scholarly journals Optimal Length of R-R Interval Segment Window for Lorenz Plot Detection of Paroxysmal Atrial Fibrillation by Machine Learning

2020 ◽  
Author(s):  
Masaya Kisohara ◽  
Yuto Masuda ◽  
Emi Yuda ◽  
Norihiro Ueda ◽  
Junichiro Hayano
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Likelihood Ratio ◽  
Test Data ◽  
Cross Validation ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Training Data ◽  
Window Length ◽  
X 32

Abstract Background: Heartbeat interval Lorenz plot (LP) imaging is a promising method for detecting atrial fibrillation (AF) in long-term monitoring, but the optimal segment window length for the LP images is unknown. We examined the performance of AF detection by LP images with different segment window lengths by machine learning with convolutional neural network (CNN). LP images with a 32 x 32-pixel resolution of non-overlapping segments with lengths between 10 and 500 beats were created from R-R intervals of 24-h ECG in 52 patients with chronic AF and 58 non-AF controls as training data and in 53 patients with paroxysmal AF and 52 non-AF controls as test data. For each segment window length, discriminant models were made by 5-fold cross-validation subsets of the training data and its classification performance was examined with the test data.Results: In machine learning with the training data, the averages of cross-validation scores were 0.995 and 0.999 for 10 and 20-beat LP images, respectively, and >0.999 for 50 to 500-beat images. The classification of test data showed good performance for all segment window lengths with an accuracy from 0.970 to 0.988. Positive likelihood ratio for detecting AF segments, however, showed a convex parabolic curve linear relationship to log segment window length and peaked at 85 beats, while negative likelihood ratio showed monotonous increase with increasing segment window length.Conclusions: This study suggests that the optimal segment window length that maximizes the positive likelihood ratio for detecting paroxysmal AF with 32 x 32-pixel LP image is 85 beats.

scholarly journals Optimal Length of R-R Interval Segment Window for Lorenz Plot Detection of Paroxysmal Atrial Fibrillation by Machine Learning

2020 ◽  
Author(s):  
Masaya Kisohara ◽  
Yuto Masuda ◽  
Emi Yuda ◽  
Norihiro Ueda ◽  
Junichiro Hayano
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Likelihood Ratio ◽  
Test Data ◽  
Cross Validation ◽  
Positive Likelihood Ratio ◽  
Training Data ◽  
Segment Length ◽  
Optimal Length ◽  
X 32

Abstract Background Machine learning of R-R interval Lorenz plot (LP) images is a promising method for the detection of atrial fibrillation (AF) in long-term ECG monitoring, but the optimal length of R-R interval segment window for the LP images is unknown. We examined the performance of LP AF detection by differing the segment length using convolutional neural network (CNN). LP images with a 32 x 32-pixel resolution of non-overlapping R-R interval segments with lengths of 10, 20, 50, 100, 200, and 500 beats were created from 24-h ECG data in 52 patients with chronic AF and 58 non-AF controls as training data and in 53 patients with paroxysmal AF and 52 non-AF controls as test data. For each segment length, classification models were made by 5-fold cross-validation subsets of the training data and its classification performance was examined with the test data. Results In machine learning with the training data, the averages of cross-validation scores were 0.995 and 0.999 for 10 and 20-beat LP images, respectively, and >0.999 for 50 to 500-beat images. The classification of test data showed good performance for all segment lengths with an accuracy from 0.970 to 0.988. Positive likelihood ratio for detecting AF segments, however, showed a convex parabolic curve linear relationship to log segment length with a peak ratio of 111 at 100 beats, while negative likelihood ratio showed monotonous increase with increasing segment length. Conclusions This study suggests that the optimal R-R interval segment window length that maximizes the positive likelihood ratio for detecting paroxysmal AF with 32 x 32-pixel LP image is about 100 beats.

scholarly journals Cardiac natriuretic peptides for diagnosis of covert atrial fibrillation after acute ischaemic stroke: a meta-analysis of diagnostic accuracy studies

