Berlin Algorithm Research Articles

Performance of clinical, laboratory and imaging features for diagnosing spondyloarthritis-a systematic literature review and meta-analysis.

The Berlin algorithm was developed to help diagnosing axial spondyloarthritis (axSpA), but new studies suggest some features typical of SpA are less specific than previously assumed. Furthermore, evidence is lacking for other SpA subtypes (e.g. peripheral SpA). We aimed to review the evidence on the performance of SpA features for diagnosing each SpA subtype. Systematic literature review of studies reporting the diagnostic performance of ≥ 1 SpA feature in patients with suspected SpA. The external reference was the rheumatologist's diagnosis of SpA. Meta-analysis was performed, separately for each SpA subtype, to estimate pooled sensitivity, specificity, positive (LR+) and negative (LR-) likelihood ratios. Meta-regression assessed the effect of covariates (e.g. feature's prevalence) on each feature's performance. Of 13 844 articles screened, 46 were included. Sacroiliitis on magnetic resonance imaging, damage on pelvic radiographs and elevated C-reactive protein (CRP) had the best balance between LR+ and LR- (LR + 3.9-17.0, LR- 0.5-0.7) for diagnosing axSpA. HLA-B27 had an LR+ lower than anticipated (LR + =3.1). Inflammatory back pain (IBP) had low LR + (LR+∼1), but substantially decreased the likelihood of axSpA when absent (LR-=0.3). Conversely, peripheral features and extra-musculoskeletal manifestations showed high LR + (LR+ 1.6-5.0), but were as common in axSpA as no-axSpA (LR-∼1). The specificity of most features was reduced in settings when these were highly prevalent. Limited data precluded a detailed analysis on diagnosing other SpA subtypes. Imaging features and CRP have good diagnostic value for axSpA. However, the specificity of other features, especially HLA-B27 and IBP, is lower than previously known.

FRI0298 ASAS MODIFICATION OF THE BERLIN ALGORITHM AND THE DUET ALGORITHM FOR DIAGNOSING AXIAL SPONDYLOARTHRITIS: RESULTS FROM THE SCREENING IN AXIAL SPONDYLOARTHRITIS FOR PSORIASIS, IRITIS, AND COLITIS COHORT

Background:Patients presenting with back pain and psoriasis, iritis, or colitis, represent a high-risk population for the presence of axial spondyloarthritis (axSpA). The Dublin Evaluation Tool (DUET)1, the Berlin algorithm2, and the ASAS modification of this algorithm3are recommended referral strategies aimed at early diagnosis of axSpA. DUET was developed for patients presenting with AAU. Validation of these algorithms in inception cohorts is limited.Objectives:1. To assess the performance of referral algorithms for diagnosis of axSpA when tested against the final local rheumatologist diagnosis in an inception cohort of patients presenting with undiagnosed back pain and extra-articular manifestations. 2. To determine whether different criteria for inflammatory back pain (IBP) impact the performance of the algorithms.Methods:The multicenter Screening for Axial Spondyloarthritis in Psoriasis, Iritis, and Colitis (SASPIC) Study at 11 sites is aimed at early detection of axial SpA in patients presenting with undiagnosed back pain to the rheumatologist. Consecutive patients ≤45 years of age with ≥3 months undiagnosed back pain with any one of psoriasis, acute anterior uveitis (AAU), or colitis diagnosed by the relevant specialist undergo routine clinical evaluation by a rheumatologist for axial SpA. The rheumatologist determines the presence or absence of axial SpA at 3 consecutive stages: 1. After the clinical evaluation; 2. After the results of labs (B27, CRP) and radiography; 3. After the results of MRI evaluation. Final diagnosis by the rheumatologist was used as external standard to test the performance of the algorithms. We tested the following criteria for IBP in the algorithm: ASAS, Berlin, rheumatologist global for likelihood of IBP >5 (0-10 scale), and DUET algorithm in AAU patients.Results:A total of 246 patients were recruited, 73 presented with iritis, 46 with psoriasis, and 127 with colitis, 47.6% were diagnosed with axSpA. The diagnosis of axSpA was established in 45.7%, 61.6%, and 40.2% of patients with psoriasis, AAU, and IBD, respectively. The performance of the ASAS-modification of the Berlin algorithm was superior to the original algorithm as reported previously3, primarily for enhanced sensitivity, and this was observed irrespective of the criteria used to define IBP (Table 1). Conversely, the performance of the Duet algorithm in the subset of patients with AAU was substantially worse than previously reported1.Conclusion:The ASAS modification of the Berlin algorithm is the preferred referral strategy for patients presenting with undiagnosed back pain to the rheumatologist.

