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"Echocardiography in Heart Failure - a volume in the exciting new Practical Echocardiography Series edited by Dr. Catherine M. Otto - provides practical, how-to guidance on effectively applying echocardiography to evaluate heart failure, make therapeutic decisions, and monitor therapy. Definitive, expert instruction from Drs. Martin St. John Sutton and Denise Wiegers is presented in a highly visual, case-based approach that facilitates understanding and equips you to accurately apply this technique while avoiding any potential pitfalls. Access the full text online at www.expertconsult.com al."

"This book is a practical guide to the use of TEE (transoesophageal echocardiography) in the diagnosis of congenital heart disease (CHD). Beginning with an introduction to TEE for CHD, the following chapters describe procedures to be used for different cardiac conditions. 3D TEE allowing multi-dimensional perspectives is also covered."

"Deteksi dini gagal jantung (GJ) penting untuk mengurangi angka kesakitan, kematian dan rawat ulang, terutama pada era pandemi COVID-19. Kecerdasan buatan berdasarkan data ekokardiografi berpotensi mempermudah identifikasi GJ, tetapi tingkat kesahihan belum diketahui. Oleh karena itu, dikembangkan model Learning Intelligent for Effective Sonography (LIFES) dengan metode deep learning menggunakan algoritme visual geometry group (VGG)-16 untuk menilai validitas model kecerdasan buatan dalam deteksi GJ dan membedakan jenis GJ dengan atau tanpa penurunan fraksi ejeksi ventrikel kiri (FEVKi) di berbagai alat ekokardiografi. Penelitian uji diagnostik ini menggunakan desain potong lintang yang dibagi dua fase yaitu fase pertama populasi pasien normal dan GJ dengan atau tanpa FEVKi menurun di RS Pusat Jantung Nasional Harapan Kita dan fase kedua di 10 RS jejaring pada bulan Januari 2020–Maret 2022. Pada fase pertama dilakukan analisis 141 rekaman video ekokardiografi dan fase kedua dianalisis 685 video meliputi tampilan apical 4 chamber (A4C), apical 2 chamber (A2C), dan parasternal long axis (PLAX). Dataset setiap fase dibagi untuk melatih (tahap training) dan menguji (tahap testing) model LIFES dalam membedakan dua kelas diagnosis (GJ dan individu normal) dan tiga kelas diagnosis (GJ dengan FEVKi menurun, GJ dengan FEVKi terjaga, dan individu normal). Pada fase 1 performa terbaik model LIFES dalam membedakan dua kelas ditunjukkan pada tampilan A2C dengan skor F1 0,94 dan area under the curve (AUC) 0,93. Klasifikasi tiga kelas terbaik ditunjukkan pada tampilan A2C dengan F1 0,78 dan AUC 0,83 sampai 0,92. Pada fase 2 klasifikasi dua kelas terbaik ditunjukkan oleh tampilan PLAX dengan skor F1 mencapai 0,93 dan AUC 0,91. Klasifikasi tiga kelas terbaik ditunjukkan pada tampilan PLAX dengan F1 0,82 dan AUC berkisar dari 0,91 hingga 0,94. Waktu pemrosesan model LIFES sekitar 0,15 sampai 0,19 detik untuk memprediksi satu sampel. Disimpulkan model LIFES berfungsi baik untuk deteksi dini GJ sesuai konsensus ahli, sekaligus dapat membedakan jenis GJ dengan atau tanpa FEVKi menurun pada berbagai mesin ekokardiografi.

