Development and validation of a simple lupus severity index using ACR criteria for classification of SLE.
Ontology highlight
ABSTRACT:
SUBMITTER: Bello GA
PROVIDER: S-EPMC4800735 | biostudies-other | 2016
REPOSITORIES: biostudies-other
Similar Datasets
Project description:ImportanceClassification criteria are the standardized definitions that are used to enroll uniform cohorts for research studies. They emphasize high specificity and are distinct from diagnostic criteria. No universally recognized classification criteria currently exist for discoid lupus erythematosus (DLE), which has led to problematic heterogeneity in observational and interventional clinical studies across the field.ObjectiveTo create and validate classification criteria for DLE using 12 previously defined candidate criteria items.Design, setting, and participantsFor this diagnostic study, candidate criteria items were prospectively applied by dermatologists and dermatopathologists at clinical visits of patients with DLE or a condition that could be confused for DLE, termed a DLE mimicker, at academic dermatology practices across the United States, Poland, Japan, and South Korea. Data were collected from December 1, 2017, to February 1, 2019, and analyzed from March 1 to September 19, 2019.Main outcomes and measuresClinical features among these 2 groups were calculated and compared with χ2 or Fisher exact tests. Candidate models were identified using best subsets logistic regression analysis. Improvement tests, fit statistics, and discrimination were considered to choose a final model.ResultsNine sites contributed 215 patients, 15 of whom had missing or incomplete data. The final model for DLE classification criteria includes only clinical variables: atrophic scarring (3 points), location in the conchal bowl (2 points), preference for the head and neck (2 points), dyspigmentation (1 point), follicular hyperkeratosis and/or plugging (1 point), and erythematous to violaceous in color (1 point), with an area under the receiving operating characteristic curve of 0.91 (95% CI, 0.87-0.95). A score of at least 5 points yields a sensitivity of 84.1% and a specificity of 75.9% in the classification of DLE, with increasing scores yielding higher specificity.Conclusions and relevanceThese findings provide the initial validation of classification criteria for DLE for use in observational and clinical trials.
| S-EPMC7301299 | biostudies-literature
Project description:BACKGROUND:Researchers have suggested models to predict the risk of postoperative AKI (PO-AKI), but an externally validated risk index that can be practically implemented before patients undergo noncardiac surgery is needed. METHODS:We performed a retrospective observational study of patients without preexisting renal failure who underwent a noncardiac operation (?1 hour) at two tertiary hospitals in Korea. We fitted a proportional odds model for an ordinal outcome consisting of three categories: critical AKI (defined as Kidney Disease Improving Global Outcomes AKI stage ?2, post-AKI death, or dialysis within 90 days after surgery), low-stage AKI (defined as PO-AKI events not fulfilling the definition of critical AKI), and no PO-AKI. RESULTS:The study included 51,041 patients in a discovery cohort and 39,764 patients in a validation cohort. The Simple Postoperative AKI Risk (SPARK) index included a summation of the integer scores of the following variables: age, sex, expected surgery duration, emergency operation, diabetes mellitus, use of renin-angiotensin-aldosterone inhibitors, baseline eGFR, dipstick albuminuria hypoalbuminemia, anemia, and hyponatremia. The model calibration plot showed tolerable distribution of observed and predicted probabilities in both cohorts. The discrimination power of the SPARK index was acceptable in both the discovery (c-statistic 0.80) and validation (c-statistic 0.72) cohorts. When four SPARK classes were defined on the basis of the sum of the risk scores, the SPARK index and classes fairly reflected the risks of PO-AKI and critical AKI. CONCLUSIONS:Clinicians may consider implementing the SPARK index and classifications to stratify patients' PO-AKI risks before performing noncardiac surgery.
