Diagnostic studies

2020 ◽  
pp. 389-404
Author(s):  
Janet L. Peacock ◽  
Philip J. Peacock
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Clinical Practice ◽  
Receiver Operating Characteristic Curve ◽  
Characteristic Curve ◽  
Diagnostic Testing ◽  
Predictive Values ◽  
Post Test ◽  
Operating Characteristic Curve ◽  
Sensitivity Specificity

This chapter describes how statistical methods are used in diagnostic testing to obtain different measures of a test’s performance. It describes how to calculate sensitivity, specificity, and positive and negative predictive values, and shows the relevance of the pre- and post-test odds and the likelihood ratio in evaluating a test in clinical practice. The chapter also describes the receiver operating characteristic curve and shows how this links with logistic regression analysis. All methods are illustrated with examples.

scholarly journals Who Is at Risk for Postdischarge Nausea and Vomiting after Ambulatory Surgery?

2012 ◽  
Vol 117 (3) ◽  
pp. 475-486 ◽  
Author(s):  
Christian C. Apfel ◽  
Beverly K. Philip ◽  
Ozlem S. Cakmakkaya ◽  
Ashley Shilling ◽  
Yun-Ying Shi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Ambulatory Surgery ◽  
Characteristic Curve ◽  
Nausea And Vomiting ◽  
Validation Dataset ◽  
Postanesthesia Care Unit ◽  
Operating Characteristic Curve

Background About one in four patients suffers from postoperative nausea and vomiting. Fortunately, risk scores have been developed to better manage this outcome in hospitalized patients, but there is currently no risk score for postdischarge nausea and vomiting (PDNV) in ambulatory surgical patients. Methods We conducted a prospective multicenter study of 2,170 adults undergoing general anesthesia at ambulatory surgery centers in the United States from 2007 to 2008. PDNV was assessed from discharge until the end of the second postoperative day. Logistic regression analysis was applied to a development dataset and the area under the receiver operating characteristic curve was calculated in a validation dataset. Results The overall incidence of PDNV was 37%. Logistic regression analysis of the development dataset (n=1,913) identified five independent predictors (odds ratio; 95% CI): female gender (1.54; 1.22 to 1.94), age less than 50 yr (2.17; 1.75 to 2.69), history of nausea and/or vomiting after previous anesthesia (1.50; 1.19 to 1.88), opioid administration in the postanesthesia care unit (1.93; 1.53 to 2.43), and nausea in the postanesthesia care unit (3.14; 2.44-4.04). In the validation dataset (n=257), zero, one, two, three, four, and five of these factors were associated with a PDNV incidence of 7%, 20%, 28%, 53%, 60%, and 89%, respectively, and an area under the receiver operating characteristic curve of 0.72 (0.69 to 0.73). Conclusions PDNV affects a substantial number of patients after ambulatory surgery. We developed and validated a simplified risk score to identify patients who would benefit from long-acting prophylactic antiemetics at discharge from the ambulatory care center.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

scholarly journals A comparison of logistic regression models with alternative machine learning methods to predict the risk of in-hospital mortality in emergency medical admissions via external validation

2018 ◽  
Vol 26 (1) ◽  
pp. 34-44 ◽  
Author(s):  
Muhammad Faisal ◽  
Andy Scally ◽  
Robin Howes ◽  
Kevin Beatson ◽  
Donald Richardson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Receiver Operating Characteristic Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
External Validation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Operating Characteristic Curve

We compare the performance of logistic regression with several alternative machine learning methods to estimate the risk of death for patients following an emergency admission to hospital based on the patients’ first blood test results and physiological measurements using an external validation approach. We trained and tested each model using data from one hospital ( n = 24,696) and compared the performance of these models in data from another hospital ( n = 13,477). We used two performance measures – the calibration slope and area under the receiver operating characteristic curve. The logistic model performed reasonably well – calibration slope: 0.90, area under the receiver operating characteristic curve: 0.847 compared to the other machine learning methods. Given the complexity of choosing tuning parameters of these methods, the performance of logistic regression with transformations for in-hospital mortality prediction was competitive with the best performing alternative machine learning methods with no evidence of overfitting.

scholarly journals Interleukin-27 as a Diagnostic Biomarker for Patients with Sepsis: A Meta-Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ying Wang ◽  
Jingyi Zhao ◽  
Yinhui Yao ◽  
Dan Zhao ◽  
Shiquan Liu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Diagnostic Biomarker ◽  
Summary Receiver Operating Characteristic ◽  
Funnel Plot ◽  
Operating Characteristic Curve ◽  
Sensitivity Specificity

Background. The present study was aimed to investigate the value of blood interleukin-27 (IL-27) as a diagnostic biomarker of sepsis. Methods. We searched PubMed, EMBASE, the Cochrane Library, and the reference lists of relevant articles. All studies published up to October 21, 2020, which evaluated the accuracy of IL-27 levels for the diagnosis of sepsis were included. All the selected papers were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). We used a bivariate random effects model to estimate sensitivity, specificity, diagnostic odds ratios (DOR), and a summary receiver operating characteristic curve (SROC). Deeks’ funnel plot was used to illustrate the potential presence of publication bias. Results. This meta-analysis included seven articles. The pooled sensitivity, specificity, and DOR were 0.85 (95% CI, 0.72-0.93), 0.72 (95% CI, 0.42-0.90), and 15 (95% CI, 3-72), respectively. The area under the summary receiver operating characteristic curve was 0.88 (95% CI, 0.84-0.90). The pooled I 2 statistic was 96.05 for the sensitivity and 96.65 for the specificity in the heterogeneity analysis. Deeks’ funnel plot indicated no publication bias in this meta-analysis ( P = 0.07 ). Conclusions. The present results showed that IL-27 is a reliable diagnostic biomarker of sepsis, but it should be investigated in combination with other clinical tests and results.

