Background and Objectives: The aim of this study was to evaluate the impact of sagittal imbalance based on pelvic incidence–lumbar lordosis (PI-LL) mismatch on the analgesic efficacy of epidural steroid injection in geriatric patients. Materials and Methods: Patients aged 65 years or older who received lumbar epidural steroid injections under fluoroscopy were enrolled. The cutoff of PI-LL mismatch >20° was used as an indicator of a marked sagittal imbalance. The cross-sectional area of the psoas and paraspinal muscles, as well as the paraspinal fat infiltration grade were measured. A 50% or more decrease in pain score at four weeks after injection was considered as good analgesia. Variables were compared between PI-LL ≤ 20° and >20° groups and multivariate analysis was used to identify factors related to pain relief after injection. Results: A total of 237 patients consisting of 150 and 87 patients in the PI-LL ≤ 20° and >20° groups, respectively, were finally analyzed. Female patients, patients with lumbar surgery history, and the smaller cross-sectional area of the psoas muscles were predominantly observed in patients with sagittal imbalance. There was no difference in analgesic outcome after injection according to the PI-LL mismatch . Multivariate analysis showed that pre-injection opioid use, moderate to severe foraminal stenosis, and high-graded paraspinal fat infiltration were significantly associated with poor analgesia after injection. Conclusions: There was no significant correlation between sagittal spinopelvic alignment and pain relief after lumbar epidural steroid injection for geriatric patients. Degenerative sagittal imbalance results from degenerative structural change of the spine such as disc space narrowing, collapsed vertebral bodies, and/or muscle degeneration in geriatric patients ,2,3. An Lumbar paraspinal muscle abnormalities evaluated with magnetic resonance imaging (MRI) are clinically applied to assess spinal sarcopenia and myosteatosis . DegenerTherefore, the aim of this retrospective observational study was to investigate the relationship between sagittal spinopelvic imbalance based on PI-LL mismatch and pain relief after ESI in geriatric patients diagnosed with degenerative lumbar disease. Another goal of this study was to identify the relevant clinical factors that affected the pain outcome of ESI in the study population.Approval from the Institutional Review Board was obtained and, in keeping with the policies for a retrospective review, informed consent was not required (No. 4-2022-0811). This manuscript adheres to the applicable STROBE checklists for observational studies. From 2016 to 2017, we enrolled patients 65 years or older with a diagnosis of degenerative lumbar spinal disease who received fluoroscopy-guided lumbar ESI, including interlaminar, transforaminal, and caudal approaches. Exclusion criteria included the following: patients with neurodegenerative and psychiatric disease or cancer; patients without pain score measurement; patients without X-ray/MRI prior to injection; and patients lacking in complete medical records and/or records that were lost in follow-up within 4 weeks of the procedure. The study flow diagram can be found in The spinopelvic parameters including PI and LL were measured from the pre-procedural lateral X-ray images of the lumbosacral spine, which were taken in the neutral standing position. Following the standard protocol ,6, angle2/m2). Calculations were performed with Image J software .T1-weighted axial images of MRI were used to evaluate the quantitative measurement of psoas major muscles and paraspinal muscles, which envelope the multifidus and erector spinae muscle as one entity. The cross sectional area (CSA), measured by individual manual outlining of each bilateral psoas muscle and paraspinal muscle, was evaluated at the axial image of the L3/4 disc level , with boA three-grade system based on previous studies was applied to access a semi-quantitative measurement of fatty content of the paraspinal muscles from the same images used to measure CSA  8,14]. ,14. 8,14A pain physician (H.J.K) performed all measurements of CSA and fat infiltration grading at the initial time point and four weeks after the first measurement. The pain physician was blinded to the first results when determining the second results. An independent radiologist (M.R), blinded to all clinical data, also evaluated the images. Both raters were blinded to the results of the other evaluator. If there was a discord in grading, a third evaluator (S.H.K) was introduced to achieve a consensus between the two initial evaluators.All ESI procedures, in accordance to the institutional standard protocol, were performed by two operators with similar training using a C-arm fluoroscopy system . After correct positioning of the needle tip in the anteroposterior and lateral views, 1–2 mL of contrast media was injected to confirm epidural spread. Then, a lidocaine and 5 mg of dexamethasone (non-particulate water-soluble steroid) was typically administered into the epidural space.Review of electronic medical records was performed to collect patient characteristics, factors associated with pain, and clinical factors. Patient characteristics included age, sex, body mass index (BMI), and previous lumbar surgery history. Factors associated with pain included duration of pain, baseline pain score measured by the numeric rating scale (NRS), and pre-opioid analgesic use one month before the procedure. Radiographic features including lumbar herniated disc, grading of stenosis ,17, spont-test, chi-square test, or Fisher’s exact test to analyze demographics, clinical variables, spinopelvic parameters, and muscle degeneration. Continuous variables with non-normal distribution were analyzed with the Mann–Whitney U test. The primary endpoint was the analgesic effects of ESI determined after 4 weeks of the injection between the two groups. The outcome was analyzed with the chi-square test. In regard to our secondary endpoint, significant univariate variables with a p-value threshold of 0.2 were entered into a multivariate logistic regression model to assess their impact on pain relief post-ESI. Values were calculated as adjusted odd ratio (aOR) with a 95% confidence interval (CI). Analyses were conducted with the Statistical Package for the Social Sciences, version 25.0 . A p-value < 0.05 was considered statistically significant.Data are expressed as mean ± standard deviation (SD) for continuous variables and number (percentages) for categorical variables. For ordinal data and discontinuous variables, we expressed the data as median and interquartile range (IQR). The normality of distribution was examined by the Shapiro–Wilk test to ensure proper statistical treatment. We used the intra-class correlation coefficient to analyze the intra and inter observer reliabilities in relation to the spinopelvic measurements, CSA, and the Cohen’s weighted kappa coefficient for grade of paraspinal fat infiltration. We performed the independent Overall, a total of 237 patients, with 150 patients (63.3%) in the PI-LL ≤ 20° group and 87 patients (36.7%) in the PI-LL > 20° group, were finally analyzed in this study. .Patient demographics, pre-procedural clinical data, spinopelvic parameters, and psoas/paraspinal muscle degeneration are summarized in The reliabilities for all spinopelvic measurements were excellent, with intra-observer ICCs of 0.912 and 0.908 and inter-observer ICCs of 0.901 and 0.921 for the PI and LL, respectively. The intra- and inter-observer reliabilities of total muscle CSAs were excellent for the psoas major  and paraspinal muscles . The intra- and inter-observer reliabilities of fat infiltration grading were also excellent for the paraspinal muscles .At four weeks after ESI, there was no difference in the proportion of patients with good analgesia according to the PI-LL mismatch before injection . Also, tp = 0.018), moderate- to severe-graded foraminal stenosis , and high-graded paraspinal fat infiltration  were identified as independent factors of poor analgesic pain relief after ESI (Univariate and multivariate analyses for factors investigated against poor pain relief after ESI can be found in fter ESI . There wDegenerative sagittal imbalance is associated with back pain and poor quality of life in the geriatric population . The spiSpinopelvic parameters were correlated with lumbar muscle volumes but not with muscle fat infiltration in asymptomatic young adults . Our stuIn the current study, the results revealed an association between the severity of foraminal stenosis and analgesic outcome of ESI. A result of the previous study indicated that although transforaminal ESI was effective in pain relief without regard to foraminal stenosis severity, patients with severe foraminal stenosis reported a significantly smaller drop in pain scores as time passed . SimilarThere are several limitations in this study. There may be information bias due to the retrospective nature of the current study. Also, this study was performed in a single-center institution with a small sample size involving a homogenous racial study population. Due to the lack of whole spine X-rays available for a majority of patients, PI and LL were measured only as spinopelvic parameters for assessment of sagittal imbalance. Although previous studies support a strong association of muscle CSA and paraspinal fat infiltration at the L3-L4 level with other levels ,24,27, aIn conclusion, sagittal imbalance based on spinopelvic measurement was not associated with pain outcome of ESI. Instead, there was a significant association between high paraspinal fat infiltration and poor pain relief after ESI. Therefore, the analgesic efficacy of ESI has a higher association to the quality of paraspinal muscles than sagittal imbalance, which might be linked to the loss of muscle mass in geriatric patients with symptomatic degenerative lumbar spinal disease.


