Frankfurt, 16. Januar 2020: In der aktuellen Ausgabe veröffentlicht die renommierte Fachzeitschrift “Journal of Electrocardiology” eine Peer-Review-Studie, in der die beeindruckende Präzision der Cardisiographie im Screening Koronarer Herzkrankheiten belegt wird. Der Artikel liefert Ergebnisse zur Sensitivität und Spezifität der Cardisiographie: Die Sensitivität liegt bei den männlichen Probanden bei 97%, bei den weiblichen bei 90%, d.h. 97% der erkrankten Männer werden als erkrankt erkannt und 90% der Frauen. Bei der Spezifität liegen die Frauen, die an der Studie teilnahmen, mit 76% vor den Männern mit 74%, d.h. 76% der gesunden Frauen werden als gesund erkannt und 74% der Männer. Die Cardisiographie ist das erste Verfahren, mit dessen Hilfe nicht-invasiv, schnell und günstig das Risiko einer Koronaren Herzerkrankung (KHK) bei symptomfreien Menschen ermittelt werden kann.

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Frankfurt, 10. März 2020: Zum zweiten Mal innerhalb kurzer Zeit gelangt eine klinische Studie zu dem Ergebnis, dass die Cardisiographie bei der Erkennung Koronarer Herzkrankheiten (KHK) vergleichbar gute Ergebnisse erzielt wie der aktuelle Goldstandard, die Koronarangiographie.

Das Team rund um Dr. Temirlan Erkenov von der Abteilung für Herzchirurgie des SANA Herzzentrums in Cottbus, Deutschland, kam zu dem Schluss, dass: „…die Cardisiographie eine einfache, präzise und hochvalide Methode ist, die sich als nicht-invasive diagnostische Modalität für die Erstbeurteilung stabiler KHK im klinischen Setting eignet…“ (Cardisiography as a novel non-invasive diagnostic tool for the detection of coronary artery disease at rest – a first prospective study of diagnostic accuracy; Temirlan Erkenov, Tomasz Stankowski, Oliver Grimmig, Sören Just, Prof. Oleg Remizov, Prof. Dirk Fritsche)

In die Studie flossen Daten von 106 Patienten ein, bei denen eine Koronarangiographie angezeigt war und auch durchgeführt wurde. Anschließend wurde die Cardisiographie durchgeführt, deren Ergebnis blind mit dem der Koronarangiographie korreliert wurde. Das Ergebnis: Bei insgesamt 86 der 106 Patienten wurde eine Gefäßerkrankung mittels Koronarangiographie bestätigt. Die Cardisiographie identifizierte 82 der 86 Fälle (95,4 Prozent), während in der konventionellen Echocardiographie lediglich 12 Fälle erkannt wurden. Daraus ergibt sich für die Cardisiographie eine Sensitivität von 95,4 Prozent, eine Spezifität von 90 Prozent und ein positiver Voraussagewert von 97,6 Prozent für die KHK.

„In westlichen Ländern ist die Koronare Herzkrankheit eine der häufigsten Todesursachen und ein häufiger Grund für körperliche Behinderungen. Der Grund für den schweren Verlauf ist die Tatsache, dass die initiale Manifestation der Erkrankung ein Herzinfarkt oder der plötzliche Herztod sein kann. Die Cardisiographie ist eine neue, einfach zu handhabende und Untersucher unabhängige Technologie, welche die Vektorcardiographie mit den modernen Analysemöglichkeiten der Künstlichen Intelligenz nutzt“, erklären die Autoren den Grund für die Durchführung der Studie – und für deren überzeugenden Verlauf.

