Internal and emergency medicine
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Coronavirus 2019 disease (COVID-19) continues to challenge healthcare systems globally as many countries are currently experiencing an increase in the morbidity and mortality. Compare baseline characteristics, clinical presentation, treatments, and clinical outcomes of patients admitted during the second peak to those admitted during the first peak. Retrospective analysis of 258 COVID-19 patients consecutively admitted to the Tel Aviv Medical Center, of which, 131 during the first peak (March 21-May 30, 2020) and 127 during the second peak (May 31-July 16, 2020). ⋯ Compared to the first peak, 30-day mortality and invasive mechanical ventilation rates as well as adjusted risk were significantly lower during the second peak (10.2%, vs 19.8% vs p = 0.028, adjusted HR 0.39, 95% CI 0.19-0.79, p = 0.009 and 8.8% vs 19.3%, p = 0.002, adjusted HR 0.29, 95% CI 0.13-0.64, p = 0.002; respectively). Rates of 30-day mortality and invasive mechanical ventilation, as well as adjusted risks, were lower in the second peak of the COVID-19 pandemic among hospitalized patients. The change in treatment strategy and the experienced gained during the first peak may have contributed to the improved outcomes.
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Observational Study
Putative invasive pulmonary aspergillosis within medical wards and intensive care units: a 4-year retrospective, observational, single-centre study.
Blot and colleagues have proposed putative invasive pulmonary aspergillosis (PIPA) definitions for troublesome diagnosis in suspected patients outside the classical criteria of immunosuppression. We retrospectively included in the study all admitted patients with an Aspergillus spp. positive culture within lower airway samples. Overall, Aspergillus spp. positivity in respiratory samples was 0.97 every 1000 hospital admissions (HA): 4.94 and 0.28/1000/HA, respectively, in intensive care units (ICUs) and medical wards (MW). 66.6% fulfilled PIPA criteria, and 33.4% were defined as colonized. 69.2% of PIPA diagnosis occurred in the ICU. ⋯ Overall mortality within 21 days was 50%: 54.2% in ICU, 36,8% in MW. Factors associated with death were length of hospitalization, influenza, pneumonia, liver transplant, AKI, ARDS, sepsis and septic shock. PIPA in the ICU had higher disease severity and needed more organ support than MW cases, despite that cases of PIPA in MW are emerging with trends difficult to demonstrate given the problematic diagnosis.
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Ultrasound-guided synovial tissue biopsy (USSB) may allow personalizing the treatment for patients with inflammatory arthritis. To this end, the quantification of tissue inflammation in synovial specimens can be crucial to adopt proper therapeutic strategies. This study aimed at investigating whether computer vision may be of aid in discriminating the grade of synovitis in patients undergoing USSB. ⋯ Cellularity in the synovial lining and sublining layers was the salient determinant of CNN prediction. This study provides a proof of concept that computer vision with transfer learning is suitable for scoring synovitis. Integrating CNN-based approach into real-life patient management may improve the workflow between rheumatologists and pathologists.
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The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record. ⋯ The generation of an exact EDD remains inaccurate. This study has shown that repeated estimates of LOS using daily ward round notes and mixed-data inputs are effective in the prediction of general medicine discharges in the next 48 h. Further research may seek to prospectively and externally validate models for prediction of upcoming discharge, as well as combination human-ML approaches for generating EDDs.