Journal of investigative medicine : the official publication of the American Federation for Clinical Research
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The ISARIC4C consortium developed and internally validated the 4C Score for prediction of mortality only in hospitalized patients. We aimed to assess the validity of the 4C Score in mortality prediction of patients with COVID-19 who had been home isolated or hospitalized. This retrospective cross-sectional study was performed after the first wave of COVID-19. ⋯ Other components of the model had non-significant predictions. In conclusion, the 4C Mortality Score has good sensitivity and specificity in early risk stratification and mortality prediction of patient with COVID-19. Within the model, only hypoxia, tachypnea, high BUN, and CRP were the independent mortality predictors with the possibility of overlooking other important predictors.
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Septic arthritis is important to consider in any patient who presents with joint pain because it is a medical emergency with an 11% fatality rate. Diagnosis and treatment may improve prognosis; however, many patients do not regain full joint function. In patients with end-stage renal disease (ESRD), immune dysfunction due to uremia and chronic vascular access leads to increased risk of infection. ⋯ Significant risk factors for septic arthritis included history of joint disease, immune compromise (diabetes, HIV, cirrhosis), bacteremia and urinary tract infection. One of the four sequelae examined (joint replacement, amputation, osteomyelitis, Clostridioides difficile infection) occurred in 25% of septic arthritis cases. The high incidence of septic arthritis and the potential for serious sequelae in patients with ESRD suggest that physicians treating individuals with ESRD and joint pain/inflammation should maintain a high clinical suspicion for septic arthritis.
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Comparative Study
Prospective predictive performance comparison between clinical gestalt and validated COVID-19 mortality scores.
Most COVID-19 mortality scores were developed at the beginning of the pandemic and clinicians now have more experience and evidence-based interventions. Therefore, we hypothesized that the predictive performance of COVID-19 mortality scores is now lower than originally reported. We aimed to prospectively evaluate the current predictive accuracy of six COVID-19 scores and compared it with the accuracy of clinical gestalt predictions. 200 patients with COVID-19 were enrolled in a tertiary hospital in Mexico City between September and December 2020. ⋯ Adjusting scores with locally derived likelihood ratios did not improve their performance; however, some scores outperformed clinical gestalt predictions when clinicians' confidence of prediction was <80%. Despite its subjective nature, clinical gestalt has relevant advantages in predicting COVID-19 clinical outcomes. The need and performance of most COVID-19 mortality scores need to be evaluated regularly.
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The psychological burden of the COVID-19 pandemic may have a lasting effect on emotional well-being of healthcare workers. Medical personnel working at the time of the pandemic may experience elevated occupational stress due to the uncontrollability of the virus, high perceived risk of infection, poor understanding of the novel virus transmission routes and unavailability of effective antiviral agents. This study used path analysis to analyze the relationship between stress and alexithymia, emotional processing and negative/positive affect in healthcare workers. ⋯ The relationship between alexithymia and emotional processing was stronger in nurses than in physicians (difference in beta=0.27; p<0.05). Individual path χ2 tests also revealed significantly different paths across these groups. The results of the study may be used to develop evidence-based intervention programs promoting healthcare workers' mental health and well-being.
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Depression entails changes in the mental health of individuals worldwide. Episodes of depression lead to mood swings and changes in the motivational dimension. Our research focused on the prevalence of depression in the adult population and on how it affected the social and affective dimensions. ⋯ As tools, we used the Hamilton Depression Rating Scale and the Hamilton Anxiety Rating Scale. The data demonstrated that women were more likely to display symptoms of depression and that individuals aged between 24 and 29 were less likely to reveal symptoms of anxiety than those aged between 18 and 23. Moreover, childless or economically dependent individuals proved to be more likely to display symptoms of depression during the pandemic.