19 авторы получили из Европейского центра по контролю и профилактике заболеваний. Авторы составили график изменения использования приложения в зависимости от количества случаев COVID-19 для нескольких стран и регионов.Данные были получены от 100 099 пользователей приложения из 214 стран и территорий. Было отмечено, что использование приложения сокращалось в праздничные дни, выходные дни и ночью, что соотносится с ожидаемыми колебаниями объема хирургических операций. Также было отмечено, что начало пандемии привело к значительному сокращению использования приложения. Авторы наблюдали сильную взаимную корреляцию между количеством случаев COVID-19 и сокращением использования приложения в странах с низким и средним уровнем дохода, но не в странах с высоким уровнем дохода. По данным для 112 стран и территорий с ненулевым использованием приложения во время исходного уровня и во время пандемии медианное сокращение использования приложения составило до 73,6% от исходного уровня.Данные приложения служат косвенной оценкой количества проводимых хирургических операций и, следовательно, могут использоваться в качестве средства мониторинга воздействия COVID-19 на хирургический потенциал в режиме реального времени. Авторы создали панель индикаторов для непрерывной визуализации этих данных, которая позволяет лицам, формирующим политику, направлять ресурсы в области, где потребности являются наиболее острыми.

Concerns about severe acute respiratory syndrome coronavirus 2 exposure in health care settings may cause patients to delay care. Among 2992 patients testing negative on admission to an academic, 3-hospital system, 8 tested positive during hospitalization or within 14 days postdischarge. Following adjudication of each instance, health care–associated infection incidence ranged from 0.8 to 5.0 cases per 10 000 patient-days.

STUDY DESIGN Retrospective hospital-registry study. OBJECTIVE To characterize the microbial epidemiology of surgical site infection (SSI) in spinal fusion surgery and the burden of resistance to standard surgical antibiotic prophylaxis. SUMMARY OF BACKGROUND DATA SSI persists as a leading complication of spinal fusion surgery despite the growth of enhanced recovery programs and improvements in other measures of surgical quality. Improved understandings of SSI microbiology and common mechanisms of failure for current prevention strategies are required to inform the development of novel approaches to prevention relevant to modern surgical practice. METHODS Spinal fusion cases performed at a single referral center between January 2011 and June 2019 were reviewed and SSI cases meeting National Healthcare Safety Network criteria were identified. Using microbiologic and procedural data from each case, we analyzed the anatomic distribution of pathogens, their differential time to presentation, and correlation with methicillin-resistant Staphylococcus aureus screening results. Susceptibility of isolates cultured from each infection were compared with the spectrum of surgical antibiotic prophylaxis administered during the index procedure on a per-case basis. Susceptibility to alternate prophylactic agents was also modeled. RESULTS Among 6727 cases, 351 infections occurred within 90 days. An anatomic gradient in the microbiology of SSI was observed across the length of the back, transitioning from cutaneous (gram-positive) flora in the cervical spine to enteric (gram-negative/anaerobic) flora in the lumbosacral region (correlation coefficient 0.94, P