| S-EPMC6317608 | biostudies-literature
Project description:A significant portion of frontotemporal lobar degeneration (FTLD) is due to inherited gene mutations, and we are unaware of a large sequential series that includes a recently discovered inherited cause of FTLD. There is also great need to develop clinical tools and approaches that will assist clinicians in the identification and counseling of patients with FTLD and their families regarding the likelihood of an identifiable genetic cause.To ascertain the frequency of inherited FTLD and develop validated pedigree classification criteria for FTLD that provide a standardized means to evaluate pedigree information and insight into the likelihood of mutation-positive genetic test results for C9orf72, MAPT, and GRN.Information about pedigrees and DNA was collected from 306 serially assessed patients with a clinical diagnosis of FTLD. This information included gene test results for C9orf72, MAPT, and GRN. Pedigree classification criteria were developed based on a literature review of FTLD genetics and pedigree tools and then refined by reviewing mutation-positive and -negative pedigrees to determine differentiating characteristics.Academic medical center.Patients with FTLD.Familial risk.The rate of C9orf72, MAPT, or GRN mutation-positive FTLD in this series was 15.4%. Categories designating the risk level for hereditary cause were termed high, medium, low, apparent sporadic, and unknown significance. Thirty-nine pedigrees (12.7%) met criteria for high, 31 (10.1%) for medium, 46 (15.0%) for low, 91 (29.7%) for apparent sporadic, and 99 (32.4%) for unknown significance. The mutation-detection rates were as follows: high, 64.1%; medium, 29%; low, 10.9%; apparent sporadic, 1.1%; and unknown significance, 7.1%. Mutation-detection rates differed significantly between the high and other categories.Mutation rates are high in FTLD spectrum disorders, and the proposed criteria provide a validated standard for the classification of FTLD pedigrees. The combination of pedigree criteria and mutation-detection rates has important implications for genetic counseling and testing in clinical settings.
| S-EPMC3906581 | biostudies-literature
Project description:Objective:To determine the measurement properties of the Cutaneous Lupus Erythematosus Disease Area and Severity Index (CLASI) and the paediatric adaptation of the Skindex29 (pSkindex27) when used in childhood-onset SLE (cSLE). Methods:Patients with mucocutaneous involvement of cSLE were evaluated at the study entry and 6 months later. Besides the CLASI and pSkindex27, the Pediatric Quality of Life Inventory Generic Core scale (PedsQL-GC), its Rheumatology Module (PedsQL-RM), the SLE Disease Activity Index (SLEDAI) and the SLE Damage Index (SDI) were completed. Results:The CLASI and pSkindex27 had high internal consistency (both Cronbach ? >0.82). Children were able to complete the pSkindex27, with self-report and caregiver proxy-reports showing excellent agreement (intraclass correlation coefficient=0.97). The CLASI Activity Score (CLASI-A) was strongly correlated with the mucocutaneous domain score of the SLEDAI as was the CLASI Damage Score (CLASI-D) with that of the SDI (both: Spearman correlation coefficients (rs) >0.68). pSkindex27 summary scores were moderately correlated with those of the PedsQL-GC and PedsQL-RM (all: rs >|0.51|), the CLASI-A and CLASI-D (both: rs > 0.64), respectively. Patients who experienced a >50% improvement of the CLASI-A between study visits had significantly higher PedsQL-GC and pSkindex27 scores than those without improvement of mucocutaneous features. Conclusion:Both CLASI and pSkindex27 are useful assessment tools in cSLE, active and chronic mucocutaneous lesions and their changes over time can be measured using the CLASI and the pSkindex27 can capture the impact of mucocutaneous involvement on patient health-related quality of life.
| S-EPMC6257379 | biostudies-other
Project description:BackgroundThe aim of this study was to validate the 2019 European League Against Rheumatism/American College of Rheumatology (EULAR/ACR) classification criteria for systemic lupus erythematosus (SLE) in antinuclear antibody (ANA)-positive Chinese patients.MethodsMedical records of all adult patients who attended the rheumatology out-patient clinics between May and September 2019 were reviewed. Patients with ever ANA positive (titre ⩾1:80) were included and evaluated for the fulfilment of the 2019 EULAR/ACR, 2012 Systemic Lupus International Collaborating Clinics (SLICC) and 1997 ACR criteria for SLE classification. The performance of these criteria in predicting a clinical diagnosis of SLE as judged by an independent panel of rheumatologists was studied and compared in different subgroups.ResultsA total of 1533 patients (88.2% women; age at first clinic attendance 45.5 ± 15.6 years) were studied and 562 patients were judged to be clinical SLE. The sensitivity and specificity of the EULAR/ACR (⩾10 points), SLICC and ACR criteria for a clinical diagnosis of SLE was 96.1%, 97.9% and 86.1%; and 85.8%, 86.3% and 94.3%, respectively. Applying the attribution rule to the non-SLE controls, the specificity of the three criteria increased to 95.0%, 92.5% and 98.8%, respectively. The specificity of the EULAR/ACR criteria was higher in male patients (97.9%), those aged >50 years (97.0%) and disease duration of ⩽3 years (97.6%). Using a cut-off of 12 points, the specificity of the EULAR/ACR criteria was further increased (96.6%) while a high sensitivity (95.0%) was maintained.ConclusionIn Chinese patients with a positive ANA, the EULAR/ACR criteria for clinical SLE perform equally well to the SLICC criteria. Both the EULAR/ACR and SLICC are more sensitive but less specific than the ACR criteria. The specificity of all the three criteria is enhanced by applying the attribution rule to controls. The specificity of the EULAR/ACR criteria is higher in certain patient subgroups or when the cut-off score is raised.
