Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy

Xinyu Zhang; Yu Meng; Mei Jiang; Lin Yang; Kuixing Zhang; Cuiting Lian; Ziwei Li; Xinyu Zhang; Yu Meng; Mei Jiang; Lin Yang; Kuixing Zhang; Cuiting Lian; Ziwei Li

doi:10.3934/mbe.2023363

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 5: 8308-8319. doi: 10.3934/mbe.2023363

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Research article Special Issues

Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy

1.
Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
2.
College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China

Academic Editor: Simon James Fong

Received: 26 August 2022 Revised: 09 February 2023 Accepted: 20 February 2023 Published: 01 March 2023

Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aims to evaluate the application value of pulse waves in the diagnosis of hypertensive disorder in pregnancy.This research a retrospective study of pregnant women who attended prenatal care and labored at Beijing Haidian District Maternal and Child Health Hospital. We extracted maternal hemodynamic factors and measured the pulse wave of the pregnant women. We developed an HDP predictive model by using support vector machine algorithms at five-gestational-week stages.At five-gestational-week stages, the area under the receiver operating characteristic curve (AUC) of the predictive model with pulse wave parameters was higher than that of the predictive model with hemodynamic factors. The AUC values of the predictive model with pulse wave parameters were 0.77 (95% CI 0.64 to 0.9), 0.83 (95% CI 0.77 to 0.9), 0.85 (95% CI 0.81 to 0.9), 0.93 (95% CI 0.9 to 0.96) and 0.88 (95% CI 0.8 to 0.95) at five-gestational-week stages, respectively. Compared to the predictive models with hemodynamic factors, the predictive model with pulse wave parameters had better prediction effects on HDP.Pulse waves had good predictive effects for HDP and provided appropriate guidance and a basis for non-invasive detection of HDP.

Keywords:

Citation: Xinyu Zhang, Yu Meng, Mei Jiang, Lin Yang, Kuixing Zhang, Cuiting Lian, Ziwei Li. Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 8308-8319. doi: 10.3934/mbe.2023363

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Abstract

1. Introduction

Hypertensive disorders in pregnancy (HDP) are pregnancy-specific systematic disorders that globally affect 5–10% of all pregnancies^[1,2,3,4]. HDP is a major cause of maternal and fetal mortality and morbidity worldwide, and it is important to make an accurate prediction of HDP to improving maternal and infant outcomes ^[5,6,7,8].

Pregnancy is accompanied by a change in the hemodynamic environment. During normal pregnancy, the cardiac output (CO), heart rate, and intravascular volume increase, which decreases the total peripheral resistance (TPR) and blood pressure ^{[9,10,11,12,13]}. The main physiological manifestation of HDP is systemic small vessel spasms, which result in increased TPR and blood pressure and reduced cardiac output in pregnant women ^[14,15,16]. Therefore, HDP can be predicted by a combination of multiple indicators of maternal hemodynamic factors ^[17,18], but some of the hemodynamic factors are obtained through invasive means, which could cause discomfort to the pregnant woman. Pulse waves originate from the rhythmic contraction and diastole of the heart, form at the root of the aorta and then propagate rapidly along the arterial tree to the peripheral vasculature with a constant reflex, making them rich in cardiovascular information about the human body and a powerful predictor of future cardiovascular events. Previous studies have shown that pulse wave parameters are an important basis for evaluating the physiological and pathological status of the human cardiovascular system ^[19,20]. The morphological parameters can reflect the cardiac ejection fraction and vascular elasticity. The presystolic wave can reflect the physiological phenomenon of the process from atrial systolic depolarization to ventricular systolic depolarization. By decomposing the pulse wave by applying a fourth-order Gaussian, the fourth Gaussian parameter can locate the presystolic wave ^[21]. The descending branch of the pulse wave is related to the peripheral resistance and vascular elasticity of the body, and it better reflects the hemodynamic information of the body ^[22]. The current research mainly focuses on exploring the value of morphological parameters in HDP. However, the application value of other parameters to HDP remains unclear.

In this study, we extracted the waveform morphology parameters, Gaussian decomposition parameters, and descending branch energy parameters of the pulse wave. We used support vector machine algorithms to establish HDP predictive models based on hemodynamic factors and pulse wave parameters, respectively. The predictive effects of the models were compared to investigate the predictive value of the pulse wave on HDP and to provide a possibility for the non-invasive monitoring of HDP.