We trained and tested (R2.2.4) two (R3-2) deep learning (DL) algorithms on a machine vision tool library platform (Cognex Vision Pro Deep Learning software) to recognize anatomic landmarks and classify chest CT as those with optimum, under-scanned, or over-scanned scan length. (2) Methods: To test our hypothesis, we performed a study with 428 consecutive chest CT examinations  performed at one of the four hospitals. CT examinations from two hospitals were used to train the DL classification algorithms to identify lung apices and bases. The developed algorithms were then tested on the data from the remaining two hospitals. For each CT, we recorded the scan lengths above and below the lung apices and bases. Model performance was assessed with receiver operating characteristics (ROC) analysis. (3) Results: The two DL models for lung apex and bases had high sensitivity, specificity, accuracy, and areas under the curve (AUC) for identifying under-scanning ) and over-scanning ). (4) Conclusions: Our DL models can accurately identify markers for missing anatomic coverage and over-scanning in chest CTs. As a result of its expanding applications, there has been a tremendous increase in the use of computed tomography (CT) R3-3) in modern medicine across the entire world. In the United States, CT imaging rates increased from 204 per 1000 individuals in 2000 to 428 per 1000 in 2016 [-3 in modAlthough CT vendors have introduced several innovative technologies to help reduce radiation dose from improved scanner efficiency and advanced image reconstruction techniques, careful designing of scan protocols and appropriate scanning procedures are critical for radiation-dose optimization . Apart fDespite specific information on anatomic landmarks for appropriate scan length, both over-scanning and under-scanning are frequent and lead to suboptimal evaluation and/or excess radiation dose . To asseWe used a novel deep learning (DL)-based vision software platform  to trainDL models for recognizing key anatomic landmarks in the chest to determine over-scanning and under-scanning at the lung apices and bases in chest CT examinations (R3-1). We used multi-site, multi-scanner, and multi-protocol image data to train and test the artificial intelligence (AI) (R3-3) models to ensure generalizability. The purpose of our study was to train and test (R2.2.4) deep DL algorithms to assess over- and under-scanning in chest CT examinations from two quaternary and two community hospitals.The retrospective study was approved by the human research committee of our institutional review board at Massachusetts General Brigham . The need for informed consent was waived. All methods were performed in accordance with the relevant guidelines and regulations . The study followed the Health Insurance Portability and Accountability Act’s guidelines (HIPAA). Mass General Brigham received funding through a collaboration with Cognex Corporation. MKK has received unrelated research grants from Coreline Corporation, Riverain Tech  and Siemens Healthineers . Other co-authors have no financial disclosures.Using keywords of “chest CT with contrast and/or without contrast” on a commercial natural language understanding-based search engine , we queried a multi-institutional radiology report search database to identify the first consecutive 428 chest CT examinations from adult patients (age > 18 years) scanned between January and March 2021. All patients had a routine chest CT as part of their standard of care. The most common clinical indications for chest CT examinations were cancer staging and response assessment, hemoptysis, persistent cough, and unresolved pneumonia. Non-routine chest CT examinations, such as for lung nodule follow-up, lung cancer screening, tracheal protocol, and CT pulmonary angiography, were excluded. However, we did not exclude CT with metal or motion-related artifacts.The first chest CT of each patient was selected from January–March 2021. Thus, the final sample size was 428 patients . All patients were scanned in one of the four hospitals .n = 277 patients), Siemens Healthineers , Canon Medical Systems USA , and Philips Healthcare . All chest CT examinations were performed using routine chest CT protocols with helical scan mode, automatic tube voltage selection (on some scanners and 100–120 kV on scanners without the technique), automatic exposure control, 0.9:1 pitch, 0.4–0.5 s gantry rotation time, 1–1.25 mm reconstructed section thickness, 0.625–0.8 mm section overlap, 512 matrix, (R1-2.2) standard-to-medium reconstruction kernels, and with commercial vendor-specific iterative reconstruction techniques.All patients underwent contrast or non-contrast chest CT on one of the six multidetector-row CT scanners. The scanner-wise distribution of patients was GE Healthcare  or a user-specified proportion (in an increment of 10%). In addition, for external validation, we also partitioned the data such that training data came from the two quaternary hospitals and CT data from the two community sites represented the testing dataset.The DL models were created using the Cognex Vision Pro Deep Learning software (Cognex Corporation). The software provides an intuitive user interface for training and testing models using a complete machine vision tool library with advanced DL techniques. The users or physicians without technical or programming experience can train DL models for specific clinical applications on the platform. The software offers four DL architectures that perform classification, segmentation, object location, and optical character recognition. The Green Tool helps classify objects based on training with labeled images from various classes, and the trained model can then be evaluated with new images that are not part of the training set. The tool can be used in high-definition mode (HDM) and focused mode. The focused mode used in our study does not display loss function, as there is no holdout validation set, so users can focus on the external test set performance as evidence that there is no overfitting of the model. (R2.2.4). It is comparable to classification neural networks such as DenseNet, ResNet, or Visual Geometry Group (VGG) but uses proprietary architectures and pre-processing steps to obtain improved performance in some settings. Further details of the architecture of the platform have been described in prior publications ,9,10.The segmentation tool is comparable to a semantic segmentation neural network (such as UNet) and is used for segmentation and detection of specific findings in images. At the time of writing this manuscript, the software is able to process a limited number of 2D images per patient as input, so we elected to train two different models on instances near the lung apices and bases (three images each at lung apices and bases) instead of including every instance in the CT series. At the apex, we de-identified and exported three images, including the cranial-most image (first image), an image just above the lung apices, and an image just below the lung apices (including a portion of lung apex/apices). Likewise, at the base from the same CT examinations, we exported three additional images, including the last, caudal-most image, an image just below the lung base, and an image including the lung base. Thus, for each CT, we exported and labeled six images from each CT examination included in our study (R3-5). Images just above the apex and just below the lung base represented the anatomic location of “optimal” scan coverage. Images below the apex and/or above the lung bases represented “incomplete” scan coverage. Finally, the first and last images represented “over-scanned” anatomy when these were >2.5 cm beyond the lung apices or bases. We recorded the distance between the first image and the image just above the lung apex  as well as the distance between the last image and the image just below the lung base .All six CT images per exam were then uploaded to the DL building platform to train two DL models from scratch  to recognize incomplete scan coverage from optimum scan coverage  and to differentiate over-scanned anatomy from optimum coverage .We used the classification tool to classify three labeled CT images for each anatomical landmark at the lung apices and bases and trained two different DL models on the DL building platform, one (R3-5) each for lung apices and bases. We used the segmentation tool to segment the lung parenchyma using the “fill tool” functionality to isolate and train the model in identifying the lung regions with similar attenuation. The statistical analysis was performed using Microsoft Excel  and SPSS version 24 . To assess the DL performance, we compared the DL performance against the radiologist labeling of anatomic landmarks at the lung bases and apex (R3-8). We predefined true positive (radiologist and DL output agreed on image labels with the lung apices or bases included in the image), true negative , false negative (disagreement with radiologist labeled apex or base not identified by DL), and false positive (DL misidentified lung base or apex on images labeled by the radiologist as without the lungs). The sensitivity, specificity, accuracy, and receiver operating characteristics (ROC) analysis with the area under the curve (AUC) were estimated for each DL model (at the lung bases and lung apices) with the five-fold cross-validations using Microsoft Excel and SPSS.p < 0.001).The mean cranial (distance between the first image and the image just above the lung apices) and caudal distances (distance between the last image and image just below the lung bases) were 19.8 ± 7.9 mm and 40.5 ± 22.5 mm, respectively. There was a significant difference between the extent of over-scanning below the lung bases compared to over-scanning above the lung apices (p > 0.9). Recognition of anatomic landmark with the DL model at the lung apices and bases allowed us to automatically determine if there was adequate scan coverage or if there was under- or over-scanning of chest (R3-9). The sensitivity, specificity, accuracy, and AUCs of DL-IA and DL-OA for external validation datasets (sites A and B as training dataset and sites C and D as test dataset) were 97.77%, 98.20%, 97.99%, 0.997  0.992–1.000), respectively. The corresponding values for DL-IB and DL-OB were 100%, 100%, 100%, and 1.000 (95%CI 1.000–1.000), respectively. The sensitivity, specificity, accuracy, and AUC of five-fold cross-validations of each DL model are summarized in The performance of both the classification and segmentation tools for differentiating incomplete coverage and over-scanning from optimum coverage at both the lung apices and bases images were identical in terms of sensitivity, specificity, accuracy, and AUC (p > 0.1). Likewise, there was no significant difference in DL performance across the different CT scanner or section thickness (1–1.25 mm) types (p > 0.9). When classified into two age groups , there was no significant difference in the performance of the DL-IA, DL-OA, DL-IB, and DL-OB between the two age groups (p > 0.1).There was no significant difference in the performance of the DL models between male  and female patients  (The physician-trained DL models in our study accurately classified image levels suggestive of under- and over-scanning for routine chest CT protocols (98–100% accuracy). The performance of the DL models did not vary with patient age, gender, scanner types, section thickness, or external validation site data sources. The performance of the DL models for determining over-scanning is similar to other DL applications reported in prior publications. For example, Colevray et al. reported 0.99 accuracy for estimating over-scanning in chest CT examinations with a convolutional neural network (CNN)-based DL algorithm developed with 250 chest CT examinations . The autAs opposed to DL algorithms reported in the abovementioned studies , the modThe primary implication of our study pertains to the ability of physicians to train DL models without any prior training or experience in deep learning, artificial intelligence, programming languages, and coding. Since radiation dose and service quality remain critical areas of concern for hospitals and imaging departments, we believe that DL models assessing proper image acquisition provides significant value to patients and healthcare systems. Some CT vendors now provide AI-enabled selection of scan start and end locations . DL modehttps://github.com/parisak88/Chest-anatomical-landmark, accessed on 11 July 2022). Fifth, we did not assess the impact of artifacts, such as from metallic spinal prosthesis or patient motion, on the performance of the DL model. However, about 10–20% of our routine chest CTs have some motion artifacts, and therefore, our results should hold in presence of such artifacts. Sixth, we excluded patients who underwent CT for non-routine and non-chest protocols, and therefore, we cannot comment on the performance of our algorithm in patients scanned with the excluded protocols. Seventh, the DL model can classify chest CT for under-scanning and over-scanning but cannot determine the actual length in centimeter of over-scanned or missed anatomic lengths. Eighth, we did not use open-access CT datasets for our research since we could include data from four sites and multiple CT vendors and scanner types from our healthcare organization. Given the high performance of our DL model regardless of the scanners and vendors, we believe that there was no critical need for open access CT data in our study (R3-6). Finally, the performance of the DL model might have been artificially high on very limited set of images used for model training and testing. Thus, the results might not hold when a larger and more diverse set of images are exposed to the DL model.Our study has limitations. First, we did not perform a power analysis to determine the required sample size although the observed performance of the AI model suggests that we had enough training and test cases. Second, we did not specifically enrich or down-sample chest CT examinations with and without abnormalities. It is possible, therefore, that a different type of datasets or abnormality spectrum will yield different results. However, we do not anticipate a substantial change in DL performance since we did not exclude chest CT examinations with apical or basilar pulmonary or pleural diseases. Third, we did not compare the DL model used in this study to other reported models since we did not have access to those models. Fourth, patient privacy rules forbid us to share our imaging datasets on an open-access platform. We have, however, uploaded the Excel datasheet of unidentifiable information on the GitHub website (In conclusion, the physician-trained deep learning-based models can identify missing anatomic coverage and over-scanning on images of routine chest CT examinations. Such determination can help audit the frequency and impact of under-scanning and over-scanning on diagnostic information and radiation doses associated with routine chest CT protocols. The DL-based identification of anatomic landmarks suggestive of under-scanning or over-scanning can help assess the quality and safety of CT scanning practices and can be leveraged to provide automated feedback to CT technologists so that errors in scan length can be reduced.


