A very uncommon complication of acute cholecystitis is the development of a pseudoaneurysm in an arterial branch of the hepatic artery. We report a rare case of a patient with acute cholecystitis who presented with a pseudoaneurysm of the right anterior hepatic artery complicated by necrosis of the bile duct and hepatic infarction. A 70-year-old woman attended the emergency department with an unusual presentation of acute cholecystitis involving abdominal discomfort and a mass in the right upper quadrant. CT demonstrated a pseudoaneurysm of the right hepatic artery. Emergency selective transcatheter arterial embolization and cholecystectomy were performed. Subsequently, bile duct necrosis and hepatic ischemic damage made it necessary to perform a right hepatectomy and bile duct resection. Once a hepatic artery pseudoaneurysm is confirmed, its embolization may be useful to ensure the patient’s safety. However, in our experience such pseudoaneurysms may be associated with hepatic and biliary ischemia.
BackgroundDamage in SLE is associated with mortality. Not every damage manifestation is associated in the same way. Some studies were made assessing this relation but in small and heterogeneous samples making difficult to obtain meaningful conclusions.ObjectivesTo evaluate patterns of damage accrual and mortality in a large sample of SLE patients.MethodsSLE patients from RELESSER were studied. After K-means cluster analysis, different clusters of patients with similar characteristics in terms of damage accrual were identified. Kaplan-Meier log-rank test and Cox regression were used to analyse mortality in each group.Results3,656 SLE patients from 45 Rheumatology Units across Spain were studied. 90.3% were women. 93.1% Caucasian, 5.2% Latinamerican and 1.6% other races. Mean age (±SD) at SLE diagnosis was 35.1±14.6 years. Mean follow-up time was 120.1±87.6 months. Mean SLICC/ACR damage index (SDI) score was 1.1±1.6. Average number of organ systems affected in terms of damage was 0.6±1.0. 207 (5.6%) patients died.The SDI organ systems most frequently damaged were: musculoskeletal (MS) (13.7%), ocular (8.5%), cardiovascular (CV) (7.9%) and renal (6.1%). The least frequently present were:gastrointestinal (1.9%), diabetes mellitus (2.4%) and premature gonadal failure (2.5%).Three clusters (C) were formed. C1 had mild or no damage. Patients in C2 had MS damage but no CV. In C3 all had CV damage.Among the 3 clusters, there were significant differences in prevalence of damage in each organ system assessed by the SDI, in average SDI score, in number of SDI systems damaged and mortality rate (p<0.001, for all comparisons). Detailed data are shown in Table 1.C3 patients were older at SLE diagnosis and had higher %of males (p<0.001, for both comparisons).Comparing survival curves of the 3 clusters, the log-rank test showed significant differences (p<0.001 for the triple and double comparisons). Analysing the survival rate at 10, 20 and 30 years from diagnosis of SLE, we found lower survival in patients of C2 and C3 compared to C1 (p=0.068 when C2 is compared to C1 at 10 years, p<0.01 for all the other cases). Between C2 and C3, there were no significant differences in survival at 10 years and itwas significantly lower in C3 at 20 and 30 years (p=0.025 for both).Cox regression analysis showed that, compared with C1, the mortality hazard ratio of C2 and C3 was 1.9 and 3.5 higher respectively (p<0.001, for both comparisons).ConclusionsSLE patients can be divided into different homogeneous groups (clusters) based on damage accrual. These clusters have different mortality rates.Disclosure of InterestJ. Pego Grant/research support from: Spanish Society of Rheumatology, FIS/ISCIII (PI11/02857), BIOCAPS from the EU 7th Framework Programme/REGPOT-2012-2013.1 (316265), GSK, Roche, Novartis,UCB., A. Lois: None declared, F. Lόpez: None declared, M. Galindo: None declared, J. Calvo: None declared, J. Uña: None declared, V. Balboa: None declared, A. Olivé: None declared, C. Mouriño: None declared, T. Otόn: None declared, J. Ibañez: None de...
