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. 2023 Apr 17;10:1154041. doi: 10.3389/fmed.2023.1154041

Table 3.

Performance of the generalized PAT-based model, the complex individualized models and comparison of the two.

Generalized PAT-based model Complex individualized models p value for comparison
Systolic blood pressure
Mean error, mmHg −0.2 −1.4
Mean absolute error (SD), mmHg 7.6 (5.3) 6.5 (4.8) <0.001*
SD of errors, mmHg 7.2 6.7 <0.001**
Median of absolute errors (IQR), mmHg 5.3 (4.5–10.7) 5.8 (4.7–7.3)
Repeated measures correlation coefficient 0.23 0.39
Correlation coefficient, all subjects pooled 0.91 0.94
Linear regression of aggregated data between model and reference***, R2 0.91 0.96
Akaike’s information criterion*** 173 154
Bayesian information criterion*** 175 156
Diebold-Mariano comparison of predictive accuracy Individualized model is significantly better 0.001
Diastolic blood pressure
Mean error, mmHg 0.2 0.0
Mean absolute error, mean (SD), mmHg 3.3 (3.3) 3.1 (2.2) <0.001*
SD of errors, mmHg −3.1 3.0 0.56**
Median of absolute errors (IQR), mmHg 2.7 (1.8–4.1) 2.2 (1.7–3.5)
Repeated measures correlation coefficient 0.29 0.33
Correlation coefficient, all subjects pooled. 0.94 0.94
Linear regression of aggregated data between model and reference***, R2 0.94 0.94
Akaike’s information criterion*** 131 130
Bayesian information criterion*** 134 133
Diebold-Mariano comparison of predictive accuracy Individualized model is non-significantly better 0.14
Mean arterial pressure
Mean error, mmHg 0.1 −0.1
Mean absolute error, mean (SD), mmHg 4.6 (3.2) 4.0 (2.9) <0.001*
SD of errors, mmHg 4.4 4.0 <0.001**
Median of absolute errors (IQR), mmHg 3.3 (2.4–6.4) 3.3 (2.5–4.5)
Repeated measures correlation coefficient 0.25 0.37
Correlation coefficient, all subjects pooled. 0.93 0.95
Linear regression of aggregated data between model and reference***, R2 0.93 0.95
Akaike’s information criterion*** 146 138
Bayesian information criterion*** 149 140
Diebold-Mariano comparison of predictive accuracy Individualized model is significantly better 0.006

*Compared using non-parametric test of difference in means of all absolute errors between the two models. **Compared using variance comparison test of equality of standard deviations. ***Means of predicted BP from each model for each subject fitted in a linear regression model against reference BP.