Do CRP levels predict severity in COVID-19 hospitalized Egyptian patients?

Background:  Coronavirus disease 2019 (COVID-19) is a rapidly spreading virus with a wide range of clinical manifestations. To manage treatment programs and promptly assess patient severity, prognostic factors must be identified early. Objectives: The purpose of this study was to investigate if there was a link between the severity of COVID-19 and the C-reactive protein (CRP) level on admission. Methods: On admission clinical and laboratory data from 323 patients with laboratory-confirmed COVID-19 were gathered from an Isolation Hospital records from April 10, 2020 to July 30, 2020. CRP was determined in all participants using an automated analyzer and a commercially available latex-enhanced immuno-turbidimetric assay. Results: The most prevalent presenting symptom was fever (39.3%), followed by cough (38.4%).  Coronavirus disease 2019 severity and ICU admission were both predicted by high CRP levels (p < /em><0.001).  C-reactive protein levels were also greater in those who had more chest discomfort, as indicated by CT chest abnormalities (p < /em><0.001). Conclusion: Serum CRP is a simple and effective prognosticator for early prediction of COVID-19 severity

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10&nbsp;years; 78.2% included were male with a median age of 37&nbsp;years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Added value of contrast-enhanced spectral mammogram in assessment of suspicious microcalcification and grading of DCIS

Abstract Background Breast microcalcifications are one of the most difficult mammographic findings to assess. The purpose of this study is to assess the ability of contrast-enhanced spectral mammography in the assessment of suspicious microcalcification and in predicting the grade of DCIS. Methods Three hundred and forty cases with suspicious microcalcification were reviewed in this study. We excluded 160 cases associated with masses. We enrolled 180 cases for analysis of suspicious microcalcification on mammograms with no underlying masses. We reviewed the microcalcification for their morphology, distribution, and associated pathological enhancement according to BI-RADS lexicon with pathology results reviewed and classified into benign and malignant which subdivided into low, intermediate, or high-grade DCIS or invasive carcinoma. Results Three hundred and forty cases with suspicious microcalcification were reviewed in this study. We excluded 160 cases associated with masses. Forty-five of 180 cases were benign, and 135/180 cases were malignant. Twenty-five of 135 cases were diagnosed as invasive breast carcinomas while 110/135 were ductal carcinoma in situ. From the latter, 110 patients with DCIS, 22/110 cases were low grade, 11/110 cases were intermediate grade, and 77/110 cases were high grade (44 with micro-invasion). A total of 25 invasive carcinomas showed pathological non-mass enhancement, 76/77 cases of high-grade DCIS, and 6/11 cases of intermediate-grade DCIS. No abnormal enhancement appeared with benign entities, low-grade DCIS, and 5/11 cases of intermediate DCIS. The diagnostic performance of CESM in anticipation of high grade in DCIS patients was sensitivity of 98%, specificity of 81.8%, and accuracy of 93.1%. CESM sensitivity, specificity, and accuracy in prediction of invasiveness or high-grade DCIS were 98.5%, 81.8%, and 87.5%, respectively. Conclusion CESM can provide a fundamental contribution in the evaluation of suspicious microcalcification as high-grade DCIS or invasive component can present by non-mass enhancement, but enhancement paucity is favorable to diagnose benign lesion or non-invasive/low-grade DCIS. </jats:sec

Comparison of hemoglobin level and neutrophil to lymphocyte ratio as prognostic markers in patients with COVID-19

Background Anemia can negatively affect the outcome of many diseases, including infections and inflammatory conditions. Aim To compare the prognostic value of hemoglobin level and the neutrophil/lymphocyte ratio (NLR) for prediction of coronavirus disease 2019 (COVID-19) severity. Methods In this retrospective cohort study, clinical data from patients with laboratory-confirmed COVID-19 were collected from hospital records from 10 April 2020 to 30 July 2020. Results The proportions of patients with mild, moderate, and severe COVID-19 differed significantly in association with hemoglobin levels, neutrophil counts, lymphocyte counts, NLR, and total leukocyte counts. Patients with severe COVID-19 had significantly lower hemoglobin levels than those with moderate or mild COVID-19. There were statistically significant negative associations between hemoglobin and D-dimer, age, and creatinine. The optimal hemoglobin cut-off value for prediction of disease severity was 11.6 g/dL. Using this cut-off value, hemoglobin had higher negative predictive value and sensitivity than NLR (92.4% and 51.3%, respectively). The specificity of hemoglobin as a prognostic marker was 79.3%. Conclusion Both NLR and hemoglobin level are of prognostic value for predicting severity of COVID-19. However, hemoglobin level displayed higher sensitivity than NLR. Hemoglobin level should be assessed upon admission in all patients and closely monitored throughout the disease course. </jats:sec