2. Materials and methods

2.1. Subjects

We performed a retrospective study on pregnant women who attended prenatal checkups at Beijing Haidian District Maternal and Child Health Hospital from 2015 to 2016. We excluded women with the following conditions: (a) long-term drug use; (b) fetal malformations; (c) suffering from chronic hypertension, diabetes or other cardiovascular diseases. A total of 168 pregnant women were included in this study, 49 of whom had HDP and 119 who were healthy pregnant women

2.2. Data acquisition

We collected maternal demographic information, blood pressure, TPR, CO, and mean arterial pressure (MAP) data from the hospital's electronic medical record. Beginning the first maternity examination of the pregnant women, the radial artery pulse wave waveform of each pregnant woman was collected and tracked.

Before testing, pregnant women were asked to remain quiet for 5 minutes. A pressure transducer was placed above their left radial artery and the recordings were captured via a Power Lab data acquisition system (ADInstruments Pty., Ltd., Power Lab 8/35, Bella Vista NSW 2153, Australia) at a rate of 1000 Hz. We repeated the sampling three times for each person and then took the average value. In the end, a total of 1269 pulse waves were obtained from the HDP group and 4160 pulse waves were obtained from the control group. As shown in Figure 1, to facilitate the calculation of feature points, we normalized the sampling points and amplitude of each pulse waveform to 0–100.

Figure 1. Normalized radial artery pulse waveform.

DownLoad: Full-Size Img PowerPoint

2.3. Feature extraction of pulse wave

In this study, three main types of features of pulse waveforms were extracted: waveform morphology parameters, Gaussian decomposition parameters ^[23], and descending branch energy parameters.

The pulse wave contained three main waveform components: main wave, tidal wave, and dicrotic wave. As shown in Figure 2, A is the main wave peak point, B is the tidal wave peak point, C is the dicrotic wave peak point, M is the pulse wave endpoint, N_A is the main wave peak point location, N_B is the tidal wave peak point location, N_C is the dicrotic wave peak point location, P_A is the main wave amplitude, P_B is the tidal wave amplitude, and P_C is the dicrotic wave amplitude. We extracted the position parameters (N_A, N_B, N_C), amplitude parameters (P_A, P_B, P_C), position difference parameters (N_B-A = N_B-N_A; N_C-A = N_C-N_A) and amplitude ratio parameters (P_B/A = P_B/P_A; P_C/A = P_C/P_A) of the three main waveform components.

Figure 2. Morphological parameters of the pulse wave.

DownLoad: Full-Size Img PowerPoint

For the acquisition of the Gaussian parameters, we used four Gaussian functions to decompose the pulse wave, as shown in Figure 3. The Gaussian equation is as follows:

${f}_{i}\left(t\right) = {\sum }_{i = 1}^{4}{H}_{i}*{e}^{-\frac{2{\left(t-{T}_{i}\right)}^{2}}{{W}_{i}^{2}}} ,$

(2.1)

Figure 3. Gaussian decomposition graph of the pulse wave.

DownLoad: Full-Size Img PowerPoint

where H_i (i = 1, 2, 3, 4) denotes the amplitude of each Gaussian waveform, T_i (i = 1, 2, 3, 4) denotes the position of each Gaussian wave peak, and W_i (i = 1, 2, 3, 4) denotes the width of each Gaussian wave.

We extracted the descending branch energy parameters of the pulse wave, which included the waveform slope parameters, the area ratio parameters and the waveform descending branch complexity parameters. For the waveform slope parameters, we extracted the slope of the peak point to the endpoint (SL_A, SL_B, SL_C, SL_D), the slope between the peak points (SL₁, SL₂) and the slope ratio (SL_2/1) as slope parameters. We calculated the area under the curve line and the area under the line between the peak point and the endpoint, and we used the ratio between the two areas as the area ratio parameters (Dr₁, Dr₂, Dr₃). We extracted the sample entropy of the waveform (SampEn_AM) between the peak point of the main wave and the endpoint as the complexity parameter. The parameters can be calculated as follows:

