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.

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.

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.

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.

Antibiotic cross-resistance in the lab and resistance co-occurrence in the clinic: Discrepancies and implications in E.coli.

INTRODUCTION: Antibiotic resistance is an important public health issue, and vast resources are invested in researching new ways to fight it. Recent experimental works have shown that resistance to some antibiotics can result in increased susceptibility to others, namely induce cross-sensitivity. This phenomenon could be utilized to increase efficiency of antibiotic treatment strategies that minimize resistance. However, as conditions in experimental settings and in the clinic may differ substantially, the implications of cross-sensitivity for clinical settings are not guaranteed and should be examined. METHODS: In this work we analyzed data of Escherichia coli isolates from patients' blood, sampled in Rabin Medical Center, Israel, to examine co-occurrence of resistance to antibiotics in the clinic. We compared the co-occurrence patterns with cross-sensitivity patterns observed in the lab. RESULTS: Our data showed only positively associated occurrence of resistance, even with antibiotics that were shown to induce cross-sensitivity in laboratory conditions. We used a mathematical model to examine the potential effects of cross-sensitivity versus co-occurrence on the spread of drug resistance. CONCLUSIONS: We conclude that resistance frequencies in the clinic can have a substantial effect on the success of treatment strategies, and should be considered alongside experimental evidence of cross-sensitivity.

A Case of Hepatotoxicity Induced by Adulterated "Tiger King", a Chinese Herbal Medicine Containing Sildenafil.

Detection of Phosphodiesterase Type 5 (PDE-5) inhibitors and their analogues in "100% natural" or "herbal" supplements have been described in numerous reports. However, few reports have been published in relation to actual harm caused by counterfeit erectile dysfunction herbal supplements. We describe a case of a 65-year old male admitted to a tertiary hospital with acute liver toxicity, possibly induced by adulterated "Chinese herbal" supplement "Tiger King" for sexual enhancement. Chemical analysis of the tablets discovered the presence of therapeutic doses of sildenafil with no other herbal components. Other medications were excluded as potential causes of the hepatic impairment. According to the Naranjo adverse drug reaction scale and the Roussel Uclaf Causality Assessment Method (RUCAM) the probability of association of Hepatotoxicity with Sildenafil was "possible" and "probable" respectively (Naranjo score of 4, RUCAM score of 7). Within three days of admission, the patient's clinical status and liver function improved without any specific treatment. His liver function tests normalized 30 days post discharge. Further pharmacovigilance actions should be taken by regulatory authorities and pharmaceutical companies in order to determine the relation between sildenafil and hepatotoxicity. This case emphasizes the importance of raising public awareness on the potential dangers of "Tiger king" in particular, and other counterfeit medications or herbal supplements of unknown origin.

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.

Clinical implications of high-sensitivity cardiac troponin measurements in hospitalized medical patients.

BACKGROUND: The increased use of high sensitivity cardiac troponins (hs-cTn), have made the diagnosis of non-ST elevation myocardial infarction (MI) challenging, especially in complex medical patients, in whom the clinical presentation of MI is nonspecific and multiple comorbidities as well as non-ischemic acute conditions may account for elevated hs-cTn levels. The aim of this study was to assess the frequency of both elevated hs-cTn levels and dynamic changes in hospitalized patients. METHODS AND FINDINGS: We conducted a retrospective study identifying all patients hospitalized in the Internal Medicine Division of Rabin Medical Center, Israel between January 2011 to December 2011, for whom at least one hs-cTn T (hs-cTnT) measurement was obtained. Collected data included patient demographics, acute and chronic diagnosis, hs-cTnT and creatinine levels and date of death. Hs-cTnT levels were obtained in 5,696 admissions and was above the 99th percentile (> = 13 ng/L) in 61.6% of the measurements. A relative change of 50% or higher was observed in 24% of the admissions. Among those with elevated hs-cTnT levels, acute coronary syndromes (ACS) accounted for only 6.1% of acute diagnoses. Maximal hs-cTnT levels above 100 ng/L but not dynamic changes discriminated between ACS and non-ACS conditions (positive and negative predictive values of 12% and 96% respectively). The frequency of elevated hs-cTnT levels was age-dependent and over 75% of patients aged >70 years-old had levels above the 99th percentile. Multivariate analysis identified hs-cTnT levels higher than the 99th percentile, as an independent, strong predictor for 30-day mortality (OR 4.58 [2.8, 7.49], p<0.0001). CONCLUSIONS: Elevated hs-cTnT levels together with dynamic changes are frequent findings among hospitalized patients and in most cases, are not related to the ACS diagnosis. These findings highlight the diagnostic challenge of ACS in this complex population. Further studies are needed in order to optimize the use of hs-cTnT measurements in hospitalized patients.

Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.

Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.

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.

Type-II myocardial infarction--patient characteristics, management and outcomes.

BACKGROUND: Type-II MI is defined as myocardial infarction (MI) secondary to ischemia due to either increased oxygen demand or decreased supply. This categorization has been used for the last five years, yet, little is known about patient characteristics and clinical outcomes. In the current work we assessed the epidemiology, causes, management and outcomes of type II MI patients. METHODS: A comparative analysis was performed between patients with type-I and type-II MI who participated in two prospective national Acute Coronary Syndrome Israeli Surveys (ACSIS) performed in 2008 and 2010. RESULTS: The surveys included 2818 patients with acute MI of whom 127 (4.5%) had type-II MI. The main causes of type-II MI were anemia (31%), sepsis (24%), and arrhythmia (17%). Patients with type-II MI tended to be older (75.6±12 vs. 63.8±13, p<0.0001), female majority (43.3% vs. 22.3%, p<0.0001), had more frequently impaired functional level (45.7% vs. 17%, p<0.0001) and a higher GRACE risk score (150±32 vs. 110±35, p<0.0001). Patients with type-II MI were significantly less often referred for coronary interventions (36% vs. 89%, p<0.0001) and less frequently prescribed guideline-directed medical therapy. Mortality rates were substantially higher among patients with type-II MI both at thirty-day (13.6% vs. 4.9%, p<0.0001) and at one-year (23.9% vs. 8.6%, p<0.0001) follow-ups. CONCLUSIONS: Patients with type-II compared to type-I MI have distinct demographics, increased prevalence of multiple comorbidities, a high-risk cardiovascular profile and an overall worse outcome. The complex medical condition of this cohort imposes a great therapeutic challenge and specific guidelines with recommended medical treatment and invasive strategies are warranted.

Predictors and outcomes of infection-related hospital admissions of heart failure patients.

BACKGROUND: Infections are one of the most common causes for hospitalization of patients with heart failure (HF). Yet, little is known regarding the prevalence and predictors of different types of acute infections as well as their impact on outcome among this growing population. METHODS AND RESULTS: We identified all patients aged 50 or older with a major diagnosis of HF and at least one echocardiography examination who had been hospitalized over a 10-year period (January 2000 and December 2009). Infection-associated admissions were identified according to discharge diagnoses. Among 9,335 HF patients, 3530 (38%) were hospitalized at least once due to infections. The most frequent diagnoses were respiratory infection (52.6%) and sepsis/bacteremia (23.6%) followed by urinary (15.7%) and skin and soft tissue infections (7.8%). Hospitalizations due to infections compared to other indications were associated with increased 30-day mortality (13% vs. 8%, p<0.0001). These higher mortality rates were predominately related to respiratory infections (OR 1.28 [95% CI 1.09, 1.5]) and sepsis\bacteremia (OR 3.13 [95% CI 2.6, 3.7]). Important predictors for these serious infections included female gender, chronic obstructive pulmonary disease, past myocardial infarction and echocardiography-defined significant right (RV) but not left ventricular dysfunction. CONCLUSIONS: Major infection-related hospitalizations are frequent among patients with HF and are associated with increased mortality rates. Elderly female patients with multiple comorbidities and those with severe RV dysfunction are at higher risk for these infections.

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.