Interpretative reading of the antibiogram:a semi-naïve Bayesian approach

AbstractBackgroundAn antibiogram (ABG) gives the results of in vitro susceptibility tests performed on a pathogen isolated from a culture of a sample taken from blood or other tissues. The institutional cross-ABG consists of the conditional probability of susceptibility for pairs of antimicrobials. This paper explores how interpretative reading of the isolate ABG can be used to replace and improve the prior probabilities stored in the institutional ABG. Probabilities were calculated by both a naïve and semi-naïve Bayesian approaches, both using the ABG for the given isolate and institutional ABGs and cross-ABGs.Methods and MaterialWe assessed an isolate database from an Israeli university hospital with ABGs from 3347 clinically significant blood isolates, where on average 19 antimicrobials were tested for susceptibility, out of 31 antimicrobials in regular use for patient treatment. For each of 14 pathogens or groups of pathogens in the database the average (prior) probability of susceptibility (also called the institutional ABG) and the institutional cross-ABG were calculated. For each isolate, the normalized Brier distance was used as a measure of the distance between susceptibility test results from the isolate ABG and respectively prior probabilities and posteriori probabilities of susceptibility. We used a 5-fold cross-validation to evaluate the performance of different approaches to predict posterior susceptibilities.ResultsThe normalized Brier distance between the prior probabilities and the susceptibility test results for all isolates in the database was reduced from 37.7% to 28.2% by the naïve Bayes method. The smallest normalized Brier distance of 25.3% was obtained with the semi-naïve min2max2 method, which uses the two smallest significant odds ratios and the two largest significant odds ratios expressing respectively cross-resistance and cross-susceptibility, calculated from the cross-ABG.ConclusionA practical method for predicting probability for antimicrobial susceptibility could be developed based on a semi-naïve Bayesian approach using statistical data on cross-susceptibilities and cross-resistances. The reduction in Brier distance from 37.7% to 25.3%, indicates a significant advantage to the proposed min2max2 method (p<10 99)

Predicting antibiotic resistance in urinary tract infection patients with prior urine cultures

To improve antibiotic prescribing, we sought to establish the probability of a resistant organism in urine culture given a previous resistant culture in a setting endemic for multidrug-resistant (MDR) organisms. We performed a retrospective analysis of inpatients with paired positive urine cultures. We focused on ciprofloxacin-resistant (cipro(r)) Gram-negative bacteria, extended-spectrum-beta-lactamase (ESBL)-producing Enterobacteriaceae, carbapenem-resistant Enterobacteriaceae (CRE), and carbapenem-resistant nonfermenters (CRNF). Comparisons were made between the frequency of each resistance phenotype following a previous culture with the same phenotype and the overall frequency of that phenotype, and odds ratios (ORs) were calculated. We performed a regression to assess the effects of other variables on the likelihood of a repeat resistant culture. A total of 4,409 patients (52.5% women; median age, 70 years) with 19,546 paired positive urine cultures were analyzed. The frequencies of cipro(r) bacteria, ESBL-producing Enterobacteriaceae, CRE, and CRNF among all cultures were 47.7%, 30.6%, 1.7%, and 2.6%, respectively. ORs for repeated resistance phenotypes were 1.87, 3.19, 48.25, and 19.02 for cipro(r) bacteria, ESBL-producing Enterobacteriaceae, CRE, and CRNF, respectively (P < 0.001 for all). At 1 month, the frequencies of repeated resistance phenotypes were 77.4%, 66.4%, 57.1%, and 33.3% for cipro(r) bacteria, ESBL-producing Enterobacteriaceae, CRE, and CRNF, respectively. Increasing time between cultures and the presence of an intervening nonresistant culture significantly reduced the chances of a repeat resistant culture. Associations were statistically significant over the duration of follow-up (60 months) for CRE and for up to 6 months for all other pathogens. Knowledge of microbiology results in the six preceding months may assist with antibiotic stewardship and improve the appropriateness of empirical treatment for urinary tract infections (UTIs)

Lifetime Cannabis Use Is Not Associated With Negative Beliefs About Medication in Patients With First Treatment Psychosis

Objective: Cannabis use is common among patients with psychosis, and along with negative beliefs about medication, it has been found to predict poor adherence to antipsychotic drug treatment. Such lack of adherence to antipsychotic drug treatment increases the risk of poor clinical outcomes and relapse in patients with first treatment for psychosis (FTP). However, to date, it is unclear whether cannabis use may be related to negative perceptions about antipsychotic drug treatment. Methods: A cross-sectional sample of 265 FTP patients with schizophrenia spectrum disorder underwent extensive clinical assessments. Three measures of cannabis use were obtained: lifetime, current and meeting diagnostic criteria for abuse or addiction. For the primary analyses we focused on lifetime cannabis use. The Beliefs about Medication Questionnaire (BMQ) was employed to assess the patients' specific concerns and perceptions of antipsychotic medications, as well as general beliefs about pharmacotherapy. The relationship between lifetime cannabis use and BMQ scores was investigated with general linear model (GLM) analyses, controlling for age and sex. Results: Patients with lifetime use of cannabis ≥10 times were more likely to be male, younger at the age of onset of psychosis and with higher levels of alcohol use and daily tobacco smoking, as compared to the non-users (p < 0.05). Neither lifetime use of cannabis, current use nor a cannabis abuse diagnosis was associated with negative beliefs about medicines as measured by the BMQ questionnaire. Conclusion: Use of cannabis is not linked to negative perceptions about antipsychotic medicines in patients with FTP. Other reasons for poor compliance to antipsychotic drug treatment in cannabis users need to be further investigated.publishedVersio