${{\text{SL}}}_{{\text{A}}}{\text{ = }}\frac{{{\text{P}}}_{{\text{A}}}}{{\text{(100-}}{{\text{N}}}_{{\text{A}}}{\text{)}}}$

(2.2)

${{\text{SL}}}_{{\text{B}}}{\text{ = }}\frac{{{\text{P}}}_{{\text{B}}}}{{\text{(100-}}{{\text{N}}}_{{\text{B}}}{\text{)}}}$

(2.3)

${{\text{SL}}}_{{\text{C}}}{\text{ = }}\frac{{{\text{P}}}_{{\text{C}}}}{{\text{(100-}}{{\text{N}}}_{{\text{C}}}{\text{)}}}$

(2.4)

${{\text{SL}}}_{{\text{D}}}{\text{ = }}\frac{{{\text{P}}}_{{\text{D}}}}{{\text{(100-}}{{\text{N}}}_{{\text{D}}}{\text{)}}}$

(2.5)

${{\text{SL}}}_{{\text{1}}}{\text{ = }}\frac{{{\text{P}}}_{{\text{A}}}{\text{-}}{{\text{P}}}_{{\text{B}}}}{{\text{(}}{{\text{N}}}_{{\text{B}}}{\text{-}}{{\text{N}}}_{{\text{A}}}{\text{)}}}$

(2.6)

${{\text{SL}}}_{{\text{2}}}{\text{ = }}\frac{{{\text{P}}}_{{\text{A}}}{\text{-}}{{\text{P}}}_{{\text{C}}}}{{\text{(}}{{\text{N}}}_{{\text{C}}}{\text{-}}{{\text{N}}}_{{\text{A}}}{\text{)}}}$

(2.7)

${{\text{SL}}}_{{\text{2/1}}}{\text{ = }}\frac{{{\text{SL}}}_{{\text{2}}}}{{{\text{SL}}}_{{\text{1}}}}$

(2.8)

${{\text{Dr}}}_{{\text{1}}}{\text{ = }}\frac{{\text{0.5*}}{{\text{P}}}_{{\text{A}}}{\text{*}}\left({\text{100-}}{{\text{N}}}_{{\text{A}}}\right){\text{-}}{{\text{S}}}_{{\text{AM}}}}{{\text{0.5*}}{{\text{P}}}_{{\text{A}}}{\text{*}}\left({\text{100-}}{{\text{N}}}_{{\text{A}}}\right)}$

(2.9)

${{\text{Dr}}}_{{\text{2}}}{\text{ = }}\frac{{\text{0.5*}}{{\text{P}}}_{{\text{B}}}{\text{*}}\left({\text{100-}}{{\text{N}}}_{{\text{B}}}\right){\text{-}}{{\text{S}}}_{{\text{BM}}}}{{\text{0.5*}}{{\text{P}}}_{{\text{B}}}{\text{*}}\left({\text{100-}}{{\text{N}}}_{{\text{B}}}\right)}$

(2.10)

${{\text{Dr}}}_{{\text{3}}}{\text{ = }}\frac{{\text{0.5*}}{{\text{P}}}_{{\text{C}}}{\text{*}}\left({\text{100-}}{{\text{N}}}_{{\text{C}}}\right){\text{-}}{{\text{S}}}_{{\text{CM}}}}{{\text{0.5*}}{{\text{P}}}_{{\text{C}}}{\text{*}}\left({\text{100-}}{{\text{N}}}_{{\text{C}}}\right)}$

(2.11)

where S_AM is the area under the waveform line from waveform point A to point M, S_BM is the area under the waveform line from waveform point B to point M, and S_CM is the area under the waveform line from waveform point C to point M.

2.4. Statistical analysis

Statistical analysis of the characteristic parameters was performed using SPSS Statistics (version 26.0, IBM, Inc.). We performed independent sample t-tests for characteristics between the HDP and control groups and selected factors with P < 0.05, and the parameter values are expressed as the X (mean) ± SD (standard deviation). In this paper, the data were characterized by a large variety of parameters and the data volume was small, so we choose the support vector machine algorithm to build the predictive model with hemodynamic factors and pulse wave parameters, respectively. A model for the prediction of HDP was constructed by using MATLAB (R2019b, MathWorks, Inc.), and the model evaluation parameters included the area under the ROC curve (AUC), accuracy, sensitivity, and specificity.

