Understanding Decision Making in Critical Care

Advancements and implications of artificial intelligence for early detection, diagnosis and tailored treatment of cancer

The complexity and heterogeneity of cancer makes early detection and effective treatment crucial to enhance patient survival and quality of life. The intrinsic creative ability of artificial intelligence (AI) offers improvements in patient screening, diagnosis, and individualized care. Advanced technologies, like computer vision, machine learning, deep learning, and natural language processing, can analyze large datasets and identify patterns that permit early cancer detection, diagnosis, management and incorporation of conclusive treatment plans, ensuring improved quality of life for patients by personalizing care and minimizing unnecessary interventions. Genomics, transcriptomics and proteomics data can be combined with AI algorithms to unveil an extensive overview of cancer biology, assisting in its detailed understanding and will help in identifying new drug targets and developing effective therapies. This can also help to identify personalized molecular signatures which can facilitate tailored interventions addressing the unique aspects of each patient. AI-driven transcriptomics, proteomics, and genomes represents a revolutionary strategy to improve patient outcome by offering precise diagnosis and tailored therapy. The inclusion of AI in oncology may boost efficiency, reduce errors, and save costs, but it cannot take the role of medical professionals. While clinicians and doctors have the final say in all matters, it might serve as their faithful assistant.

Unique considerations in the assessment and management of traumatic brain injury in older adults

The age-specific incidence of traumatic brain injury in older adults is rising in high-income countries, mainly due to an increase in the incidence of falls. The severity of traumatic brain injury in older adults can be underestimated because of a delay in the development of mass effect and symptoms of intracranial haemorrhage. Management and rehabilitation in older adults must consider comorbidities and frailty, the treatment of pre-existing disorders, the reduced potential for recovery, the likelihood of cognitive decline, and the avoidance of future falls. Older age is associated with worse outcomes after traumatic brain injury, but premorbid health is an important predictor and good outcomes are achievable. Although prognostication is uncertain, unsubstantiated nihilism (eg, early withdrawal decisions from the assumption that old age necessarily leads to poor outcomes) should be avoided. The absence of management recommendations for older adults highlights the need for stronger evidence to enhance prognostication. In the meantime, decision making should be multidisciplinary, transparent, personalised, and inclusive of patients and relatives.

Physicians' decision making when weaning patients from mechanical ventilation: A qualitative content analysis

BACKGROUND

Weaning from mechanical ventilation is a complex and central intensive care process. This complexity indicates that the challenges of weaning must be explored from different perspectives. Furthermore, physicians' experiences and the factors influencing their decision-making regarding weaning are unclear.

OBJECTIVES

This study aimed to explore and describe the factors influencing physicians' decision-making when weaning patients from invasive mechanical ventilation in Swedish intensive care units (ICUs).

METHODS

This qualitative study used an exploratory and descriptive design with qualitative content analysis. Sixteen physicians from five ICUs across Sweden were purposively included and interviewed regarding their weaning experiences.

FINDINGS

The physicians expressed that prioritising the patient's well-being was evident, and there was agreement that both the physical and mental condition of the patient had a substantial impact on decision-making. Furthermore, there was a lack of agreement on whether patients should be involved in the weaning process and how their resources, needs, and wishes should be included in decision-making. In addition, there were factors not directly linked to the patient but which still influenced decision-making, such as the available resources and teamwork. Sometimes, it was difficult to point out the basis for decisions; in that decisions were made by gut feeling, intuition, or clinical experience.

CONCLUSION

Physicians' decision-making regarding weaning was a dynamic process influenced by several factors. These factors were related to the patient's condition and the structure for weaning. Increased understanding of weaning from the physicians' and ICU teams' perspectives may improve the weaning process by broadening the knowledge about the aspects influencing the decision-making.

A conceptual analysis of facilitation to improve clinical outcomes in critical care units

Objectives

Effective facilitation is crucial to improve critical care outcomes in life-threatening conditions through improved teamwork, caring, decision-making, and problem-solving. The meaning of facilitation remains unprecise in a critical care context despite its frequent usage in nursing education and clinical practice. This study aimed to report a thorough concept analysis to clarify the meaning of facilitation in the critical care context by formulating attributes, antecedents, and consequences and providing model cases related to facilitation.

Methods

This analysis was performed by searching online sources published from 1999 to 2023. EBSCOhost, CINAHL, PubMed, and Google Scholar databases were searched using online search engines. The analysis also included the manual search of books, thesaurus and dictionaries that showed relevance to facilitation. Walker and Avant's eight-step approach was applied to explore and analyze the meaning of facilitation in critical care units.