This survey indirectly shows that SARS-CoV-2 circulates in canine and feline populations, but its circulation appears to be too low for pets to act as a significant viral reservoir.Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for COVID-19 in people, has been detected in companion animals on rare occasions. A limited number of large-scale studies have investigated the exposure of companion animals to SARS-CoV-2. The objective of this prospective study was to estimate seroprevalence in privately owned dogs and cats presented in veterinary clinics in different French regions and to test the hypothesis that the occurrence of an episode of COVID-19 in the household and close contact with the owner would increase the chances of the animals being seropositive. One hundred and sixty-five dogs and 143 cats were blood-sampled between March 2020 and December 2021. Neutralizing SARS-CoV-2 antibodies were detected in 8.4% of cats (12/143) and 5.4% of dogs (9/165). Seven animals (three dogs and four cats) were seropositive in the absence of an episode of COVID-19 in the household. Despite not being statistically significant (chi-square test,  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 in China and rapidly spread worldwide ,4, detecThe animal experiments were approved by the Sciences et Santé Animales  N°115 Ethics Committee . Written consent from the pets’ owners was obtained prior to the beginning of the study.The survey was conducted prospectively during healthcare visits at veterinary clinics or veterinary university hospitals between March 2020 and December 2021 throughout metropolitan France. Information on each sampled animal was registered by the veterinarian. It included the signalment of the animal , its lifestyle (indoor/outdoor and time spent outdoors), the occurrence of clinical signs suggestive of SARS-CoV-2 infection , and the proximity between the pet and the owner . The information obtained from the owners included the occurrence of a COVID-19 episode in the household and, if any, the severity of the owner’s symptoms and the availability of a positive test result were recorded. The data were entered online on a dedicated platform allowing pseudonymization. Blood samples were taken by standard venepuncture. The serum was collected by centrifugation, and stored at −20 °C until analyses.v/v in PBS. To detect xenoreactive sera, for every sample, a negative control was performed using PBS instead of the IH4-RBD reagent. Feline serum from a previous experiment was used as a positive control. Plates were incubated for 1 h at room temperature and briefly centrifuged. Then, they were tilted to detect the lack of teardrop formation indicative of a positive reaction.The hemagglutination test (HAT) was used as previously described . The ass® SARS-CoV-2 Double Antigen Multi-species ELISA, ID.vet, Grabels, France).The serological status of the animals was assessed using a commercial SARS-CoV-2 N double antigen enzyme-linked immunosorbent assay (ELISA) kit in accordance with the manufacturer’s instructions (ID Screen50 of SARS-CoV-2 (strain hCoV-19/France/OCC-IHAP-VIR12/2020), in a final volume of 100 µL. After being incubated for 1 h, virus serum mixtures were deposited onto cells. Cells were washed with PBS 1 h later and then incubated in infection media  for 72 h at 37 °C, with 5% CO2. They were then screened for cytopathic effects. Serum neutralization titers were defined as the highest serum dilution inhibiting viral growth. Experiments were carried out in a biosafety level 3 facility at the National Veterinary School of Toulouse. ELISA and HAT positive or inconclusive sera were further tested using a virus serum neutralization assay, which was carried out using Vero-E6 cells, as described previously . BrieflyThe univariable associations between the serological test results and all putative risk variables were tested using a Fisher test for all animals together and for cats and dogs separately. A multivariable logistic regression on the serological status was also performed using both the species and the previous occurrence of a COVID-19 episode in the household as explanatory variables, as these two variables were the main focus of the analysis. The other variables studied were the severity of the COVID-19 episode , whether the owners had performed a test  to confirm their infection, and the nature of their relationship with their pet (close or distant).We collected 314 samples. After the exclusion of six cats (owing to an unfilled questionnaire), a total of 308 privately owned dogs (165) and cats (143) from different French regions  were incSera were first screened using two techniques: ELISA and HAT. Sample positivity was then confirmed by a virus serum neutralization assay. Sera were considered positive only if the virus serum neutralization assay was positive. Neutralizing SARS-CoV-2 antibodies were detected in 12 cats  and 9 dogs .While all ELISA positive sera were serum neutralization positive, seven feline samples  and seven canine samples  were HAT positive and ELISA negative, but serum neutralization positive. HAT thus appears more sensitive than ELISA, and possibly also more sensitive than serum neutralization because nine cat and 20 dog sera were HAT positive, but showed no viral neutralization activity in our assay. On the other hand, a sizeable number of sera , from 16 cats and five dogs, contained xenoreactive antibodies, causing hemagglutination in the HAT negative control, and were all found to be negative in the virus neutralization assay. Detailed information regarding all sera and tests results can be found in In the univariable analysis, none of the tested predictor variables were significantly associated with the animals’ serological status . In the This study reveals that, although not systematic, the transmission of SARS-CoV-2 from humans to domestic carnivores is far from rare. Anti-SARS-CoV-2 antibodies were found in 8.4% and 5.4% of cat and dog samples, respectively. Interestingly, whether or not the owners had contracted COVID-19 was not statistically significantly associated with the serological status of the animals, in contrast to the results of a large-scale study . HoweverThe different seroprevalence studies performed in Europe reported variable positivity rates. While studies performed in Germany, the United Kingdom, and Croatia found positivity rates below 3% ,13,14, ahttps://covidtracker.fr/covidexplorer/; accessed on 25 May 2022), the epidemiological situation in the Occitanie region was broadly similar to that of the rest of the country throughout the first waves.Our study has several limitations. Firstly, the size of our cohort was limited—we collected about three hundred samples, while other studies have reached a thousand ,9. SeconWe also tested the HAT designed by Townsend et al. , which iIn conclusion, SARS-CoV-2 has been shown to circulate in canine and feline populations, but its circulation appears to be too low for pets to act as a viral reservoir. Nevertheless, viral circulation needs to be monitored, as it cannot be excluded that variants (such as immune-escaping variants) may emerge in domestic carnivores.


