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{{AFC comment|1=A good start but needs more citations. Cite where you found facts, whole sections are currently unreferenced. [[User:Bobby Cohn|Bobby Cohn]] ([[User talk:Bobby Cohn|talk]]) 13:37, 22 November 2024 (UTC)}}

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{{Short description|New stage of Heart Failure}}
{{Short description|New stage of Heart Failure}}
{{Draft topics|medicine-and-health}}
{{Draft topics|medicine-and-health}}
{{AfC topic|other}}
{{AfC topic|other}}
{{AfC submission|||ts=20241124172745|u=HMSDnSingh|ns=118}}
{{Draft article}}


'''Ray's disease''', also known as '''Impending Heart Failure''' ('''IHF'''), is a preclinical stage of heart failure in which asymptomatic patients (AHA stages A or B)<ref>{{cite journal |last1=Heidenreich |first1=Paul A. |last2=Bozkurt |first2=Biykem |last3=Aguilar |first3=David |last4=Allen |first4=Larry A. |last5=Byun |first5=Joni J. |last6=Colvin |first6=Monica M. |last7=Deswal |first7=Anita |last8=Drazner |first8=Mark H. |last9=Dunlay |first9=Shannon M. |last10=Evers |first10=Linda R. |last11=Fang |first11=James C. |last12=Fedson |first12=Savitri E. |last13=Fonarow |first13=Gregg C. |last14=Hayek |first14=Salim S. |last15=Hernandez |first15=Adrian F. |last16=Khazanie |first16=Prateeti |last17=Kittleson |first17=Michelle M. |last18=Lee |first18=Christopher S. |last19=Link |first19=Mark S. |last20=Milano |first20=Carmelo A. |last21=Nnacheta |first21=Lorraine C. |last22=Sandhu |first22=Alexander T. |last23=Stevenson |first23=Lynne Warner |last24=Vardeny |first24=Orly |last25=Vest |first25=Amanda R. |last26=Yancy |first26=Clyde W. |title=2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines |journal=Circulation |date=3 May 2022 |volume=145 |issue=18 |doi=10.1161/CIR.0000000000001063|pmid=35363499 }}</ref> are identified as having a high risk of developing symptomatic heart failure (AHA stage C or D)<ref>{{cite journal |last1=Heidenreich |first1=Paul A. |last2=Bozkurt |first2=Biykem |last3=Aguilar |first3=David |last4=Allen |first4=Larry A. |last5=Byun |first5=Joni J. |last6=Colvin |first6=Monica M. |last7=Deswal |first7=Anita |last8=Drazner |first8=Mark H. |last9=Dunlay |first9=Shannon M. |last10=Evers |first10=Linda R. |last11=Fang |first11=James C. |last12=Fedson |first12=Savitri E. |last13=Fonarow |first13=Gregg C. |last14=Hayek |first14=Salim S. |last15=Hernandez |first15=Adrian F. |last16=Khazanie |first16=Prateeti |last17=Kittleson |first17=Michelle M. |last18=Lee |first18=Christopher S. |last19=Link |first19=Mark S. |last20=Milano |first20=Carmelo A. |last21=Nnacheta |first21=Lorraine C. |last22=Sandhu |first22=Alexander T. |last23=Stevenson |first23=Lynne Warner |last24=Vardeny |first24=Orly |last25=Vest |first25=Amanda R. |last26=Yancy |first26=Clyde W. |title=2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines |journal=Circulation |date=3 May 2022 |volume=145 |issue=18 |doi=10.1161/CIR.0000000000001063|pmid=35363499 }}</ref> within a specific time frame. This stage is diagnosed through the Heart Failure Predictor (HFP), an AI-based software tool designed to detect early signs of heart failure using 12-lead ECG data alone.
'''Ray's Disease''', also known as '''Impending Heart Failure''' ('''IHF'''), is a preclinical stage of heart failure in which asymptomatic patients are identified as having a high risk of developing symptomatic heart failure within a specific time frame. This stage is diagnosed through the Heart Failure Predictor (HFP), an AI-based software tool designed to detect early signs of heart failure using 12-lead ECG data alone.


