scholarly journals Early clinical markers of aggressive multiple sclerosis

Brain ◽  
2020 ◽  
Vol 143 (5) ◽  
pp. 1400-1413 ◽  
Author(s):  
Charles B Malpas ◽  
Ali Manouchehrinia ◽  
Sifat Sharmin ◽  
Izanne Roos ◽  
Dana Horakova ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Symptom Onset ◽  
Predictive Value ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
First Year ◽  
Clinical Markers

Abstract Patients with the ‘aggressive’ form of multiple sclerosis accrue disability at an accelerated rate, typically reaching Expanded Disability Status Score (EDSS) ≥ 6 within 10 years of symptom onset. Several clinicodemographic factors have been associated with aggressive multiple sclerosis, but less research has focused on clinical markers that are present in the first year of disease. The development of early predictive models of aggressive multiple sclerosis is essential to optimize treatment in this multiple sclerosis subtype. We evaluated whether patients who will develop aggressive multiple sclerosis can be identified based on early clinical markers. We then replicated this analysis in an independent cohort. Patient data were obtained from the MSBase observational study. Inclusion criteria were (i) first recorded disability score (EDSS) within 12 months of symptom onset; (ii) at least two recorded EDSS scores; and (iii) at least 10 years of observation time, based on time of last recorded EDSS score. Patients were classified as having ‘aggressive multiple sclerosis’ if all of the following criteria were met: (i) EDSS ≥ 6 reached within 10 years of symptom onset; (ii) EDSS ≥ 6 confirmed and sustained over ≥6 months; and (iii) EDSS ≥ 6 sustained until the end of follow-up. Clinical predictors included patient variables (sex, age at onset, baseline EDSS, disease duration at first visit) and recorded relapses in the first 12 months since disease onset (count, pyramidal signs, bowel-bladder symptoms, cerebellar signs, incomplete relapse recovery, steroid administration, hospitalization). Predictors were evaluated using Bayesian model averaging. Independent validation was performed using data from the Swedish Multiple Sclerosis Registry. Of the 2403 patients identified, 145 were classified as having aggressive multiple sclerosis (6%). Bayesian model averaging identified three statistical predictors: age > 35 at symptom onset, EDSS ≥ 3 in the first year, and the presence of pyramidal signs in the first year. This model significantly predicted aggressive multiple sclerosis [area under the curve (AUC) = 0.80, 95% confidence intervals (CIs): 0.75, 0.84, positive predictive value = 0.15, negative predictive value = 0.98]. The presence of all three signs was strongly predictive, with 32% of such patients meeting aggressive disease criteria. The absence of all three signs was associated with a 1.4% risk. Of the 556 eligible patients in the Swedish Multiple Sclerosis Registry cohort, 34 (6%) met criteria for aggressive multiple sclerosis. The combination of all three signs was also predictive in this cohort (AUC = 0.75, 95% CIs: 0.66, 0.84, positive predictive value = 0.15, negative predictive value = 0.97). Taken together, these findings suggest that older age at symptom onset, greater disability during the first year, and pyramidal signs in the first year are early indicators of aggressive multiple sclerosis.

scholarly journals Early clinical markers of aggressive multiple sclerosis

2019 ◽  
Author(s):  
Charles B Malpas ◽  
Ali Manouchehrinia ◽  
Sifat Sharmin ◽  
Izanne Roos ◽  
Dana Horakova ◽  
...  
Keyword(s):  
Symptom Onset ◽  
Bayesian Model Averaging ◽  
Age At Onset ◽  
Disease Onset ◽  
Model Averaging ◽  
Observation Time ◽  
First Year ◽  
Clinical Markers ◽  
Clinical Predictors ◽  
Disability Score

