Performance of the 2016 diagnostic criteria for fibromyalgia in a tertiary care pain rehabilitation setting: a diagnostic accuracy study

Objectives With the International Classification of Diseases 11th revision (classifying fibromyalgia as a primary pain disorder) soon to be implemented, the importance of pain physicians being able to identify patients with fibromyalgia is emphasized. The diagnostic criteria proposed in 2016 are based on self-reported pain distribution and symptom severity. The study aimed to evaluate the diagnostic accuracy of the 2016 diagnostic criteria for fibromyalgia applied in a population of patients with high impact chronic pain referred for pain rehabilitation. Methods The study was performed as a diagnostic accuracy study at two Danish interdisciplinary pain rehabilitation centers, including 215 participants. All participants were evaluated clinically to identify patients with fibromyalgia. The diagnosis was based on expert opinion, but the minimum requirements were: (1) pain in all four body quadrants and axially for at least three months and (2) minimum 8 of 18 positive tender points. Participants filled in the fibromyalgia survey questionnaire, the patient version of the 2016 diagnostic criteria. Sensitivity, specificity, likelihood ratios, and positive and negative post-test probabilities were calculated using a clinical diagnosis of fibromyalgia as the reference standard. Results Based on clinical diagnosis 45% of the participants were diagnosed with fibromyalgia; of these, only 19% had been diagnosed previously. The 2016 diagnostic criteria demonstrated a sensitivity of 88.5%, a specificity of 81.5%, a positive likelihood ratio of 4.79, a negative likelihood ratio of 0.14, a positive post-test probability of 79.4%, and a negative post-test probability of 10.2%. Conclusions Fibromyalgia was severely under-diagnosed among patients with high impact chronic pain referred to tertiary care in two pain rehabilitation centers in Denmark. The 2016 diagnostic criteria showed sufficient discriminatory properties suggesting that the fibromyalgia survey questionnaire can be used as a screening tool assisting the identification of fibromyalgia in this patient population.

Femoroacetabular impingement syndrome and labral injuries: grading the evidence on diagnosis and non-operative treatment—a statement paper commissioned by the Danish Society of Sports Physical Therapy (DSSF)

This statement summarises and appraises the evidence on diagnostic tests and clinical information, and non-operative treatment of femoroacetabular impingement (FAI) syndrome and labral injuries. We included studies based on the highest available level of evidence as judged by study design. We evaluated the certainty of evidence using the Grading of Recommendations Assessment Development and Evaluation framework. We found 29 studies reporting 23 clinical tests and 14 different forms of clinical information, respectively. Restricted internal hip rotation in 0° hip flexion with or without pain was best to rule in FAI syndrome (low diagnostic effectiveness; low quality of evidence; interpretation of evidence: may increase post-test probability slightly), whereas no pain in Flexion Adduction Internal Rotation test or no restricted range of motion in Flexion Abduction External Rotation test compared with the unaffected side were best to rule out (very low to high diagnostic effectiveness; very low to moderate quality of evidence; interpretation of evidence: very uncertain, but may reduce post-test probability slightly). No forms of clinical information were found useful for diagnosis. For treatment of FAI syndrome, 14 randomised controlled trials were found. Prescribed physiotherapy, consisting of hip strengthening, hip joint manual therapy techniques, functional activity-specific retraining and education showed a small to medium effect size compared with a combination of passive modalities, stretching and advice (very low to low quality of evidence; interpretation of evidence: very uncertain, but may slightly improve outcomes). Prescribed physiotherapy was, however, inferior to hip arthroscopy (small effect size; moderate quality of evidence; interpretation of evidence: hip arthroscopy probably increases outcome slightly). For both domains, the overall quality of evidence ranged from very low to moderate indicating that future research on diagnosis and treatment may alter the conclusions from this review.