== Background ==
== Background ==


Heart failure (HF) is a chronic and progressive condition characterized by the heart's inability to pump blood efficiently, ultimately leading to symptoms such as shortness of breath, fatigue, and edema.<ref>{{cite journal |last1=Bozkurt |last2=Biykem |title=Universal Definition and Classification of Heart Failure |journal=Journal of Cardiac Failure, |issue=4 |pages=387 - 413 |doi=10.1016/j.cardfail.2021.01.022}}</ref> Traditional diagnostics often rely on symptom-based assessments, which may miss early, asymptomatic phases of the disease. Ray's Disease represents an important clinical advantage by identifying patients at risk for both [[heart failure with reduced ejection fraction]] (HFrEF) and [[heart failure with preserved ejection fraction]] (HFpEF) even before symptoms develop, allowing for timely intervention.
Heart failure (HF) is a chronic and progressive condition characterized by the heart's inability to pump blood efficiently, ultimately leading to symptoms such as shortness of breath, fatigue, and edema.<ref>{{cite journal |last1=Bozkurt |last2=Biykem |title=Universal Definition and Classification of Heart Failure |journal=Journal of Cardiac Failure |date=2021 |volume=27 |issue=4 |pages=387–413 |doi=10.1016/j.cardfail.2021.01.022}}</ref> Traditional diagnostics often rely on symptom-based assessments, which may miss early, asymptomatic phases of the disease<ref>{{cite journal |last1=Ramani |first1=Gautam V. |last2=Uber |first2=Patricia A. |last3=Mehra |first3=Mandeep R. |title=Chronic Heart Failure: Contemporary Diagnosis and Management |journal=Mayo Clinic Proceedings |date=February 2010 |volume=85 |issue=2 |pages=180–195 |doi=10.4065/mcp.2009.0494|pmid=20118395 |pmc=2813829 }}</ref>. Ray's disease represents an important clinical advantage by identifying patients at risk for both [[heart failure with reduced ejection fraction]] (HFrEF) and [[heart failure with preserved ejection fraction]] (HFpEF) even before symptoms develop, allowing for timely intervention<ref>{{cite journal |last1=Wang |first1=H |last2=Gao |first2=C |last3=Guignard-Duff |first3=M |last4=Cole |first4=C |last5=Hall |first5=C |last6=Larman |first6=M |last7=Baruah |first7=R |last8=Gao |first8=H |last9=Mamza |first9=J B |last10=Lang |first10=C C |last11=Mordi |first11=I |title=Importance of early diagnosis and treatment of heart failure across the spectrum of ejection fraction |journal=European Heart Journal |date=9 November 2023 |volume=44 |issue=Supplement_2 |doi=10.1093/eurheartj/ehad655.892}}</ref>.


Ray's Disease was introduced by Dr. Abhijit Ray following extensive research aimed at improving early detection and management of heart failure. This classification provides a more actionable approach for at-risk patients, as studies have shown that individuals identified with Ray's Disease have a high probability of experiencing symptomatic heart failure within a specific time frame—usually within 3 to 24 months.
Ray's disease was introduced by Dr. Abhijit Ray following extensive research aimed at improving early detection and management of heart failure<ref>{{cite journal |last1=Ray |first1=Abhijit |title=Understanding Ray's Disease : A Study of its Incidence and Diagnostic Approaches |journal=American Journal of Biomedical Science & Research |date=November 12, 2024 |volume=24 |issue=5 |doi=10.34297/AJBSR.2024.24.003242|doi-broken-date=2024-11-23 }}</ref>. This classification provides a more actionable approach for at-risk patients, as studies have shown that individuals identified with Ray's disease have a high probability of experiencing symptomatic heart failure within a specific time frame—usually within 3 to 24 months<ref>{{cite news |title=Introducing Ray's Disease: A revolution in cardiac care |url=https://www.londondaily.news/introducing-rays-disease-a-revolution-in-cardiac-care/}}</ref>.


