Derivation and internal validation of prediction models for pulmonary hypertension risk assessment in a cohort inhabiting Tibet, China
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eLife Assessment
This study retrospectively analyzed clinical data to develop a risk prediction model for pulmonary hypertension in high-altitude populations. The evidence is solid, and the findings are useful and hold clinical significance as the model can be used for intuitive and individualized prediction of pulmonary hypertension risk in these populations.
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Abstract
Individuals residing in plateau regions are susceptible to pulmonary hypertension (PH) and there is an urgent need for a prediction nomogram to assess the risk of PH in this population. A total of 6603 subjects were randomly divided into a derivation set and a validation set at a ratio of 7:3. Optimal predictive features were identified through the least absolute shrinkage and selection operator regression technique, and nomograms were constructed using multivariate logistic regression. The performance of these nomograms was evaluated and validated using the area under the curve (AUC), calibration curves, the Hosmer–Lemeshow test, and decision curve analysis. Comparisons between nomograms were conducted using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. Nomogram I was established based on independent risk factors, including gender, Tibetan ethnicity, age, incomplete right bundle branch block (IRBBB), atrial fibrillation (AF), sinus tachycardia (ST), and T wave changes (TC). The AUCs for Nomogram I were 0.716 in the derivation set and 0.718 in the validation set. Nomogram II was established based on independent risk factors, including Tibetan ethnicity, age, right axis deviation, high voltage in the right ventricle, IRBBB, AF, pulmonary P waves, ST, and TC. The AUCs for Nomogram II were 0.844 in the derivation set and 0.801 in the validation set. Both nomograms demonstrated satisfactory clinical consistency. The IDI and NRI indices confirmed that Nomogram II outperformed Nomogram I . Therefore, the online dynamic Nomogram II was established to predict the risks of PH in the plateau population.
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eLife Assessment
This study retrospectively analyzed clinical data to develop a risk prediction model for pulmonary hypertension in high-altitude populations. The evidence is solid, and the findings are useful and hold clinical significance as the model can be used for intuitive and individualized prediction of pulmonary hypertension risk in these populations.
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Reviewer #1 (Public Review):
Summary:
This study retrospectively analyzed clinical data to develop a risk prediction model for pulmonary hypertension in high-altitude populations. This finding holds clinical significance as it can be used for intuitive and individualized prediction of pulmonary hypertension risk in these populations. The strength of evidence is high, utilizing a large cohort of 6,603 patients and employing statistical methods such as LASSO regression. The model demonstrates satisfactory performance metrics, including AUC values and calibration curves, enhancing its clinical applicability.
Strengths:
(1) Large Sample Size: The study utilizes a substantial cohort of 6,603 subjects, enhancing the reliability and generalizability of the findings.
(2) Robust Methodology: The use of advanced statistical techniques, including …
Reviewer #1 (Public Review):
Summary:
This study retrospectively analyzed clinical data to develop a risk prediction model for pulmonary hypertension in high-altitude populations. This finding holds clinical significance as it can be used for intuitive and individualized prediction of pulmonary hypertension risk in these populations. The strength of evidence is high, utilizing a large cohort of 6,603 patients and employing statistical methods such as LASSO regression. The model demonstrates satisfactory performance metrics, including AUC values and calibration curves, enhancing its clinical applicability.
Strengths:
(1) Large Sample Size: The study utilizes a substantial cohort of 6,603 subjects, enhancing the reliability and generalizability of the findings.
(2) Robust Methodology: The use of advanced statistical techniques, including least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, ensures the selection of optimal predictive features.
(3) Clinical Utility: The developed nomograms are user-friendly and can be easily implemented in clinical settings, particularly in resource-limited high-altitude regions.
(4) Performance Metrics: The models demonstrate satisfactory performance, with strong AUC values and well-calibrated curves, indicating accurate predictions.
Weaknesses:
(1) Lack of External Validation: The models were validated internally, but external validation with cohorts from other high-altitude regions is necessary to confirm their generalizability.
(2) Simplistic Predictors: The reliance on ECG and basic demographic data may overlook other potential predictors that could improve the models' accuracy and predictive power.
(3) Regional Specificity: The study's cohort is limited to Tibet, and the findings may not be directly applicable to other high-altitude populations without further validation.
Comments on revised version:
The authors have made revisions in response to the primary concerns raised in the initial review, leading to significant improvements in the manuscript's technical accuracy, formatting consistency, and overall clarity. They have provided a detailed explanation of the selection criteria for the final model variables, which has enhanced the transparency and robustness of the study's methodology. Additionally, the authors have acknowledged the limitation of lacking external validation in cohorts from other high-altitude regions and outlined their plans for future research to address this issue.
