Clinical Utility of a Biomarker to Detect Contrast-Induced Acute Kidney Injury during Percutaneous Cardiovascular Procedures

<b><i>Introduction:</i></b> Contrast-induced acute kidney injury (CI-AKI) is a major clinical complication of percutaneous cardiovascular procedures requiring iodinated contrast. Despite its relative frequency, practicing physicians are unlikely to identify or treat this condition. <b><i>Methods:</i></b> In a 2-round clinical trial of simulated patients, we examined the clinical utility of a urine-based assay that measures liver-type fatty acid-binding protein (L-FABP), a novel marker of CI-AKI. We sought to determine if interventional cardiologists’ ability to diagnose and treat potential CI-AKI improved using the biomarker assay for 3 different patient types: pre-procedure, peri-procedure, and post-procedure patients. <b><i>Results:</i></b> 154 participating cardiologists were randomly divided into either control or intervention. At baseline, we found no difference in the demographics or how they identified and treated potential complications of AKI, with both groups providing less than half the necessary care to their patients (46.4% for control vs. 47.6% for intervention, <i>p</i> = 0.250). The introduction of L-FABP into patient care resulted in a statistically significant improvement of 4.6% (<i>p</i> = 0.001). Compared to controls, physicians receiving L-FABP results were 2.9 times more likely to correctly identify their patients’ risk for AKI (95% CI 2.1–4.0) and were more than twice as likely to treat for AKI by providing volume expansion and withholding nephrotoxic medications. We found the greatest clinical utility in the pre-procedure and peri-procedure settings but limited value in the post-procedure setting. <b><i>Conclusion:</i></b> This study suggests L-FABP as a clinical marker for assessing the risk of potential CI-AKI, has clinical utility, and can lead to more accurate diagnosis and treatment.

Accuracy and Validity Outpatient Diagnosis Code Base On ICD-10 at Imogiri I Health Center Bantul Yogyakarta

Background: Analysis of accuracy and validity fill code diagnosis on medical record document is very important because if diagnosis code is not appropriate with ICD-10, will cause decline in quality services health center, generated data have this validation data level is low, because accuracy code very important for health center such as index process and statistical report, as basis for making outpatient morbidity report and top ten diseases reports, as well as influencing policies will be taken by primary health center management. This study aims to analyze accuracy and validity diagnosis disease code based on ICD-10 fourth quarter in 2020 Imogiri I Health Center Bantul.Methods: Descriptive qualitative approach, case study design. Subject is a doctor, nurse, head record medical and staff. Object is outpatients medical record document in Imogiri I Health Center Bantul. Total sample 99 medical record file. Obtaining data from this study through interviews and observations.Results: Number of complete accurate diagnosis codes is 60 (60,6%), incomplete accurate diagnosis codes is 26 (26.3%) and inaccurate diagnosis codes is 13 (13.1%). Inaccuracies include errors in determining code, errors in determining 4th character ICD-10 code, not adding 4th and 5th characters, not including external cause, and multiple diseases.Conclusions: Inaccuracy factors are not competence medical record staff, incomplete diagnosis writing and no training, no evaluation or coding audit has been carried out, and standard operational procedure is not socialized.

Comparing Diagnosis and Treatment of Pulmonary Hypertension Patients at a Pulmonary Hypertension Center versus Community Centers

Once patients are diagnosed with pulmonary hypertension it is important to identify the correct diagnostic group as it will have implications on the disease state management. Pulmonary hypertension is increasingly diagnosed and treated in general medical practices; however, evidence-based guidelines recommend evaluation and treatment in pulmonary hypertension centers for accurate diagnosis and appropriate treatment recommendations. We conducted a retrospective cohort study of 509 random patients 18 years and older who were evaluated in our pulmonary hypertension clinic from January 2005 to December 2018. 68.4% (n = 348) had their diagnostic group clarified or changed. Pulmonary hypertension was deemed an incorrect diagnosis in 12.4% (n = 63). A total of 114 patients (22.4%) had been initiated on pulmonary hypertension specific treatment prior to presentation. Pulmonary hypertension specific medication was stopped in 57 (50.0%) cases. The estimated monthly saving of the stopped medication based on wholesale acquisition costs was USD 396,988.05–419,641.05, a monthly saving of USD 6964.70–7362.12 per patient. Evaluation outside of a pulmonary hypertension center may lead to misdiagnosis and inappropriate or inadequate treatment. Pulmonary arterial hypertension directed therapy improves median survival, but inappropriate therapy may cause harm; therefore, patients benefit from a specialized center with multiple resources to secure an accurate diagnosis and tailored treatment for their condition.

Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning

Atopic dermatitis (AD) is a common skin disease in childhood whose diagnosis requires expertise in dermatology. Recent studies have indicated that host genes–microbial interactions in the gut contribute to human diseases including AD. We sought to develop an accurate and automated pipeline for AD diagnosis based on transcriptome and microbiota data. Using these data of 161 subjects including AD patients and healthy controls, we trained a machine learning classifier to predict the risk of AD. We found that the classifier could accurately differentiate subjects with AD and healthy individuals based on the omics data with an average F1-score of 0.84. With this classifier, we also identified a set of 35 genes and 50 microbiota features that are predictive for AD. Among the selected features, we discovered at least three genes and three microorganisms directly or indirectly associated with AD. Although further replications in other cohorts are needed, our findings suggest that these genes and microbiota features may provide novel biological insights and may be developed into useful biomarkers of AD prediction.

Lymphoma versus Carcinoma and Other Collaborations

David Mason started his research career at a time when lymphoma diagnosis was based primarily on cellular morphology, clinical symptoms and special cytochemical stains using formalin fixed tissue sections. There were occasions, however, where the morphology was unhelpful, such as in the case of anaplastic or poorly differentiated tumours, where a distinction between lymphoma and a non-haematopoietic tumour was often problematical. Accurate diagnosis became even more important with the developments in the clinical staging of lymphoma and the availability of more effective treatments. One way forward to improve diagnosis was to use immunohistochemistry to study the antigens expressed by the tumor cells.

Electroanalysis of Fentanyl and Its New Analogs: A Review

The review describes fentanyl and its analogs as new synthetic opioids and the possibilities of their identification and determination using electrochemical methods (e.g., voltammetry, potentiometry, electrochemiluminescence) and electrochemical methods combined with various separation methods. The review also covers the analysis of new synthetic opioids, their parent compounds, and corresponding metabolites in body fluids, such as urine, blood, serum, and plasma, necessary for a fast and accurate diagnosis of intoxication. Identifying and quantifying these addictive and illicit substances and their metabolites is necessary for clinical, toxicological, and forensic purposes. As a reaction to the growing number of new synthetic opioid intoxications and increasing fatalities observed over the past ten years, we provide thorough background for developing new biosensors, screen-printed electrodes, or other point-of-care devices.