Results

A total of 68 articles were included in the analysis of this study. Eleven attributes, six antecedents, and seven consequences related to facilitation were formulated. The attributes included dynamic, interactive processes, creating a positive environment, mobilizing resources, assistance, student-centered, shared goals, collaboration, engagement, participation, and feedback. Antecedents were facilitator qualities, motivation, a positive learning environment, student-facilitator relationship, time availability, and specified learning outcomes. The consequences of facilitation were identified as follows: change, professional development, competency, quality development, increased job satisfaction, staff retention, and self-confidence.

Conclusions

The findings from the analysis indicated that effective facilitation results in nurses and critical care staff developing competency, caring, critical thinking, and independence. Therefore, clinical outcomes in critical care environments are improved through teamwork, decision-making, and problem-solving in life-threatening situations.

The value of artificial intelligence for the treatment of mechanically ventilated intensive care unit patients: An early health technology assessment

PURPOSE

The health and economic consequences of artificial intelligence (AI) systems for mechanically ventilated intensive care unit patients often remain unstudied. Early health technology assessments (HTA) can examine the potential impact of AI systems by using available data and simulations. Therefore, we developed a generic health-economic model suitable for early HTA of AI systems for mechanically ventilated patients.

MATERIALS AND METHODS

Our generic health-economic model simulates mechanically ventilated patients from their hospitalisation until their death. The model simulates two scenarios, care as usual and care with the AI system, and compares these scenarios to estimate their cost-effectiveness.

RESULTS

The generic health-economic model we developed is suitable for estimating the cost-effectiveness of various AI systems. By varying input parameters and assumptions, the model can examine the cost-effectiveness of AI systems across a wide range of different clinical settings.

CONCLUSIONS

Using the proposed generic health-economic model, investors and innovators can easily assess whether implementing a certain AI system is likely to be cost-effective before an exact clinical impact is determined. The results of the early HTA can aid investors and innovators in deployment of AI systems by supporting development decisions, informing value-based pricing, clinical trial design, and selection of target patient groups.

Intergroup time bias and aversive racism in the medical context

Time is fundamental to organizing all aspects of human life. When invested in relationships, it has a psychological meaning as it indicates how much individuals value others and their interest in maintaining social relationships. Previous research has identified an intergroup time bias (ITB) in racialized social relations, defined as a discriminatory behavior in which White individuals invest more time in evaluating White than Black individuals. This research proposes an aversive racism explanation for the ITB effect and examines its consequences in the medical context. In four experimental studies (N = 434), we found that White medical trainees invested more time in forming impressions of White (vs. Black) male patients. Study 5 (N = 193) further revealed more time investment in diagnosing, assessing pain, and prescribing opioids for White than Black male patients. This biased time effect mediated the impact of patients' skin color on health care outcomes, leading to greater diagnostic accuracy and pain perception, and lower opioid prescriptions. A meta-analytical integration of the results (Study 6) confirmed the ITB effect reliability across experiments and that it is stronger in participants with an aversive racist profile (vs. consistently prejudiced or nonprejudiced). These findings provide the first evidence that bias in time investment favoring White (vs. Black) patients is associated with aversive racism and impacts medical health care outcomes. Furthermore, these results offer insights into the sociopsychological meaning of time investment in health care and provide a theoretical explanation for an understudied insidious form of discrimination that is critical to comprehending the persistency of racial health care disparities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Ten misconceptions regarding decision-making in critical care

Diagnostic errors are prevalent in critical care practice and are associated with patient harm and costs for providers and the healthcare system. Patient complexity, illness severity, and the urgency in initiating proper treatment all contribute to decision-making errors. Clinician-related factors such as fatigue, cognitive overload, and inexperience further interfere with effective decision-making. Cognitive science has provided insight into the clinical decision-making process that can be used to reduce error. This evidence-based review discusses ten common misconceptions regarding critical care decision-making. By understanding how practitioners make clinical decisions and examining how errors occur, strategies may be developed and implemented to decrease errors in Decision-making and improve patient outcomes.

Sports Medicine Patient Experience: Implicit Bias Mitigation and Communication Strategies

Unconscious bias, also known as implicit bias, is the principal contributor to the perpetuation of discrimination and is a robust determinant of people's decision-making. Unconscious bias occurs despite conscious nonprejudiced intentions and interferes with the actions of the reflective and conscious side. Education and self-awareness about implicit bias and its potentially harmful effects on judgment and behavior may lead individuals to pursue corrective action and follow implicit bias mitigation communication strategies. Team physicians must follow existing communication strategies and guidelines to mitigate unconscious bias and begin an evolution toward nonbiased judgment and decision-making to improve athlete care.

