Prediction of Antibiotic Resistance in Patients With a Urinary Tract Infection: Algorithm Development and Validation.

BACKGROUND: The early prediction of antibiotic resistance in patients with a urinary tract infection (UTI) is important to guide appropriate antibiotic therapy selection. OBJECTIVE: In this study, we aimed to predict antibiotic resistance in patients with a UTI. Additionally, we aimed to interpret the machine learning models we developed. METHODS: The electronic medical records of patients who were admitted to Yongin Severance Hospital, South Korea were used. A total of 71 features extracted from patients' admission, diagnosis, prescription, and microbiology records were used for classification. UTI pathogens were classified as either sensitive or resistant to cephalosporin, piperacillin-tazobactam (TZP), carbapenem, trimethoprim-sulfamethoxazole (TMP-SMX), and fluoroquinolone. To analyze how each variable contributed to the machine learning model's predictions of antibiotic resistance, we used the Shapley Additive Explanations method. Finally, a prototype machine learning-based clinical decision support system was proposed to provide clinicians the resistance probabilities for each antibiotic. RESULTS: The data set included 3535, 737, 708, 1582, and 1365 samples for cephalosporin, TZP, TMP-SMX, fluoroquinolone, and carbapenem resistance prediction models, respectively. The area under the receiver operating characteristic curve values of the random forest models were 0.777 (95% CI 0.775-0.779), 0.864 (95% CI 0.862-0.867), 0.877 (95% CI 0.874-0.880), 0.881 (95% CI 0.879-0.882), and 0.884 (95% CI 0.884-0.885) in the training set and 0.638 (95% CI 0.635-0.642), 0.630 (95% CI 0.626-0.634), 0.665 (95% CI 0.659-0.671), 0.670 (95% CI 0.666-0.673), and 0.721 (95% CI 0.718-0.724) in the test set for predicting resistance to cephalosporin, TZP, carbapenem, TMP-SMX, and fluoroquinolone, respectively. The number of previous visits, first culture after admission, chronic lower respiratory diseases, administration of drugs before infection, and exposure time to these drugs were found to be important variables for predicting antibiotic resistance. CONCLUSIONS: The study results demonstrated the potential of machine learning to predict antibiotic resistance in patients with a UTI. Machine learning can assist clinicians in making decisions regarding the selection of appropriate antibiotic therapy in patients with a UTI.

Evaluation of bone marrow findings in hemodialysis patients with erythropoietin-resistant anemia.

INTRODUCTION: Treatment with erythropoietin-stimulating agents (ESAs) is widely used in anemia of chronic kidney disease (CKD). Acquired ESA resistance is an important problem. The aim of this study is to examine the bone marrow findings in hemodialysis patients with ESA-resistant anemia. METHODS: The data of 210 patients with acquired ESA resistance were reviewed retrospectively. The patients were divided into groups according to having diagnosis of dysplasia and hematological disease, and survival analysis was performed. RESULTS: A total of 26 patients were included in the study. While dysplasia was present in 10 (38.5%) patients, five of them were diagnosed hematologically. When survival analysis was performed between those with and without a hematological diagnosis, a difference in survival was observed against the group with the diagnosis (24.4 vs. 72 months, p = 0.045). CONCLUSION: Unresponsiveness to ESA treatment in CKD patients or a decrease in one of the other cell lines along with hemoglobin, it would be appropriate to perform early bone marrow examination.

Cohen's d and physicians' opinion on effect size: a questionnaire on anemia treatment.

