Dizziness as a first alarming symptom to neurological hospital admission: reasons and differentiation problem - a pilot study.

Aim. This prospective study aimed to assess the diversity of diagnoses in patients hospitalized in the neurology department, in whom the occurrence of dizziness was the presenting complaint during qualification for hospitalization, based on a joint assessment performed by a doctor and a physiotherapist and the implementation of treatment, including physiotherapy. Material and Methods. The study included consecutive patients selected from 2155 individuals hospitalized between 2018 and 2020 in the Neurology Unit who reported dizziness as the presenting complaint. Results. 100 patients (the mean age 58.68±16.57) were qualified for the study: 53 men (the mean age 59.47±15.44) and 47 women (the mean age 57.79±17.88). In the overwhelming number of cases, dizziness was associated with a vascular incident. However, cases of vertigo were also reported. Conclusion. A variety of diagnoses were made in patients hospitalized in the neurological department in whom the occurrence of dizziness was the presenting complaint during qualification for hospitalization.

Upper Limb Movement Measurement Systems for Cerebral Palsy: A Systematic Literature Review

Quantifying the quality of upper limb movements is fundamental to the therapeutic process of patients with cerebral palsy (CP). Several clinical methods are currently available to assess the upper limb range of motion (ROM) in children with CP. This paper focuses on identifying and describing available techniques for the quantitative assessment of the upper limb active range of motion (AROM) and kinematics in children with CP. Following the screening and exclusion of articles that did not meet the selection criteria, we analyzed 14 studies involving objective upper extremity assessments of the AROM and kinematics using optoelectronic devices, wearable sensors, and low-cost Kinect sensors in children with CP aged 4–18 years. An increase in the motor function of the upper extremity and an improvement in most of the daily tasks reviewed were reported. In the population of this study, the potential of wearable sensors and the Kinect sensor natural user interface as complementary devices for the quantitative evaluation of the upper extremity was evident. The Kinect sensor is a clinical assessment tool with a unique markerless motion capture system. Few authors had described the kinematic models and algorithms used to estimate their kinematic analysis in detail. However, the kinematic models in these studies varied from 4 to 10 segments. In addition, few authors had followed the joint assessment recommendations proposed by the International Society of Biomechanics (ISB). This review showed that three-dimensional analysis systems were used primarily for monitoring and evaluating spatiotemporal variables and kinematic parameters of upper limb movements. The results indicated that optoelectronic devices were the most commonly used systems. The joint assessment recommendations proposed by the ISB should be used because they are approved standards for human kinematic assessments. This review was registered in the PROSPERO database (CRD42021257211).

Ultrasound shows swollen joints are the better proxy for synovitis than tender joints in DMARD-naïve, early psoriatic arthritis

Objective To evaluate the relationship between clinical examination/ultrasound (US) synovitis in DMARD-naïve early PsA. Methods Eligible patients underwent matched clinical/US 44 joint assessment for tender and/or swollen joints (TJ/SJ) and US synovitis [grey scale (GS) ≥2 or power Doppler (PD) ≥1]. Statistical agreement between TJ/SJ, GS ≥ 2 or PD ≥ 1 was calculated by prevalence-adjusted and bias-adjusted kappa (PABAK). To derive probabilities of GS ≥ 2/PD ≥ 1, mixed-effects logistic regression modelled odds of US synovitis in TJ/SJ were conducted. Results In 155 patients, 5,616 joints underwent clinical/US examination. Of these joints, 1039/5616 (18.5%) were tender, 550/5616 (9.8%) were swollen, 1144/5616 (20.4%) had GS ≥ 2, and 292/5616 (5.2%) had PD ≥ 1. GS ≥ 2 was most prevalent in concomitantly tender and swollen joints [205/462 (44%)] followed by swollen non-tender joints [32/88 (36.4%)], tender non-swollen joints [148/577 (25.7%)], and non-tender non-swollen joints (subclinical synovitis) [759/4489 (16.9%)]. Agreement between SJ/PD ≥ 1 was high at the individual joint level (82.6%-96.3%, PABAK 0.65–0.93) and for total joints combined (89.9%, PABAK 0.80). SJ/GS ≥ 2 agreement was greater than between TJ/GS ≥ 2 [73.5%-92.6% vs 51.0%-87.4% (PABAK 0.47–0.85 vs PABAK 0.35–0.75) respectively]. Swelling was independently associated with higher odds of GS ≥ 2 [odds ratio (OR) (95% CI); 4.37 (2.62, 7.29); p < 0.001] but not tenderness [OR = 1.33 (0.87, 2.06); p = 0.192]. Swelling [OR = 8.78 (3.92, 19.66); p < 0.001] or tenderness [OR = 3.38 (1.53, 7.50); p = 0.003] were independently associated with higher odds of PD ≥ 1. Conclusion Synovitis (GS ≥ 2 and/or PD ≥ 1) was more likely in swollen joints than tender joints in DMARD-naïve, early PsA. Agreement indicated swollen joints were the better proxy for synovitis, adding to greater understanding between clinical/US assessments.