Predicting Severe/Critical Outcomes in Patients With SARS-CoV2 Pneumonia: Development of the prediCtion seveRe/crItical ouTcome in COVID-19 (CRITIC) Model

Objective: To create a prediction model of the risk of severe/critical disease in patients with Coronavirus disease (COVID-19).Methods: Clinical, laboratory, and lung computed tomography (CT) severity score were collected from patients admitted for COVID-19 pneumonia and considered as independent variables for the risk of severe/critical disease in a logistic regression analysis. The discriminative properties of the variables were analyzed through the area under the receiver operating characteristic curve analysis and included in a prediction model based on Fagan's nomogram to calculate the post-test probability of severe/critical disease. All analyses were conducted using Medcalc (version 19.0, MedCalc Software, Ostend, Belgium).Results: One hundred seventy-one patients with COVID-19 pneumonia, including 37 severe/critical cases (21.6%) and 134 mild/moderate cases were evaluated. Among all the analyzed variables, Charlson Comorbidity Index (CCI) was that with the highest relative importance (p = 0.0001), followed by CT severity score (p = 0.0002), and age (p = 0.0009). The optimal cut-off points for the predictive variables resulted: 3 for CCI [sensitivity 83.8%, specificity 69.6%, positive likelihood ratio (+LR) 2.76], 69.9 for age (sensitivity 94.6%, specificity 68.1, +LR 2.97), and 53 for CT severity score (sensitivity 64.9%, specificity 84.4%, +LR 4.17).Conclusion: The nomogram including CCI, age, and CT severity score, may be used to stratify patients with COVID-19 pneumonia.

A Mobile Terminal-based Nomogram for Early Predicting Severity of Acute Pancreatitis

Background: Early prediction of the severity of acute pancreatitis (AP) is important but there is no preferred method in China. We aimed to develop and validate a simple-to-use predictive nomogram for persistent organ failure (POF) on admission in patients with AP. Methods: Data from 816 consecutive patients was obtained from internal (Chengdu) retrospective datasets and formed the training cohort for nomogram development. Data from 398 and 880 consecutive patients from internal (Chengdu) and external (Nanchang) prospective datasets formed the validation cohorts (all admitted < 48 hours of symptom onset). Univariate and multivariate logistic regressions were used to identify independent prognostic factors to establish the nomogram for POF. The calibration curves, concordance index (C-index), decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the performance of the nomogram and its clinical utility. The area under the receiver-operating characteristic curve (AUC) with 95% CI and likelihood ratio as well as post-test probability were applied. Measurements and main results: Age, respiratory rate, albumin, lactate dehydrogenase, oxygen support, and pleural effusion were identified as independent prognostic factors for POF and were included in the nomogram model (web-based calculator: https://shina.shinyapps.io/DynNomapp/). This predictive nomogram had good predictive ability for POF (C-indexes of 0.88, 0.91 and 0.81 for the training and two validation cohorts) and promising clinical utility (DCA: better or equivalent than prognostic scores; CIC: high clinical net benefit). The AUC of (0.91 [0.88-0.94] and 0.81 [0.79-0.84]), negative likelihood ratio (NLR 0.11 and 0.29), post-test probability of negative (0.9% and 6.7%) of the nomogram were superior in predicting POF than all other routinely used clinical prognostic scoring systems in both validation cohorts. Similar findings were observed for predicting major infection (superior to other prognostic scores) and mortality (superior or equally to others). Conclusions: The validated nomogram comprises 6 independent prognostic factors to predict major clinical outcomes of patients with AP in two distinct Chinese centers. This mobile terminal-based nomogram should be validated in other settings and considered for clinical practice and trial allocation, until more accurate biomarkers are discovered.

Estimating the Post-Test Probability of Long QT Syndrome Diagnosis for Rare KCNH2 Variants

Background - The proliferation of genetic profiling has revealed many associations between genetic variations and disease. However, large-scale phenotyping efforts in largely healthy populations, coupled with DNA sequencing, suggest variants currently annotated as pathogenic are more common in healthy populations than previously thought. In addition, novel and rare variants are frequently observed in genes associated with disease both in healthy individuals and those under suspicion of disease. This raises the question of whether these variants can be useful predictors of disease. To answer this question, we assessed the degree to which the presence of a variant in the cardiac potassium channel gene KCNH2 was diagnostically predictive for the autosomal dominant long QT syndrome. Methods - We estimated the probability of a long QT diagnosis given the presence of each KCNH2 variant using Bayesian methods that incorporated variant features such as changes in variant function, protein structure, and in silico predictions. We call this estimate the post-test probability of disease. Our method was applied to over 4,000 individuals heterozygous for 871 missense or in-frame insertion/deletion variants in KCNH2 and validated against a separate international cohort of 933 individuals heterozygous for 266 missense or in-frame insertion/deletion variants. Results - Our method was well-calibrated for the observed fraction of heterozygotes diagnosed with Long QT. Heuristically, we found that the innate diagnostic information one learns about a variant from three-dimensional variant location, in vitro functional data, and in silico predictors is equivalent to the diagnostic information one learns about that same variant by clinically phenotyping 10 heterozygotes. Most importantly, these data can be obtained in the absence of any clinical observations. Conclusions - We show how variant-specific features can inform a prior probability of disease for rare variants even in the absence of clinically-phenotyped heterozygotes.

