Introduction: There are insufficient data on the aetiologic factors underlying splenic infarction (SI). Therefore, there is no consensus regarding the appropriate diagnostic approach. Methods: We conducted a retrospective analysis of all patients admitted with SI from January 2004 to December 2014. Medical records were screened for the clinical presentation, underlying causes, associated medical conditions and methods of patient evaluation. Results: We found 89 subjects with 90 episodes of SI. Presentation of SI was characterized by abdominal, flank and chest pain (82.2%, 18.9%, 7.8%, respectively); leukocytosis (in 67% of tested subjects); elevated LDH (72%), CRP (97.5%) and D-Dimer (100%). The main underlying mechanisms were cardioembolic (54.4%), vascular (20%), haematologic disorders (15.6%) and multiple causes (21.1%). Atrial fibrillation and atherosclerosis were common in older patients (age > 70 years) while antiphospholipid syndrome occurred exclusively in younger individuals. SI was the presentation of previously unknown medical conditions in 38% of patients. Abdominal CT, ECG, echocardiography and blood cultures demonstrated the highest diagnostic yield. Conclusions: Contributing factors are identified in the majority of SI patients. We recommend CT, ECG, echocardiography and blood cultures in all cases. Atrial fibrillation should be sought in older patients, while APLS and haematologic disorders should be suspected in younger ones.

Background SLE is an independent risk factor for cardiovascular disease (CVD). This study aimed to determine which among QRISK2, QRISK3, Framingham Risk Score (FRS), modified Framingham Risk Score (mFRS) and SLE Cardiovascular Risk Equation (SLECRE) best predicts CVD. Methods This is a single-centre analysis on 1887 patients with SLE followed prospectively according to a standard protocol. Tools’ scores were evaluated against CVD development at/within 10 years for patients with CVD and without CVD. For patients with CVD, the index date for risk score calculation was chosen as close to 10 years prior to CVD event. For patients without CVD, risk scores were calculated as close to 10 years prior to the most recent clinic appointment. Proportions of low-risk (20%) patients for developing CVD according to each tool were determined, allowing sensitivity, specificity, positive/negative predictive value and concordance (c) statistics analysis. Results Among 1887 patients, 232 CVD events occurred. QRISK2 and FRS, and QRISK3 and mFRS, performed similarly. SLECRE classified the highest number of patients as intermediate and high risk. Sensitivities and specificities were 19% and 93% for QRISK2, 22% and 93% for FRS, 46% and 83% for mFRS, 47% and 78% for QRISK3, and 61% and 64% for SLECRE. Tools were similar in negative predictive value, ranging from 89% (QRISK2) to 92% (SLECRE). FRS and mFRS had the greatest c-statistics (0.73), while QRISK3 and SLECRE had the lowest (0. 67). Conclusion mFRS was superior to FRS and was not outperformed by the QRISK tools. SLECRE had the highest sensitivity but the lowest specificity. mFRS is an SLE-adjusted practical tool with a simple, intuitive scoring system reasonably appropriate for ambulatory settings, with more research needed to develop more accurate CVD risk prediction tools in this population.

Objectives: To study the clinical phenotypes, determined based on cumulative disease activity manifestations, and sociodemographic factors associated with depression and anxiety in SLE. Methods: Patients attending a single centre were assessed for depression and anxiety. SLE clinical phenotypes were based on the organ systems of cumulative 10-year SLE Disease Activity Index 2000 (SLEDAI-2K), prior to visit. Multivariable logistic regression analyses for depression, anxiety, and coexisting anxiety and depression were performed to study associated SLE clinical phenotypes and other factors. Results: Among 341 patients, the prevalence of anxiety and depression was 34% and 27%, respectively, while 21% had coexisting anxiety and depression. Patients with skin involvement had significantly higher likelihood of anxiety compared with patients with no skin involvement [adjusted odds ratio (aOR) = 1.8; 95% CI: 1.1, 3.0]. Patients with skin involvement also had higher likelihood of having coexisting anxiety and depression (aOR = 2.0, 95% CI: 1.2, 3.9). Patients with musculoskeletal (MSK) (aOR = 1.9; 95% CI: 1.1, 3.5) and skin system (aOR = 1.8; 95% CI: 1.04, 3.2) involvement had higher likelihood of depression compared with patients without skin or musculoskeletal involvement. Employment status and fibromyalgia at the time of the visit, and inception status were significantly associated with anxiety, depression, and coexisting anxiety and depression, respectively. Conclusion: SLE clinical phenotypes, specifically skin or MSK systems, along with fibromyalgia, employment and shorter disease duration were associated with anxiety or depression. Routine patient screening, especially among patients with shorter disease duration, for these associations may facilitate the diagnosis of these mental health disorders, and allow for more timely diagnosis.