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Early detection of heart failure (HF) is important to reduce morbidity, mortality, and re-hospitalization, especially in the era of the COVID-19 pandemic. Artificial intelligence based on echocardiographic data has the potential to facilitate the identification of HF, but the level of validity is unknown. Therefore, Learning Intelligent for Effective Sonography (LIFES) model was developed with a deep learning method using the visual geometry group (VGG)-16 algorithm to assess the validity of the artificial intelligence model in the detection of HF and distinguish the type of HF with reduced ejection fraction (HFrEF) or preserved in left ventricular ejection fraction (HFpEF) in various echocardiographic devices. This diagnostic test study used a cross-sectional design, which was divided into two phases, namely the population of normal and HFrEF or HFpEF patients at the Harapan Kita National Heart Center Hospital and ten network hospitals from January 2020 to March 2022. In the first phase, 141 echocardiographic video recordings were analyzed and in the second phase, 685 videos were analyzed, including apical-4 chamber (A4C), apical-2 chamber (A2C), and parasternal-longaxis (PLAX) displays. The dataset for each phase was divided between training and testing the LIFES model in distinguishing two-diagnostic classes (HF and normal individuals) and three-diagnostic classes (HFrEF, HFpEF, and normal individuals). In phase 1, the best performance of the LIFES model in distinguishing the two classes is shown on the A2C display with an F1 score of 0.94 and an area under the curve (AUC) 0.93. The best three-class classifications are shown on the A2C display with an F1 of 0.78 and an AUC of 0.83 to 0.92. In phase 2, the best twoclass classifications are shown by the PLAX display with F1 scores reaching 0.93 and AUC 0.91. he best three-class classifications are shown on the PLAX display, with an F1 of 0.82 and an AUC ranging from 0.91 to 0.94. The

processing time of the LIFES model is about 0.15 to 0.19 seconds to predict a single sample. It is concluded that the LIFES model works well for the early detection of HF, according to expert consensus while at the same time being able to distinguish the type of HF (HFrEF or HFpEF) on various echocardiographic machines."

"Gagal jantung adalah sindrom progresif yang menyebabkan kualitas hidup yang buruk bagi pasien. Insidensi dan prevalensi gagal jantung terus meningkat. Saat ini, banyak bukti menunjukkan bahwa gagal jantung kronis dikarakteristikkan oleh aktivitas kompensasi neurohormonal yang berlebihan, termasuk overaktivitas simpatis yang kemudian menjadi landasan terapi. Diperlukan penatalaksanaan yang holistik dan komprehensif meliputi modifikasi gaya hidup, diet, serta intervensi farmakologi. Beberapa penelitian klinis menunjukkan bahwa akupunktur memiliki efek terapeutik dan modulatoris pada kondisi yang menjadi faktor risiko gagal jantung. Salah satu modalitas akupunktur adalah elektroakupunktur yang dapat menurunkan aktivitas simpatis dan menghambat respon reflek simpatoeksistoris kardiovaskuler. Penelitian ini merupakan uji klinis double blind randomized controlled trial (RCT), yang melibatkan 42 orang pasien gagal jantung dengan kriteria NYHA II-III, EF <40% terbagi dalam kelompok medikamentosa dan elektroakupunktur, medikamentosa dan elektroakupunktur sham, dan medikamentosa tanpa elektroakupunktur. Terapi dilakukan sebanyak 16 sesi selama 8 minggu. Pengukuran dilakukan pada awal terapi, pertengahan terapi, dan akhir terapi. Hasil menunjukkan pemberian elektroakupunktur pada terapi utama medikamentosa pada pasien gagal jantung mampu meningkatkan fraksi ejeksi, mean arterial pressure, dan menurunkan LVEDP lebih cepat, mempertahankan stabilitas dari heart rate variability, serta meningkatkan kualitas hidup yang diukur menggunakan uji jalan 6 menit secara signifikan.

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Heart failure is a progressive syndrome that causes poor quality of life for patients. The incidence and prevalence of heart failure continues to increase. At present, much evidence shows that chronic heart failure is characterized by excessive neurohormonal compensatory activity, including sympathetic overactivity which later became the basis of therapy. Holistic and comprehensive management is needed including lifestyle modification, diet, and pharmacological interventions. Some clinical studies show that acupuncture has a therapeutic and modulator effect on conditions that are risk factors for heart failure. This study is a double blind clinical trial randomized controlled trial (RCT), involving 42 people with heart failure patients with NYHA II-III criteria, EF <40% divided into medical and electroacupuncture, medical and electroacupuncture sham, and medical without electroacupuncture groups. Therapy was done 16 sessions for 8 weeks. Measurements of the variables were carried out at the beginning of therapy, mid-therapy, and end of therapy. The results of showed that electroacupuncture in the top of guidlines medical therapy in heart failure patients were able to increase ejection fraction, mean arterial pressure, and to decrease LVEDP faster, maintain stability of heart rate variability, and improve quality of life measured using the 6 minute road test significantly."