AbstractPatients with the ‘aggressive’ form of MS accrue disability at an accelerated rate, typically reaching EDSS >= 6 within 10 years of symptom onset. Several clinicodemographic factors have been associated with aggressive MS, but less research has focused on clinical markers that are present in the first year of disease. The development of early predictive models of aggressive MS is essential to optimise treatment in this MS subtype. We evaluated whether patients who will develop aggressive MS can be identified based on early clinical markers, and to replicate this analysis in an independent cohort. Patient data were obtained from MSBase. Inclusion criteria were (a) first recorded disability score (EDSS) within 12 months of symptom onset, (b) at least 2 recorded EDSS scores, and (c) at least 10 years of observation time. Patients were classified as having ‘aggressive MS’ if they: (a) reached EDSS >= 6 within 10 years of symptom onset, (b) EDSS >=6 was confirmed and sustained over >=6 months, and (c) EDSS >=6 was sustained until the end of follow-up. Clinical predictors included patient variables (sex, age at onset, baseline EDSS, disease duration at first visit) and recorded relapses in the first 12 months since disease onset (count, pyramidal signs, bowel-bladder symptoms, cerebellar signs, incomplete relapse recovery, steroid administration, hospitalisation). Predictors were evaluated using Bayesian Model Averaging (BMA). Independent validation was performed using data from the Swedish MS Registry. Of the 2,403 patients identified, 145 were classified as having aggressive MS (6%). BMA identified three statistical predictors: age > 35 at symptom onset, EDSS >= 3 in the first year, and the presence of pyramidal signs in the first year. This model significantly predicted aggressive MS (AUC = .80, 95% CIs = .75, .84). The presence of all three signs was strongly predictive, with 32% of such patients meeting aggressive disease criteria. The absence of all three signs was associated with a 1.4% risk. Of the 556 eligible patients in the Swedish MS Registry cohort, 34 (6%) met criteria for aggressive MS. The combination of all three signs was also predictive in this cohort (AUC = .75, 95% CIs = .66, .84). Taken together, these findings suggest that older age at symptom onset, greater disability during the first year, and pyramidal signs in the first year are early indicators of aggressive MS.

Screening for cognitive impairment in multiple sclerosis using the Symbol Digit Modalities Test

2007 ◽  
Vol 13 (1) ◽  
pp. 52-57 ◽  
Author(s):  
B A Parmenter ◽  
B Weinstock-Guttman ◽  
N Garg ◽  
F Munschauer ◽  
R HB Benedict
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Impairment ◽  
Cognitive Function ◽  
Positive Predictive Value ◽  
Bayesian Statistics ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Demographic Variables ◽  
Healthy Controls ◽  
Neurologic Examination

Cognitive impairment is common in multiple sclerosis (MS), yet difficult to detect duringroutine neurologic examination. Therefore, briefscreening tests that identify patients who may benefit from a more thorough assessment or treatment are needed. We investigated the utility of the Symbol Digit Modalities Test (SDMT) as a screen for cognitive dysfunction because it can be administered and scored in about 5 minutes. One hundred MS patients and 50 healthy controls, matched on demographic variables, participated in the study. Examination procedures included the neuropsychological (NP) tests included in the Minimal Assessment of Cognitive Function in MS (MACFIMS) battery. Patients were considered impaired if they performed one and a half standard deviations below controls on two or more MACFIMS variables, excluding theSDMT. Bayesian statistics showed that a total score of 55 or lower onthe SDMT accurately categorized 72% of patients, yielding sensitivityof 0.82, specificity of 0.60, positive predictive value (PPV) of 0.71, and negative predictive value (NPV) of 0.73. These results suggest that the effectiveness of the SDMT as a screen for cognitive impairment in MS is roughly equal to that of other psychometric and questionnaire methods.

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models

scholarly journals Added value of diffusion-weighted MRI in assessment of pleural lesions

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Youssriah Yahia Sabri ◽  
Ikram Hamed Mahmoud ◽  
Lamis Tarek El-Gendy ◽  
Mohamed Raafat Abd El-Mageed ◽  
Sally Fouad Tadros
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Value ◽  
Added Value ◽  
Adc Value ◽  
Pleural Diseases ◽  
Conventional Mri ◽  
Malignant Lesions

Abstract Background There are many causes of pleural disease including variable benign and malignant etiologies. DWI is a non-enhanced functional MRI technique that allows qualitative and quantitative characterization of tissues based on their water molecules diffusivity. The aim of this study was to evaluate the diagnostic value of DWI-MRI in detection and characterization of pleural diseases and its capability in differentiating benign from malignant pleural lesions. Results Conventional MRI was able to discriminate benign from malignant lesions by using morphological features (contour and thickness) with sensitivity 89.29%, specificity 76%, positive predictive value 89%, negative predictive value 76.92%, and accuracy 85.37%. ADC value as a quantitative parameter of DWI found that ADC values of malignant pleural diseases were significantly lower than that of benign lesions (P < 0.001). Hence, we discovered that using ADC mean value of 1.68 × 10-3 mm2/s as a cutoff value can differentiate malignant from benign pleural diseases with sensitivity 89.3%, specificity 100%, positive predictive value 100%, negative predictive value 81.2%, and accuracy 92.68% (P < 0.001). Conclusion Although DWI-MRI is unable to differentiate between malignant and benign pleural effusion, its combined morphological and functional information provide valid non-invasive method to accurately characterize pleural soft tissue diseases differentiating benign from malignant lesions with higher specificity and accuracy than conventional MRI.

