SynthesisCritical pathways in cardiology2020
Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification.
Synthesis in Critical pathways in cardiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07461415 (CONNECT-HTN Trial - Coaching and Navigation by CHWs Through Telehealth for High-risk Hypertension), which is not on this map. Cited by 11 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.
CONNECT-HTN Trial - Coaching and Navigation by CHWs Through Telehealth for High-risk Hypertension
Who cites it
11 citing papers in PubMed, 22 citations in OpenAlex.
- Artificial intelligence in prehospital assessment of acute coronary syndrome: a scoping review.BMC emergency medicine · 2026Article
- Enhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems.BMC medical informatics and decision making · 2025Article
- Enhancing Prehospital Decision-Making: Exploring User Needs and Design Considerations for Clinical Decision Support Systems.Research square · 2024Article
- Characterizing chest pain in patients with acute coronary syndrome at Vietnam National Heart Institute: a case-control study.The Journal of international medical research · 2024Article
- Prediction of emergency department presentations for acute coronary syndrome using a machine learning approach.Scientific reports · 2024Article
- Article
- Integrated Hands-Free Electronic Patient Care Report (ePCR) Charting (IHeC): Designing the Architecture.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study.Scientific reports · 2022Observational
- Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction: Preangiogram prediction remains difficult.Clinical cardiology · 2022Article
- Article
- Prediction of acute neurovascular syndromes with prehospital clinical features witnessed by bystanders.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTimely prehospital diagnosis and treatment of acute coronary syndrome (ACS) are required to achieve optimal outcomes. Clinical decision support systems (CDSS) are platforms designed to integrate multiple data and can aid with management decisions in the prehospital environment. The review aim was to describe the accuracy of CDSS and individual components in the prehospital ACS management.
methodsThis systematic review examined the current literature regarding the accuracy of CDSS for ACS in the prehospital setting, the influence of computer-aided decision-making and of 4 components: electrocardiogram, biomarkers, patient history, and examination findings. The impact of these components on sensitivity, specificity, and positive and negative predictive values was assessed.
resultsA total of 11,439 articles were identified from a search of databases, of which 199 were screened against the eligibility criteria. Eight studies were found to meet the eligibility and quality criteria. There was marked heterogeneity between studies which precluded formal meta-analysis. However, individual components analysis found that patient history led to significant improvement in the sensitivity and negative predictive values. CDSS which incorporated all 4 components tended to show higher sensitivities and negative predictive values. CDSS incorporating computer-aided electrocardiogram diagnosis showed higher specificities and positive predictive values.
conclusionsAlthough heterogeneity precluded meta-analysis, this review emphasizes the potential of ACS CDSS in prehospital environments that incorporate patient history in addition to integration of multiple components. The higher sensitivity of certain components, along with higher specificity of computer-aided decision-making, highlights the opportunity for developing an integrated algorithm with computer-aided decision support.
Indexed as
Identifiers
What 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.