scholarly journals A Nomogram for Predicting the Risk of Common Bile Duct Stones Based on Preoperative Laboratory Tests and Ultrasonography 

2021 ◽  
Author(s):  
Kai Wu ◽  
Dong Wang ◽  
Jiegao Zhu ◽  
Kun Liu ◽  
Hongwei Wu ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Bile Duct ◽  
Common Bile Duct ◽  
Predictive Factors ◽  
Roc Curve ◽  
Laboratory Tests ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Validation Set ◽  
Cbd Stone

Abstract Objective: The objective of this study was to determine the predictive factors for common bile duct (CBD) stone and establish a nomogram model based on the preoperative laboratory tests and imaging findings.Methods: A total of 1701 patients who underwent laparoscopic cholecystectomy (LC) combined with common bile duct exploration (CBDE) for suspected choledochlithiasis from November 2014 to October 2020 were eligible for this analysis. All patients were divided into the training set (from November 2014 to November 2019, n=1,453) and validation set (from November 2019 to October 2020, n=248) based on the admission time. The predictive factors for CBD stone were determined by the univariate and multivariate logistic regression model. A nomogram model for predicting the presence of CBD stone was developed based on significant variables, and receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. Results: The results of multivariate logistic regression analysis demonstrated that multiple gallbladder stones (OR: 7.463, 95%CI: 5.437-10.243, P<0.001), the maximal diameter of CBD stone measured by preoperative ultrasonography (OR for 0.8-1.5 cm: 4.756, 95%CI: 3.513-6.438, P<0.001; OR for 1.5-2.0 cm: 9.597, 95%CI: 4.621-19.931, P<0.001; OR for >2.0 cm: 24.473, 95%CI: 2.809-213.207, P<0.001), preoperative GGT level (OR for 90-225 U/L: 2.828, 95%CI: 1.898-4.214, P<0.001; OR for 225-450 U/L: 9.994, 95%CI: 4.668-21.403, P<0.001; OR for >450 U/L: 12.535, 95%CI: 4.452-35.292, P<0.001) and DB/TB ratio (OR: 394.329, 95%CI: 79.575-1954.064, P<0.001) were independent predictive factors for CBD stone. The nomogram model for predicting the presence of CBD stone was developed based on the above-mentioned variables. ROC curve showed that the C-index of the nomogram model for the training set and validation set was 0.875 (95% CI: 0.857-0.893) and 0.834 (95% CI: 0.784-0.883), which were better than that of MRCP for preoperative diagnosis of CBD stone. The calibration curve and DCA curve demonstrated that the nomogram model had a good clinical utility for predicting the presence of CBD stone .Conclusion: The nomogram based on preoperative laboratory tests and ultrasonography had an excellent predictive power for CBD stone, and it might provide useful information for making treatment strategies.

scholarly journals A Nomogram Based on Clinical Characteristics for Predicting Multiple Organ Dysfunction Syndrome(MODS) Following Multiple Trauma Patients

2020 ◽  
Author(s):  
ZhenJun Miao ◽  
Faxing Wei ◽  
Feng Zhou
Keyword(s):  
Logistic Regression ◽  
Multiple Trauma ◽  
Clinical Characteristics ◽  
Area Under The Curve ◽  
Trauma Patients ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Lemeshow Test ◽  
Validation Set ◽  
Multiple Trauma Patients

Abstract BackgroundMultiple organ dysfunction syndrome (MODS) is the one of common complications,and the leading cause of late mortality in multiple trauma patients.The present study aims to develop and validate a nomogram based on clinical characteristics in order to identify the patients with multiple trauma who were at risk of developing MODS.MethodsAn retrospective cohort study was performed with data from January 2011 to December 2019,totally 770 patients with multiple trauma were enrolled in our study.They were randomly categorized into training set (n=514) and validation set (n=256).The univariate and multivariate logistic regression analyses were used to screen the predictors for multiple trauma patients who were at risk of developing MODS from training set data.Then we established a nomogram based on these above predictors.The discriminative capacity was assessed by receiver operating characteristic (ROC) curve area under the curve (AUC), and the predictive precision was depicted by calibration plot.The Hosmer-Lemeshow test was used to evaluate the the model’s goodness of fit.ResultsOur study showed that age,ISS,hemorrhagic shock,heart rate,blood glucose,D-dimer and APTT were independent risk factors for MODS in patients with multiple trauma by multivariate logistic regression analysis.A nomogram was established on basis of these above risk factors.The area under the curve (AUC) was 0.868 (95% confidence interval [CI]:0.829-0.908) in the training set and 0.884 (95% confidence interval [CI]:0.833-0.935) in the validation set.The Hosmer-Lemeshow test has a p value of 0.227 in training set and 0.554 in validation set respectively,which confirm the model’s goodness of fit.Calibration plot showed that the predicted and actual incidence of MODS probability were fitted well on both internal and external validations.ConclusionsThe present nomogram had a well predictive precision and discrimination capacity,which can facilitate improved screening and early identification of multiple trauma patients who were at high risk of developing MODS.

