The number of beds occupied is an independent risk factor for discharge of trauma patients

Reducing the burden of limited capacity on medical practitioners and public health systems requires a time-dependent characterization of hospitalization rates, such that inferences can be drawn about the underlying causes for hospitalization and patient discharge. The aim of this study was to analyze non-medical risk factors that lead to the discharge of trauma patients. This retrospective cohort study includes trauma patients who were treated in Switzerland between 2011 and 2018. The national Swiss database for quality assurance in surgery (AQC) was reviewed for trauma diagnoses according to the ICD-10 code. Non-medical risk factors include seasonal changes, daily changes, holidays, and number of beds occupied by trauma patients across Switzerland. Individual patient information was aggregated into counts per day of total patients, as well as counts per day of levels of each categorical variable of interest. The ARIMA-modeling was utilized to model the number of discharges per day as a function of auto aggressive function of all previously mentioned risk factors. This study includes 226,708 patients, 118,059 male (age 48.18, standard deviation (SD) 22.34 years) and 108,649 female (age 62.57, SD 22.89 years) trauma patients. The mean length of stay was 7.16 (SD 14.84) days and most patients were discharged home (n = 168,582, 74.8%). A weekly and yearly seasonality trend can be observed in admission trends. The mean number of occupied trauma beds ranges from 3700 to 4000 per day. The number of occupied beds increases on weekdays and decreases on holidays. The number of occupied beds is a positive, independent risk factor for discharge in trauma patients; as the number of occupied beds increases at any given time, so does the risk for discharge. The number of beds occupied represents an independent non-medical risk factor for discharge. Capacity determines triage of hospitalized patients and therefore might increase the risk of premature discharge

Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP).

OBJECTIVES We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays. RESULTS A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO. CONCLUSION Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. Trial registration number ISRCTN51255782; Pre-results

Federated Learning for Breast Density Classification: A Real-World Implementation

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.Comment: Accepted at the 1st MICCAI Workshop on "Distributed And Collaborative Learning"; add citation to Fig. 1 & 2 and update Fig.

Molecular, microbiological and clinical characterization of Clostridium difficile isolates from tertiary care hospitals in Colombia

In Colombia, the epidemiology and circulating genotypes of Clostridium difficile have not yet been described. Therefore, we molecularly characterized clinical isolates of C.difficile from patients with suspicion of C.difficile infection (CDI) in three tertiary care hospitals. C.difficile was isolated from stool samples by culture, the presence of A/B toxins were detected by enzyme immunoassay, cytotoxicity was tested by cell culture and the antimicrobial susceptibility determined. After DNA extraction, tcdA, tcdB and binary toxin (CDTa/CDTb) genes were detected by PCR, and PCR-ribotyping performed. From a total of 913 stool samples collected during 2013–2014, 775 were included in the study. The frequency of A/B toxins-positive samples was 9.7% (75/775). A total of 143 isolates of C.difficile were recovered from culture, 110 (76.9%) produced cytotoxic effect in cell culture, 100 (69.9%) were tcdA+/tcdB+, 11 (7.7%) tcdA-/tcdB+, 32 (22.4%) tcdA-/tcdB- and 25 (17.5%) CDTa+/CDTb+. From 37 ribotypes identified, ribotypes 591 (20%), 106 (9%) and 002 (7.9%) were the most prevalent; only one isolate corresponded to ribotype 027, four to ribotype 078 and four were new ribotypes (794,795, 804,805). All isolates were susceptible to vancomycin and metronidazole, while 85% and 7.7% were resistant to clindamycin and moxifloxacin, respectively. By multivariate analysis, significant risk factors associated to CDI were, staying in orthopedic service, exposure to third-generation cephalosporins and staying in an ICU before CDI symptoms; moreover, steroids showed to be a protector factor. These results revealed new C. difficile ribotypes and a high diversity profile circulating in Colombia different from those reported in America and European countries

Activation of MEK1 or MEK2 isoform is sufficient to fully transform intestinal epithelial cells and induce the formation of metastatic tumors