2.5. Ethical approval

The studies were approved by the Ethics Committee of Science and Technology of Beijing University of Technology.

3. Results

3.1. Baseline characteristics

In this research, 168 women were included. We compared the basic information of the pregnant women in the HDP and control groups. We found that there were no statistically significant differences in age and height, and that HDP pregnant women had a significantly higher pre-pregnancy body mass index than the control group (P < 0.05). The gestational age of delivery of those with HDP was significantly lower than that of the control group. The basic information is shown in Table 1.

Table 1. Comparison of basic information on HDP group and control group.

Factor	HDP group	Control group
Age (years)	30.08 ± 3.83	30.16 ± 3.85
Height (cm)	162.1 ± 4.88	162.03 ± 5.29
Pre-BMI	24.35 ± 4.56*	21.34 ± 2.26
Gestational week of delivery (weeks)	37.88 ± 1.87*	39.01 ± 1.05
Note: *P < 0.05. Pre-BMI, pre-pregnancy body mass index.

| Show Table

DownLoad: CSV

3.2. Model construction results

We compared the differences in CO, TPR, systolic blood pressure (SBP), diastolic blood pressure (DBP), and MAP between the HDP group and control group during pregnancy. As shown in Table 2, significant differences in the CO and TPR between the HDP and control groups at 35–40 weeks (P < 0.05). The SBP, DBP, and MAP of the HDP group and control group showed significant differences after the 14th week (P < 0.05). As shown in Table 3, all pulse wave parameters, except W_4, were significantly different in at least one gestational week stage (P < 0.05).

Table 2. Comparison of CO, TPR, SBP, DBP and MAP during pregnancy between HDP group and control group.

Factor	Group	0–13 weeks	14–20 weeks	21–27 weeks	28–34 weeks	35–40 weeks
CO	Control	2.68±0.71	3.02±0.93	2.65±0.67	2.7±0.69	2.67±0.75
	HDP	2.73±0.79	2.73±0.86	2.52±0.71	2.64±0.67	2.41±0.81*
TPR	Control	1.4±0.39	1.18±0.40	1.24±0.40	1.18±0.38	1.23±0.46
	HDP	1.36±0.43	1.3±0.34	1.31±0.34	1.22±0.34	1.45±0.45*
SBP	Control	115.03±10.55	112.1±9.18	107.11±9.88	106.45±9.61	111.5±10.47
	HDP	118.38±8.83	120.14±8.83*	114.68±10.09*	116.83±8.16*	122.5±15.19*
DBP	Control	73.45±8.25	68.66±7.46	67.14±7.82	66.3±7.46	69.81±8.11
	HDP	75±8.14	75.49±9.57*	73.78±9.23*	73.45±7.31*	79.57±12.67*
Map	Control	90.78±8.87	85.94±7.14	83.12±8.48	82.11±7.84	86.43±8.68
	HDP	93.02±8.40	93.78±9.45*	90.38±9.53*	90.7±7.19*	97.35±12.89*
Note: * P < 0.05 compared to Control

| Show Table

DownLoad: CSV

Table 3. Comparison of pulse wave parameters during pregnancy between HDP group and control group.

