The impact of hospitalization on inappropriate prescribing and polypharmacy in older patients: A descriptive cross-sectional study.

BACKGROUND: Polypharmacy and the use of potentially inappropriate medications (PIMs) in older individuals are widespread phenomena that are associated with an increase in morbidity and mortality. The Beers Criteria is a tool that helps to identify patients that are prescribed with PIMs, thereby reducing the risk of associated harm. Amongst other populations, the criteria identify drugs that should not be used by the majority of older patients. AIM: Determining the proportion of older inpatients who were discharged from hospitalization with polypharmacy (a prescription for more than seven drugs), or with a PIM as defined by the Beers Criteria. METHODS: A descriptive cross-sectional study based on patients aged 65 and over who were hospitalized in the years 2019-2021 in the internal medicine, orthopedic and surgical wards at a medium-size hospital. Demographic information and details about drug treatment were collected from the electronic patient records system. Patients who died during hospitalization were excluded from the study group. MAIN OUTCOME MEASURES: The proportion of inpatients with polypharmacy or a PIM as part of their regular prescription, at the time of admission and at discharge. RESULTS: 49 564 patients were included in the study cohort. At discharge, 19% of the patients were given a prescription for a PIM, with a small but significant decrease compared with the rate admission (22.1%). At discharge, 42.8% of patients had polypharmacy, representing a small but significant increase compared with the rate on admission (40.6%). CONCLUSIONS: The study demonstrated high baseline rates of PIM prescription and polypharmacy. Hospitalization was associated with a decrease in PIM prescription and an increase in polypharmacy. This highlights the importance of medication review during admission to reduce the potential risk to older adults from polypharmacy and PIM prescription.

Screening for Medication Errors and Adverse Events Using Outlier Detection Screening Algorithms in an Inpatient Setting.

OBJECTIVES: To evaluate the potential of a novel system using outlier detection screening algorithms and to identify medication related risks in an inpatient setting. METHODS: In the first phase of the study, we evaluated the transferability of models refined at another medical center using a different electronic medical record system (EMR) on 3 years of historical data (2017-2019), extracted from the local EMR system. Following the retrospective analysis, the system's models were fine-tuned to the specific local practice patterns. In the second, prospective phase of the study, the system was fully integrated in the local EMR and after a short run-in period was activated live. All alerts generated by the system, in both phases, were analyzed by a clinical team of physicians and pharmacists for accuracy and clinical relevance. RESULTS: In the retrospective phase of the study, 226,804 medical orders were analyzed, generating a total of 2731 alerts (1.2% of medical orders). Of the alerts analyzed, 69% were clinically relevant alerts and 31% were false alerts. In the prospective phase of the study, 399 alerts were generated by the system (1.6% of medical orders). The vast majority of the alerts (72%) were considered clinically relevant, and 41% of the alerts caused a change in prescriber behavior (i.e. cancel/modify the medical order). CONCLUSION: In an inpatient setting of a 600 bed computerized decision support system (CDSS) -naïve medical center, the system generated accurate and clinically valid alerts with low alert burden enabling physicians to improve daily medical practice.

Predicting Antibiotic Resistance in Hospitalized Patients by Applying Machine Learning to Electronic Medical Records.

BACKGROUND: Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning (ML) algorithms. However, they are scarcely used for empiric antibiotic therapy. Here, we predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using ML algorithms applied to patients' electronic medical records (EMRs). METHODS: The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their EMRs. Five antibiotics were examined: ceftazidime (n = 2942), gentamicin (n = 4360), imipenem (n = 2235), ofloxacin (n = 3117), and sulfamethoxazole-trimethoprim (n = 3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble that combined all 3 algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis. RESULTS: The ensemble outperformed the separate models and produced accurate predictions on test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble yielded area under the receiver-operating characteristic (auROC) scores of 0.73-0.79 for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. Variables' effects on predictions were assessed and found to be consistent with previously identified risk factors for antibiotic resistance. CONCLUSIONS: We demonstrate the potential of ML to predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapidly gained information regarding the infecting bacterial species can improve predictions substantially. Clinicians should consider the implementation of such systems to aid correct empiric therapy and to potentially reduce antibiotic misuse.

Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling.

