Contribution of the patient microbiome to surgical site infection and antibiotic prophylaxis failure in spine surgery.

Despite modern antiseptic techniques, surgical site infection (SSI) remains a leading complication of surgery. However, the origins of SSI and the high rates of antimicrobial resistance observed in these infections are poorly understood. Using instrumented spine surgery as a model of clean (class I) skin incision, we prospectively sampled preoperative microbiomes and postoperative SSI isolates in a cohort of 204 patients. Combining multiple forms of genomic analysis, we correlated the identity, anatomic distribution, and antimicrobial resistance profiles of SSI pathogens with those of preoperative strains obtained from the patient skin microbiome. We found that 86% of SSIs, comprising a broad range of bacterial species, originated endogenously from preoperative strains, with no evidence of common source infection among a superset of 1610 patients. Most SSI isolates (59%) were resistant to the prophylactic antibiotic administered during surgery, and their resistance phenotypes correlated with the patient's preoperative resistome (P = 0.0002). These findings indicate the need for SSI prevention strategies tailored to the preoperative microbiome and resistome present in individual patients.

2023 Neer Award for Basic Science: Genetics of Cutibacterium acnes in Revision Shoulder Arthroplasty: A Large-Scale Bacterial Whole-Genome Sequencing Study.

BACKGROUND: Cutibacterium acnes is the bacterium most commonly responsible for shoulder periprosthetic joint infection (PJI) and is often cultured from samples obtained at the time of revision for failed shoulder arthroplasty. We sought to determine whether these bacteria originate from the patient or from exogenous sources. We also sought to identify which C. acnes genetic traits were associated with the development of shoulder PJI. METHODS: We performed bacterial whole-genome sequencing of C. acnes from a single-institution repository of cultures obtained before or during primary and revision shoulder arthroplasty and correlated the molecular epidemiology and genetic content of strains with clinical features of infection. RESULTS: A total of 341 isolates collected over a four-year period from 88 patients were sequenced. C. acnes cultured from surgical specimens demonstrated significant similarity to the strains colonizing the skin of the same patient (p<0.001). Infrequently, there was evidence of strains shared across unrelated patients, suggesting that exogenous sources of C. acnes culture-positivity were uncommon. Phylotypes IB and II were modestly associated with clinical features of PJI, but all phylotypes appeared inherently capable of causing disease. Chronic shoulder PJI was associated with the absence of common C. acnes genes involved in bacterial quorum-sensing (luxS, tqsA). CONCLUSION: C. acnes strains cultured from deep intraoperative sources during revision shoulder arthroplasty demonstrate strong genetic similarity to the strains colonizing a patient's skin. Some phylotypes of C. acnes commonly colonizing human skin are modestly more virulent than others, but all phylotypes have a capacity for PJI. C. acnes cultured from cases of PJI commonly demonstrated genetic hallmarks associated with adaptation from acute to chronic phases of infection. This is the strongest evidence to date supporting the role of the patient's own, cutaneous C. acnes strains in the pathogenesis of shoulder arthroplasty infection. Our findings support the importance of further research focused on perioperative decolonization and management of endogenous bacteria that are likely to be introduced into the arthroplasty wound at the time of skin incision.

Transposon sequencing identifies genes impacting Staphylococcus aureus invasion in a human macrophage model.

Staphylococcus aureus is a facultative intracellular pathogen in many host cell types, facilitating its persistence in chronic infections. The genes contributing to intracellular pathogenesis have not yet been fully enumerated. Here, we cataloged genes influencing S. aureus invasion and survival within human THP-1 derived macrophages using two laboratory strains (ATCC2913 and JE2). We developed an in vitro transposition method to produce highly saturated transposon mutant libraries in S. aureus and performed transposon insertion sequencing (Tn-Seq) to identify candidate genes with significantly altered abundance following macrophage invasion. While some significant genes were strain-specific, 108 were identified as common across both S. aureus strains, with most (n = 106) being required for optimal macrophage infection. We used CRISPR interference (CRISPRi) to functionally validate phenotypic contributions for a subset of genes. Of the 20 genes passing validation, seven had previously identified roles in S. aureus virulence, and 13 were newly implicated. Validated genes frequently evidenced strain-specific effects, yielding opposing phenotypes when knocked down in the alternative strain. Genomic analysis of de novo mutations occurring in groups (n = 237) of clonally related S. aureus isolates from the airways of chronically infected individuals with cystic fibrosis (CF) revealed significantly greater in vivo purifying selection in conditionally essential candidate genes than those not associated with macrophage invasion. This study implicates a core set of genes necessary to support macrophage invasion by S. aureus, highlights strain-specific differences in phenotypic effects of effector genes, and provides evidence for selection of candidate genes identified by Tn-Seq analyses during chronic airway infection in CF patients in vivo.

