Troponin | Point of Care Testing in Rural and Remote Community Health Centres
Notice bibliographique
Résumé
What Is the Issue? Acute coronary syndrome (ACS), including myocardial infarction (MI), is a leading cause of morbidity and mortality worldwide and survival rates for MI may be lower in rural and remote settings compared to urban settings. While chest pain is a common symptom of ACS, most patients with this symptom will ultimately not be diagnosed with MI and diagnosis can be challenging. Recommendations for diagnosis include many factors, one of which is serial blood sampling to test for cardiac troponin (cTn), a biomarker for cardiac injury. High sensitivity, laboratory-based assays are typically recommended for cTn testing ideally within 60 minutes of a patient’s presentation. In many rural and remote community health centres, including in Canada, laboratory services are not easily accessible, and results may not be available for hours or days. The current clinical practice in many of these regions is to transfer patients presenting with suspected ACS to the nearest hospital or tertiary care centre, sometimes over long distances and by aeromedical transfer, and often at a high cost. Given many of these patients will not ultimately be diagnosed with MI or require hospital-based care, this poses a concern for patients, staff, and resources. Point-of-care tests (POCT) for cTn may offer an interesting opportunity in these settings. Efficient access to cTn results may potentially improve the ability for health care workers in community settings to more easily diagnose and triage patients at risk for MI and potentially avoid unnecessary patient transfers. However, cTn POCTs are generally less sensitive than laboratory-based assays with varied performance between devices and staff training is required for implementation and quality control. It is important to understand the clinical utility and recommendations regarding the use of cTn POCTs in remote and rural settings. What Did We Do? This rapid review has been conducted in response to a request from a jurisdictional service provider to help determine if implementation of POCTs for a cardiac biomarker, cardiac troponin I (cTnI), should be considered in rural and remote community health or primary care centres. The research questions were codeveloped in response to this request. We aimed to identify and summarize recent evidence on the current use, clinical utility, and guideline recommendations regarding cTnI POCTs to support patient diagnosis and triage in these settings, where access to laboratory-based cTn results may be limited. We searched key resources including journal citation databases and conducted a focused internet search for relevant evidence published since January 2009. No critical appraisal of identified information was conducted. What Did We Find? We identified 2 point-of-care testing programs implemented in rural jurisdictions that include cTnI POCTs, 1 in Australia and 1 in New Zealand; no records met our eligibility criteria regarding cTnI POCT use in rural and remote settings in Canada. The included reports describe program-related processes and experiences, including approaches to structured governance and oversight, training, and quality control. We also identified 2 surveys of general practitioners’ (GPs) current and desired use of cTnI POCT, 1 from Germany and 1 from South Africa, with limited applicability to the setting in Canada. We identified 2 health technology assessments (HTAs (with 3 eligible studies in 4 reports) and 2 primary observational studies examining the clinical utility of cTnI POCT in community health centres in rural and remote settings. One HTA, including 2 studies, and 1 primary observational study assessed cTnI POCT as part of an accelerated diagnostic protocol (ADP), a clinical decision algorithm, incorporating cTnI POCT results with electrocardiogram (ECG) results, clinical assessment, and patient history. The results suggest that cTnI POCTs, when used within the context of an ACS-ADP, have the potential to identify low-risk patients (high sensitivity and negative predictive value), and reduce unnecessary referrals or transfers when there is limited or no efficient access to laboratory-based results. While the included HTAs appear to be well conducted, they identified few primary research studies, studies were focused primarily on 1 cTnI POCT used within specific ADPs, the studies lacked a control group, and no studies assessed high-sensitivity cTnI POCTs. The results cannot be translated to other cTnI POCT devices nor those used within other ADPs. We identified no studies conducted in rural or remote jurisdictions in Canada. While some existing guidelines provide specific recommendations on components of our questions, we did not identify evidence-based guideline recommendations on the use of cTnI POCT in rural or remote community health centres. What Does It Mean? The studies we identified suggest that cTnI POCTs may be useful in rural and remote community care settings to help identify patients presenting with symptoms of ACS who may be at low risk for MI, potentially avoiding unnecessary patient transfers to hospital. As the clinical utility of cTnI POCTs is dependent on the device used, patient-management protocols and contextual factors such as underlying community risk, social acceptability and resources — and we identified no studies conducted in rural and remote community care settings in Canada — the applicability of the results is unclear. To better understand their use, cTnl POCTs should be assessed within a suspected ACS-ADP in the context of interest to better understand their utility. The experiences published about rural or remote community POCT programs outside of Canada highlight various factors that may inform decision-making in Canada including the importance of governance and oversight structures; continuing resources for training, accreditation and quality control; and the challenges with high staff turnover in rural and remote regions. As high-sensitivity cTn POCTs become available, it will be important to assess their clinical utility in settings of interest.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».