Frankfurt, 16 de enero de 2020: En el número actual, la reconocida revista “Journal of Electrocardiology” publica un estudio revisado por pares que demuestra la impresionante precisión de la cardiosiografía en la detección de enfermedades coronarias. El artículo proporciona resultados sobre la sensibilidad y especificidad de la cardiosiografía: La sensibilidad es del 97% para los hombres y del 90% para las mujeres, es decir, el 97% de los hombres enfermos son reconocidos como enfermos y el 90% de las mujeres. En cuanto a la especificidad, las mujeres que participaron en el estudio superan a los hombres con un 74% y un 76%, es decir, el 76% de las mujeres sanas son reconocidas como sanas y el 74% de los hombres. La cardiografía es el primer procedimiento que se puede utilizar para determinar de forma no invasiva, rápida y económica el riesgo de enfermedad coronaria (CHD) en personas sin síntomas.

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Frankfurt, 10 de marzo de 2020: Por segunda vez en poco tiempo, un estudio clínico ha llegado a la conclusión de que la cardiosiografía logra resultados comparables en la detección de enfermedades coronarias (CHD) a los del estándar de oro actual, la angiografía coronaria.

El equipo alrededor del Dr. Temirlan Erkenov del Departamento de Cirugía Cardíaca del Centro Cardíaco SANA en Cottbus, Alemania, concluyó que: “…la cardiografía es un método simple, preciso y altamente válido que ha demostrado ser una modalidad de diagnóstico no invasivo para la evaluación inicial del estado estable. CAD en entornos clínicos El entorno es adecuado…” (La cardiosiografía como nueva herramienta de diagnóstico no invasiva para la detección de enfermedad arterial coronaria en reposo: un primer estudio prospectivo de precisión diagnóstica; Temirlan Erkenov, Tomasz Stankowski, Oliver Grimmig, Sören Just, Prof. . Oleg Remizov, Prof. Dirk Fritsche)

El estudio incluyó datos de 106 pacientes a los que se les indicó y también se realizó una angiografía coronaria. Luego se realizó una cardiosiografía, cuyo resultado se correlacionó ciegamente con el de la angiografía coronaria. El resultado: la enfermedad vascular se confirmó mediante angiografía coronaria en un total de 86 de los 106 pacientes. La cardiosiografía identificó 82 de los 86 casos (95,4 por ciento), mientras que la ecocardiografía convencional detectó sólo 12 casos. Esto da como resultado una sensibilidad del 95,4 por ciento para la cardiosiografía, una especificidad del 90 por ciento y un valor predictivo positivo del 97,6 por ciento para la enfermedad coronaria.

“En los países occidentales, la enfermedad coronaria es una de las causas más comunes de muerte y una causa común de discapacidad física. La razón del curso severo es el hecho de que la manifestación inicial de la enfermedad puede ser un ataque cardíaco o una muerte súbita cardíaca. La cardiografía es una tecnología nueva, fácil de usar e independiente del examinador, que utiliza la cardiografía vectorial con las modernas posibilidades de análisis de la inteligencia artificial», explican los autores el motivo del estudio y su convincente resultadoo.

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Un estudio del Centro de Diabetes y Corazón de Renania del Norte-Westfalia en Bad Oeynhausen confirma la importancia diagnóstica de la cardiosiografía

Comparación de la cardiosiografía (CSG) con la SPECT miocárdica en enfermedad coronaria sospechada y enfermedad coronaria conocida:

• La cardiosiografía (CSG) muestra una conexión significativa con MPS en el diagnóstico temprano de enfermedad coronaria
• Un CSG normal se correlaciona con una MPS de normal a ligeramente patológica, lo que corresponde a un alto valor predictivo negativo del 98 %.
• El CSG es adecuado como herramienta de preselección para consultas de atención primaria o cardiología a la hora de decidir sobre imágenes no invasivas en pacientes con sospecha de enfermedad coronaria.

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Resumen e interpretación:

Se comparó el índice CSG (parámetro CSG) con la puntuación CVRF en términos de su poder predictivo para la presencia de enfermedad coronaria.

Puntaje PROCAM modificado (puntuación CVRF)
Puntuación de riesgo clásica para determinar la probabilidad de enfermedad coronaria antes de la prueba
Análisis actual:

• 407 pacientes
• 225 pacientes HDZ, Bad Oeynhausen
• 182 pacientes de una consulta general de Berlín

Resultado

“El índice CSG diferencia a aquellos sin signos ni síntomas de enfermedad coronaria de los pacientes con enfermedad coronaria y es un mejor predictor de riesgo cardiovascular que los factores de riesgo clásicos”

Para la evaluación no invasiva de la enfermedad coronaria, el CSG es superior a la puntuación CVRF
El índice CSG se correlaciona significativamente (p < 0,001) con el estado de CAD clínicamente confirmado
El VPN (valor predictivo negativo) del CSG fue del 91%

Al estudio

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. 

Al estudio

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. 

Al estudio

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) 

Al estudio

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.

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