== Diagnosis ==
== Diagnosis ==


Ray's Disease is diagnosed using the Heart Failure Predictor (HFP) software,<ref>{{cite journal |last1=Ray |first1=Abhijit |title=Impending Heart Failure : An Artificial Intellectual Reality |journal=medRxiv |date=August 14, 2024 |url=https://www.medrxiv.org/content/10.1101/2024.08.12.24311907v1}}</ref> which assesses the [[left ventricular ejection fraction]] (LVEF) and other cardiac parameters from a standard 12-lead ECG. The HFP algorithm leverages machine learning and a patented formula<ref>{{cite web |last1=Ray |first1=Abhijit |title=Determining LVEF using electrocardiographic signals |url=https://patents.google.com/patent/US11517204B2/en?oq=11517204 |language=en |date=6 December 2022}}</ref> to analyze cardiac indicators and assign a risk classification. Those categorized as "High Risk" by the HFP algorithm are diagnosed as Ray's Disease, or Impending Heart Failure.
Ray's disease is diagnosed using the Heart Failure Predictor (HFP) software,<ref>{{cite journal |last1=Ray |first1=Abhijit |title=Impending Heart Failure : An Artificial Intellectual Reality |journal=medRxiv |date=August 14, 2024 |doi=10.1101/2024.08.12.24311907 |url=https://www.medrxiv.org/content/10.1101/2024.08.12.24311907v1}}</ref> which assesses the [[left ventricular ejection fraction]] (LVEF) and other cardiac parameters<ref>{{cite journal |last1=Struthers |first1=A. D |title=HEART FAILURE: The diagnosis of heart failure |journal=Heart |date=1 September 2000 |volume=84 |issue=3 |pages=334–338 |doi=10.1136/heart.84.3.334}}</ref> from a standard 12-lead ECG. The HFP algorithm leverages linear regression model of machine learning<ref>{{cite book |last1=Montgomery |first1=Douglas C. |last2=Peck |first2=Elizabeth A. |last3=Vining |first3=G. Geoffrey |title=Introduction to linear regression analysis |date=2020 |publisher=Wiley |location=Hoboken, New Jersey |isbn=9781119578727 |edition=Fifth |url=https://ocd.lcwu.edu.pk/cfiles/Statistics/Stat-503/IntroductiontoLinearRegressionAnalysisbyDouglasC.MontgomeryElizabethA.PeckG.GeoffreyViningz-lib.org.pdf}}</ref> and a patented formula<ref>{{cite web |last1=Ray |first1=Abhijit |title=Determining LVEF using electrocardiographic signals |url=https://patents.google.com/patent/US11517204B2/en?oq=11517204 |language=en |date=6 December 2022}}</ref> to analyze cardiac indicators and assign a risk classification. Those categorized as "High Risk" by the HFP algorithm are diagnosed as Ray's disease, or Impending Heart Failure.

⚫
Data Acquisition: A [[12-lead ECG]] is obtained from the patient.

Data Processing and Analysis: The ECG data undergoes signal quality checks<ref>{{cite journal |last1=Rahman |first1=Saifur |last2=Karmakar |first2=Chandan |last3=Natgunanathan |first3=Iynkaran |last4=Yearwood |first4=John |last5=Palaniswami |first5=Marimuthu |title=Robustness of electrocardiogram signal quality indices |journal=Journal of the Royal Society Interface |date=April 2022 |volume=19 |issue=189 |doi=10.1098/rsif.2022.0012}}</ref>, and the HFP algorithm calculates LVEF and analyzes additional indicators.

⚫
Risk Prediction: Using machine learning, the algorithm classifies the patient's risk as either "High Risk" (Ray's disease) or "Low Risk".


⚫
Data Acquisition: A 12-lead ECG is obtained from the patient.
Data Processing and Analysis: The ECG data undergoes signal quality checks, and the HFP algorithm calculates LVEF and analyzes additional indicators.
⚫
Risk Prediction: Using machine learning, the algorithm classifies the patient's risk as either "High Risk" (Ray's Disease) or "Low Risk."
Output: Results are displayed in an easy-to-interpret format for clinicians.
Output: Results are displayed in an easy-to-interpret format for clinicians.