Factor	Group	0–13 weeks	14–20 weeks	21–27 weeks	28–34 weeks	35–40 weeks
N_A	Control	15.84±2.74	17.26±1.98	17.8±1.92	17.78±2.15	18.09±2.02
	HDP	16.6±2.70*	18.37±2.19*	17.88±2.13	17.33±2.02*	17.71±2.30*
N_B	Control	31.08±4.01	33.17±2.91	33.75±2.93	33.56±3.15	33.6±2.94
	HDP	32.26±4.26*	34.91±3.24*	34.33±3.31*	33.13±3.13*	33.48±3.38
N_C	Control	54.25±6.00	59±5.92	59.48±5.61	59.83±6.10	58.75±5.67
	HDP	55.64±6.43*	60.31±6.07*	60.56±5.95*	59.38±7.14	57.71±5.52*
P_B	Control	72.79±7.95	64.87±8.56	63.46±8.16	61.97±8.84	65.05±9.06
	HDP	73.32±10.01	66.21±10.88*	68.07±8.00*	68.33±10.64*	69.05±11.37*
P_C	Control	40.75±8.08	35.94±8.45	35.55±6.62	34.5±6.87	34.86±7.53
	HDP	40.5±12.29	35.39±7.87	35.73±8.03	34.69±7.67	38.05±6.16*
N_B-A	Control	15.25±1.59	15.91±1.37	15.96±1.26	15.78±1.39	15.51±1.26
	HDP	15.63±1.73*	16.55±1.48*	16.45±1.50*	15.83±1.44	15.73±1.42
N_C-A	Control	38.41±4.35	41.74±4.88	41.68±4.54	42.04±4.95	40.65±4.70
	HDP	39.03±4.50	41.94±5.20	42.68±4.72*	42.05±6.18	39.99±4.53
P_B/A	Control	0.73±0.08	0.65±0.09	0.63±0.08	0.62±0.09	0.65±0.09
	HDP	0.73±0.10	0.66±0.11*	0.68±0.08*	0.68±0.11*	0.69±0.11*
P_C/A	Control	0.41±0.08	0.36±0.08	0.36±0.07	0.35±0.07	0.35±0.08
	HDP	0.41±0.12	0.35±0.08	0.36±0.08	0.35±0.08	0.38±0.06*
H₁	Control	98.14±3.12	98.9±1.92	99.03±1.36	99.18±1.44	99.53±1.09
	HDP	98.35±1.76	99.38±1.11*	98.84±1.54	98.94±1.62	99.62±1.11
H₂	Control	59.43±10.73	52.13±9.48	48.82±7.95	47.38±9.32	47.62±9.15
	HDP	58.6±9.82	50.81±11.50	54.4±9.69*	53.43±10.56*	52.21±10.36*
H₃	Control	38.77±7.71	34.66±8.23	34.69±6.46	33.57±6.75	33.89±7.34
	HDP	38.71±11.18	34.2±7.82	34.72±7.87	33.7±7.20	36.78±6.19*
H₄	Control	8.65±3.77	4.26±3.81	3.22±2.55	3.16±2.73	4.86±3.34
	HDP	6.83±4.19	4.03±3.62	4.29±2.75*	3.87±2.94	5.12±2.65
T₁	Control	15.84±2.74	17.26±1.98	17.8±1.92	17.78±2.15	18.09±2.02
	HDP	16.6±2.70*	18.37±2.19*	17.88±2.13	17.33±2.02*	17.71±2.30*
T₂	Control	31.09±4.00	33.17±2.89	33.75±2.90	33.56±3.14	33.6±2.92
	HDP	32.24±4.23*	34.92±3.23*	34.33±3.29*	33.16±3.09*	33.44±3.37
T₃	Control	54.25±6.00	59±5.92	59.48±5.61	59.83±6.10	58.75±5.67
	HDP	55.64±6.43*	60.31±6.07*	60.56±5.95*	59.38±7.14	57.71±5.52*
T₄	Control	81.07±5.00	79.94±8.46	79.88±8.85	79.45±8.15	82.09±8.65
	HDP	82.25±5.43	79.95±8.62	82.71±6.74*	81.4±7.46*	81.28±6.14
W₁	Control	18.08±3.41	19.76±2.61	20.22±2.53	20.24±2.95	20.46±2.55
	HDP	19.22±3.82*	21.34±2.86*	20.61±2.90*	19.88±2.71*	20.3±3.29
W₂	Control	20.02±2.94	20.73±2.76	20.38±2.31	20.11±2.49	19.2±2.28
	HDP	20.05±2.51	20.7±2.92	20.82±2.58*	20.22±2.80	19.5±2.67
W₃	Control	37.47±5.93	38.57±6.08	40.48±5.72	40.17±5.92	40.95±6.12
	HDP	37.49±5.53	38.95±5.11	40.22±5.43	41.5±5.62*	39.82±5.57*
W₄	Control	26.54±7.56	25.92±10.54	25.98±12.22	26.94±11.04	24.33±10.36
	HDP	25.88±8.55	23.48±11.02	25.05±10.55	24.33±10.74	26.38±8.56
SL_A	Control	1.17±0.04	1.19±0.03	1.2±0.03	1.2±0.03	1.21±0.03
	HDP	1.19±0.04*	1.21±0.03*	1.2±0.03	1.2±0.03*	1.2±0.03*
SL_B	Control	1.04±0.12	0.96±0.12	0.94±0.12	0.92±0.13	0.97±0.13
	HDP	1.07±0.15	1±0.14*	1.02±0.13*	1.01±0.14*	1.03±0.18*
SL_C	Control	0.88±0.16	0.86±0.17	0.86±0.12	0.84±0.15	0.83±0.15
	HDP	0.89±0.23	0.89±0.23	0.89±0.23*	0.89±0.23	0.89±0.23*
SL_D	Control	0.7±0.16	0.77±0.18	0.79±0.17	0.76±0.16	0.8±0.17
	HDP	0.69±0.17	0.81±0.16*	0.83±0.18*	0.8±0.16*	0.8±0.19
SL₁	Control	1.79±0.53	2.22±0.58	2.3±0.55	2.42±0.60	2.27±0.64
	HDP	1.72±0.66	2.03±0.62*	1.96±0.58*	1.99±0.65*	1.97±0.73*
SL₂	Control	1.55±0.20	1.54±0.19	1.55±0.15	1.57±0.18	1.61±0.19
	HDP	1.52±0.26	1.55±0.21	1.51±0.18*	1.57±0.18	1.56±0.18*
SL_2/1	Control	0.88±0.17	0.69±0.12	0.68±0.14	0.67±0.14	0.72±0.13
	HDP	1±0.27	0.74±0.16*	0.84±0.12*	0.81±0.19*	0.94±0.31*
Dr₁	Control	0.48±0.04	0.48±0.04	0.48±0.03	0.48±0.03	0.48±0.03
	HDP	0.47±0.04	0.47±0.04	0.47±0.03*	0.47±0.03*	0.47±0.03
Dr₂	Control	0.34±0.07	0.41±0.08	0.41±0.07	0.43±0.07	0.41±0.08
	HDP	0.34±0.1	0.39±0.09*	0.39±0.06*	0.4±0.09*	0.37±0.07*
Dr₃	Control	0.53±0.06	0.51±0.07	0.49±0.07	0.49±0.06	0.48±0.06
	HDP	0.53±0.05	0.5±0.07	0.48±0.07*	0.48±0.06*	0.5±0.07*
SampEn_AM	Control	0.07±0.01	0.06±0.02	0.06±0.02	0.06±0.03	0.06±0.02
	HDP	0.07±0.02	0.06±0.02	0.06±0.02*	0.06±0.02	0.06±0.02
Note: * P < 0.05 compared to Control

