Clinical Application of a Multiparameter-Based Nomogram Model in Predicting Preeclampsia.

Based on single-center data, the related predictive factors of preeclampsia (PE) were investigated, and a nomogram prediction model was established and validated. A retrospective collection of 93 PE patients admitted to our hospital from January 2019 to January 2021 were included in the PE group. In addition, non-PE pregnant women were selected for physical examination during the same period for matching, and 170 normal pregnant women who met the matching conditions were found as the normal pregnancy group. Clinical data of the selected candidates were collected. The risk factors of PE were screened by logistic regression analysis, and the lipopograph prediction model was constructed and verified. Logistic analysis results showed that age (OR = 3.069, 95% CI = 1.233-7.638), prepregnancy BMI (OR = 2.896, 95% CI = 1.193-7.029), vitamin E deficiency (OR = 2.803, 95% CI = 1.134-6.928), 25-(OH)D (OR = 0.944, 95% CI = 0.903∼9.988), PLGF (OR = 0.887, 95% CI = 0.851∼0.924), PAPP-A (OR = 1.240, 95% CI = 1.131∼1.360), and PI (OR = 6.376, 95% CI = 1.163∼34.967) were the independent risk factors for PE prediction (P < 0.05). The ROC curve showed that the AUC of the model for predicting the risk of PE was 0.957 (95% CI: 0.935-0.979), and the specificity and sensitivity were 0.912 and 0.892, respectively. H-L goodness of the fit test showed that there was no statistical significance in the deviation between the actual observed value and the predicted value of the risk in the line graph model (χ 2 = 7.001, P=0.536). The bootstrap test was used for internal verification, and the original data were repeatedly sampled 1000 times. The average absolute error of the calibration curve is 0.014, and the fitting degree between the calibration curve and the ideal curve is good. Age, prepregnancy BMI, lack of vitamin E, 25-(OH)D, PLGF, PAPP-A, and PI are independent risk factors for predicting PE. The establishment of a nomogram prediction model based on the above parameters can help identify PE high-risk groups in the early clinical stage and provide a reference for individualized clinical diagnosis and treatment.

1. Introduction

Preeclampsia (PE) is a type of hypertensive disease during pregnancy, which is a specific disease in pregnant women. Studies have reported that the incidence of this disease is about 3.2%–12% in all pregnancies, and it is an important factor leading to maternal and infant mortality [1]. At present, the etiology and pathogenesis of PE have not been fully clarified, and only symptomatic treatment can be performed clinically. Some scholars have found that in the process of disease, different individuals have different organs involved, and each individual is not evenly involved in various organs at the same time, showing nonparallel disease development among individuals [2]; the mechanism and multipathway and multifactor pathogenic point of view indicate that PE is a syndrome [3]. This feature makes it difficult for a single parameter to comprehensively predict the occurrence and development of PE. This prediction model uses a multifactor model to estimate the probability of an outcome, which comprehensively considers the interaction between various factors and the impact on the outcome, and is more suitable for predicting the occurrence of PE than a single parameter. The nomogram model can graphically and visually use the logistic regression analysis results to predict individual risk factors. It has been used in medical fields such as oncology and has achieved a good prediction performance. Based on single-center data, this study explored PE-related predictive factors and constructed a PE nomogram prediction model to provide a reference for the early detection of PE patients.

2. Materials and Methods

2.1. Normal Information

A retrospective collection of 93 PE patients admitted to our hospital from January 2019 to January 2021 were included in the PE group. The inclusion criteria were as follows: PE patients meet the diagnostic criteria of “Guidelines for the Diagnosis and Treatment of Hypertensive Diseases in Pregnancy (2015)” [4]; age ≥18 years; no malignant tumor disease. The exclusion criteria were as follows: essential hypertension; patients with endocrine diseases and mental illness; hyperthyroidism; twins or multiple births; those whose data cannot be obtained from the system. In addition, non-PE pregnant women were selected for physical examination during the same period for matching, and 170 normal pregnant women who met the matching conditions were found as the normal pregnancy group. All subjects in the study had no complications before pregnancy (hypertension, diabetes, cardiovascular disease, and blood disease), and the liver and kidney functions were normal. No contraceptive pills were used for one month before pregnancy, and there was no fever or various infections during the examination. This study was approved by the hospital's medical ethics committee.