Evaluating the clinical utility of artificial intelligence assistance and its explanation on the glioma grading task

Clinical evaluation evidence and model explainability are key gatekeepers to ensure the safe, accountable, and effective use of artificial intelligence (AI) in clinical settings. We conducted a clinical user-centered evaluation with 35 neurosurgeons to assess the utility of AI assistance and its explanation on the glioma grading task. Each participant read 25 brain MRI scans of patients with gliomas, and gave their judgment on the glioma grading without and with the assistance of AI prediction and explanation. The AI model was trained on the BraTS dataset with 88.0% accuracy. The AI explanation was generated using the explainable AI algorithm of SmoothGrad, which was selected from 16 algorithms based on the criterion of being truthful to the AI decision process. Results showed that compared to the average accuracy of 82.5±8.7% when physicians performed the task alone, physicians' task performance increased to 87.7±7.3% with statistical significance (p-value = 0.002) when assisted by AI prediction, and remained at almost the same level of 88.5±7.0% (p-value = 0.35) with the additional assistance of AI explanation. Based on quantitative and qualitative results, the observed improvement in physicians' task performance assisted by AI prediction was mainly because physicians' decision patterns converged to be similar to AI, as physicians only switched their decisions when disagreeing with AI. The insignificant change in physicians' performance with the additional assistance of AI explanation was because the AI explanations did not provide explicit reasons, contexts, or descriptions of clinical features to help doctors discern potentially incorrect AI predictions. The evaluation showed the clinical utility of AI to assist physicians on the glioma grading task, and identified the limitations and clinical usage gaps of existing explainable AI techniques for future improvement.

Identifying the need for infection-related consultations in intensive care patients using machine learning models

Infection-related consultations on intensive care units (ICU) have a positive impact on quality of care and clinical outcome. However, timing of these consultations is essential and to date they are typically event-triggered and reactive. Here, we investigate a proactive approach to identify patients in need for infection-related consultations by machine learning models using routine electronic health records. Data was retrieved from a mixed ICU at a large academic tertiary care hospital including 9684 admissions. Infection-related consultations were predicted using logistic regression, random forest, gradient boosting machines, and long short-term memory neural networks (LSTM). Overall, 7.8% of admitted patients received an infection-related consultation. Time-sensitive modelling approaches performed better than static approaches. Using LSTM resulted in the prediction of infection-related consultations in the next clinical shift (up to eight hours in advance) with an area under the receiver operating curve (AUROC) of 0.921 and an area under the precision recall curve (AUPRC) of 0.541. The successful prediction of infection-related consultations for ICU patients was done without the use of classical triggers, such as (interim) microbiology reports. Predicting this key event can potentially streamline ICU and consultant workflows and improve care as well as outcome for critically ill patients with (suspected) infections.

Healthcare providers consistently overestimate the diagnostic probability of ventilator-associated pneumonia

OBJECTIVE

To assess the accuracy of provider estimates of ventilator-associated pneumonia (VAP) diagnostic probability in various clinical scenarios.

DESIGN

We conducted a clinical vignette-based survey of intensive care unit (ICU) physicians to evaluate provider estimates of VAP diagnostic probability before and after isolated cardinal VAP clinical changes and VAP diagnostic test results. Responses were used to calculate imputed diagnostic likelihood ratios (LRs), which were compared to evidence-based LRs.

SETTING

Michigan Medicine University Hospital, a tertiary-care center.

PARTICIPANTS

This study included 133 ICU clinical faculty and house staff.

RESULTS

Provider estimates of VAP diagnostic probability were consistently higher than evidence-based diagnostic probabilities. Similarly, imputed LRs from provider-estimated diagnostic probabilities were consistently higher than evidence-based LRs. These differences were most notable for positive bronchoalveolar lavage culture (provider-estimated LR 5.7 vs evidence-based LR 1.4; P < .01), chest radiograph with air bronchogram (provider-estimated LR 6.0 vs evidence-based LR 3.6; P < .01), and isolated purulent endotracheal secretions (provider-estimated LR 1.6 vs evidence-based LR 0.8; P < .01). Attending physicians and infectious disease physicians were more accurate in their LR estimates than trainees (P = .04) and non-ID physicians (P = .03).

CONCLUSIONS

Physicians routinely overestimated the diagnostic probability of VAP as well as the positive LRs of isolated cardinal VAP clinical changes and VAP diagnostic test results. Diagnostic stewardship initiatives, including educational outreach and clinical decision support systems, may be useful adjuncts in minimizing VAP overdiagnosis and ICU antibiotic overuse.

