Evidence map›Paper›PMID 40770682›Full record

Observational studyBMC cardiovascular disorders2025

Machine learning prediction and explanation of high intraoperative blood pressure variability for noncardiac surgery using preoperative factors.

Zheng Zhang, Yi Duan, Zuozhi Li, Zhifeng Gao, Huan Zhang

Registry-linked trialAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05698433 (Development of a Predictive Model for Intraoperative Blood Pressure Variability), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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.

NCT05698433 completednot on this map

Development of a Predictive Model for Intraoperative Blood Pressure Variability: a Retrospective Cohort Study

TypeobservationalSponsorBeijing Tsinghua Chang Gung HospitalRan2023 to 2023Enrolled47,520ConditionsBlood Pressure ImmeasurableArmsNo intervention
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. 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

5 authors.

Zheng Zhang *Department of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, 102218, China.
Yi Duan *Department of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, 102218, China.
Zuozhi Li *Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Disease, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhifeng GaoDepartment of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, 102218, China. btchgzf@hotmail.com.
Huan ZhangDepartment of Anesthesiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, 102218, China.

Funding

Tsinghua Precision Medicine Foundation 2022TS016
6 · The paper itself

Abstract

backgroundThe objective of this study is to construct an explainable machine learning predictive model for high intraoperative blood pressure variability(IBPV) based on preoperative characteristics, to enhance intraoperative circulatory management and surgical outcomes. This study utilized a retrospective observational design, employing the eXtreme Gradient Boosting (XGBoost) algorithm to create a predictive model for high IBPV.

methodThe data for the study were obtained from the central operating room of a major hospital in Beijing, China, covering the period from March 2016 to April 2022. A total of 37,756 noncardiac surgeries were included in the analysis. The dataset comprised demographic, preoperative laboratory, and diagnostic information. Selection criteria included all noncardiac surgeries with complete preoperative data. High IBPV was defined as a coefficient of variation exceeding 20% during the surgical procedure. The main outcome measure was the prediction of high IBPV, assessed using the area under the receiver operating characteristic curve (AUC), accuracy, and specificity.

resultsThe XGBoost-based model achieved an accuracy of 0.81 and a specificity of 0.99, with a moderate discriminative ability (AUC = 0.60). SHAP analysis identified age and American Society of Anesthesiologists (ASA) classification as the top positive predictors for high IBPV, with maximum SHAP values of 0.4 and 0.2, respectively. Preoperative plasma albumin level was the key negative predictor, with a maximum SHAP value of -0.6. Interactions between preoperative blood calcium and age, and weight and age, were also influential. The model quantified individual high IBPV risk probabilities and variable contributions.

conclusionsThe XGBoost model effectively identifies significant predictors of high IBPV, including age, ASA classification, and plasma albumin levels, and is capable of estimating individual risk probabilities. However, external validation of the model in different clinical settings and populations is needed to further confirm its predictive performance and generalizability.

trial registrationNCT05698433.

Indexed as

Blood PressureDecision Support TechniquesHypertensionMachine LearningPredictive Learning ModelsSurgical Procedures, OperativeAgedBeijingBoosting Machine Learning AlgorithmsClassification AlgorithmsFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Value of TestsBlood pressure variabilityMachine learningModel explainabilityPerioperative

Identifiers

PMID40770682
PMCPMC12326602

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Registered trials

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.