Evidence map›Paper›PMID 38128272›Full record

ArticleAnnals of medicine2023

A novel interpretative tool for early prediction of low cardiac output syndrome after valve surgery: online machine learning models.

Liang Hong, Tianling Feng, Runze Qiu, Shiteng Lin, Yinying Xue, Kaizong Huang, Chen Chen, Jiawen Wang, Rongrong Xie, Sanbing Song and 2 more

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

Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 22% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. Observational
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

12 authors at 3 institutions in 1 country.

Liang HongCardiovascular Intensive Care Unit, Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Tianling FengSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Runze QiuDepartment of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Shiteng LinSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Yinying XueCardiovascular Intensive Care Unit, Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Kaizong HuangDepartment of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Chen ChenDepartment of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Jiawen WangDepartment of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Rongrong XieCardiovascular Intensive Care Unit, Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Sanbing SongCardiovascular Intensive Care Unit, Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Cui ZhangCardiovascular Intensive Care Unit, Department of Critical Care Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Jianjun ZouDepartment of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Nanjing Medical University · CNChina Pharmaceutical University · CNNanjing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveLow cardiac output syndrome (LCOS) is a severe complication after valve surgery, with no uniform standard for early identification. We developed interpretative machine learning (ML) models for predicting LCOS risk preoperatively and 0.5 h postoperatively for intervention in advance.

methodsA total of 2218 patients undergoing valve surgery from June 2019 to Dec 2021 were finally enrolled to construct preoperative and postoperative models. Logistic regression, support vector machine (SVM), random forest classifier, extreme gradient boosting, and deep neural network were executed for model construction, and the performance of models was evaluated by area under the curve (AUC) of the receiver operating characteristic and calibration curves. Our models were interpreted through SHapley Additive exPlanations, and presented as an online tool to improve clinical operability.

resultsThe SVM algorithm was chosen for modeling due to better AUC and calibration capability. The AUCs of the preoperative and postoperative models were 0.786 (95% CI 0.729-0.843) and 0.863 (95% CI 0.824-0.902), and the Brier scores were 0.123 and 0.107. Our models have higher timeliness and interpretability, and wider coverage than the vasoactive-inotropic score, and the AUC of the postoperative model was significantly higher. Our preoperative and postoperative models are available online at http://njfh-yxb.com.cn:2022/lcos.

conclusionsThe first interpretable ML tool with two prediction periods for online early prediction of LCOS risk after valve surgery was successfully built in this study, in which the SVM model has the best performance, reserving enough time for early precise intervention in critical care.

Indexed as

AlgorithmsCardiac Output, LowArea Under CurveCritical CareHumansMachine LearninginterpretativeLow cardiac output syndromemachine learningonlinepredictionvalve surgery

Identifiers

PMID38128272
PMCPMC10763875
OpenAlexW4390042809

What OpenQuestion holds

Textmetadata
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.