Aluminium leaching in fluid warming devices – A Review

Current guidelines recommend the use of an intravenous fluid warmer to prevent perioperative hypothermia. Among the various methods of warming intravenous fluids, contact warmers are among the most effective and accurate, particularly in clinical conditions requiring rapid infusions of refrigerated blood or fluids. Contact warmers put the infusate in direct contact with a heating block. Some fluid warmers use heating blocks manufactured from aluminium. Several recent publications, however, have shown that uncoated aluminium blocks can leach potentially toxic amounts of aluminium into the body. In this review we performed a systematic literature review on aluminium leaching with contact fluid warmers and describe what manufacturer and competent authorities did in the past years to ensure patient safety. The search resulted in five articles describing the aluminium leaching. Four different devices (Level 1 Fluid Warmer from Smiths Medical, ThermaCor from Smisson-Cartledge Biomedical, Recirculator 8.0 from Eight Medical International BV, enFlow from Vyaire) were shown to leach high levels of aluminium when heating certain intravenous fluids. One manufacturer (Vyaire) voluntarily removed their product from the market, while three manufacturers (Eight Medical International BV, Smisson-Cartledge Biomedical, and Smiths Medical) revised the instructions for use for the affected devices. The enFlow fluid warmer was subsequently redesigned with a parylene coating over the heating block. The scientific literature shows that by using a thin parylene layer on the heating block, the leaching of aluminium can be nearly eliminated without affecting the heating performance of the device.

Postmarketing safety of orphan drugs: a longitudinal analysis of the US Food and Drug Administration database between 1999 and 2018

Background Information about the specific regulatory environment of orphan drugs is scarce and inconsistent. Uncertainties surrounding the postmarketing long-term safety of orphan drugs remain. This study aimed to evaluate the labelling changes of orphan drugs and to identify postmarketing safety-associated approval factors. Methods This retrospective cohort study includes all drugs with orphan drug designation approved by the Center for Drug Evaluation and Research of the US Food and Drug Administration between 1999 and 2018. Main outcomes are safety-related labelling changes up to 31 December 2019. We defined any safety-related labelling changes as postmarketing safety events (PMSE). Safety-related withdrawals, suspensions, and boxed warnings were further categorised as severe postmarketing safety events (SPSE). Outcome measurements include frequencies of PMSE, SPSE, and association between approval factors and the occurrence of safety events. Results Amongst the 214 drugs identified with orphan drug designation (25.7% biologics), 83.6% were approved through at least one expedited programme, and 29.4% were approved with boxed warnings. During a median follow-up of 6.74 years since approval, 69.2% and 14.5% of the analysed orphan drugs had PMSE and SPSE, respectively. Safety-related withdrawal (0%, 0/214), suspended marketing (0.46%, 1/214) and new boxed warnings are uncommon (3.7%, 8/214). The safety-related labelling changes were more frequent in the drugs approved with boxed warnings [Incidence rate ratio (IRR): 1.95 (1.02–3.73)] and approved for long-term use [IRR: 2.76 (1.52–5.00)]. Conclusions and Relevance In this long-term postmarketing analysis, approximately 70% of FDA-approved orphan drugs had safety-related labelling changes although severe safety events were rare. While maintaining early access to orphan drugs, the drug regulatory body has taken timely regulatory action with postmarketing surveillance to ensure patient safety.

Opinion of the junior doctors on the existing morale, support and autonomy at their workplace and the factors positively influencing them

Background: Junior doctors form the majority of the workforce in patient care. Their job is perilous, highly critical, tedious and exhausting and it is imperative that they stay motivated while at work. Improving the morale of physicians has the potential to increase efficiency, ensure patient safety and improve patient outcomes. We aimed to identify the existing status and explore the factors affecting junior doctors’ morale, their sense of feeling supported and their levels of autonomy in 2 large teaching hospitals in Bangladesh. Methods: This cross-sectional observational study was done across 2 large tertiary hospitals- Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders(BIRDEM) General Hospital and Dhaka Shishu Hospital, over 4 months period from September- December 2020. The study was carried out on 120 junior doctors by an online questionnaire, distributed through emails and Facebook messenger, asking junior doctors to rate their morale, sense of feeling supported and autonomy and rank the top factors that positively affected them. Results: Data were finally collected from 117 junior doctors after 3 incomplete data were discarded. Most of the junior doctors felt ‘neither good nor bad’ in the domains of existing ‘morale’ (44.4%), ‘feeling supported’ (46.5%) and ‘autonomy’ (48.7%). Additionally, ‘good’ morale was seen in 39.3%, while around 34% rated their support system as ‘good’ and around 24% reported a ‘good’ autonomy. The most important factor positively affecting morale was recognition and reward for good performance (70.1%), factor influencing support was an easy access to senior clinicians (70.4%) and that defining autonomy was constant senior supervision of the everyday work (61.1%). Conclusion: The study aims to identify the existing level of morale, support and autonomy of the junior doctors at their workplace and explore the factors positively affecting them. It is concluded from this study that the junior doctors rated their existing morale, support and autonomy as ‘average’. According to the opinions of the doctors, this study also concludes that, to improve their morale, there is a need to recognize and reward their good work and provide positive feedback. Doctors identified an easy access to senior clinicians with a problem was the primary factor influencing support. Finally, junior doctors wanted constant senior supervision of their everyday work in the wards to improve output. BIRDEM Med J 2022; 12(1): 30-35