OP0048 DIAGNOSING AXIAL SPONDYLOARTHRITIS: ESTIMATION OF THE DISEASE PROBABILITY IN PATIENTS WITH A PRIORI DIFFERENT LIKELIHOODS OF THE DIAGNOSIS

Background:The diagnostic approach in axial spondyloarthritis (SpA) relies on a estimation of the post-test disease probability that is based on evaluation of positive and negative results of diagnostic tests in the context of the pre-test disease probability.Objectives:To evaluate the diagnostic value of SpA parameters and their combination for the diagnosis of axial SpA in patients with an a priori different probability of the diagnosis.Methods:A total of 361 patients with chronic back pain and suspicion of axial SpA (181 referred by primary care physicians or orthopaedists, 180 recruited via an online screening tool) received a structured rheumatologic examination as a part of the OptiRef study [1], which resulted into a diagnosis or exclusion of axial SpA. The prevalence of axial SpA indicating the pre-test probability was 40% in the physician-referred subgroup and 20% in the online screening subgroup. Sensitivities, specificities, and likelihood ratios (LRs) for SpA features were determined in both subgroups and the respective post-test probabilities of axial SpA were calculated.Results:The relative diagnostic value of single SpA features varied substantially between the groups with different referral pathways – see the online disease probability calculator http://www.axspa.de/calculator.html. It can be seen that the diagnostic values of the SpA parameters vary substantially between the groups. For instance, HLA-B27 positivity increased the probability of the presence of axial SpA by 35% to 55% in online-screened patients and by 22% to 62% in physician-referred patients. Furthermore, the absence of HLA-B27 resulted in a sharp decrease in the probability of the presence of axial SpA in physician-referred patients (from 40% to 6%). This decrease was less sharp in the online screening group (from 20% to 10%). Furthermore, combinations of parameters performed differently in the studied subgroups. Figure 1 illustrates that the observed differences in the diagnostic values of the SpA parameters in different subgroups were only clinically relevant in the presence of a low number of positive test results. For instance, combining IBP with anterior uveitis increased the post-test probability for axial SpA to 78% in the online screening group and to 87% in the physician-referred group, whereas using HLA-B27 positivity and active sacroiliitis on MRI in combination with IBP resulted in a surge in the post-test probability of the presence of axial SpA to around 95% in both groups.Conclusion:The diagnostic value of a single diagnostic test in the clinical practice is not fixed and a number of factors including the referral pathway can affect it. Fluctuation of the diagnostic values is especially relevant when the number of positive parameters is low (1-2).References:[1]Proft F, et al. Semin Arthritis Rheum. 2020;50:1015-1021.Acknowledgements:The OptiRef study was supported by a research grant from Novartis.Disclosure of Interests:None declared

Hip aspiration culture: analysing data from a single operator series investigating periprosthetic joint infection

Introduction: We undertook this study to know the sensitivity, specificity and post-test probabilities of hip aspiration when diagnosing periprosthetic hip infections. We also examined “dry tap” (injection with saline and aspiration) results and aspiration volumes. Methods: This is a retrospective cohort study of patients aspirated for suspected periprosthetic joint infection between July 2012 and October 2016. All aspirations were carried out by one trained surgical care practitioner (SCP). All aspirations followed an aseptic technique and fluoroscopic guidance. Aspiration was compared to tissue biopsy taken at revision. Aspiration volumes were analysed for comparison. Results: Between January 2012 and September 2016, 461 hip aspirations were performed by our SCP. Of these 125 progressed to revision. We calculated sensitivity 59 % (confidence interval (CI) 35 %–82 %) and specificity 94 % (CI 89 %–98 %). Pre-test probability for our cohort was 0.14. Positive post-test probability was 0.59 and negative post-test probability 0.06. Aspiration volume for infected (n=17) and non-infected (n=108) joints was compared and showed no significant difference. Dry taps were experienced five times; in each instance the dry tap agreed with the biopsy result. Conclusions: Our data show that hip aspiration culture is a highly specific investigation for diagnosing infection but that it is not sensitive. Aspiration volume showed no significant difference between infected and non-infected groups. Each time a joint was infiltrated with saline to achieve a result, the result matched tissue sampling.

