Evidence map›Paper›PMID 37828597›Full record

ArticleBMC surgery2023

A novel prediction model of pancreatic fistula after pancreaticoduodenectomy using only preoperative markers.

Hiroto Matsui, Yoshitaro Shindo, Daisaku Yamada, Hiroyuki Ogihara, Yukio Tokumitsu, Masao Nakajima, Michihisa Iida, Nobuaki Suzuki, Shigeru Takeda, Yuki Nakagami and 5 more

Open access · goldAbstract read
In one paragraph

Article in BMC surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.8field-weighted citation impact, top 13% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

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  5. Assessing the Adequacy of a Prediction Model.Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine
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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

15 authors at 5 institutions in 1 country.

Hiroto MatsuiDepartment of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, Ube, 755-8505, Yamaguchi, Japan.
Yoshitaro ShindoDepartment of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, Ube, 755-8505, Yamaguchi, Japan.
Daisaku YamadaDepartment of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan.
Hiroyuki OgiharaDepartment of Computer Science and Electronic Engineering, National Institute of Technology, Tokuyama College, Shunan, Yamaguchi, Japan.
Yukio TokumitsuDepartment of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, Ube, 755-8505, Yamaguchi, Japan.
Masao NakajimaDepartment of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, Ube, 755-8505, Yamaguchi, Japan.
Michihisa IidaDepartment of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, Ube, 755-8505, Yamaguchi, Japan.
Nobuaki SuzukiDepartment of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, Ube, 755-8505, Yamaguchi, Japan.
Shigeru TakedaDepartment of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, Ube, 755-8505, Yamaguchi, Japan.
Yuki NakagamiDepartment of Data Science, Graduate School of Economics, Shimonoseki City University, Shimonoseki, Yamaguchi, Japan.
Shogo KobayashiDepartment of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan.
Hidetoshi EguchiDepartment of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, Osaka, Japan.
Tatsuya IokaOncology Center, Yamaguchi University Hospital, Ube, Yamaguchi, Japan.
Yoshihiko HamamotoDivision of Electrical, Electronic and Information Engineering, Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Yamaguchi, Japan.
Hiroaki NaganoDepartment of Gastroenterological, Breast and Endocrine Surgery, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi, Ube, 755-8505, Yamaguchi, Japan. hnagano@yamaguchi-u.ac.jp.
Yamaguchi University · JPOsaka University · JPNational Institute of Technology, Tokuyama College · JPShimonoseki City University · JPYamaguchi University Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSince clinically relevant postoperative pancreatic fistula (CR-POPF) can cause intra-abdominal hemorrhage and abscesses, leading to surgery-related deaths after pancreaticoduodenectomy (PD), its preoperative prediction is important to develop strategies for surgical procedures and perioperative management. This study aimed to establish a novel prediction model for CR-POPF using preoperative markers.

methodsOn a training set of 180 patients who underwent PD at the Yamaguchi University Hospital, a combination of CR-POPF predictors were explored using the leave-one-out method with a unique discrete Bayes classifier. This predictive model was confirmed using a validation set of 366 patients who underwent PD at the Osaka University Hospital.

resultsIn the training set, CR-POPF occurred in 60 (33%) of 180 patients and 130 (36%) of 366 patients in the validation set using selected markers. In patients with pancreatic ductal adenocarcinoma (PDAC), the main pancreatic duct (MPD) index showed the highest prognostic performance and could differentiate CR-POPF with 87% sensitivity and 81% specificity among 84 patients in the training set. In the validation set, the sensitivity and specificity of the MPD index-based model for 130 PDAC samples were 93% and 87%, respectively. In patients with non-PDAC, the MPD index/body mass index (BMI) combination showed the highest prognostic performance and could differentiate CR-POPF with 84% sensitivity and 57% specificity among 96 patients in the training set. In the validation set, the sensitivity and specificity of the MPD index/BMI-based model for 236 non-PDAC samples were 85% and 53%, respectively.

conclusionWe developed a novel prediction model for pancreatic fistulas after PD using only preoperative markers. The MPD index and MPD index/BMI combination will be useful for CR-POPF assessment in PDAC and non-PDAC samples, respectively.

Indexed as

Carcinoma, Pancreatic DuctalPancreatic NeoplasmsBayes TheoremHumansPancreatic FistulaPancreaticoduodenectomyPostoperative ComplicationsRetrospective StudiesRisk FactorsDiscrete Bayes classifierPancreaticoduodenectomyPostoperative pancreatic fistulaPrediction model

Identifiers

PMID37828597
PMCPMC10571374
OpenAlexW4387569829

What OpenQuestion holds

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LicenceCC BY
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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.