The possible onset of fibromyalgia following acute COVID-19 infection.

INTRODUCTION: The exact pathogenesis of fibromyalgia (FM) syndrome is unclear. However, various infectious have been implicated with the development of FM after their acute phase. We aimed to investigate the incidence of FM syndrome among convalesced individuals following hospitalization for Acute Coronavirus Disease-2019 (COVID-19). METHODS: We performed a cross-sectional study on patients who were discharged after COVID-19 hospitalization from the Sheba Medical Center, Israel, between July 2020 to November 2020. A phone interview was performed consisting of the following questionnaires: the Fibromyalgia Survey Diagnostic Criteria Questionnaire, Sense of Coherence Questionnaire to evaluate resilience, and the Subjective Traumatic Outlook Questionnaire to assess the associated psychological aspects of the trauma. The incidence of post-COVID FM was calculated and regression models were performed to identify predictors. RESULTS: The study population consisted of 198 eligible patients who completed the phone interview. The median age was 64 (52-72) and 37% were women. The median follow-up was 5.2 months (IQR 4.4-5.8). The incidence of FM was 15% (30 patients) and 87% (172 patients) had at least one FM-related symptom. Female gender was significantly associated with post-COVID FM (OR 3.65, p = 0.002). In addition, high median Subjective Traumatic Outlook scores and low median Sense of Coherence scores were both significantly associated with post-COVID FM (OR 1.19, p<0.001 and OR 0.92, p<0.001, respectively). CONCLUSIONS: FM is highly prevalent among COVID-19 convalescent patients. Our finding suggests that a significant subjective traumatic experience and a low resilience are highly associated with post-COVID FM.

Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study.

BACKGROUND: The burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews. OBJECTIVES: The goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers. METHODS: We evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms. RESULTS: The overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73). CONCLUSION: The data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.

The intercorrelations between blood levels of ferritin, sCD163, and IL-18 in COVID-19 patients and their association to prognosis.

Coronavirus disease 2019 (COVID-19) is associated with immune dysregulation, severe respiratory failure, and multiple organ dysfunction caused by a cytokine storm involving high blood levels of ferritin and IL-18. Furthermore, there is a resemblance between COVID-19 and macrophage activation syndrome (MAS) characterized by high concentrations of soluble CD163 (sCD163) receptor and IL-18. High levels of ferritin, IL-18, and sCD163 receptor are associated with "hyperferritinemic syndrome", a family of diseases that appears to include COVID-19. In this retrospective cohort study, we tested the association and intercorrelations in the serum levels of ferritin, sCD163, and IL-18 and their impact on the prognosis of COVID-19. We analyzed data of 70 hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The levels of sCD163, ferritin, and IL-18 were measured and the correlation of these parameters with the respiratory deterioration and overall 30-day survival was assessed. Among the 70 patients, 60 survived 30 days from hospitalization. There were substantial differences between the subjects who were alive following 30 days compared to those who expired. The differences were referring to lymphocyte and leukocyte count, CRP, D-dimer, ferritin, sCD163, and IL-18. Results showed high levels of IL-18 (median, 444 pg/mL in the survival group compared with 916 pg/mL in the mortality group, p-value 8.54 × 10-2), a statistically significant rise in levels of ferritin (median, 484 ng/mL in the survival group compared with 1004 ng/mL in the mortality group p-value, 7.94 × 10-3), and an elevated value of in sCD163 (mean, 559 ng/mL in the survival group compared with 840 ng/mL in the mortality group, p-value 1.68 × 10-2). There was no significant correlation between the rise of ferritin and the levels sCD163 or IL-18. Taken together, sCD163, ferritin, and IL-18 were found to correlate with the severity of COVID-19 infection. Although these markers are associated with COVID-19 and might contribute to the cytokine storm, no intercorrelation was found among them. It cannot be excluded though that the results depend on the timing of sampling, assuming that they play distinct roles in different stages of the disease course. The data represented herein may provide clinical benefit in improving our understanding of the pathological course of the disease. Furthermore, measuring these biomarkers during the disease progression may help target them at the right time and refine the decision-making regarding the requirement for hospitalization.

Automated processing of thermal imaging to detect COVID-19.

Rapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on advanced image processing algorithms. We captured thermal images of the backs of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in COVID-19 detection with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. In summary, we show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.

Clinical Course and Outcomes of Severe Covid-19: A National Scale Study

Knowledge of the outcomes of critically ill patients is crucial for health and government officials who are planning how to address local outbreaks. The factors associated with outcomes of critically ill patients with coronavirus disease 2019 (Covid-19) who required treatment in an intensive care unit (ICU) are yet to be determined. Methods: This was a retrospective registry-based case series of patients with laboratory-confirmed SARS-CoV-2 who were referred for ICU admission and treated in the ICUs of the 13 participating centers in Israel between 5 March and 27 April 2020. Demographic and clinical data including clinical management were collected and subjected to a multivariable analysis;primary outcome was mortality. Results: This study included 156 patients (median age = 72 years (range = 22-97 years));69% (108 of 156) were male. Eighty-nine percent (139 of 156) of patients had at least one comorbidity. One hundred three patients (66%) required invasive mechanical ventilation. As of 8 May 2020, the median length of stay in the ICU was 10 days (range = 0-37 days). The overall mortality rate was 56%;a multivariable regression model revealed that increasing age (OR = 1.08 for each year of age, 95%CI = 1.03-1.13), the presence of sepsis (OR = 1.08 for each year of age, 95%CI = 1.03-1.13), and a shorter ICU stay(OR = 0.90 for each day, 95% CI = 0.84-0.96) were independent prognostic factors. Conclusions: In our case series, we found lower mortality rates than those in exhausted health systems. The results of our multivariable model suggest that further evaluation is needed of antiviral and antibacterial agents in the treatment of sepsis and secondary infection.