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.

Predictors of thermal increase in magnetic resonance–guided focused ultrasound treatment for essential tremor: histogram analysis of skull density ratio values for 1024 elements

2021 ◽  
pp. 1-6
Author(s):  
Ken Iijima ◽  
Hajime Yokota ◽  
Toshio Yamaguchi ◽  
Masayuki Nakano ◽  
Takahiro Ouchi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Operating Characteristic ◽  
Focused Ultrasound ◽  
Characteristic Curve ◽  
Density Ratio ◽  
Predictive Ability ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Operating Characteristic Curve

OBJECTIVE Sufficient thermal increase capable of generating thermocoagulation is indispensable for an effective clinical outcome in patients undergoing magnetic resonance–guided focused ultrasound (MRgFUS). The skull density ratio (SDR) is one of the most dominant predictors of thermal increase prior to treatment. However, users currently rely only on the average SDR value (SDRmean) as a screening criterion, although some patients with low SDRmean values can achieve sufficient thermal increase. The present study aimed to examine the numerical distribution of SDR values across 1024 elements to identify more precise predictors of thermal increase during MRgFUS. METHODS The authors retrospectively analyzed the correlations between the skull parameters and the maximum temperature achieved during unilateral ventral intermediate nucleus thalamotomy with MRgFUS in a cohort of 55 patients. In addition, the numerical distribution of SDR values was quantified across 1024 elements by using the skewness, kurtosis, entropy, and uniformity of the SDR histogram. Next, the authors evaluated the correlation between the aforementioned indices and a peak temperature > 55°C by using univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis was performed to compare the predictive ability of the indices. The diagnostic performance of significant factors was also assessed. RESULTS The SDR skewness (SDRskewness) was identified as a significant predictor of thermal increase in the univariate and multivariate logistic regression analyses (p < 0.001, p = 0.013). Moreover, the receiver operating characteristic curve analysis indicated that the SDRskewness exhibited a better predictive ability than the SDRmean, with area under the curve values of 0.847 and 0.784, respectively. CONCLUSIONS The SDRskewness is a more accurate predictor of thermal increase than the conventional SDRmean. The authors suggest setting the SDRskewness cutoff value to 0.68. SDRskewness may allow for the inclusion of treatable patients with essential tremor who would have been screened out based on the SDRmean exclusion criterion.

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 A clinical scoring system for pediatric hand-foot-mouth disease

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui Huang ◽  
Li Deng ◽  
Liping Jia ◽  
Runan Zhu
Keyword(s):  
Receiver Operating Characteristic Curve ◽  
Scoring System ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Test Set ◽  
Hand Foot Mouth Disease ◽  
Clinical Scoring ◽  
Exposure History ◽  
Operating Characteristic Curve

Abstract Background The aim of the present study was to develop a clinical scoring system for the diagnosis of hand-foot-mouth disease (HFMD) with improved accuracy. Methods A retrospective analysis was performed on standardized patient history and clinical examination data obtained from 1435 pediatric patients under the age of three years who presented with acute rash illness and underwent enterovirus nucleic acid detection. Patients were then divided into the HFMD (1094 patients) group or non-HFMD (341 patients) group based on a positive or a negative result from the assay, respectively. We then divided the data into a training set (1004 cases, 70%) and a test set (431 cases, 30%) using a random number method. Multivariate logistic regression was performed on 15 clinical variables (e.g. age, exposure history, number of rash spots in a single body region) to identify variables highly predictive of a positive diagnosis in the training set. Using the variables with high impact on the diagnostic accuracy, we generated a scoring system for predicting HFMD and subsequently evaluated this system in the test set by receiver operating characteristic curve (ROC curve). Results Using the logistic model, we identified seven clinical variables (age, exposure history, and rash density at specific regions of the body) to be included into the scoring system. The final scores ranged from − 5 to 24 (higher scores positively predicted HFMD diagnosis). Through our training set, a cutoff score of 7 resulted in a sensitivity of 0.76 and specificity of 0.68. The area under the receiver operating characteristic curve (AUC) was 0.804 (95% confidence interval [CI]: 0.773–0.835) (P < 0.001). Using the test set, we obtained an AUC of 0.76 (95% CI: 0.710–0.810) with a sensitivity of 0.76 and a specificity of 0.62. These results from the test set were consistent with those from the training set. Conclusions This study establishes an objective scoring system for the diagnosis of typical and atypical HFMD using measures accessible through routine clinical encounters. Due to the accuracy and sensitivity achieved by this scoring system, it can be employed as a rapid, low-cost method for establishing diagnoses in children with acute rash illness.