2020 ◽  
pp. svn-2020-000440
Author(s):  
Kejia Zhang ◽  
Joseph Kamtchum-Tatuene ◽  
Mingxi Li ◽  
Glen C. Jickling
Keyword(s):  
Atrial Fibrillation ◽  
Ischaemic Stroke ◽  
Likelihood Ratio ◽  
Acute Ischaemic Stroke ◽  
Meta Analysis ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Heart Rhythm ◽  
Post Test ◽  
Cardiac Natriuretic Peptides

Background and purposeDetection of atrial fibrillation (AF) after acute ischaemic stroke is pivotal for the timely initiation of anticoagulation to prevent recurrence. Besides heart rhythm monitoring, various blood biomarkers have been suggested as complimentary diagnostic tools for AF. We aimed to summarise data on the performance of cardiac natriuretic peptides for the diagnosis of covert AF after acute ischaemic stroke and to assess their potential clinical utility.MethodsWe searched PubMed and Embase for prospective studies reporting the performance of B-type natriuretic peptide (BNP) or N-terminal pro-BNP (NT-proBNP) for the diagnosis of covert AF after acute ischaemic stroke. Summary diagnostic performance measures were pooled using bivariate meta-analysis with a random-effect model.ResultsWe included six studies focusing on BNP (n=1930) and three studies focusing on NT-proBNP (n=623). BNP had a sensitivity of 0.83 (95% CI 0.64 to 0.93), a specificity of 0.74 (0.67 to 0.81), a positive likelihood ratio of 3.2 (2.6 to 4.0) and a negative likelihood ratio of 0.23 (0.11 to 0.49). NT-proBNP had a sensitivity of 0.91 (0.65 to 0.98), a specificity of 0.77 (0.52 to 0.91), a positive likelihood ratio of 3.9 (1.8 to 8.7) and a negative likelihood ratio of 0.12 (0.03 to 0.48). Considering a pretest probability of 20%, BNP and NT-proBNP had post-test probabilities of 45% and 50%.ConclusionsNT-proBNP has a better performance than BNP for the diagnosis of covert AF after acute ischaemic stroke. Both biomarkers have low post-test probabilities and may not be used as a stand-alone decision-making tool for the diagnosis of covert AF in patients with acute ischaemic stroke. However, they may be useful for a screening strategy aiming to select patients for long-term monitoring of the heart rhythm.

scholarly journals Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation

2021 ◽  
Vol 18 (18) ◽  
pp. 9873
Author(s):  
Meng Ji ◽  
Wenxiu Xie ◽  
Riliu Huang ◽  
Xiaobo Qian
Keyword(s):  
Machine Learning ◽  
Machine Translation ◽  
Likelihood Ratio ◽  
Clinical Symptoms ◽  
Digital Health ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Semantic Features ◽  
Disease Symptoms ◽  
Vulnerable People

Background: Machine translation (MT) technologies have increasing applications in healthcare. Despite their convenience, cost-effectiveness, and constantly improved accuracy, research shows that the use of MT tools in medical or healthcare settings poses risks to vulnerable populations. Objectives: We aimed to develop machine learning classifiers (MNB and RVM) to forecast nuanced yet significant MT errors of clinical symptoms in Chinese neural MT outputs. Methods: We screened human translations of MSD Manuals for information on self-diagnosis of infectious diseases and produced their matching neural MT outputs for subsequent pairwise quality assessment by trained bilingual health researchers. Different feature optimisation and normalisation techniques were used to identify the best feature set. Results: The RVM classifier using optimised, normalised (L2 normalisation) semantic features achieved the highest sensitivity, specificity, AUC, and accuracy. MNB achieved similar high performance using the same optimised semantic feature set. The best probability threshold of the best performing RVM classifier was found at 0.6, with a very high positive likelihood ratio (LR+) of 27.82 (95% CI: 3.99, 193.76), and a low negative likelihood ratio (LR−) of 0.19 (95% CI: 0.08, 046), suggesting the high diagnostic utility of our model to predict the probabilities of erroneous MT of disease symptoms to help reverse potential inaccurate self-diagnosis of diseases among vulnerable people without adequate medical knowledge or an ability to ascertain the reliability of MT outputs. Conclusion: Our study demonstrated the viability, flexibility, and efficiency of introducing machine learning models to help promote risk-aware use of MT technologies to achieve optimal, safer digital health outcomes for vulnerable people.