Accuracy of Medical Examiner’s Assessment for Near–Real-Time Surveillance of Fatal Drug Overdoses, King County, Washington, March 2017–February 2018

2021 ◽  
pp. 003335492110084
Author(s):  
Kirsten Vannice ◽  
Julia Hood ◽  
Nicole Yarid ◽  
Meagan Kay ◽  
Richard Harruff ◽  
...  
Keyword(s):  
Public Health ◽  
Positive Predictive Value ◽  
Real Time ◽  
Negative Predictive Value ◽  
Drug Overdose ◽  
Predictive Value ◽  
Death Certificate ◽  
King County ◽  
Public Health Response ◽  
Drug Overdoses

Objectives Up-to-date information on the occurrence of drug overdose is critical to guide public health response. The objective of our study was to evaluate a near–real-time fatal drug overdose surveillance system to improve timeliness of drug overdose monitoring. Methods We analyzed data on deaths in the King County (Washington) Medical Examiner’s Office (KCMEO) jurisdiction that occurred during March 1, 2017–February 28, 2018, and that had routine toxicology test results. Medical examiners (MEs) classified probable drug overdoses on the basis of information obtained through the death investigation and autopsy. We calculated sensitivity, positive predictive value, specificity, and negative predictive value of MEs’ classification by using the final death certificate as the gold standard. Results KCMEO investigated 2480 deaths; 1389 underwent routine toxicology testing, and 361 were toxicologically confirmed drug overdoses from opioid, stimulant, or euphoric drugs. Sensitivity of the probable overdose classification was 83%, positive predictive value was 89%, specificity was 96%, and negative predictive value was 94%. Probable overdoses were classified a median of 1 day after the event, whereas the final death certificate confirming an overdose was received by KCMEO an average of 63 days after the event. Conclusions King County MEs’ probable overdose classification provides a near–real-time indicator of fatal drug overdoses, which can guide rapid local public health responses to the drug overdose epidemic.

scholarly journals Role of annexin A2 and osteopontin for early diagnosis of hepatocellular carcinoma in hepatitis C virus patients

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Abd El-Fattah F. Hanno ◽  
Fatma M. Abd El-Aziz ◽  
Akram A. Deghady ◽  
Ehab H. El-Kholy ◽  
Aborawy I. Aborawy
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Early Diagnosis ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Chronic Hepatitis C ◽  
Predictive Value ◽  
Annexin A2 ◽  
Alpha Fetoprotein

Abstract Background Liver cancer is the fifth most common cancer and the second most frequent cause of cancer-related death globally. Early stages of hepatocellular carcinoma (0&A) can be treated with curative procedures. The aim of this work was to evaluate the role of annexin A2 and osteopontin for early diagnosis of hepatocellular carcinoma in hepatitis C virus patients. Methods The study was carried out on 80 patients classified into two groups. Group A had 40 chronic hepatitis C patients without hepatocellular carcinoma, while group B had 40 chronic hepatitis C patients with early hepatocellular carcinoma (stages; 0&A). All patients were subjected to thorough history taking, clinical examination, liver function tests, renal function tests, serum alpha-fetoprotein, serum osteopontin, and serum annexin A2. Results Serum alpha-fetoprotein was found to be statistically significantly higher in patients with the hepatocellular carcinoma group than the chronic hepatitis C group. The ROC curve for alpha-fetoprotein for detection of HCC was significant, its diagnostic performance was 0.818* (p < 0.001*), and the cutoff point for predicting the probability for HCC was 6.0 (ng/ml) with sensitivity of 77.50%, specificity of 82.50%, positive predictive value of 81.60%, negative predictive value of 78.6%, and accuracy of 80%. Serum osteopontin was found to be statistically significantly higher in patients from the hepatocellular carcinoma group than the chronic hepatitis C group. The ROC curve for osteopontin was significant, its diagnostic performance was 0.739* (p < 0.001*), the cutoff point was 13.2 (ng/ml) with sensitivity of 65.0%, specificity of 90.0%, positive predictive value of 86.70%, negative predictive value of 72.0%, and accuracy of 77.0%. Serum annexin A2 was found to be statistically significantly higher in patients from the hepatocellular carcinoma group than the chronic hepatitis C group. The ROC curve for annexin A2 was significant, its diagnostic performance was 0.927* (p < 0.001*), the cutoff point was 10.1(ng/ml) with sensitivity of 85.0%, specificity of 85.0%, positive predictive value of 85.0%, negative predictive value of 85.0%, and accuracy of 85.0%. Conclusions Osteopontin had better specificity but lower sensitivity than serum alpha-fetoprotein for early diagnosis of hepatocellular carcinoma. Annexin A2 had better diagnostic sensitivity and specificity than alpha-fetoprotein for early diagnosis of hepatocellular carcinoma.

