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"Background: several studies have been reported piperacillin-tazobactam (TAZ / PIPC)-associated AKI with various frequencies. The aim of this study was to determine the frequency of TAZ/PIPC- associated AKI among our patients and to identify the risk factors for this clinical entity. Methods: this retrospective cross-sectional study was conducted at Hamad General Hospital; it involved adult patients who were admitted from January 2017 to December 2017. Results: we involved 917 patients, of whom 635 (69.25%) were males and 282 (30.75%) were females. The mean age of the patients was 52 (SD 19) years, and 98 (10.7%) patients were diagnosed with AKI. The patients with AKI were significantly older than without AKI [59.71 (SD 19.79) versus 51.06 (SD 18.67); P <0.001]. After TAZ/PIPC initiation, the mean creatinine level in the AKI group was higher than the mean creatinine level in the non-AKI group, [158.91 (SD 81.93) versus 66.78 (SD 21.42); P<001]. The mean time of onset of AKI after PIPC/TAZ initiation was 4.46 (SD 3.20) (1-12 days). AKI was significantly associated with low mean serum albumin (P<0.001), high mean fasting blood glucose (P<0.001), coronary artery diseases (P<0.001), heart failure (P<0.001), liver diseases (P=0.047), diabetes mellitus (P=0.021) and hypertension (P<0.001). The in-hospital mortality was significantly higher in the AKI group [38.78% versus 5.13% in the non-AKI group; P<0.001], and only advanced age and heart failure were found as independent risk factors for TAZ/PIPC-associated AKI. Conclusion: TAZ/PIPC was significantly associated with AKI. Advanced age and heart failure were identified as independent risk factors for TAZ/PIPC-associated AKI"

"Introduksi: Gagal jantung (GJ) adalah kondisi dimana jantung tidak mampu secara optimal memompa darah untuk konsumsi tubuh. Walaupun dalam tingkat global prevalansi GJ itu tinggi, studi mengenai hubungan status New York Heart Association (NYHA) dan IMT dari pasien GJ masih minim. Sebagai tambahan, tidak seperti di negara lain, studi mengenai profil pasien GJ di Indonesia sudah tua. Oleh karena itu, studi ini bertujuan untuk menyediakan informasi mengenai karakteristik klinik pasien GJ di RSCM dan mengidentifikasi jika adanya korelasi signifikan diantara IMT dan status NYHA pasien GJ.Metode: Ini adalah studi penampang lintang data sekunder yang dilakukan pada tahun 2021. Data dari pasien GJ yang memiliki indikator IMT dan NYHA jelas dari rekam medis PJT dan pusat RSCM dikumpulkan. Semua data berasal dari kunjungan pertama pasien ke RSCM. Data tersebut dianalisa menggunakan SPSS, dimana frekuensi, median, dan interquartile range dari variabel ditelusuri. Hubungan antara IMT dan NYHA diobservasi melalui ANOVA dan regresi logistik. Hasil: 224 data pasien dari RSCM berhasil terkumpulkan. Median usia pasien GJ di RSCM adalah 57 tahun (IQR=13.75). Populasi pria melebihi dibandingkan wanita (66.1%). Pasien obesitas meliputi sepertiga (39.7) dari total populasi. NYHA 2 adalah status NYHA yang paling kerap muncul dalam populasi sample (62.1%). Gejala yang paling sering ditemukan adalah sesak nafas (75.0%), sedangkan sejarah medis yang paling acap adalah gejala jantung iskemik (69.2%). Semua tanda vital dalam jarak normal. Manifestasi klinis yang paling sering muncul adalah ronkhi (36.80%). Pola ECG yang paling sering ditemukan adalah irama sinus normal (61.50%). Kebanyakan pasien memiliki ejeksi fraksi berkurang (58%). Semua indikator lab normal, terkecuali untuk biomarker renal yang tinggi. Terapi farmakologi teradministrasi paling sering adalah B-Blocker dan Antagonis Aldosterone (64.6% dan 66.5%). One way ANOV A menunjukan adanya perbedaan rata rata IMT signifikan (! (2,211) = 7.964, " = <.001, #2 = .06). Konklusi: Profil pasien studi ini sesuai ekspektasi dari kondisi rujukan awal pasien GJ. Profil pasien sample ini dengan studi lain dari Indonesia, akan tetapi menemukan beberapa perbedaan dengan studi dari negara lain. IMT dan NYHA juga ditemukan mempunyai korelasi linear, dengan catatan ada faktor eksternal yang menentukan progresi menuju NYHA 4.

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Introduction: Heart failure is a condition where the heart cannot pump blood for the body. Even though it is high on prevalence globally, the relationship between the presenting New York Heart Association (NYHA) and patients' BMI is still minimally studied. Additionally, HF patients' profile in Indonesia is significantly outdated. This study aims to provide a clinical characteristic of HF patients in RSCM and identify the relationship between BMI and present NYHA status. Methods: This study is a cross sectional secondary data study conducted on 2021. Data of HF patients from PJT and central RSCM medical records with a clear indicator of BMI and NYHA were collected. All data came from the patient's first visit to RSCM. Data were then analyzed using SPSS, where the frequency, median, interquartile range of variables was explored. The relationship between BMI and NYHA was observed using ANOVA and logistic regression. Results: 224 data were collected on this study. The median age of HF patients in RSCM was 57 years old (IQR=13.75). Compared to females, males are more frequent (66.1%). Obese patients comprise one third (39.7%) of the population. NYHA 2 is the most common presenting NYHA, which constitutes half the sample (62.1%). The most common symptom is dyspnea (75.0%), while the most common medical history is previous ischemic heart disease diagnosis (69.2%). All vital signs are within normal range upon inspection. The most common physical manifestation is Ronchi (36.80%). ECG pattern most commonly found is normal sinus rhythm (61.50%). Most patients have reduced ejection fraction (58%). Lab indicators are within the normal range, except for renal biomarkers, which is mainly elevated. Most common medication administered is B- Blocker and Aldosterone Antagonist (64.6% and 66.5%). ANOVA test found significant mean differences between severe NYHA and BMI (! (2,211) = 7.964, " = <.001, #2 = .06) Conclusion: In conclusion, patients from this study are more common to have NYHA 2. Additionally, BMI and NYHA is linear correlated, where other factors apart from BMI may be a significant cause of progression to NYHA 4."

"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."

"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."