A nomogram integrating non-ECG factors with ECG to screen left ventricular hypertrophy among hypertensive patients from northern China
Supplemental Figure 1. Flow chart for the selection and grouping of hypertensive patients.
Transthoracic Echocardiogram not performed (n=197); Electro- cardiogram not performed (n=123); Height or weight not available(n=26)
Participants with SBP≥140mmHg or DBP≥90mmHg, or previously diagnosed hypertension (n=6,141) Participants aged ≥35 years were recruited from rural areas of Liaoning province from January 2012 to August 2013 (n=11, 956)
Participants with normal blood pressure (n=5,815)
Participants with congenital heart diseases, coronary heart disease, heart failure, pericardial diseases, valvular diseases, or serious arrhythmias were also excluded (n=841)
Eligible hypertensive patients to establish the LVH diagnostic
nomograms (n=4954)
Dataset randomly divided into training set and validation set
at a ratio of 2:1
Training dataset (n=3,303):
LVM indexed to BSA:
Non-LVH (n=2,726, 82.5%);
LVH (n=577, 17.5%) LVM indexed to height2.7:
Non-LVH (n=2,631, 79.7%);
LVH (n=672, 20.3%) ECG diagnosed LVH:
Non-LVH (n=2,870, 86.9%);
LVH (n=433, 13.1%)
Validation dataset (n=1,651):
LVM indexed to BSA:
Non-LVH (n=1,366, 80.9%);
LVH (n=315, 19.1%) LVM indexed to height2.7:
Non-LVH (n=1,284, 77.8%);
LVH (n=367, 22.2%) ECG diagnosed LVH:
Non-LVH (n=1,438, 87.1%);
LVH (n=213, 12.9%)
Supplemental Table 1. The distributions of LV geometry in the training dataset and validation dataset, respectively
LV geometry
LVM indexed to BSA LVM indexed to height2.7 Training
n (%)
Validation n (%)
Training n (%)
Validation n (%) Normal geometry 2364 (71.6) 1177 (71.3) 2300 (69.6) 1132 (68.6) Concentric remodeling 362 (11.0) 159 (9.6) 331 (10.0) 152 (9.2) Concentric hypertrophy 237 (7.2) 132 (7.2) 268 (8.1) 139 (8.4) Eccentric hypertrophy 340 (10.3) 183 (11.1) 404 (12.2) 228 (13.8)
P value 0.32 0.36
Note: The relative wall thickness (RWT) was calculated according to the formula (IVSTd + PWTd)/LVIDd [1]. Elevated RWT was defined as RWT>0.42. LV geometry was classified into four patterns based on LVMI and RWT values: normal geometry, normal LVMI and RWT≤0.42; concentric remodeling, normal LVMI and RWT>0.42;
concentric hypertrophy, increased LVMI and RWT>0.42; eccentric hypertrophy, increased LVMI and RWT≤0.42 [2].
Supplemental Figure 2. Screening of non-ECG risk factors for echo-LVH indexed to BSA using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (a) LASSO coefficient profiles of all candidate variables. (b) The optimal parameters (λ) for the LASSO model were determined by tenfold cross-validation. Notes: The grey and red dotted vertical lines indicate the optimal λ values which were achieved using the minimum criteria and one standard error of the minimum criteria (1-SE criteria), respectively. Nine variables with non-
Supplemental Figure 3. Screening of non-ECG risk factors for echo-LVH indexed to BSA among hypertensive patients without ECG-LVH using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (a) LASSO coefficient profiles of all candidate variables. (b) The optimal parameters (λ) for the LASSO model were determined by tenfold cross-validation. Notes: The grey and red dotted vertical lines indicate the optimal λ values which were achieved using the minimum criteria and one standard error of the minimum criteria (1-SE criteria), respectively. Eight variables with non-zero coefficients were selected based on the 1-SE criteria.
Supplemental Figure 4. Screening of non-ECG risk factors for echo-LVH indexed to height2.7 using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (a) LASSO coefficient profiles of all candidate variables. (b) The optimal parameters (λ) for the LASSO model were determined by tenfold cross-validation. Notes: The grey and red dotted vertical lines indicate the optimal λ values which were achieved using the minimum criteria and one standard error of the minimum criteria (1-SE criteria), respectively. Six variables with non- zero coefficients were selected based on the 1-SE criteria.