scholarly journals POS0706 PERFORMANCES OF DIFFERENT CLASSIFICATION CRITERIA FOR SYSTEMIC LUPUS ERYTHEMATOSUS IN A SINGLE CENTER COHORT FROM TURKEY

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 602.1-603
Author(s):  
E. S. Torun ◽  
E. Bektaş ◽  
F. Kemik ◽  
M. Bektaş ◽  
C. Cetin ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Antiphospholipid Syndrome ◽  
Lupus Erythematosus ◽  
Predictive Value ◽  
Sjogren's Syndrome ◽  
Classification Criteria ◽  
Systemic Lupus ◽  
Acr Criteria

Background:Recently developed EULAR/ACR classification criteria for systemic lupus erythematosus (SLE) have important differences compared to the 2012 Systemic Lupus International Collaborating Clinics (SLICC) SLE classification criteria and the revised 1997 American College of Rheumatology (ACR) criteria: The obligatory entry criterion of antinuclear antibody (ANA) positivity is introduced and a “weighted” approach is used1. Sensitivity and specificity of these three criteria have been debated and may vary in different populations and clinical settings.Objectives:We aim to compare the performances of three criteria sets/rules in a large cohort of patients and relevant diseased controls from a reference center with dedicated clinics for SLE and other autoimmune/inflammatory connective tissue diseases from Turkey.Methods:We reviewed the medical records of SLE patients and diseased controls for clinical and laboratory features relevant to all sets of criteria. Criteria sets/rules were analysed based on sensitivity, positive predictive value, specificity and negative predictive value, using clinical diagnosis with at least 6 months of follow-up as the gold standard. A subgroup analysis was performed in ANA positive patients for both SLE patients and diseased controls. SLE patients that did not fulfil 2012 SLICC criteria and 2019 EULAR/ACR criteria and diseased controls that fulfilled these criteria were evaluated.Results:A total of 392 SLE patients and 294 non-SLE diseased controls (48 undifferentiated connective tissue disease, 51 Sjögren’s syndrome, 43 idiopathic inflammatory myopathy, 50 systemic sclerosis, 52 primary antiphospholipid syndrome, 15 rheumatoid arthritis, 15 psoriatic arthritis and 20 ANCA associated vasculitis) were included into the study. Hundred and fourteen patients (16.6%) were ANA negative.Sensitivity was more than 90% for 2012 SLICC criteria and 2019 EULAR/ACR criteria and positive predictive value was more than 90% for all three criteria (Table 1). Specificity was the highest for 1997 ACR criteria. Negative predictive value was 76.9% for ACR criteria, 88.4% for SLICC criteria and 91.7% for EULAR/ACR criteria.In only ANA positive patients, sensitivity was 79.6% for 1997 ACR criteria, 92.2% for 2012 SLICC criteria and 96.1% for 2019 EULAR/ACR criteria. Specificity was 92.6% for ACR criteria, 87.8% for SLICC criteria 85.2% for EULAR/ACR criteria.Eleven clinically diagnosed SLE patients had insufficient number of items for both 2012 SLICC and 2019 EULAR/ACR criteria. Both criteria were fulfilled by 16 diseased controls: 9 with Sjögren’s syndrome, 5 with antiphospholipid syndrome, one with dermatomyositis and one with systemic sclerosis.Table 1.Sensitivity, positive predictive value, specificity and negative predictive value of 1997 ACR, 2012 SLICC and 2019 EULAR/ACR classification criteriaSLE (+)SLE (-)Sensitivity (%)Positive Predictive Value (%)Specificity (%)Negative Predictive Value (%)1997 ACR(+) 308(-) 841527978.695.494.976.92012 SLICC(+) 357(-) 352626891.193.291.288.42019 EULAR/ACR(+) 368(-) 242826693.892.990.591.7Conclusion:In this cohort, although all three criteria have sufficient specificity, sensitivity and negative predictive value of 1997 ACR criteria are the lowest. Overall, 2019 EULAR/ACR and 2012 SLICC criteria have a comparable performance, but if only ANA positive cases and controls are analysed, the specificity of both criteria decrease to less than 90%. Some SLE patients with a clinical diagnosis lacked sufficient number of criteria. Mostly, patients with Sjögren’s syndrome or antiphospholipid syndrome are prone to misclassification by both recent criteria.References:[1]Aringer M, Costenbader K, Daikh D, et al. 2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus. Ann Rheum Dis 2019;78:1151-1159.Disclosure of Interests:None declared

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