== Clinical Performance ==
== Clinical performance ==


The Heart Failure Predictor (HFP) has been clinically validated to ensure reliable predictions. Studies have demonstrated high accuracy, with sensitivity at 97.5% and specificity at 99.9%, meaning it correctly identifies both those likely to develop heart failure and those at low risk. Patients classified as high risk were found to develop heart failure symptoms at rates consistent with HFP's positive predictive value of 98.6%.
The Heart Failure Predictor (HFP) has been clinically validated to ensure reliable predictions. Studies have demonstrated high accuracy, with [[sensitivity]] at 97.5% and [[specificity]] at 99.9%<ref>{{cite news |title=Introducing Ray's Disease: A revolution in cardiac care |url=https://www.londondaily.news/introducing-rays-disease-a-revolution-in-cardiac-care/ |work=www.londondaily.news/ |language=en}}</ref>, meaning it correctly identifies both those likely to develop heart failure and those at low risk. Patients classified as high risk were found to develop heart failure symptoms at rates consistent with HFP's positive predictive value of 98.6%<ref>{{cite journal |last1=Ray |first1=Abhijit |title=Impending Heart Failure : An Artificial Intellectual Reality |journal=medRxiv |date=August 14, 2024 |doi=10.1101/2024.08.12.24311907}}</ref>.


== Clinical Significance ==
== Clinical significance ==


Identifying patients with Ray's disease provides an opportunity to prevent or delay heart failure progression through early interventions, potentially reducing healthcare costs<ref>{{cite journal |last1=Heidenreich |first1=Paul A. |last2=Fonarow |first2=Gregg C. |last3=Opsha |first3=Yekaterina |last4=Sandhu |first4=Alexander T. |last5=Sweitzer |first5=Nancy K. |last6=Warraich |first6=Haider J. |last7=Butler |first7=Javed |last8=Hsich |first8=Eileen |last9=Pressler |first9=Susan Bennett |last10=Shah |first10=Kevin |last11=Taylor |first11=Kenneth |last12=Sabe |first12=Marwa |last13=Ng |first13=Tien |title=Economic Issues in Heart Failure in the United States |journal=Journal of Cardiac Failure |date=March 2022 |volume=28 |issue=3 |pages=453–466 |doi=10.1016/j.cardfail.2021.12.017|pmid=35085762 }}</ref> and improving patient outcomes<ref>{{cite journal |last1=Herscovici |first1=Romana |last2=Kutyifa |first2=Valentina |last3=Barsheshet |first3=Alon |last4=Solomon |first4=Scott |last5=McNitt |first5=Scott |last6=Polonsky |first6=Bronislava |last7=Lee |first7=Andy Y. |last8=Zareba |first8=Wojciech |last9=Moss |first9=Arthur J. |last10=Goldenberg |first10=Ilan |title=Early intervention and long-term outcome with cardiac resynchronization therapy in patients without a history of advanced heart failure symptoms |journal=European Journal of Heart Failure |date=September 2015 |volume=17 |issue=9 |pages=964–970 |doi=10.1002/ejhf.281|pmid=25921965 }}</ref>. As a predictive category, Ray's disease shifts the focus of cardiac care from reactive treatment to proactive risk management<ref>{{cite web |title=Proactive Care Framework for Heart Failure |url=https://uclpartners.com/our-priorities/cardiovascular/proactive-care/proactive-care-framework-for-heart-failure/ |website=UCLPartners}}</ref>.
Identifying patients with Ray's Disease provides an opportunity to prevent or delay heart failure progression through early interventions, potentially reducing healthcare costs and improving patient outcomes. As a predictive category, Ray's Disease shifts the focus of cardiac care from reactive treatment to proactive risk management.