Background:The early diagnosis of rheumatic diseases improves their prognosis. However, patients take several months to reach the rheumatologist from the beginning of the first symptoms. Thermography is a safe and fast technique that captures the heat of an object through infrared photography. The inflammation of the joints causes an increase in temperature and, therefore, can be measured by thermography. Machine learning methods have shown that they are capable of analyzing medical images with an accuracy similar or superior to that of a healthcare professional.Objectives:Develop an algorithm that, based on thermographic images of hands and machine learning, differentiates healthy subjects from patients with rheumatoid arthritis (RA), psoriatic arthritis (PA), undifferentiated arthritis (UA) and arthritis of hands secondary to other diseases (SA).Methods:Multicenter observational study conducted in the rheumatology and radiology service of two hospitals. Patients with RA, PA, UA and SA who attended the followup visit and healthy subjects (companions and healthcare proffesionals) were recruited. In all cases, a thermal image of the hands was taken using a Flir One Pro or Thermal Expert TE-Q1 camera connected to the mobile and an ultrasound of both hands. The degree of synovial hypertrophy (SH) and power doppler (PD) was assessed for each joint (score from 0 to 3). Inflammation was defined as the presence of SH> 1 or PD> 0. Machine learning was used to classify patients with RA, PA, UA and SA with inflammation evidenced by ultrasound and healthy subjects from thermographic images. The evaluation of the classifier was performed by leave-one-out cross-validation and the area under the ROC curve (AUCROC) in those subjects whose thermal image was performed with the Thermal Expert TE-Q1 camera. The study was approved by the Clinical Ethics and Research Committee of the centers.Results:500 subjects were recruited from March 2018 to January 2020, of these 73 were excluded due to poor quality in the thermal image (moved or absence of temperature contrast between hand and background). Of the 427 subjects analyzed, 129 corresponded to healthy subjects, 138 to patients without evidence of inflammation and 160 to patients with inflammation evidenced by ultrasound (116 RA and 44 PA, UA or SA). Of these, 42% were taken using the Thermal Expert TE-Q1 camera. An AUCROC of 0.73 (p-value <0.01) was obtained for the healthy classifier vs RA and 0.72 (p-value <0.01) for the healthy classifier vs PA, UA and SA.Conclusion:A classification model has been developed capable of differentiating patients with RA, PA, UA and SA with evidence of inflammation from healthy subjects. These results open an opportunity to develop tools that facilitate early diagnosis.References:[1]Barhamain AS, Magliah RF, Shaheen MH, Munassar SF, Falemban AM, Alshareef MM, Almoallim HM. The journey of rheumatoid arthritis patients: a review of reported lag times from the onset of symptoms. Open Access Rheumatol. 2017 Jul 28;9:139-150. doi: 10.2147/OARRR.S138830. eCollection 2017. Review.[2]Lynch CJ, Liston C. New machine-learning technologies for computer-aided diagnosis. Nat Med. 2018 Sep;24(9):1304-1305. doi: 10.1038/s41591-018-0178-4.[3]Brenner M, Braun C, Oster M, Gulko PS. Thermal signature analysis as a novel method for evaluating inflammatory arthritis activity. Ann Rheum Dis. 2006 Mar;65(3):306-11.Disclosure of Interests:None declared
BackgroundA high proportion of patients with rheumatoid arthritis (RA) in clinical remission (CR) present subclinical synovitis when evaluated by sonography. Most prospective studies on baseline biomarkers of radiographic progression in this patients not include immunological biomarkers that could be of interest to better identify the residual inflammatory activity.ObjectivesTo determine clinical, serological and ultrasonografic variables predicting progression of structural damage as evaluated by MRI at 12 months (mo.) of follow-up in a cohort of RA patients in CR.MethodsWe included 42 RA patients in CR defined as DAS28-ESR<2.6 for >6 mo. Complete clinical and biological assessment, ultrasonography of two hands and MRI of dominant hand were performed at baseline and after 12 mo. We analyzed risk factors related with RAMRIS progression with multivariate lineal models.Results42 patients were included (76.7% female). Mean age was 53.5 years, body mass index (BMI) 26.7 kg/m2. Disease and remission median duration were 94 and 37 mo. respectively. 73.8% RF[+] patients and 85.7% ACPA [+]. RAMRIS 10.3 and erosion (RAMRIS) 18.8. At baseline, 66.7% had power-doppler (PD) signal and 45.2% also had synovial hyperplasia grade ≥2, so fulfilling the criteria of previously defined ultrasound defined active synovitis (UdAS)1. The risk factors more related with RAMRIS progression at 12 mo. were baseline RAMRIS, BMI, disease duration, low-dose prednisone treatment, absence of csDMARDs treatment and presence of UdAS. Synovitis as defined by only PD positive signal was no associated to erosions.We excluded the baseline RAMRIS in the multivariate model because it was the strongest predictor factor. We observed that higher serum levels of calprotectin and ENA78 at baseline were significant predictors. Absence of csDMARD treatment, disease duration and the presence of UdAS also remained as independent predictive factors (Table 1).Table 1.Risk factors and prognostic biomarkers associated with a higher score at one year RAMRISRisk factorUnadjustedAdjustedβ (95% CI)p-valueβ (95% CI)p-valueBaseline calprotectin levelsr=0.1950.2164.6 (1.2–8)0.009Baseline log2(ENA78) levelsr=0.3420.0275.7 (0.4–11.1)0.037Longer disease evolution0.08 (0.003–0.15)0.0430.07 (0.01–0.13)0.027No treatment with DMARDs19.9 (2.6–37.2)0.02618.6 (3.8–33.3)0.015UdAS16.7 (3.1–30.3)0.01717.1 (6.1–28.2)0.003Predictive value of the modelModel indexesR, R2 (R2 adjusted)70%, 49%, (41.9%)Overall significance of the model<0.0001ConclusionsThis prospective study confirms a high prevalence of UdAS in patients with RA in CR, which is an independent risk factor to develop structural damage progression at 12 months of follow-up. Baseline RAMRIS, BMI, disease duration, absence of csDMARDs therapy, UdAS, and baseline levels of calprotectin and ENA78, were independent predictors of RAMRIS progression.ReferencesRamírez J, et al. Patients with rheumatoid arthritis in clinical remission and ultrasound-defined active synovitis exhibit higher disease activity and increased serum levels of ang...
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