"The kidney in heart failure focuses on the changes that occur in kidney physiology as a function of a failing heart. This comprehensive resource covers epidemiology, pathophysiology, management of kidney disorders and advances in nephropathy management. In addition, the latest therapies, common heart failure dilemmas and kidney disease markers are included. Each chapter is co-authored by a Nephrologist and Cardiologist, offering a unified perspective to these chronic conditions. This indispensible volume provides the reader with the depth-of-knowledge needed for assessing and treating the cardio renal patient."

"Latar Belakang: Konfirmasi diagnosis gagal jantung dengan fraksi ejeksi terjaga (HFpEF) berdasarkan algoritma HFA-PEFF masih dirasa sulit untuk diaplikasikan karena terdapat komponen uji lanjutan berupa uji ekokardiografi dengan protokol uji latih beban dan hemodinamik secara invasif. Belum terdapat pemeriksaan untuk mengkonfirmasi diagnosis HFpEF secara lebih mudah dan terbukti secara klinis.Tujuan: Mengevaluasi apakah pemeriksaan ekokardiografi melalui protokol uji angkat kaki secara aktif (AKA) dapat menjadi alternatif diagnostik uji ekokardiografi dengan protokol latih beban menggunakan ergocycle (ergocycle stress echo) dalam mengkonfirmasi diagnosis pada populasi suspek HFpEF berdasarkan algoritma HFA-PEFF.Metode: Studi komparatif diagnostik yang mengevaluasi pasien suspek HFpEF berdasarkan algoritma HFA-PEFF dengan skor intermediate dan high. Pasien suspek HFpEF menjalani pemeriksaan ekokardiografi protokol uji AKA dan protokol uji latih beban menggunakan ergocycle (ergocycle stress echo) yang sesuai algoritma HFA-PEFF. Hasil: Dari 66 pasien suspek HFpEF, terdapat 14 pasien (21%) dengan hasil ergocycle stress echo positif (average E/e' >=15). Dari 14 pasien, protokol ekokardiografi dengan uji AKA positif terhadap 8 pasien (57,1%). Sensitivitas dan spesifisitas ekokardiografi dengan protokol uji AKA bila dibandingkan dengan protokol ergocycle stress echo adalah 57,1% dan 100% secara berurutan. Area di bawah kurva receiver operating characteristic (ROC) adalah 0,96 (p<0,001). Didapatkan tiga nilai potong untuk average E/e’ (>=15, >12,8 dan >12,2) pada protokol uji AKA yang memiliki sensitivitas dan spesifisitas sebesar 57,1% dan 100%; 85,7% dan 88,5; dan 100% dan 84,6% secara berurutan.Kesimpulan: Pemeriksaan ekokardiografi dengan protokol uji AKA dapat menjadi alternatif diagnostik ekokardiografi dengan protokol ergocycle stress echo dalam mengkonfirmasi diagnosis pada pasien suspek HFpEF dengan skorintermediate dan high berdasarkan algoritma HFA-PEFF.

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Background: Confirmation of heart failure with preserved ejection fraction (HFpEF) diagnosis based on the ESC HFA-PEFF algorithm is still difficult to apply because further tests are needed through echocardiography stress test and invasive hemodynamic test. There are currently no clinically proven and less complex tests to confirm the diagnosis of HFpEF.

Objective: To evaluate whether echocardiography through the active leg raise (ALR) protocol can become an alternative diagnostic test to ergocycle stress echocardiography protocol (ergocycle stress echo) in confirming the diagnosis of suspected HFpEF patients based on HFA-PEFF algorithm.

Methods: This is a comparative diagnostic study that evaluated patients with HFA-PEFF algorithm suspected HFpEF patients (intermediate and high scores). Suspected HFpEF patients underwent echocardiographic examination through the ALR protocol and ergocycle stress echo according to the HFA-PEFF algorithm.

