Plasma Exchange in Anti-Signal Recognition Particle Myopathy: A Systematic Review and Combined Analysis of Patient Individual Data.

Anti-signal recognition particle myopathy (anti-SRP myopathy) is a rare subtype of immune-mediated inflammatory myopathy characterized by muscle weakness and anti-SRP autoantibodies. Although plasma exchange (PE) is used in severe cases, its role remains unclear. A systematic review was conducted following PRISMA guidelines, identifying 23 patients with anti-SRP myopathy treated with PE. Data on demographics, clinical features, laboratory findings, treatments, and outcomes were analyzed combining individual patient data if available. Sixteen (69.6%) patients were male, with muscle weakness as the predominant symptom in 100% of cases. After PE, most patients showed improvement in symptoms, and the proportion of patients with muscle weakness was reduced (p = 0.001). Relapse occurred in 17.4% of the cases. The incidence of adverse events was low (8.7%). Despite limitations, including a small sample size and heterogeneous data, our systematic review suggests that PE may be effective in inducing remission and controlling symptoms in anti-SRP myopathy, particularly in severe cases. Since evidence on PE in anti-SRP myopathy is limited, further research, including prospective multicenter studies, is warranted to understand better its efficacy and safety and establish its role in treatment algorithms.

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

Disnea de un mes de evolución en una mujer de 62 años / Dyspnea of one-month duration in a 62 years-old woman

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