Dynamics of Cutibacterium repopulation onto the skin surface of the shoulder after chlorhexidine application.

PURPOSE: The objective of this study was to characterize the temporal dynamics of Cutibacterium repopulation of the skin surface after application of chlorhexidine to the shoulder. METHODS: Ten shoulders in five male subjects were used. A skin swab was taken prior to (0 minutes) and then at three, 30, 60, 120, and 240 minutes after skin preparation with 2% chlorhexidine gluconate and 70% isopropyl alcohol. Semi-quantitative bacterial load was measured for each timepoint. RESULTS: From zero minutes (pre-treatment) to three minutes, chlorhexidine-isopropyl alcohol reduced the skin bacterial load in eight out of ten shoulders. Of these eight shoulders, four (50%) had growth by 30 minutes, seven (88%) had growth by 60 minutes, and all eight (100%) had growth by 240 minutes. Compared to the three minutes after chlorhexidine application, bacterial load had significantly increased by 60 minutes but were still significantly lower than the pre-prep bacterial load (0 minutes). CONCLUSION: Following standard surgical skin preparation with chlorhexidine-isopropyl alcohol, the surface of the shoulder is repopulated with Cutibacterium within one hour, presumably from reservoirs in sebaceous glands not penetrated by topical antiseptic agents. Since these dermal glands are transected by skin incision for shoulder arthroplasty, this study suggests that they may be sources of wound contamination during surgery in spite of skin preparation with chlorhexidine.

In Vitro Selection Identifies Staphylococcus aureus Genes Influencing Biofilm Formation.

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. Here, we performed in vitro selection of four S. aureus laboratory strains (ATCC 29213, JE2, N315, and Newman) to identify mutations associated with enhanced biofilm production. Biofilm formation increased in passaged isolates from all strains, exhibiting from 1.2- to 5-fold the capacity of parental lines. Whole-genome sequencing identified nonsynonymous mutations affecting 23 candidate genes and a genomic duplication encompassing sigB. Six candidate genes significantly impacted biofilm formation as isogenic transposon knockouts: three were previously reported to impact S. aureus biofilm formation (icaR, spdC, and codY), while the remaining three (manA, narH, and fruB) were newly implicated by this study. Plasmid-mediated genetic complementation of manA, narH, and fruB transposon mutants corrected biofilm deficiencies, with high-level expression of manA and fruB further enhancing biofilm formation over basal levels. This work recognizes genes not previously identified as contributing to biofilm formation in S. aureus and reveals genetic changes able to augment biofilm production by that organism.

Genomic reconstruction and directed interventions in a multidrug-resistant Shigellosis outbreak in Seattle, WA, USA: a genomic surveillance study.

BACKGROUND: Shigella spp have been associated with community-wide outbreaks in urban settings. We analysed a sustained shigellosis outbreak in Seattle, WA, USA, to understand its origins and mechanisms of antimicrobial resistance, define ongoing transmission patterns, and optimise strategies for treatment and infection control. METHODS: We did a retrospective study of all Shigella isolates identified from stool samples at the clinical laboratories at Harborview Medical Center and University of Washington Medical Center (Seattle, WA, USA) from May 1, 2017, to Feb 28, 2022. We characterised isolates by species identification, phenotypic susceptibility testing, and whole-genome sequencing. Demographic characteristics and clinical outcomes of the patients were retrospectively examined. FINDINGS: 171 cases of shigellosis were included. 78 (46%) patients were men who have sex with men (MSM), and 88 (52%) were people experiencing homelessness (PEH). Although 84 (51%) isolates were multidrug resistant, 100 (70%) of 143 patients with data on antimicrobial therapy received appropriate empirical therapy. Phylogenomic analysis identified sequential outbreaks of multiple distinct lineages of Shigella flexneri and Shigella sonnei. Discrete clonal lineages (ten in S flexneri and nine in S sonnei) and resistance traits were responsible for infection in different at-risk populations (ie, MSM, PEH), enabling development of effective guidelines for empirical treatment. The most prevalent lineage in Seattle was probably introduced to Washington State via international travel, with subsequent domestic transmission between at-risk groups. INTERPRETATION: An outbreak in Seattle was driven by parallel emergence of multidrug-resistant strains involving international transmission networks and domestic transmission between at-risk populations. Genomic analysis elucidated not only outbreak origin, but directed optimal approaches to testing, treatment, and public health response. Rapid diagnostics combined with detailed knowledge of local epidemiology can enable high rates of appropriate empirical therapy even in multidrug-resistant infection. FUNDING: None.

