ArticleAmerican journal of translational research2021
Effectiveness of artificial intelligent cardiac remote monitoring system for evaluating asymptomatic myocardial ischemia in patients with coronary heart disease.
Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- ANMCO position paper E-health integration in cardiology clinical care framework.European heart journal supplements : journal of the European Society of Cardiology · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo explore the effectiveness of cardiac remote monitoring system (CRMS) based on artificial intelligence-enabled ECG algorithm mode for evaluating asymptomatic myocardial ischemia (AMI) in patients with coronary artery disease (CAD).
methodsTwo hundred CAD patients confirmed by coronary angiography (CA) in our hospital were included as the study subjects, 120 of whom developed myocardial ischemia (MI). All patients received 12-lead telephone remote ECG monitoring and evaluation. After monitoring, artificial intelligence-enabled ECG algorithm was performed to observe the detection rate of MI.
resultsCompared with artificial intelligence-enabled ECG algorithm combined with remote ECG monitoring system, the detection rate of remote ECG monitoring system in 120 MI patients was lower (96.67% vs. 86.67%, P<0.01). Among the 120 MI patients, there were 26 patients (21.67%) with symptomatic myocardial ischemia (SMI) and 94 patients (78.33%) with AMI. There was no difference between the two detection methods in the diagnosis of SMI (P>0.05), while there was a difference in the diagnosis of AMI (P<0.01). The degree and duration of ST segment decline and the threshold variability of MI were higher in SMI patients than those in AMI patients (P<0.001). It showed that the lowest frequency of MI was from 0:00 to 06:00, and the highest from 06:01 to 12:00, with significant difference compared with other time periods (P<0.05).
conclusionThe CRMS based on artificial intelligence-enabled ECG algorithm mode can significantly improve the detection rate of AMI. Moreover, small changes of ST segment in AMI patients and circadian rhythm of disease onset were presented.
Indexed as
Identifiers
34786091PMC8581875W3213797419What OpenQuestion holds
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.