Results: Of the 66 patients with suspected HFpEF included in the study, 14 patients (21%) had positive ergocycle stress echo results (average E/e' >=15). Of the 14 patients, the echocardiography protocol with the ALR was positive for 8 patients (57.1%). The sensitivity and specificity of echocardiography with the ALR when compared with the ergocycle stress echo protocol were 57.1% (95%CI 28.9% - 82.3%) and 100% (95%CI 93.2% - 100%), respectively. The area under the receiver operating characteristic (ROC) curve is 0.96 (p<0.001). Three cutoff values for the average E/e' >=15, >12.8 and >12.2 in the ALR protocol ​​were proposed which had a sensitivity and specificity of 57.1% and 100%; 85.7% and 88.5%; and 100% and 84.6%, respectively.

Conclusion: Echocardiography with the ALR protocol may become an alternative to ergocycle stress echocardiography in diagnosis confirmation of suspected HFpEF patients with intermediate and high scores based on the HFA-PEFF algorithm."

"This timely book is a road map for defining the care of acute heart failure patients in the short stay or observation unit setting. Produced in collaboration with the Society of Chest Pain Centers, this book provides an understanding of the diverse medical needs and solutions, administrative processes, and regulatory issues necessary for successful management. In an environment of increasing financial consciousness, medical practice is changing drastically. Short stay care is premier among the new specialties that cater to the complex balance of optimizing patient outcomes while minimizing fiscal burdens. The observation unit has proven to be an excellent arena for the care of acute heart failure, replete with opportunities to improve both medical management and quality metrics. Unique to the field, Short stay management of acute heart failure, providing the medical, regulatory, and economic tools necessary to create and implement successful short stay management protocols and units for the care of the heart failure patient. It is an essential guide for health care professionals and for hospitals and institutions wishing to be recognized as quality heart failure centers as accredited by the Society of Chest Pain Centers."

"Cardiovascular diseases (CVD) merupakan salah satu penyebab utama kematian di dunia. WHO memperkirakan angka 17,9 juta kematian pada tahun 2021 disebabkan oleh CVD. Di Indonesia sendiri, prevalensi penyakit jantung mencapai angka 1,5% atau sekitar 2,7 juta orang pada tahun 2018. CVD mencakup berbagai macam jenis penyakit jantung. Salah satu tipe penyakit jantung tersebut adalah congestive heart failure. Congestive heart failure (CHF) adalah kondisi dimana jantung tidak dapat memompa darah yang cukup ke seluruh bagian tubuh. CHF dapat terjadi dikarenakan melemahnya kemampuan otot jantung untuk memompa darah sehingga mempengaruhi heart rate atau detak jantung manusia. Heart rate dapat direpresentasikan menggunakan sinyal yang dapat diukur menggunakan alat rekaman electrocardiogram (ECG/EKG). EKG adalah rekaman aktivitas elektrik jantung yang ditangkap melalui bagian permukaan tubuh. Heart rate variability (HRV) diketahui berkorelasi dengan berbagai penyakit jantung dan salah satunya adalah CHF. Dengan berkembangnya teknologi, terdapat beberapa penelitian mengenai implementasi artificial intelligence (AI) untuk mendeteksi keberadaan CHF menggunakan model machine learning dan HRV sebagai fitur bagi model. Pada penelitian ini, akan dibangun dan dievaluasi kinerja model XGBoost untuk mendeteksi eksistensi penyakit CHF pada short-term HRV dari rekaman EKG 5 menit. Dataset yang digunakan berasal dari empat database yang berbeda yang diambil dari situs PhysioNet, yaitu NSRDB dan NSR2DB sebagai kelas sehat dan CHFDB dan CHF2DB sebagai kelas CHF. Masing-masing database memiliki rekaman long-term EKG. Seluruh rekaman tersebut dilakukan segmentasi selama 5 menit pada 2 jam pertama rekaman. Dari hasil segmentasi rekaman 5 menit tersebut akan dihitung nilai HRV yang akan menjadi fitur bagi model XGBoost. XGBoost dilatih menggunakan kombinasi teknik Grid Search dan K-Fold Cross Validation dengan nilai 𝐾 = 10. Terdapat 4 metrik yang dijadikan objektif optimisasi Grid Search, yaitu akurasi, sensitivitas, spesifisitas, dan skor AUC. XGBoost yang dilatih dengan mengoptimasi akurasi berhasil mencapai nilai akurasi sebesar 0,954, sensitivitas sebesar 0,935, spesifisitas sebesar 0,96, dan skor AUC sebesar 0,947. XGBoost yang dilatih dengan mengoptimasi sensitivitas berhasil mencapai nilai akurasi sebesar 0,966, sensitivitas sebesar 0,977, spesifisitas sebesar 0,963, dan skor AUC sebesar 0,97. XGBoost yang dilatih dengan mengoptimasi spesifisitas berhasil mencapai nilai akurasi sebesar 0,962, sensitivitas sebesar 0,931, spesifisitas sebesar 0,971, dan skor AUC sebesar 0,951. Kemudian XGBoost yang dilatih dengan mengoptimasi skor AUC berhasil mencapai nilai akurasi sebesar 0,955, sensitivitas sebesar 0,935, spesifisitas sebesar 0,962, dan skor AUC sebesar 0,948.

