Logistic regression analysis and a risk prediction model of pneumothorax after CT-guided needle biopsy

Zhao, Yanfeng; Wang, Xiaoyi; Wang, Yong; Zhu, Zheng

doi:10.21037/jtd.2017.09.47

Cited by 29 publications

(31 citation statements)

References 38 publications

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“…Zhao et al created a predictive model that obtained 0.735 AUC of ROC in a study cohort with 864 cases. The study however did not consider the size of lesions, and the model has not been validated in their study [16]. In the present study, a risk prediction model was established based on 5 predictors proposed by shrinking the regression coefficients with the Lasso regression.…”

Section: Discussionmentioning

confidence: 96%

Development and Validation of a Prediction Pneumothorax Model in CT-Guided Transthoracic Needle Biopsy for Solitary Pulmonary Nodule

Wang

Chen

2019

BioMed Research International

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Computed tomography-guided transthoracic needle biopsy (CT-TNB) is widely used in the diagnosis of solitary pulmonary nodule (SPN). However, CT-TNB-induced pneumothorax occurs frequently. This study aimed to establish a predictive model for pneumothorax following CT-TNB for SPN. The prediction model was developed in a cohort that consisted of 311 patients with SPN who underwent CT-TNB. An independent external validation cohort contained 227 consecutive patients. The least absolute shrinkage and selection operator (Lasso) regression analysis was used for data dimension reduction and predictors selection. Multivariable logistic regression was used to develop the predictive model, which was presented with a nomogram. Area under the curve (AUC) was used to determine the discrimination of the proposed model. The calibration was used to test the goodness-of-fit of the model, and decision curve analysis (DCA) was used for evaluating its clinical usefulness. Five variables (age, diagnosis of nodule, puncture times, puncture distance, and puncture position) were filtered by Lasso regression. AUC of the predictive model and the validation were 0.801 (95% CI, 0.738-0.865) and 0.738 (95% CI, 0.656-0.820), respectively. The model was well-calibrated (P > 0.05), and DCA demonstrated its clinical usefulness. Thus, this predictive model might facilitate the individualized preoperative prediction of pneumothorax in CT-TNB for SPN.

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Section: Discussionmentioning

confidence: 96%

Development and Validation of a Prediction Pneumothorax Model in CT-Guided Transthoracic Needle Biopsy for Solitary Pulmonary Nodule

Wang

Chen

2019

BioMed Research International

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“…Cluster analysis collects infinite information in some limited set variables and uses these set variables to group the observed objects. In short, set analysis is a data processing method to classify a group of observation objects reasonably [14]. It is an important part of experimental machine learning and a common technology for analyzing statistical data.…”

Section: Cluster Regression Analysis Modelmentioning

confidence: 99%

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Jin

Yi²

2021

Mathematical Problems in Engineering

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Cluster regression analysis model is an effective theory for a reasonable and fair player scoring game. It can roughly predict and evaluate the performance of athletes after the game with limited data and provide scientific predictions for the performance of athletes. The purpose of this research is to achieve the player’s postmatch scoring through the cluster regression model. Through the research and analysis of past ball games, the comparison and experiment of multiple objects based on different regression analysis theories, the following conclusions are drawn. Different regression models have different standard errors, but if the data in other model categories are put into the centroid model expression, the standard error and the error of the original model are within 0.3, which can replace other models for calculation. In the player’s postmatch scoring, although the expert’s prediction of the result is very accurate, within the error range of 1 copy, the player’s postmatch scoring mechanism based on the cluster regression analysis model is more accurate, and the error formula is in the 0.5 range. It is best to switch the data of the regression model twice to compare the scoring mechanism using different regression experiments.

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“…The model could identify high-risk patients with pneumothorax after CCNB, so that it could assist clinicians in avoiding risk factors, reducing puncture injury, and in preparing oxygen intake and thoracic cavity drainage equipment in advance. However, all 3 previous studies about the predictive model of pneumothorax after pulmonary biopsy focused on the non-coaxial or non-core needle biopsy [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Nomogram model to predict pneumothorax after computed tomography-guided coaxial core needle lung biopsy

Yang

Liang

et al. 2021

European Journal of Radiology

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To develop a predictive model to determine risk factors of pneumothorax in patients undergoing the computed tomography (CT) 1 -guided coaxial core needle lung biopsy (CCNB). Methods: A total of 489 patients who underwent CCNBs with an 18-gauge coaxial core needle were retrospectively included. Patient characteristics, primary pulmonary disease, target lesion image characteristics and biopsy-related variables were evaluated as potential risk factors of pneumothorax which was determined on the chest X-ray and CT scans. Univariate and multivariate logistic regressions were used to identify the independent risk factors of pneumothorax and establish the predictive model, which was presented in the form of a nomogram. The discrimination and calibration of the model were evaluated as well. Results: The incidence of pneumothorax was 32.91 % and 31.42 % in the development and validation groups, respectively. Age, emphysema, pleural thickening, lesion location, lobulation sign, and size grade were identified independent risk factors of pneumothorax at the multivariate logistic regression model. The forming model produced an area under the curve of 0.718 (95 % CI = 0.660− 0.776) and 0.722 (95 % CI = 0.638− 0.805) in development and validation group, respectively. The calibration curve showed good agreement between predicted and actual probability. Conclusions: The predictive model for pneumothorax after CCNBs had good discrimination and calibration, which could help in clinical practice.

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Logistic regression analysis and a risk prediction model of pneumothorax after CT-guided needle biopsy

Cited by 29 publications

References 38 publications

Development and Validation of a Prediction Pneumothorax Model in CT-Guided Transthoracic Needle Biopsy for Solitary Pulmonary Nodule

Development and Validation of a Prediction Pneumothorax Model in CT-Guided Transthoracic Needle Biopsy for Solitary Pulmonary Nodule

The Scoring Mechanism of Players after Game Based on Cluster Regression Analysis Model

Nomogram model to predict pneumothorax after computed tomography-guided coaxial core needle lung biopsy

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