Evidence map›Paper›PMID 35455733›Full record

ArticleJournal of personalized medicine2022

Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning.

Na Shi, Lan Lan, Jiawei Luo, Ping Zhu, Thomas R W Ward, Peter Szatmary, Robert Sutton, Wei Huang, John A Windsor, Xiaobo Zhou and 1 more

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

Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
1.5field-weighted citation impact, top 18% 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, 2 syntheses or guidelines pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Review
  6. Article
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

11 authors at 6 institutions in 4 countries.

Na ShiDepartment of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre and West China-Liverpool Biomedical Research Centre, West China Hospital, Sichuan University, Chengdu 610044, China.
Lan LanIT Center, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
Jiawei LuoWest China Biomedical Big Data Centre, West China Hospital, Sichuan University, Chengdu 610041, China.
Ping ZhuDepartment of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre and West China-Liverpool Biomedical Research Centre, West China Hospital, Sichuan University, Chengdu 610044, China.
Thomas R W WardLiverpool Pancreatitis Study Group, Royal Liverpool University Hospital, Institute of Translational Medicine, University of Liverpool, Liverpool L6 93BX, UK.ORCID 0000-0002-9527-4324
Peter SzatmaryLiverpool Pancreatitis Study Group, Royal Liverpool University Hospital, Institute of Translational Medicine, University of Liverpool, Liverpool L6 93BX, UK.
Robert SuttonLiverpool Pancreatitis Study Group, Royal Liverpool University Hospital, Institute of Translational Medicine, University of Liverpool, Liverpool L6 93BX, UK.
Wei HuangDepartment of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre and West China-Liverpool Biomedical Research Centre, West China Hospital, Sichuan University, Chengdu 610044, China.
John A WindsorSurgical and Translational Research Centre, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand.
Xiaobo ZhouSchool of Biomedical Informatics, University of Texas Health Science Centre at Houston, Houston, TX 77030, USA.
Qing XiaDepartment of Integrated Traditional Chinese and Western Medicine, Sichuan Provincial Pancreatitis Centre and West China-Liverpool Biomedical Research Centre, West China Hospital, Sichuan University, Chengdu 610044, China.
University of Liverpool · GBSichuan University · CNCapital Medical University · CNThe University of Texas Health Science Center at Houston · USUniversity of Auckland · NZWest China Medical Center of Sichuan University · CN

Funding

1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University ZYJC18010Center of Excellence-International Collaboration Initiative Grant 139170052NZ-China Strategic Research Alliance 2016 Award 2016YFE0101800Sichuan Provincial Department of Science and Technology 19ZDYF1765Sichuan Provincial Department of Science and Technology 2020YFS0235
6 · The paper itself

Abstract

backgroundCurrent approaches to predicting intervention needs and mortality have reached 65-85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML).

methodsData were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model's performance.

resultsThree datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores.

conclusionsML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP.

Indexed as

acute pancreatitisinterventionsmachine learningmortalitypredictor

Identifiers

PMID35455733
PMCPMC9031087
OpenAlexW4223481503

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.