OBJECTIVES: Microbial resistance exhibits dependency patterns between different antibiotics, termed cross-resistance and collateral sensitivity. These patterns differ between experimental and clinical settings. It is unclear whether the differences result from biological reasons or from confounding, biasing results found in clinical settings. We set out to elucidate the underlying dependency patterns between resistance to different antibiotics from clinical data, while accounting for patient characteristics and previous antibiotic usage. METHODS: Additive Bayesian network modelling was employed to simultaneously estimate relationships between variables in a dataset of bacterial cultures derived from hospitalized patients and tested for resistance to multiple antibiotics. Data contained resistance results, patient demographics and previous antibiotic usage, for five bacterial species: Escherichia coli (n = 1054), Klebsiella pneumoniae (n = 664), Pseudomonas aeruginosa (n = 571), CoNS (n = 495) and Proteus mirabilis (n = 415). RESULTS: All links between resistance to the various antibiotics were positive. Multiple direct links between resistance of antibiotics from different classes were observed across bacterial species. For example, resistance to gentamicin in E. coli was directly linked with resistance to ciprofloxacin (OR = 8.39, 95% credible interval 5.58-13.30) and sulfamethoxazole/trimethoprim (OR = 2.95, 95% credible interval 1.97-4.51). In addition, resistance to various antibiotics was directly linked with previous antibiotic usage. CONCLUSIONS: Robust relationships among resistance to antibiotics belonging to different classes, as well as resistance being linked to having taken antibiotics of a different class, exist even when taking into account multiple covariate dependencies. These relationships could help inform choices of antibiotic treatment in clinical settings.

Antibiotic modification versus withhold in febrile patients without evidence of bacterial infection, unresponsive to initial empiric regimen: a multicentre retrospective study conducted in Israel.

Prescribing antibiotics for febrile patients without proof of bacterial infection contributes to antimicrobial resistance. Lack of clinical response in these patients often leads to antibiotic escalation, although data supporting this strategy are scarce. This study compared outcomes of modifying, withholding, or continuing the same antibiotic regimen for such patients. Febrile or hypothermic stable patients with suspected infection, unresponsive to empiric antibiotic treatment, admitted to one of 15 internal medicine departments in three hospitals during a 5-year study period, were included. Patients with a definitive clinical or microbiological bacterial infection, malignancy, immunodeficiency, altered mental status, or need for mechanical ventilation were excluded. Participants were divided into groups based on treatment strategy determined 72 h after antibiotic initiation: antibiotic modified, withheld or continued. Outcomes measured included in-hospital and 30-day post-discharge-mortality rates, length of hospital stay (LOS) and days of antimicrobial therapy (DOT). A total of 486 patients met the inclusion criteria: 124 in the Antibiotic modified group, 67 in the Antibiotic withheld group and 295 in the Initial antibiotic continued group. Patient characteristics were similar among groups with no differences in mortality rates in-hospital (23% vs. 25% vs. 20%, p = 0.58) and within 30 days after discharge (5% vs. 3% vs. 4%, p = 0.83). Changing antibiotics led to longer LOS (9.0 ± 6.8 vs. 6.2 ± 5.6 days, p = 0.003) and more DOT (8.6 ± 6.0 vs. 3.2 ± 1.0 days, p < 0.001) compared to withholding treatment. Withholding as compared to modifying antibiotics, in febrile patients with no clear evidence of bacterial infection, is a safe strategy associated with decreased LOS and DOT.

Structural and Functional Echocardiographic Changes Following Kidney Transplantation: The Role of Allograft Function.

BACKGROUND: Kidney transplantation is associated with early improvement in cardiac function and structure; however, data on cardiac adaptation and its relation to kidney allograft function remain sparse. OBJECTIVES: To investigate the relationship between post-transplant kidney function and echocardiographic measures in patients with normal/preserved pre-transplant cardiac structure and function. METHODS: The study included 113 patients who underwent kidney transplantation at a single tertiary medical center from 2000 to 2012. The patients were evaluated by echocardiography before and after transplantation, and the relation between allograft function and echocardiographic changes was evaluated. Echocardiography was performed at a median of 510 days after transplantation. RESULTS: The post-transplantation estimated glomerular filtration rate (eGFR) was directly correlated with left ventricular (LV) systolic function and inversely correlated with LV dimensions, LV wall thickness, left atrial diameter, and estimated systolic pulmonary arterial pressure. In patients with significant allograft dysfunction (eGFR ≤ 45 ml/min), LV hypertrophy worsened, with no improvement in LV dimensions. In contrast, in patients with preserved kidney function, there was a significant reduction in both LV diameter and arterial pulmonary systolic pressure. CONCLUSIONS: Our results show that in kidney transplant recipients, allograft function significantly affects cardiac structure and function. Periodic echocardiographic follow-up is advisable, especially in patients with kidney graft dysfunction.

