Evidence map›Paper›PMID 39254914›Full record

ArticleSleep & breathing = Schlaf & Atmung2024

Screening for moderate to severe obstructive sleep apnea by using heart rate variability features based on random forest algorithm.

Chenxu Zhang, Liangcai Yu, Lin Li, Ping Zeng, Xiaoqing Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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.

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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Chenxu Zhang *West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Liangcai Yu *West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Lin LiWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Ping ZengWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China.
Xiaoqing ZhangWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China. zxq0529@163.com.

Funding

Science and Technology Department of Sichuan Province 2021YFH0165
6 · The paper itself

Abstract

purposeMore than 80% of patients with moderate to severe obstructive sleep apnea (OSA) are still not diagnosed timely. The prediction model based on random forest (RF) algorithm was established by using heart rate variability (HRV), clinical and demographic features so as to screen for the patients with high risk of moderate and severe obstructive sleep apnea.

methodsThe sleep monitoring data of 798 patients were randomly divided into training set (n = 558) and test set (n = 240) in 7:3 proportion. Grid search was applied to determine the best parameters of the RF model. 10-fold cross validation was utilized to evaluate the predictive performance of the RF model, which was then compared to the performance of the Logistic regression model.

resultsAmong the 798 patients, 638 were males and 160 were females, with the average age of 43.51 years old and the mean body mass index (BMI) of 25.92 kg/m

conclusionsThe RF prediction model can effectively distinguish patients with moderate to severe OSA, which is expected to carry out in a large-scale population in order to screening for high-risk patients, and helps to evaluate the effect of OSA treatment continuously.

Indexed as

AlgorithmsHeart RateSleep Apnea, ObstructiveAdultFemaleHumansMaleMass ScreeningMiddle AgedPolysomnographyRandom ForestSensitivity and SpecificityHeart rate variabilityMachine learningObstructive sleep apneaRandom forestScreening

Identifiers

PMID39254914
PMCPMC11567982

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.