We consider machine-learning-based lesion identification and malignancy prediction from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal or wide-field images. Specifically, we propose a two-stage approach in which we first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy that can be used for high-level screening processes. Further, we consider augmenting the proposed approach with clinical covariates  and publicly available data (the ISIC dataset). Comprehensive experiments validated on an independent test dataset demonstrate that (1) the proposed approach outperforms alternative model architectures; (2) the model based on images outperforms a pure clinical model by a large margin, and the combination of images and clinical data does not significantly improves over the image-only model; and (3) the proposed framework offers comparable performance in terms of malignancy classification relative to three board certified dermatologists with different levels of experience. During the COVID-19 pandemic, the number of dermatology consultations were reduced by 80-90% to urgent issues only, leading to delay in care of dermatologic concerns. Moreover, the issue of access is very significant for the growing Medicare population, expected to account for 1 in 5 patients by 20302, due to a higher incidence of skin cancer.Prior to the COVID-19 pandemic, access to dermatology care was challenging due to limited supply and increasing demand. According to a survey study of dermatologists3. Many of the skin lesions of concern are screened by primary care physicians (PCPs). In fact, up to one third of primary care visits contend with at least one skin problem, and skin tumors are the most common reason for referral to dermatology4. High volume of referrals places a strain on specialty care, delaying visits for high-risk cases. Given the expected rise in baby boomers, with significantly increased risk of skin cancer, there is an urgent need to equip primary care providers to help screen and risk stratify patients in real time, high quality and cost-conscious fashion. PCPs have variable experience and training in dermatology, causing often low concordance between their evaluation and dermatology4. A consistent clinical decision support (CDS) system has the potential to mitigate this variability, and to create a powerful risk stratification tool, leveraging the frontline network of providers to enhance access to quality and valuable care. In addition, such a tool can aid tele-dermatology workflows that have emerged during the global pandemic.Access issues in dermatology are concerning as there has been an increasing incidence of skin cancers, particularly a threefold increase in melanoma over the last 40 years6, with dermoscopy-based machine learning (ML) algorithms reaching sensitivities and specificities for melanoma diagnosis at 87.6% (95% CI 72.72–100.0) and 83.5% (95% CI 60.92–100.0), respectively, by meta-analysis7. Several authors have reported superior performance of ML algorithms for classification of squamous cell carcinoma (SCC) and basal cell carcinomas (BCC), with larger datasets improving performance8.Over the last decade, several studies in the field of dermatology have demonstrated the promise of deep learning models such as convolutional neural networks (CNN) in terms of classification of skin lesions9 or AlexNet after image pre-processing, e.g., background removal10. Alternatively, some approaches consider lesion sub-types independently11, sonified images12, or by combining clinical data with images to increase the information available to the model for prediction13. However, dermoscopy images are generally of good quality, high resolution and minimal background noise, making them less challenging to recognize compared to clinical, wide-field, images.From a machine-learning methods perspective, a common approach for classification with dermoscopy images consists on refining pre-trained CNN architectures such as VGG16 as in14, with additional pre-processing (illumination correction)15, by using detection models to account for the non-informative background17, or by first extracting features with CNN-based models, e.g., Inception v2, to then perform feature classification with other machine learning methods12. Moreover, comparative studies18 have shown that models based on deep learning architectures can perform similarly to dermatologists on various classification tasks.Beyond dermoscopy images, similar refinement approaches have been proposed based on architectures such as ResNet1528. The use of such algorithms trained on images often acquired from high quality cameras and/or dermatoscopes may be limited to specialty healthcare facilities and research settings, with questionable transmissibility in resource-limited settings and the primary care, thus creating a gap between healthcare providers and patients. Smartphone-based imaging is a promising image capture platform for bridging this gap and offering several advantages including portability, cost-effectiveness and connectivity to electronic medical records for secure image transfer and storage. To democratize screening and triage in primary care setting, an ideal ML-based CDS tool should be trained, validated and tested on smartphone-acquired clinical and dermoscopy images, representative of the clinical setting and patient populations for the greatest usability and validity.However, these ML algorithms are often developed with curated image datasets containing high quality clinical and dermoscopy photographs with limited skin variability, i.e., majority Caucasian or Asian sets in the ISIC dataset (dermoscopy), Asan dataset, Hallym dataset, MED-NODE, Edinburgh datasetWhile there are challenges to consumer grade smartphone image quality such as variability in angles, lighting, distance from lesion of interest and blurriness, they show promise to improve clinical workflows. Herein, we propose a two-stage approach to detect skin lesions of interest in wide-field images taken from consumer grade smartphone devices, followed by binary lesion classification into two groups: Malignant vs. Benign, for all skin cancers  and most common benign tumors. Ground truth malignancy was ascertained via biopsy, as apposed to consensus adjudication. As a result, the proposed approach can be integrated and generalized into primary care and dermatology clinical workflows. Importantly, our work also differs from existing approaches in that our framework can detect lesions from both wide-field clinical and dermoscopy images acquired with smartphones.In this section, we will first explain how we formulate the problem and introduce the model details. Then we describe the datasets we used in this study. Finally, we present the evaluation metrics used for each task.N is the number of instances in the dataset, ith region of interest (ROI) represented as a bounding box with coordinates We represent a set of annotated images as In our specific use case, the images in Below, we introduce deep-learning-based models for lesion identification, malignancy prediction and image-level classification for end-to-end processing. An illustration of the two-step lesion identification and malignancy prediction framework is presented in Fig. 19 with additional details described in the “Assuming we know the position of the ROIs, i.e., C groups of interest, i.e., C, namely, (1) one-class where the model seeks to identify any lesion regardless of type; (2) malignancy in which the model seeks to separately identify malignant and benign lesions; and (3) sub-type, thus the model is aware of all lesion types of interest.Above we assume that the location (ROI) of the lesions is known, which may be the case in dermoscopy images as illustrated in Fig. 20 with a feature pyramid network (FPN)21 and a ResNet-5019 backbone as object detection architecture. Implementation details can be found in the “Note that we are mainly interested in finding malignant lesions among all lesions present in an image as opposed to identifying the type of all lesions in the image. Nevertheless, it may be beneficial for the model to be aware that different types of lesions may have common characteristics which may be leveraged for improved detection. Alternatively, provided that some lesion types are substantially rarer than others  options such as noisy ANDOne-step approachsub-types lesion identification model in Eq.  or  or One-sher Eqs. .sub-type detection model with the simpler malignancy detection model. Akin to this simplified one-step approach, the two-stage approach provides a balanced trade-off between the ability of estimating the location of the lesions and the need to identify lesion sub-types. All these options will be quantitatively compared in the experiments below.From the options described above, the direct image-level classification approach is conceptually simpler and easier to implement but it does not provide explanation (lesion locations) to its predictions. The one-step approach is a more principled end-to-end system that directly estimates lesion locations, lesion sub-type likelihood, and overall likelihood of malignancy, however, it may not be suitable in situations where the availability of labeled sub-type lesions may be limited, in which case, one may also consider replacing the 19 as shown in Fig. t and For malignancy classification we use a ResNet-50 architecture20 with a FPN21 and a ResNet-5019 backbone. The feature extraction module is a ResNet-50 truncated to the 4th block. The FPN then reconstructs the features to higher resolutions for better multi-scale detection21. Higher resolution feature maps are built as a combination of the same-resolution ResNet-50 feature map and the next lower-resolution feature map from the FPN, as illustrated in Fig. 21. The model was trained using an SGD optimizer for 25 epochs, with batch size of 512 per image, initial learning rate 0.001, momentum 0.9 and weight decay 1e-4. Learning rate was decayed 10x at 60,000-th and 80,000-th step, respectively. We augment the data by random resizing.The lesion identification model is specified as a Faster-RCNNThe direct image-level classification model in the “The clinical model was built using logistic regression with standardized input covariates and discrete  covariates encoded as one-hot-vectors.In order to combine the clinical covariates with the images into a single model, we use the malignancy classification model as the backbone while freezing all convolutional layers during training. Then, we concatenate the standardized input covariates and the global average-pooled convolutional feature maps, and feed them through a fully connected layer with sigmoid activation that produces the likelihood of malignancy. The combined model was trained using an SGD optimizer for 30 epochs, with batch size 64, initial learning rate 0.001, momentum 0.9 and weight decay 1e-4. The learning rate was decayed using a half-period cosine function as in the malignancy classification model.24 for the lesion identification model. All other models were coded in Pyhton 3.6.3 using the PyTorch 1.3.0 framework except for the clinical model that was implemented using scikit-learn 0.19.1. The source code for all the models used in the experiments is available (upon publication) at https://github.com/mx41-m/Lesion-Identification-and-Malignant-Prediction.git.We used Detectrondiscovery dataset consists of 6819 images from 3853 patients with 7196 manually annotated lesions. Malignancy was ascertained by separate Histopathological diagnosis. There are 4113 (57%) lesions in 3894 images diagnosed as malignant. For images with multiple lesions, the image is deemed malignant if it contains at least one malignant lesion. In terms of skin tone, the Fitzpatrick scale was used to group skin tones: Light (Fitzpatrick type 1 &2), Medium (Fitzpatrick type 3 &4), Dark (Fitzpatrick type 5 &6). Specifically, 6022 lesions (5721 images) are light, 1073 lesions (1020 images) are medium and 101 lesions (96 images) are dark tone (lesions from one image may be different skin tone.) Lesions were manually annotated as bounding boxes (ROIs) by a dermatology trained medical doctor  using a in-house annotation application. Diagnoses taken from the biopsy reports associated with the lesion images were