| S-EPMC9150246 | biostudies-literature
Project description:ObjectiveTo develop, validate and apply a generic clinical severity index applicable to all adult patients with inflammatory bowel disease (IBD).DesignA review of the literature and an expert focus group consultation were carried out in order to draw out relevant items from existing literature. The new index was called the IBD Index (IBDEX). Standard psychometric analysis was carried out. The construct validity was assessed against biochemical markers, clinical and endoscopic indices. The new index was completed again within 6 weeks to check responsiveness and reproducibility.ResultsIBDEX was used to assess 255 adult patients with IBD (125 with Crohn's disease and 130 with ulcerative colitis), and 64 patients were re-evaluated within 6 weeks. It had good internal consistency (Cronbach's α=0.79) and correlated very well with the Harvey Bradshaw Index (r=0.94), the Simple Clinical Colitis Activity Index (r=0.92), the Mayo Clinic Index (r=0.87) and the Simple Endoscopic Score (r=0.76), all with p values <0.05. IBDEX had a moderate but positive correlation with C reactive protein (r=0.51) and erythrocyte sedimentation rate (r=0.36) p values both <0.05. The test-retest reliability was good (intraclass correlation coefficient 0.97) and responsiveness ratio was 2.27.ConclusionsIBDEX is the first properly validated Clinical Disease Severity Index in IBD. Our results showed that it is valid, reliable and reproducible and has the potential to be used in clinical practice.
| S-EPMC5369573 | biostudies-literature
Project description:BackgroundClinical indices to characterize the severity of inflammatory bowel disease (IBD) are widely used in clinical trials and real-world practice. However, there are few validated instruments for assessing IBD severity in administrative claims-based studies.MethodsPatients (18-89 years) diagnosed with ulcerative colitis (UC) or Crohn's disease (CD) and receiving ≥1 prescription claim for IBD therapy were identified using administrative claims data from the Optum Clinformatics, IMS PharMetrics, and Truven MarketScan databases (January 1, 2013-September 30, 2017). Regression modeling identified independent predictors of IBD-related hospitalization (inpatient stay or emergency department visit resulting in hospitalization), which were used to develop IBD severity indices. The index was validated against all-cause hospitalization and total cost and IBD-related hospitalization and total cost.ResultsThere were 51,767 patients diagnosed with UC (n = 30,993) or CD (n = 20,774) who were initiated treatment with IBD therapy. Independent predictors of IBD-related hospitalization were Charlson Comorbidity Index score >1, anemia, weight loss, intravenous corticosteroid use, prior gastrointestinal-related emergency department visit and hospitalization, and unspecified disease location or more extensive disease. Female sex, renal comorbidities, intestinal fistula, and stricture were additional risk factors for patients with CD, whereas age <40 years was a UC-specific risk factor. Median IBD severity scores were 8 and 13 for UC and CD, respectively, from possible total scores of 51 and 37. Inflammatory bowel disease severity score correlated with significantly higher all-cause hospitalization and cost, all-cause total cost, IBD-related hospitalization cost, and total cost.ConclusionsThese validated UC and CD severity indices can be used to predict IBD-related outcomes using administrative claims databases.
