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Background: Risk stratification of COVID-19 patients upon hospital admission is key for their successful treatment and efficient utilization of hospital resources. Objective: To evaluate the risk factors associated with ventilation need and mortality. Design, setting and participants: We established a retrospective cohort of COVID-19 patients from Mass General Brigham hospitals. Demographic, clinical, and admission laboratory data were obtained from electronic medical records of patients admitted to hospital with laboratory-confirmed COVID-19 before May 19th, 2020. Using patients admitted to Massachusetts General Hospital (MGH, derivation cohort), multivariable logistic regression analyses were used to construct the Ventilation in COVID Estimator (VICE) and Death in COVID Estimator (DICE) risk scores. Measurements: The primary outcomes were ventilation status and death. Results: The entire cohort included 1042 patients (median age, 64 years; 56.8% male). The derivation and validation cohorts for the risk scores included 578 and 464 patients, respectively. We found seven factors to be independently predictive for ventilation requirement (diabetes mellitus, dyspnea, alanine aminotransferase, troponin, C-reactive protein, neutrophil-lymphocyte ratio, and lactate dehydrogenase), and 10 factors to be predictors of in-hospital mortality (age, sex, diabetes mellitus, chronic statin use, albumin, C-reactive protein, neutrophil-lymphocyte ratio, mean corpuscular volume, platelet count, and procalcitonin). Using these factors, we constructed the VICE and DICE risk scores, which performed with C-statistics of at least 0.8 in our cohorts. Importantly, the chronic use of a statin was associated with protection against death due to COVID-19. The VICE and DICE score calculators have been placed on an interactive website freely available to the public (https://covid-calculator.com/). Limitations: One potential limitation is the modest sample sizes in both our derivation and validation cohorts. Conclusion: The risk scores developed in this study may help clinicians more appropriately determine which COVID-19 patients will need to be managed with greater intensity.

Background Spinal Muscular Atrophy (SMA) is a neurodegenerative motor neuron disorder resulting from a homozygous mutation of the survival of motor neuron 1 (SMN1) gene. The gene product, SMN protein, functions in RNA biosynthesis in all tissues. In humans, a nearly identical gene, SMN2, rescues an otherwise lethal phenotype by producing a small amount of full-length SMN protein. SMN2 copy number inversely correlates with disease severity. Identifying other novel biomarkers could inform clinical trial design and identify novel therapeutic targets. Objective: To identify novel candidate biomarkers associated with disease severity in SMA using unbiased proteomic, metabolomic and transcriptomic approaches. Materials and Methods: A cross-sectional single evaluation was performed in 108 children with genetically confirmed SMA, aged 2–12 years, manifesting a broad range of disease severity and selected to distinguish factors associated with SMA type and present functional ability independent of age. Blood and urine specimens from these and 22 age-matched healthy controls were interrogated using proteomic, metabolomic and transcriptomic discovery platforms. Analyte associations were evaluated against a primary measure of disease severity, the Modified Hammersmith Functional Motor Scale (MHFMS) and to a number of secondary clinical measures. Results A total of 200 candidate biomarkers correlate with MHFMS scores: 97 plasma proteins, 59 plasma metabolites (9 amino acids, 10 free fatty acids, 12 lipids and 28 GC/MS metabolites) and 44 urine metabolites. No transcripts correlated with MHFMS. Discussion In this cross-sectional study, “BforSMA” (Biomarkers for SMA), candidate protein and metabolite markers were identified. No transcript biomarker candidates were identified. Additional mining of this rich dataset may yield important insights into relevant SMA-related pathophysiology and biological network associations. Additional prospective studies are needed to confirm these findings, demonstrate sensitivity to change with disease progression, and assess potential impact on clinical trial design. Trial Registry Clinicaltrials.gov NCT00756821.

BACKGROUNDThe universal presence of a gene (SMN2) nearly identical to the mutated SMN1 gene responsible for Spinal Muscular Atrophy (SMA) has proved an enticing incentive to therapeutics development. Early disappointments from putative SMN-enhancing agent clinical trials have increased interest in improving the assessment of SMN expression in blood as an early "biomarker" of treatment effect.METHODSA cross-sectional, single visit, multi-center design assessed SMN transcript and protein in 108 SMA and 22 age and gender-matched healthy control subjects, while motor function was assessed by the Modified Hammersmith Functional Motor Scale (MHFMS). Enrollment selectively targeted a broad range of SMA subjects that would permit maximum power to distinguish the relative influence of SMN2 copy number, SMA type, present motor function, and age.RESULTSSMN2 copy number and levels of full-length SMN2 transcripts correlated with SMA type, and like SMN protein levels, were lower in SMA subjects compared to controls. No measure of SMN expression correlated strongly with MHFMS. A key finding is that SMN2 copy number, levels of transcript and protein showed no correlation with each other.CONCLUSIONThis is a prospective study that uses the most advanced techniques of SMN transcript and protein measurement in a large selectively-recruited cohort of individuals with SMA. There is a relationship between measures of SMN expression in blood and SMA type, but not a strong correlation to motor function as measured by the MHFMS. Low SMN transcript and protein levels in the SMA subjects relative to controls suggest that these measures of SMN in accessible tissues may be amenable to an "early look" for target engagement in clinical trials of putative SMN-enhancing agents. Full length SMN transcript abundance may provide insight into the molecular mechanism of phenotypic variation as a function of SMN2 copy number.TRIAL REGISTRYClinicaltrials.gov NCT00756821.