scholarly journals Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

JMIR Diabetes ◽  
10.2196/22458 ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. e22458
Author(s):  
Satoru Kodama ◽  
Kazuya Fujihara ◽  
Haruka Shiozaki ◽  
Chika Horikawa ◽  
Mayuko Harada Yamada ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Likelihood Ratio ◽  
Meta Analysis ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Machine Learning Algorithms ◽  
Diabetes Research ◽  
Summary Receiver Operating Characteristic ◽  
Patients With Diabetes

Background Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. Objective The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). Methods Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. Results A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. Conclusions Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682

scholarly journals Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis (Preprint)

2020 ◽  
Author(s):  
Satoru Kodama ◽  
Kazuya Fujihara ◽  
Haruka Shiozaki ◽  
Chika Horikawa ◽  
Mayuko Harada Yamada ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Likelihood Ratio ◽  
Meta Analysis ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Machine Learning Algorithms ◽  
Diabetes Research ◽  
Summary Receiver Operating Characteristic ◽  
Patients With Diabetes

BACKGROUND Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. OBJECTIVE The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). METHODS Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. RESULTS A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. CONCLUSIONS Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682

scholarly journals The Potential Diagnostic Accuracy of Circulating MicroRNAs for Leukemia: A Meta-Analysis

2021 ◽  
Vol 20 ◽  
pp. 153303382110119
Author(s):  
Wen-Ting Zhang ◽  
Guo-Xun Zhang ◽  
Shuai-Shuai Gao
Keyword(s):  
Diagnostic Accuracy ◽  
Likelihood Ratio ◽  
Subgroup Analysis ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Diagnostic Odds Ratio ◽  
Circulating Micrornas ◽  
Sensitivity Specificity

Background: Leukemia is a common malignant disease in the human blood system. Many researchers have proposed circulating microRNAs as biomarkers for the diagnosis of leukemia. We conducted a meta-analysis to evaluate the diagnostic accuracy of circulating miRNAs in the diagnosis of leukemia. Methods: A comprehensive literature search (updated to October 13, 2020) in PubMed, EMBASE, Web of Science, Cochrane Library, Wanfang database and China National Knowledge Infrastructure (CNKI) was performed to identify eligible studies. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) for diagnosing leukemia were pooled for both overall and subgroup analysis. The meta-regression and subgroup analysis were performed to explore heterogeneity and Deeks’ funnel plot was used to assess publication bias. Results: 49 studies from 22 publications with a total of 3,489 leukemia patients and 2,756 healthy controls were included in this meta-analysis. The overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under the curve were 0.83, 0.92, 10.8, 0.18, 59 and 0.94, respectively. Subgroup analysis shows that the microRNA clusters of plasma type could carry out a better diagnostic accuracy of leukemia patients. In addition, publication bias was not found. Conclusions: Circulating microRNAs can be used as a promising noninvasive biomarker in the early diagnosis of leukemia.

scholarly journals The value of glycosylated hemoglobin in the diagnosis of diabetic retinopathy: a systematic review and Meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bo Zhang ◽  
Bingjie Zhang ◽  
Zhulin Zhou ◽  
Yutong Guo ◽  
Dan Wang
Keyword(s):  
Diabetic Retinopathy ◽  
Likelihood Ratio ◽  
Roc Curve ◽  
Meta Analysis ◽  
Glycosylated Hemoglobin ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Biomedical Literature ◽  
Cochrane Library ◽  
Clinical Value

AbstractObjectiveGlycosylated hemoglobin (HbA1c) has obvious clinical value in the diagnosis of diabetes, but the conclusions on the diagnostic value of diabetic retinopathy (DR) are not consistent. This study aims to comprehensively evaluate the accuracy of glycosylated hemoglobin in the diagnosis of diabetic retinopathy through the meta-analysis of diagnostic tests.MethodsCochrane Library, Embase, PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), China Wanfang Database, Chinese Biomedical Literature Database (CBM) were searched until November, 2020. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of the included studies. The pooled sensitivity, specificity, positive likelihood ratio (+LR), negative likelihood ratio (-LR), diagnostic odds ratio (DOR) and areas under the receiver operating characteristic (ROC) curve were calculated by Stata 15.0 software.ResultsAfter screening, 18 high-quality papers were included. The results of meta-analysis showed that the combined DOR = 18.19 (95% CI: 10.99–30.11), the sensitivity= 0.81 (95% CI): 0.75 ~ 0.87), specificity = 0.81 (95%CI: 0.72 ~ 0.87), +LR = 4.2 (95%CI: 2.95 ~ 6.00), −LR = 0.23 (95%CI: 0.17 ~ 0.31), and the area under the Summary ROC curve was 0.88 (95%CI:  0.85 ~ 0.90).ConclusionThe overall accuracy of HbA1cC forin diagnosing diabetic retinopathy is good. As it is more stable than blood sugar and is not affected by meals, it may be a suitable indicator for diabetic retinopathy.

