Israel Medical Association Journal

Background: Hookah smoking is a common activity around the world and has recently become a trend among youth. Studies have indicated a relationship between hookah smoking and a high prevalence of chronic diseases, cancer, cardiovascular, and infectious diseases. In Israel, there has been a sharp increase in hookah smoking among the Arabs. Most studies have focused mainly on hookah smoking among young people.

Objectives: To examine the association between hookah smoking and socioeconomic characteristics, health status and behaviors, and knowledge in the adult Arab population and to build a prediction model using machine learning methods.

Methods: This quantitative study based is on data from the Health and Environment Survey conducted by the Galilee Society in 2015–2016. The data were collected through face-to-face interviews with 2046 adults aged 18 years and older.

Results: Using machine learning, a prediction model was built based on eight features. Of the total study population, 13.0% smoked hookah. In the 18–34 age group, 19.5% smoked. Men, people with lower level of health knowledge, heavy consumers of energy drinks and alcohol, and unemployed people were more likely to smoke hookah. Younger and more educated people were more likely to smoke hookah.

Conclusions: Hookah smoking is a widespread behavior among adult Arabs in Israel. The model generated by our study is intended to help health organizations reach people at risk for smoking hookah and to suggest different approaches to eliminate this phenomenon.

Background: Benign rolandic epilepsy or benign childhood epilepsy with centrotemporal spikes (BCECTS) is a common childhood epileptic syndrome. The syndrome resolves in adolescence, but 1–7% of patients have an atypical presentation, some of which require aggressive medical treatment. Early treatment may prevent complications and neurocognitive deterioration. Variants include Landau-Kleffner syndrome (LKS) and electrical status epilepticus during sleep (ESES).

Objectives: To determine data driven identification of risk factors and characterization of new subtypes of BCECTS based on anontology. To use data mining analysis and correlation between the identified groups and known clinical variants.

Methods: We conducted a retrospective cohort study comprised of 104 patients with a diagnosis of BCECTS and a minimum of 2 years of follow-up, between the years 2005 and 2017. The medical records were obtained from the epilepsy service unit of the pediatric neurology department at Dana–Dwek Hospital, Tel Aviv Sourasky Medical Center. We developed a BCECTS ontology and performed data preprocessing and analysis using the R Project for Statistical Computing (https://www.r-project.org/) and machine learning tools to identify risk factors and characterize subgroups.

Results: The ontology created a uniform and understandable infrastructure for research. With the ontology, a more precise characterization of clinical symptoms and EEG activity of BCECTS was possible. Risk factors for the development of severe atypical presentations were identified: electroencephalography (EEG) with spike wave (P < 0.05), EEG without evidence of left lateralization (P < 0.05), and EEG localization (centrotemporal, frontal, or frontotemporal) (P < 0.01).

Conclusions: Future use of the ontology infrastructure for expanding characterization for multicenter studies as well as future studies of the disease are needed. Identifying subgroups and adapting them to known clinical variants will enable identification of risk factors, improve prediction of disease progression, and facilitate adaptation of more accurate therapy. Early identification and frequent follow-up may have a significant impact on the prognosis of the atypical variants.

Abstract

Background: Heart rate variability (HRV) analysis has been shown to be a predictor of sudden cardiac death and all-cause mortality in patients with cardiac disease.

Objectives: To examine whether newer HRV analysis algorithms, as used by the HeartTrends device, are superior to exercise stress testing (EST) for the detection of myocardial ischemia in patients without known coronary artery disease (CAD).

Methods: We present pilot data of the first 100 subjects enrolled in a clinical trial designed to evaluate the yield of short-term (1 hour) HRV testing for the detection of myocardial ischemia. The study population comprised subjects without known CAD referred to a tertiary medical center for EST with single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). All patients underwent a 1 hour electrocardiographic acquisition for HRV analysis with a HeartTrends device prior to EST with MPI. Sensitivity, specificity, and positive and negative predictive values (PPV and NPV, respectively) were calculated for EST and HRV analysis, using MPI as the gold standard for the non-invasive detection of myocardial ischemia.

Results: In this cohort 15% had a pathologic MPI result. HRV analysis showed superior sensitivity (85%), PPV (50%) and NPV (97%) as compared to standard EST (53%, 42%, 90%, respectively), while the specificity of the two tests was similar (86% and 85%, respectively). The close agreement between HRV and MPI was even more pronounced among patients > 65 years of age.

Conclusions: Our pilot data suggest that the diagnostic yield of the novel HeartTrends HRV algorithm is superior to conventional EST for the non-invasive detection of myocardial ischemia.

Background: Syndromic surveillance systems have been developed for early detection of bioterrorist attacks, but few validation studies exist for these systems and their efficacy has been questioned.

Objectives: To assess the capabilities of a syndromic surveillance system based on community clinics in conjunction with the WSARE[1] algorithm in identifying early signals of a localized unusual influenza outbreak.

Methods: This retrospective study used data on a documented influenza B outbreak in an elementary school in central Israel. The WSARE algorithm for anomalous pattern detection was applied to individual records of daily patient visits to clinics of one of the four health management organizations in the country.

Results: Two successive significant anomalies were detected in the HMO’s[2] data set that could signal the influenza outbreak. If data were available for analysis in real time, the first anomaly could be detected on day 3 of the outbreak, 1 day after the school principal reported the outbreak to the public health authorities.

Conclusions: Early detection is difficult in this type of fast-developing institutionalized outbreak. However, the information derived from WSARE could help define the outbreak in terms of time, place and the population at risk.

Objectives: To examine whether a military medic, using a structured questionnaire and an algorithm, can appropriately triage patients to receive over-the-counter medications and refer more complicated cases to a physician.

Methods: The study group comprised 190 consecutive soldiers who presented to a military primary care clinic with symptoms of ARVI. Using a questionnaire, a medic recorded the patient's history and measured oral temperature, pulse rate and blood pressure. All patients were referred to a doctor. Physicians were “blind” to the medic’s anamnesis and to the algorithm diagnosis. We compared the medic’s anamnesis and therapeutic decisions to those of the doctors.

Results: Patients were young (21.1 ± 3.7 years) and generally healthy (93% without background illness). They usually had a minor disease (64% without fever), which was mostly diagnosed as viral ARVI (83% of cases). Ninety-nine percent were also examined by a physician. According to the patients' data, the medics showed high overall agreement with the doctors (83–97.9%). The proposed algorithm could have saved 37% of referrals to physicians, with a sensitivity of 95.2%. Had the medics been allowed to examine the pharynx for an exudate, the sensitivity might have been 97.6%.

Conclusions: Medics, equipped with a questionnaire and algorithm but without special training and without performing a physical examination, can appropriately triage patients and thereby reduce the number of referrals to physicians.

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[1] ARVI = acute respiratory viral infection

Objective: To present our algorithm of urgent and elective donor selection.

Methods: Urgent selection is expeditious and protocol‑based. Elective selection permits a comprehensive process. Both include medical, psychosocial and surgical-anatomic evaluations. Liver volumes and vascular anatomy are evaluated with computerized tomographic angiography. Informed consent is obtained after painstaking explanations. Independent institutional committees review and approve all cases.

Results: Between July 2003 and June 2004 we evaluated 43 potential live donors for 12 potential recipients (fulminant hepatic failure, n=5; chronic end-stage liver disease, n=6); primary graft non-function, n=1). Thirty-three candidates (76%) were excluded due to blood type incompatibility (n=14, 42%), incompatible anatomy (n=8, 24%) – including problematic volume distribution (n=2) or vascular anatomy (n=6) – psychosocial issues (n=4, 12%), or medical co-morbidity (n=7, 22%). Five recipients (FHF[1], n=4; chronic ESLD[2], n=1) were successfully transplanted from living donors. In the acute setting, two patients (FHF, PGNF[3]) died in the absence of an appropriate donor (cadaveric or living donor). In the elective group, one patient died of unexpected variceal bleeding and one received a cadaveric graft just before the planned living donor transplantation was performed. One candidate was transplanted overseas and two cases are scheduled. The ratio of compatibility for donation was 34% (10/29) for blood type-compatible candidates.

Conclusions: Donor selection for living donor liver transplantation is a complex, labor-intensive multidisciplinary process. Most exclusions are due to blood type incompatibility or anatomic details. Psychosocial aspects of these donations warrant special attention.

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[1] FHF = fulminant hepatic failure

[2] ESLD = chronic end-stage liver disease

[3] PGNF = primary graft non-function