Validation of Methicillin-Resistant Staphylococcus aureus (MRSA) Risk Factors in Predicting MRSA Community-Acquired Pneumonia at an Academic Medical Center

Background: The 2019 Infectious Diseases Society of America community-acquired pneumonia (CAP) guidelines recommend antimethicillin- resistant Staphylococcus aureus (MRSA) therapy in patients with CAP based on previously identified risk factors for MRSA with an emphasis on local epidemiology and institutional validation of risk. Thus, we sought to assess the ability of guideline-recognized risk factors to predict MRSA CAP at our institution. Methods: This was a single-center, retrospective cohort study from January 2016 to March 2020. Patients were included if they were >18 years old, diagnosed with CAP, and had a MRSA nasal screen and respiratory culture obtained on admission. Patients were excluded if CAP diagnosis was not met, respiratory cultures were not obtained within 48 hours of antibiotic initiation, or they had cystic fibrosis. Sensitivity, specificity, negative predictive value, positive predictive value, and likelihood ratios (LR) were calculated using Vasser Stats 2019. Pre/post-test odds and pre/post-test probabilities were calculated using Excel 2019. Results: Of 705 screened patients, 221 were included. MRSA prevalence in CAP patients at our institution was 3.6%. History of MRSA isolated from a respiratory specimen had high specificity (98%), high positive LR of 20 (95% CI 5.3–74.8), and high post-test probability of 42.8%. Receipt of IV antibiotics during hospitalization within the past 90 days had a positive LR of 1.9 (95% CI 0.74–4.84). A positive MRSA nasal screen on admission had a positive LR of 6.9 (95% CI 4.0–12.1), negative LR 0.28 (95% CI 0.08–0.93), positive post-test probability of 20.7%, and negative post-test probability of 1.04%. Conclusion: Our study utilized institutional data to validate guideline recognized risk factors for MRSA CAP specifically at our institution. Risk factors including history of MRSA isolated from a respiratory specimen, and positive post-admission MRSA nasal screen were validated as significant risk factors; receipt of IV antibiotics during hospitalization within the past 90 days was not shown to be a risk factor for MRSA CAP based on our institutional data. Validated risk factors may help providers discern which patients with CAP at our institution would benefit most from empiric MRSA treatment.

“Clinician’s Probability Calculator” to Convert Pre-Test to Post-Test Probability of COVID-19, Based on Method Validation from Each Laboratory

Identifying the SARS-CoV-2 virus has been a unique challenge for the scientific community. In this paper, we discuss a practical solution to help guide clinicians with interpretation of the probability that a positive, or negative, COVID-19 test result indicates an infected person, based on their clinical estimate of pre-test probability of infection.The authors conducted a small survey on LinkedIn to confirm that hypothesis that that the clinical pre-test probability of COVID-19 increases relative to local prevalence of disease plus patient age, known contact, and severity of symptoms. We examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 individual laboratory experiments for molecular tests for SARS-CoV-2 as reported to the FIND database 1, and for selected methods in FDA EUA submissions2,3. Authors calculated likelihood ratios to convert pre-test to post-test probability of disease and designed an online calculator to create graphics and text to report results. Despite best efforts, false positive and false negative Covid-19 test results are unavoidable4,5. A positive or negative test result from one laboratory has a different probability for the presence of disease than the same result from another laboratory. Likelihood ratios and confidence intervals can convert the physician or other healthcare professional&rsquo;s clinical estimate of pre-test probability to post-test probability of disease. Ranges of probabilities differ depending on proven method PPA and NPA in each laboratory. We recommend that laboratories verify PPA and NPA and utilize a the &ldquo;Clinician&rsquo;s Probability Calculator&rdquo; to verify acceptable test performance and create reports to help guide clinicians with estimation of post-test probability of COVID-19.