scholarly journals Preoperative Albumin-to-Fibrinogen Ratio Predicts Postoperative Pneumonia in Patients with Esophageal Squamous Cell Carcinoma: A Retrospective Study

2020 ◽  
Author(s):  
Peng Zhang ◽  
Song Zhao
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Logistic Regression ◽  
Roc Curve ◽  
Laboratory Tests ◽  
Curve Analysis ◽  
Postoperative Pneumonia ◽  
Function Parameter ◽  
Multivariate Logistic Regression ◽  
Roc Curve Analysis

Abstract Background: Postoperative pneumonia is the most common postoperative complication in patients with esophageal cancer. Prediction of postoperative pneumonia by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin, fibrinogen, albumin-to-fibrinogen ratio(AFR) , and other preoperative laboratory tests and postoperative pneumonia in patients with esophageal cancer after esophagectomy.Methods: Retrospective analysis was performed on 177 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from December 2018 to December 2019.Postoperative pneumonia was defined according to the revised Uniform Pneumonia Score(rUPS).Patients were divided into pneumonia and non-pneumonia groups for comparison of baseline data, perioperative indicators, and laboratory examination data.(Receiver operating characteristic)ROC curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR and other laboratory tests data. Univariate and multivariate logistic regression analyses were used to assess the risk factors for postoperative pneumoniaResults: Of the 177 patients, 32 (18%) developed postoperative pneumonia. The AUC value predicted by AFR using ROC curve analysis was 0.767, 65.6% sensitivity and 83.4% specificity. Multivariate logistic regression analysis showed that albumin (P=0.013), creatinine (P=0.01), and AFR (P=0.016) were independent risk factors for postoperative pneumonia.Conclusion: Preoperative AFR can effectively predict the occurrence of postoperative pneumonia in patients with esophageal cancer

scholarly journals Preoperative Evaluation of Perineural Invasion in Cervical Cancer: Development and Independent Validation of a Novel Predictive Nomogram

2021 ◽  
Vol 11 ◽  
Author(s):  
Ting Wan ◽  
Guangyao Cai ◽  
Shangbin Gao ◽  
Yanling Feng ◽  
He Huang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Logistic Regression ◽  
Cancer Patients ◽  
Perineural Invasion ◽  
Preoperative Evaluation ◽  
False Negative ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Surgical Strategies ◽  
Validation Set

BackgroundPerineural invasion (PNI) is associated with a poor prognosis for cervical cancer and influences surgical strategies. However, a preoperative evaluation that can determine PNI in cervical cancer patients is lacking.MethodsAfter 1:1 propensity score matching, 162 cervical cancer patients with PNI and 162 cervical cancer patients without PNI were included in the training set. Forty-nine eligible patients were enrolled in the validation set. The PNI-positive and PNI-negative groups were compared. Multivariate logistic regression was performed to build the PNI prediction nomogram.ResultsAge [odds ratio (OR), 1.028; 95% confidence interval (CI), 0.999–1.058], adenocarcinoma (OR, 1.169; 95% CI, 0.675–2.028), tumor size (OR, 1.216; 95% CI, 0.927–1.607), neoadjuvant chemotherapy (OR, 0.544; 95% CI, 0.269–1.083), lymph node enlargement (OR, 1.953; 95% CI, 1.086–3.550), deep stromal invasion (OR, 1.639; 95% CI, 0.977–2.742), and full-layer invasion (OR, 5.119; 95% CI, 2.788–9.799) were integrated in the PNI prediction nomogram based on multivariate logistic regression. The PNI prediction nomogram exhibited satisfactory performance, with areas under the curve of 0.763 (95% CI, 0.712–0.815) for the training set and 0.860 (95% CI, 0.758–0.961) for the validation set. Moreover, after reviewing the pathological slides of patients in the validation set, four patients initially diagnosed as PNI-negative were recognized as PNI-positive. All these four patients with false-negative PNI were correctly predicted to be PNI-positive (predicted p &gt; 0.5) by the nomogram, which improved the PNI detection rate.ConclusionThe nomogram has potential to assist clinicians when evaluating the PNI status, reduce misdiagnosis, and optimize surgical strategies for patients with cervical cancer.