| Show Table

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We used SVMs to build the predictive models with hemodynamic factors and pulse wave parameters, respectively. As shown in Table 4, we found that the AUC of the predictive model with hemodynamic factors was between 0.5 and 0.6, and the prediction effects were not good. The AUC of the predictive model with pulse wave parameters was higher than that of the predictive model with hemodynamic factors at the same stage. As shown in Table 4 and Figure 1, the prediction effects of the model with pulse wave parameters were poor in the first trimester, but the AUC was higher than 0.8 after 14 weeks, and the prediction effect was good. The predictive model with pulse wave parameters had the best prediction effects at 28 – 34 weeks (ROC-AUC = 0.93, accuracy = 84.86%, PR-AUC = 0.91).

Table 4. Test results of the models.

Gestational weeks	Model	AUC	accuracy	Sensitivity	Specificity
0–13 weeks	Hemodynamic	0.62[0.45, 0.78]	65.44%	61.52%	52.30%
	Pulse wave	0.67[0.52, 0.82]	61.82%	76.92%	68.28%
14–20 weeks	Hemodynamic	0.64[0.45, 0.82]	75.51%	88.24%	46.67%
	Pulse wave	0.83[0.77, 0.9]	74.83%	79.73%	69.68%
21–27 weeks	Hemodynamic	0.68[0.47, 0.89]	57.14%	47.06%	72.73%
	Pulse wave	0.85[0.81, 0.9]	76.86%	85.47%	68.80%
28–34 weeks	Hemodynamic	0.70[0.5, 0.89]	58.62%	81.25%	30.77%
	Pulse wave	0.93[0.9, 0.96]	84.86%	91.15%	78.10%
35–40 weeks	Hemodynamic	0.60[0.39, 0.8]	78.75%	86.57%	38.46%
	Pulse wave	0.88[0.8, 0.95]	77.00%	64.71%	89.80%