Intraoperative prediction of postanaesthesia care unit hypotension.

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.

Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.

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.

A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

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.

Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients.

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).

Occurrence and Timing of Subsequent Severe Acute Respiratory Syndrome Coronavirus 2 Reverse-transcription Polymerase Chain Reaction Positivity Among Initially Negative Patients.

Using data for 20 912 patients from 2 large academic health systems, we analyzed the frequency of severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction test discordance among individuals initially testing negative by nasopharyngeal swab who were retested on clinical grounds within 7 days. The frequency of subsequent positivity within this window was 3.5% and was similar across institutions.

2021 Young Investigator Award Winner: Anatomic Gradients in the Microbiology of Spinal Fusion Surgical Site Infection and Resistance to Surgical Antimicrobial Prophylaxis.

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 < 0.001). The majority (57.5%) of infections were resistant to the prophylaxis administered during the procedure. Cephalosporin-resistant gram-negative infection was common at lumbosacral levels and undetected methicillin-resistance was common at cervical levels. CONCLUSION: Individualized infection prevention strategies tailored to operative level are needed in spine surgery. Endogenous wound contamination with enteric flora may be a common mechanism of infection in lumbosacral fusion. Novel approaches to prophylaxis and prevention should be prioritized in this population.Level of Evidence: 3.

Polyclonality, Shared Strains, and Convergent Evolution in Chronic Cystic Fibrosis Staphylococcus aureus Airway Infection.

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 have been described for Pseudomonas aeruginosa, resulting in improved diagnosis and treatment, these features remain more poorly defined for S. aureus.Objectives: To characterize the molecular epidemiology and genetic adaptation of S. aureus during chronic CF airway infection and in response to antibiotic therapy.Methods: We performed whole-genome sequencing of 1,382 S. aureus isolates collected longitudinally over a mean 2.2 years from 246 children with CF at five U.S. centers between 2008 and 2017. Results were integrated with clinical and demographic data to characterize bacterial population dynamics and identify common genetic targets of in vivo adaptation.Measurements and Main Results: Results showed that 45.5% of patients carried multiple, coexisting S. aureus lineages, often having different antibiotic susceptibility profiles. Adaptation during the course of infection commonly occurred in a set of genes related to persistence and antimicrobial resistance. Individual sequence types demonstrated wide geographic distribution, and we identified limited strain-sharing among children linked by common household or clinical exposures. Unlike P. aeruginosa, S. aureus genetic diversity was unconstrained, with an ongoing flow of new genetic elements into the population of isolates from children with CF.Conclusions: CF airways are frequently coinfected by multiple, genetically distinct S. aureus lineages, indicating that current clinical procedures for sampling isolates and selecting antibiotics are likely inadequate. Strains can be shared by patients in close domestic or clinical contact and can undergo convergent evolution in key persistence and antimicrobial-resistance genes, suggesting novel diagnostic and therapeutic approaches for future study.

Impact of COVID-19 response on global surgical volumes: an ongoing observational study.

OBJECTIVE: 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. METHODS: 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. FINDINGS: 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. CONCLUSION: 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.

Incidence of Health Care-Associated COVID-19 During Universal Testing of Medical and Surgical Admissions in a Large US Health System.

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.

Identifying Optimal Loci for the Molecular Diagnosis of Microsatellite Instability.

BACKGROUND: Microsatellite 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. METHODS: To 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. RESULTS: Twenty-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. CONCLUSIONS: Most 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.

Real-time assessment of COVID-19 impact on global surgical case volumes.

Importance: The 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. Objective: To 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. Design: Subset of data from an ongoing observational cohort study of users of the application collected from October 1, 2018 to April 18, 2020. Setting: The mobile application is available from public sources; users download and use the application per their own clinical needs on personal mobile devices. Participants: No user data was excluded from the study. Exposures: Events with impacts on surgical case volumes, including weekends, holidays, and the COVID-19 pandemic. Main Outcomes and Measures: 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. Results: 4,300,975 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 Relevance: Application 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.