scholarly journals Development and Validation of a Nomogram Based on Noninvasive Liver Reserve and Fibrosis (PALBI and FIB-4) Model to Predict Posthepatectomy Liver Failure Grade B-C in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenhui Zhong ◽  
Feng Zhang ◽  
Kaijun Huang ◽  
Yiping Zou ◽  
Yubin Liu
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Logistic Regression ◽  
Liver Failure ◽  
Roc Curves ◽  
Postoperative Liver Failure ◽  
Multivariate Logistic Regression ◽  
Training Set ◽  
Independent Risk Factors ◽  
Validation Set

Hepatectomy is currently one of the most effective treatments for hepatocellular carcinoma (HCC). However, postoperative liver failure (PHLF) is a serious complication and the leading cause of mortality in patients with HCC after hepatectomy. This study attempted to develop a novel nomogram based on noninvasive liver reserve and fibrosis models, platelet-albumin-bilirubin grade (PALBI) and fibrosis-4 index (FIB-4), able to predict PHLF grade B-C. This was a single-centre retrospective study of 574 patients with HCC undergoing hepatectomy between 2014 and 2018. The independent risk factors of PHLF were screened using univariate and multivariate logistic regression analyses. Multivariate logistic regression was performed using the training set, and the nomogram was developed and visualised. The utility of the model was evaluated in a validation set using the receiver operating characteristic (ROC) curve. A total of 574 HCC patients were included (383 in the training set and 191 for the validation set) and included PHLF grade B-C complications of 14.8, 15.4, and 13.6%, respectively. Overall, cirrhosis ( P < 0.026 , OR = 2.296, 95% confidence interval (CI) 1.1.02–4.786), major hepatectomy ( P = 0.031 , OR = 2.211, 95% CI 1.077–4.542), ascites ( P = 0.014 , OR = 3.588, 95% 1.299–9.913), intraoperative blood loss ( P < 0.001 , OR = 4.683, 95% CI 2.281–9.616), PALBI score >−2.53 (, OR = 3.609, 95% CI 1.486–8.764), and FIB-4 score ≥1.45 ( P < 0.001 , OR = 5.267, 95% CI 2.077–13.351) were identified as independent risk factors associated with PHLF grade B-C in the training set. The areas under the ROC curves for the nomogram model in predicting PHLF grade B-C were significant for both the training and validation sets (0.832 vs 0.803). The proposed nomogram predicted PHLF grade B-C among patients with HCC with a better prognostic accuracy than other currently available fibrosis and noninvasive liver reserve models.

scholarly journals Predictive Factors for the Post Embolization Fever after TACE for Hepatocellular Carcinoma Patients: A Single-Center Study in China.

2020 ◽  
Author(s):  
Dan TIAN ◽  
Xiaoyu LI ◽  
Qianzhou LV
Keyword(s):  
Logistic Regression ◽  
Predictive Factors ◽  
Roc Curve ◽  
Validation Cohort ◽  
Laboratory Data ◽  
Multivariate Logistic Regression ◽  
Discriminative Ability ◽  
Derivation Cohort ◽  
Embolism Syndrome ◽  
Multivariate Logistic Model