scholarly journals Circulating tumour DNA methylation in hepatocellular carcinoma diagnosis using digital droplet PCR

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199296
Author(s):  
Juan Wang ◽  
Liu Yang ◽  
Yanjun Diao ◽  
Jiayun Liu ◽  
Jinjie Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Likelihood Ratio ◽  
Predictive Value ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Control Group ◽  
Control Subjects ◽  
Droplet Pcr ◽  
Healthy Control

Objective To evaluate the performance of a DNA methylation-based digital droplet polymerase chain reaction (ddPCR) assay to detect aberrant DNA methylation in cell-free DNA (cfDNA) and to determine its application in the detection of hepatocellular carcinoma (HCC). Methods The present study recruited patients with liver-related diseases and healthy control subjects. Blood samples were used for the extraction of cfDNA, which was then bisulfite converted and the extent of DNA methylation quantified using a ddPCR platform. Results A total of 97 patients with HCC, 80 healthy control subjects and 46 patients with chronic hepatitis B/C virus infection were enrolled in the study. The level of cfDNA in the HCC group was significantly higher than that in the healthy control group. For the detection of HCC, based on a cut-off value of 15.7% for the cfDNA methylation ratio, the sensitivity and specificity were 78.57% and 89.38%, respectively. The diagnostic accuracy was 85.27%, the positive predictive value was 81.91% and the negative predictive value was 87.20%. The positive likelihood ratio of 15.7% in HCC diagnosis was 7.40, while the negative likelihood ratio was 0.24. Conclusions A sensitive methylation-based assay might serve as a liquid biopsy test for diagnosing HCC.

scholarly journals Test characteristics of history, examination and investigations in the evaluation for septic arthritis in the child presenting with acute non-traumatic limp. A systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e038088
Author(s):  
Jacky Tu ◽  
Peter Gowdie ◽  
Julian Cassar ◽  
Simon Craig
Keyword(s):  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Septic Arthritis ◽  
Quality Assessment ◽  
Likelihood Ratio ◽  
Risk Prediction ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Prediction Tools ◽  
Clinical Risk

BackgroundSeptic arthritis is an uncommon but potentially significant diagnosis to be considered when a child presents to the emergency department (ED) with non-traumatic limp. Our objective was to determine the diagnostic accuracy of clinical findings (history and examination) and investigation results (pathology tests and imaging) for the diagnosis of septic arthritis among children presenting with acute non-traumatic limp to the ED.MethodsSystematic review of the literature published between 1966 and June 2019 on MEDLINE and EMBASE databases. Studies were included if they evaluated children presenting with lower limb complaints and evaluated diagnostic performance of items from history, physical examination, laboratory testing or radiological examination. Data were independently extracted by two authors, and quality assessment was performed using the Quality Assessment Tool for Diagnostic Accuracy Studies 2 tool.Results18 studies were identified, and included 2672 children (560 with a final diagnosis of septic arthritis). There was substantial heterogeneity in inclusion criteria, study setting, definitions of specific variables and the gold standard used to confirm septic arthritis. Clinical and investigation findings were reported using varying definitions and cut-offs, and applied to differing study populations. Spectrum bias and poor-to-moderate study design quality limit their applicability to the ED setting.Single studies suggest that the presence of joint tenderness (n=189; positive likelihood ratio 11.4 (95% CI 5.9 to 22.0); negative likelihood ratio 0.2 (95% CI 0.0 to 1.2)) and joint effusion on ultrasound (n=127; positive likelihood ratio 8.4 (95% CI 4.1 to 17.1); negative likelihood ratio 0.2 (95% CI 0.1 to 0.3)) appear to be useful. Two promising clinical risk prediction tools were identified, however, their performance was notably lower when tested in external validation studies.DiscussionDifferentiating children with septic arthritis from non-emergent disorders of non-traumatic limp remains a key diagnostic challenge for emergency physicians. There is a need for prospectively derived and validated ED-based clinical risk prediction tools.