132 Assessment Tools to Measure Clinical Reasoning While Attending Simulation-Based Courses

Clinical reasoning is interconnected with decision-making which is a critical element to ensure patient safety [1]. To avoid practice mistakes, healthcare professionals should be competent with effective clinical reasoning skills. To develop effective clinical reasoning skills, healthcare professionals should get the chance to practise and be exposed to various experiences and levels of patient complexities. Simulation can immerse learners in scenarios that mimic clinical situations, simultaneously mitigating safety risks and increasing standardization in healthcare education [2]. Through simulation, learners can get the chance to practise clinical reasoning with focussed learning opportunities [3]. Several assessment tools have been used to measure clinical reasoning while attending simulation-based activities. However, we would like to explore the most valid and reliable tools to assess clinical reasoning while attending simulation, in addition to finding out whether these tools have considered the seniority and competency levels of their users.A scoping review was undertaken to answer the questions: What are the best available valid and reliable tools to evaluate clinical reasoning while attending simulation-based activities? Do we have valid and reliable clinical reasoning assessment tools for simulation that measure clinical reasoning considering different seniority and competency levels? We searched Medline, Scopus, Education Research Complete, and Google Scholar to identify relevant recent primary research conducted on this topic from 2000 onwards. The search included MeSH topics of: ‘Clinical reasoning’, ‘Simulation-based courses’ and ‘Clinical Reasoning tools’. The inclusion criteria were primary studies that described the use of tools measuring clinical reasoning while attending simulation-based courses. Two independent researchers agreed on the inclusion of the identified papers for full-text review. This review followed the review guidelines of Joanne Briggs institute.There are valid and reliable tools to evaluate clinical reasoning while attending simulation which is Clinical Reasoning Evaluation Simulation Tool CREST [1]; Lasater Clinical Judgment Rubric LCJR [4]; Creighton Competency Evaluation Instrument Creighton C-SEI- Tool [5]. However, the validity and reliability of these tools were tested on undergraduate student nurses, and there was no consideration for different seniority and competence levels, and applicability to other healthcare professions.There is an adequate number of tools to measure clinical reasoning while attending simulation. However, there is a significant basis to test the reliability and validity of these tools against different competence and seniority levels, and applicability to other healthcare professions.

Digital Teaching Methodology among the Dental Students a Questionnaire Based Survey

Background: The implementation of digital technologies in dental curricula has started globally and reached varying levels of penetration counting on local resources and demands. One of the biggest challenges in digital education is the need to continuously adapt and adjust to the developments in technology and apply these to dental practice in communicating with dental professionals, medical doctors, dental technicians, and insurance providers, dental students need to be prepared to manage digitized data, ensure patient safety, and understand the advantages and limitations of conventional and digital processes. Aim: To create awareness about digital teaching methodology among the dental students. Materials and Methods: A cross-sectional survey was conducted among the adolescent population with a sample size of 120. A self administered structured questionnaire was prepared based on digital teaching methodology and consisted of 13 questions. It was circulated to participants through an online platform (google form). The statistics were done using SPSS software, chi-square test was used to check the association and P value of 0.05 was said to be statistically significant.The pros of the survey is that the adolescents of different lifestyles and cultures were surveyed. Children and adults were excluded from the survey. Simple random sampling method was the sampling method used to minimise the sampling bias. Results: The results showed that the dental students are aware about the digital teaching methodology. Conclusion: The people are aware of the digital teaching methodology. But more awareness needs to be spread so that digital handling can be improvised in the near future.

The Relative Contribution of Medical Comorbidities in the OARA Score: A Machine Learning Analysis

Background/Objective: The Outpatient Arthroplasty Risk Assessment (OARA) score has been used successfully to identify patients who can safely undergo outpatient primary total joint arthroplasty (TJA) based on medical risk stratification. The targeted score (0 to 79) was conservatively established to ensure patient safety. However, the number of points associated with each of the 52 comorbidities in the OARA score were assigned based on physician experience with early discharge. This study applied machine learning (ML) to empirically identify the relative contribution/importance of each medical comorbidity to safe same-day discharge (SDD). Methods: 3,047 patients who underwent primary unilateral TJA by a single surgeon at a single institution were included in the analysis; 573 were SDDs. Before ML analysis, associations among binary (yes/no) comorbidities were examined using Cramér's V. A CART decision tree model using Gini method was used to develop a model for SDD (yes/no) based on the presence or absence of the comorbidities. Results: To produce interpretable results with acceptable face validity the 52 comorbidities were grouped in 19 common medical categories (heart disease, liver disease, etc.). Although the resulting model was less than perfectly predictive (AROC = 0.652, 95% CI 0.629–0.675), it resulted in an interpretable classification tree identifying heart disease, kidney disease, immunosuppression, chronic sedative use, pulmonary disease, thrombophilia, anemia, and history of stroke, in order, as the most important predictors of SDD. Conclusion: Model limitations expressed as AROC were not unexpected because the relative contribution (expressed as points) of comorbidities to the OARA score are based on physician decision-making, not empirical identification of the importance of each medical condition to safe SDD. Study results moved the goal of empirical classification forward but the low prevalence of many of the comorbidities limited variability and hence model performance and accuracy. Future work with a larger sample is being planned.