2.2. Research Methods

The clinical data of the participants were collected, including age, prepregnancy BMI, gestational age, pregnancy history, abortion history, preeclampsia history, gravity labor, edema, oligohydramnios, and urinary protein. Laboratory indicators were as follows: vitamin A (serum vitamin A < 0.3 mg/L is deficient [5]), vitamin E (serum vitamin E < 5.0 mg/L is deficient [5]), white blood cell count, neutrophil count, lymphocyte ratio, hemoglobin level (Hb), platelet count (PLT), mean platelet volume (MPV), alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin, uric acid, creatinine, plasma prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer (D-D), 25-hydroxyvitamin D (25-(OH)D), placental growth factor (PLGF), and pregnancy-associated protein A (PAPP-A). Ultrasound parameters were as follows: Uterine Artery Resistance Index (RI), Pulsation Index (PI), and uterine artery systolic maximum blood flow velocity to end-diastolic blood flow velocity ratio (S/D).

2.3. Statistical Methods

The data in this study were analyzed and processed by SPSS 24.0. Measurement data (consistent with normal distribution) were expressed by , and the t-test was adopted. Using the t-test comparison, qualitative data (N (%)) were described by the chi-square (χ2) test. Statistically significant parameters were included in the multivariate logistic regression analysis to obtain the risk factors for predicting PE. Based on the screened risk factors, R 4.0.3 was used to construct the prediction model of the lipopograph and the receiver operator characteristic curve (ROC) and the calibration curve were used to evaluate the lipopograph model. For the evaluation of the fitting degree of the model through the H-L test, P > 0.05 was considered to be statistically significant.

3. Results

3.1. Baseline Data

There was no significant difference in gestational age, systolic blood pressure, diastolic blood pressure, pregnancy history, abortion history, preeclampsia history, hypertension history, diabetes history, gravity labor, edema, and oligohydramnios between the PE group and the normal pregnancy group (P > 0.05). There were 95 cases (63.44%) in the PE group aged ≥35 years and had a prepregnancy BMI ≥24. Compared with the normal pregnancy group, the PE group had a higher incidence, and the difference was statistically significant (P < 0.05; Table 1).

Table 1

Comparison of baseline data between the two groups.

Group	PE group (n = 93)	Normal pregnancy group (n = 170)	t/χ2	P
Age (years)			16.065	＜0.001
≥35	59 (63.44)∗	64 (37.64)
<35	34 (36.56)	106 (62.35)
Prepregnancy BMI (Kg/m2)			22.726	＜0.001
≥24	59 (63.44)∗	56 (32.94)
<24	34 (36.56)	114 (67.06)
Gestational week (week)	38.18 ± 3.82	39.04 ± 3.64	1.800	0.073
Systolic blood pressure (mmHg)	115.38 ± 10.97	112.97 ± 12.89	1.526	0.128
Diastolic blood pressure (mmHg)	70.41 ± 9.56	68.28 ± 9.37	1.750	0.081
Maternity history (n (%))			0.106	0.745
First birth	77 (82.80)	138 (81.18)
Birth	16 (17.20)	32 (18.82)
History of miscarriage (n (%))			0.696	0.404
Have	26 (27.96)	56 (32.94)
None	67 (72.04)	114 (67.06)
Gravity labor (n (%))			0.368	0.534
Yes	24 (25.81)	50 (29.41)
No	69 (74.19)	120 (70.59)
Edema (n (%))			0.094	0.759
Have	30 (32.26)	57 (33.53)
None	63 (67.74)	113 (66.47)
Oligohydramnios (n (%))			1.931	0.165
Yes	22 (23.66)	53 (31.17)
No	71 (76.34)	117 (68.82)
Urine protein (n (%))			2.357	0.125
Positive	46 (49.46)	68 (40.0)
Negative	47 (50.54)	102 (60.00)

Comparison with the normal pregnancy group, P < 0.05.

3.2. Comparison of Laboratory Parameters and Ultrasound Parameters between the Two Groups

The PE group lacked vitamin E, 25-(OH)D, PLGF, PAPP-A, and PI compared with the normal pregnancy group, and the difference was statistically significant (P < 0.05). There was no significant difference in other indexes between the two groups (P > 0.05), as shown in Table 2.

Table 2

Comparison of laboratory parameters and ultrasound parameters between the two groups.