designated as the ground truth . Further, there are 589 (9%) dermoscopy images and 6230 (91%) wide-field images. Based on the guidelines of the International Skin Imaging Collaboration (ISIC) dataset, the lesions in our dataset can be further divided into several specific lesion types, whose counts and proportions are shown as Table To develop the model we consider a single institution, retrospective collection of skin lesion images taken with smartphones with and without dermoscopy from Duke University Medical Center patients of age 18 and older from 2013 to 2018. These data are collected under the approval of the Duke Institute for Health Innovation and each participant has provided written informed consent. The We also consider a subset of 4130 images from 2270 patients for which we also have demographic , lesion characteristics (location and number of previous dermatology visits), comorbidities  and skin-cancer-related medications , their risk (Low vs. High), and frequency of administration. Among these patients, 1411 (2537 images) are diagnosed as malignant and 859 (1593 images) as benign. Similar to the discovery dataset, we split these data into 85% for training and the remaining 15% for validation.26 consisting of 10,015 dermoscopy images, from which 1954 correspond to malignant lesions and 8061 benign lesions. Detailed lesion type counts are presented in Table Provided that we have a smaller number of dermoscopy images, we also consider augmenting our discovery dataset with the ISIC2018 training datasetIn order to evaluate the performance of the model relative to human diagnosis, we consider an independent set of 488 images also from Duke University Medical Center patients. In terms of skin tone, 369 lesions (359 images) are light, 122 lesions (118 images) are medium and 11 lesions (11 images) are dark. From these images, 242 are malignant and 246 are benign. Consistent with the Discovery Dataset, we use the same malignancy and skin tone definitions. To compare the proposed model with human experts, we had three dermatology trained medical doctors with different levels of experience label each of the images without access to the biopsy report or context from the medical record. In terms of experience, MJ has 3 years dermoscopy experience, AS has 6 years of dermoscopy experience and MK has 10 year dermoscopy experience. Provided that MK also participated in lesion annotation with access to biopsy report information, we allowed 12 months separation between the lesion annotation and malignancy adjudication sessions. Detailed lesion type counts are presented in Table t.For malignancy prediction, two threshold-free metrics of performance are reported, namely, area under the curve (AUC) of the receiving operating characteristic (ROC) and the average precision (AP) of the precision recall curve, both described below. AUC is calculated as:precision for threshold t. The calculation for the AUC and AP areas follow the trapezoid rule.Similarly, the AP is calculated as:The intersection over union (IoU) is defined as the ratio between the overlap or ground truth and estimated ROIs, t set to 0.5, 0.75 and  in the experiments. These values are standard in object detection benchmarks, see for instance27.The mean average precision (mAP) is the AP calculated on the binarized predictions from the detection model such that predictions with an IoUWe also report the recall with IoUComprehensive experiments to analyze the performance of the proposed approach were performed. First, we evaluate and compare various design choices based on the evaluation metrics described in “First, we present results for the malignancy prediction task, for which we assume that lesions in the form of bounding boxes (ROIs) have been pre-identified from smartphone (wide-field) or dermoscopy images. Specifically, we use ground truth lesions extracted from larger images using manual annotations as previously described. Table one-class: for all types of lesions combined; (2) malignancy: for all types of lesions combined into malignant and benign; and (3) sub-type: for all types of lesions separately. Table one-class lesion identification model outperforms the more granular malignancy and sub-type approaches. These observation is also consistent in terms of Recall and IoU.Provided that in practice lesions are not likely to be pre-identified by clinicians, we present automatic detection  results using the models presented in the “ign; and  sub-typeone-class model specifically, 79.3% regions predicted are true lesions at at IoUone-class model is able to capture most of the true lesions at IoUFor the one-class detection model in the “The image-level prediction results of malignancy are reported in Fig. one-class lesion identification, and one-step with (3) malignancy or (4) sub-type identification models with max pooling aggregation are presented in Table Additional results comparing the different image-level malignancy prediction strategies described in the “nancy or  sub-typeone-class, malignancy and sub-types) described in the “Further, we also compare different lesion identification models  the logistic regression model using only clinical covariates; (2) the malignancy classification model; and 3) the combined model described in the “ the combone-class lesion identification. As expected, data augmentation consistently improve the performance metrics of all models considered. But, performance metrics on dermoscopy images do not improve. We think it may be caused by the domain gap between our dermoscopy images and ISIC dataset since the dermoscopy parameters, resolutions and light conditions are different.Finally, we consider whether augmenting the discovery dataset with the publicly available ISIC2018 dataset improves the performance characteristics of the proposed model. Specifically, the ISIC20128 (training) dataset which consists of only dermoscopy images is meant to compensate for the low representation of dermoscopy images in our discovery dataset, i.e., only 9% of the discovery images are dermoscopy. Results in Table one-class lesion identification model described in the “t-SNE map28 that the representations produced by the lesion detection model (combined backbone and FPN features) roughly discriminate between malignant and benign lesions, while also clustering in terms of lesion types.Figure 10. Specifically, Esteva et al.5 showed that when trained with over 100,000 images, Inception v3 achieved 6 showed that Inception V4, an improved version of model architecture over Inception V3, obtained a higher specificity of et al.8 applied ResNet-152 in classifying 12 types of skin diseases then tested the model performance on three different public datasets, i.e., Asan, Hallym and Edinburgh. They further analyzed the performance of the models built separately on each dataset and on each type of skin disease, and proposed several hypothesis for the observed performance differences, e.g., unequal lighting and background of images, imbalanced number of images for each class. However, usability of these algorithms in the real-world remains questionable and must be tested prospectively in clinical settings. Consumer-grade devices produce images of variable quality, however, this approach mimics the clinical work flow and provides a universally applicable image capture for any care setting. The utility of wide-field clinical images taken with smartphone was recently demonstrated by Soenksen et. al for detection of “ugly duckling” suspicious pigmented lesions vs. non-suspicious lesions with 90.3% sensitivity (95% CI 90.0–90.6) and 89.9% specificity (95% CI 89.6–90.2) validated against three board certified dermatologists29. This use case demonstrates how clinical work flow in dermatology can be replicated with ML-based CDS. However, the limitation is that the number needed to treat (NNT) for true melanoma detection from pigmented lesion biopsies by dermatologists is 9.60 (95% CI 6.97–13.41) by meta-analysis30. Hence, the task of detecting suspicious pigmented lesions should be compared against histological ground truth rather than concordance with dermatologists, for improved accuracy and comparability of model performance. Furthermore, pigmented lesions are a small subset of the overall task to detect skin cancer, as melanomas constitute fewer than 5% of all skin cancers. Our approach utilizing wide-field images to detect lesions of interest demonstrated encouraging mAP, IoU and Recall metrics, considering the sample size used. This primary step is critical in the clinical workflow where images are captured for lesions of interest but lesion annotation is not possible in real time. An ideal ML-based CDS would identify lesion of interest and also provide the likelihood of malignancy and the sub-type annotations as feedback to the user. Our study demonstrates malignancy classification for the three most common skin cancers  vs. benign tumors with smartphone images  with encouraging accuracy when validated against histopathological ground truth. The usability of this algorithm is further validated by comparison with dermatologists with variable levels of dermoscopy experience, showing comparable performance to dermatologists in both clinical and dermoscopy binary classification tasks, despite low dermoscopy image data (9%) in the Discovery set. This two-stage model, with the current performance level, could be satisfactorily utilized in a PCP triage to dermatology  at scale for images concerning for malignancy as a complete end-to-end system. Interestingly, the additional ISIC high-quality dataset (predominantly dermoscopy images) improved performance across both clinical and dermoscopy image sets. This suggests that smartphone image data can be enriched by adding higher quality images. It is unclear if this benefit is due to improvement in image quality or volume, and remains an area of further study.The early skin lesion classification literature used largely high-quality clinical and dermoscopy images for proof of concept6. The value of clinical context in model performance has not been studied extensively and remains an area of further study in larger datasets.Finally, we demonstrated that comprehensive demographic and clinical data is not critical for improving model performance in a subset of patients, as the image classification model alone performs at par with the combination model. Clinicians often make contextual diagnostic and management decisions when evaluating skin lesions to improve their accuracy. Interestingly, this clinical-context effect that improves diagnostic accuracy at least in pigmented lesions maybe dependent on years of dermoscopy experience30.Limitations of the study include a small discovery image dataset, predominantly including light and medium skin tones, and with less than 2% of images included with dark skin tone. However, this may represent the bias in the task itself as skin cancers are more prevalent in light- followed by medium-skin tones. Given the large range of skin types and lesions encountered in clinical practice, additional images may improve performance and generalizability. At scale, image data pipelines with associated metadata are a key resource needed to obtain inclusive ML-based CDS for dermatology. Improved image quality and/or volume improves performance as demonstrated by the ISIC dataset incorporation into the model, however, this theoretical improvement in performance needs validation in prospective clinical settings. While the pure clinical model incorporates a comprehensive list and accounts for temporal association of this metadata with detection of lesions, it is not an exhaustive list as it does not include social determinants such as sun-exposure behavior and tanning bed usage; two critical factors contributing to increasing incidence of skin cancer. In particular, metadata including lesion symptoms and evolution is missing and should be incorporated in future studies. Finally, it should be noted that lesions included in this study were evaluated and selected for biopsies in dermatology clinics. If this model was to be utilized in other clinical settings such as primary care, additional validation will be needed as pre-test probability of lesion detection is different among clinical settingsSupplementary Information.