Supplemental Figure 5. The Cornell product (a) and non-ECG nomograms (b) predicting the probability of echo-LVH indexed to BSA. Abbreviations: DBP, diastolic blood pressure; eGFR, estimating glomerular filtration rate; LVH, left ventricular hypertrophy; SBP, systolic blood pressure.
Supplemental Table 2. The points corresponding to specific value of each variable for the non-ECG and integrated nomograms in predicting echo-LVH indexed to BSA, respectively.
Variables Points
Non-ECG nomogram Integrated nomogram Age group, years
35~44 0 0
45~54 21 9
55~64 23 12
≥65 49 24
Male vs. female 0 vs. 32 0 vs. 17
Educational level (years)
≤6 32 16
>6 0 0
Hypertension duration (years)
≤1 0 0
2~5 26 10
>5 50 20
SBP grade (mmHg)
<150 0 0
150~159 27 12
160~169 37 14
170~179 75 32
≥180 100 42
DBP grade (mmHg)
<100 0 0
100~109 17 5
≥110 47 11
eGFR (mL/min/1.73m2)
≥90 0 0
<90 47 5
Sleep duration (h per day)
≤8 38 17
>8 0 0
Tea consumption (yes vs. no) 0 vs. 19 0 vs. 9
Cornell product (mm*ms)
≤500 NA 0
501~1000 NA 11
1001~1500 NA 18
1501~2000 NA 39
2001~2500 NA 55
2501~3000 NA 71
3001~3500 NA 77
>3500 NA 100
Supplemental Table 3. The diagnostic LVH probabilities corresponding to specific total points for the non-ECG and integrated nomograms in predicting echo-LVH indexed to BSA, respectively.
Diagnostic probability Total points
Non-ECG nomogram Integrated nomogram
0.10 116 92
0.15 152 110
0.20 180 123
0.25 202 134
0.30 222 143
0.35 240 152
0.40 257 160
0.45 273 168
0.50 289 175
0.55 305 183
0.60 321 191
0.65 338 199
0.70 356 208
0.75 376 217
0.80 399 228
0.85 NA 241
Supplemental Figure 6. The Cornell product (a), non-ECG (b), and integrated nomograms (c) predicting the probability of echo-LVH indexed to BSA among hypertensive patients without ECG-LVH. Abbreviations: LVH, left ventricular hypertrophy; SBP, systolic blood pressure.
Supplemental Figure 7. The Cornell product (a), non-ECG (b), and integrated nomograms (c) predicting the probability of echo-LVH indexed to height2.7. Abbreviations: BMI, body mass index; LVH, left ventricular hypertrophy; SBP, systolic blood pressure.
Supplemental Figure 8. ROC curves showing the discrimination power for echo-LVH indexed to BSA (without ECG-LVH) of the three different nomograms in the (a) training dataset and (b) validation dataset. Abbreviations: AUC, area under curve; CI, confidence interval; TN, true negative; TP, true positive.
Supplemental Figure 9. ROC curves showing the discrimination power for echo-LVH indexed to height2.7 of three different nomograms in the (a) training dataset and (b) validation dataset. Abbreviations: AUC, area under curve; CI, confidence interval; TN, true negative; TP, true positive.
Supplemental Figure 10. Calibration curves (echo-LVH indexed to BSA among participants without ECG-LVH) for the three nomograms in the (a) training dataset and (b) validation dataset. Notes: These curves aim at describing the consistency between the predicted risk and actual LVH diagnosis. The 45° diagonal grey line indicates a perfect prediction by an ideal model. The black, yellow, and blue lines represent the performance of the Cornell product, non-ECG, and integrating nomogram models, respectively.
Supplemental Figure 11. Calibration curves (echo-LVH indexed to height2.7) for the three nomograms in the (a) training dataset and (b) validation dataset. Notes: These curves aim at describing the consistency between the predicted risk and actual LVH diagnosis. The 45° diagonal grey line indicates a perfect prediction by an ideal model.