Group	PE group (n = 93)	Normal pregnancy group (n = 170)	t/χ2	P
Lack of vitamin A (n (%))			0.352	0.553
Yes	21 (22.58)	44 (25.88)
No	72 (77.42)	126 (74.12)
Lack of vitamin E (n (%))			25.671	＜0.001
Yes	62 (66.67)∗	58 (34.12)
No	31 (33.33)	112 (65.88)
White blood cell count (×109/L)	8.92 ± 2.94	9.26 ± 3.11	0.864	0.388
Neutrophil count (×109/L)	12.44 ± 3.42	13.26 ± 3.26	1.917	0.056
Lymphocyte ratio (%)	19.30 ± 5.05	17.99 ± 5.22	1.968	0.050
Hb (g/L)	122.26 ± 26.05	126.41 ± 27.50	1.192	0.234
PLT (109/L)	220.71 ± 39.94	211.19 ± 38.76	1.915	0.057
MPV (fl)	10.22 ± 1.52	9.93 ± 1.92	1.257	0.210
ALT (U/L)	20.34 ± 5.84	18.97 ± 5.95	1.797	0.074
AST (U/L)	20.20 ± 6.55	21.54 ± 7.15	1.496	0.136
Albumin (g/L)	30.85 ± 4.87	32.08 ± 5.24	1.867	0.063
Uric acid (μmol/L)	325.56 ± 84.56	346.71 ± 85.67	1.923	0.056
Creatinine (μmol/L)	57.73 ± 13.10	62.19 ± 21.92	1.794	0.074
LDH (U/L)	258.62 ± 94.56	235.73 ± 88.10	1.963	0.051
PT(s)	10.82 ± 0.98	11.06 ± 1.05	1.814	0.071
APTT(s)	31.98 ± 1.56	32.17 ± 1.47	0.981	0.328
FIB (g/L)	5.20 ± 1.24	4.90 ± 1.26	1.856	0.064
D-D (mg/L)	2.01 ± 0.42	1.91 ± 0.40	1.904	0.058
25-(OH)D (ng/mL)	28.03 ± 8.14∗	34.83 ± 11.27	5.131	＜0.001
PLGF (pg/mL)	68.78 ± 8.84∗	89.66 ± 16.33	11.440	＜0.001
PAPP-A (mg/mL)	25.04 ± 7.89∗	16.68 ± 4.34	11.090	＜0.001
RI	0.54 ± 0.10∗	0.51 ± 0.09	1.656	0.099
PI	1.14 ± 0.34∗	0.96 ± 0.19	5.511	＜0.001
S/D	2.71 ± 0.78	2.29 ± 0.62	0.858	0.391

Comparison with the normal pregnancy group, P < 0.05.

3.3. Multivariate Logistic Regression Analysis

The items with statistical significance in Tables 1 and 2 were included in multivariate logistic regression analysis, and categorical variables were assigned values (age (≥35 years = 1, <35 years = 0), prepregnancy BMI (≥24 = 1, <24 = 0), and lack of vitamin E (yes = 1, no = 0)); the measurement data are entered with actual values, as shown in Table 3. Logistic analysis results show that age (OR = 3.069, 95% CI = 1.233–7.638), prepregnancy BMI (OR = 2.896, 95% CI = 1.193–7.029), vitamin E deficiency (OR = 2.803, 95% CI = 1.134–6.928), 25-(OH)D (OR = 0.944, 95% CI = 0.903∼9.988), PLGF (OR = 0.887, 95% CI = 0.851∼0.924), PAPP-A (OR = 1.240, 95% CI = 1.131∼1.360), and PI (OR = 6.376, 95% CI = 1.163∼34.967) were the independent risk factors for PE, as shown in Table 4.

Table 3

Variable assignment table.

Variable	Code	Specific assignment
Age	X1	≥35 years old = 1, <35 years old = 0
Prepregnancy BMI	X2	≥24 = 1, <24 = 0
Lack of vitamin E	X3	yes = 1, no = 0
25-(OH)D	X4	Actual value entry
PLGF	X5	Actual value entry
PAPP-A	X6	Actual value entry
PI	X7	Actual value entry

Table 4

PE multivariate logistic regression analysis.

Variable	B	SE	Wald	P	OR	95% CI
Age	1.121	0.465	5.810	0.016	3.069	1.233∼7.638
Prepregnancy BMI	1.063	0.452	5.525	0.019	2.896	1.193∼7.029
Lack of vitamin E	1.031	0.462	4.983	0.026	2.803	1.134∼6.928
25-(OH)D	−0.057	0.023	6.300	0.012	0.944	0.903∼9.988
PLGF	−0.012	0.021	32.475	＜0.001	0.887	0.851∼0.924
PAPP-A	0.215	0.047	20.954	＜0.001	1.240	1.131∼1.360
PI	1.853	0.868	4.522	0.033	6.376	1.163∼34.967

3.4. The Establishment and Example of Nomogram

Based on the 7 risk factors screened out from the aforementioned multivariate analysis (age, PRE-pregnancy BMI, vitamin E deficiency, 25-(OH)D, PLGF, PAPP-A, and PI), a rosette risk model for PE prediction was established. The total score was obtained by adding the corresponding scores of each index value of the risk nomogram model, and the total score was converted into the risk prediction probability of PE incidence according to the nomogram (as shown in Figure 1). For example, a pregnant woman aged 35 years with a prepregnancy BMI of 24.20 Kg/m2, deficient vitamin E, 25-(OH)D of 25.25 ng/mL, PLGF of 88.90 pg/mL, and PAPP-A of 17.5 mg/mL, and the PI was 1.15, and its corresponding scores were 10, 10, 10, 22.5, 45, 37.5, and 18.5, with a total score of 153 points, and the corresponding PE occurrence probability was 38%.