ASSESSMENT OF ECOLOGICAL STATE OF WATER BODIES USING INTEGRAL HYDROBIOLOGICAL AND HYDROCHEMICAL INDICATORS

The paper considers the features of the joint use of hydrobiological and hydrochemical indicators to characterize the ecological state of water bodies. The comparison of effectiveness of the joint assessment of the state of aquatic ecosystems based on hydrobiological and hydrochemical indicators is carried out. In some cases, a discrepancy between the integral assessment characteristics was revealed. It is due to the fact that the comparison of data of the Roshydromet hydrochemical and hydrobiological monitoring is carried out at the same site not taking into account hydrological features of water bodies.

OP0300 USE OF THERMOGRAPHY OF HANDS AND MACHINE LEARNING TO QUANTIFY JOINT INFLAMMATION AND ESTIMATE DAS28, CDAI, SDAI IN PATIENTS WITH RHEUMATOID ARTHRITIS

Background:Disease activity scores such as DAS28, CDAI and SDAI are used in the follow-up of patients with rheumatoid arthritis (RA). These scores include variables obtained on physical examination such as the tender joint count (TJC) and the swollen joint count (SJC). In telematic consultations, it is not possible to determine these variables by physical joint assessment. Therefore, it is necessary to develop new tools that allow detecting joint inflammation in places close to the patient. Thermography is a safe and fast technique that measures heat through infrared imaging. Inflammation of the joints causes an increase in temperature and can therefore be detect by thermography. Machine learning methods are highly accurate in analyzing medical images automatically.Objectives:To develop an algorithm that, based on thermographic images of hands and machine learning, learn to quantify joint inflammation in patients with RA and estimate the DAS28, CDAI, SDAI by including the patient global health (PGH).Methods:Multicenter observational study conducted in the rheumatology and radiology service of two hospitals. Patients with RA, psoriatic arthritis (PA), undifferentiated arthritis (UA) and arthritis of hands secondary to other diseases (SA) that attended the follow-up visits were recruited. Companions of patients and healthcare professionals were also recruited as healthy subjects (HS). In all cases, a thermographic image of the hands was taken using a Flir One Pro or a Thermal Expert TE-Q1 camera connected to a smartphone. Ultrasound (US) of both hands was performed in patients with RA, PA, UA and SA. The degree of synovial hypertrophy (SH) and power doppler (PD) was assessed for each joint (score from 0 to 3). Machine learning was used to quantify joint inflammation (SH+PD) from the thermal images using US as ground truth. RA patients whose thermal image was taken with the Thermal Expert TE-Q1 camera were used to evaluate the performance (test dataset). The other participants were used as training dataset. The TJC, SJC, PGH, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were also assessed in the test dataset. A linear regression was used to estimate the DAS28, CDAI and SDAI with the resultant joint inflammation quantification from the thermal images and the PGH. Performance was evaluated by means of Pearson’s correlation coefficient. The study was approved by the Clinical Ethics and Research Committee of both centers.Results:The total number of recruited subjects was 521 (422 for the training and 99 for the testing dataset). In the training dataset, the thermography of 296 patients was taken with the Flir One Pro (163 RA, 17 PA, 22 UA, 12 SA and 82 HS) and 126 with the Thermal Expert TE-Q1 camera (6 RA without clinical data, 20 PA, 7 UA, 23 SA and 70 HS).We found higher correlations between joint inflammation variables (US and SJC) and thermography (0.48, p<0.01 for US and 0.48, p<0.01 for SJC) than between joint inflammation variables (US and SJC) and the PGH (0.29, p<0.01 for US and 0.35, p<0.01 for SJC). Thermography did not show statistically significant correlation with the PGH (0.14, p=0.164). The linear regression of thermography and the PGH showed strong correlation with the DAS28 (0.73, p<0.01), CDAI (0.84, p<0.01) and SDAI (0.82, p<0.01).Conclusion:Thermography of hands and machine learning can effectively quantify joint inflammation and can be used in combination with the PGH to estimate disease activity scores. These results open an opportunity to develop tools that facilitate telematic consultations in patients with RA.References:[1]Brenner M, Braun C, Oster M, Gulko PS. Thermal signature analysis as a novel method for evaluating inflammatory arthritis activity. Ann Rheum Dis. 2006;65(3):306-11[2]Lynch CJ, Liston C. New machine-learning technologies for computer-aided diagnosis. Nat Med. 2018;24(9):1304-1305[3]Tan YK, Hong C, Li H, Allen JC Jr, Thumboo J. Thermography in rheumatoid arthritis: a comparison with ultrasonography and clinical joint assessment. Clin Radiol. 2020;75(12):963Disclosure of Interests:None declared.