A Nomogram for Predicting the Risk of Tuberculosis Infection

Abstract Background: Tuberculosis (TB) has become one of the main causes of deaths worldwide. Because of certain conditions prevent the early TB diagnosis and treatment to some extent. This study aimed to develop a tuberculosis (TB) infection risk model and validate the ability of nomogram to predict risk for TB infection in a Chinese population.Methods: A prediction model based on the training dataset of 272 patients was established. Minimum absolute shrinkage and selection operator regression model were adopted to optimize the feature selection of the TB infection risk model. Using multivariate logistic regression analysis, a predictive model combining the features selected in the minimum absolute shrinkage and the selected operator regression model was constructed. The ability of this predictive model to discriminate and calibrate TB infection risk and its utility in clinical settings were assessed via concordance index (C-index), calibration plot, area under time-dependent receiver operating characteristic curve (AUC), and decision curve analysis (DCA). The clinical practicality of nomogram was evaluated via net reclassification index (NRI) and integrated discrimination improvement (IDI). Bootstrapping validation allowed internal validation.Results: According to this predictive nomogram, the main predictors of TB infection risk were gender, age, smoking history, fever, hemoptysis, fatigue, emaciation, CD8, CD4/CD8, ESR, CRP, and abnormal liver function. The model exhibited superior risk calibration and discrimination with a C-index of 0.737 (95% CI: 0.685–0.789). The internal validation reached a C-index value of 0.688. The predictive model was able to produce an AUC of 0.729 (95% CI: 0.677–0.781). Analysis of the decision curve revealed the TB infection probability nomogram manifested its clinical usefulness on the condition that intervention was decided at the TB probability threshold of 13%. Moreover, results demonstrated that nomogram could be utilized as an effective prognostic tool according to NRI and IDI.Conclusion: The new TB probability nomogram for predicting TB infection risk developed herein that combines various factors, such gender, age, smoking history, fever, hemoptysis, fatigue, emaciation, CD8, CD4/CD8, ESR, CRP, and abnormal liver function is convenient and useful in predicting individual TB risks among patients.

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Bootstrap internal validation command for predictive logistic regression models

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x211025836 ◽

2021 ◽

Vol 21 (2) ◽

pp. 498-509

Author(s):

B. M. Fernandez-Felix ◽

E. García-Esquinas ◽

A. Muriel ◽

A. Royuela ◽

J. Zamora

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Predictive Model ◽

Performance Measures ◽

Predictive Models ◽

Regression Models ◽

Logistic Regression Model ◽

Model Performance ◽

Internal Validation ◽

Logistic Regression Models

Overfitting is a common problem in the development of predictive models. It leads to an optimistic estimation of apparent model performance. Internal validation using bootstrapping techniques allows one to quantify the optimism of a predictive model and provide a more realistic estimate of its performance measures. Our objective is to build an easy-to-use command, bsvalidation, aimed to perform a bootstrap internal validation of a logistic regression model.

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Predictive Model of Cerebrospinal Fluid Leakage After Posterior Circumferential Decompression for Thoracic Ossification of Posterior Longitudinal Ligament

10.21203/rs.3.rs-580487/v1 ◽

2021 ◽

Author(s):

Jun Zhong ◽

Bingtao Wen ◽

Zhongqiang Chen

Keyword(s):