BackgroundSARS-CoV-2 reverse transcriptase polymerase chain reaction (RT-PCR) testing remains the cornerstone of laboratory-based identification of patients with COVID-19. As the availability and speed of SARS-CoV-2 testing platforms improve, results are increasingly relied upon to inform critical decisions related to therapy, use of personal protective equipment, and workforce readiness. However, early reports of RT-PCR test performance have left clinicians and the public with concerns regarding the reliability of this predominant testing modality and the interpretation of negative results. In this work, two independent research teams report the frequency of discordant SARS-CoV-2 test results among initially negative, repeatedly tested patients in regions of the United States with early community transmission and access to testing.MethodsAll patients at the University of Washington (UW) and Stanford Health Care undergoing initial testing by nasopharyngeal (NP) swab between March 2nd and April 7th, 2020 were included. SARS-CoV-2 RT-PCR was performed targeting the N, RdRp, S, and E genes and ORF1ab, using a combination of Emergency Use Authorization laboratory-developed tests and commercial assays. Results through April 14th were extracted to allow for a complete 7-day observation period and an additional day for reporting.ResultsA total of 23,126 SARS-CoV-2 RT-PCR tests (10,583 UW, 12,543 Stanford) were performed in 20,912 eligible patients (8,977 UW, 11,935 Stanford) undergoing initial testing by NP swab; 626 initially test-negative patients were re-tested within 7 days. Among this group, repeat testing within 7 days yielded a positive result in 3.5% (4.3% UW, 2.8% Stanford) of cases, suggesting an initial false negative RT-PCR result; the majority (96.5%) of patients with an initial negative result who warranted reevaluation for any reason remained negative on all subsequent tests performed within this window.ConclusionsTwo independent research teams report the similar finding that, among initially negative patients subjected to repeat SARS-CoV-2 RT-PCR testing, the occurrence of a newly positive result within 7 days is uncommon. These observations suggest that false negative results at the time of initial presentation do occur, but potentially at a lower frequency than is currently believed. Although it is not possible to infer the clinical sensitivity of NP SARS-CoV-2 RT-PCR testing using these data, they may be used in combination with other reports to guide the use and interpretation of this common testing modality.

ImportanceThe COVID-19 pandemic has disrupted global surgical capacity. The impact of the pandemic in low and middle income countries has the potential to worsen already strained access to surgical care. Timely assessment of surgical volumes in these countries remains challenging.ObjectiveTo determine whether usage data from a globally used anesthesiology calculator mobile application can serve as a proxy for global surgical case volume and contribute to monitoring of the impact of the COVID-19 pandemic, particularly in World Bank low income countries where official data collection is not currently practical.DesignSubset of data from an ongoing observational cohort study of users of the application collected from October 1, 2018 to April 18, 2020.SettingThe mobile application is available from public sources; users download and use the application per their own clinical needs on personal mobile devices.ParticipantsNo user data was excluded from the study.Exposure(s)Events with impacts on surgical case volumes, including weekends, holidays, and the COVID-19 pandemic.Main Outcome(s) and Measure(s)It was previously noted that application usage was decreased on weekends and during winter holidays. We subsequently hypothesized that more detailed analysis would reveal impacts of country-specific or region-specific holidays on the volume of app use.Results4,300,975 data points from 92,878 unique users were analyzed. Physicians and other anesthesia providers comprised 85.8% of the study population. Application use was reduced on holidays and weekends and correlated with fluctuations in surgical volume. The COVID-19 pandemic was associated with substantial reductions in app use globally and regionally. There was strong cross correlation between COVID-19 case count and reductions in app use. By country, there was a median global reduction in app use to 58% of baseline (interquartile range, 46%-75%). Application use in low-income continues to decline but in high-income countries has stabilized.Conclusions and RelevanceApplication usage metadata provides a real-time indicator of surgical volume. This data may be used to identify impacted regions where disruptions to surgical care are disproportionate or prolonged. A dashboard for continuous visualization of these data has been deployed.Key PointsQuestionCan usage data from a globally used anesthesiology calculator mobile application contribute to monitoring of the impacts to global surgical case volume caused by the COVID-19 pandemic, particularly in resource-limited environments such as World Bank low income countries?FindingsIn this ongoing observational cohort study, application usage data from 92,878 unique users in 221 countries was found to serve well as a qualitative proxy for surgical case volume, with clear impacts to app use during weekend, holidays, and during the COVID-19 pandemic.MeaningThis proxy of surgical volume will provide insight into the impact of and recovery from the COVID-19 pandemic where official data collection is not currently practical. A real time dashboard tracking this proxy of global surgical volume is live and under continued development.