Abstract Background Fever is one of the main symptoms for post-embolism syndrome (PES). This study aimed to determine and validate a model to predict fever after transcatheter arterial chemoembolization (TACE) in patients receiving platinum as the main regimen. Materials and Methods Clinical data of HCC patients who underwent TACE with platinum was retrospectively collected in the Fudan University Zhongshan Hospital during January 2016 to January 2018. According to post-TACE medical records, patients were divided into fever group and non-fever group. Predictive factors were selected by multivariate logistic regression. The receiver operating characteristic (ROC) curve were then performed to detect accuracy and discriminative ability of these factors using the derivation cohort and an independent validation cohort.Results Fevers were detected in 44 of 252 patients. Demographics, laboratory data were statistically similar within fever group and non-fever group. Strongest predictors identified in multivariate logistic regression included Iopiodol emulsion dose (OR, 1.081; 95%CI, 1.006-1.162), number of hepatoprotectants (OR, 0.619; 95%CI, 0.419-0.914), K+ (OR, 2.992; 95%CI, 1.225-7.308), and albumin-bilirubin (ALBI) grade (OR, 2.249; 95%CI, 1.040-4.862). Furthermore, the area under the ROC curve of derivation cohort and validation cohort were 0.798 and 0.874 respectively, which indicated comparative stability and discriminative ability of this model. Conclusions Iopiodol emulsion dose, number of hepatoprotectants, K+, and ALBI grade are strong predictors for PEF. The multivariate logistic model of these factors shows a discriminative ability to predict PEF in the validation cohort.

scholarly journals CD8+ T cells predicted the conversion of common covid-19 to severe

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Li Liu ◽  
Zhiyong Chen ◽  
Yingrong Du ◽  
Jianpeng Gao ◽  
Junyi Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
T Cells ◽  
Logistic Regression ◽  
Prediction Model ◽  
Roc Curve ◽  
Multiple Logistic Regression Analysis ◽  
Laboratory Data ◽  
Training Set ◽  
Area Under Roc Curve ◽  
Validation Set

AbstractTo evaluate the predictive effect of T-lymphoid subsets on the conversion of common covid-19 to severe. The laboratory data were collected retrospectively from common covid-19 patients in the First People's Hospital of Zaoyang, Hubei Province, China and the Third People's Hospital of Kunming, Yunnan Province, China, between January 20, 2020 and March 15, 2020 and divided into training set and validation set. Univariate and multivariate logistic regression was performed to investigate the risk factors for the conversion of common covid-19 to severe in the training set, the prediction model was established and verified externally in the validation set. 60 (14.71%) of 408 patients with common covid-19 became severe in 6–10 days after diagnosis. Univariate and multiple logistic regression analysis revealed that lactate (P = 0.042, OR = 1097.983, 95% CI 1.303, 924,798.262) and CD8+ T cells (P = 0.010, OR = 0.903, 95% CI 0.835, 0.975) were independent risk factors for general type patients to turn to severe type. The area under ROC curve of lactate and CD8+ T cells was 0.754 (0.581, 0.928) and 0.842 (0.713, 0.970), respectively. The actual observation value was highly consistent with the prediction model value in curve fitting. The established prediction model was verified in 78 COVID-19 patients in the verification set, the area under the ROC curve was 0.906 (0.861, 0.981), and the calibration curve was consistent. CD8+ T cells, as an independent risk factor, could predict the transition from common covid-19 to severe.

Comparison of BP Neural Network Model and Logistic Regression in the Analysis of Influencing Factors of Violence in Hospitals

2011 ◽  
Vol 50-51 ◽  
pp. 964-967 ◽  
Author(s):  
Jian Hui Wu ◽  
Guo Li Wang ◽  
Xiao Ming Li ◽  
Su Feng Yin
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Roc Curve ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Training Set ◽  
Hospital Violence ◽  
Bp Neural Network Model

Collecting violence cases for medical personnel from different levels of the hospital of Tangshan, we create a model for influential factors of hospital violence, and respectively with BP Nerve Network and logistic regression, by sensitivity, specificity and ROC curve, it is compared with two methods,in order to discovering effective analytical method . The training set and testing set sensitivity of BP Neural Network Model are 0.916 and 0.935,and the specificity is 0.447 and 0.526,the area of ROC curve is 0.769 and 0.785；for logistic regression Model ,for its the training set and testing set, sensitivity is 0.907 and 0.925, the specificity is 0.377 and 0.404, the area of ROC curve is 0.663and0.666. In hospital violence influencing factors, the forecast capability of BP Neural Network Model is better than logistic regression Model and it has farther extend value.

scholarly journals Ectopic eruption of the maxillary second molar: Predictive factors

