Figures and data
Baseline characteristics of individuals in the derivation and validation sets
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.
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.
Risk factors for PH≥I grade in the derivation set
Risk factors for PH≥II grade in the derivation set
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.
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).
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.
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).
