Acute Pancreatitis in Pregnancy : A Propensity Score Matching Analysis and Dynamic Nomogram for Risk Assessment
© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature..
BACKGROUND: Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6-18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment.
OBJECTIVE: The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish, and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients.
STUDY DESIGN: Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The dataset after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated.
RESULTS: A total of 111 APIP were included. In all APIP patients, hyperlipidemic pancreatitis was the most important reason. The levels of serum amylase, creatinine, albumin, triglyceride, high-density lipoprotein cholesterol, and apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, and the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability.
CONCLUSION: The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Digestive diseases and sciences - (2024) vom: 11. Apr. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tang, Xiaowei [VerfasserIn] |
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| Links: |
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| Themen: |
Acute pancreatitis in pregnancy |
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| Anmerkungen: |
Date Revised 11.04.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s10620-024-08415-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 245 | 1 | 0 | |a Acute Pancreatitis in Pregnancy |b A Propensity Score Matching Analysis and Dynamic Nomogram for Risk Assessment |
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| 520 | |a © 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. | ||
| 520 | |a BACKGROUND: Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6-18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment | ||
| 520 | |a OBJECTIVE: The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish, and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients | ||
| 520 | |a STUDY DESIGN: Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The dataset after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated | ||
| 520 | |a RESULTS: A total of 111 APIP were included. In all APIP patients, hyperlipidemic pancreatitis was the most important reason. The levels of serum amylase, creatinine, albumin, triglyceride, high-density lipoprotein cholesterol, and apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, and the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability | ||
| 520 | |a CONCLUSION: The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Acute pancreatitis in pregnancy | |
| 650 | 4 | |a Dynamic nomogram | |
| 650 | 4 | |a Propensity score match | |
| 700 | 1 | |a Chen, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Shu |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Jiao |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, Bei |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Wensen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Xueqin |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Xiaomin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Shi, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhong, Xiaolin |e verfasserin |4 aut | |
| 700 | 1 | |a Lü, Muhan |e verfasserin |4 aut | |
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