A comparison of predictors for mortality and bacteraemia in patients suspected of infection

Abstract Background Stratification by clinical scores of patients suspected of infection can be used to support decisions on treatment and diagnostic workup. Seven clinical scores, SepsisFinder (SF), National Early Warning Score (NEWS), Sequential Orgen Failure Assessment (SOFA), Mortality in Emergency Department Sepsis (MEDS), quick SOFA (qSOFA), Shapiro Decision Rule (SDR) and Systemic Inflammatory Response Syndrome (SIRS), were evaluated for their ability to predict 30-day mortality and bacteraemia and for their ability to identify a low risk group, where blood culture may not be cost-effective and a high risk group where direct-from-blood PCR (dfbPCR) may be cost effective. Methods Retrospective data from two Danish and an Israeli hospital with a total of 1816 patients were used to calculate the seven scores. Results SF had higher Area Under the Receiver Operating curve than the clinical scores for prediction of mortality and bacteraemia, significantly so for MEDS, qSOFA and SIRS. For mortality predictions SF also had significantly higher area under the curve than SDR. In a low risk group identified by SF, consisting of 33% of the patients only 1.7% had bacteraemia and mortality was 4.2%, giving a cost of € 1976 for one positive result by blood culture. This was higher than the cost of € 502 of one positive dfbPCR from a high risk group consisting of 10% of the patients, where 25.3% had bacteraemia and mortality was 24.2%. Conclusion This may motivate a health economic study of whether resources spent on low risk blood cultures might be better spent on high risk dfbPCR

Sex-Specific Effect of Serum Lipids and Body Mass Index on Psychotic Symptoms, a Cross-Sectional Study of First-Episode Psychosis Patients

Background: Schizophrenia is a disorder with considerable heterogeneity in course and outcomes, which is in part related to the patients' sex. Studies report a link between serum lipids, body mass index (BMI), and therapeutic response. However, the role of sex in these relationships is poorly understood. In a cross-sectional sample of first-episode psychosis (FEP) patients, we investigated if the relationship between serum lipid levels (total cholesterol, HDL-C, LDL-C, and triglycerides), BMI, and symptoms differs between the sexes. Methods: We included 435 FEP patients (males: N = 283, 65%) from the ongoing Thematically Organized Psychosis (TOP) study. Data on clinical status, antipsychotics, lifestyle, serum lipid levels, and BMI were obtained. The Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS) were used to assess psychotic and depressive symptoms. General linear models were employed to examine the relationship between metabolic variables and symptomatology. Results: We observed a female-specific association between serum HDL-C levels and negative symptoms (B = −2.24, p = 0.03) and between triglycerides levels (B = 1.48, p = 0.04) and BMI (B = 0.27, p = 0.001) with depressive symptoms. When controlling for BMI, only the association between serum HDL-C levels and negative symptoms remained significant. Moreover, the HDL-C and BMI associations remained significant after controlling for demography, lifestyle, and illness-related factors. Conclusion: We found a relationship between metabolic factors and psychiatric symptoms in FEP patients that was sex-dependent.publishedVersio

Energy expenditure in critically ill patients estimated by population-based equations, indirect calorimetry and CO2-based indirect calorimetry

Background: Indirect calorimetry (IC) is the reference method for measurement of energy expenditure (EE) in mechanically ventilated critically ill patients. When IC is unavailable, EE can be calculated by predictive equations or by VCO2-based calorimetry. This study compares the bias, quality and accuracy of these methods. Methods: EE was determined by IC over a 30-min period in patients from a mixed medical/postsurgical intensive care unit and compared to seven predictive equations and to VCO2-based calorimetry. The bias was described by the mean difference between predicted EE and IC, the quality by the root mean square error (RMSE) of the difference and the accuracy by the number of patients with estimates within 10 % of IC. Errors of VCO2-based calorimetry due to choice of respiratory quotient (RQ) were determined by a sensitivity analysis, and errors due to fluctuations in ventilation were explored by a qualitative analysis. Results: In 18 patients (mean age 61 ± 17 years, five women), EE averaged 2347 kcal/day. All predictive equations were accurate in less than 50 % of the patients with an RMSE ≥ 15 %. VCO2-based calorimetry was accurate in 89 % of patients, significantly better than all predictive equations, and remained better for any choice of RQ within published range (0.76–0.89). Errors due to fluctuations in ventilation are about equal in IC and VCO2-based calorimetry, and filtering reduced these errors. Conclusions: This study confirmed the inaccuracy of predictive equations and established VCO2-based calorimetry as a more accurate alternative. Both IC and VCO2-based calorimetry are sensitive to fluctuations in respiration.SCOPUS: ar.jinfo:eu-repo/semantics/publishe