== Co-morbidities and Risk Factors ==
== Co-morbidities and risk factors ==


Patients identified with Ray's Disease commonly present with higher incidences of conditions such as [[hypertension]], [[diabetes]], [[coronary artery disease]], and [[chronic kidney disease]] compared to the general population.<ref>{{cite journal |last1=Ray |first1=Abhijit |title=Understanding Ray's Disease : A Study of its Incidence and Diagnostic Approaches |journal=American Journal of Biomedical Science & Research |date=November 12, 2024 |volume=24 |issue=5 |doi=10.34297/AJBSR.2024.24.003242}}</ref> This correlation underscores the importance of a holistic approach to treatment, addressing both Ray's Disease and associated co-morbidities.
Patients identified with Ray's disease commonly present with higher incidences of conditions such as [[hypertension]], [[diabetes]], [[coronary artery disease]], and [[chronic kidney disease]] compared to the general population<ref>{{cite journal |last1=Ray |first1=Abhijit |title=Understanding Ray's Disease : A Study of its Incidence and Diagnostic Approaches |journal=American Journal of Biomedical Science & Research |date=November 12, 2024 |volume=24 |issue=5 |doi=10.34297/AJBSR.2024.24.003242|doi-broken-date=2024-11-23 }}</ref><ref>{{cite web |title=Hypertension |url=https://www.who.int/news-room/fact-sheets/detail/hypertension |website=www.who.int |language=en}}</ref><ref>{{cite web |title=Diabetes |url=https://www.who.int/news-room/fact-sheets/detail/diabetes |website=www.who.int |language=en}}</ref><ref>{{cite journal |last1=Stark |first1=Benjamin |last2=Johnson |first2=Catherine |last3=Roth |first3=Gregory Andrew |title=Global Prevalence of Coronary Artery Disease: An Update from the Global Burden of Disease Study |journal=Journal of the American College of Cardiology |date=April 2024 |volume=83 |issue=13 |pages=2320 |doi=10.1016/S0735-1097(24)04310-9}}</ref><ref>{{cite journal |last1=Kovesdy |first1=Csaba P. |title=Epidemiology of chronic kidney disease: an update 2022 |journal=Kidney International Supplements |date=April 2022 |volume=12 |issue=1 |pages=7–11 |doi=10.1016/j.kisu.2021.11.003|pmid=35529086 |pmc=9073222 }}</ref>. This correlation underscores the importance of a holistic approach to treatment, addressing both Ray's disease and associated co-morbidities.


== Future Research ==
== Future research ==


The HFP algorithm is currently being explored for possible integration with additional biomarkers, such as [[cardiac MRI]], to enhance its predictive capabilities.
The HFP algorithm is currently being explored for possible integration with additional biomarkers, such as [[cardiac MRI]], to enhance its predictive capabilities.

Revision as of 02:47, 25 November 2024

Ray's disease, also known as Impending Heart Failure (IHF), is a preclinical stage of heart failure in which asymptomatic patients (AHA stages A or B)[1] are identified as having a high risk of developing symptomatic heart failure (AHA stage C or D)[2] within a specific time frame. This stage is diagnosed through the Heart Failure Predictor (HFP), an AI-based software tool designed to detect early signs of heart failure using 12-lead ECG data alone.

Background

Heart failure (HF) is a chronic and progressive condition characterized by the heart's inability to pump blood efficiently, ultimately leading to symptoms such as shortness of breath, fatigue, and edema.[3] Traditional diagnostics often rely on symptom-based assessments, which may miss early, asymptomatic phases of the disease[4]. Ray's disease represents an important clinical advantage by identifying patients at risk for both heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF) even before symptoms develop, allowing for timely intervention[5].

Ray's disease was introduced by Dr. Abhijit Ray following extensive research aimed at improving early detection and management of heart failure[6]. This classification provides a more actionable approach for at-risk patients, as studies have shown that individuals identified with Ray's disease have a high probability of experiencing symptomatic heart failure within a specific time frame—usually within 3 to 24 months[7].

Diagnosis

Ray's disease is diagnosed using the Heart Failure Predictor (HFP) software,[8] which assesses the left ventricular ejection fraction (LVEF) and other cardiac parameters[9] from a standard 12-lead ECG. The HFP algorithm leverages linear regression model of machine learning[10] and a patented formula[11] to analyze cardiac indicators and assign a risk classification. Those categorized as "High Risk" by the HFP algorithm are diagnosed as Ray's disease, or Impending Heart Failure.

Data Acquisition: A 12-lead ECG is obtained from the patient.

Data Processing and Analysis: The ECG data undergoes signal quality checks[12], and the HFP algorithm calculates LVEF and analyzes additional indicators.

