Pre-Hospital Point-of-Care Troponin: Is It Possible to Anticipate the Diagnosis? A Preliminary Report
Abstract
:1. Introduction
2. Materials and Methods
Study Design
- The primary aims of this study are:
- To evaluate the non-inferiority of the 0/1-h ESC algorithm using Atellica hs-cTnI point-of-care (POC) testing compared to the conventional laboratory technique (hs-cTnT Elecsys®, Roche, Basel, Switzerland) for the early diagnosis of acute myocardial infarction (AMI). A non-inferiority margin of 5% difference in negative predictive value is defined. This aims to determine if the new POC test can effectively rule out AMI with a probability at least equivalent to the standard laboratory test, considering the initial sample collection at the patient’s location followed by a second sample in the Emergency Department (ED).
- To analyze rule-out/rule-in times for non-ST-elevation acute coronary syndromes (NSTE-ACS), as well as ED stay times, and correlate these with corresponding times obtained using the standard 0/3-h intra-hospital protocol (involving laboratory-based hs-cTnT testing).
- The secondary aims of the study are:
- Evaluate the feasibility of the first hs-cTn POCT sampling performed in the pre-hospital environment, with the instrumentation provided.
- Study population and recruitment criteria:
- Inclusion criteria:
- over 18 years of age;
- acute non-traumatic chest pain;
- informed consent obtained from the patient.
- Criteria for exclusion:
- less than 18 years of age;
- pregnant women;
- SARS-CoV-2-positive patients;
- patients with ST-elevation myocardial infarction EKG;
- hemodynamically unstable patients.
- Same activation code: (patients were transported to the ER of Arezzo without Atellica VTLi) during the same reference period (June–September 2023).
- Similar inclusion/exclusion criteria.
- Stratification based on cardiovascular risk factors and HEART score.
3. Results
4. Discussion
- Adoption of a faster algorithm: The 0/1 h algorithm used with Atellica VTLi is significantly faster than the traditional 0/3 h algorithm.
- Pre-hospital blood sampling: Obtaining the first blood sample before hospital arrival accelerates the diagnostic process.
- Rapid processing time: Atellica VTLi provides results much faster than traditional laboratory analysis (7.5 min compared to 45–60 min).
- Early identification and treatment of NSTEMI.
- Reduced time spent in the emergency department.
- Potential for improved patient outcomes.
- Reduced healthcare resource utilization and associated costs.
- Stable surface: The Atellica VTLi was always placed on a stable, level surface during operation to minimize the effects of external vibrations. We did not analyze the sample in the ambulance.
- Operator training: Operators were thoroughly trained on proper handling and placement of the device to ensure minimal errors during sample processing.
- Monitoring of device stability: The stability of the device was monitored throughout the study by laboratory experts.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Total Patients (n = 36) | Males (n = 20) | Females (n = 16) | |
---|---|---|---|
Age [age: average ± Standard deviation (DS)] | 66.78 ± 14.29 | 62.05 ± 14.03 | 72.69 ± 13.17 |
Hypercholesterolemia | 13 (36%) | 8 (40%) | 5 (38%) |
Arterial hypertension | 25 (69%) | 15 (75%) | 10 (63%) |
Diabetes mellitus | 8 (22%) | 4 (20%) | 4 (25%) |
Obesity | 6 (17%) | 3 (15%) | 3 (19%) |
Active smoking | 6 (17%) | 2 (10%) | 4 (25%) |
Familiarity for Coronary Artery Dissease (CAD) | 5 (14%) | 3 (15%) | 2 (13%) |
Atherosclerotic pathology * | 11 (31%) | 6 (30%) | 5 (38%) |