<p>Abstract</p> <p>Background</p> <p>The Ras-dependent ERK1/2 MAP kinase signaling pathway plays a central role in cell proliferation control and is frequently activated in human colorectal cancer. Small-molecule inhibitors of MEK1/MEK2 are therefore viewed as attractive drug candidates for the targeted therapy of this malignancy. However, the exact contribution of MEK1 and MEK2 to the pathogenesis of colorectal cancer remains to be established.</p> <p>Methods</p> <p>Wild type and constitutively active forms of MEK1 and MEK2 were ectopically expressed by retroviral gene transfer in the normal intestinal epithelial cell line IEC-6. We studied the impact of MEK1 and MEK2 activation on cellular morphology, cell proliferation, survival, migration, invasiveness, and tumorigenesis in mice. RNA interference was used to test the requirement for MEK1 and MEK2 function in maintaining the proliferation of human colorectal cancer cells.</p> <p>Results</p> <p>We found that expression of activated MEK1 or MEK2 is sufficient to morphologically transform intestinal epithelial cells, dysregulate cell proliferation and induce the formation of high-grade adenocarcinomas after orthotopic transplantation in mice. A large proportion of these intestinal tumors metastasize to the liver and lung. Mechanistically, activation of MEK1 or MEK2 up-regulates the expression of matrix metalloproteinases, promotes invasiveness and protects cells from undergoing anoikis. Importantly, we show that silencing of MEK2 expression completely suppresses the proliferation of human colon carcinoma cell lines, whereas inactivation of MEK1 has a much weaker effect.</p> <p>Conclusion</p> <p>MEK1 and MEK2 isoforms have similar transforming properties and are able to induce the formation of metastatic intestinal tumors in mice. Our results suggest that MEK2 plays a more important role than MEK1 in sustaining the proliferation of human colorectal cancer cells.</p

A comprehensive overview of radioguided surgery using gamma detection probe technology

The concept of radioguided surgery, which was first developed some 60 years ago, involves the use of a radiation detection probe system for the intraoperative detection of radionuclides. The use of gamma detection probe technology in radioguided surgery has tremendously expanded and has evolved into what is now considered an established discipline within the practice of surgery, revolutionizing the surgical management of many malignancies, including breast cancer, melanoma, and colorectal cancer, as well as the surgical management of parathyroid disease. The impact of radioguided surgery on the surgical management of cancer patients includes providing vital and real-time information to the surgeon regarding the location and extent of disease, as well as regarding the assessment of surgical resection margins. Additionally, it has allowed the surgeon to minimize the surgical invasiveness of many diagnostic and therapeutic procedures, while still maintaining maximum benefit to the cancer patient. In the current review, we have attempted to comprehensively evaluate the history, technical aspects, and clinical applications of radioguided surgery using gamma detection probe technology

Sex-specific differences in physiological parameters related to SARS-CoV-2 infections among a national cohort (COVI-GAPP study).

Considering sex as a biological variable in modern digital health solutions, we investigated sex-specific differences in the trajectory of four physiological parameters across a COVID-19 infection. A wearable medical device measured breathing rate, heart rate, heart rate variability, and wrist skin temperature in 1163 participants (mean age = 44.1 years, standard deviation [SD] = 5.6; 667 [57%] females). Participants reported daily symptoms and confounders in a complementary app. A machine learning algorithm retrospectively ingested daily biophysical parameters to detect COVID-19 infections. COVID-19 serology samples were collected from all participants at baseline and follow-up. We analysed potential sex-specific differences in physiology and antibody titres using multilevel modelling and t-tests. Over 1.5 million hours of physiological data were recorded. During the symptomatic period of infection, men demonstrated larger increases in skin temperature, breathing rate, and heart rate as well as larger decreases in heart rate variability than women. The COVID-19 infection detection algorithm performed similarly well for men and women. Our study belongs to the first research to provide evidence for differential physiological responses to COVID-19 between females and males, highlighting the potential of wearable technology to inform future precision medicine approaches