Quantitative evaluation of the response to neo-adjuvant chemotherapy based on non-rigid registration of CEM

International audienceThis paper introduces a new intensity-compensated longitudinal non-rigid registration method for Contrast Enhanced Mammography monitoring neoadjuvant chemotherapy (NAC). The registered NAC image subtraction, called residual fields, allows to discriminate between different lesion responses to treatment using features extraction. The approach registers low-energy 2D images of the exams acquired before and after the chemotherapy. The measured motion is then applied to the corresponding dual-energy recombined images. Consequently, the difference in registered images allows identifying local density and iodine uptake changes, especially in the lesion area. The registration converged for all 51 patients with 208 image pairs. Finally, the residual fields can be used to extract clinically relevant features used to assess the response of the treatment. The lesion response classification model, evaluated with the AUC and F-score, performed better when taking into account the residual features (AUC: 0.92 vs 0.79)

Deformable registration with intensity correction for CESM monitoring response to Neoadjuvant Chemotherapy

Abstract This paper proposes a robust longitudinal registration method for Contrast Enhanced Spectral Mammography in monitoring neoadjuvant chemotherapy. Because breast texture intensity changes with the treatment, a non-rigid registration procedure with local intensity compensations is developed. The approach allows registering the low energy images of the exams acquired before and after the chemotherapy. The measured motion is then applied to the corresponding recombined images. The difference of registered images, called residual, makes vanishing the breast texture that did not changed between the two exams. Consequently, this registered residual allows identifying local density and iodine changes, especially in the lesion area. The method is validated with a synthetic NAC case where ground truths are available. Then the procedure is applied to 51 patients with 208 CESM image pairs acquired before and after the chemotherapy treatment. The proposed registration converged in all 208 cases. The intensity-compensated registration approach is evaluated with different mathematical metrics and through the repositioning of clinical landmarks (RMSE: 5.9 mm) and outperforms state-of-the-art registration techniques.</jats:p

AI-Based Cancer Detection Model for Contrast-Enhanced Mammography

Background: The recent development of deep neural network models for the analysis of breast images has been a breakthrough in computer-aided diagnostics (CAD). Contrast-enhanced mammography (CEM) is a recent mammography modality providing anatomical and functional imaging of the breast. Despite the clinical benefits it could bring, only a few research studies have been conducted around deep-learning (DL) based CAD for CEM, especially because the access to large databases is still limited. This study presents the development and evaluation of a CEM-CAD for enhancing lesion detection and breast classification. Materials & Methods: A deep learning enhanced cancer detection model based on a YOLO architecture has been optimized and trained on a large CEM dataset of 1673 patients (7443 images) with biopsy-proven lesions from various hospitals and acquisition systems. The evaluation was conducted using metrics derived from the free receiver operating characteristic (FROC) for the lesion detection and the receiver operating characteristic (ROC) to evaluate the overall breast classification performance. The performances were evaluated for different types of image input and for each patient background parenchymal enhancement (BPE) level. Results: The optimized model achieved an area under the curve (AUROC) of 0.964 for breast classification. Using both low-energy and recombined image as inputs for the DL model shows greater performance than using only the recombined image. For the lesion detection, the model was able to detect 90% of all cancers with a false positive (non-cancer) rate of 0.128 per image. This study demonstrates a high impact of BPE on classification and detection performance. Conclusion: The developed CEM CAD outperforms previously published papers and its performance is comparable to radiologist-reported classification and detection capability