Cerebrospinal Fluid ◽

Regression Model ◽

Predictive Model ◽

Roc Curve ◽

Posterior Longitudinal Ligament ◽

Thoracic Segment ◽

Smoking History ◽

Cerebrospinal Fluid Leakage ◽

Fluid Leakage ◽

The Mean

Abstract Background: Cerebrospinal fluid leakage(CSFL) is one of the most common complications after posterior transarticular osteotomy and circumferential decompression for thoracic ossification of posterior longitudinal ligament(OPLL). It is of great usefulness If the cerebrospinal fluid leakage can be predicted preoperatively. These predictors help to attract the attention of the surgeon in advance and warn the patient. Therefore, the aim of this study is to find out the factors that can predict the CSFL prior to operation and try to build a predictive model.Methods: A total of 61 patients with thoracic OPLL underwent posterior transarticular osteotomy and circumferential decompression from August 2015 to June 2020 in our hospital were included in this study, including 29 males and 32 females. The patients were divided into CSLF group and non-CSFL group according to whether they suffered cerebrospinal fluid leakage. Univariate analysis was used to identify possible predictors in Demographic characteristics, clinical and radiological data. A logistic regression model was developed by multivariate analyses to predict probability of CSFL. Model validation was done using the receiver operating characteristic(ROC) curve.Results: The incidence of CSFL was 31.1%, including 7 males and 12 females, with an average age of 49.8 ±11.4 years. The mean drainage indwelling time in CSFL group was 5.6±1.0 days, which was significantly higher than that in non-CSFL group (4.2±1.3 days, P < 0.001). The mean length of hospital stay was 16.3±6.3 days, slightly higher than that of the non-CSF group (15.8±6.7 days), but there was no statistical difference (P=0.77). Among them, 12 patients (63.2%) suffered low intracranial pressure manifested as headache; 1 patient (5.3%) had cerebrospinal fluid outflow from the incision, and the wound healed successfully after debridement.1 patient (5.3%) was re-admitted to the hospital due to fever after 3 weeks, considering deep wound effusion and pleural effusion. The wound effusion was found to be cured after 2 weeks of anti-infective treatment. Univariate regression analysis showed statistical differences (P<0.05) in smoking history, segment of circumferential decompression, combined with ossification of the ligamentum flavum (OLF), number of laminectomy, occupying ratio and OPLL base ratio. Multivariate regression model showed smoking history (OR=30.1, P=0.003), the upper thoracic segment (OR= 188.0, P= 0.002), the middle thoracic segment (OR= 57.4, P= 0.005) and OPLL base ratio (OR=1.3, P=0.007) were the predictors of CSFL. The ROC curve was in the upper left corner (area under the curve = 0.955, 95% CI 0.91-1.00, P< 0.001), indicating good predictability of the model.Conclusion: The predictive model established in this study has a high predictive effect. When the patients with thoracic OPLL have smoking history or the segment of circumferential decompression is located in the upper or middle thoracic spine or the OPLL has a wide base, the operator should be highly alert to the possibility of postoperative CSFL and warn the patient before surgery. Evidence level: level II-2

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P-O06 A scoring system to support surgical decision-making for cardial submucosal tumors

British Journal of Surgery ◽

10.1093/bjs/znab430.116 ◽

2021 ◽

Vol 108 (Supplement_9) ◽

Author(s):

Zi-Han Geng ◽

Yan Zhu ◽

Wei-Feng Chen ◽

Quan-Lin Li ◽

Ping-Hong Zhou

Keyword(s):

Regression Model ◽

Endoscopic Resection ◽

Scoring System ◽

Goodness Of Fit ◽

Validation Cohort ◽

Multivariate Logistic Regression Analysis ◽

Muscularis Propria ◽

Internal Validation ◽

Training Cohort ◽

Submucosal Tumors

Abstract Background Submucosal tunneling endoscopic resection (STER) and non-tunneling techniques are two alternative options for the treatment of cardial submucosal tumors (SMTs). We aimed to establish a regression model and develop a simple scoring system to help clinicians make surgical decisions for cardial submucosal tumors. Methods A total of 246 patients who suffered cardial SMTs and received endoscopic resection were included in this study. All of them were randomized into the training cohort (n = 147) or internal validation cohort (n = 99). Then, the scoring system was proposed based on multivariate logistic regression analysis in the training cohort and assessed in the validation cohort. Results Of 246 patients, 97 were treated with STER, and the others with non-tunneling endoscopic resection. In the training stage, four factors were weighted with points based on the β coefficient from the regression model, including irregular morphology (-2 points), ulcer (2 points), the direction of the gastroscope (-2 points for reversing direction and 1 point for entering direction), and originating from the muscularis propria (-2 points). The patients were categorized into low-score (< -4), medium-score (-4 - -3) and high-score (> -3) groups, and those with low scores were more likely to be treated with STER. Our score model performed satisfying discriminatory power in internal validation (Areas under the receiver-operator characteristic curve (AUC), 0.829; 95% confidence interval (CI), 0.694-0.964) and goodness-of-fit in the Hosmer-Lemeshow test (P = .4721). Conclusions This scoring system could provide clinicians the references for making decisions about the treatment of cardial submucosal tumors.

