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. 2025 Apr 3:18:4681-4693.
doi: 10.2147/JIR.S504183. eCollection 2025.

Diagnosis of Tuberculous Pericarditis in Zhejiang, China: A Diagnostic Prediction Model Based on LASSO Logistic Regression

Xiaoqun Xu #  1 , Xiao Liu #  2 , Chao Yang #  2 , Long Cai  1 , Libin Liu  1 , Tielong Chen  3 , Houyong Zhu  3 , Hui Wei  1
Affiliations

Diagnosis of Tuberculous Pericarditis in Zhejiang, China: A Diagnostic Prediction Model Based on LASSO Logistic Regression

Xiaoqun Xu et al. J Inflamm Res. .

Abstract

Background and aims: Tuberculous pericarditis (TBP) is a severe, life-threatening complication, yet its diagnosis is highly challenging due to the lack of sufficient diagnostic tools. The aim of this study was to develop and validate a diagnostic prediction model suitable for primary healthcare institutions to predict the risk of TBP.

Methods: We collected detailed medical histories, imaging examination results, laboratory test data, and clinical characteristics of patients and used the Least Absolute Shrinkage and Selection Operator (LASSO) technique combined with logistic regression analysis to construct a predictive model. The diagnostic efficacy of the model was assessed using the Receiver Operating Characteristic (ROC) curve, calibration curve, and Decision Curve Analysis (DCA).

Results: A total of 304 patients were included in the study, with a median age of 64 years, of which 144 were diagnosed with tuberculous pericarditis. Patients were randomly assigned to the training and validation sets in a 7:3 ratio. LASSO logistic regression analysis revealed that weight loss (P=0.011), body mass index (BMI) (P=0.061), history of tuberculosis (P=0.022), history of dust exposure (P=0.03), moderate to severe kidney disease (P=0.005), erythrocyte sedimentation rate (ESR) (P=0.084), and B-type natriuretic peptide (BNP) (P<0.001) are independent risk factors for TBP. Based on these factors, we constructed a nomogram with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.757 in both the training and validation sets, indicating high discriminative ability of the model. Calibration curve analysis showed good consistency of the model. DCA results indicated that the model has significant clinical application value when the threshold probability is set between 1-100% (training set) and 30-100% (validation set).

Conclusion: We successfully developed a nomogram model for predicting tuberculous pericarditis, which can assist clinicians in improving diagnostic accuracy and reducing misdiagnoses and missed diagnoses in primary healthcare settings.

Keywords: LASSO logistic regression; diagnostic prediction model; primary healthcare; tuberculous pericarditis.

Plain language summary

Imagine you have a tool that helps doctors figure out if someone has a serious heart issue called tuberculous pericarditis, which is tough to detect. Our team collected data from 304 patients, looking at everything from their medical history to lab results. We used a smart method to pinpoint key risk factors like weight loss, body mass index, past tuberculosis, exposure to dust, moderate or severe kidney disease, a measure of inflammation called erythrocyte sedimentation rate, and a heart failure marker known as B-type natriuretic peptide. From there, we crafted a simple scoring tool that predicts the likelihood of having this heart problem. When we put our tool to the test, it did a great job, especially when we set the risk level just right. This means we have developed a helpful guide for doctors, especially in places with limited resources, to diagnose this condition more accurately and avoid mistakes. In simple terms, our research has led to a better way to spot a dangerous heart condition that can be tricky to find. This not only helps patients get the right treatment but also raises awareness about the importance of medical research and its impact on public health.

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Conflict of interest statement

Xiaoqun Xu reports article publishing charges and statistical analysis were provided by Zhejiang Administration Bureau of Traditional Chinese Medicine. Xiaoqun Xu reports article publishing charges and statistical analysis were provided by Health Commission of Zhejiang Province. Xiaoqun Xu reports article publishing charges and statistical analysis were provided by Hangzhou Municipal Health Commission. Houyong Zhu reports article publishing charges and statistical analysis were provided by Zhejiang Administration Bureau of Traditional Chinese Medicine. Houyong Zhu reports article publishing charges and statistical analysis were provided by 2024 Zhejiang Chinese Medical University Research Project. Hui Wei reports article publishing charges and statistical analysis were provided by Hangzhou Biomedical and Health Industry Support Science and Technology Project. Chao Yang reports article publishing charges and statistical analysis were provided by Zhejiang Chinese Medicine University Graduate Student Scientific Research Fund Project. The authors report no other conflicts of interest in this work.

Figures

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Graphical abstract
Figure 1
Figure 1
Nomogram Developed from the LASSO Logistic Regression Model.
Figure 2
Figure 2
Analysis of the ROC and Calibration Plots. (A) Model performance in diagnosing tuberculous pericarditis within the training set. (B) Model performance in diagnosing tuberculous pericarditis within the validation set. (C) Calibration of the model for diagnosing tuberculous pericarditis within the training set. (D) Calibration of the model for diagnosing tuberculous pericarditis within the validation set. The sawtooth pattern in red above represents patients diagnosed with tuberculous pericarditis (coded as 1), while the sawtooth pattern below represents patients diagnosed with non-specific pericarditis (coded as 0).
Figure 3
Figure 3
Decision Curve Analysis of the Nomogram. (A) Decision curve analysis for the nomogram in the training set. (B) Decision curve analysis for the nomogram in the validation set.
Figure 4
Figure 4
Comparison of Clinical Application Based on the Model and Clinical Experience. (A) Misdiagnosis Due to Model’s High-Risk Group Threshold. (B) Misdiagnosis Due to Physicians’ Clinical Experience. (C) Missed Diagnoses Due to Model’s Low-Risk Group Threshold. (D) Missed Diagnoses Due to Physicians’ Clinical Experience. (E) Overuse of Anti-Tuberculosis Treatment.
See this image and copyright information in PMC

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