Hospital employees are at risk of SARS-CoV-2 infection through transmission in 3 settings: (1) the community, (2) within the hospital from patient care, and (3) within the hospital from other employees. We evaluated probable sources of infection among hospital employees based on reported exposures before infection.A structured survey was distributed to participants to evaluate presumed COVID-19 exposures  and mask usage. Participants were stratified into high, medium, low, and unknown risk categories based on exposure characteristics and personal protective equipment.Tertiary-care hospital in Boston, Massachusetts.Hospital employees with a positive SARS-CoV-2 PCR test result between March 2020 and January 2021. During this period, 573 employees tested positive, of whom 187 (31.5%) participated.P = .03).We did not detect a statistically significant difference in the proportion of employees who reported any exposure  in the community compared with any exposure in the hospital, from either patients or employees. In total, 131 participants (70.0%) reported no known high-risk exposure  in any setting. Among those who could identify a high-risk exposure, employees were more likely to have had a high-risk exposure in the community than in both hospital settings combined (odds ratio, 1.89; Hospital employees experienced exposure risks in both community and hospital settings. Most employees were unable to identify high-risk exposures prior to infection. When respondents identified high-risk exposures, they were more likely to have occurred in the community. Even after the advent of vaccination against SARS-CoV-2, healthcare workers have been at increased risk of infection due to their risk of occupational exposure through patients and coworkers.The coronavirus disease 2019 (COVID-19) pandemic has put strain on healthcare workers worldwide since it emerged in December 2019 and was declared a pandemic by the World Health Organization in March 2020. Many studies have investigated risk factors for healthcare worker SARS-CoV-2 infection through interviews, questionnaires, assessing seroprevalence, and screening asymptomatic individuals. Results of these studies have been variable, with some finding high-risk exposure outside the workplace as the strongest risk factor and others finding that exposure to patients increases the probability of a positive SARS-CoV-2 test. Staff-to-staff transmission also represents a significant driver of employee infection. Prior studies have been limited in that most investigated patient-facing job roles as potential risk factors and relatively few considered the potential contribution of transmission from coworkers. Additionally, mask use was investigated in some studies but not in others. Universal masking of both healthcare workers and patients has been associated with a lower rate of SARS-CoV-2 positivity among healthcare workers.Considerable research has been conducted to better understand the potential increased risk of infection for people working in the hospital, but questions remain.Recognizing the gaps in the literature, we devised a study to evaluate exposures among all hospital employees, not just those who were patient facing. We evaluated exposures in the hospital and in the community among employees who tested positive for SARS-CoV-2 by nasal polymerase chain reaction (PCR) testing at a large, urban, tertiary-care hospital in the United States. We sought to understand exposures, and we evaluated mask usage and breaches during hospital and community interactions to stratify risk as well as potential employee-to-employee transmission.We used a structured survey to assess exposures among hospital employees infected with COVID-19 between March 1, 2020, and January 15, 2021. “Employees” refers to all individuals who work within the hospital premises, whether they are patient facing or non–patient facing, or full-time employees or contract workers. Employees who tested positive for SARS-CoV-2 by nasal PCR testing were asked to complete an anonymous survey detailing potential exposures in the 2 weeks prior to the start of their symptoms or prior to their positive test result. Exposures were considered in 3 different settings: (1) in the community , (2) in the hospital from contact with patients, and (3) in the hospital from contact with other employees. N95 masks were only recommended for use during interactions with COVID-19 patients in intensive care units or during or after aerosol-generating procedures. Universal use of N95 masks for all patients with confirmed or suspected COVID-19 in the hospital was implemented on June 11, 2020.This study was conducted at Tufts Medical Center (TMC), a tertiary-care academic medical center in Boston, Massachusetts, with ∼7,000 employees. Universal masking was implemented on March 27, 2020, for employees, patients, and visitors, but notably was not consistently enforced for inpatients. From March to June 2020, by hospital policy and consistent with CDC guidelines during times of severe shortage,Study participants were TMC employees who had a positive SARS-CoV-2 PCR test result between March 1, 2020, and January 15, 2021, either according to the employee’s self-report or as documented by the TMC employee health department. The employee health department maintained a record of employees who tested positive through the hospital’s testing facility or from an outside facility per self-report.A structured questionnaire was fielded to participants to evaluate COVID-19 exposures and personal protective equipment (PPE) in community and hospital settings. Close contact was defined as being within 6 feet (2 meters) of someone with confirmed or probable COVID-19 for at least 15 minutes or having direct contact with their secretions , regardless of whether PPE was worn. Participants described mask usage in each reported exposure, specifically whether both individuals were masked, one individual was masked, or neither individual was masked. When describing contact with TMC patients, participants answered questions regarding eye protection, gown, and glove usage, and participation in aerosol-generating procedures. They were also asked to explain their perceived source of infection using open-ended free text. Participants provided demographic data and COVID-19 symptoms. This survey was offered in 5 different languages: English, Spanish, Haitian Creole, Portuguese, Cantonese, and Mandarin, with online and paper versions. This initial survey wave involved outreach to all TMC employees, regardless of COVID-19 status. Employees who self-reported testing positive for SARS-CoV-2 were asked to answer additional survey questions. The second survey wave was distributed in English between March 1, 2021, and June 10, 2021, to all SARS-CoV-2-positive employees who had not already participated, as identified through TMC employee health department records. Only the first wave of participants were compensated with a $10 gift card. Participants were recruited by e-mail, and our team followed up with a single telephone call to those who did not initially respond to encourage participation.The survey was distributed in 2 waves. The first wave of surveys was distributed in July and August 2020 as part of another TMC study evaluating disparities in SARS-CoV-2 testing among employees.Participants were first grouped into categories based on their exposures in each of the 3 settings: community, in the hospital via patient, or in the hospital via another employee. Notably, participants could report exposures in multiple settings (ie community and/or patient and/or employee), and be grouped in multiple risk categories. These categories included “no exposure”  or subcategories among those who had exposure . These subcategories were “exposure with both masked,” , “exposure with one masked” , “exposure with no masking,” , or “exposure with masking responses missing” . Likewise, participants were grouped into an “exposure responses missing” category, if they did not answer exposure questions.Participants were then classified into risk categories based on their reported exposure status in each setting Fig. . These r Respondents’ own perceived sources of exposure based on their free-text responses were analyzed to determine which of the 3 risk settings  they believed was the most likely source of their infection. Free-text responses were independently coded and categorized by 2 authors, and differences were reconciled by consensus.To assess the representativeness of survey respondents to the total population of infected employees, characteristics of survey participants were compared to all TMC employees who tested positive for SARS-CoV-2 in the same period of March 2020-January 15, 2021. Frequencies of respondents categorized with any risk or high-risk–only exposures in each of the settings were calculated. Given that participants could have reported risk in multiple settings, frequencies are reported for the various combination of the 3 settings. Frequencies of exposure in each of the 3 settings based on level of mask use was also calculated. McNemar tests were used to determine whether there were any significant differences in risk in the community setting compared to risk in the hospital setting , for any risk level .Between March 1, 2020, and January 15, 2021, 573 employees tested positive for SARS-COV-2 infection by PCR testing, of whom 187 (31.5%) responded to the survey. Participant characteristics were similar to those of all TMC employees who tested positive in terms of gender, age, and job role (Table P = .75).In the 14 days prior to being infected, 79 participants 42.2%) identified any exposure  in the community setting, while 37 participants (19.7%) identified any exposure with a coworker in the hospital, and 59 (29.9%) identified any exposure with a patient. Across both in-hospital settings (patient and coworker interactions), 75 participants (40%) identified any exposure. Moreover, 66 participants (35.3%) were unable to identify exposures in any setting. Table .2% identTable C).After stratifying survey participants into risk categories, a higher proportion of survey participants experienced at least 1 high-risk exposure in the community compared to either of the hospital settings Table . A higheP = .03).We then used these risk categories to evaluate the proportion of employees reporting high-risk exposures in 1 setting, multiple settings, or no setting Table . Most paD). A higher proportion of participants perceived that they were infected by a patient than by a coworker, 32 (17.1%) versus 18 (9.6%) respectively, whereas 16 (8.6%) perceived that they could have been infected by either.When asked their perception of their source of infection, participants were equally likely to report that their exposure was in the community setting alone  or in the hospital setting  , as has been implemented in schools, may be an alternative strategy for decreasing in-hospital transmission, although real-world data evaluating this strategy in hospitals is limited.Transmission in the hospital setting is not limited to patient-facing areas, and employee-to-employee transmission cannot be discounted. Despite widespread vaccinations, this variant was immune evasive, depleting the healthcare workforce and stressing hospital systems. This situation further demonstrates the need for continued mitigation strategies both in the community and in the hospital, especially during future COVID-19 surges. During surges, hospitals should consider developing education strategies emphasizing the importance of employees reducing high-risk interactions with coworkers and in community settings. Hospitals may also consider providing high-quality PPE to employees to use outside of the hospital .Data for this study were collected early in the pandemic and reflect the period prior to mass vaccination. The earliest part of the study period was also before testing became widely available and when PPE availability and usage policies were rapidly changing. Although our study ended in June 2021, the emergence of the SARS-CoV-2 omicron variant led to another substantial wave of healthcare worker infections.Although high-risk exposures related to patient care were the lowest of the 3 settings, among participants who did have close contacts with patients, about half reported breaches in PPE usage. Some of these breaches may have occurred due to lack of knowledge that a patient had COVID-19, a problem that likely decreased after introduction of universal SARS-CoV-2 testing of patients at admission. Hospitals should consider reinforcing education on appropriate PPE use with patients to ensure higher adherence by patient-facing employees during periods of high transmission. Thus, we decided that exposures in which one or both parties were masked would be classified as “low or intermediate risk.” Exposures in which neither party were masked were classified as “high risk.” However, for community and coworker exposures we did not ask about the type of mask worn, which may impact risk reduction.This study had several limitations. Our data were based on recall from participants. Given that participants had positive SARS-CoV-2 tests between March 2020 and January 2021 and our study was conducted between July 2020 and June 2021, considerable time may have lapsed between their positive test and survey completion. Also, one-third of participants could not identify a setting in which they were exposed to COVID-19. Although this finding is consistent with or lower than rates in published literature, it is a limitation of our study in that it limits our understanding of the setting in which people were exposed. Additionally, our risk categories were based on knowledge from the literature about how masks reduce infection transmission.Overall, the survey data collected from TMC employees who tested positive for SARS-CoV-2 between March 2020 and January 2021 showed that there was meaningful risk among all 3 settings examined. Additionally, one-third of employees were unable to identify any exposure, and most employees were unable to identify a high-risk (unmasked) exposure in the 14 days prior to their SARS-CoV-2 infection. There was no significant difference in the proportion of employees who experienced any exposure to a person with SARS-CoV-2 infection in the community compared to hospital settings. However, among those employees who could identify a close contact, they were significantly more likely to report a high-risk exposure in the community than in the hospital. Moreover, within the hospital, high-risk exposures were at least as common with coworkers as with patients. During future surges of COVID-19 or other respiratory viruses, hospitals should expand their transmission mitigation strategies beyond patient-facing areas to help reduce risk for employees in staff workspaces and, most importantly, in community settings.


