Development of hypertension models for lung cancer screening cohorts using clinical and thoracic aorta imaging factors
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View abstract on PubMed
Summary
This summary is machine-generated.New nomogram models using clinical and thoracic aorta imaging predict hypertension risk in lung cancer screening patients. These tools aid early intervention, potentially preventing or delaying hypertension onset.
Area Of Science
- Cardiology
- Radiology
- Oncology
Background
- Hypertension is a significant comorbidity.
- Lung cancer screening identifies at-risk populations.
- Predictive tools for hypertension in this cohort are needed.
Purpose Of The Study
- Develop and validate nomogram models for hypertension risk prediction.
- Utilize clinical and thoracic aorta imaging factors.
- Assess model performance for clinical utility.
Main Methods
- Included 804 patients for training (70%) and validation (30%) sets.
- Selected thoracic aorta imaging features using statistical methods and LASSO.
- Constructed five nomogram models (AIMeasure, BasicClinical, TotalClinical, AIBasicClinical, AITotalClinical).
- Evaluated models using ROC curves, calibration curves, and decision curve analysis (DCA).
Main Results
- Models demonstrated good predictive capability in both training and validation sets (AUCs ranging from 0.73 to 0.84).
- Calibration curves and DCAs confirmed accuracy and clinical practicality.
- Nomogram models showed significant predictive performance for hypertension risk.
Conclusions
- Nomogram models integrating clinical and thoracic aorta imaging factors are effective for hypertension risk assessment.
- These models offer clinical utility for early hypertension risk stratification.
- Facilitates timely non-pharmacological interventions to manage or prevent hypertension.
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