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Background <p>We aimed to analyze the correlation between serum lipid levels [total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C)] and recurrence after uterine fibroids (UF) resection, and explore the predictive value of serum lipid levels in determining recurrence after myomectomy.</p> Methods <p>In this retrospective cohort study, 323 patients undergoing first myomectomy who came from Li Huili Hospital, Ningbo Medical Center between December 2019 and January 2023 were included. The primary endpoint was the recurrence of UF within 12&#xa0;months following surgery. Univariate and multivariate logistic regression analyses were adopted to evaluate the association between four serum lipid parameters and the risk of UF recurrence. All included patients were randomly assigned to the training group for nomogram development and the testing group for nomogram validation, with a ratio of 7:3. Receiver operator characteristic, calibration curves, and decision curve analysis were used to assess the predicting performance of constructed nomograms.</p> Results <p>Totally, 98 developed the recurrence of UF within 12&#xa0;months following surgery. Multivariate logistic regression analyses indicated that high levels of TC [odds ratio (OR) = 9.98, 95% confidence interval (CI): 4.28–23.30], LDL-C (OR = 11.31, 95% CI: 4.66–27.47) and HDL-C (OR = 2.37, 95% CI: 1.21–4.64) were associated with recurrence of UF risk. The association between TG level and UF recurrence risk did not statistical significance (<i>P</i> &gt; 0.05). Four online prediction nomograms by integrating serum lipid levels and clinical features for predicting the risk of recurrence of UF were developed (TC-model, TG-model, LDL-C-model and HDL-C-model). Through verification, these models may have good prediction performance for predicting the recurrence of UF risk.</p> Conclusion <p>This study developed and validated prediction nomograms for predicting the risk of UF recurrence. These nomograms can provide individual risk assessment for UF recurrence.</p>

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Impact of serum lipid on recurrence of uterine fibroids: a single center retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2024年12月31日

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  • Yimin Ma,
  • Jingjing Weng,
  • Yingying Zhu

摘要

Background

We aimed to analyze the correlation between serum lipid levels [total cholesterol (TC), triglyceride (TG), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C)] and recurrence after uterine fibroids (UF) resection, and explore the predictive value of serum lipid levels in determining recurrence after myomectomy.

Methods

In this retrospective cohort study, 323 patients undergoing first myomectomy who came from Li Huili Hospital, Ningbo Medical Center between December 2019 and January 2023 were included. The primary endpoint was the recurrence of UF within 12 months following surgery. Univariate and multivariate logistic regression analyses were adopted to evaluate the association between four serum lipid parameters and the risk of UF recurrence. All included patients were randomly assigned to the training group for nomogram development and the testing group for nomogram validation, with a ratio of 7:3. Receiver operator characteristic, calibration curves, and decision curve analysis were used to assess the predicting performance of constructed nomograms.

Results

Totally, 98 developed the recurrence of UF within 12 months following surgery. Multivariate logistic regression analyses indicated that high levels of TC [odds ratio (OR) = 9.98, 95% confidence interval (CI): 4.28–23.30], LDL-C (OR = 11.31, 95% CI: 4.66–27.47) and HDL-C (OR = 2.37, 95% CI: 1.21–4.64) were associated with recurrence of UF risk. The association between TG level and UF recurrence risk did not statistical significance (P > 0.05). Four online prediction nomograms by integrating serum lipid levels and clinical features for predicting the risk of recurrence of UF were developed (TC-model, TG-model, LDL-C-model and HDL-C-model). Through verification, these models may have good prediction performance for predicting the recurrence of UF risk.

Conclusion

This study developed and validated prediction nomograms for predicting the risk of UF recurrence. These nomograms can provide individual risk assessment for UF recurrence.