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Cardiovascular diseases (CVD) is one of the major causes of death in the world. WHO estimated that 17.9 million of deaths during 2021 are caused by CVD. In Indonesia alone, the prevalence of heart diseases reached 1.5% or around 2,7 million people in 2018. CVD consists of various types of heart disease. Congestive heart failure is one of them. Congestive heart failure (CHF) is a condition where the heart cannot pump enough blood for the entire body. CHF can occur due to a weakening of the heart muscle's ability to pump blood, thereby affecting the human heart rate. Heart rate can be represented using signal that can be measured using electrocardiogram (ECG/EKG) recording. EKG is a recording of the heart's electrical activity captured through the surface of the body. Heart rate variability (HRV) have been known to be correlated with various heart diseases with CHF is one of it. With the advance of technology, there have been various research regarding the implementation of artificial intelligence (AI) to detect the presence of CHF using machine learning model and HRV as features for the model. In this research, we built and evaluated the performance of XGBoost model to detect the existence of CHF on short-term HRV from 5 minutes EKG recording. The dataset came from four different databases that can be accessed from PhysioNet website. Those are NSRDB and NSR2DB datasets to represent healthy class and CHFDB and CHF2DB to represent CHF class. Each database contains long-term EKG. All records are segmented by 5 minutes on the first 2 hours of the recording. HRV metrics are calculated from those 5 minutes segments to become features for the XGBoost model. XGBoost was trained using a combination of Grid Search and K-Fold Cross Validation techniques with 𝐾 = 10. There are 4 metrics that become the objective scoring function for the Grid Search. Those are accuracy, sensitivity, specificity, and AUC score. XGBoost trained to optimize accuracy managed to achieve 0.954 accuracy, 0.935 sensitivity, 0.96 specificity, and 0.947 AUC score. XGBoost trained to optimize sensitivity managed to achieve 0.966 accuracy, 0.977 sensitivity, 0.963 specificity, and 0.97 AUC score. XGBoost trained to optimize specificity managed to achieve 0.962 accuracy, 0.931 sensitivity, 0.971 specificity, and 0.951 AUC score. Lastly, XGBoost trained to optimize AUC score managed to achieve 0.955 accuracy, 0.935 sensitivity, 0.962 specificity, and 0.948 AUC score."

"Agen penghambat beta telah menunjukkan penurunan resiko perawatan atau kematian pasien dengan gagal jantung ringan sampai sedang, tetapi hanya sedikit diketahui mengenai manfaat atau keamanan agen ini pada gagal jantung berat. Dilaporkan satu kasus penggunaan penghambat beta pada gagal jantung berat dengan fraksi ejeksi kurang dari 25%. Laporan manfaat penghambat beta terhadap kesakitan dan kematian pasien dengan gagal jantung ringan sampai sedang juga ditemukan pada pasien dengan gagal jantung berat seperti yang dilaporkan pada kasus ini. (Med J Indones 2002; 11: 174-5)

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Beta-blocking agents have been shown to reduce the risk of hospitalization and death in patients with mild to moderate heart failure, but little is known about the efficacy or safety of these agents in severe heart failure. A case of beta blocker administration in severe heart failure with ejection fraction less than 25% is reported. The reported benefits of beta blockers with regard to morbidity and mortality in patients with mild to moderate heart failure were also found in the patient with severe heart failure as reported in this case. (Med J Indones 2002; 11: 174-5)"