Rapid breath analysis for acute respiratory distress syndrome diagnostics using a portable two-dimensional gas chromatography device.

Acute respiratory distress syndrome (ARDS) is the most severe form of acute lung injury, responsible for high mortality and long-term morbidity. As a dynamic syndrome with multiple etiologies, its timely diagnosis is difficult as is tracking the course of the syndrome. Therefore, there is a significant need for early, rapid detection and diagnosis as well as clinical trajectory monitoring of ARDS. Here, we report our work on using human breath to differentiate ARDS and non-ARDS causes of respiratory failure. A fully automated portable 2-dimensional gas chromatography device with high peak capacity (> 200 at the resolution of 1), high sensitivity (sub-ppb), and rapid analysis capability (~ 30min) was designed and made in-house for on-site analysis of patients' breath. A total of 85 breath samples from 48 ARDS patients and controls were collected. Ninety-seven elution peaks were separated and detected in 13min. An algorithm based on machine learning, principal component analysis (PCA), and linear discriminant analysis (LDA) was developed. As compared to the adjudications done by physicians based on the Berlin criteria, our device and algorithm achieved an overall accuracy of 87.1% with 94.1% positive predictive value and 82.4% negative predictive value. The high overall accuracy and high positive predicative value suggest that the breath analysis method can accurately diagnose ARDS. The ability to continuously and non-invasively monitor exhaled breath for early diagnosis, disease trajectory tracking, and outcome prediction monitoring of ARDS may have a significant impact on changing practice and improving patient outcomes. Graphical abstract.

FRI0470 Modification of ‘the asas modification of the berlin algorithm’ in patients with chronic low back pain can be useful for primary care

BackgroundRecently the ASAS modification of the Berlin algorithm (ASAS algorithm) for diagnosing axial spondyloarthritis (aSpA) was published1. The entry criterion, chronic low back pain (CLBP), started before the age of...

ASAS modification of the Berlin algorithm for diagnosing axial spondyloarthritis: results from the SPondyloArthritis Caught Early (SPACE)-cohort and from the Assessment of SpondyloArthritis international Society (ASAS)-cohort

ObjectiveTo compare the original Berlin algorithm for diagnosing axial Spondyloarthritis (axSpA) with two modifications in the SPondyloArthritis Caught Early (SPACE)-cohort and the Assessment of SpondyloArthritis international Society (ASAS) axSpA criteria...

Verification of Berlin algorithm for diagnosing undifferentiated spondyloarthropathy patients in Chinese population

Objective To evaluate the diagnosing value of the Berlin algorithm, comparing to that of the established ESSG and Amor criteria in Chinese patients with undifferentiated spondyloarthropathy. Methods A total of 92 clinically diagnosed undifferentiated spondyloarthropathy patients with axial involvement were compared to 123 patients with other kinds of rheumatic diseases by using the parameters listed in the Berlin algorithm, ESSG criteria, and Amor criteria. Results In the 92 undifferentiated spondyloarthropathy patients with axial involvement, the prevalence rate of HLA-B27 was 71.76% (61/85). Elevated ESR and/or CRP was found in 40.96% (34/83) SpA patients and abnormal MRI manifestation of sacroiliac joint was found in 91% (39/43) SpA patients. The specificity of HLA-B27 was 78% and similar with the Berlin study. The sensitivity/specificity of ESSG, Amor criteria and Berlin algorithm on diagnosing USpA was 72.83%/92.68%, 64.13%/93.50% and 67.39%/95.93%, respectively. The coincidence between the three diagnosing criteria and the rheumatologist's opinion was moderate. Conclusion Our study showed the new Berlin algorithm has important value of diagnosing undifferentiated spondyloarthropathy in China, which has the similar diagnosing capacity comparing to the traditional criteria ESSG and Amor criteria.

Algorithms for optimizing the treatment of depression: making the right decision at the right time.

Medication algorithms for the treatment of depression are designed to optimize both treatment implementation and the appropriateness of treatment strategies. Thus, they are essential tools for treating and avoiding refractory depression. Treatment algorithms are explicit treatment protocols that provide specific therapeutic pathways and decision-making tools at critical decision points throughout the treatment process. The present article provides an overview of major projects of algorithm research in the field of antidepressant therapy. The Berlin Algorithm Project and the Texas Medication Algorithm Project (TMAP) compare algorithm-guided treatments with treatment as usual. The Sequenced Treatment Alternatives to Relieve Depression Project (STAR*D) compares different treatment strategies in treatment-resistant patients.