Figure 1

The nomogram model for predicting PE.

3.5. Internal Validation of Nomogram

The ROC curve showed that the AUC of the model for predicting the risk of PE was 0.957 (95% CI: 0.935–0.979), and the specificity and sensitivity were 0.912 and 0.892, respectively, as shown in Figure 2(a). The H-L goodness of the fit test showed that there was no statistical significance in the deviation between the actual observed value and the predicted value of the risk in the line graph model (χ2 = 7.001, P=0.536), indicating the fitness of the nomogram model better. The nomogram model was internally verified by the bootstrap test method. The original data was repeatedly sampled 1000 times. The average absolute error of the calibration curve was 0.014, and the fitting degree between the calibration curve and the ideal curve was good, as shown in Figure 2(b).

Figure 2

The ROC curve and the calibration curve of the line graph model to predict the risk of PE occurrence.

4. Discussion

PE is a common disease in pregnant women and seriously threatens the health of mothers and infants. At present, the pathogenesis of PE is not clear, and its pathogenesis may involve maternal, fetal, placental, and other factors, but no single factor can explain all the etiology and mechanism of PE [6]. PE pathological physiology of the disease, including impaired placental trophoblastic invasion, leads to a lack of oxygen to the subsequent placenta, activates the release of the vascular endothelial inflammatory factor, causes vascular endothelial damage in patients with PE, causes systemic small vascular spasm, so as to reduce the perfusion in body organs such as liver and kidney and function is impaired, leading to eclampsia, placental abruption, and the occurrence of complications such as maternal deaths [7, 8]. Therefore, early prediction of PE occurrence, prevention, and intervention are particularly important to reduce the incidence of PE and improve maternal and infant pregnancy outcomes. In this study, PE prediction factors were discussed based on single-center data. A multiparameter PE line chart prediction model was established, and the accuracy of the model was verified.