A well-known effect size (ES) indicator is Cohen's d. Cohen defined d measures of small, medium, and large ES as 0.2, 0.5, and 0.8, respectively. This approach has been criticized because practical and clinical importance depends on the context of research. The aim of the study was to examine physicians' perception of ES using iron deficiency anemia treatment as an example and observing the effects of pretreatment level and duration of treatment on the magnitude of ES. We prepared a questionnaire describing four different clinical studies: (1) 1 month of treatment of anemia in a group of patients with a mean hemoglobin (Hb) of 10 g/dL; (2) 3 months of treatment at an Hb level of 10 g/dL; (3) 1 month of treatment at an Hb level of 8 g/dL; and (4) 3 months of treatment at an Hb level of 8 g/dL. In each scenario, respondents were required to evaluate six various levels of Hb improvement as being very small, small, medium, large, or very large effect: 0.1 g/dL, 0.3 g/dL, 0.7 g/dL, 1.1 g/dL, 1.7 g/dL, and 2.8 g/dL. The responses of 35 physicians were evaluated. For 10 mg/dL, the Cohen's d for small, medium, and large ES was 0.5, 0.8, and 1.2 respectively, for 1 month of treatment. In terms of 3 months of treatment, the Cohen's d was 0.8, 1.2, and 2, respectively. Two separate pretreatment Hb levels (8 g/dL and 10 g/dL) demonstrated a minor difference. Determination of ES during the planning phase of studies requires thorough evaluation of specific clinical cases. Our results are divergent from the classic Cohen's d values. Additionally, duration of treatment affects ES perception.

ALS and fertility: does ALS affect number of children patients have?

OBJECTIVE: Amyotrophic Lateral Sclerosis is one major disease in the group of neurodegenerative conditions. As with most other neurodegenerative diseases, clinical signs of the disease usually show among the elderly population, and most commonly around 60-65 years of age. Therefore the disease is not expected to impact the fertility of ALS patients. When examined from an evolutionary medicine and evolutionary biology perspective, there should be no selection pressure on the patient population due to the late onset of ALS. Methods: In this study, we tested the hypothesis that ALS does not affect fertility on a group of patients with ALS that we collected in a multi-center study. We recruited 511 patients diagnosed with ALS according to the revised El Escorial criteria, and 236 control cases without a neurodegenerative disease. We compared the ALS group's number of offspring with the control group in three consecutive generations. Results: No statistically significant difference was found between the number of siblings of ALS and control groups (p = 0.44). A statistically significant difference was found between the number of children of ALS and control groups (p < 0.001), indicating ALS patients had more children than controls. When the number of children is assessed by gender, for women, there was no statistically significant difference between the number of children of ALS and control groups (p = 0.067). Conclusions: This finding supports the view that ALS does not have a negative selection pressure on the patient population's fertility.

DREEM in primary care: students' perspectives on educational environment of family medicine internship in primary care centres: experiences at Akdeniz University Faculty of Medicine in Turkey.

Objective: In many countries, medical education programmes are expanding, and educational environments are starting to change from hospitals to primary health care centres. International organizations for medical education highlight that medical students should also be educated at primary and secondary care centres in addition to third-step health care. In this study, the evaluation of sixth-year (final year) medical students' perspectives on the educational environment of family medicine internship in primary care is presented, using the Dundee Ready Educational Environment Measurement (DREEM)Material and methods: This descriptive cross-sectional study was conducted with sixth-year medical students. Starting in the 2017-2018 academic year, sixth-year medical students started to enrol in educational courses in primary care. A total of 223 students completed internships in primary care for a two-week period, during which we applied the Dundee Ready Educational Environment Measure (DREEM) to evaluate the educational environment at the end of the internship. The collected questionnaires were analysed by using IBM-SPSS version 23 for Mac OS. Student's t-test and Mann-Whitney U Test were used for comparisons.Results: Of the 223 students, 192 (86%) completed the questionnaire. The total DREEM score was 138.62/200 (%63.5). The students rated the learning environment as positive. The mean score for students' perceptions of teaching, students' perceptions of teachers, students' academic self-perceptions, students' perceptions of the atmosphere and students' social self-perceptions were 31.65/48, 30.45/44, 23.46/32, 34.20/48 and were 18.56/28, respectively. The total mean score and subscale scores were not significantly different between males and females.Conclusion: This study suggests that students' perception of the global learning environment in primary care was more positive than negative. The lowest scores were recorded for Students' Perceptions of Learning and Students' Social Self-Perceptions, and the highest score was recorded for Students' Academic Self-Perceptions.