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Studie am Herz- und Diabeteszentrum Nordrhein-Westfalen in Bad Oeynhausen bestätigt diagnostische Relevanz der Cardisiographie

Vergleich der Cardisiographie (CSG) mit der Myokard-SPECT bei Verdacht auf KHK und bekannter KHK:

• Die Cardisiographie (CSG) zeigt in der KHK Vorfelddiagnostik einen signifikanten Zusammenhang mit der MPS
• Eine normale CSG korreliert mit einer normalen bis gering pathologischer MPS, entsprechend hoher negativer prädiktiver Wert von 98 %
• Die CSG eignet sich als Präselektions-Tool für hausärztliche oder kardiologische Praxen für Entscheidung zur nicht-invasiven Bildgebung bei Patienten mit Verdacht auf KHK

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Zusammenfassung und Interpretation:

Verglichen wurde der CSG-Index (Parameter der CSG) mit dem CVRF-Score in Bezug auf die Vorhersagekraft auf das Vorhandensein einer KHK

• Modified PROCAM-Score (CVRF-Score)
  • Klassischer Risiko-Score zur Ermittlung der Vortestwahrscheinlichkeit einer koronaren Herzerkrankung
• Aktuelle Analyse:
  • 407 Patienten
    • 225 Patienten HDZ, Bad Oeynhausen
    • 182 Patienten einer hausärztlichen Praxis, Berlin

Ergebnis

„The CSG Index differentiated those with no signs and symptoms of CHD and patients with CHD and is a better predictor for cardiovascular risk than the classical risk factors”

• Zur nicht-invasiven Beurteilung einer KHK ist die CSG dem CVRF-Score überlegen
• CSG-Index korreliert signifikant (p < 0,001) mit klinisch bestätigtem KHK-Status
• NPV (negativer prädiktiver Wert) der CSG lag bei 91%

Zur Studie

Originally presented at the DGK Herztage 2023. Published in the American Heart Association Circulation. 2023;148:A15181

Purpose of the study  

• Validate Artificial Intelligence-based 5-lead 3D-vectorcardiography (5L3DVCG-AI) 
• Use additional information of 5L3DVCG-AI over standard 12-lead electrocardiography (ECG) in the detection of coronary vascular disease (CVD) at rest  
• Basis for investigation of 5L3DVCG-AI as a new screening tool for CVD in ongoing prospective multinational trials 

Conclusion
These data extend the previous findings of 5L3DVCG-AI identifying CVD patients with cardiac ischaemia from those without to now differentiating healthy controls from CVD and those with higher risk for CVD. 5L3DVCG-AI may thus be a further scalable screening method to identify patients at risk for CVD in need for risk modification or further diagnostic procedures.
5L3DVCG-AI-derived ECG showed high correlation and low bias compared to standard 12-lead ECG. The ongoing prospective large-scale performance clinical trials will have to confirm these preliminary data to verify the diagnostic accuracy. 

Zur Studie

Purpose of the study 

• Validate Artificial Intelligence-based 5-lead 3D-vectorcardiography (5L3DVCG-AI)  
• use additional information of 5L3DVCG-AI over standard 12-lead electrocardiography (ECG) in the detection of coronary vascular disease (CVD) at rest  
• basis for investigation of 5L3DVCG-AI as a new screening tool for CVD in ongoing prospective multinational trials 

Conclusion

• Data extend the previous findings of 5L3DVCG-AI identifying CVD patients with cardiac ischaemia 
• Now differentiating healthy controls from CVD and those with higher risk for CVD 
• Confirmation of results in female population 
• Validation of ECG-reconstruction via heart axis 
• CSG-Index is superior to CVRF-Score in identification of CVD  
• The ongoing prospective large-scale performance clinical trials will have to confirm these preliminary data to verify the diagnostic accuracy. 

Zur Studie

Originally presented at the AHA 23 (American Heart Association Scientific Sessions 2023) and published in the American Heart Association Circulation. 2023;148:A16473.

