Israel Medical Association Journal

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.

Despite recent advances in the pharmacological and endoscopic treatments for obesity, bariatric surgery is still considered one of the most effective and safe treatments for morbid obesity with over 250,000 bariatric procedures performed each year in the United States. While these procedures are considered safe, they are not free of complications. It has been reported that the primary short-term major complication after Roux-en-Y gastric bypass (RYGB), one-anastomosis (Mini) gastric bypass (OAGB), or sleeve gastrectomy (SG) is gastrointestinal leakage, with a reported leak rate of 0.1–8.3%, 0–5.1%, and 0–7%, respectively [1,2]. While the etiology of gastrointestinal leakage following bariatric procedure is multifactorial, including preoperative, intraoperative, and postoperative factors, a single factor can rarely be attributed to this misfortunate complication. We describe a case of a 30-year-old woman who presented on postoperative day (POD) 10 of a OAGB with a gastrointestinal leakage after treated with a high dose of oral misoprostol.

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.

A 69-year-old woman with a 30-year history of rheumatoid arthritis (RA) on leflunomide presented with dizziness and weakness. Vital signs, cardiopulmonary auscultation, and laboratory studies were normal. The serological status of her RA was unknown. She exhibited ulnar deviation and swan-necking of the hands but no nodular skin lesions. She was an active smoker. Chest radiography revealed an opacity in the right lung. Computed tomography (CT) showed multiple pulmonary nodules and a dominant thick-walled cavitary mass in the periphery of the right lower lobe [Figure 1A]. Due to concern for a malignancy or infection, she underwent a bronchoscopy with a biopsy of the mass, which was non-diagnostic. A subsequent transthoracic needle biopsy demonstrated a central zone of necrosis surrounded by a cuff of palisading epithelioid histiocytes with the presence of occasional giant cells [Figure 1B]. There was no malignancy, and stains for micro-organisms were negative. In this clinical context, biopsy results were consistent with a pulmonary rheumatoid nodule (PRN).

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 (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.

Artificial intelligence (AI) has emerged as a transformative group of technologies in the field of medicine. Specifically in cardiology, numerous applications have materialized, and these are developing exponentially. AI-based risk prediction models leverage machine learning algorithms and large datasets to probe multiple variables, aid in the identification of individuals at high risk for adverse events, facilitate early interventions, and enable personalized risk assessments. Unique algorithms analyze medical images, such as electrocardiograms, echocardiograms, and cardiac computed tomography scans to enable rapid detection of abnormalities and aid in the accurate identification of cardiac pathologies. AI has also shown promise in guiding treatment decisions during coronary catheterization. In addition, AI has revolutionized remote patient monitoring and disease management by means of wearable and implantable sensing technologies. In this review, we discussed the field of cardiovascular genetics and personalized medicine, where AI holds great promise. While the applications of AI in cardiology are promising, challenges such as data privacy, interpretability of the findings, and multiple matters regarding ethics need to be addressed. We presented a succinct overview of the applications of AI in cardiology, highlighting its potential to revolutionize risk prediction, diagnosis, treatment, and personalized patient care.

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.

Medical imaging data has been at the frontier of artificial intelligence innovation in medicine with many clinical applications. There have been many challenges, including patient data protection, algorithm performance, radiology workflow, user interface, and IT integration, which have been addressed and mitigated over the last decade. The AI products in imaging now fall into three main categories: triage artificial intelligence (AI), productivity AI, and augmented AI, each providing a different utility for radiologists, clinicians, and patients. Adoption of AI products into the healthcare system has been slow, but it is growing. It is typically dictated by return on investment, which can be demonstrated in each use case. It is expected to lead to wider adoption of AI products in imaging into the clinical workflow in the future.

Background: The use of proprotein convertase subtilisin/kexin type 9 monoclonal antibodies (PCSK9 mAbs) is emerging for lowering low-density lipoprotein cholesterol (LDL-C). However, real-world data is lacking for their use among elderly patients.

Objective: To define the characteristics of elderly patients treated with PCSK9 mAbs and to evaluate the efficacy and tolerability compared with younger patients.

Methods: We conducted a retrospective cohort study of elderly patients (≥ 75 years at enrollment) treated with PCSK9 mAbs for primary and secondary cardiovascular prevention. Data were retrieved for demographic and clinical characteristics; indications for treatment; agents and dosages; concomitant lipid lowering treatment; LDL-C levels at baseline, 6, 12 months, and at the end of follow up. Data also included achieving LDL-C target levels and adverse effects.

Results: The cohort included 91 elderly patients and 92 younger patients, mean age 75.2 ± 3.76 and 58.9 ± 7.4 years (P < 0.0001). Most patients (82%, 80%) were in high/very high-risk categories. For almost all (98%, 99%), the indication was statin intolerance, with PCSK9 mAb monotherapy the most prevalent regimen. The average follow-up was 38.1 ± 20.5 and 30.9 ± 15.8 months (P = 0.0258). Within 6 months the LDL-C levels were reduced by 57% in the elderly group and by 59% in the control group (P = 0.2371). Only 53% and 57% reached their LDL-C target levels. No clinically significant side effects were documented.

Conclusion: PCSK9 mAbs have similar effects and are well tolerated among elderly patients as in younger patients.

Background: The coronavirus disease 2019 (COVID-19) pandemic caused significant global turmoil, including changes in social and societal conduct such as lockdowns, social isolation, and extensive regulations. These changes can be major sources of stress. The first wave of the pandemic (April–May 2020) was a time of global uncertainty. We evaluated symptom severity among 29 Israeli children and adolescents with obsessive-compulsive disorder (OCD). Our previous study found that most of these participants did not experience an exacerbation of symptoms.

Objective: To re-evaluate the OCD symptoms of 18 participants from the original group of 29 children and adolescents during three time points: before the pandemic, during the first wave, and 2 years later.

Methods: Obsessive-compulsive symptoms (OCS) were assessed using the Clinical Global Impression Scale (CGI), a functional questionnaire, and the Obsessive-Compulsive Inventory-child version (OCI-CV).

Results: OCS in patients did not change significantly during the three time points. Participants reported minimal changes in their general functioning 2 years after the outbreak of COVID-19 and showed minimal change in OCI-CV scale scores.

Conclusions: Our results indicated clinical stability of OCD symptoms among most of the participants.