Evidence map›Paper›PMID 36934238›Full record

ArticleBMC pregnancy and childbirth2023

A novel clinical prediction model of severity based on red cell distribution width, neutrophil-lymphocyte ratio and intra-abdominal pressure in acute pancreatitis in pregnancy.

Wenyan Liao, Guangwei Tao, Guodong Chen, Jun He, Chunfen Yang, Xiaohua Lei, Shuo Qi, Jiafeng Hou, Yi Xie, Can Feng and 3 more

Open access · goldFull text read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pregnancy and childbirth, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.5field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 1 institution in 1 country.

Wenyan LiaoThe First Affiliated Hospital, Department of Gynaecology and Obstetrics, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Guangwei TaoThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Guodong ChenThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Jun HeThe Nanhua Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Chunfen YangThe First Affiliated Hospital, Department of Gynaecology and Obstetrics, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Xiaohua LeiThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Shuo QiThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Jiafeng HouThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Yi XieThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Can FengThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Xinmiao JiangThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Xin DengThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China.
Chengming DingThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, No. 69, Chuanshan Road, Hengyang, Hunan, 421001, China. dingchengming83@163.com.
University of South China · CN

Funding

Health Commission of Hunan Province 20201950Health Commission of Hunan Province 202105010225Natural Science Foundation of Hunan Province 2021JJ40495, 2022JJ30530Natural Science Foundation of Hunan Province 2022JJ30526Science and Technology Program of Hunan Province 2020SK5181Science and Technology Program of Hunan Province 2021SK51812Science and Technology Research Project (Basic Applied Research Program) from Department of Science and Technology of Hengyang City 202150063929
6 · The paper itself

Abstract

backgroundAcute pancreatitis in pregnancy (APIP) with a high risk of death is extremely harmful to mother and fetus. There are few models specifically designed to assess the severity of APIP. Our study aimed to establish a clinical model for early prediction of severity of APIP.

methodsA retrospective study in a total of 188 patients with APIP was enrolled. The hematological indicators, IAP (intra-abdominal pressure) and clinical data were obtained for statistical analysis and prediction model construction.

resultsAccording to univariate and multivariate logistic regression analysis, we found that red cell distribution width (RDW), neutrophil-lymphocyte ratio (NLR) and Intra-abdominal pressure (IAP) are prediction indexes of the severity in APIP (p-value < 0.05). Our novel clinical prediction model was created by based on the above three risk factors and showed superior predictive power in primary cohort (AUC = 0.895) and validation cohort (AUC = 0.863). A nomogram for severe acute pancreatitis in pregnancy (SAPIP) was created based on the three indicators. The nomogram was well-calibrated.

conclusionRDW, NLR and IAP were the independent risk factors of APIP. Our clinical prediction model of severity in APIP based on RDW, NLR and IAP with predictive evaluation is accurate and effective.

Indexed as

PancreatitisAcute DiseaseErythrocyte IndicesFemaleHumansLymphocytesModels, StatisticalNeutrophilsPregnancyPrognosisRetrospective StudiesAcute pancreatitis in pregnancyInflammationIntra-abdominal pressureNeutrophil-lymphocyte ratioPrediction modelRed cell distribution width

Identifiers

PMID36934238
PMCPMC10024436
OpenAlexW4327761289

What OpenQuestion holds

Textfull text, public
LicenceCC BY
reference markers read1
measurements read11
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.