Visible Human Project® female surface based computational phantom (Nelly) for radio-frequency safety evaluation in MRI coils

Quantitative modeling of specific absorption rate and temperature rise within the human body during 1.5 T and 3 T MRI scans is of clinical significance to ensure patient safety. This work presents justification, via validation and comparison, of the potential use of the Visible Human Project (VHP) derived Computer Aided Design (CAD) female full body computational human model for non-clinical assessment of female patients of age 50–65 years with a BMI of 30–36 during 1.5 T and 3 T based MRI procedures. The initial segmentation validation and four different application examples have been identified and used to compare to numerical simulation results obtained using VHP Female computational human model under the same or similar conditions. The first application example provides a simulation-to-simulation validation while the latter three application examples compare with measured experimental data. Given the same or similar coil settings, the computational human model generates meaningful results for SAR, B1 field, and temperature rise when used in conjunction with the 1.5 T birdcage MRI coils or at higher frequencies corresponding to 3 T MRI. Notably, the deviation in temperature rise from experiment did not exceed 2.75° C for three different heating scenarios considered in the study with relative deviations of 10%, 25%, and 20%. This study provides a reasonably systematic validation and comparison of the VHP-Female CAD v.3.0–5.0 surface-based computational human model starting with the segmentation validation and following four different application examples.

Improving Follow-Up of Tests Pending at Discharge

BACKGROUND AND OBJECTIVES Follow-up on results of inpatient tests pending at discharge (TPAD) must occur to ensure patient safety and high-quality care continue after discharge. We identified a need to improve follow-up of TPAD and began a quality improvement initiative with an aim of reducing the rate of missed follow-up of TPAD to ≤20% within 12 months. METHODS The team used the Plan-Do-Study-Act method of quality improvement and implemented a process using reminder messages in the electronic health record. We collected data via retrospective chart review for the 6 months before the intervention and monthly thereafter. The primary outcome measure was the percentage of patients with missed follow-up of TPAD, defined as no documented follow-up within 72 hours of a result being available. The use of a reminder message was monitored as a process measure. RESULTS We reviewed charts of 764 discharged patients, and 216 (28%) were noted to have TPAD. At baseline, the average percentage of patients with missed follow-up was 80%. The use of reminder messages was quickly adopted. The average percentage of patients with missed follow-up of TPAD after beginning the quality improvement interventions was 35%. CONCLUSIONS We had significant improvement in follow-up after our interventions. Additional work is needed to ensure continued and sustained improvement, focused on reducing variability in performance between providers and investing in technology to allow for automation of the follow-up process.

Nurses' efforts to maintain competence: a qualitative study

Background: The clinical competence of nurses should be maintained to ensure patient safety. Competence is the integration of knowledge, skills, and attitudes. Nurse competency also improves the quality of nursing care and reduces the incidence of missed nursing care. This study aimed to explore the experiences of nurses maintaining a nurse competence system in hospitals through continuing education.Design and Methods: The research method used was qualitative phenomenological research, and the data was collected through an in-depth interview which was consist of six participants.Results: The results of the research were 1). continuing professional development to maintain the competence, 2) credentialing system in the career ladder system, 3). missed care still occurred in the implementation of nursing services 4). Nurses had hopes that managers supported the efforts to maintain competence through continuing professional development.Conclusion: Nurse managers are expected to improve the supervision program to maintain clinical competence and decrease missed care.

Understanding machine learning classifier decisions in automated radiotherapy quality assurance

The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional radiotherapy treatment planning QA process. However, one important gap in relying on classifiers for QA of radiotherapy treatment plans is the lack of understanding behind a specific classifier prediction. We develop explanation methods to understand the decisions of two automated QA classifiers: (1) a region of interest (ROI) segmentation/labeling classifier, and (2) a treatment plan acceptance classifier. For each classifier, a local interpretable model-agnostic explanation (LIME) framework and a novel adaption of team-based Shapley values framework are constructed. We test these methods in datasets for two radiotherapy treatment sites (prostate and breast), and demonstrate the importance of evaluating QA classifiers using interpretable machine learning approaches. We additionally develop a notion of explanation consistency to assess classifier performance. Our explanation method allows for easy visualization and human expert assessment of classifier decisions in radiotherapy QA. Notably, we find that our team-based Shapley approach is more consistent than LIME. The ability to explain and validate automated decision-making is critical in medical treatments. This analysis allows us to conclude that both QA classifiers are moderately trustworthy and can be used to confirm expert decisions, though the current QA classifiers should not be viewed as a replacement for the human QA process.