Decision Fatigue in Emergency Medicine: An Exploration of Its Validity

Emergency physicians face a relentless stream of complex, high-stakes decisions in a fast-paced and dynamic environment. The concept of decision fatigue, a phenomenon characterized by a decline in the quality of decision-making after a long sequence of choices, has garnered increasing attention within healthcare. Several investigations show that the number and complexity of decisions made during prolonged shifts correlate with increased self-reported fatigue; however, the effect on clinical decision quality is uncertain. Conversely, a subset of studies found no clear relationship between decision fatigue and errors in clinical judgment. Importantly, some researchers argue that decision fatigue may be mitigated by factors such as experience, training, and support systems. This narrative review highlights the existing literature on decision fatigue among emergency physicians and explores whether this concept holds as a valid concern or remains a myth in the context of their practice.

Temporal Clustering of Critical Illness Events on Medical Wards

Importance

Recognizing and preventing patient deterioration is important for hospital safety.

Objective

To investigate whether critical illness events (in-hospital death or intensive care unit [ICU] transfer) are associated with greater risk of subsequent critical illness events for other patients on the same medical ward.

Design, Setting, and Participants

Retrospective cohort study in 5 hospitals in Toronto, Canada, including 118 529 hospitalizations. Patients were admitted to general internal medicine wards between April 1, 2010, and October 31, 2017. Data were analyzed between January 1, 2020, and April 10, 2023.

Exposures

Critical illness events (in-hospital death or ICU transfer).

Main Outcomes and Measures

The primary outcome was the composite of in-hospital death or ICU transfer. The association between critical illness events on the same ward across 6-hour intervals was studied using discrete-time survival analysis, adjusting for patient and situational factors. The association between critical illness events on different comparable wards in the same hospital was measured as a negative control.

Results

The cohort included 118 529 hospitalizations (median age, 72 years [IQR, 56-83 years]; 50.7% male). Death or ICU transfer occurred in 8785 hospitalizations (7.4%). Patients were more likely to experience the primary outcome after exposure to 1 prior event (adjusted odds ratio [AOR], 1.39; 95% CI, 1.30-1.48) and more than 1 prior event (AOR, 1.49; 95% CI, 1.33-1.68) in the prior 6-hour interval compared with no exposure. The exposure was associated with increased odds of subsequent ICU transfer (1 event: AOR, 1.67; 95% CI, 1.54-1.81; >1 event: AOR, 2.05; 95% CI, 1.79-2.36) but not death alone (1 event: AOR, 1.08; 95% CI, 0.97-1.19; >1 event: AOR, 0.88; 95% CI, 0.71-1.09). There was no significant association between critical illness events on different wards within the same hospital.

Conclusions and Relevance

Findings of this cohort study suggest that patients are more likely to be transferred to the ICU in the hours after another patient's critical illness event on the same ward. This phenomenon could have several explanations, including increased recognition of critical illness and preemptive ICU transfers, resource diversion to the first event, or fluctuations in ward or ICU capacity. Patient safety may be improved by better understanding the clustering of ICU transfers on medical wards.

Design, validation and piloting of clinical vignettes to analyze critical care clinical decision processes during the COVID-19 pandemic in three different countries (Spain, Chile and United States)

Background

The use of heuristics in clinical decision-making processes increases in contexts of high uncertainty, such as those in Intensive Care Units (ICU. Given the impossibility of empirically studying their impact on real-world conditions, clinical vignettes were developed with the goal of identifying the use of heuristics in the care of critically ill patients during the COVID-19 pandemic in different clinical contexts.

Methodology

Vignettes were designed by critical care physicians in Spain to assess the use of representativeness, availability, and status quo heuristics in the care of critically ill patients during the COVID-19 pandemic. The construct, internal and external validity of the vignettes designed in Spain, the United States and Chile were evaluated. A questionnaire was piloted with the vignettes being validated in the three aforementioned countries through a computer application built for this purpose.

Results

16 study vignettes grouped into 5 models were created: each model included between 2 and 4 vignettes. The vignettes designed were closed-response vignettes with 2-3 possible alternatives. The vignettes, initially developed in Spain in Spanish, were translated to English and adapted to the Spanish used in Chile. The clinical content of the vignettes was not modified during the translation process.

Conclusions

The vignettes allow for the study of the use of heuristics in critical care clinical decision making in the context of the COVID-19 pandemic. The piloting and validation process used can serve as a model for similar multinational studies exploring clinical decision making.

Predicting onset of disease progression using temporal disease occurrence networks

OBJECTIVE

Early recognition and prevention are crucial for reducing the risk of disease progression. This study aimed to develop a novel technique based on a temporal disease occurrence network to analyze and predict disease progression.

METHODS

This study used a total of 3.9 million patient records. Patient health records were transformed into temporal disease occurrence networks, and a supervised depth first search was used to find frequent disease sequences to predict the onset of disease progression. The diseases represented nodes in the network and paths between nodes represented edges that co-occurred in a patient cohort with temporal order. The node and edge level attributes contained meta-information about patients' gender, age group, and identity as labels where the disease occurred. The node and edge level attributes guided the depth first search to identify frequent disease occurrences in specific genders and age groups. The patient history was used to match the most frequent disease occurrences and then the obtained sequences were merged together to generate a ranked list of diseases with their conditional probability and relative risk.