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Author response:
The following is the authors’ response to the original reviews.
Reviewer #1:
(1) Correct capitalization errors, ensuring the first letter of each sentence is capitalized.
Thank you for your comment. We have corrected capitalization errors.
(2) Ensure that all technical terms and abbreviations are introduced in full when first mentioned and consistently used throughout the text.
Thank you for your comment. we have checked and corrected the issue.
(3) Review the manuscript for grammatical errors and improve sentence structures to enhance readability.
Thank you for your comment. we have checked and corrected the issue.
(4) Ensure all figures referenced in the text, such as Fig. 3G, are appropriately discussed and integrated into the narrative.
Thank you for your comment. we have discussed and integrated Fig. 3G …
Author response:
The following is the authors’ response to the original reviews.
Reviewer #1:
(1) Correct capitalization errors, ensuring the first letter of each sentence is capitalized.
Thank you for your comment. We have corrected capitalization errors.
(2) Ensure that all technical terms and abbreviations are introduced in full when first mentioned and consistently used throughout the text.
Thank you for your comment. we have checked and corrected the issue.
(3) Review the manuscript for grammatical errors and improve sentence structures to enhance readability.
Thank you for your comment. we have checked and corrected the issue.
(4) Ensure all figures referenced in the text, such as Fig. 3G, are appropriately discussed and integrated into the narrative.
Thank you for your comment. we have discussed and integrated Fig. 3G into the narrative (Page 12, Line 162-166).
(5) Maintain consistent formatting, including first-line indentation and spacing before paragraphs, to improve the document's visual coherence.
Thank you for your comment. we have checked and corrected the issue.
(6) Provide additional explanations for the selection criteria of final model variables, particularly the rationale behind choosing the λ_1se criterion in the LASSO regression.
Thank you for your comment. we have provided explanations for choosing the λ_1se criterion in the LASSO regression (Page 25, Line 315-316; Page 27, Line 363-364).
(7) Conduct validation studies with cohorts from other high-altitude regions to assess the generalizability and robustness of the prediction models.
Thank you for your comment. The lack of validation of cohorts from other high-altitude regions is a weakness in this study, and in our follow-up study, we will conduct external validation with cohorts from more other high-altitude regions to assess the generalizability and robustness of our prediction models.
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eLife assessment
This study retrospectively analyzed clinical data to develop a risk prediction model for pulmonary hypertension in high-altitude populations. The evidence is solid and the findings are useful and hold clinical significance as the model can be used for intuitive and individualized prediction of pulmonary hypertension risk in these populations.
-
Joint Public Review:
Summary:
This study retrospectively analyzed clinical data to develop a risk prediction model for pulmonary hypertension in high-altitude populations. This finding holds clinical significance as it can be used for intuitive and individualized prediction of pulmonary hypertension risk in these populations. The strength of evidence is high, utilizing a large cohort of 6,603 patients and employing statistical methods such as LASSO regression. The model demonstrates satisfactory performance metrics, including AUC values and calibration curves, enhancing its clinical applicability.
Strengths:
(1) Large Sample Size: The study utilizes a substantial cohort of 6,603 subjects, enhancing the reliability and generalizability of the findings.
(2) Robust Methodology: The use of advanced statistical techniques, including least …
Joint Public Review:
Summary:
This study retrospectively analyzed clinical data to develop a risk prediction model for pulmonary hypertension in high-altitude populations. This finding holds clinical significance as it can be used for intuitive and individualized prediction of pulmonary hypertension risk in these populations. The strength of evidence is high, utilizing a large cohort of 6,603 patients and employing statistical methods such as LASSO regression. The model demonstrates satisfactory performance metrics, including AUC values and calibration curves, enhancing its clinical applicability.
Strengths:
(1) Large Sample Size: The study utilizes a substantial cohort of 6,603 subjects, enhancing the reliability and generalizability of the findings.
(2) Robust Methodology: The use of advanced statistical techniques, including least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, ensures the selection of optimal predictive features.
(3) Clinical Utility: The developed nomograms are user-friendly and can be easily implemented in clinical settings, particularly in resource-limited high-altitude regions.
(4) Performance Metrics: The models demonstrate satisfactory performance, with strong AUC values and well-calibrated curves, indicating accurate predictions.
Weaknesses:
(1) Lack of External Validation: The models were validated internally, but external validation with cohorts from other high-altitude regions is necessary to confirm their generalizability.
(2) Simplistic Predictors: The reliance on ECG and basic demographic data may overlook other potential predictors that could improve the models' accuracy and predictive power.
(3) Regional Specificity: The study's cohort is limited to Tibet, and the findings may not be directly applicable to other high-altitude populations without further validation.
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