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Predicting Risk of Insulin Resistance in a Chinese Population with Polycystic Ovary Syndrome: Designing and Testing a New Predictive Nomogram

BioMed Research International ◽

10.1155/2020/8031497 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Feng Jiang ◽

Ke Wei ◽

Wenjun Lyu ◽

Chuyan Wu

Keyword(s):

Insulin Resistance ◽

Polycystic Ovary Syndrome ◽

Regression Model ◽

Chinese Population ◽

Risk Model ◽

Polycystic Ovary ◽

Training Dataset ◽

Multivariable Logistic Regression Analysis ◽

Ovary Syndrome ◽

Selection Operator

Background. This research is aimed at establishing and internally validating the risk nomogram of insulin resistance (IR) in a Chinese population of patients with polycystic ovary syndrome (PCOS). Methods. We developed a predictive model based on a training dataset of 145 PCOS patients, and data were collected between March 2018 and May 2019. The least absolute shrinkage and selection operator regression model was used to optimize function selection for the insulin resistance risk model. Multivariable logistic regression analysis was used to construct a prediction model integrating the function selected in the regression model of the least absolute shrinkage and selection operator. The predicting model’s characteristics of prejudice, disease, and lifestyle were analyzed using the C-index, the calibration diagram, and the study of the decision curve. External validity was assessed using the validation of bootstrapping. Results. Predictors contained in the prediction nomogram included occupation, disease durations (years), BMI, current use of metformin, and activities. With a C-index of 0.739 (95 percent confidence interval: 0.644–0.830), the model showed good differentiation and proper calibration. In the interval validation, a high C-index value of 0.681 could still be achieved. Examination of the decision curve found that the IR nomogram was clinically useful when the intervention was determined at the 11 percent IR potential threshold. Conclusion. This novel IR nomogram incorporates occupation, disease durations (years), BMI, current use of metformin, and activities. This nomogram could be used to promote the estimation of individual IR risk in patients with PCOS.

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Development of a Predictive Model of Tuberculosis Transmission among Household Contacts

Canadian Journal of Infectious Diseases and Medical Microbiology ◽

10.1155/2019/5214124 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Saibin Wang

Keyword(s):

Predictive Model ◽

Secondary Analysis ◽

Multivariate Logistic Regression Analysis ◽

Lasso Regression ◽

Internal Validation ◽

Tuberculosis Transmission ◽

Household Contacts ◽

Study Sites ◽

Individualized Prediction ◽

Index Cases

Background. Household contacts of patients with tuberculosis (TB) are at great risk of TB infection. The aim of this study was to develop a predictive model of TB transmission among household contacts. Method. This was a secondary analysis of data from a prospective cohort study, in which a total of 700 TB patients and 3417 household contacts were enrolled between 2010 and 2013 at two study sites in Peru. The incidence of secondary TB cases among household contacts of index cases was recorded. The LASSO regression method was used to reduce the data dimension and to filter variables. Multivariate logistic regression analysis was applied to develop the predictive model, and internal validation was performed. A nomogram was constructed to display the model, and the AUC was calculated. The calibration curve and decision curve analysis (DCA) were also evaluated. Results. The incidence of TB disease among the contacts of index cases was 4.4% (149/3417). Ten variables (gender, age, TB history, diabetes, HIV, index patient’s drug resistance, socioeconomic status, spoligotypes, and the index-contact share sleeping room status) filtered through the LASSO regression technique were finally included in the predictive model. The model showed good discriminatory ability, with an AUC value of 0.761 (95% CI, 0.723–0.800) for the derivation and 0.759 (95% CI, 0.717–0.796) for the internal validation. The predictive model showed good calibration, and the DCA demonstrated that the model was clinically useful. Conclusion. A predictive model was developed that incorporates characteristics of both the index patients and the contacts, which may be of great value for the individualized prediction of TB transmission among household contacts.

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A novel nomogram to predict the risk of anastomotic leakage in patients after oesophagectomy

10.21203/rs.2.12279/v1 ◽

2019 ◽

Author(s):

Chengya Huang ◽

Haixia Yao ◽

Qi Huang ◽

Huijie Lu ◽

Meiying Xu ◽

...