The black, yellow, and blue lines represent the performance of the Cornell product, non- ECG, and integrating nomogram models, respectively.
Supplemental Table 4. Reclassifications of echo-LVH indexed to BSA among hypertensive patients without ECG-LVH by the integrated nomogram compared with the Cornell product nomogram or non-ECG nomogram.
Model comparisons Training dataset Validation dataset NRI or IDI (95% CI) P value NRI or IDI (95% CI) P value Integrated vs. Cornell product
NRI 0.542 (0.440,0.628) <0.001 0.574 (0.438,0.721) <0.001 NRI+ 0.229 (0.140,0.312) <0.001 0.245 (0.119,0.376) <0.001 NRI- 0.313 (0.271,0.350) <0.001 0.329 (0.279,0.379) <0.001 Absolute IDI 0.069 (0.057,0.081) <0.001 0.064 (0.049,0.080) <0.001
Relative IDI 227% <0.001 310% <0.001
Integrated vs. non-ECG
NRI 0.386 (0.302,0.485) <0.001 0.252 (0.088,0.379) <0.001 NRI+ 0.371 (0.286,0.459) <0.001 0.227 (0.097,0.361) <0.001
NRI- 0.015 (-0.023,0.054) 0.46 0.024 (-0.040,0.079) 0.42
Absolute IDI 0.021 (0.014,0.028) 0.02 0.011 (0.002,0.020) 0.02
Relative IDI 26.0% 0.02 14.9% 0.02
Abbreviations: LVH, left ventricular hypertrophy; IDI, integrated discrimination improvement; NRI, net reclassification improvement; NRI+, net reclassification improvement in LVH patients; NRI-, net reclassification improvement in non-LVH patients.
Supplemental Table 5. Reclassifications of echo-LVH indexed to height2.7 by the integrated nomogram compared with the Cornell product nomogram or non-ECG nomogram.
Model comparisons
Training dataset Validation dataset NRI or IDI (95% CI) P value NRI or IDI (95% CI) P
value Integrated vs. Cornell product
NRI 0.640 (0.557,0.726) <0.001 0.717 (0.614,0.828) <0.001 NRI+ 0.259 (0.179,0.338) <0.001 0.324 (0.232,0.412) <0.001 NRI- 0.381 (0.347,0.419) <0.001 0.393 (0.342,0.445) <0.001 Absolute IDI 0.108 (0.095,0.122) <0.001 0.109 (0.091,0.127) <0.001
Relative IDI 140% <0.001 183% <0.001
Integrated vs. non-ECG
NRI 0.510 (0.432,0.601) <0.001 0.320 (0.206,0.429) <0.001 NRI+ 0.065 (-0.003,0.144) 0.09 -0.101 (-0.204,0.005) 0.06 NRI- 0.444 (0.405,0.478) <0.001 0.421 (0.368,0.471) <0.001 Absolute IDI 0.051 (0.041,0.060) <0.001 0.033 (0.019,0.046) <0.001
Relative IDI 37.3% <0.001 23.9% <0.001
Abbreviations: LVH, left ventricular hypertrophy; IDI, integrated discrimination improvement; NRI, net reclassification improvement; NRI+, net reclassification improvement in LVH patients; NRI-, net reclassification improvement in non-LVH patients.
Supplemental Figure 12. Decision curve analysis (echo-LVH indexed to BSA among participants without ECG-LVH) for the three nomograms in the (a) training dataset and (b) validation dataset. Notes: The x- and y-axis measures the threshold probability and net benefit, respectively. The horizontal black line represents the assumption that none of the hypertensive patients will be intervened; the grey solid line represents the assumption that interventions are offered to all hypertensive patients; Other lines represent the net benefit of offering interventions according to the threshold probability derived from different nomograms.
Supplemental Figure 13. Decision curve analysis (echo-LVH indexed to height2.7) for the three nomograms in the (a) training dataset and (b) validation dataset. Notes: The x- and y-axis measures the threshold probability and net benefit, respectively. The horizontal black line represents the assumption that none of the hypertensive patients will be intervened; the grey solid line represents the assumption that interventions are offered to all hypertensive patients; Other lines represent the net benefit of offering interventions according to the threshold probability derived from different nomograms.
Reference:
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