Adherence and Characteristics of HIV Post-Exposure Prophylaxis for a Population in Tel Aviv of Men who have Sex with Men.

BACKGROUND: Pre-exposure prophylaxis (PrEP) for populations at high risk for human immunodeficiency virus (HIV) is still not available in Israel. OBJECTIVES: To analyze post-exposure prophylaxis (PEP) treatment adherence rates among adult men in Tel Aviv, Israel, who have sex with men (MSM), and to obtain data on the demographics of PEP users, exposure types, timeline of exposure and PEP administration, incidence of side effects, number of treatments per individual, and satisfaction with selected elements of treatment provision. METHODS: The authors conducted an observational cohort study of adult MSM who requested PEP treatment in the Tel Aviv Sourasky Medical Center. Information from patients receiving treatment between January 2013 and June 2014 was obtained through telephone interviews by means of a 30-item questionnaire. RESULTS: Of 336 individuals requesting PEP treatment, 255 (75.9%) were adult MSM, and 100 (39.2%) satisfactorily completed the interview. The average age of the study cohort was 32.4 years (standard deviation of 7.5). Ninety-one (91%) reported completing a full 28-day course of treatment, 84% reported side effects, and 20% underwent multiple courses. Satisfaction was high for interactions with the HIV specialists. Patient experience with PEP treatment in the emergency room setting, and follow-up were inadequate deficient. CONCLUSIONS: PEP adherence rates in Tel Aviv were significantly higher than previously reported. PEP should be administered in designated community settings. PrEP as a general treatment policy might suit the MSM population in Tel Aviv.

Immunogenicity of Sci-B-Vac (a Third-Generation Hepatitis B Vaccine) in HIV-Positive Adults.

BACKGROUND: Guidelines recommend hepatitis B virus (HBV) vaccination of all adults positive for human immunodeficiency virus (HIV). Immune responses to single-antigen HBV vaccine among HIV-positive patients are low when compared with HIV-negative adults. Sci-B-Vac™ is a recombinant third-generation HBV that may be advantageous in this population. OBJECTIVES: To examine the immune responses to Sci-B-Vac among HIV-positive adults. METHODS: We conducted a prospective cohort study involving HIV-positive adults who had negative HBV serology (HBSAg, HBSAb, HBcoreAb). Sci-B-Vac at 10 µg/dose was administered intramuscularly upon recruitment and after 1 and 6 months. HBSAb levels were checked 1 month after each dose; a level > 10 mlU/ml was considered protective. Data regarding age, gender, CD4 level, and viral load were collected. RESULTS: The study group comprised 31 patients. Average CD4 count was 503 ± 281 cells/ml, and average viral load was 44 copies/ml. Median interquartile range (IQR) HBVAb titers after the first, second and third immunizations were 0 (0, 3.5), 30 (6, 126) and 253 (81, 408) mlU/ml. Significant titer elevations were found between the second and third immunizations (P = 0.0003). The rate of patients considered protected was 16% after the first, 65% after the second (P < 0.0001), and 84% after the third dose (P = 0.045). No adverse events were reported. More patients under the age of 40 years responded to the first immunization (28% vs. 0%, P = 0.038). CD4 level had no influence on immunization rates. CONCLUSIONS: Sci-B-Vac might achieve better immunization rates among HIV-positive adults compared to the single-antigen vaccine and thus deserves further evaluation in a randomized, double-blind study in this population.

Antibiotic Restriction Might Facilitate the Emergence of Multi-drug Resistance.