Purpose of the study  

• Validate Artificial Intelligence based 5 lead 3D vectorcardiography (5 L 3 DVCG AI)  
• Use additional information of 5 L 3 DVCG AI over standard 12 lead electrocardiography (ECG) in the detection of cardiac pathology at rest.  
• Basis for investigation of 5 L 3 DVCG AI as a new screening tool for cardiac pathology in ongoing prospective multinational trials

Conclusion 

• 5 L 3 DVCG AI derived ECG showed high correlation and low bias compared to standard 12 lead ECG 
• Easy to use 5 lead ECG may replace 12 lead ECG without major training or expertise  
• Shorter intervals to be considered when interpreting 5 L 12 L ECG and “normal” values in the ongoing prospective large scale performance clinical trials  
• 5 L 3 DVCG AI identifies persons at risk for CVD (s abstract 15181 PSu 3119) 

Zur Studie

Originally presented at the 90. DGK-Jahrestagung 2024.

Purpose of the study

• Artificial Intelligence (AI) and access to a large global clinical data repository have the potential to boost the performance of Vectorcardiography (VCG) beyond conventional techniques.
• Quantifying cardiovascular risk (CVR) according to SCORE2, QRISK3 or ASCVD is not always feasible, especially in hard to reach populations.
• The modified PROCAM-Score (CVRF-Score) is a validated alternative.

Conclusion

• AI further improves the easy-to-use and inexpensive 5L3DVCG
• 5L3DVCG-AI identifies asymptomatic females at high risk for CVD
• CSG-Index differentiated between no signs and symptoms of CVD and patients with cardiac pathology or CVD
• 5L3DVCG-AI identifies patients at risk for CVD and cardiac pathology
• 5L3DVCG-AI opens up a diagnostic window for early detection of CVD
• CSG is superior to CVRF-Score in differentiating people at risk of CVD or cardiopathology, especially for women and hard-to-reach population
  

Zur Studie

The SBRI study (SBRIH21P3013) investigated how the Cardisiography could be incorporated into the NHS, focusing on its application within Primary Care to enhance early detection of cardiovascular issues and improve the efficiency of patient referrals to Secondary Care.

Originally presented at the DGK-Herztage 2024. https://doi.org/10.1007/s00392-024-02526-y

Introduction:
Artificial Intelligence-based 5-lead 3D-vectorcardiography (5L3DVCG-AI) is easy to use, quantifies the individual risk for cardiac pathologies in need for further diagnostic procedures and may, with good results in women, facilitate and align the cardiological diagnostic pathway for coronary heart disease.

Methods:
In this multicentre retro- and prospective study, recordings from 5L3DVCG-AI were externally validated against automated 12-lead ECG in 287 patients with and without cardiac pathologies. 5L3DVCG-AI derived 12-lead ECG (VCG-ECG) was reconstructed from 5L3DVCG-AI with an algorithm (hgh-1.1.23) and time intervals were derived from 5L3DVCG-AI. Two independent specialist cardiologists masked for 12-lead ECG results interpreted VCG-ECG qualitatively and quantitatively. The following variables were compared between 5L3DVCG-AI and 12-lead ECG: electric heart axis and rhythm, HR, and time intervals for P, PQ, QT, QTcB. Presence of cardiac pathology (CP) was categorised as exclusion of any CP (control), mild CP or overt CP by 2 independent cardiologists from clinical practice with a follow-up period of 16.2 ± 7.5 months. Diagnostic accuracy was assessed for ECG findings and abnormalities. Correlation between VCG-ECG and 12-lead ECG was calculated for electric heart axis (Spearman’s rank correlation coefficient). Agreement was tested with Bland-Altman analyses. The modified PROCAM-score was used for cardiovascular risk factor (CVRF) assessment.