Keyword(s):

Prediction Model ◽

Anastomotic Leakage ◽

Cardiac Arrhythmia ◽

Risk Model ◽

Discrimination Performance ◽

Smoking History ◽

Multivariable Logistic Regression Analysis ◽

Consecutive Series ◽

Lasso Regression ◽

Internal Validation

Abstract Background: Anastomotic leakage is a dangerous postoperative complication of oesophageal surgery. The present study aimed to develop a simple and practical scoring system to predict the risk of anastomotic leakage after oesophageal resection. Methods: A consecutive series of 330 patients who underwent oesophageal cancer surgery from January 2016 to January 2018 in the Shanghai Chest Hospital were included to develop a prediction model. Anastomotic leakage was evaluated using oesophagography, computed tomography, or flexible endoscopy. A LASSO regression based on a generalized linear model was used to select the variables for the anastomotic leakage risk model, while avoid overfitting. Multivariable logistic regression analysis was applied to build forest plots and a prediction model. The C-index or the area under the curve was used to judge the discrimination. The calibration plots verified the consistency. Internal validation of the model was conducted. The clinical usefulness and threshold screening of the model were evaluated by decision curve analysis. Results: The factors included in the predictive nomogram included gender, diabetes history, anastomotic mode, reconstruction route, smoking history, CRP level and presence of cardiac arrhythmia. The model displayed a discrimination performance with a C-index of 0.690 (95% confidence interval: 0.620-0.760) and good calibration. A C-index value of 0.664 could still be reached during the internal validation. The calibration curve showed good agreement between the actual observations and the predicted results. Conclusion: The present prediction model, which requires only seven variables including gender, diabetes history, anastomotic mode, reconstruction route, smoking history, CRP level and presence of cardiac arrhythmia, may be useful in predicting anastomotic leakage in patients after oesophagectomy.

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Predicting bleeding risk in a Chinese immune thrombocytopenia (ITP) population: development and assessment of a new predictive nomogram

Scientific Reports ◽

10.1038/s41598-020-72275-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Mingjing Wang ◽

Weiyi Liu ◽

Yonggang Xu ◽

Hongzhi Wang ◽

Xiaoqing Guo ◽

...

Keyword(s):

Feature Selection ◽

Predictive Model ◽

Risk Model ◽

Validation Cohort ◽

Multivariate Logistic Regression Analysis ◽

Bleeding Risk ◽

Inflammatory Factors ◽

Calibration Plot ◽

Ifn Γ ◽

Selection Operator

Abstract The aim of this study was to develop a model that could be used to forecast the bleeding risk of ITP based on proinflammatory and anti-inflammatory factors. One hundred ITP patients were recruited to build a new predictive nomogram, another eighty-eight ITP patients were enrolled as validation cohort, and data were collected from January 2016 to January 2019. Four demographic characteristics and fifteen clinical characteristics were taken into account. Eleven cytokines (IFN-γ, IL-1, IL-4, IL-6, IL-8, IL-10, IL-17A, IL-22, IL-23, TNF-α and TGF-β) were used to study and the levels of them were detected by using a cytometric bead array (CBA) human inflammation kit. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariate logistic regression analysis was applied to build a new predictive nomogram based on the results of the least absolute shrinkage and selection operator regress ion model. The application of C-index, ROC curve, calibration plot, and decision curve analyses were used to assess the discrimination, calibration, and clinical practicability of the predictive model. Bootstrapping validation was used for testing and verifying the predictive model. After feature selection, cytokines IL-1, IL-6, IL-8, IL-23 and TGF-β were excluded, cytokines IFN-γ, IL-4, IL-10, IL-17A, IL-22, TGF-β, the count of PLT and the length of time of ITP were used as predictive factors in the predictive nomogram. The model showed good discrimination with a C-index of 0.82 (95% confidence interval 0.73376–0.90 624) in training cohortn and 0.89 (95% CI 0.868, 0.902) in validation cohort, an AUC of 0.795 in training cohort, 0.94 in validation cohort and good calibration. A high C-index value of 0.66 was reached in the interval validation assessment. Decision curve analysis showed that the bleeding risk nomogram was clinically useful when intervention was decided at the possibility threshold of 16–84%. The bleeding risk model based on IFN-γ, IL-4, IL-10, IL-17A, IL-22, TGF-β, the count of PLT and the length of time of ITP could be conveniently used to predict the bleeding risk of ITP.

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