[This consensus document is intended to provide guidance for the effective and efficient treatment of asymptomatic individuals with high uric acid levels and gout patients.]

[Diuretics have remained the cornerstone of the antihypertensive treatment since their widespreading in the 1960s. According to the 2018 ESC/ESH Guidelines for the management of arterial hypertension, in the absence of evidence from direct comparator trials and recognizing that many of the approved single-pill combinations are based on hydrochlorothiazide, this drug and thiazide-like indapamide can be considered suitable antihypertensive agents. In the 2018 Hungarian guidelines indapamide is named as the most efficacious diuretic in the treatment of patients with hypertension. The aim of the publication is redefining thiazide- and thiazide-like diuretic use in the treatment of hypertensive patients, with particular attention to presently available hydrochlorothia­zide and indapamide, and their combination drugs in Hungary.]

Short-lasting unilateral neuralgiform headache with conjunctival injection and tearing/short-lasting unilateral neuralgiform headache with cranial autonomic features (SUNCT/SUNA) is a rare severe headache. At the time of an attack, it can hinder a patient from eating and requires acute intervention. The sphenopalatine ganglion is an extracranial parasympathetic ganglion with both sensory and autonomic fibers. Sphenopalatine ganglion block has long been used in the treatment of headache, particularly when conventional methods have failed. Here, we present a patient who was resistant to intravenous lidocaine, but responded rapidly to sphenopalatine ganglion block during an acute episode of SUNCT/SUNA.

Delirium is a syndrome frequently encountered in intensive care and associated with a poor prognosis. Intensive care delirium is mostly based on general and palliative intensive care data in the literature. In this study, we aimed to investigate the incidence of delirium in coronary intensive care unit (CICU), related factors, its relationship with inhospital and follow up prognosis, incidence of age-related delirium and its effect on outcomes. This study was conducted with patients hospitalized in CICU of a tertiary university hospital between 01 August 2017 and 01 August 2018. Files of all patients were examined in details, and demographic, clinic and laboratory parameters were recorded. Patients confirmed with psychiatry consultation were included in the groups of patients who developed delirium. Patients were divided into groups with and without delirium developed, and baseline features, inhospital and follow up prognoses were investigated. In addition, patients were divided into four groups as <65 years old, 65-75 yo, 75-84 yo and> 85 yo, and the incidence of delirium, related factors and prognoses were compared among these groups. A total of 1108 patients (mean age: 64.4 ± 13.9 years; 66% men) who were followed in the intensive care unit with variable indications were included in the study. Of all patients 11.1% developed delirium in the CICU. Patients who developed delirium were older, comorbidities were more frequent, and these patients showed increased inflammation findings, and significant increase in inhospital mortality compared to those who did not develop delirium (p<0.05). At median 9-month follow up period, rehospitalization, reinfarction, cognitive dysfunction, initiation of psychiatric therapy and mortality were significantly higher in the delirium group (p<0.05). When patients who developed delirium were divided into four groups by age and analyzed, incidence of delirium and mortality rate in delirium group were significantly increased by age (p<0.05). Development of delirium in coronary intensive care unit is associated with increased inhospital and follow up morbidity and mortality. Delirium is more commonly seen in geriatric patients and those with comorbidity, and is associated with a poorer prognosis. High-risk patients should be more carefully monitored for the risk of delirium.

[What has Go to do with making clinical decisions? One of the greatest intellectual challenges of bedside medicine is making decisions under uncertainty. Besides the psychological traps of traditionally intuitive and heuristic medical decision making, lack of information, scarce resources and characteristics of doctor-patient relationship contribute equally to this uncertainty. Formal, mathematical model based analysis of decisions used widely in developing clinical guidelines and in health technology assessment provides a good tool in theoretical terms to avoid pitfalls of intuitive decision making. Nevertheless it can be hardly used in individual situations and most physicians dislike it as well. This method, however, has its own limitations, especially while tailoring individual decisions, under inclusion of potential lack of input data used for calculations, or its large imprecision, and the low capability of the current mathematical models to represent the full complexity and variability of processes in complex systems. Nevertheless, clinical decision support systems can be helpful in the individual decision making of physicians if they are well integrated in the health information systems, and do not break down the physicians’ autonomy of making decisions. Classical decision support systems are knowledge based and rely on system of rules and problem specific algorithms. They are utilized widely from health administration to image processing. The current information revolution created the so-called artificial intelligence by machine learning methods, i.e. machines can learn indeed. This new generation of artificial intelligence is not based on particular system of rules but on neuronal networks teaching themselves by huge databases and general learning algorithms. This type of artificial intelligence outperforms humans already in certain fields like chess, Go, or aerial combat. Its development is full of challenges and threats, while it presents a technological breakthrough, which cannot be stopped and will transform our world. Its development and application has already started also in the healthcare. Health professionals must participate in this development to steer it into the right direction. Lee Sedol, 18-times Go world champion retired three years after his historical defeat from AlphaGo artificial intelligence, be­cause “Even if I become the No. 1, there is an entity that cannot be defeated”. It is our great luck that we do not need to compete or defeat it, we must ensure instead that it would be safe and trustworthy, and in collaboration with humans this entity would make healthcare more effective and efficient. ]