RESULTS

The study found that the proposed method had improved performance compared to other methods. Specifically, when predicting a single disease, the method achieved an area under the receiver operating characteristic curve (AUC) of 0.65 and an F1-score of 0.11. When predicting a set of diseases relative to ground truth, the method achieved an AUC of 0.68 and an F1-score of 0.13.

CONCLUSION

The ranked list generated by the proposed method, which includes the probability of occurrence and relative risk score, can provide physicians with valuable information about the sequential development of diseases in patients. This information can help physicians to take preventive measures in a timely manner, based on the best available information.

Investigating the impact of brief training in decision-making on treatment escalation to intensive care using objective structured clinical examination-style scenarios

Background

The decision to admit patients to the intensive care unit (ICU) is complex. Structuring the decision-making process may be beneficial to patients and decision-makers alike. The aim of this study was to investigate the feasibility and impact of a brief training intervention on ICU treatment escalation decisions using the Warwick model- a structured decision-making framework for treatment escalation decisions.

Methods

Treatment escalation decisions were assessed using Objective Structured Clinical Examination-style scenarios. Participants were ICU and anaesthetic registrars with experience of making ICU admission decisions. Participants completed one scenario, followed by training with the decision-making framework and subsequently a second scenario. Decision-making data was collected using checklists, note entries and post-scenario questionnaires.

Results

Twelve participants were enrolled. Brief decision-making training was successfully delivered during the normal ICU working day. Following training participants demonstrated greater evidence of balancing the burdens and benefits of treatment escalation. On visual analogue scales of 0-10, participants felt better trained to make treatment escalation decisions (4.9 vs 6.8, p = 0.017) and felt their decision-making was more structured (4.7 vs 8.1, p = 0.017).Overall, participants provided positive feedback and reported feeling more prepared for the task of making treatment escalation decisions.

Conclusion

Our findings suggest that a brief training intervention is a feasible way to improve the decision-making process by improving decision-making structure, reasoning and documentation. Training was implemented successfully, acceptable to participants and participants were able to apply their learning. Further studies of regional and national cohorts are needed to determine if training benefit is sustained and generalisable.

Classification of Level of Consciousness in a Neurological ICU Using Physiological Data

BACKGROUND

Impaired consciousness is common in intensive care unit (ICU) patients, and an individual's degree of consciousness is crucial to determining their care and prognosis. However, there are no methods that continuously monitor consciousness and alert clinicians to changes. We investigated the use of physiological signals collected in the ICU to classify levels of consciousness in critically ill patients.

METHODS

We studied 61 patients with subarachnoid hemorrhage (SAH) and 178 patients with intracerebral hemorrhage (ICH) from the neurological ICU at Columbia University Medical Center in a retrospective observational study of prospectively collected data. The level of consciousness was determined on the basis of neurological examination and mapped to comatose, vegetative state or unresponsive wakefulness syndrome (VS/UWS), minimally conscious minus state (MCS-), and command following. For each physiological signal, we extracted time-series features and performed classification using extreme gradient boosting on multiple clinically relevant tasks across subsets of physiological signals. We applied this approach independently on both SAH and ICH patient groups for three sets of variables: (1) a minimal set common to most hospital patients (e.g., heart rate), (2) variables available in most ICUs (e.g., body temperature), and (3) an extended set recorded mainly in neurological ICUs (absent for the ICH patient group; e.g., brain temperature).

RESULTS

On the commonly performed classification task of VS/UWS versus MCS-, we achieved an area under the receiver operating characteristic curve (AUROC) in the SAH patient group of 0.72 (sensitivity 82%, specificity 57%; 95% confidence interval [CI] 0.63-0.81) using the extended set, 0.69 (sensitivity 83%, specificity 51%; 95% CI 0.59-0.78) on the variable set available in most ICUs, and 0.69 (sensitivity 56%, specificity 78%; 95% CI 0.60-0.78) on the minimal set. In the ICH patient group, AUROC was 0.64 (sensitivity 56%, specificity 65%; 95% CI 0.55-0.74) using the minimal set and 0.61 (sensitivity 50%, specificity 80%; 95% CI 0.51-0.71) using the variables available in most ICUs.

CONCLUSIONS

We find that physiological signals can be used to classify states of consciousness for patients in the ICU. Building on this with intraday assessments and increasing sensitivity and specificity may enable alarm systems that alert physicians to changes in consciousness and frequent monitoring of consciousness throughout the day, both of which may improve patient care and outcomes.

Anchoring Errors in Emergency Medicine Residents and Faculties

Background: Clinical reasoning is the basis of all clinical activities in the health team, and diagnostic reasoning is perhaps the most critical of a physician's skills. Despite many advances, medical errors have not been reduced. Studies have shown that most diagnostic errors made in emergency rooms are cognitive errors, and anchoring error was identified as the most common cognitive error in clinical settings. This research intends to determine the frequency and compare the percentage of anchoring bias perceived among faculty members versus residents in the emergency medicine department. Methods: In this quasi-experimental study, Emergency Medicine's Faculties and Residents are evaluated in clinical reasoning by nine written clinical cases. The clinical data for each clinical case was presented to the participants over three pages, based on receiving clinical and para-clinical information in real situations. At the end of each page, participants were asked to write up diagnoses. Data were analyzed using one-way ANOVA test. The SPSS software (Version 16.0) was employed to conduct statistical tests, and a P value < 0.05 was considered to be statistically significant. Results: Seventy-seven participants of the residency program in the Emergency Medical group volunteered to participate in this study. Data showed Faculties were significantly higher in writing correct diagnoses than residents (66% vs. 41%), but the anchoring error ratio was significantly lower in residents (33% vs. 75%). In addition, the number of written diagnoses, time for writing diagnoses, and Clinical experience in faculties and residents were compared. Conclusion: Findings showed that increasing clinical experience increased diagnostic accuracy and changed cognitive medical errors. Faculties were higher than residents in anchoring error ratio. This error could be the result of more exposure and more decision-making in the mode of heuristic or intuitive thinking in faculties.

Prediction models for SIRS, sepsis and associated organ dysfunctions in paediatric intensive care: study protocol for a diagnostic test accuracy study

INTRODUCTION

Systemic inflammatory response syndrome (SIRS), sepsis and associated organ dysfunctions are life-threating conditions occurring at paediatric intensive care units (PICUs). Early recognition and treatment within the first hours of onset are critical. However, time pressure, lack of personnel resources, and the need for complex age-dependent diagnoses impede an accurate and timely diagnosis by PICU physicians. Data-driven prediction models integrated in clinical decision support systems (CDSS) could facilitate early recognition of disease onset.

OBJECTIVES

To estimate the sensitivity and specificity of previously developed prediction models (index tests) for the detection of SIRS, sepsis and associated organ dysfunctions in critically ill children up to 12 hours before reference standard diagnosis is possible.

METHODS AND ANALYSIS

We conduct a monocentre, prospective diagnostic test accuracy study. Clinicians in the PICU of the tertiary care centre Hannover Medical School, Germany, continuously screen and recruit patients until the adaptive sample size (originally intended sample size of 500 patients) is enrolled. Eligible are children (0-17 years, all sexes) who stay in the PICU for ≥12 hours and for whom an informed consent is given. All eligible patients are independently assessed for SIRS, sepsis and organ dysfunctions using corresponding predictive and knowledge-based CDSS models. The knowledge-based CDSS models serve as imperfect reference standards. The assessments are used to estimate the sensitivities and specificities of each predictive model using a clustered nonparametric approach (main analysis). Subgroup analyses ('age groups', 'sex' and 'age groups by sex') are predefined.

ETHICS AND DISSEMINATION

This study obtained ethics approval from the Hannover Medical School Ethics Committee (No. 10188_BO_SK_2022). Results will be disseminated as peer-reviewed publications, at scientific conferences, and to patients in an appropriate dissemination approach.

TRIAL REGISTRATION NUMBER

This study was registered with the German Clinical Trial Register (DRKS00029071) on 2022-05-23.

PROTOCOL VERSION

10188_BO_SK_2022_V.2.0-20220330_4_Studienprotokoll.

What are the essential components to implement individual-focused interventions for well-being and burnout in critical care healthcare professionals? A realist expert opinion

Background

This study aimed to determine what, how, and under what circumstances individual-focused interventions improve well-being and decrease burnout for critical care healthcare professionals.

Method

This realist approach, expert opinion interview, was guided by the Realist And Meta-narrative Evidence Synthesis: Evolving Standards II (RAMESES II) guidelines. Semi-structured interviews with critical care experts were conducted to ascertain current and nuanced information on a set of pre-defined individual interventions summarized from a previous umbrella review. The data were appraised, and relationships between context, mechanisms, and outcomes were extracted, which created theory prepositions that refined the initial program theory.

