Frontiers in medicine
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Frontiers in medicine · Jan 2020
Case ReportsFalse Positive COVID-19 Antibody Test in a Case of Granulomatosis With Polyangiitis.
Collateral damage due to 2019 novel coronavirus disease (COVID-19) represents an emerging issue. Symptoms of COVID-19 are not disease-specific. Differential diagnosis is challenging and the exclusion of other life-threatening diseases has major caveats. ⋯ Pulses of methylprednisolone along with cyclophosphamide were applied. At day 10, treatment response was noticed as indicated by respiratory and renal function improvement. This report highlights the need for meticulous patient evaluation in order to avoid misdiagnosis in the era of COVID-19.
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Frontiers in medicine · Jan 2020
Mixed Method Study to Explore Ethical Dilemmas and Health Care Workers' Willingness to Work Amid COVID-19 Pandemic in Palestine.
Background: The high potential risks involved in working in a healthcare setting during a pandemic and the associated fear that may affect health care workers' (HCWs') willingness to work are important to understand to eliminate potential barriers to working. This study aimed to assess Palestinian HCWs' willingness to work and the related factors as well as to explore their ethical dilemmas during the coronavirus disease 2019 (COVID-19) pandemic. Materials and Methods: Quantitative (survey questionnaire) and qualitative (semi-structured interviews) data were collected. ⋯ Conclusion: Physicians and nurses were more likely to comply with a commitment to their professional ethics and the duty or obligation to work. Stress levels could be mitigated in the future with better leadership, adding supports to address mental health and psychosocial challenges to enhance HCWs' well-being and improve quality of care. The realities of the occupation added additional threats and uncertainty.
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Frontiers in medicine · Jan 2020
Development and Validation of a Sepsis Mortality Risk Score for Sepsis-3 Patients in Intensive Care Unit.
Background: Many severity scores are widely used for clinical outcome prediction for critically ill patients in the intensive care unit (ICU). However, for patients identified by sepsis-3 criteria, none of these have been developed. This study aimed to develop and validate a risk stratification score for mortality prediction in sepsis-3 patients. ⋯ As shown by the decision curve analysis, the score always had a positive net benefit. Conclusion: We observed moderate discrimination and calibration for the score termed Sepsis Mortality Risk Score (SMRS), allowing stratification of patients according to mortality risk. However, we still require further modification and external validation.
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Frontiers in medicine · Jan 2020
Effect of a Multimodal Movement Intervention in Patients With Neurogenic Claudication Based on Lumbar Spinal Stenosis and/or Degenerative Spondylolisthesis-A Pilot Study.
Chronic low-back pain is a major individual, social, and economic burden. The impairment ranges from deterioration of gait, limited mobility, to psychosocial distress. Due to this complexity, the demand for multimodal treatments is huge. ⋯ For the subsequent study, further kinematic and cognitive parameters should be analyzed, and the number of participants has to be increased. Clinical Trial Registration: German Clinical Trial Register (ID: DRKS00021026/URL: https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID=DRKS00021026).
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Frontiers in medicine · Jan 2020
A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.
Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients. Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis. Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). ⋯ The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837-0.846) and 0.803 (95% CI: 0.798-0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653-0.667) and SIC scores (0.752; 95% CI: 0.747-0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable. Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.