Figures and data
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Baseline characteristics of individuals in the derivation and validation sets
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Flow diagram. Based on the exclusion and inclusion criteria, 6,603 patients were included in this study. Patients were divided into a validation set and a derivation set randomly following a 7:3 ratio. pulmonary hypertension, PH; right axis deviation, RAD; high voltage in the right ventricle, HVRV; incomplete right bundle branch block, IRBBB; atrial fibrillation, AF; sinus tachycardia, ST; T wave changes, TC; Pulmonary P waves, PP.
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illustrates the optimal predictive variables as determined by the LASSO binary logistic regression model. Panels A and B depict the measurement of tricuspid regurgitation spectra via transthoracic echocardiography in patients with Grade I PH (A) and Grade III PH (B). Panels C to J demonstrate the identification of the optimal penalisation coefficient lambda (λ) in the LASSO model using 10-fold cross-validation for the PH≥I grade group (C) and the PH≥II grade group (D). The dotted line on the left (λ_min) represents the value of the harmonic parameter log(λ) at which the model’s error is minimised, and the dotted line on the right (λ_1se) indicates the value of the harmonic parameter log(λ) at which the model’s error is minimal minus 1 standard deviation. The LASSO coefficient profiles of 22 predictive factors for the PH≥I grade group (E) and the PH≥II grade group (F) show that as the value of λ decreased, the degree of model compression increased, enhancing the model’s ability to select significant variables. ROC curves were constructed for three models (LASSO, LASSO-λ_min, LASSO-λ_1se) in both the PH ≥I grade group (G) and the PH≥II grade group (H). Histograms depict the final variables selected according to λ_1se and their coefficients for the PH≥I grade group (I) and the PH≥II grade group (J). Asterisks denote levels of statistical significance: *P < 0.05, **P < 0.01, ***P < 0.001.
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Risk factors for PH≥I grade in the derivation set
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Risk factors for PH≥II grade in the derivation set
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Nomogram for predicting PH and risk stratification based on total score. (A-C) NomogramI for the prediction of PH≥I grade in the PH≥I grade group. Points for each independent factor are summed to calculate total points, determining the corresponding ‘risk’ level. Patients were divided into ‘High-risk’ and ‘Low-risk’ subgroups according to the cutoff of the total points (A). Histograms illustrate the odds ratio (OR) comparing the ‘High-risk’ group to the ‘Low-risk’ group in the derivation set (B) and validation set (C). (D-F) NomogramII for predicting PH≥II grade within the PH≥II grade group: Similarly, points from each independent factor are totalled, and the corresponding ‘risk’ level is ascertained. Patients are divided into ‘High-risk’ and ‘Low-risk’ groups based on the cut-off value of the total points (D). Histograms display the OR for the ‘High-risk’ group compared to the ‘Low-risk’ group in the derivation (E) and validation set (F). *** P < 0.001. (G) Screenshot of dynamic NomogramII’s web page.
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Receiver operating characteristic (ROC) curves and area under the curve (AUC) for NomogramI in PH≥I and NomogramII in PH≥II grade groups. (A-F) In the PH≥I grade group, the ROC and corresponding AUC of NomogramI and independent factors in the derivation set (A-C) and validation set (D-F). (G-L) In the PH≥II grade group, the ROC and corresponding AUC of NomogramII and independent factors in the derivation set (G-I) and validation set (J-L).
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Calibration plots and Hosmer-Lemeshow test results for NomogramI in PH≥I and NomogramII in PH≥II grade groups. (A-B) In the PH≥I grade group, the calibration plots of NomogramI in the derivation set (A) and the validation set (B). (C-D) In the PH≥II grade group, the calibration plots of NomogramII in the derivation set (C) and the validation set (D). (E) In the PH≥I grade group, Hosmer-Lemeshow test results for NomogramI in the derivation set and the validation set. (F) In the PH≥II grade group, Hosmer-Lemeshow test results for NomogramII in the derivation set and the validation set.
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Decision curve analysis (DCA) for NomogramI in the PH≥I grade and NomogramII in the PH≥II grade group. (A-D) In the PH≥I grade group, the DCAs of NomogramI and independent factors in the derivation (A, C) and validation set (B, D). (E-H) In the PH≥II grade group, the DCAs of NomogramII and independent factors in the derivation (E, G) and validation set (F, H).