scholarly journals THU0346 SARC-F PERFORMANCE FOR SARCOPENIA SCREENING IN PATIENTS WITH SYSTEMIC SCLEROSIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 404.1-404
Author(s):  
V. Hax ◽  
R. Santo ◽  
L. Santos ◽  
M. Farinon ◽  
M. De Oliveira ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Likelihood Ratio ◽  
Handgrip Strength ◽  
Pulmonary Function Tests ◽  
Positive Likelihood Ratio ◽  
Negative Likelihood Ratio ◽  
Youden Index ◽  
Routine Practice ◽  
Test Accuracy ◽  
European Working

Background:Because the method of diagnosing sarcopenia is complex and is considered to be difficult to introduce into routine practice, the European Working Group on Sarcopenia in Older People’s (EWGSOP) recommends use of the SARC-F questionnaire as a way to introduce assessment and treatment of sarcopenia into clinical practice1. Only recently, some studies have focused their attention on the presence of sarcopenia in systemic sclerosis (SSc) and there is no data about the performance of SARC-F in this population.Objectives:To test the diagnostic properties of the SARC-F questionnaire for sarcopenia screening in SSc patients.Methods:Cross-sectional study, including 94 SSc patients assessed by clinical evaluation, laboratory and pulmonary function tests. Sarcopenia was evaluated using the EWGSOP diagnostic criteria updated in 2019 (EWGSOP2): dual-energy X-ray absorptiometry, handgrip strength, and short physical performance battery (SPPB)1. Participants also completed the SARC-F questionnaire. The questionnaires’ performances were evaluated through receiver operating characteristic (ROC) curves and standard measures of diagnostic accuracy were computed using the EWGSOP2 criteria as the gold standard for diagnosis of sarcopenia.Results:Sarcopenia was identified in 15 (15,9%) SSc patients by the EWGSOP2 criteria. Area under the ROC curve of SARC-F screening for sarcopenia was 0.588 (95% confidence interval (CI) 0.482, 0.688) (figure 1). The results of sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR) with the EWGSOP2 criteria as the reference standard were 35.71 [95% CI, 12.76-64.86], 81.01 (95% CI, 70.62-88.97), 1.88 (95% CI, 0.81-4.35) and 0.79 (95% CI, 0.53-1.19), respectively. The optimal cut-off point of SARC-F in our sample was ≥ 4 (Youden index: 0.21), the same cut-off point recommended in the literature2,3. Only 6 (40%) out of the 15 participants with sarcopenia were identified by the SARC-F questionnaire in our population. However, the SARC-F properly identified 4 out of 5 patients who had severe sarcopenia.Conclusion:This is the first study to evaluate the performance of SARC-F questionnaire for sarcopenia screening in patients with SSc. Although it appropriately identifies severe cases of sarcopenia, the SARC-F alone may not be an adequate screening tool in high-risk populations, such as SSc, that may benefit from early intervention and treatment.References:[1]Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16-31.[2]Malmstrom TK, Morley JE. SARC-F: A simple questionnaire to rapidly diagnose sarcopenia. J Am Med Dir Assoc. 2013;14(8):531-532.[3]Ida S, Kaneko R, Murata K. SARC-F for Screening of Sarcopenia Among Older Adults: A Meta-analysis of Screening Test Accuracy. J Am Med Dir Assoc. 2018;19(8):685-689.Disclosure of Interests:Vanessa Hax: None declared, Rafaela Santo: None declared, Leonardo Santos: None declared, Mirian Farinon: None declared, Marianne de Oliveira: None declared, Guilherme Levi Três: None declared, Andrese Aline Gasparin: None declared, M Bredemeier: None declared, Ricardo Xavier Consultant of: AbbVie, Pfizer, Novartis, Janssen, Eli Lilly, Roche, Rafael Mendonça da Silva Chakr: None declared

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