[Red man syndrome as a complication of vancomycin therapy]. / Zespól czerwonego czlowieka jako powiklanie leczenia wankomycyna.

Red man syndrome (RMS) is a side effect of vancomycin therapy and manifests itself mainly by a red blotchy rash with itching and sometimes muscle pain and a decrease of arterial blood pressure. A CASE REPORT: 24-year-old patient admitted to hospital with a history of chest pain radiating to the back. EKG has shown the depression of PQ, in the ECHO mark of liquid in the pericardial cavity and the increase of CRP and troponin concentrations. The patient was diagnosed with acute pericarditis and treated with ibuprofen and colchicine. Due to the increasing parameters of inflammation, a bacterial etiology was suspected and vancomycin was administered. During antibiotic therapy, there were symptoms of a mild adverse reaction in the form of a maculopapular rash and periodic decreases in blood pressure. RMS was diagnosed and symptoms resolved after treatment with cetirizine. CONCLUSIONS: RMS should be distinguished from anaphylaxis and treated according to the diagnosis.

Dyspnoe, cough and fever in 80-year old patient with chronic heart failure.

The prevalence of heart failure (HF) in developed countries exceeds 10% in adults over 70 year old. At the following report we aim to present a case of HF worsening complicated by gout attack. CASE REPORT: 80-year old patient was admitted to the hospital, with the suspicion of pneumonia, because of 3-day history of dyspnoe, cough and fever. Patient reported redness, swelling and pain in the area of left elbow. Prior to admission patient was diagnosed with bursitis and treated with antibiotic without symptoms resolution. There was past medical history of atrial fibrillation, hypertension, intermittent claudication, COPD. One month before, patient was hospitalized due to HF worsening. Diuretics' dosage was increased at that time and symptoms resolved. On admission: blood pressure 145/88 mm Hg, HR 96/min irregular, saturation O2 88% without oxygen therapy. On physical examination, bilateral pulmonary crackles, redness, tenderness of left elbow were found. Laboratory tests demonstrated elevated parameters of inflammation - leukocytosis 13.4G/L, neutrophilia 11G/L, CRP 142.5 mg/L, but normal procalcitonin 0.27 ng/ml. Moreover, high NTproBNP 8573 pg/ml and hyperuricemia 13.1 mg/dl were detected. Chest X-ray indicated pulmonary venous congestion. ECG revealed atrial fibrillation with QRS rate of 100/min, left axis deviation. Therefore, gout attack was diagnosed and after colchicine administration symptoms resolved quickly. CONCLUSIONS: Clinical signs including fever, elevated parameters of inflammation and dyspnoe justify pneumonia consideration in differential diagnosis. Importantly, non-infectious causes of inflammatory conditions, like gout must be also considered. Patients with HF often develop hyperuricemia due to diuretic treatment, aggravated catabolism and often co-prevalent chronic kidney disease.

Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?

Background: The guidelines recommend intensive blood pressure control. Randomized trials have focused on the relevance of the systolic blood pressure (SBP) lowering, leaving the safety of the diastolic blood pressure (DBP) reduction unresolved. There are data available which show that low DBP should not stop clinicians from achieving SBP targets; however, registries and analyses of randomized trials present conflicting results. The purpose of the study was to apply machine learning (ML) algorithms to determine, whether DBP is an important risk factor to predict stroke, heart failure (HF), myocardial infarction (MI), and primary outcome in the SPRINT trial database. Methods: ML experiments were performed using decision tree, random forest, k-nearest neighbor, naive Bayesian, multi-layer perceptron, and logistic regression algorithms, including and excluding DBP as the risk factor in an unselected and selected (DBP < 70 mmHg) study population. Results: Including DBP as the risk factor did not change the performance of the machine learning models evaluated using accuracy, AUC, mean, and weighted F-measure, and was not required to make proper predictions of stroke, MI, HF, and primary outcome. Conclusions: Analyses of the SPRINT trial data using ML algorithms imply that DBP should not be treated as an independent risk factor when intensifying blood pressure control.