Acute partial transverse myelitis (APTM) may be the first clinical manifestation of multiple sclerosis (MS), of relapsing myelitis, or remain a monophasic event. Identification of risk factors associated with relapse or conversion to MS is important, as prognostic information might help to guide management. The objective of this study was to define clinical, laboratory and neuroimaging factors in patients with first-ever APTM that predict relapses or conversion to MS. We identified 73 patients with a first-ever APTM admitted to our institution from January 1999 to June 2005. The follow-up time ranged from 12 to 90 months (mean follow-up 46 months). Patient demographics, clinical impairment at onset and after 3 months, ancillary tests including cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), evoked potentials, recurrent and new symptoms and signs during follow-up were analysed. APTM remained a monophasic event in 35 patients (47.9%), conversion to MS occurred in 32 (43.8%) and recurred as relapsing myelitis in six patients (8.2%). According to univariate analysis, a family history of MS (P = 0.02), higher expanded disability status scale (EDSS) at onset (P = 0.03) and lesions on brain MRI (P = 0.03) were predictive factors for conversion to MS. CSF-specific oligoclonal bands (P = 0.04) or abnormal IgG-index (P = 0.04) were associated with increased risk for MS as well. In patients with a first-ever APTM, a family history of MS, high EDSS at presentation, lesions on brain MRI, CSF-specific oligoclonal bands or abnormal IgG-index may indicate an increased risk for conversion to MS.

Study design: Retrospective 9-year survey. Objectives: Clinical presentation of acute myelitis syndromes is variable, and neuroimaging and laboratory findings are not specific enough to establish the diagnosis with certainty. We evaluated the spectrum clinical features and paraclinical findings encountered during diagnostic workup and aiding the diagnosis. Setting: Department of Neurology, Inselspital Bern, Switzerland. Material: Charts and magnetic resonance imaging (MRI) of 63 patients discharged with the diagnosis of acute transverse myelitis. Results: The diagnosis was supported by abnormal MRI and cerebrospinal fluid (CSF) findings in 52 patients (82.5%) and suspected in the remaining either because of a spinal cord MRI lesion suggestive of myelitis (n ¼ 5), or abnormal CSF findings (n ¼ 4), or electrophysiological evidence of a spinal cord dysfunction (n ¼ 2). Clinical impairment was mild (ASIA D) in the majority. All patients had sensory disturbances, whereas motor deficit and autonomic dysfunction were less frequent. Neurological levels were mainly located in cervical or thoracic dermatomes. Spinal cord lesions were visualized by MRI in 90.4% of the patients and distributed either in the cervical or thoracic cord, or both. Multiple lesions were present in more than half of the patients, and lateral, centromedullary and posterior locations were most common. A high percentage of multiple sclerosis (MS)-typical brain lesions and CSF findings suggested a substantial number of MS-related myelitis in our cohort. Conclusion: The diagnostic workup of acute myelitis discloses a broad spectrum of CSF or MRI findings, and may be associated with diagnostic uncertainty due to lack of specific CSF or MRI features, or pathological findings.

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.