Risk Prediction: Using machine learning, the algorithm classifies the patient's risk as either "High Risk" (Ray's disease) or "Low Risk".

Output: Results are displayed in an easy-to-interpret format for clinicians.

Clinical performance

The Heart Failure Predictor (HFP) has been clinically validated to ensure reliable predictions. Studies have demonstrated high accuracy, with sensitivity at 97.5% and specificity at 99.9%[13], meaning it correctly identifies both those likely to develop heart failure and those at low risk. Patients classified as high risk were found to develop heart failure symptoms at rates consistent with HFP's positive predictive value of 98.6%[14].

Clinical significance

Identifying patients with Ray's disease provides an opportunity to prevent or delay heart failure progression through early interventions, potentially reducing healthcare costs[15] and improving patient outcomes[16]. As a predictive category, Ray's disease shifts the focus of cardiac care from reactive treatment to proactive risk management[17].

Co-morbidities and risk factors

Patients identified with Ray's disease commonly present with higher incidences of conditions such as hypertension, diabetes, coronary artery disease, and chronic kidney disease compared to the general population[18][19][20][21][22]. This correlation underscores the importance of a holistic approach to treatment, addressing both Ray's disease and associated co-morbidities.

Future research

The HFP algorithm is currently being explored for possible integration with additional biomarkers, such as cardiac MRI, to enhance its predictive capabilities.