scholarly journals Postoperative Ultrasound, Unassisted Maturation, and Subsequent Primary Patency of Arteriovenous Fistulas

2018 ◽  
Vol 13 (9) ◽  
pp. 1364-1372 ◽  
Author(s):  
Crystal A. Farrington ◽  
Michelle L. Robbin ◽  
Timmy Lee ◽  
Jill Barker-Finkel ◽  
Michael Allon
Keyword(s):  
Blood Flow ◽  
Logistic Regression ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariable Logistic Regression ◽  
Ultrasound Measurements ◽  
Operating Characteristic Curve

Background and objectivesPostoperative ultrasound is commonly used to assess arteriovenous fistula (AVF) maturation for hemodialysis, but its utility for predicting unassisted AVF maturation or primary AVF patency for hemodialysis has not been well defined. This study assessed the predictive value of postoperative AVF ultrasound measurements for these clinical AVF outcomes.Design, setting, participants, & measurementsWe queried a prospective vascular access database to identify 246 patients on catheter-dependent hemodialysis who underwent AVF creation between 2010 and 2016 and obtained a postoperative ultrasound within 90 days. Multivariable logistic regression was used to evaluate the association of clinical characteristics and postoperative ultrasound measurements with unassisted AVF maturation. A receiver operating characteristic curve estimated the predictive value of these factors for unassisted AVF maturation. Finally, multivariable survival analysis was used to identify factors associated with primary AVF patency in patients with unassisted AVF maturation.ResultsUnassisted AVF maturation occurred in 121 out of 246 patients (49%), assisted maturation in 55 patients (22%), and failure to mature in 70 patients (28%). Using multivariable logistic regression, unassisted AVF maturation was associated with AVF blood flow (odds ratio [OR], 1.30; 95% confidence interval [95% CI], 1.18 to 1.45 per 100 ml/min increase; P<0.001), forearm location (OR, 0.37; 95% CI, 0.08 to 1.78; P=0.21), presence of stenosis (OR, 0.45; 95% CI, 0.23 to 0.88; P=0.02); AVF depth (OR, 0.88; 95% CI, 0.77 to 1.00 per 1 mm increase; P=0.05), and AVF location interaction with depth (OR, 0.50; 95% CI, 0.28 to 0.84; P=0.02). The area under the receiver operating characteristic curve, using all these factors, was 0.84 (95% CI, 0.79 to 0.89; P<0.001). Primary AVF patency in patients with unassisted maturation was associated only with AVF diameter (hazard ratio, 0.84; 95% CI, 0.76 to 0.94 per 1 mm increase; P=0.002).ConclusionsUnassisted AVF maturation is predicted by AVF blood flow, location, depth, and stenosis. AVF patency after unassisted maturation is predicted only by the postoperative AVF diameter.

Discriminating Malignancy in Thyroid Nodules: The Nomogram Versus the Kwak and ACR TI-RADS

2020 ◽  
Vol 163 (6) ◽  
pp. 1156-1165
Author(s):  
Juan Xiao ◽  
Qiang Xiao ◽  
Wei Cong ◽  
Ting Li ◽  
Shouluan Ding ◽  
...  
Keyword(s):  
Thyroid Nodules ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Diagnostic Study ◽  
Diagnostic Efficiency ◽  
Training Set ◽  
Multivariable Logistic Regression Model ◽  
Predictive Values ◽  
Validation Set ◽  
Sensitivity Specificity

Objective To develop an easy-to-use nomogram for discrimination of malignant thyroid nodules and to compare diagnostic efficiency with the Kwak and American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Study Design Retrospective diagnostic study. Setting The Second Hospital of Shandong University. Subjects and Methods From March 2017 to April 2019, 792 patients with 1940 thyroid nodules were included into the training set; from May 2019 to December 2019, 174 patients with 389 nodules were included into the validation set. Multivariable logistic regression model was used to develop a nomogram for discriminating malignant nodules. To compare the diagnostic performance of the nomogram with the Kwak and ACR TI-RADS, the area under the receiver operating characteristic curve, sensitivity, specificity, and positive and negative predictive values were calculated. Results The nomogram consisted of 7 factors: composition, orientation, echogenicity, border, margin, extrathyroidal extension, and calcification. In the training set, for all nodules, the area under the curve (AUC) for the nomogram was 0.844, which was higher than the Kwak TI-RADS (0.826, P = .008) and the ACR TI-RADS (0.810, P < .001). For the 822 nodules >1 cm, the AUC of the nomogram was 0.891, which was higher than the Kwak TI-RADS (0.852, P < .001) and the ACR TI-RADS (0.853, P < .001). In the validation set, the AUC of the nomogram was also higher than the Kwak and ACR TI-RADS ( P < .05), each in the whole series and separately for nodules >1 or ≤1 cm. Conclusions When compared with the Kwak and ACR TI-RADS, the nomogram had a better performance in discriminating malignant thyroid nodules.

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