Results

A total of 21 critical care experts were individually interviewed. By understanding the complex interplay between organizational and personal factors that influenced intervention uptake, it was possible to decipher the most likely implementable intervention for critical care healthcare professionals. The expert recommendation suggested that interventions should be evidence-based, accessible, inclusive, and collaborative, and promote knowledge and skill development. Unique mechanisms were also required to achieve the positive effects of the intervention due to the presence of contextual factors within critical care settings. Mechanisms identified in this study included the facilitation of self-awareness, self-regulation, autonomy, collaboration, acceptance, and inclusion (to enable a larger reach to different social groups).

Conclusion

This validation of a theoretical understanding of intervention that addressed well-being and burnout in critical care healthcare professionals by expert opinion demonstrated essential mechanisms and contextual factors to consider when designing and implementing interventions. Future research would benefit by piloting individual interventions and integrating these new theoretical findings to understand better their effectiveness for future translation into the "real-world" setting.

REACT: Rapid Evaluation Assessment of Clinical Reasoning Tool

INTRODUCTION

Clinical reasoning encompasses the process of data collection, synthesis, and interpretation to generate a working diagnosis and make management decisions. Situated cognition theory suggests that knowledge is relative to contextual factors, and clinical reasoning in urgent situations is framed by pressure of consequential, time-sensitive decision-making for diagnosis and management. These unique aspects of urgent clinical care may limit the effectiveness of traditional tools to assess, teach, and remediate clinical reasoning.

METHODS

Using two validated frameworks, a multidisciplinary group of clinicians trained to remediate clinical reasoning and with experience in urgent clinical care encounters designed the novel Rapid Evaluation Assessment of Clinical Reasoning Tool (REACT). REACT is a behaviorally anchored assessment tool scoring five domains used to provide formative feedback to learners evaluating patients during urgent clinical situations. A pilot study was performed to assess fourth-year medical students during simulated urgent clinical scenarios. Learners were scored using REACT by a separate, multidisciplinary group of clinician educators with no additional training in the clinical reasoning process. REACT scores were analyzed for internal consistency across raters and observations.

RESULTS

Overall internal consistency for the 41 patient simulations as measured by Cronbach's alpha was 0.86. A weighted kappa statistic was used to assess the overall score inter-rater reliability. Moderate reliability was observed at 0.56.

DISCUSSION

To our knowledge, REACT is the first tool designed specifically for formative assessment of a learner's clinical reasoning performance during simulated urgent clinical situations. With evidence of reliability and content validity, this tool guides feedback to learners during high-risk urgent clinical scenarios, with the goal of reducing diagnostic and management errors to limit patient harm.

The use of deliberate reflection to reduce confirmation bias among orthopedic surgery residents

Introduction: cognitive biases might affect decision-making processes such as clinical reasoning and confirmation bias is among the most important ones. The use of strategies that stimulate deliberate reflection during the diagnostic process seems to reduce availability bias, but its effect in reducing confirmation bias needs to be evaluated.Aims: to examine whether deliberate reflection reduces confirmation bias and increases the diagnostic accuracy of orthopedic residents solving written clinical cases.Methods: experimental study comparing the diagnostic accuracy of orthopedic residents in the resolution of eight written clinical cases containing a referral diagnosis. Half of the written cases had a wrong referral diagnosis. One group of residents used deliberate reflection (RG), which stimulates comparison and contrast of clinical hypotheses in a systematic manner, and a control group (CG), was asked to provide differential diagnoses with no further instruction. The study included 55 third-year orthopedic residents, 27 allocated to the RG and 28 to the CG.Results: residents on the RG had higher diagnostic scores than the CG for clinical cases with a correct referral diagnosis (62.0±20.1 vs. 49.1±21.0 respectively; p = 0.021). For clinical cases with incorrect referral diagnosis, diagnostic accuracy was similar between residents on the RG and those on the CG (39.8±24.3 vs. 44.6±26.7 respectively; p = 0.662). We observed an overall confirmation bias in 26.3% of initial diagnoses (non-analytic phase) and 19.5% of final diagnoses (analytic phase) when solving clinical cases with incorrect referral diagnosis. Residents from RG showed a reduction in confirmation of incorrect referral diagnosis when comparing the initial diagnosis given in the non-analytic phase with the one provided as the final diagnosis (25.9±17.7 vs. 17.6±18.1, respectively; Cohen d: 0.46; p = 0.003). In the CG, the reduction in the confirmation of incorrect diagnosis was not statistically significant.Conclusions: confirmation bias was present when residents solved written clinical cases with incorrect referral diagnoses, and deliberate reflection reduced such bias. Despite the reduction in confirmation bias, diagnostic accuracy of residents from the RG was similar to those from the CG when solving the set of clinical cases with a wrong referral diagnosis.

The Impact of Choice Architecture on Sepsis Fluid Resuscitation Decisions: An Exploratory Survey-Based Study

Background

Discordance with well-known sepsis resuscitation guidelines is often attributed to rational assessments of patients at the point of care. Conversely, we sought to explore the impact of choice architecture (i.e., the environment, manner, and behavioral psychology within which options are presented and decisions are made) on decisions to prescribe guideline-discordant fluid volumes.

