Israel Medical Association Journal

Background: Cardiac amyloidosis (CA) is characterized by the extracellular deposition of misfolded protein in the heart. Precise identification of the amyloid type is often challenging, but critical, since the treatment and prognosis depend on the disease form and the type of deposited amyloid. Coexistence of clinical conditions such as old age, monoclonal gammopathy, chronic inflammation, or peripheral neuropathy in a patient with cardiomyopathy creates a differential diagnosis between the major types of CA: amyloidosis light chains (AL), amyloidosis transthyretin (ATTR) and amyloidosis A (AA).

Objectives: To demonstrate the utility of the Western blotting (WB)-based amyloid typing method in patients diagnosed with cardiac amyloidosis where the type of amyloid was not obvious based on the clinical context.

Methods: Congo red positive endomyocardial biopsy specimens were studied in patients where the type of amyloid was uncertain. Amyloid proteins were extracted and identified by WB. Mass spectrometry (MS) of the electrophoretically resolved protein-in-gel bands was used for confirmation of WB data.

Results: WB analysis allowed differentiation between AL, AA, and ATTR in cardiac biopsies based on specific immunoreactivity of the electrophoretically separated proteins and their characteristic molecular weight. The obtained results were confirmed by MS.

Conclusions: WB-based amyloid typing method is cheaper and more readily available than the complex and expensive gold standard techniques such as MS analysis or immunoelectron microscopy. Notably, it is more sensitive and specific than the commonly used immunohistochemical techniques and may provide an accessible diagnostic service to patients with amyloidosis in Israel.

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.

Background: Patients with inflammatory bowel disease (IBD) are at increased risk after percutaneous coronary intervention (PCI).

Objectives: To compare the clinical outcomes within 30 days, one year, and five years of undergoing PCI.

Methods: We conducted a retrospective cohort study of adult patients with IBD who underwent PCI in a tertiary care center from January 2009 to December 2019.

Results: We included 44 patients, 26 with Crohn’s disease (CD) and 18 with ulcerative colitis (UC), who underwent PCI. Patients with CD underwent PCI at a younger age compared to UC (57.8 vs. 68.9 years, P < 0.001) and were more likely to be male (88.46% of CD vs. 61.1% of UC, P < 0.03). CD patients had a higher rate of non-steroidal treatment compared to UC patients (50% vs. 5.56%, P < 0.001). Acute coronary syndromes (ACS) and/or the need for revascularization (e.g., PCI) were the most common clinical events to occur following PCI, in both groups. Of patients who experienced ACS and/or unplanned revascularization within 5 years, 25% of UC vs. 40% of CD had target lesion failure (TLF) due to in-stent restenosis and 10% of CD had TLF due to stent thrombosis.

Conclusions: We observed higher rates of TLF in IBD patients compared to the general population as well as differences in clinical outcomes between UC and CD patients. A better understanding of the prognostic factors and pathophysiology of these differences may have clinical importance in tailoring the appropriate treatment or type of revascularization for this high-risk group.

Background: Obliterative vaginal procedures may offer lower perioperative morbidity and equal success rates as reconstructive procedures for frail and elderly women who no longer desire future coital function. The combination of vaginal hysterectomy with either reconstructive or obliterative vaginal procedures has not yet been investigated.

Objectives: To compare peri- and postoperative outcomes of vaginal hysterectomy with pelvic floor reconstruction (VHR) vs. vaginal hysterectomy with colpocleisis (VHC).

Methods: We conducted a retrospective study comparing medical and surgical data of patients undergoing either VHR or VHC between 2006 and 2015. Data were obtained from inpatient and outpatient medical records including peri- and postoperative course, as well as long-term (24 months) follow-up data.

Results: We identified 172 patients who underwent VHR and 44 who underwent VHC. Patients in the VHC group were significantly older (71.3 ± 4.5 vs. 68.6 ± 6.5 years, P = 0.01), and more likely to have medical co-morbidities (P = 0.001 and P = 0.029, respectively). Patients in the VHC group experienced shorter operative time (2.3 ± 0.58 vs. 2.7 ± 1.02 hours, P = 0.007), lower perioperative blood loss (P < 0.0001), shorter hospital stay (P < 0.0001), and lower rates of postoperative urinary retention. Long-term pelvic organ prolapse (POP) recurrence rates were significantly higher among the VHR group. Postoperative resolution of both stress urinary incontinence and overactive bladder were common in both groups.

Conclusions: VHC is associated with lower perioperative blood loss, shorter operative time, shorter hospital stay, shorter time with an indwelling catheter, and lower long-term objective POP recurrence rates.

Background: Opioid-base sedation is considered the first line choice in ventilated patients in intensive care units (ICU). Few studies have examined sedation in ventilated patients outside the ICU. A pilot program was initiated in the internal medicine ward A at Meir Hospital in Kfar Saba, Israel. A new sedation protocol was implemented for opioid-based versus benzodiazepine-based sedation in ventilated patients.

Objectives: To compare the rates and intensity of delirium between patients who received opioid-based sedation vs. benzodiazepine-based sedation. To compare parameters related to morbidity and mortality.

Methods: We conducted a retrospective before-after intervention study based on data collection. Patients who were admitted to the internal medicine ward A from January 2020 to January 2021 and required sedation and ventilation were included. Demographic data, medical history data, admission data, Richmond Agitation and Sedation Scale scores, hemodynamic parameters, reports of falls and self-harm, and data regarding unplanned extubation were collected, as well as the need for additional sedative drugs.

Results: Chronic hypertension was more common in the opioid group. Delirium intensity tended to be higher in the benzodiazepine group. The number of ventilation days was significantly higher in the benzodiazepine group, as was the number of times adjuvant sedation was required.

Conclusions: Opioid-based sedation outside the ICU was associated with shorter ventilation days, tendency toward lower intensity of delirium, and reduction in requirement of adjuvant sedative drugs compared to benzodiazepine-based sedation. Further studies are required to confirm the findings.

Background: Bronchiectasis is an obstructive chronic lung disease characterized by structural changes in large and small airways, namely permanent widening of bronchial lumen resulting in chronic inflammation and infection. Nontuberculous mycobacteria (NTM) are environmental mycobacteria that may cause human infection or colonization with over 150 species identified to date. Bronchiectasis with NTM colonization or infection is often encountered but with varying prevalence and unknown clinical or prognostic significance.

Objectives: To find the prevalence of NTM among patients with bronchiectasis in the Jerusalem district. To assess whether there were clinical differences between patients with bronchiectasis who were isolated with NTM and those without.

Methods: In this retrospective observational research study, we reviewed all computerized medical charts of patients over 18 years of age, who were diagnosed with bronchiectasis at Hadassah Medical Centers in Jerusalem between 2012 and 2017. We assessed the prevalence of NTM pulmonary disease. To compare patients with and without NTM, we reviewed and analyzed clinical, radiological, and microbiological data of all NTM patients and a group of controls in a 4:1 ratio.

Results: Prevalence of NTM among bronchiectasis patients was 5.1%, slightly lower than previously reported in Israel. We did not find clinically or radiological significant differences in patients with NTM disease compared to controls. This result included a similar number of exacerbations, hospitalization rates, number of lobes involved, and pulmonary function tests.

Conclusions: Bronchiectasis patients with isolation of Pseudomonas aeruginosa experienced more exacerbations than patients with other isolates, consistent with previous studies.

Background: Survivors of critical illness are at increased risk of long-term impairments, referred to as post-intensive care unit (ICU) syndrome (PICS). Post-traumatic stress disorder (PTSD) is common among ICU survivors with reported rates of up to 27%. The prevalence of PTSD among Israeli ICU survivors has not been reported to date.

Objectives: To evaluate the prevalence of new onset PTSD diagnosed in a post-ICU clinic at a tertiary center in Israel.

Methods: We conducted a retrospective, single center, cohort study. Data were collected from medical records of all patients who visited the Tel Aviv Sourasky Medical Center post-ICU clinic between October 2017 and June 2020. New onset PTSD was defined as PTSD diagnosed by a certified board psychiatrist during the post-ICU clinic visit. Data were analyzed using descriptive statistics.

Results: Overall, 39 patients (mean age 51 ± 17 years, 15/39 females [38%]) attended the post-ICU clinic during the study period. They were evaluated 82 ± 57 days after hospital discharge. After excluding 7 patients due to missing proper psychiatric analysis, 32 patients remained eligible for the primary analysis. New PTSD was diagnosed in one patient (3%).

Conclusions: We found lower incidence of PTSD in our cohort when compared to existing literature. Possible explanations include different diagnostic tools and low risk factors rate. Unique national, cultural, and/or religious perspectives might have contributed to the observed low PTSD rate. Further research in larger study populations is required to establish the prevalence of PTSD among Israeli ICU survivors.

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.

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.

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.

Background: Completing internal medicine specialty training in Israel involves passing the Israel National Internal Medicine Exam (Shlav Aleph), a challenging multiple-choice test. multiple-choice test. Chat generative pre-trained transformer (ChatGPT) 3.5, a language model, is increasingly used for exam preparation.

Objectives: To assess the ability of ChatGPT 3.5 to pass the Israel National Internal Medicine Exam in Hebrew.

Methods: Using the 2023 Shlav Aleph exam questions, ChatGPT received prompts in Hebrew. Textual questions were analyzed after the appeal, comparing its answers to the official key.

Results: ChatGPT 3.5 correctly answered 36.6% of the 133 analyzed questions, with consistent performance across topics, except for challenges in nephrology and biostatistics.

Conclusions: While ChatGPT 3.5 has excelled in English medical exams, its performance in the Hebrew Shlav Aleph was suboptimal. Factors include limited training data in Hebrew, translation complexities, and unique language structures. Further investigation is essential for its effective adaptation to Hebrew medical exam preparation.

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.

A rise in the incidence of chronic health conditions, notably heart failure, is expected due to demographic shifts. Such an increase places an onerous burden on healthcare infrastructures, with recurring hospital admissions and heightened mortality rates being prominent factors. Efficient chronic disease management hinges on regular ambulatory care and preemptive action. The application of intelligent computational models is showing promise as a key resource in the ongoing management of chronic diseases, particularly in forecasting disease trajectory and informing timely interventions. In this review, we explored a pioneering intelligent computational model by Diagnostic Robotics, an Israeli start-up company. This model uses data sourced from insurance claims to forecast the progression of heart failure. The goal of the model is to identify individuals at increased risk for heart failure, thus enabling interventions to be initiated early, mitigating the risk of disease worsening, and relieving the pressure on healthcare facilities, which will result in economic efficiencies.

Over the last decade the use of artificial intelligence (AI) has reformed academic research. While clinical diagnosis of psoriasis and psoriatic arthritis is largely straightforward, the determining factors of a clinical response to therapy, and specifically to biologic agents, have not yet been found. AI may meaningfully impact attempts to unravel the prognostic factors that affect response to therapy, assist experimental techniques being used to investigate immune cell populations, examine whether these populations are associated with treatment responses, and incorporate immunophenotype data in prediction models. The aim of this mini review was to present the current state of the AI-mediated attempts in the field. We executed a Medline search in October 2023. Selection and presentation of studies were conducted following the principles of a narrative–review design. We present data regarding the impact AI can have on the management of psoriatic disease by predicting responses utilizing clinical or biological parameters. We also reviewed the ways AI has been implemented to assist development of models that revolutionize the investigation of peripheral immune cell subsets that can be used as biomarkers of response to biologic treatment. Last, we discussed future perspectives and ethical considerations regarding the use of machine learning models in the management of immune-mediated diseases.

In recent years, we have been experiencing a technological revolution, which signifies an ethical and societal transformation. Artificial intelligence (AI) based technologies have gradually permeated all aspects of life and solidified their position. Within this context, the emergence of these technologies offers new opportunities in the medical field, including palliative care, which is aimed at alleviating suffering and improving the quality of life for terminally ill patients and their families. In Israel, the Dying Patient Act of 2005 (the law), which promotes values such as the sanctity of life and individual autonomy, allows terminally ill patients to determine their preferred treatment, and withhold life-saving treatment under certain circumstances. The law represents a significant step toward improving care for terminally ill patients, reducing pain and suffering, and respecting the patient's wishes and worldviews in their final days. However, the practical implementation of the law has encountered numerous challenges, ranging from lack of familiarity among doctors and healthcare professionals and the requirement to determining life expectancy to fulfilling the law's purpose. These challenges are associated with ethical, cultural, and religious perspectives. In this article, we describe how AI-based technologies hold immense potential in applying the law and providing palliative care based on their predictive capabilities, prognostic accuracy, and optimization of treatment as well as communication between patients and healthcare providers. However, as an innovative, developing, and complex technology, it is crucial not to overlook the ethical, societal, and legal challenges inherent in implementing and using AI-based technologies in the context of palliative care.