HEART score | |||
High risk (score 7–10) | 7 (19%) | 2 (10%) | 5 (31%) |
Medium risk (score 4–6) | 18 (50%) | 12 (60%) | 6 (38%) |
Low risk (score 0–3) | 11 (31%) | 6 (30%) | 5 (31%) |
Total Patients (n = 61) | Males (n = 41) | Females (n = 20) | |
---|---|---|---|
Age (ages: averages ± DS) | 67.56 ± 12.92 | 65.46 ± 12.29 | 71.85 ± 13.74 |
Comorbidity | |||
Hypercholesterolemia | 26 (43%) | 21 (51%) | 5 (25%) |
Arterial hypertension | 43 (70%) | 30 (73%) | 13 (65%) |
Diabetes mellitus | 13 (21%) | 7 (17%) | 6 (30%) |
Obesity | 9 (15%) | 5 (12%) | 4 (20%) |
Active smoking | 15 (25%) | 13 (32%) | 2 (10%) |
Familiarity for CAD | 9 (15%) | 7 (17%) | 2 (10%) |
Atherosclerotic pathology * | 25 (41%) | 21 (51%) | 4 (20%) |
HEART score | |||
High risk (score 7–10) | 7 (19%) | 2 (10%) | 5 (31%) |
Medium risk (score 4–6) | 18 (50%) | 12 (60%) | 6 (38%) |
Low risk (score 0–3) | 11 (31%) | 6 (30%) | 5 (31%) |
hs-cTnI Atellica t 0 h | hs-cTnT Roche t 0 h | hs-cTnI Atellica t 1 h | hs-cTnT Roche t 1 h |
---|---|---|---|
8.1 | 9 | 7.0 | 6 |
11.9 | 13 | 11.2 | 16 |
5.5 | 7 | 7.3 | 14 |
6.4 | 7 | 9.6 | 16 |
3.0 | 5 | 3.5 | 4 |
3.9 | 4 | 4.2 | 6 |
9.1 | 10 | 7.0 | 8 |
47.8 | 80 | 44.6 | 74 |
8.7 | 11 | 8.2 | 8 |
14.3 | 27 | 15.0 | 27 |
5.0 | 4 | 5.0 | 5 |
15.2 | 21 | 16.4 | 22 |
19.0 | 17 | 18.2 | 17 |
11.2 | 12 | 12.0 | 14 |
1250.0 | 505 | 1125.0 | 480 |
6.0 | 7 | 5.7 | 8 |
9.0 | 4 | 8.1 | 5 |
9.7 | 9 | 10.3 | 8 |
24.0 | 18 | 26.0 | 19 |
22.0 | 16 | 20.0 | 15 |
21.0 | 17 | 21.8 | 15 |
661.0 | 190 | 600.0 | 210 |
8.1 | 9 | 4.0 | 10 |
8.1 | 8 | 6.5 | 9 |
15.9 | 7 | 14.5 | 9 |
36.1 | 19 | 65.3 | 53 |
15.0 | 21 | 20.3 | 32 |
12.9 | 19 | 15.0 | 18 |
11.0 | 14 | 18.0 | 16 |
15.7 | 25 | 40.0 | 85 |
153.0 | 101 | 498.0 | 213 |
4.6 | 11 | 6.4 | 12 |
9.1 | 10 | 5.7 | 9 |
9.2 | 15 | 8.2 | 14 |
31.6 | 80 | 31.6 | 81 |
32.7 | 47 | 81.0 | 140 |
Group | Atellica Acc-Ric | Elecsys Acc-Ric |
---|---|---|
Mean | 60.8 | 281.4 |
SD a | 9.58 | 93.25 |
SEM b | 4.28 | 41.7 |
Mean.Diff c | 220.6 | |
DF d | 4 | |
t-value | 4.863 | |
p-value | 0.0083 |
Group | Atellica Acc-Dim | Roche Acc-Dim |
---|---|---|
Mean | 174.81 | 322.52 |
SD a | 126.7 | 73.94 |
SEM b | 22.76 | 13.28 |
Mean.Diff c | 147.71 | |
DF d | 30 | |
t-value | 6.409 | |
p-value | <0.0001 |
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Lazzari, C.; Montemerani, S.; Fabrizi, C.; Sacchi, C.; Belperio, A.; Fantacci, M.; Sbrana, G.; Ognibene, A.; Zanobetti, M.; Nocentini, S. Pre-Hospital Point-of-Care Troponin: Is It Possible to Anticipate the Diagnosis? A Preliminary Report. Diagnostics 2025, 15, 220. https://doi.org/10.3390/diagnostics15020220
Lazzari C, Montemerani S, Fabrizi C, Sacchi C, Belperio A, Fantacci M, Sbrana G, Ognibene A, Zanobetti M, Nocentini S. Pre-Hospital Point-of-Care Troponin: Is It Possible to Anticipate the Diagnosis? A Preliminary Report. Diagnostics. 2025; 15(2):220. https://doi.org/10.3390/diagnostics15020220
Chicago/Turabian StyleLazzari, Cristian, Sara Montemerani, Cosimo Fabrizi, Cecilia Sacchi, Antoine Belperio, Marilena Fantacci, Giovanni Sbrana, Agostino Ognibene, Maurizio Zanobetti, and Simone Nocentini. 2025. "Pre-Hospital Point-of-Care Troponin: Is It Possible to Anticipate the Diagnosis? A Preliminary Report" Diagnostics 15, no. 2: 220. https://doi.org/10.3390/diagnostics15020220
APA StyleLazzari, C., Montemerani, S., Fabrizi, C., Sacchi, C., Belperio, A., Fantacci, M., Sbrana, G., Ognibene, A., Zanobetti, M., & Nocentini, S. (2025). Pre-Hospital Point-of-Care Troponin: Is It Possible to Anticipate the Diagnosis? A Preliminary Report. Diagnostics, 15(2), 220. https://doi.org/10.3390/diagnostics15020220