OBJECTIVE: Evaluate the dose-response relationship between intraoperative fluid administration and postoperative outcomes in a large cohort of surgical patients.BACKGROUND: Healthy humans may live in a state of fluid responsiveness without the need for fluid supplementation. Goal-directed protocols driven by such measures are limited in their ability to define the optimal fluid state during surgery.METHODS: This analysis of data on file included 92,094 adult patients undergoing noncardiac surgery with endotracheal intubation between 2007 and 2014 at an academic tertiary care hospital and two affiliated community hospitals. The primary exposure variable was total intraoperative volume of crystalloid and colloid administered. The primary outcome was 30-day survival. Secondary outcomes were respiratory complications within three postoperative days (pulmonary edema, reintubation, pneumonia, or respiratory failure) and acute kidney injury. Exploratory outcomes were postoperative length of stay and total cost of care. Our models were adjusted for patient-, procedure-, and anesthesia-related factors.RESULTS: A U-shaped association was observed between the volume of fluid administered intraoperatively and 30-day mortality, costs, and postoperative length of stay. Liberal fluid volumes (highest quintile of fluid administration practice) were significantly associated with respiratory complications whereas both liberal and restrictive (lowest quintile) volumes were significantly associated with acute kidney injury. Moderately restrictive volumes (second quintile) were consistently associated with optimal postoperative outcomes and were characterized by volumes approximately 40% less than traditional textbook estimates: infusion rates of approximately 6-7 mL/kg/hr or 1 L of fluid for a 3-hour case.CONCLUSIONS: Intraoperative fluid dosing at the liberal and restrictive margins of observed practice is associated with increased morbidity, mortality, cost, and length of stay.

Background The use of intraoperative opioids may influence the rate of postoperative complications. This study evaluated the association between intraoperative opioid dose and the risk of 30-day hospital readmission. Methods We conducted a pre-specified analysis of existing registry data for 153 902 surgical cases performed under general anaesthesia at Massachusetts General Hospital and two affiliated medical centres. We examined the association between total intraoperative opioid dose (categorised in quintiles) and 30-day hospital readmission, controlling for several patient-, anaesthetist-, and case-specific factors. Results Compared with low intraoperative opioid dosing [quintile 1, median (inter-quartile range): 8 (4–9) mg morphine equivalents], exposure to high-dose opioids during surgery [quintile 5: 32 (27–41) equivalents] is an independent predictor of 30-day readmission [odds ratio (OR) 1.15 (95% confidence interval 1.07–1.24); P Conclusions High intraoperative opioid dose is a modifiable anaesthetic factor that varies in the practice of individual anaesthetists and affects postoperative outcomes. Conservative standards for intraoperative opioid dosing may reduce the risk of postoperative readmission, particularly in ambulatory surgery.

Background:: The COVID-19 pandemic has disrupted global surgical capacity. The impact of the pandemic in low and middle income countries (LMIC) has the potential to worsen already strained access to surgical care. However, timely assessment of surgical volumes in LMIC remains challenging. Methods: Data from a globally available anesthesia mobile application were examined to assess utilization as a real-time qualitative proxy for surgical case volume. Users consent to cloud-based data collection for an ongoing app utilization study. These data were analyzed to identify temporal and geographic trends indicative of COVID-19-associated changes in surgical volume. Findings: Between 1 October 2018 and 18 April 2020, data from 92,878 unique users in 221 countries were collected. App use was reduced on major holidays and weekends, strongly correlated with known fluctuations in surgical volume. The COVID-19 pandemic was associated with substantial reductions in app use globally and regionally. In individual countries and regions, there was strong cross correlation between COVID-19 case count and reductions in app use. By country, there was a median global reduction in app use to 54% of baseline (interquartile range, 42%-64%). Overall app use in LMIC continues to decline but in high-income countries has stabilized. Interpretation: The metadata from this app offers an inexpensive real-time indicator of surgical volume. This data may be used to identify impacted regions where disruptions to surgical care are disproportionate or prolonged. A dashboard for continuous visualization of these data will be deployed to ensure access to this proxy for global surgical volumes. Funding Statement: No funding to declare in support of the present work. Declaration of Interests: All authors declare: no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work. The Anesthesiologist app was initially released in 2011 by Vikas O'Reilly-Shah with advertising in the free version and a paid companion app to remove the ads. The app intellectual property was transferred to Emory University in 2015 and advertisements were subsequently removed, and the companion app to remove ads made freely available for legacy users not updating to the ad-free version. Following review by the Emory University Research Conflict of Interest Committee, Vikas O'Reilly-Shah was released from any conflict of interest management plan or oversight. Ethics Approval Statement: The study was reviewed and approved by the Emory University Institutional Review Board (study number 00082571), and there is a reliance agreement in place with the University of Washington Institutional Review Board. The approval includes a waiver of written informed consent. Participants anonymously gave electronic consent before participating in any data collection.