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 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.

scholarly journals IER-SICH Nomogram to Predict Symptomatic Intracerebral Hemorrhage After Thrombectomy for Stroke

Stroke ◽  
2019 ◽  
Vol 50 (4) ◽  
pp. 909-916 ◽  
Author(s):  
Manuel Cappellari ◽  
Salvatore Mangiafico ◽  
Valentina Saia ◽  
Giovanni Pracucci ◽  
Sergio Nappini ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Training Set ◽  
Test Set ◽  
Symptomatic Intracerebral Hemorrhage ◽  
Operating Characteristic Curve ◽  
Receiver Operating

Background and Purpose— As a reliable scoring system to detect the risk of symptomatic intracerebral hemorrhage after thrombectomy for ischemic stroke is not yet available, we developed a nomogram for predicting symptomatic intracerebral hemorrhage in patients with large vessel occlusion in the anterior circulation who received bridging of thrombectomy with intravenous thrombolysis (training set), and to validate the model by using a cohort of patients treated with direct thrombectomy (test set). Methods— We conducted a cohort study on prospectively collected data from 3714 patients enrolled in the IER (Italian Registry of Endovascular Stroke Treatment in Acute Stroke). Symptomatic intracerebral hemorrhage was defined as any type of intracerebral hemorrhage with increase of ≥4 National Institutes of Health Stroke Scale score points from baseline ≤24 hours or death. Based on multivariate logistic models, the nomogram was generated. We assessed the discriminative performance by using the area under the receiver operating characteristic curve. Results— National Institutes of Health Stroke Scale score, onset-to-end procedure time, age, unsuccessful recanalization, and Careggi collateral score composed the IER-SICH nomogram. After removing Careggi collateral score from the first model, a second model including Alberta Stroke Program Early CT Score was developed. The area under the receiver operating characteristic curve of the IER-SICH nomogram was 0.778 in the training set (n=492) and 0.709 in the test set (n=399). The area under the receiver operating characteristic curve of the second model was 0.733 in the training set (n=988) and 0.685 in the test set (n=779). Conclusions— The IER-SICH nomogram is the first model developed and validated for predicting symptomatic intracerebral hemorrhage after thrombectomy. It may provide indications on early identification of patients for more or less postprocedural intensive management.

Conventional administration and scoring procedures suppress the diagnostic accuracy of a performance-based test designed to assess balance ability in lower limb prosthesis users

2019 ◽  
Vol 43 (4) ◽  
pp. 402-408 ◽  
Author(s):  
Andrew Sawers ◽  
Brian J Hafner
Keyword(s):  
Diagnostic Accuracy ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Practice Effects ◽  
Balance Test ◽  
Walking Test ◽  
History Of ◽  
Operating Characteristic Curve

Background:Practice effects have been observed among performance-based clinical tests administered to prosthesis-users. Their impact on test applications remains unknown.Objective:To determine whether scoring a clinical balance test using conventional procedures that do not accommodate practice effects reduces its diagnostic accuracy relative to scoring it using recommended procedures that do accommodate practice effects.Study Design:Cross-sectional study.Methods:Narrowing Beam Walking Test data from 40 prosthesis users was scored using recommended methods (i.e. average of trials 3–5) and conventional methods applied to other tests (i.e. mean or best of trials 1–3). Area under the receiver operating characteristic curve for each method was compared to 0.50, to determine if it was better than chance at identifying prosthesis-users with a history of falls, and to 0.80, to determine if it surpassed a threshold recommended for diagnostic accuracy.Results:Receiver operating characteristic curve area decreased when the Narrowing Beam Walking Test was scored using conventional rather than recommended procedures. Furthermore, when scored using conventional procedures, the NBWT no longer discriminated between prosthesis-users with and without a history of falls with a probability greater than chance, or exceeded recommended diagnostic thresholds.Conclusion:Scoring the Narrowing Beam Walking Test using conventional procedures that do not accommodate practice effects decreased its diagnostic accuracy among prosthesis-users relative to recommended procedures. Conventional scoring procedures may limit the effectiveness of performance-based tests used to screen for fall risk in prosthesis-users because they do not mitigate practice effects. The influence of practice effects on other tests, and test applications (e.g. clinical evaluation and prediction), is warranted.Clinical relevanceScoring a clinical balance test using conventional procedures that do not mitigate practice effects reduced its diagnostic accuracy. Changing administration and scoring procedures to accommodate practice effects should be considered to improve the diagnostic accuracy of other performance-based balance tests.

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