High antibiotic resistance frequencies have become a major public health issue. The decrease in new antibiotics' production, combined with increasing frequencies of multi-drug resistant (MDR) bacteria, cause substantial limitations in treatment options for some bacterial infections. To diminish overall resistance, and especially the occurrence of bacteria that are resistant to all antibiotics, certain drugs are deliberately scarcely used--mainly when other options are exhausted. We use a mathematical model to explore the efficiency of such antibiotic restrictions. We assume two commonly used drugs and one restricted drug. The model is examined for the mixing strategy of antibiotic prescription, in which one of the drugs is randomly assigned to each incoming patient. Data obtained from Rabin medical center, Israel, is used to estimate realistic single and double antibiotic resistance frequencies in incoming patients. We find that broad usage of the hitherto restricted drug can reduce the number of incorrectly treated patients, and reduce the spread of bacteria resistant to both common antibiotics. Such double resistant infections are often eventually treated with the restricted drug, and therefore are prone to become resistant to all three antibiotics. Thus, counterintuitively, a broader usage of a formerly restricted drug can sometimes lead to a decrease in the emergence of bacteria resistant to all drugs. We recommend re-examining restriction of specific drugs, when multiple resistance to the relevant alternative drugs already exists.

Estimating Risk of Venous-Thromboembolic Events in Hospitalized Medical Patients: Comparison between 2008 and 2012 Guidelines.

BACKGROUND: Prophylaxis for hospitalized venous-thromboembolic events (VTEs) is frequently underutilized, in part due to lack of a simple risk assessment model (RAM). OBJECTIVES: To compare patient selection and administration of VTE prophylaxis according to the American College of Chest Physicians (ACCP) 2008 guidelines versus the newer 2012 guidelines, and assess the feasibility of developing simpler local RAMs. METHODS: We conducted a prospective assessment of VTE risk among 300 unselected consecutive patients admitted to a medical hospital ward, using the 2008 and 2012 ACCP guidelines. The frequency and relative weight of each risk factor in the 2012 ACCP guidelines were used to develop a local VTE RAM. RESULTS: VTE prophylaxis was indicated by the 2008 and 2012 ACCP guidelines in 40% and 42% of the cohort respectively, and was administered in 28% and 26% of eligible patients, respectively. Contraindication to VTE prophylaxis was found in 29% of patients according to both guidelines. In comparison to the 2008 guidelines, sensitivity and specificity of the 2012 guidelines were 96% and 88%, respectively. A local RAM based on the following concise score, comprising age, malignancy and immobility, correctly identified 99% of at-risk patients based on the 2012 guidelines, with a sensitivity and specificity of 98% and 95%, respectively. CONCLUSIONS: Both guidelines performed to a similar degree and were poorly implemented in daily practice. A simplified RAM accurately identified the vast majority of these eligible patients. The development of local RAMs is feasible and may result in higher utilization rates.

Resistance profiles of coagulase-negative staphylococci contaminating blood cultures predict pathogen resistance and patient mortality.

OBJECTIVES: Blood culture isolates are the cornerstone of adequate antibiotic treatment. However, many blood cultures are contaminated with bacteria residing on the skin, the most common contaminants being coagulase-negative staphylococci (CoNS). Such contaminated cultures are mostly disregarded. In this retrospective study, we show that contaminated cultures contain diagnostic information. We tested the association between resistance profiles of CoNS contaminants and those of the actual infecting bacteria isolated subsequently from the same patient, as well as their association with short-term mortality. METHODS: We identified all patients in Rabin Medical Center, Israel, with positive blood cultures during 2009-12. Data included patient demographics, hospitalization records, comorbidities, blood culture results and date of death. RESULTS: Our cohort consists of 2518 patients with 5290 blood cultures, where 1124 patients had 1664 blood cultures with CoNS contaminants. High overall CoNS resistance predicted high overall resistance of the subsequent bacterial isolates (P<0.004 and P<0.0006, for Gram-positive and -negative bacteria, respectively). Moreover, the resistance of CoNS contaminants to a specific antibiotic predicted the resistance of the subsequent bacterial isolates to that antibiotic (OR=5.55, 95% CI=3.54-8.66, P<10(-15) and OR=2.47, 95% CI=1.61-3.78, P<3 ×10(-5), for Gram-positive and -negative bacteria, respectively). Finally, highly resistant CoNS isolates were associated with higher short-term mortality (hazard ratio=1.71, 95% CI=1.4-2.11, P<10(-6)). CONCLUSIONS: Resistance patterns of CoNS contaminants predict specific and overall resistance of subsequent blood culture isolates and short-term mortality. These results may help predict patient mortality and correct empirical antibiotic therapy if blood cultures yield contaminant bacteria and imply that skin commensals may serve as an additional, non-invasive, diagnostic tool.

A method for inferring medical diagnoses from patient similarities.

