scholarly journals Have you had bleeding from your gums? Self‐report to Identify giNGival inflammation (The SING diagnostic accuracy and diagnostic model development study)

2021 ◽  
Author(s):  
Beatriz Goulão ◽  
Graeme S MacLennan ◽  
Craig R Ramsay
Keyword(s):  
Diagnostic Accuracy ◽  
Model Development ◽  
Self Report ◽  
Diagnostic Model ◽  
Gingival Inflammation ◽  
Development Study

scholarly journals Machine-learning algorithm that can improve the diagnostic accuracy of septic arthritis of the knee

2021 ◽  
Author(s):  
Eun-Seok Choi ◽  
Jae Ang Sim ◽  
Young Gon Na ◽  
Jong- Keun Seon ◽  
Hyun Dae Shin
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Confidence Interval ◽  
Diagnostic Accuracy ◽  
Septic Arthritis ◽  
Inflammatory Arthritis ◽  
Supervised Machine Learning ◽  
Diagnostic Model ◽  
Extreme Gradient Boosting ◽  
Wbc Count

Abstract Purpose Prompt diagnosis and treatment of septic arthritis of the knee is crucial. Nevertheless, the quality of evidence for the diagnosis of septic arthritis is low. In this study, the authors developed a machine learning-based diagnostic algorithm for septic arthritis of the native knee using clinical data in an emergency department and validated its diagnostic accuracy. Methods Patients (n = 326) who underwent synovial fluid analysis at the emergency department for suspected septic arthritis of the knee were enrolled. Septic arthritis was diagnosed in 164 of the patients (50.3%) using modified Newman criteria. Clinical characteristics of septic and inflammatory arthritis were compared. Area under the receiver-operating characteristic (ROC) curve (AUC) statistics was applied to evaluate the efficacy of each variable for the diagnosis of septic arthritis. The dataset was divided into independent training and test sets (comprising 80% and 20%, respectively, of the data). Supervised machine-learning techniques (random forest and eXtreme Gradient Boosting: XGBoost) were applied to develop a diagnostic model using the training dataset. The test dataset was subsequently used to validate the developed model. The ROC curves of the machine-learning model and each variable were compared. Results Synovial white blood cell (WBC) count was significantly higher in septic arthritis than in inflammatory arthritis in the multivariate analysis (P = 0.001). In the ROC comparison analysis, synovial WBC count yielded a significantly higher AUC than all other single variables (P = 0.002). The diagnostic model using the XGBoost algorithm yielded a higher AUC (0.831, 95% confidence interval 0.751–0.923) than synovial WBC count (0.740, 95% confidence interval 0.684–0.791; P = 0.033). The developed algorithm was deployed as a free access web-based application (www.septicknee.com). Conclusion The diagnosis of septic arthritis of the knee might be improved using a machine learning-based prediction model. Level of evidence Diagnostic study Level III (Case–control study).

Diagnostic accuracy of self-report and subjective history in the diagnosis of low back pain with non-specific lower extremity symptoms: A systematic review

2015 ◽  
Vol 20 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Susan Shultz ◽  
Kristina Averell ◽  
Angela Eickelman ◽  
Holly Sanker ◽  
Megan Burrowbridge Donaldson
Keyword(s):  
Systematic Review ◽  
Low Back Pain ◽  
Back Pain ◽  
Lower Extremity ◽  
Diagnostic Accuracy ◽  
Self Report ◽  
Low Back

scholarly journals Diagnostic accuracy for major depression in multiple sclerosis using self‐report questionnaires

2015 ◽  
Vol 5 (9) ◽  
Author(s):  
Anja Fischer ◽  
Marcus Fischer ◽  
Robert A. Nicholls ◽  
Stephanie Lau ◽  
Jana Poettgen ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Major Depression ◽  
Diagnostic Accuracy ◽  
Self Report

Development and External Validation of Diagnostic Model for Intra-Procedural Hypotension during Primary Percutaneous Coronary Intervention: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Yong Li
Keyword(s):  
Percutaneous Coronary Intervention ◽  
Primary Percutaneous Coronary Intervention ◽  
External Validation ◽  
Model Development ◽  
Coronary Intervention ◽  
University Hospital ◽  
Diagnostic Model ◽  
Validation Data ◽  
Acute Stemi ◽  
Percutaneous Coronary

BACKGROUND Intra-procedural hypotension weaken the benefit of primary percutaneous coronary intervention (PPCI) and worsens the prognosis of acute ST elevation myocardial infarction ( STEMI ) patients. OBJECTIVE The objective of our study was to develop and externally validate a diagnostic model of intra-procedural hypotension. METHODS Design:Multivariable logistic regression of a cohort of acute STEMI patients. Setting: Emergency department ward of a university hospital. Participants: Diagno The objective of our study was to develop and externally validate a diagnostic model of intra-procedural hypotension. stic model development: A total of 1239 acute STEMI patients who were consecutively treated with PPCI from November 2007 to December 2013. External validation: A total of 1294 acute STEMI patients who were treated with PPCI from January 2014 to June 2018. Outcomes: Intra-procedural hypotension. Intra-procedural hypotension was defined as pre-procedural systolic blood pressure (SBP) was > 90mmHg, intra-procedural SBP less than or equal to 90 mmHg persistent or transient. RESULTS Intra-procedural hypotension occurred in121 out of 1,239 participants (9.8%) in the development data set.The strongest predictors of intra-procedural hypotension were no-reflow(odds ratios (OR) 1.911; 95% confidence interval(CI), 1.177～3.102 ; P =.009), the culprit vessel was left anterior descending(OR.488;95% CI, .326～.732 ; P =.001), complete occlusion of culprit vessel(OR4.351;95% CI, 2.076～9.12 ; P<.001), using thrombus aspiration devices during operation(OR 1.793;95% CI, 1.058～3.039 ; P =.03) ,and history of diabetes (OR .589;95% CI, .353~.983 ; P =.042). We developed a diagnostic model of intra-procedural hypotension. The area under the receiver operating characteristic (ROC) curve (AUC)was .685 ± .022, 95% CI= .641 ~ .729 in the development set. We constructed a nomogram using the development database based on predictors of intra-procedural hypotension. Intra-procedural hypotension occurred in 123 out of 1,294 participants (9.5%)patients in the validation data set.The AUC was .718 ±.022, 95% CI= .674 ~ .761 in the validation set . Discrimination, calibration, and decision curve analysis were satisfactory. Date of approved by ethic committee: 2 September 2019. Date of data collection start: 10 September 2019. Numbers recruited as of submission of the manuscript:2,533. CONCLUSIONS We developed and externally validated a diagnostic model of intra-procedural hypotension during PPCI . We can use the formula or nomogram to predict intra-procedural hypotension. CLINICALTRIAL This study was registered with WHO International Clinical Trials Registry Platform (ICTRP) on 6 September 2019 (registration number:ChiCTR1900025706). http://www.chictr.org.cn/edit.aspx?pid=42913&htm=4.

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