References

  1. ^ Heidenreich, Paul A.; Bozkurt, Biykem; Aguilar, David; Allen, Larry A.; Byun, Joni J.; Colvin, Monica M.; Deswal, Anita; Drazner, Mark H.; Dunlay, Shannon M.; Evers, Linda R.; Fang, James C.; Fedson, Savitri E.; Fonarow, Gregg C.; Hayek, Salim S.; Hernandez, Adrian F.; Khazanie, Prateeti; Kittleson, Michelle M.; Lee, Christopher S.; Link, Mark S.; Milano, Carmelo A.; Nnacheta, Lorraine C.; Sandhu, Alexander T.; Stevenson, Lynne Warner; Vardeny, Orly; Vest, Amanda R.; Yancy, Clyde W. (3 May 2022). "2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines". Circulation. 145 (18). doi:10.1161/CIR.0000000000001063. PMID 35363499.
  2. ^ Heidenreich, Paul A.; Bozkurt, Biykem; Aguilar, David; Allen, Larry A.; Byun, Joni J.; Colvin, Monica M.; Deswal, Anita; Drazner, Mark H.; Dunlay, Shannon M.; Evers, Linda R.; Fang, James C.; Fedson, Savitri E.; Fonarow, Gregg C.; Hayek, Salim S.; Hernandez, Adrian F.; Khazanie, Prateeti; Kittleson, Michelle M.; Lee, Christopher S.; Link, Mark S.; Milano, Carmelo A.; Nnacheta, Lorraine C.; Sandhu, Alexander T.; Stevenson, Lynne Warner; Vardeny, Orly; Vest, Amanda R.; Yancy, Clyde W. (3 May 2022). "2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines". Circulation. 145 (18). doi:10.1161/CIR.0000000000001063. PMID 35363499.
  3. ^ Bozkurt; Biykem (2021). "Universal Definition and Classification of Heart Failure". Journal of Cardiac Failure. 27 (4): 387–413. doi:10.1016/j.cardfail.2021.01.022.
  4. ^ Ramani, Gautam V.; Uber, Patricia A.; Mehra, Mandeep R. (February 2010). "Chronic Heart Failure: Contemporary Diagnosis and Management". Mayo Clinic Proceedings. 85 (2): 180–195. doi:10.4065/mcp.2009.0494. PMC 2813829. PMID 20118395.
  5. ^ Wang, H; Gao, C; Guignard-Duff, M; Cole, C; Hall, C; Larman, M; Baruah, R; Gao, H; Mamza, J B; Lang, C C; Mordi, I (9 November 2023). "Importance of early diagnosis and treatment of heart failure across the spectrum of ejection fraction". European Heart Journal. 44 (Supplement_2). doi:10.1093/eurheartj/ehad655.892.
  6. ^ Ray, Abhijit (November 12, 2024). "Understanding Ray's Disease : A Study of its Incidence and Diagnostic Approaches". American Journal of Biomedical Science & Research. 24 (5). doi:10.34297/AJBSR.2024.24.003242 (inactive 2024-11-23).{{cite journal}}: CS1 maint: DOI inactive as of November 2024 (link)
  7. ^ "Introducing Ray's Disease: A revolution in cardiac care".
  8. ^ Ray, Abhijit (August 14, 2024). "Impending Heart Failure : An Artificial Intellectual Reality". medRxiv. doi:10.1101/2024.08.12.24311907.
  9. ^ Struthers, A. D (1 September 2000). "HEART FAILURE: The diagnosis of heart failure". Heart. 84 (3): 334–338. doi:10.1136/heart.84.3.334.
  10. ^ Montgomery, Douglas C.; Peck, Elizabeth A.; Vining, G. Geoffrey (2020). Introduction to linear regression analysis (PDF) (Fifth ed.). Hoboken, New Jersey: Wiley. ISBN 9781119578727.
  11. ^ Ray, Abhijit (6 December 2022). "Determining LVEF using electrocardiographic signals".
  12. ^ Rahman, Saifur; Karmakar, Chandan; Natgunanathan, Iynkaran; Yearwood, John; Palaniswami, Marimuthu (April 2022). "Robustness of electrocardiogram signal quality indices". Journal of the Royal Society Interface. 19 (189). doi:10.1098/rsif.2022.0012.
  13. ^ "Introducing Ray's Disease: A revolution in cardiac care". www.londondaily.news/.
  14. ^ Ray, Abhijit (August 14, 2024). "Impending Heart Failure : An Artificial Intellectual Reality". medRxiv. doi:10.1101/2024.08.12.24311907.
  15. ^ Heidenreich, Paul A.; Fonarow, Gregg C.; Opsha, Yekaterina; Sandhu, Alexander T.; Sweitzer, Nancy K.; Warraich, Haider J.; Butler, Javed; Hsich, Eileen; Pressler, Susan Bennett; Shah, Kevin; Taylor, Kenneth; Sabe, Marwa; Ng, Tien (March 2022). "Economic Issues in Heart Failure in the United States". Journal of Cardiac Failure. 28 (3): 453–466. doi:10.1016/j.cardfail.2021.12.017. PMID 35085762.
  16. ^ Herscovici, Romana; Kutyifa, Valentina; Barsheshet, Alon; Solomon, Scott; McNitt, Scott; Polonsky, Bronislava; Lee, Andy Y.; Zareba, Wojciech; Moss, Arthur J.; Goldenberg, Ilan (September 2015). "Early intervention and long-term outcome with cardiac resynchronization therapy in patients without a history of advanced heart failure symptoms". European Journal of Heart Failure. 17 (9): 964–970. doi:10.1002/ejhf.281. PMID 25921965.
  17. ^ "Proactive Care Framework for Heart Failure". UCLPartners.
  18. ^ Ray, Abhijit (November 12, 2024). "Understanding Ray's Disease : A Study of its Incidence and Diagnostic Approaches". American Journal of Biomedical Science & Research. 24 (5). doi:10.34297/AJBSR.2024.24.003242 (inactive 2024-11-23).{{cite journal}}: CS1 maint: DOI inactive as of November 2024 (link)
  19. ^ "Hypertension". www.who.int.
  20. ^ "Diabetes". www.who.int.
  21. ^ Stark, Benjamin; Johnson, Catherine; Roth, Gregory Andrew (April 2024). "Global Prevalence of Coronary Artery Disease: An Update from the Global Burden of Disease Study". Journal of the American College of Cardiology. 83 (13): 2320. doi:10.1016/S0735-1097(24)04310-9.
  22. ^ Kovesdy, Csaba P. (April 2022). "Epidemiology of chronic kidney disease: an update 2022". Kidney International Supplements. 12 (1): 7–11. doi:10.1016/j.kisu.2021.11.003. PMC 9073222. PMID 35529086.
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