BACKGROUND: Clinical decision support systems assist physicians in interpreting complex patient data. However, they typically operate on a per-patient basis and do not exploit the extensive latent medical knowledge in electronic health records (EHRs). The emergence of large EHR systems offers the opportunity to integrate population information actively into these tools. METHODS: Here, we assess the ability of a large corpus of electronic records to predict individual discharge diagnoses. We present a method that exploits similarities between patients along multiple dimensions to predict the eventual discharge diagnoses. RESULTS: Using demographic, initial blood and electrocardiography measurements, as well as medical history of hospitalized patients from two independent hospitals, we obtained high performance in cross-validation (area under the curve >0.88) and correctly predicted at least one diagnosis among the top ten predictions for more than 84% of the patients tested. Importantly, our method provides accurate predictions (>0.86 precision in cross validation) for major disease categories, including infectious and parasitic diseases, endocrine and metabolic diseases and diseases of the circulatory systems. Our performance applies to both chronic and acute diagnoses. CONCLUSIONS: Our results suggest that one can harness the wealth of population-based information embedded in electronic health records for patient-specific predictive tasks.

INDI: a computational framework for inferring drug interactions and their associated recommendations.

Inferring drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve) ≥0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/~bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.

PREDICT: a method for inferring novel drug indications with application to personalized medicine.

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large-scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug-drug and disease-disease similarity measures for the prediction task. On cross-validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue-specific expression information on the drug targets. We further show that disease-specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease-specific signatures.

The diversity of heart failure in a hospitalized population: the role of age.

BACKGROUND: The prevalence of heart failure (HF) among hospitalized elderly patients is high and steadily growing. However, because most studies have focused mostly on young patients, little is known about the clinical characteristics, echocardiographic measures, prognostic factors, and outcome of hospitalized elderly HF patients. METHODS AND RESULTS: We identified all HF patients aged ≥50 years who had undergone ≥1 echocardiography study and had been hospitalized during January 2000 to December 2009. A comparative analysis was performed between 3,897 "young" patients (aged 50-75 years) and 5,438 "elderly" patients (aged >75 years), followed for a mean 2.8 ± 2.6 years. Elderly HF patients were more often female (50% vs 35%; P < .0001) and had a higher prevalence of HF with preserved ejection fraction (64.8% vs 53%; P < .0001), more significant valvular disease (35.7% vs 32.5%; P < .0001), and lower rates of ischemic heart disease (65.5% vs 70.9%; P < .0001) and diabetes (34.4% vs 53.9%; P < .0001). Thirty-day and 1-year mortality rates were significantly higher among the elderly population (12.2% vs 6.9% [P < .0001] and 34.3% vs 21.2% [P < .0001], respectively). Prognostic markers differed significantly between age groups. Young-specific predictors were chronic renal failure, diastolic dysfunction, malignancy, and tricuspid regurgitation, whereas elderly-specific predictors were HF with reduced ejection fraction, chronic obstructive pulmonary disease, pulmonary hypertension, and mitral regurgitation. CONCLUSIONS: Hospitalized elderly, compared with young, HF patients differed in prevalence of cardiac and noncardiac comorbid conditions, echocardiographic parameters, and predictors of short- and intermediate-term mortality. Identifying unique features in the elderly population may render age-tailored therapeutics.

Sex differences in the epidemiology, seasonal variation, and trends in the management of patients with acute appendicitis.

BACKGROUND: Acute appendicitis is the most common indication for acute surgical abdominal intervention. In this study, we analyzed the gender correlation with demographic, epidemiologic, diurnal, and seasonal trends in relation to the incidence and management of patients with acute appendicitis in our medical center. METHODS: Data of patients, 18 years of age or older who underwent emergency appendectomies at the Rabin Medical Center during the last 13 years, were collected. The data collected included demographic parameters, hospitalization, procedures, and use of preoperative imaging. RESULTS: Data were available for 3,736 patients. Males had more appendicitis attacks than females (p < 0.0001), whereas females had more normal appendixes than males (p < 0.0001). The overall rate of normal appendixes was 19.6 %, with a decline in the past 10 years from a yearly average of 23.5 % between 1998 and 2002 to 15 % between 2003 and 2007 (p < 0.0001) with a reverse correlation with the preoperative use of abdominal CT. A distinct seasonal pattern was observed; more appendectomies for acute appendicitis occurred during the summer months (p < 0.0001). Ten percent of patients had a complicated course with a mortality rate of 0.33 %; most of them were elderly, male/female ratio 0.4. CONCLUSIONS: We found distinct gender, epidemiological, seasonal, and diurnal trends influencing the incidence of acute appendicitis. The incidence rate of false-positive surgery has been gradually declining, probably due to the increased use of preoperative abdominal CT and ultrasound. Acute appendicitis was more common in males and during the summer months.