BACKGROUND In this prespecified cohort study, we investigated the influence of postoperative admission to the intensive care unit versus surgical ward on health care utilization among patients undergoing intermediate-risk surgery. METHODS Of adult surgical patients who underwent general anesthesia without an absolute indication for postoperative intensive care unit admission, 3530 patients admitted postoperatively to an intensive care unit were matched to 3530 patients admitted postoperatively to a surgical ward using a propensity score based on 23 important preoperative and intraoperative predictor variables. Postoperative hospital length of stay and hospital costs were defined as primary and secondary end points, respectively. RESULTS Among patients with low propensity for postoperative intensive care unit admission, initial triage to an intensive care unit was associated with increased postoperative length of stay (incidence rate ratio, 1.69 [95% CI, 1.59-1.79]; P < .001) and hospital costs (incidence rate ratio, 1.92 [95% CI, 1.81-2.03]; P < .001). By contrast, postoperative intensive care unit admission of patients with high propensity was associated with decreased postoperative length of stay (incidence rate ratio, 0.90 [95% CI, 0.85-0.95]; P < .001) and costs (incidence rate ratio, 0.92 [95% CI, 0.88-0.97]; P = .001). Decisions regarding postoperative intensive care unit resource utilization were influenced by individual preferences of anesthesiologists and surgeons. CONCLUSIONS In patients with an unclear indication for postoperative critical care, intensive care unit admission may negatively impact postoperative hospital length of stay and costs. Postoperative discharge disposition varies substantially based on anesthesia and surgical provider preferences but should optimally be driven by an objective assessment of a patient's status at the end of surgery.

West Nile virus (WNV) infection results in a diverse spectrum of outcomes, and host genetics are likely to influence susceptibility to neuroinvasive disease (West Nile neuroinvasive disease (WNND)). We performed whole-exome sequencing of 44 individuals with WNND and identified alleles associated with severe disease by variant filtration in cases, kernel association testing in cases and controls and single-nucleotide polymorphism (SNP) imputation into a larger cohort of WNND cases and seropositive controls followed by genome-wide association analysis. Variant filtration prioritized genes based on the enrichment of otherwise rare variants, but did not unambiguously implicate variants shared by a majority of cases. Kernel association demonstrated enrichment for risk and protective alleles in the human leukocyte antigen (HLA)-A and HLA-DQB1 loci that have well understood roles in antiviral immunity. Two loci, HERC5 and an intergenic region between CD83 and JARID2, were implicated by multiple imputed SNPs and exceeded genome-wide significance in a discovery cohort (n=862). SNPs at two additional loci, TFCP2L1 and CACNA1H, achieved genome-wide significance after association testing of directly genotyped and imputed SNPs in a discovery cohort (n=862) and a separate replication cohort (n=1387). The context of these loci suggests that immunoregulatory, ion channel and endothelial barrier functions may be important elements of the host response to WNV.

Purpose. Wide variability persists in the preparation and storage of common anesthetic medications despite the recognition of anesthesia workspace standardization as a national quality improvement priority. Syringe contamination and medication swaps continue to pose significant hazards to patient safety. Methods. We assessed differences in practice related to the availability of commonly prepared anesthetic medications. Using baseline provider surveys (n = 87) and anesthesia workspace audits (n = 80), we designed a custom syringe organization device using 3D printing techniques to serve as a cognitive aid and organizational tool. We iteratively tested and then deployed this device in all 60 operating rooms at a single institution, and then, repeated postintervention surveys (n = 79) and workspace audits (n = 75) one year after introduction. Results. Implementation was associated with significant improvements in provider-reported medication availability during coverage and handoff situations (43.7% versus 76.2% reporting 95% confidence preintervention versus postintervention, p<0.001). This was substantiated by audits of the anesthesia workspace which demonstrated reduced variability in the location (p<0.001) and availability (p<0.001) of key medications. Provider confidence in the cleanliness of syringes was also improved (p=0.01). A high degree of acceptance and compliance with the intervention was reported, with 80.4% of syringes observed to be stored in the device one year after implementation and approximately 95% of respondents reporting positive measures of usability and convenience. Conclusion. Use of a simple organizational device for syringes in the anesthesia workspace has numerous safety benefits. 3D printing offers improvements in adaptability and affordability compared with prior approaches.

