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Objectives <p>The unpredictability of HELLP syndrome and the severe adverse outcomes for both mother and children make it especially important for us to seek predictive model. This study aimed to develop a clinically accessible prediction model for assessing the risk of HELLP syndrome progression in patients with hypertensive disorders of pregnancy (HDP) and find effective factors that may predict the progression of HELLP within 3&#xa0;days.</p> Methods <p>We used electronic data from Women’s Hospital, Zhejiang University School of Medicine, between January 1,2014 and December 31,2023. A total of 808 patients were included in this study, including 607 patients in the non-HELLP syndrome group and 201 patients in the HELLP syndrome group. We collected clinical and laboratory information, and conducted single- and multiple-factor logistic regression analyses to identify independent factors influencing the occurrence of HELLP syndrome and the onset of HELLP syndrome within 3&#xa0;days. A nomogram was constructed based on these predictors to provide a visual representation of risk estimation. The model’s performance was evaluated through internal and external validation, with metrics such as the area under the&#xa0;curve(AUC), receiver operating characteristic curve (ROC), precision, recall, and F1 score. Calibration and decision curve analyses were also performed to assess model robustness and clinical utility.</p> Results <p>Multiple logistic regression analysis indicated prenatal BMI, neurologic symptoms, other system symptoms, 24-h urine protein, lowest SBP at admission, lowest DBP at admission, prenatal albumin, prenatal platelet and prenatal blood urea nitrogen as independent factors of HELLP syndrome. The prediction model achieved an AUC of 0.975 (95% CI: 0.966–0.985) in the internal validation dataset with a sensitivity of 0.962(95% CI: 0.962–1.000) and specificity of 0.885(95% CI: 0.962–1.000). The AUC of the external validation dataset was 0.838 (95% CI: 0.785–0.892). The optimal cutoff value calculated using Youden’s index was 0.613, with a sensitivity of 0.891(95% CI: 0.473–0.836) and specificity of 0.722(95% CI: 0.667–0.818). In multivariate regression analysis, blood urea nitrogen and the creatinine-to-blood urea nitrogen ratio were significant predictors in predicting HELLP syndrome within 3&#xa0;days. The sensitivity was found to be 0.68 and 0.65, specificity to be 0.74 and 0.686 respectively.</p> Conclusions <p>This study successfully developed and validated a prediction model that can reliably predict the risk of HELLP syndrome in HDP patients. And blood urine nitrogen and the ratio of creatinine over blood urea nitrogen could be efficient predictors of HELLP syndrome occurring within 3&#xa0;days.</p>

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Development a nomogram for predicting HELLP syndrome in women complicated with gestational hypertension

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月10日

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  • Ying Jiang,
  • Lu-Jiao Chen,
  • Hui-Hui Hu,
  • Neng Jin,
  • Shi-Rui Lv,
  • Chen Fang,
  • Chun-Mei Zhu,
  • Meng-Meng Yang,
  • Dong Xu,
  • Qiong Luo

摘要

Objectives

The unpredictability of HELLP syndrome and the severe adverse outcomes for both mother and children make it especially important for us to seek predictive model. This study aimed to develop a clinically accessible prediction model for assessing the risk of HELLP syndrome progression in patients with hypertensive disorders of pregnancy (HDP) and find effective factors that may predict the progression of HELLP within 3 days.

Methods

We used electronic data from Women’s Hospital, Zhejiang University School of Medicine, between January 1,2014 and December 31,2023. A total of 808 patients were included in this study, including 607 patients in the non-HELLP syndrome group and 201 patients in the HELLP syndrome group. We collected clinical and laboratory information, and conducted single- and multiple-factor logistic regression analyses to identify independent factors influencing the occurrence of HELLP syndrome and the onset of HELLP syndrome within 3 days. A nomogram was constructed based on these predictors to provide a visual representation of risk estimation. The model’s performance was evaluated through internal and external validation, with metrics such as the area under the curve(AUC), receiver operating characteristic curve (ROC), precision, recall, and F1 score. Calibration and decision curve analyses were also performed to assess model robustness and clinical utility.

Results

Multiple logistic regression analysis indicated prenatal BMI, neurologic symptoms, other system symptoms, 24-h urine protein, lowest SBP at admission, lowest DBP at admission, prenatal albumin, prenatal platelet and prenatal blood urea nitrogen as independent factors of HELLP syndrome. The prediction model achieved an AUC of 0.975 (95% CI: 0.966–0.985) in the internal validation dataset with a sensitivity of 0.962(95% CI: 0.962–1.000) and specificity of 0.885(95% CI: 0.962–1.000). The AUC of the external validation dataset was 0.838 (95% CI: 0.785–0.892). The optimal cutoff value calculated using Youden’s index was 0.613, with a sensitivity of 0.891(95% CI: 0.473–0.836) and specificity of 0.722(95% CI: 0.667–0.818). In multivariate regression analysis, blood urea nitrogen and the creatinine-to-blood urea nitrogen ratio were significant predictors in predicting HELLP syndrome within 3 days. The sensitivity was found to be 0.68 and 0.65, specificity to be 0.74 and 0.686 respectively.

Conclusions

This study successfully developed and validated a prediction model that can reliably predict the risk of HELLP syndrome in HDP patients. And blood urine nitrogen and the ratio of creatinine over blood urea nitrogen could be efficient predictors of HELLP syndrome occurring within 3 days.