Challenging management dogma where evidence is non-existent, weak or outdated

Medical practice is dogged by dogma. A conclusive evidence base is lacking for many aspects of patient management. Clinicians, therefore, rely upon engrained treatment strategies as the dogma seems to work, or at least is assumed to do so. Evidence is often distorted, overlooked or misapplied in the re-telling. However, it is incorporated as fact in textbooks, policies, guidelines and protocols with resource and medicolegal implications. We provide here four examples of medical dogma that underline the above points: loop diuretic treatment for acute heart failure; the effectiveness of heparin thromboprophylaxis; the rate of sodium correction for hyponatraemia; and the mantra of “each hour counts” for treating meningitis. It is notable that the underpinning evidence is largely unsupportive of these doctrines. We do not necessarily advocate change, but rather encourage critical reflection on current practices and the need for prospective studies

Eye-tracking to observe compliance with hand hygiene in the intensive care unit – a randomised feasibility study

Background: Healthcare-associated infections are associated with increased patient mortality. Hand hygiene is the most effective method to reduce these infections. Despite simplification of this easy intervention, compliance with hand disinfection remains low. Current assessment of hand hygiene is mainly based on observation by hygiene specialists. The aim of this study was to investigate additional benefits of eye-tracking during the analysis of hand hygiene compliance of healthcare professionals in the intensive care unit. Methods: In a simulated, randomised cross-over study conducted at the interdisciplinary intensive care unit of the University Hospital Zurich (Switzerland), doctors and nurses underwent eye-tracking and completed two everyday tasks (injection of 10 micrograms of norepinephrine via a central venous line, blood removal from the central line) in two scenarios where alcoholic dispenser locations differed ("in-sight" and "out-of-sight"). The primary outcomes were dwell time, revisits, first fixation duration and average fixation time on three areas of interest (central venous line, alcohol dispenser, protective glove box) for both scenarios. Compliance with hand hygiene guidelines was analysed. Findings: 49 participants (35 nurses, 14 doctors) were included. Eye-tracking provided additional useful information compared to conventional observations. Dwell time, revisits, first fixation duration and average fixation time did not differ between the two scenarios for all areas of interest. Overall compliance with recommended hand hygiene measures was low in both doctors (mean 20%) and nurses (mean 42.9%). Conclusion: Compared to conventional observations offered additional helpful insights and provided an in-depth analysis of gaze patterns during the recording of hand hygiene compliance in the intensive care unit. Keywords: compliance; eye-tracking; hand hygiene; intensive care unit

Where do ICU trainees really look? An eye-tracking analysis of gaze patterns during central venous catheter insertion

Background: There is limited knowledge about gaze patterns of intensive care unit (ICU) trainee doctors during the insertion of a central venous catheter (CVC). The primary objective of this study was to examine visual patterns exhibited by ICU trainee doctors during CVC insertion. Additionally, the study investigated whether differences in gaze patterns could be identified between more and less experienced trainee doctors. Methods: In a real-life, prospective observational study conducted at the interdisciplinary ICU at the University Hospital Zurich, Switzerland, ICU trainee doctors underwent eye-tracking during CVC insertion in a real ICU patient. Using mixed-effects model analyses, the primary outcomes were dwell time, first fixation duration, revisits, fixation count, and average fixation time on different areas of interest (AOI). Secondary outcomes were above eye-tracking outcome measures stratified according to experience level of participants. Results: Eighteen participants were included, of whom 10 were inexperienced and eight more experienced. Dwell time was highest for CVC preparation table ( p = 0.02), jugular vein on ultrasound image ( p < 0.001) and cervical puncture location ( p < 0.001). Concerning experience, dwell time and revisits on jugular vein on ultrasound image ( p = 0.02 and p = 0.04, respectively) and cervical puncture location ( p = 0.004 and p = 0.01, respectively) were decreased in more experienced ICU trainees. Conclusions: Various AOIs have distinct significance for ICU trainee doctors during CVC insertion. Experienced participants exhibited different gaze behavior, requiring less attention for preparation and handling tasks, emphasizing the importance of hand-eye coordination

Association Between Hypocholesterolemia and Mortality in Critically Ill Patients With Sepsis: A Systematic Review and Meta-Analysis

OBJECTIVE: To ascertain the association between cholesterol and triglyceride levels on ICU admission and mortality in patients with sepsis. DATA SOURCES: Systematic review and meta-analysis of published studies on PubMed and Embase. STUDY SELECTION: All observational studies reporting ICU admission cholesterol and triglyceride levels in critically ill patients with sepsis were included. Authors were contacted for further data. DATA EXTRACTION: Eighteen observational studies were identified, including 1,283 patients with a crude overall mortality of 33.3%. Data were assessed using Revman (Version 5.1, Cochrane Collaboration, Oxford, United Kingdom) and presented as mean difference (MD) with 95% CIs, p values, and I2 values. DATA SYNTHESIS: Admission levels of total cholesterol (17 studies, 1,204 patients; MD = 0.52 mmol/L [0.27–0.77 mmol/L]; p < 0.001; I2 = 91%), high-density lipoprotein (HDL)-cholesterol (14 studies, 991 patients; MD = 0.08 mmol/L [0.01–0.15 mmol/L]; p = 0.02; I2 = 61%), and low-density lipoprotein (LDL) cholesterol (15 studies, 1,017 patients; MD = 0.18 mmol/L [0.04–0.32 mmol/L]; p = 0.01; I2 = 71%) were significantly lower in eventual nonsurvivors compared with survivors. No association was seen between admission triglyceride levels and mortality (15 studies, 1,070 patients; MD = 0.00 mmol/L [–0.16 to 0.15 mmol/L]; p = –0.95; I2 = 79%). CONCLUSIONS: Mortality was associated with lower levels of total cholesterol, HDL-cholesterol, and LDL-cholesterol, but not triglyceride levels, in patients admitted to ICU with sepsis. The impact of cholesterol replacement on patient outcomes in sepsis, particularly in at-risk groups, merits investigation. KEYWORDS: cholesterol levels; intensive care unit; lipids; sepsis; triglyceride

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Background: The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. Methods: A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. Results: A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. Conclusions: The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice