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עמוד בית
Mon, 15.07.24

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June 2024
Ehud Jacobzon MD, Avital Lifschitz RN, Danny Fink MD, Tal Hasin MD

Background: Left ventricular assist devices (LVAD) are a staple element in contemporary treatment of advanced heart failure. LVAD surgeries are mostly done in heart transplantations centers, as a destination therapy or as a bridge to heart transplantation.

Objectives: To describe our step-by-step experience in establishing and implementing a new LVAD program in a non-heart transplant center. To give insight to our short- and long-term results of our first 25 LVAD patients.

Methods: Preliminary steps included identifying the need for a new program and establishing the leading team. Next is defining protocols for pre-operative evaluation, operating room, post-operative management, and outpatient follow-up. The leading team needs to educate other relevant units in the hospital that will be involved in the care of these patients. It is essential to work in collaboration with a heart transplant center from the very beginning. Patient selection is of major importance especially in the early experience. Initially “low risk” patients should be enrolled.

Results: We describe our first 25 LVAD patients. Our first five patients all survived beyond 2 years, with no major complications. Overall, there was one operative death due to massive GI bleeding. There were four late deaths due to septic events.

Conclusions: Establishing a new LVAD program can be successful also with small- and medium-size programs. With careful and meticulous planning LVAD implantation can be extended to more centers thus offering an excellent solution for advanced heart failure patients.

Yacov Shacham MD

Among patients admitted with acute decompensated heart failure (ADHF), deterioration of renal function with resulting acute kidney injury (AKI) is reported in up to 70% of patients with cardiogenic shock. Twenty percent of heart failure patients with AKI progress to dialysis (AKI-D). Optimal timing for initiation of renal replacement therapies (RRT) has been researched; however, minimal studies discuss guidelines for weaning from RRT [1]. Electronic monitoring of urine output (UO) may serve as a tool to aid in withdrawal from RRT. We present a case of ADHF with severe AKI requiring continuous renal replacement therapy (CRRT) where real-time electronic monitoring of UO was implemented for the first time to guide de-escalation therapy from CRRT until successful withdrawal.

May 2024
Jonathan Eisenberger BSc, Shmuel Somer BSc, Eyal Nachum MD, Eilon Ram MD, Jacob Lavee MD, Leonid Sternik MD, Jeffrey Morgan MD

Background: Long-term support with a HeartMate 3 (HM3) left ventricular assist device (LVAD) has improved outcomes of patients with end-stage heart failure. However, there is a paucity of data on the outcomes of patients who underwent concomitant cardiac surgical procedure (CCSP) during HM3-LVAD implantation.

Objectives: To assess our single-center experience with patients who underwent CCSP during the implantation of an HM3-LVAD.

Methods: From December 2016 until April 2022, 131 adult patients underwent HM3-LVAD implantation. A total of 23 patients underwent CCSP during the HM3-LVAD implantation+CCSP, and 108 underwent only HM3-LVAD implantation (HM3-only).

Results: The median age was 59 ± 11 years (range 54-67), 82% (n=108) were male, and 76% (n=100) were implanted as a bridge-to-transplant. The concomitant procedures performed during the implantation included 8 aortic valve repairs/replacements, 14 tricuspid valve repairs, 4 patent foramen ovales or atrial septal defect closures, and 3 other cardiac procedures. The mean cardiopulmonary bypass time was 113 ± 58 minutes for the HM3-only group and 155 ± 47 minutes for the HM3+CCSP group (P = 0.007). The mortality rates at 30 days, 6 months, and 12 months post-implantation were 2 (9%), 5 (22%), and 6 (26%) respectively for the HM3+CCSP group, and 7 (6%), 18 (17%), and 30 (28%) for the HM3-only group (P = 0.658, 0.554, and 1.000).

Conclusions: Our experience demonstrated no significant difference in the 30-day, 6-month, and 12-month mortality rates for patients who underwent a CCSP during HM3-LVAD implantation compared to patients who did not undergo CCSP during HM3-LVAD implantation.

Oshrit Hoffer PhD, Moriya Cohen BS, Maya Gerstein MD, Vered Shkalim Zemer MD, Yael Richenberg MD, Shay Nathanson MD, Herman Avner Cohen MD

Background: Group A Streptococcus (GAS) is the predominant bacterial pathogen of pharyngitis in children. However, distinguishing GAS from viral pharyngitis is sometimes difficult. Unnecessary antibiotic use contributes to unwanted side effects, such as allergic reactions and diarrhea. It also may increase antibiotic resistance. 

Objectives: To evaluate the effect of a machine learning algorithm on the clinical evaluation of bacterial pharyngitis in children.

Methods: We assessed 54 children aged 2–17 years who presented to a primary healthcare clinic with a sore throat and fever over 38°C from 1 November 2021 to 30 April 2022. All children were tested with a streptococcal rapid antigen detection test (RADT). If negative, a throat culture was performed. Children with a positive RADT or throat culture were considered GAS-positive and treated antibiotically for 10 days, as per guidelines. Children with negative RADT tests throat cultures were considered positive for viral pharyngitis. The children were allocated into two groups: Group A streptococcal pharyngitis (GAS-P) (n=36) and viral pharyngitis (n=18). All patients underwent a McIsaac score evaluation. A linear support vector machine algorithm was used for classification.

Results: The machine learning algorithm resulted in a positive predictive value of 80.6 % (27 of 36) for GAS-P infection. The false discovery rates for GAS-P infection were 19.4 % (7 of 36).

Conclusions: Applying the machine-learning strategy resulted in a high positive predictive value for the detection of streptococcal pharyngitis and can contribute as a medical decision aid in the diagnosis and treatment of GAS-P.

Thelma L Skare MD PhD, Jozélio Freire de Carvalho MD PhD

Hearing and vestibular function may be affected by gout and/or hyperuricemia. We performed a systematic review of the literature on ear involvement in patients with gout and hyperuricemia. We selected 24 articles: 8 case reports and 16 original articles. Case reports mainly focused on the presence of tophi in the middle ear, which was resolved with surgical treatment. Seven articles studied the hearing function in relationship to serum uric acid and 10 articles studied the occurrence of vertigo, with one of them studying both aspects. Regarding results on vertigo, five studies showed an association with uric acid elevation, three with lowering of uric acid, and two found no differences. Concerning hearing loss, five studies detected poor hearing function in association with high uric acid levels while other two did not.) Most of the studies showed an association of hearing loss with high uric acid/gout. Regarding vestibular function, the results are too heterogeneous to make any conclusions.

Fadi Hassan MD, Mohammad E. Naffaa MD

Since the introduction of the international study group (ISG) criteria for the diagnosis of Behçet's disease (BD) in the early 1990s by Yazici and colleagues [1] and the international criteria for BD (ICBD) by Davatchi and colleagues in 2014 [2], great progress has been achieved in the diagnosis of BD with fairly high sensitivity and specificity rates. However, a small, but very challenging and unique minority might not fulfill these criteria, at least at presentation. These patients are most challenging as they may present with life-threatening vascular or neurological manifestations. If the diagnosis BD is delayed, the risk for morbidity and even mortality might be increased. Therefore, we should aim for early diagnosis and prompt treatment.

March 2024
Lea Ohana Sarna Cahan MD, Dina Qaraen Saloni MD, Mevaseret Avital MD, Naama Pines MD, Itai Gross MD, Giora Wieser MD, Saar Hashavya MD

Background: Hypothermia, as a sign of serious bacterial infection (SBI) in children and infants older than 90 days is poorly characterized, especially in the post-pneumococcal vaccine era.

Objectives: To assess the prevalence of SBI in children and infants presenting to the pediatric emergency department (PED) with reported or documented hypothermia.

Methods: Retrospective data analysis was conducted of all well-appearing children aged 0–16 years who presented with a diagnosis of hypothermia at two tertiary PEDs from 2010 to 2019.

Results: The study comprised 99 children, 15 (15.2%) age 0–3 months, 71 (71.7%) 3–36 months, and 13 (13.1%) > 36 months. The youngest age group had increased length of stay in the hospital (P < 0.001) and increased rates of pediatric intensive care unit admissions (P < 0.001). Empirical antibiotic coverage was initiated in 80% of the children in the 0–3 months group, 21.1% in the 3–36 months group, and 15.4% in > 36 months (P < 0.001). Only one case of SBI was recorded and no bacteremia or meningitis. Hypothermia of unknown origin was the most common diagnosis in all age groups (34%, 42%, 46%), respectively, followed by bronchiolitis (26%) and hypoglycemia (13.3%) for 0–3 month-old children, unspecified viral infection (20%) and otitis media (7%) for 3–36-month old, and unspecified viral infection (23%) and alcohol intoxication (15.2%) in > 36 months.

Conclusion: There is a low incidence of SBI in well-appearing children presenting to the PED with hypothermia and a benign course and outcome in those older than 3 months.

Mohammad Haydar MD, Uriel Levinger MD, George Habib MD MPH

Takotsubo syndrome (TTS) or Takotsubo cardiomyopathy (TCM) is a cardiomyopathy that develops rapidly and is usually caused by mental or physical stress. It is usually a transient cardiomyopathy. The presumed cause of the onset of the syndrome is the increase and extreme secretion of adrenaline and norepinephrine due to extreme stress. An infectious disease such as sepsis can also be the cause [1].

One of the most widespread diagnostic tools is the revised version of Mayo Clinic Diagnostic Criteria for TTS (2008) [2], which incorporates transient wall-motion abnormalities, absence of a potential coronary culprit, myocarditis, and pheochromocytoma. The prognosis for TTS is usually favorable and resolves with complete recovery in 4–8 weeks in more than 90% of patients.

Marco Harari MD

Since 1980 dermatologists have been interested in the exceptional healing reported by patients who underwent treatments at the Dead Sea. Tens of thousands of patients have visited this area and more than 10,000 cases have been the subject of clinical and laboratory studies since this natural therapeutic option was discovered for psoriasis management. Through evaluation of the published articles on climatotherapy, we tried to reach a global assessment of the usefulness of this approach and to discover whether this treatment still can be recommended in the era of biologic treatments. I conducted a review of the available literature on clinical trials through PubMed, Medline, and Google Scholar using the terms psoriasis and Dead Sea. I found 26 studies published between 1982 and 2021. Assessment of patients showed major improvement through several selected parameters. Length of the stay and medical supervision positively influenced the major outcomes observed. Duration of improvement and possible long-term side effects of this natural treatment still need to be more precisely determined. Exposure to the unique climatic factors of the region, essentially the sun and the sea, induces fast and significant results with high clearance rates of psoriasis plaques. Dead Sea climatotherapy still has its place for the control of psoriasis symptoms.

February 2024
Yoad M. Dvir, Arnon Blum MD MSc

In this special issue of Israel Medical Association Journal (IMAJ) we expose readers to the topic of artificial intelligence (AI) in medicine. AI has become a powerful tool, which enables healthcare professionals to personalize treatment based on many factors, including genetic analyses of tumors, and to consider other co-morbidities affecting a specific patient. AI gives physicians the ability to analyze huge amounts of data and to combine data from different sources. AI can be implemented make a diagnosis based on computed tomography (CT) scans and magnetic resonance imaging (MRI) scans using deep machine learning and data that are stored in the memory of mega computers. AI assists in tailoring more precise surgery to train surgeons before surgery and to support surgeons during procedures. This advancement may benefit surgical procedures by making them more accurate and faster without cutting unnecessary tissues (e.g., nerves and blood vessels); thus, patients face fewer complications, lower rates of infection, and more operation theater time. In this issue, we include three original studies that describe the use of AI in academia and eight review articles that discuss applications of AI in different specialties in medicine. One of the review articles addresses ethical issues and concerns that are raised due to the more advanced use of AI in medicine.

Nadav Loebl MSc, Eytan Wirtheim MD, Leor Perl MD

Background: The field of artificial intelligence (AI) is poised to significantly influence the future of medicine. With the accumulation of vast databases and recent advancements in computer science methods, AI's capabilities have been demonstrated in numerous areas, from diagnosis and morbidity prediction to patient treatment. Establishing an AI research and development unit within a medical center offers multiple advantages, particularly in fostering research and tapping into the immediate potential of AI at the patient's bedside.

Objectives: To outline the steps taken to establish a center for AI and big data within an innovation center at a tertiary hospital in Israel.

Methods: We conducted a retrospective analysis of projects developed in the field of AI at the Artificial Intelligence Center at the Rabin Medical Center, examining trends, clinical domains, and the predominant sectors over a specific period.

Results: Between 2019 and 2023, data from 49 AI projects were gathered. A substantial and consistent growth in the number of projects was observed. Following the inauguration of the Artificial Intelligence Center we observed an increase of over 150% in the volume of activity. Dominant sectors included cardiology, gastroenterology, and anesthesia. Most projects (79.6%) were spearheaded by physicians, with the remainder by other hospital sectors. Approximately 59.2% of the projects were applied research. The remainder were research-based or a mix of both.

Conclusions: Developing technological projects based on in-hospital medical data, in collaboration with clinicians, is promising. We anticipate the establishment of more centers dedicated to medical innovation, particularly involving AI.

Idit Tessler MD PhD MPH, Amit Wolfovitz MD, Nir Livneh MD, Nir A. Gecel MD, Vera Sorin MD, Yiftach Barash MD, Eli Konen MD, Eyal Klang MD

Background: Advancements in artificial intelligence (AI) and natural language processing (NLP) have led to the development of language models such as ChatGPT. These models have the potential to transform healthcare and medical research. However, understanding their applications and limitations is essential.

Objectives: To present a view of ChatGPT research and to critically assess ChatGPT's role in medical writing and clinical environments.

Methods: We performed a literature review via the PubMed search engine from 20 November 2022, to 23 April 2023. The search terms included ChatGPT, OpenAI, and large language models. We included studies that focused on ChatGPT, explored its use or implications in medicine, and were original research articles. The selected studies were analyzed considering study design, NLP tasks, main findings, and limitations.

Results: Our study included 27 articles that examined ChatGPT's performance in various tasks and medical fields. These studies covered knowledge assessment, writing, and analysis tasks. While ChatGPT was found to be useful in tasks such as generating research ideas, aiding clinical reasoning, and streamlining workflows, limitations were also identified. These limitations included inaccuracies, inconsistencies, fictitious information, and limited knowledge, highlighting the need for further improvements.

Conclusions: The review underscores ChatGPT's potential in various medical applications. Yet, it also points to limitations that require careful human oversight and responsible use to improve patient care, education, and decision-making.

Yoad M. Dvir, Yehuda Shoenfeld MD FRCP MaACR

In the grand theater of modern medicine, artificial intelligence (AI) has swiped the lead role, with a performance so riveting it deserves an Oscar, or at least a Nobel. From the intricate labyrinths of our arteries to the profound depths of our peepers, AI is the new maestro, conducting symphonies of data with the finesse of a seasoned virtuoso [1,2].

Orly Gal-Or MD, Alon Tiosano MD, Inbar Perchik BSc, Yogev Giladi MD, Irit Bahar MD

Artificial intelligence in ophthalmology is used for automatic diagnosis, data analysis, and predicting responses to possible treatments. The potential challenges in the application and assimilation of artificial intelligence include technical challenges of the algorithms, the ability to explain the algorithm, and the ability to diagnose and manage the medical course of patients. Despite these challenges, artificial intelligence is expected to revolutionize the way ophthalmology will be practiced. In this review, we compiled recent reports on the use and application of deep learning in various fields of ophthalmology, potential challenges in clinical deployment, and future directions.

Diana Shair MD, Shiri Soudry MD

Artificial intelligence (AI) has emerged as a powerful technology in medicine, with a potential to revolutionize various aspects of disease management. In recent years, substantial progress has been made in the development and implementation of AI algorithms and models for the diagnosis, screening, and monitoring of retinal diseases. We present a brief update on recent advancements in the implementation of AI in the field of retinal medicine, with a focus on age-related macular degeneration, diabetic retinopathy, and retinopathy of prematurity. AI algorithms have demonstrated remarkable capabilities in automating image analysis tasks, thus enabling accurate segmentation and classification of retinal pathologies. AI-based screening programs hold great promise in cost-effective identification of individuals at risk, thereby facilitating early intervention and prevention. Future integration of multimodal imaging data including optical coherence tomography with additional clinical parameters, will further enhance the diagnostic accuracy and support the development of personalized medicine, thus aiding in treatment selection and optimizing therapeutic outcomes. Further research and collaboration will drive the transformation of AI into an indispensable tool for improving patient outcomes and enhancing the field of retinal medicine.

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