Systemic Connective Tissue Disease and Neuromyelitis Optica Spectrum Disorder Coexistence: A Systematic Review and Meta-Analysis.

BACKGROUND: Several results support the hypothesis that a group of pathologies falling within the Neuromyelitis Optica Spectrum Disorders (NMOSD) diagnostic criteria may coexist with Connective Tissue Diseases (CTD) in patients with a high susceptibility to autoimmune conditions. However, the relationship between NMOSD and rheumatologic diseases deserves further investigations to clarify all clinical aspects of this coexistence. We designed a systematic review and a proportional meta-analysis to estimate the association between CTD and MNOSD, with the aim of helping to plan the best strategy to achieve the most significant public health benefit for these conditions. METHODS: We conducted a systematic review of the literature published until February 2023, searching in four databases: PubMed, Web of Science, EmBase, and OVID. Then, we conducted a random-effects proportional meta-analysis and assessed the risk of bias of the included studies using the Joanna Briggs Institute checklist. RESULTS: The literature search yielded an overall result of 3176 publications (272 from PubMed, 880 from Web of Science, 634 from EmBase and 1390 from OVID). Of these, 29 were included in this systematic review. Analyzing studies that recruited unselected patients with Systemic Lupus Erythematosus (SLE) and Sjogren Syndrome (SjS), the pooled percentages of NMOSD overlapping were 0.6% (95% Confidence Interval [95% CI]: 0.1%-1.4%,) and 6.5% (95% CI: 4.7-8.6), respectively. Studies enrolling rheumatologic patients with nervous system symptoms involvement reported higher percentage of NMOSD (i.e., among SjS patients, a pooled percentage of 26.5%, 95% CI: 5.5-54.6%, was found). Similarly, recruiting patients with NMOSD, we found pooled percentages of SjS or SLE respectively of 7.0% and 3.5%. CONCLUSIONS: Our research found that the coexistence of these two disorders was more frequent in female rheumatologic patients with a SjS diagnosis with neurological manifestations and in neurologic patients for whom a SjS diagnosis was suspected. Similarly, NMOSD are less frequently found in SLE and very rarely incident in Mixed Connective Tissue Disease (MCTD) patients. These considerations should be taken into account in clinical experience of rheumatologists and neurologists, since early diagnosis of both conditions may influence the timing of immunosuppressive therapy and the prevention of systemic disabilities.

Monocyte Distribution Width as a predictor of community acquired sepsis in patients prospectively enrolled at the Emergency Department.

BACKGROUND: Monocyte Distribution Width (MDW), a simple cellular marker of innate monocyte activation, can be used for the early recognition of sepsis. We performed an observational prospective monocentric study to assess the predictive role of MDW in detecting sepsis in a sample of consecutive patients presenting at the Emergency Department. METHODS: Prospective observational study using demographic and clinical characteristics, past medical history and other laboratory measurements to predict confirmed sepsis using multivariate logistic regression. RESULTS: A total of 2724 patients were included in the study, of which 272 (10%) had sepsis or septic shock. After adjusting for known and potential risk factors, logistic regression found the following independent predictors of sepsis: SIRS equal to 1 (OR: 2.32, 1.16-4.89) and 2 or more (OR: 27.8, 14.8-56.4), MDW > 22 (OR: 3.73, 2.46-5.70), smoking (OR: 3.0, 1.22-7.31), end stage renal function (OR: 2.3, 1.25-4.22), neurodegenerative disease (OR: 2.2, 1.31-3.68), Neutrophils ≥ 8.9 × 103/µL (OR: 2.73, 1.82-4.11), Lymphocytes < 1.3 × 103/µL (OR: 1.72, 1.17-2.53) and CRP ≥ 19.1 mg/L (OR: 2.57, 1.63-4.08). A risk score derived from predictive models achieved high accuracy by using an optimal threshold (AUC: 95%; 93-97%). CONCLUSIONS: The study suggests that incorporating MDW in the clinical decision process may improve the early identification of sepsis, with minimal additional effort on the standard procedures adopted during emergency care.

Reliability of predictive models to support early decision making in the emergency department for patients with confirmed diagnosis of COVID-19: the Pescara Covid Hospital score.

BACKGROUND: The hospital management of patients diagnosed with COVID-19 can be hampered by heterogeneous characteristics at entry into the emergency department. We aimed to identify demographic, clinical and laboratory parameters associated with higher risks of hospitalisation, oxygen support, admission to intensive care and death, to build a risk score for clinical decision making at presentation to the emergency department. METHODS: We carried out a retrospective study using linked administrative data and laboratory parameters available in the initial phase of the pandemic at the emergency department of the regional reference hospital of Pescara, Abruzzo, Italy, March-June 2020. Logistic regression and Cox modelling were used to identify independent predictors for risk stratification. Validation was carried out collecting data from an extended timeframe covering other variants of concern, including Alpha (December 2020-January 2021) and Delta/Omicron (January-March 2022). RESULTS: Several clinical and laboratory parameters were significantly associated to the outcomes of interest, independently from age and gender. The strongest predictors were: for hospitalisation, monocyte distribution width ≥ 22 (4.09; 2.21-7.72) and diabetes (OR = 3.04; 1.09-9.84); for oxygen support: saturation < 95% (OR = 11.01; 3.75-41.14), lactate dehydrogenase≥237 U/L (OR = 5.93; 2.40-15.39) and lymphocytes< 1.2 × 103/µL (OR = 4.49; 1.84-11.53); for intensive care, end stage renal disease (OR = 59.42; 2.43-2230.60), lactate dehydrogenase≥334 U/L (OR = 5.59; 2.46-13.84), D-dimer≥2.37 mg/L (OR = 5.18; 1.14-26.36), monocyte distribution width ≥ 25 (OR = 3.32; 1.39-8.50); for death, procalcitonin≥0.2 ng/mL (HR = 2.86; 1.95-4.19) and saturation < 96% (HR = 2.74; 1.76-4.28). Risk scores derived from predictive models using optimal thresholds achieved values of the area under the curve between 81 and 91%. Validation of the scoring algorithm for the evolving virus achieved accuracy between 65 and 84%. CONCLUSIONS: A set of parameters that are normally available at emergency departments of any hospital can be used to stratify patients with COVID-19 at risk of severe conditions. The method shall be calibrated to support timely clinical decision during the first hours of admission with different variants of concern.