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Objectives: We examined whether an array of scleroderma autoantibodies associates with risk of cancer and could be useful tools for risk stratification. Methods: Scleroderma cancer cases and scleroderma controls without cancer from the Johns Hopkins Scleroderma Center and the University of Pittsburgh Scleroderma Center were studied. Sera were assayed by Lineblot and enzyme‐linked immunosorbent assay (ELISA) for autoantibodies against centromere, topoisomerase 1, RNA polymerase (POLR) 3, PM/Scl, Th/To, NOR90, U3 RNP, Ku, Ro52, U1RNP, and RNPC3. Logistic regression models were constructed to examine whether distinct autoantibodies associated with overall cancer at any time and cancer‐associated scleroderma (cancer occurring three years before and after scleroderma onset). The effects of having more than one autoantibody on cancer were further examined using random forest analysis. Results: A total of 676 cases and 687 controls were studied. After adjusting for relevant covariates, anti‐POLR3 (odds ratio [OR] 1.47, 95% confidence interval [CI] 1.03–2.11) and monospecific anti‐Ro52 (OR 2.19, 95% CI 1.29–3.74) were associated with an increased overall cancer risk, whereas anticentromere (OR 0.69, 95% CI 0.51–0.93) and anti‐U1RNP (OR 0.63, 95% CI 0.43–0.93) were associated with lower risk. When examining risk of cancer‐associated scleroderma, these immune responses remained associated with increased or decreased risk: anti‐POLR3 (OR 2.28, 95% CI 1.33–3.91), monospecific anti‐Ro52 (OR 2.58, 95% CI 1.05–6.30), anticentromere (OR 0.39, 95% CI 0.20–0.74), and anti‐U1RNP (OR 0.32, 95% CI 0.11–0.93). Anti‐Ro52 plus anti‐U1RNP or anti‐Th/To was associated with decreased cancer risk compared with anti‐Ro52 alone. Conclusions: These data suggest that five distinct scleroderma immune responses, alone or in combination, may be useful tools to stratify the risk of cancer for scleroderma patients. Further study examining cancer risk in autoantibody subgroups relative to the general population is warranted. [ABSTRACT FROM AUTHOR]

Objectives Ectopic calcification (calcinosis) is a common complication of SSc, but a subset of SSc patients has a heavy burden of calcinosis. We examined whether there are unique risk factors for a heavy burden of calcinosis, as compared with a light burden or no calcinosis. Methods We reviewed the medical records of all patients in the Johns Hopkins Scleroderma Center Research Registry with calcinosis to quantify calcinosis burden using pre-specified definitions. We performed latent class analysis to identify SSc phenotypic classes. We used multinomial logistic regression to determine whether latent phenotypic classes and autoantibodies were independent risk factors for calcinosis burden. Results Of all patients, 29.4% (997/3388) had calcinosis, and 13.5% (130/963) of those with calcinosis had a heavy burden. The latent phenotypic class with predominantly diffuse skin disease and higher disease severity (characterized by pulmonary hypertension, interstitial lung disease, cardiomyopathy, severe RP, gastrointestinal involvement, renal crisis, myopathy and/or tendon friction rubs) was associated with an increased risk of both a heavy burden [odds ratio (OR) 6.92, 95% CI 3.66, 13.08; P  < 0.001] and a light burden (OR 2.88, 95% CI 2.11, 3.95; P  < 0.001) of calcinosis compared with the phenotypic class with predominantly limited skin disease. Autoantibodies to PM/Scl were strongly associated with a heavy burden of calcinosis (OR 17.31, 95% CI 7.72, 38.81; P  < 0.001) and to a lesser degree a light burden of calcinosis (OR 3.59, 95% CI 1.84, 7.00; P  < 0.001). Conclusions Calcinosis burden is associated with cumulative SSc-related tissue damage. Independent of disease severity, autoantibodies to PM/Scl are also associated with a heavy burden of calcinosis. [ABSTRACT FROM AUTHOR]

Longitudinal trajectories of vital signs and biomarkers during hospital admission of patients with COVID-19 remain poorly characterized despite their potential to provide critical insights about disease progression. We studied 1884 patients with severe acute respiratory syndrome coronavirus 2 infection from April 3, 2020, to June 25, 2020, within 1 Maryland hospital system and used a retrospective longitudinal framework with linear mixed-effects models to investigate relevant biomarker trajectories leading up to 3 critical outcomes: mechanical ventilation, discharge, and death. Trajectories of 4 vital signs (respiratory rate, ratio of oxygen saturation (Sp o 2) to fraction of inspired oxygen (Fi o 2), pulse, and temperature) and 4 laboratory values (C-reactive protein (CRP), absolute lymphocyte count (ALC), estimated glomerular filtration rate, and D-dimer) clearly distinguished the trajectories of patients with COVID-19. Before any ventilation, log(CRP), log(ALC), respiratory rate, and Sp o 2-to-Fi o 2 ratio trajectories diverge approximately 8–10 days before discharge or death. After ventilation, log(CRP), log(ALC), respiratory rate, Sp o 2-to-Fi o 2 ratio, and estimated glomerular filtration rate trajectories again diverge 10–20 days before death or discharge. Trajectories improved until discharge and remained unchanged or worsened until death. Our approach characterizes the distribution of biomarker trajectories leading up to competing outcomes of discharge versus death. Moving forward, this model can contribute to quantifying the joint probability of biomarkers and outcomes when provided clinical data up to a given moment. [ABSTRACT FROM AUTHOR]