Results:
Of 287 patients (m:w 62:38%, 55.9 ± 16.1 years) of mixed ethnicity and moderate CVRF (2.1 ± 1.2), 70% were controls, 21% had mild CP and 9% overt CP. Strong correlations were seen for HR and electric heart axis  (r=0.97 and r=0.71, p<0.001 respectively) with 12-lead ECG which was visually confirmed. Quantification of ECG variables such as times for P, PQ, QT, QTcB showed strong correlations (all p<0.001), low systematic bias (SB; -0.9 to -3.9%) and narrow 95% upper and lower limits of agreement (uLoA; 5.6 to 17.3%, lLoA; -8.5 to 19.1%). In women, 5L3DVCG-AI at rest is strong in detecting CVR (r=0.71, p<0.001, mod. PROCAM-Score) and in differentiating cardiac pathologies (β=0.24, T=2.64, p<0.05, corrected for CVR). ECG abnormalities (AF, delayed R-progression, cardiac ischaemias, LVOT VES, sinus bradycardia, sinus tachycardia, AT, LAHB, LHH) were visually detected from 12-lead ECG and VCG-ECG with a sensitivity of 75% and specificity of 100% with low interrater variability. All clinically relevant ECG pathologies were displayed in both systems. The VCG-ECG had only small differences regarding the amplitude of the QRS-complex in three cases.

Conclusion:
In summary, 5L3DVCG-AI is an easy-to-use and feasible technology with good accuracy and reproducibility for electric heart axis, ECG-parameters and intervals and thus offers additional value in detecting individuals with cardiac pathologies or cardiac risks. 5L3DVCG-AI may replace conventional 12-lead ECG in the General Practice or cardiological outpatient departments. Especially for women this may offer additional value.

Zur Studie

Originally presented at the 91st annual German Cardiac Society meeting 2025.

Background:
Reduced left ventricular ejection fraction (LV-EF) and left ventricular hypertrophy (LVH) are associated with high mortality and morbidity. This study evaluated the diagnostic performance of a modified vectorcardiography (VCG) algorithm, a technique which records cardiac electrical activity in three dimensions, for detecting reduced LV-EF and LVH.

Methods:
This study included patients with reduced LV-EF (< 40%) and LVH (indexed LV mass > 55 g/m²) compared with controls without cardiac pathology. Cardiac magnetic resonance imaging (CMR) was performed and used as the reference standard for measuring LV-EF and LV mass. The modified VCG analyzed 533 VCG parameters per heartbeat to classify cardiac status. In total, 533 regular parameters and 50 Frenet-Serret parameters were analyzed, amounting to 583 parameters. Parameters were selected based on a low p-value from the Mann-Whitney-Wilcoxon test, indicating statistical significance. Bayes‘ theorem was used to update prior probabilities and to derive posterior probabilities for reduced LV-EF and LVH when compared with controls.

Results:
A total of 280 patients were included. The group with reduced LV-EF (n=40) had a mean age of 56 ± 16 years and was predominantly male (78%), with a median LV-EF of 31.5% (23.4-36.3%). The LVH group (n=209) had a mean age of 60 ± 16 years (87% male) with an indexed LVM of 67g/m² (61-77 g/m²). Controls (n=31) had a mean age of 50 ± 16 years (61% male), normal LV-EF of 62 ± 5.6% and indexed LV mass of 40g/m² (35-47g/m²). In total 4 VCG parameters were used. A sensitivity of 80.0%, a specificity of 85.7% and an accuracy of 82.9% were achieved for the detection of reduced LV-EF. A Sensitivity of 74.5%, a specificity of 68.6% and an accuracy of 73.6% were reached for the detection of LVH. In with patients haing both reduced LV-EF and LVH (n=64), the VCG demonstrated a sensitivity of 79.3% and a specificity of 85.7% with an accuracy of 82.8%.

Conclusion:
The modified VCG algorithm showed notable diagnostic value for detecting reduced LV-EF and LVH. The VCG could serve as a fast, non-invasive, and cost-effective method to assist clinicians in identifying significant cardiac pathologies and guiding further diagnostic steps. Further optimization of the VCG algorithm is necessary to improve differentiation between healthy individuals and those with cardiac disease, enhancing its clinical use.

Zur Studie

Özyüksel A, Salatzki J, and Steen H (2024). Artificial intelligence- supervised vectorcardiography for the diagnosis of a young adult with abnormal origin of the right coronary artery from aorta. Cardiology in the Young, page 1 of 3.

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