| S-EPMC8314106 | biostudies-literature
Project description:PurposeTo develop classification criteria for 25 of the most common uveitides.DesignMachine learning using 5,766 cases of 25 uveitides.MethodsCases were collected in an informatics-designed preliminary database. Using formal consensus techniques, a final database was constructed of 4,046 cases achieving supermajority agreement on the diagnosis. Cases were analyzed within uveitic class and were split into a training set and a validation set. Machine learning used multinomial logistic regression with lasso regularization on the training set to determine a parsimonious set of criteria for each disease and to minimize misclassification rates. The resulting criteria were evaluated in the validation set. Accuracy of the rules developed to express the machine learning criteria was evaluated by a masked observer in a 10% random sample of cases.ResultsOverall accuracy estimates by uveitic class in the validation set were as follows: anterior uveitides 96.7% (95% confidence interval [CI] 92.4, 98.6); intermediate uveitides 99.3% (95% CI 96.1, 99.9); posterior uveitides 98.0% (95% CI 94.3, 99.3); panuveitides 94.0% (95% CI 89.0, 96.8); and infectious posterior uveitides / panuveitides 93.3% (95% CI 89.1, 96.3). Accuracies of the masked evaluation of the "rules" were anterior uveitides 96.5% (95% CI 91.4, 98.6) intermediate uveitides 98.4% (91.5, 99.7), posterior uveitides 99.2% (95% CI 95.4, 99.9), panuveitides 98.9% (95% CI 94.3, 99.8), and infectious posterior uveitides / panuveitides 98.8% (95% CI 93.4, 99.9).ConclusionsThe classification criteria for these 25 uveitides had high overall accuracy (ie, low misclassification rates) and seemed to perform well enough for use in clinical and translational research.
| S-EPMC8526627 | biostudies-literature
Project description:BACKGROUND:Eligibility criteria are the main strategy for screening appropriate participants for clinical trials. Automatic analysis of clinical trial eligibility criteria by digital screening, leveraging natural language processing techniques, can improve recruitment efficiency and reduce the costs involved in promoting clinical research. OBJECTIVE:We aimed to create a natural language processing model to automatically classify clinical trial eligibility criteria. METHODS:We proposed a classifier for short text eligibility criteria based on ensemble learning, where a set of pretrained models was integrated. The pretrained models included state-of-the-art deep learning methods for training and classification, including Bidirectional Encoder Representations from Transformers (BERT), XLNet, and A Robustly Optimized BERT Pretraining Approach (RoBERTa). The classification results by the integrated models were combined as new features for training a Light Gradient Boosting Machine (LightGBM) model for eligibility criteria classification. RESULTS:Our proposed method obtained an accuracy of 0.846, a precision of 0.803, and a recall of 0.817 on a standard data set from a shared task of an international conference. The macro F1 value was 0.807, outperforming the state-of-the-art baseline methods on the shared task. CONCLUSIONS:We designed a model for screening short text classification criteria for clinical trials based on multimodel ensemble learning. Through experiments, we concluded that performance was improved significantly with a model ensemble compared to a single model. The introduction of focal loss could reduce the impact of class imbalance to achieve better performance.
| S-EPMC7367522 | biostudies-literature
Project description:European League Against Rheumatism and are jointly supporting multiphase development of systemic lupus erythematosus (SLE) classification criteria based on weighted criteria and a continuous probability scale. Prior steps included item generation, item reduction and hierarchical organisation of candidate criteria using an evidence-based approach. Our objectives were to determine relative weights using multicriteria decision analysis (MCDA) and to set a provisional threshold score for SLE classification. An SLE Expert Panel (8 European, 9 North American) submitted 164 real, unique cases with a wide range of SLE probability in a standardised format. Using the candidate criteria, experts scored and rank-ordered 20 representative cases. At an in-person meeting, experts reviewed inter-rater reliability of scoring, further refined criteria definitions and participated in an MCDA exercise. Based on expert consensus decisions on pairwise comparisons of criteria, 1000minds software calculated criteria weights and rank-ordered the remaining 144 cases based on their additive scores. The score of the lowest-ranked case for which complete expert consensus was achieved defined the provisional threshold classification score. Inter-rater reliability of scoring cases with the candidate criteria was good. MCDA involved 74 pairwise decisions and was repeated for the arthritis and mucocutaneous domains when the initial ranking of some cases did not match expert opinion. After criteria weights and additive scores were recalculated once, experts reached consensus for SLE classification for all cases scoring>83. Using an iterative process, the candidate criteria definitions were refined, preliminary weights were calculated and a provisional threshold score for SLE classification was determined.
| S-EPMC7057251 | biostudies-literature