| Show Table

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Figure 1. PR curves for the predictive model with pulse wave parameters. (A) PR curve for 0–13 weeks; (B) PR curve for 14–20 weeks; (C) PR curve for 21–27 weeks; (D) PR curve for 28–34 weeks; (E) PR curve for 35–40 weeks.

DownLoad: Full-Size Img PowerPoint

4. Discussion

HDP are diseases that coexist with pregnancy and hypertension, which are major causes of increased maternal morbidity and mortality. We developed a predictive model with hemodynamic factors and with pulse wave parameters, respectively, and then we compared the prediction effects of the two models. We found that the predictive model with pulse wave parameters outperformed the predictive model with hemodynamic factors.

Blood pressure monitoring has long been used as an important diagnostic tool for prenatal screening ^[24]. In this study, we found that the SBP and DBP showed significant differences between the HDP and control groups. However, the predictive value of these hemodynamic factors, including the blood pressure, for HDP was not high, which is similar to the findings of Xu et al. ^[25]. Xu et al. found that hemodynamic parameters were only 77.03% effective in predicting HDP. The pulse wave originates from the rhythmic contraction and diastole of the heart and travels through arterial relationships throughout the body, therefore, the pulse wave contains information about cardiovascular pathologies in the body. Pulse waves were closely related to the cardiovascular physiological state, including waveform amplitude and waveform period and other waveform morphological information, which largely reflected many physiopathological characteristics of the human. In this study, after the 14th week, the AUC of the predictive model with pulse wave parameters was over 80%. Compared to predictive models with hemodynamic factors, we found that the predictive model with pulse wave parameters made better predictinos. We took into account the fact that the number of healthy pregnant women and HDP pregnant women in the data was not 1:1, and we further investigated the results by constructing a Precision-Recall curve. As shown in Figure 1, the PR-AUC after 14 weeks was greater than that from 0–13 weeks, and the PR-AUC was greatest at 28–34 weeks, indicating that the best prediction was achieved at that stage. Both the predictive model with hemodynamic factors and the predictive model with pulse wave parameterspoorly predicted at 0–13 weeks, because a pregnant woman's body has not yet shown any obvious abnormal state in the first trimester.

Finally, there were some limitations in this research. First, this research had a high number of collections from pregnant women, but the total number of pregnant women was not large. The number of pregnant women participating in the study needs to be increased. Second, this study was a retrospective study, and further prospective studies should be added to evaluate and improve the model.

5. Conclusions

We developed HDP predictive models by using hemodynamic factors and pulse wave parameters, respectively, and found that the predictive model with pulse wave parameters had better prediction effects. The pulse wave has a good predictive value for HDP, which provided guidance for the non-invasive detection of HDP and offered the possibility of self-monitoring during pregnancy.

Acknowledgments

This research was funded by the National Key R & D Program of China (2019YFC0119700) and National Natural Science Foundation of China (U20A201163). We would like to thank all participants in this research and the obstetrics staff of Beijing Haidian District Maternal and Child Health Hospital for their work in patient registration.

Conflict of interest

We declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Mathematical Biosciences and Engineering

Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy

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1. Introduction

2. Materials and methods

2.1. Subjects

2.2. Data acquisition

2.3. Feature extraction of pulse wave

2.4. Statistical analysis

2.5. Ethical approval

3. Results

3.1. Baseline characteristics

3.2. Model construction results

4. Discussion

5. Conclusions

Acknowledgments

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Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy

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Abstract

1. Introduction

2. Materials and methods

2.1. Subjects

2.2. Data acquisition

2.3. Feature extraction of pulse wave

2.4. Statistical analysis

2.5. Ethical approval

3. Results

3.1. Baseline characteristics

3.2. Model construction results

4. Discussion

5. Conclusions

Acknowledgments

Conflict of interest

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