Multivariate logistic regression analysis showed that age, prepregnancy BMI, vitamin E deficiency, 25-(OH)D, PLGF, PAPP-A, and PI were independent predictive variables of PE (P < 0.05). The reasons are as follows: (1) With the increase of age, the female body will produce many physiological changes, such as gradual muscle atrophy, gradual abdominal fat accumulation, the functional level of various organs declines, and the metabolic capacity decreases [9]. In addition, in the labor process of severe energy consumption, elderly pregnant women are more prone to fatigue and physical exhaustion, which is the reason for the increased risk of cesarean section in elderly pregnant women. (2) High lipid peroxide concentration or abnormal lipid metabolism can lead to oxidative stress and vascular dysfunction. During normal pregnancy, the cytotrophoblast of the uterine artery is transformed from an epithelial to an endothelial phenotype called pseudoangiogenesis, and this remodeling provides nutrients and oxygen to the fetus [10, 11]. For pregnant women with a high BMI, it can easily lead to incomplete pseudoangiogenesis, which in turn causes placental ischemia, triggers the hypoxia-inducible factor, and other placenta-derived factors, and leads to the occurrence of PE [12]. (3) Vitamin E is a fat-soluble vitamin that has the functions of antioxidation, scavenging free radicals, and improving lipid metabolism and can reduce body damage through various ways [13]. The metabolism of pregnant women is vigorous, the production of free radicals increases, and the lipid peroxidation reaction is enhanced. Once the vitamin E content in pregnant women is low or lacks vitamin E, it will lead to the colonization of excess free radicals and damage to the placental vascular endothelium, thereby increasing the incidence of adverse pregnancy and the hazard rate for the outcome [14]. (4) Vitamin D is closely related to gestational obesity, gestational diabetes, and other diseases. Low vitamin D content will affect fetal bone development and increase the risk of puerperal infection and threatened abortion [15]. Vitamin D mainly exists in the form of 25-(OH) D in the body, which can be used as the best indicator to evaluate whether vitamin D deficiency exists in the body. Therefore, the low or lack of 25-(OH)D in early pregnancy may disrupt the balance between anti-inflammatory cytokines and proinflammatory cytokines, thus becoming one of the risk factors for PE [16]. (5) PLGF mainly exists in the heart, lung, and placenta, and is closely related to angiogenesis, vascular endothelial cell growth, and endothelial cell apoptosis. In pregnant women, PLGF is mainly expressed in the trophoblast cells. Some prospective studies have confirmed that those with significantly lower serum PLGF levels in early pregnancy are more likely to develop PE as pregnancy progresses, and low levels of PLGF are associated with the severity of the PE disease. Do away with SEX [17, 18]. This may be because the ability of trophoblast cells to synthesize PLGF is weakened, which affects the proliferation and infiltration ability of extravillous trophoblast cells, limits the dilation of the vascular lumen, causes placental ischemia and hypoxia, and inhibits syncytiotrophoblast PLGF secretion and endothelial cell damage. The postrepair effect is weakened, which further promotes the development of placental ischemia and hypoxia, secondary to PE [19]. (6) PAPP-A belongs to the superfamily of metalloproteinases. It is a macromolecular glycoprotein produced by the syncytiotrophoblast layer and decidua of the placenta. It cannot pass through the placenta and can be detected throughout pregnancy. During pregnancy, the level of PAPP-A in the maternal serum can indirectly reflect placental function to a certain extent [20]. Correlation analysis showed that the serum PAPP-A level in the preeclampsia group was positively correlated with pathological changes such as villus vasopenia, interstitial fibrosis, and fibrinoid necrosis of the placenta [21]. The reason may be that in the hypoxic environment, PAPP-A can increase the biological activity of insulin-like growth factors, promote the migration, proliferation, and differentiation of vascular smooth muscle cells, lead to the proliferation and stenosis of the vascular endothelium, and also increase the level of other inflammatory factors. The migration of endothelial cells and the remodeling of the placental capillary network lead to pathological changes in the placenta and promote the occurrence of PE [22]. (7) Uterine artery blood flow can reflect the uteroplacental circulation impedance, and PI can reflect the overall situation of the uterine artery waveform. Research says, PE patients have higher PI values than normal [23]. This may be due to a series of vascular and physiological changes in the mother during the occurrence and development of PE, such as vascular endothelial cell damage and systemic arteriolar spasm, which make the placenta thick and grow, placental blood supply and circulation disorders, placental perfusion decline, ischemia and hypoxia, resulting in placental villus arteriole spasm, edema, infarction, etc., resulting in changes in umbilical artery hemodynamics, increased resistance, and a higher PI value [24].

The construction of the multiparameter prediction model can effectively predict the occurrence of PE, help clinical decision-making and doctor-patient communication, and improve clinical benefits. Therefore, establishing a PE prediction model is of great value for the diagnosis and treatment of PE. Scholars at home and abroad have developed some prediction models for predicting adverse pregnancy outcomes of pregnant women with PE and their related parameters to establish prediction models for predicting PE. Thangaratinam S et al constructed two predictive models (PREP-L and PREP-S) using COX regression analysis and logistic regression analysis, and incorporated the effects of treatment on outcomes (such as blood pressure and spasmolysis) into the models, but due to the complexity of the equation itself, it is not convenient for clinicians to apply [25]. The nomogram is a graphically represented mathematical formula that is widely used in clinical medicine such as oncology. It has become a commonly used multiparameter prediction model in clinical practice due to its simplicity, convenience, intuition, and little information loss [26]. The statistical regression model is expressed by visual geometric shapes, and the superposition and interaction between predicted indicators are presented in an intuitive form, which can individually predict the risk of clinical events in patients. Based on the risk factors screened out by the single-center data, this study established a nomogram prediction model for PE. After verification, it was found that the AUC under the curve of the nomogram model for predicting the risk of PE was 0.957 (95% CI: 0.935–0.979), and the specificity and sensitivity were 0.912 and 0.892, respectively. This indicates that the nomogram model has good discrimination. The H-l test results show that there is no significant difference between the prediction deviation of the risk prediction value of the rosette model and the actual observed value, suggesting that the rosette model has a good fit. The nomogram model was internally verified by the bootstrap test method. The original data was repeatedly sampled 1000 times. The average absolute error of the calibration curve is 0.014, and the fitting degree between the calibration curve and the ideal curve is good.

5. Conclusion

Age, prepregnancy BMI, lack of vitamin E, 25-(OH)D, PLGF, PAPP-A, and PI are independent risk factors for predicting PE. The establishment of a nomogram prediction model based on the above parameters can help identify PE high-risk groups in the early clinical stage and provide a reference for individualized clinical diagnosis and treatment.