Disease characterization of Post-Acute Sequelae of SARS-CoV-2 (PASC) does not account for pre-existing conditions and time course of incidence. We utilized longitudinal data and matching to a COVID PCR-negative population to discriminate PASC conditions over time within our patient population during 2020. Clinical Classification Software was used to identify PASC condition groupings. Conditions were specified acute and persistent (occurring 0-30 days post COVID PCR and persisted 30–120 days post-test) or late . We matched 3:1 COVID PCR-negative COVIDPCR-positive by age, sex, testing month and service area, controlling for pre-existing conditions up to four years prior; 28,118 PCR-positive to 70,293 PCR-negative patients resulted. We estimated PASC risk from the matched cohort. Risk of any PASC condition was 12% greater for PCR-positive patients in the late period with a significantly higher risk of anosmia, cardiac dysrhythmia, diabetes, genitourinary disorders, malaise, and nonspecific chest pain. Our findings contribute to a more refined PASC definition which can enhance clinical care. In this study, the authors use electronic health record data from the US to characterise post-acute sequelae of SARS-CoV-2 infection (PASC). They identify 17 common PASC conditions and find an overall ~12% increase in risk of PASC conditions in the post-acute period among people with a SARS-CoV-2 positive test compared to matched test-negative controls. The acute effects of COVID-19 are well documented, but longer-term effects are actively being investigated and defined. Post-acute sequelae of SARS-CoV-2 infection (PASC) has been described by the World Health Organization as the persistence of symptoms or new symptoms more than 30 days post-SARS-CoV-2 infection4. Lingering symptoms can persist months after acute infection, including recurring fatigue, muscle weakness, dyspnea, anxiety, and depression. The symptomatology of PASC and time course, however, have been based on self-selected cases and not drawn from a well-defined PCR-positive population, compared to an appropriate control group and followed longitudinally.SARS-CoV-2, the viral cause of COVID-19, has triggered a global pandemic infecting over 536 million people and over 6.3 million deaths worldwide as of June 20225. Al-Aly and colleagues laid the groundwork by performing a comprehensive analysis of potential PASC symptomology within the Veterans Health Administration (VHA)3. Estiri et al. expanded PASC investigation to a non-hospitalized based cohort and identified 33 phenotypes in 3-6 month and 6-9-month periods post-COVID6. However, questions remain regarding the timing of conditions, and importantly what symptoms persisted from acute infection to late periods and which symptoms developed in the late period. Additionally, comparisons to matched populations with a negative SARS-CoV-2 test result (PCR-negative) have not been systematically conducted and are crucial to differentiating the impact of the pandemic from the impact of viral infection. A case-control approach permits a better curated PASC definition and condition identification.Disease characterization and definition have changed over time and identification via standard ICD-10 diagnosis codes were only enacted in later 2021Our primary aim is to define a set of PASC conditions, and to describe the timing of the conditions, by applying to a diverse population and comparison group of similar PCR-negative individuals. We selected a longitudinal cohort of COVIDPCR-positive patients and matched them to COVIDPCR-negative patients within the Kaiser Permanente Mid-Atlantic States (KPMAS), identified the clinical conditions for which there is an increased risk for those PCR-positive (vs. PCR-negative) and estimated PASC incidence among those PCR-positive.2). The most frequent pre-existing conditions co-morbidity was diabetes mellitus.31,390 total PCR-positive patients were identified. The majority were female and over half were less than 50 years old  and likely an effect of overall cohort size. Overall, 37.7% of PCR-positive patients had at least one condition (in the acute and persistent or late period) and 16.5% of PCR-positive patients had at least one PASC-related condition in either period. 20.4% of our PCR-positive patients had at least one condition and 4.1% had a PASC-related condition in the acute and persistent period. Late period results were 26.1% and 13.6%, respectively. Among PCR-negative, 2.5% had a PASC-related condition in the acute period and 22.1% had any condition in this period (more than the PCR-positive). Further, among PCR-negative, 12.1% had a PASC-related condition in the late period, and 25.2% had any condition (fewer than the PCR-positive).The scaled matching algorithm resulted in a study population of 28,118 PCR-positive and 70,293 PCR-negative. 1:3 case to control matching represented 66.8% of the identified cohort, followed by 16.2% with 1:2 and 16.8% with 1:1 matching. Overall, both case and control groups had ~57% female patients, a higher distribution of Black (~40–43%) and Hispanic (~20–24%) compared with white (~18–22%) patients, ~87% distribution less than 65 years old, and 30%-33% distribution of patients with a BMI ≥ 30 kg/mThere was an increased risk of numerous conditions in those PCR-positive compared to PCR-negative in both acute and persistent and late time periods Table ; Fig. 1.Some of these conditions also had increased risk among PCR-positive (vs. PCR-negative) in the acute and persistent period, including cardiac dysrhythmias , diabetes , malaise and fatigue , nonspecific chest pain . Additional PASC related conditions that had increased risk in the acute and persistent period include other lower respiratory disease  and respiratory failure/insufficiency/arrest .There was some variation in the demographic distributions for those experiencing at least one PASC-related condition in the acute and persistent and/or late periods Table ; Fig. 1.The sensitivity analysis attenuated the increased risk ratios for PASC related conditions by allowing visible conditions to occur in multiple time periods; thus, removing the requirement of mutual exclusivity between time periods. None of the significant increased risk ratios changed statistical significance Table . AbdominP = 0.89) had a significant, but weak, association , between cases and controls and corticosteroid use during the acute and persistent time period. Only 32% of diabetic patients (cases and controls) were on corticosteroids during the late period and only 18% of diabetic patients (cases and controls) were on corticosteroids during the acute and persistent time period. The risk ratio for diabetes in the late time period was 1.20  and 1.96  in the acute and persistent time period; therefore, we can say that corticosteroid use and/or abuse likely has little to no association to the increased diabetes risk.The case/control diabetes and corticosteroid sensitivity analysis showed no significant association between cases and controls and corticosteroid use during the late time period  to avoid the influence of later variants and vaccinations, and to only those with a PCR test resultOur study also reveals a presence of a disease burden among PCR-negative persons. While any conditions and our PASC-related conditions are distinctly different, the comparison of any condition provides additional evidence that the resulting PASC-related conditions are not only higher risk in the PCR-positive group, but higher risk compared to patients experiencing other symptoms/conditions. This comparison reenforces that the resulting PASC-related conditions are truly representative of patients experiencing PASC. Also, note that among patients without COVID, many experienced these same symptoms, providing further context for our results.In contrast, the absolute differences in PASC-related conditions, or any conditions, are not large when comparing PCR-positive to PCR-negative groups. In fact, any condition  was more common among PCR-negative than PCR-positive in the acute period. This has further implications for an operational PASC definition, in that while many conditions have been cited as potentially part of PASC, they are occurring with similar frequencies among PCR-negative patients. It is important, thus, to recognize that these symptoms, while elevated in patients with SARS-CoV-2 infection, are not unusual either in PCR-negative persons. Further, it is important to acknowledge the toll the pandemic has taken on all patients and while many of these PASC-defining conditions do not have large incidence rates, these conditions are still very impactful to the patients that experience them and require attention from their medical providers.11. It also should be noted that we did not compare our results or rates to a separate viral condition, such as HIV, as COVID and PASC were unique to this time and the focus of our study. While these PASC condition categories are multifaceted, such as GU disorders, all have been described by others13. Respiratory symptoms were more prominent in the acute and persistent period, which is consistent with COVID-19 symptomatology14. However, most of these did not occur in the late period, and many patients had pre-existing pulmonary conditions and respiratory-related diagnoses. As described in the literature, anosmia was seen at a higher incidence for PCR-positive patients7.Our acute and persistent and late period PASC-related conditions are not surprising, as most have been described in case reports in the literature to date or are commonly seen in sub-acute viral illnesses15. Consistent with the findings from Al-Aly3, diabetes mellitus had increased incidence during the post-COVID period. One possibility is that diabetic patients were simply undiagnosed until they sought care for their COVID-19 infection and were laterally diagnosed17. Another possibility is that COVID affects blood sugar and pancreatic endocrine function17. As noted in our sensitivity analysis, corticosteroid use did not greatly impact diabetes incidence. However, diabetes is relatively common in the KPMAS population, and an even further increased risk of the disease is of considerable concern for patient health. Future research is needed to understand the relationship between diabetes and COVID-19.An acknowledged limitation is that not all conditions described in the literature or popular press can be coded consistently during our study period in the EHR, most notably brain fog; however, malaise and mental health were prominent PASC-related conditions and likely associated with brain fog symptomatology3 provided an encompassing view of PASC conditions within the VHA population. Our study provides supporting evidence around specific conditions identified and negates their limitations around having a primarily older (average 60 years), white and male population3. We also utilized a time interval analysis which provides context and support for our most profound PASC-related conditions. Our results differ from the VHA study as they have an overall higher level of risk for most of their identified conditions. One potential reason for this difference is that our control group required testing negative for COVID-19, while their control group includes those who had no evidence of testing. Demographic differences may have also contributed to the result divergence. Our study also improves upon Estiri and colleagues’ PCR-negative comparison group by utilizing data from a closed integrated healthcare system with accurate membership accounting, applying a matching algorithm to better control for confounding variables, and most importantly, providing additional comparison periods to provide supporting evidence for the late conditions6.Al-Aly and colleaguesOther limitations are relevant to our study. It is possible that pre-existing conditions could be found more than four years prior to the PCR test date. The intent of going back four years was to ensure that we capture conditions that may have been missed on more recent encounters prior to the COVID test. Individual risk estimates are also heavily dependent on time period length, by which larger time periods, such as the 4-year pre-existing condition period, likely have a high probability of diagnosis capture compared to the 30-day acute and persistent period. This time period length discrepancy has been attenuated in our analysis as our overall comparisons between the PCR-negative vs PCR-positive cohorts are compared within each equitable time interval. Additionally, our sensitivity analysis found no effect on the significance of our results when removing the mutual exclusivity requirement for time periods and allowing symptom-based conditions to count in the acute and persistent period as well as the late period, regardless of if a condition was pre-existing.18. While we capture all telehealth visits, there is the potential for missing PASC diagnoses and encounters as some patients may not have sought medical care for their symptoms. We tested this limitation and found >76% of our PCR-positive and PCR-negative cohorts had at least one encounter, virtual or in-person, in the late period. Lastly, we acknowledge that our current study period does not include the outside influence of other variants, vaccination, and widely distributed home testing which may impact future definitions and symptomatology of PASC.Additionally, KPMAS healthcare utilization patterns were also found to be significantly altered by the pandemic5. We also cannot rule out the potential for missing data around PCR testing, especially in early 2020. Diagnoses, lab results and death, which was not compared with the National Death Index (NDI) for the cause of death, also have the potential for missingness; however, we believe our care model and connection to external data sources significantly reduces much of this limitation. Lastly, we cannot rule out the possibility of false negative COVID tests but given the high community prevalence of SARS-CoV-2 during the study period, and the KPMAS protocol for testing 5-14 days post COVID-19 exposure, the likelihood of missed COVID-19 diagnosis is low. Our strengths include examining data within an integrated and closed health system and drawing from a well-defined patient population of over 800,000 members. Further, our system has comprehensive capture of both PCR-positive and PCR-negative test results from both internal and external sources, as well as a comprehensive capture of PASC recorded symptoms and conditions. Additionally, our ability to create a matched PCR-negative population, with majority of PCR-positive cases being matched to three COVID controls, and our analysis of distinct time periods associated with condition manifestation are key distinctions of this study compared to others in the literature16.Our study population consists of insured patients only which includes Medicare and Medicaid, and charity care, so a wide demographic is included. While geographically limited, our population represents the general population well in the DC/VA/MD areaOur study demonstrates a clearly defined set of conditions for PASC definition and delineation within an integrated care system. This delineation compared the acute and persistent time period conditions with conditions identified in the late time period. These conditions are at a significantly higher risk when compared with a PCR-negative population matched on similar demographics. However, PASC-related conditions do occur among PCR-negative populations and should not be neglected among these patients. Additionally, our study found that the overall cumulative incidence of PASC, as defined by COVID positive patients with a PASC-related diagnosis in the acute and persistent or late periods, is 16.5%. Importantly, the low-risk levels, defined by the cumulative incidence of each individual condition, provide context to the overall low burden of disease for PASC-related conditions in the KPMAS population. These findings contribute to the overall evaluation of PASC and can be employed by clinicians in their care of patients who are diagnosed with COVID-19. Our research provides supporting evidence for an accepted operational definition for PASC; however, understanding of the severity and duration of these conditions will be crucial.19. They are provided comprehensive integrated health care, including, but not limited to, primary and specialty care, ambulatory and inpatient care . Their healthcare is coordinated through an integrated electronic health record (EHR) system which includes clinical data, financial information (claims data) on services received external to KPMAS, and data from the Geographically Enriched Member Sociodemographic (GEMS) database20. KPMAS is a closed healthcare system with high ascertainment of COVID-19 in the population, as well as potential PASC conditions and symptoms.Kaiser Permanente (KP) is an integrated health system in the United States, with over 800,000 members in the Mid-Atlantic region, representing Maryland, the District of Columbia, and Northern Virginia. KPMAS members are a diverse population and their demographics represent their respective jurisdictionsOur study was approved by the KPMAS Institutional Review Board on an expedited basis.21. Given the magnitude of testing performed within our system and external testing linkages, we classified COVID positive patients as those with a confirmed PCR result and refer to those as PCR-positive. We refer to those with only COVID PCR-negative results as PCR-negative.SARS-CoV-2 RT-PCR (PCR) testing, the most widely available test during our study period, has been regarded as the gold standard for COVID patient identification9, we identified adult patients (≥18 years) who had a PCR result between January 1, 2020, through December 31, 2020. We limited our cohort to this period to avoid the influence of later variants and vaccinations, and only to those with a PCR test result9. Of note, the KPMAS protocol did not test patients prior to five days post-exposure or greater than 14 days post-exposure. We prioritized PCR-positive results for each patient, then selected the first positive date as our index date. Patients were classified into cases when they received a PCR-positive COVID result or controls if they only tested negative. We excluded patients not enrolled in KPMAS 120 days post-PCR test date.Utilizing KPMAS EHR, including internal and external records incorporated into the EHR For each PCR-positive patient (case), we matched up to three PCR-negative patients, without replacement, by PCR testing month and year (using their first negative test date), age group at the time of PCR test , race/ethnicity , sex , and service area (to account for any physician differences in diagnostic practices). When 1:3 matching was not possible, cases were matched to controls 1:2 or 1:1.2), insurance type , pregnancy status, service area, and hospitalizations (30-120 days post-test) and known deaths post-index date.Demographic covariates of interest abstracted from the EHR included: race/ethnicity age, comorbidities , Body Mass Index  was the PCR test date. Three, mutually exclusive, diagnostic time intervals were identified and anchored on T0: (1) pre-existing conditions time interval - diagnoses up to four years prior to T0, (2) acute and persistent time interval – diagnoses occurring 0–30 days post-T0 and persisted into the 30–120 days period, but not previously identified in the pre-existing conditions time interval, and (3) late time interval - new disease diagnoses 30–120 days post-T0, but not previously identified in the pre-existing conditions or acute and persistent time intervals  Clinical Classifications Software (CCS)d Tables . This meTo determine which CCS conditions provided the signal for PASC diagnosis, distributions were calculated for PCR-positive patients by taking the distinct number of patients with a particular CCS condition over the total number of distinct patient-CCS condition combinations, within each respective time interval. We used an aggregated total distribution percentage, summed between all time periods, of 0.04% as a cutoff for CCS Conditions that merited clinical review. The .04% cutoff was determined by review of distribution counts for the CCS conditions, whereby the .04% cutoff merited an appropriate number of conditions for risk analysis. From the remaining symptoms and diagnoses, higher frequency conditions were reviewed by two KPMAS infectious disease physicians. Conditions flagged by the infection disease physicians, based on biologic plausibility and review of the medical literature to date of initial analysis April 2021), were then further refined and grouped on clinical similarities and/or a more defined condition classification. For example, Genitourinary symptoms were grouped together while CCS conditions related to trauma were deleted from the analysis. These condition groupings were again presented to the KPMAS infectious disease physicians with a final determination made to which conditions had a plausible biologic association to PASC  were tested via Cramer’s V to investigate distribution equality in cases and controls. Further distribution analyses were performed on those experiencing at least one PASC-related condition in the acute and persistent and/or late periods. Overall counts, cumulative incidence, and unadjusted risk ratios with 95% confidence intervals  were calculated for each CCS condition category within each time interval. Cumulative incidence was defined as the total number of distinct patients with a particular CCS condition, within each respective time period, over the total number of patients in the observed cohort. Additionally, totals for a patient having at least one CCS condition or at least one PASC-related condition were estimated and stratified by time interval. Data collection and analyses were performed using SAS software , SQL developer (version 17.3.1) and Tableau (version 2019.1). A p-value <0.05 guided statistical interpretation.To mitigate a potential limiting effect of our CCS selection criteria, we performed a sensitivity analysis on the PASC-related conditions deemed symptoms. CCS condition counts for abdominal pain, anosmia, conditions associated with dizziness/vertigo, malaise and fatigue, nausea and vomiting, nonspecific chest pain were recalculated by removing the pre-existing conditions diagnosis exclusion requirement for identification of acute and persistent and late diagnoses, thus allowing the presence of these symptoms to be counted irrespectively. Statistical analysis was performed in the same manner as our primary analysis.In addition, to account for the potential effects of the use and/or abuse of corticosteroids on glucose levels, we performed a sensitivity analysis on patients identified as having diabetes mellitus in the acute and persistent or late periods. Dispensed corticosteroid medications during the time periods in question were identified for those patients. A chi-squared test was used to determine if an association was present between case and control patients with a diabetic diagnosis and corticosteroid use.Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary