Israel Medical Association Journal

Objectives: To characterize the clinical features and outcomes of body packers hospitalized at the Sheba Medical Center.

Methods: We conducted a retrospective case series of body packers hospitalized between January 2010 and October 2012 in our medical center. Electronic medical records and imaging files were reviewed to extract clinical, laboratory and radiological data as well as details on medical treatments.

Results: We identified 23 body packers (mean age 38 ± 10 years), 20 of whom smuggled cocaine from South America. The number of packets transported ranged from 1 to 242 (median 42) and duration of hospitalization from 1 to 14 days (median 2). Two subjects required surgical intervention. All others were treated conservatively by polyethylene glycol-electrolyte lavage solution, laxatives, or watchful waiting. Ten patients underwent a urinary screen for illicit drugs, 7 of whom tested positive for cocaine and 2 for cannabinoids. Abdominal X-rays were performed in all patients at admission, and 14 had follow-up imaging, including abdominal CT scans without contrast media in 8.

Conclusions: The main treatment goals for body packers are the rapid excretion of drug packets and early detection of complications, i.e., drug intoxication and bowel obstruction. We suggest the use of a structured treatment approach for the in-hospital management of body packers.

Background: Infections with blood-borne viruses are a major health problem among illicit drug users. There is little information about infection rates and risk factors for hepatitis virus B, C or the human immunodeficiency virus in drug users in Israel.

Objectives: To determine the prevalence of HCV[1], HBV[2] and HIV[3] infections in a large cohort of drug users in Israel; to compare rates of HCV, HBV and HIV between injecting versus non-injecting drug users and between different origin countries; and to identify risk factors for HCV among illicit drug users.

Methods: We conducted a cross-sectional interviewer-administered questionnaire and serological screening for HCV, HBV and HIV in 1443 consecutive drug users diagnosed at the Israeli National Center for Diagnosis of Drug Addicts between January 2003 and December 2005.

Results: Fourteen (0.9%), 51 (3.5%) and 515 (35.7%) subjects tested positive for HIV, HBV and HCV, respectively. All three infections (HIV, HBV and HCV) were significantly more common among injecting drug users and immigrants from the former Soviet Union and other East European countries compared to native Israelis. Multivariate analysis showed that HCV infection was associated with age (> 40 years) (OR=2.06, 95% CI 1.40–3.03), immigration from East European countries and the former Soviet Union (OR=4.54, 95% CI 3.28–6.28), and injecting drug use (OR=16.44, 95% CI 10.79–25.05).

Conclusions: HIV, HBV and HCV prevalence among drug users in Israel is significantly lower than in North America and West Europe. Risk factors for HCV infection in this population include injecting drug use, older age, and immigration from the former Soviet Union.

Background: Oral anticoagulation with warfarin can lead to life-threatening events as a result of either over-anticoagulation or undertreatment. One of the main contributors to an undesirable warfarin effect is the need to adjust its daily dose for a specific patient. The dose is adjusted empirically based on the experience of the clinician, a method that is often imprecise. There is currently no other well-accepted method for predicting the maintenance dose of warfarin.

Objective: To describe the application of an artificial neural network to the problem of warfarin maintenance dose prediction.

Methods: We designed a neural network that predicts the maintenance dose of warfarin. Data on 148 patients attending a large anticoagulant clinic were collected by file review. Using correlational analysis of the patients' data we selected the best input variables. The network was trained by using the back-propagation algorithm on a subset of our data and the results were validated against the rest of the data. We used a multivariate linear regression to create a comparable model.

Results: The neural network generated reasonable predictions of the maintenance dose (r = 0.823). The results of the linear regression model were similar (r = 0.800).

Conclusion: Neural networks can be applied successfully for warfarin maintenance dose prediction. The results are promising, but further investigation is needed.