2017 ◽  
Vol 87 (4) ◽  
pp. 583-589 ◽  
Author(s):  
Soonshin Hwang ◽  
Yoon Jeong Choi ◽  
Ji Yeon Lee ◽  
Chooryung Chung ◽  
Kyung-Ho Kim
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Predictive Factors ◽  
Multivariate Logistic Regression Analysis ◽  
Key Factors ◽  
Multivariate Logistic Regression ◽  
Factors Associated ◽  
Ectopic Eruption ◽  
Maxillary Tuberosity

ABSTRACT Objective: The purpose of this study was to investigate the diagnostic aspects, contributing conditions, and predictive key factors associated with ectopic eruption of maxillary second molars. Material and Methods: This retrospective study evaluated the study models, lateral cephalographs, and panoramic radiographs of 40 adult subjects (20 men, 20 women) with bilateral ectopic eruption and 40 subjects (20 men, 20 women) with normal eruption of the maxillary second molars. Studied variables were analyzed statistically by independent t-tests, univariate and multivariate logistic regression analysis, followed by receiver-operating characteristic analysis. Results: Tooth widths of bilateral lateral incisors, canines, and premolars were wider in the ectopic group, which resulted in greater arch lengths. The ANB angle and maxillary tuberosity distance (PTV-M1, PTV-M2) were smaller in the ectopic group. The long axes of the maxillary molars showed significant distal inclination in the ectopic group. The multivariate logistic regression analysis showed that three key factors—arch length, ANB angle, and PTV-M1 distance—were significantly associated with ectopic eruption of the second molars. The area under the curve (AUC) was the largest for the combination of the three key factors with an AUC greater than 0.75. PTV-M1 alone was the single factor that showed the strongest association with ectopic eruption (AUC = 0.7363). Conclusions: An increase in arch length, decrease in ANB angle, and decrease in maxillary tuberosity distance to the distal aspect of the maxillary first molar (PTV-M1) were the most predictive factors associated with ectopic eruption of maxillary second molars.

Endoscopic third ventriculostomy in the treatment of childhood hydrocephalus in Uganda: report of a scoring system that predicts success

2010 ◽  
Vol 5 (2) ◽  
pp. 143-148 ◽  
Author(s):  
Benjamin C. Warf ◽  
John Mugamba ◽  
Abhaya V. Kulkarni
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Endoscopic Third Ventriculostomy ◽  
Successful Outcome ◽  
Third Ventriculostomy ◽  
P Value ◽  
Training Set ◽  
Childhood Hydrocephalus ◽  
Validation Set

Object In Uganda, childhood hydrocephalus is common and difficult to treat. In some children, endoscopic third ventriculostomy (ETV) can be successful and avoid dependence on a shunt. This can be especially beneficial in Uganda, because of the high risk of infection and long-term failure associated with shunting. Therefore, the authors developed and validated a model to predict the chances of ETV success, taking into account the unique characteristics of a large sub-Saharan African population. Methods All children presenting with hydrocephalus at CURE Children's Hospital of Uganda (CCHU) between 2001 and 2007 were offered ETV as first-line treatment and were prospectively followed up. A multivariable logistic regression model was built using ETV success at 6 months as the outcome. The model was derived on 70% of the sample (training set) and validated on the remaining 30% (validation set). Results Endoscopic third ventriculostomy was attempted in 1406 patients. Of these, 427 were lost to follow-up prior to 6 months. In the remaining 979 patients, the ETV was aborted in 281 due to poor anatomy/visibility and in 310 the ETV failed during the first 6 months. Therefore, a total of 388 of 979 (39.6% and [55.6% of completed ETVs]) procedures were successful at 6 months. The mean age at ETV was 12.6 months, and 57.8% of cases were postinfectious in origin. The authors' logistic regression model contained the following significant variables: patient age at ETV, cause of hydrocephalus, and whether choroid plexus cauterization was performed. In the training set (676 patients) and validation set (303 patients), the model was able to accurately predict the probability of successful ETV (Hosmer-Lemeshow p value > 0.60 and C statistic > 0.70). The authors developed the simplified CCHU ETV Success Score that can be used in the field to predict the probability of ETV success. Conclusions The authors' model will allow clinicians to accurately identify children with a good chance of successful outcome with ETV, taking into account the unique characteristics and circumstances of the Ugandan population.

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