Background Although barriers to granting surgical residents autonomy in the operating room are well described, few have proposed practical strategies to overcome these barriers. Our department adopted a multidisciplinary approach to develop a rotation that aimed to grant chief residents structured operative autonomy. In this study, we assess the feasibility of implementation, impact on patient safety, and educational benefit to residents after the program's pilot year. Study Design During a 1-month rotation, chief residents began cases alone using their own operative block time. The attending surgeon was notified when the critical portion of the operation was reached and supervised its completion. Postoperative complications, intraoperative adverse events, readmissions, operation duration, and length of stay in a subset of patients that underwent a cholecystectomy or appendectomy were compared with patients operated on by standard resident services. Follow-up surveys were administered to residents 1 year after graduation. Results One hundred and twenty-four operations, which ranged in complexity, were performed by chief residents. Unadjusted subset analysis comparing the structured operative autonomy (n = 54) and standard resident (n = 718) services outcomes for appendectomies and cholecystectomies revealed no significant differences in 30-day postoperative complications (5.6% vs 4.0%; p = 0.59), major intraoperative adverse events, or readmissions (3.7% vs 3.8%; p = 1.00), respectively. Multivariate analysis performed for 30-day complications (odds ratio 0.8; 95% CI 0.2 to 3.2; p = 0.76) and readmissions (odds ratio 0.4; 95% CI 0.1 to 2.1; p = 0.3) corroborated unadjusted findings. All participants (n = 8) strongly agreed that the rotation eased their transition to fellowship or independent practice. Conclusions Structured operative autonomy overcomes known barriers to granting chief residents autonomy in the operating room. When used for select general surgery cases, resident education is enhanced without impacting patient outcomes. This training model has the potential to improve the surgical independence of graduating residents.

We thought that the rate of postoperative pulmonary complications might be higher after pressure-controlled ventilation than after volume-controlled ventilation. We analysed peri-operative data recorded for 109,360 adults, whose lungs were mechanically ventilated during surgery at three hospitals in Massachusetts, USA. We used multivariable regression and propensity score matching. Postoperative pulmonary complications were more common after pressure-controlled ventilation, odds ratio (95%CI) 1.29 (1.21–1.37), p < 0.001. Tidal volumes and driving pressures were more varied with pressure-controlled ventilation compared with volume-controlled ventilation: mean (SD) variance from the median 1.61 (1.36) ml.kg(–1) vs. 1.23 (1.11) ml.kg(–1), p < 0.001; and 3.91 (3.47) cmH(2)O vs. 3.40 (2.69) cmH(2)O, p < 0.001. The odds ratio (95%CI) of pulmonary complications after pressure-controlled ventilation compared with volume-controlled ventilation at positive end-expiratory pressures < 5 cmH(2)O was 1.40 (1.26–1.55) and 1.20 (1.11–1.31) when ≥ 5 cmH(2)O, both p < 0.001, a relative risk ratio of 1.17 (1.03–1.33), p = 0.023. The odds ratio (95%CI) of pulmonary complications after pressure-controlled ventilation compared with volume-controlled ventilation at driving pressures of < 19 cmH(2)O was 1.37 (1.27–1.48), p < 0.001, and 1.16 (1.04–1.30) when ≥ 19 cmH(2)O, p = 0.011, a relative risk ratio of 1.18 (1.07–1.30), p = 0.016. Our data support volume-controlled ventilation during surgery, particularly for patients more likely to suffer postoperative pulmonary complications.