The echogenicity of the normal fetal kidneys during different stages of pregnancy determined objectively.

PURPOSE: To determine echogenicity of normal fetal kidneys during pregnancy by objective computerized method. METHODS: Computerized-based numerical method was developed, quantifying echogenicity of kidneys. 166 digital pictures of kidneys and liver were collected between 14 and 41 weeks of gestation. Calculating liver echogenicity was used to overcome gain problems. Women were healthy, delivered normal babies. Digital pictures were processed by software capable of identifying and labeling 256 shades of gray, numerically. In each picture, kidney was identified, region of interest was outlined. Average, standard deviation and entropy of pixel values were calculated and divided into three: 14-24, 24-36, 37-41 weeks of gestation: early, intermediate, late. RESULTS: Mean color intensities were 70.2 ± 23, 50.6 ± 17, 47.3 ± 14 for early, intermediate, late groups, respectively (p < 0.0001, comparison between early and other groups). Standard deviation, which represents the echogenic homogenicity of the kidney, was 18 ± 4, 16.5 ± 3 and 17.2 ± 3 pixels for early, intermediate, and late, respectively (p = 0.003, between early and intermediate groups; p = 0.03, between the intermediate and late). Liver echogenicity remained constant throughout pregnancy. CONCLUSIONS: Objective sonographic assessment of the echogenicity of the fetal kidney is presented here for the first time. It was found that kidneys are more echogenic during early pregnancy and more homogenous in appearance in mid-gestation.

Long-term response to rituximab in patients with relapsing thrombotic thrombocytopenic purpura.

Acquired thrombotic thrombocytopenic purpura (TTP) is an uncommon disease in adults, characterized by fever, neurological manifestations, microangiopathic hemolytic anemia, thrombocytopenia, renal dysfunction, and the presence of antibodies against the enzyme ADAMTS13. Treatment with plasmapheresis has increased the survival from 10% to more than 90%. Still, there is a subset of patients with resistant TTP who fail to respond to plasmapheresis or remain dependent on this procedure. There is mounting evidence that rituximab may play an important role in remission induction of resistant/relapsing TTP, but the extent of the remission is unknown. We present here four patients with chronic-relapsing TTP who responded favorably to rituximab. All four patients achieved prolonged remission of 23 to 82 months after the treatment. One patient relapsed 6 years afterthe initial treatment with rituximab and re-entered remission following retreatment.

Associations of physicians' prescribing experience, work hours, and workload with prescription errors.

OBJECTIVE: We aimed to assess associations of physician's work overload, successive work shifts, and work experience with physicians' risk to err. MATERIALS AND METHODS: This large-scale study included physicians who prescribed at least 100 systemic medications at Sheba Medical Center during 2012-2017 in all acute care departments, excluding intensive care units. Presumed medication errors were flagged by a high-accuracy computerized decision support system that uses machine-learning algorithms to detect potential medication prescription errors. Physicians' successive work shifts (first or only shift, second, and third shifts), workload (assessed by the number of prescriptions during a shift) and work-experience, as well as a novel measurement of physicians' prescribing experience with a specific drug, were assessed per prescription. The risk to err was determined for various work conditions. RESULTS: 1 652 896 medical orders were prescribed by 1066 physicians; The system flagged 3738 (0.23%) prescriptions as erroneous. Physicians were 8.2 times more likely to err during high than normal-low workload shifts (5.19% vs 0.63%, P < .0001). Physicians on their third or second successive shift (compared to a first or single shift) were more likely to err (2.1%, 1.8%, and 0.88%, respectively, P < .001). Lack of experience in prescribing a specific medication was associated with higher error rate (0.37% for the first 5 prescriptions vs 0.13% after over 40, P < .001). DISCUSSION: Longer hours and less experience in prescribing a specific medication increase risk of erroneous prescribing. CONCLUSION: Restricting successive shifts, reducing workload, increasing training and supervision, and implementing smart clinical decision support systems may help reduce prescription errors.