Design

We conducted an electronic, survey-based study using a septic shock clinical vignette. Physicians from multiple specialties and training levels at an academic tertiary-care hospital and academic safety-net hospital were randomized to distinct answer sets: control (6 fluid options), time constraint (6 fluid options with a 10-s limit to answer), or choice overload (25 fluid options). The primary outcome was discordance with Surviving Sepsis Campaign fluid resuscitation guidelines. We also measured response times and examined the relationship between each choice architecture intervention group, response time, and guideline discordance.

Results

A total of 189 of 624 (30.3%) physicians completed the survey. Time spent answering the vignette was reduced in time constraint (9.5 s, interquartile range [IQR] 7.3 s to 10.0 s, P < 0.001) and increased in choice overload (56.8 s, IQR 35.9 s to 86.7 s, P < 0.001) groups compared with control (28.3 s, IQR 20.0 s to 44.6 s). In contrast, the relative risk of guideline discordance was higher in time constraint (2.07, 1.33 to 3.23, P = 0.001) and lower in choice overload (0.75, 0.60, to 0.95, P =0.02) groups. After controlling for time spent reading the vignette, the overall odds of choosing guideline-discordant fluid volumes were reduced for every additional second spent answering the vignette (OR 0.98, 0.97, to 0.99, P < 0.001).

Conclusions

Choice architecture may affect fluid resuscitation decisions in sepsis regardless of patient conditions, warranting further investigation in real-world contexts. These effects should be considered when implementing practice guidelines.

Highlights

The Impact of Delayed Symptomatic Treatment Implementation in the Intensive Care Unit

We estimated the harm related to medication delivery delays across 12,474 medication administration instances in an intensive care unit using retrospective data in a large urban academic medical center between 2012 and 2015. We leveraged an instrumental variables (IV) approach that addresses unobserved confounds in this setting. We focused on nurse shift changes as disruptors of timely medication (vasodilators, antipyretics, and bronchodilators) delivery to estimate the impact of delay. The average delay around a nurse shift change was 60.8 min (p < 0.001) for antipyretics, 39.5 min (p < 0.001) for bronchodilators, and 57.1 min (p < 0.001) for vasodilators. This delay can increase the odds of developing a fever by 32.94%, tachypnea by 79.5%, and hypertension by 134%, respectively. Compared to estimates generated by a naïve regression approach, our IV estimates tend to be higher, suggesting the existence of a bias from providers prioritizing more critical patients.

Prediction of hemodynamic tolerance of intermittent hemodialysis in critically ill patients: a cohort study

The evaluation and management of fluid balance are key challenges when caring for critically ill patients requiring renal replacement therapy. The aim of this study was to assess the ability of clinical judgment and other variables to predict the occurrence of hypotension during intermittent hemodialysis (IHD) in critically ill patients. This was a prospective, observational, single-center study involving critically ill patients undergoing IHD. The clinical judgment of hypervolemia was determined by the managing nephrologists and critical care physicians in charge of the patients on the basis of the clinical data used to calculate the ultrafiltration volume and rate for each dialysis treatment. Seventy-nine (31.9%) patients presented with hypotension during IHD. Patients were perceived as being hypervolemic in 109 (43.9%) of the cases by nephrologists and in 107 (43.1%) by intensivists. The agreement between nephrologists and intensivists was weak (kappa = 0.561). Receiver operating characteristic curve analysis yielded an AUC of 0.81 (95% CI 0.75 to 0.84; P < 0.0001), and a cutoff value of 70 mm for the vascular pedicle width (VPW) had the highest accuracy for the prediction of the absence of hypotension. The clinical judgment of hypervolemia did not predict hypotension during IHD. The high predictive ability of the VPW may assist clinicians with critical thinking.

Retroperitoneal Hematoma Following Elective Abdominal Aortic Aneurysm Repair: A Case Report

Retroperitoneal hematoma formation following elective open abdominal aortic aneurysm (AAA) repair may be occult. We report a case of recurrent hypotensive episodes in the postanesthesia care unit (PACU) that were temporarily treated successfully with fluid, blood products, and vasopressors. At reoperation, active bleeding was excluded; however, upon reopening the bovine pericardial patch closure of the retroperitoneum, hematoma between the aneurysmal sac and inferior vena cava (IVC) had caused IVC compression. Evacuation of the hematoma rapidly restored venous return and hemodynamics. This report describes a case of retroperitoneal hematoma formation and highlights challenges associated with diagnosing bleeding in this compartment.

GP decisions to participate in emergencies: a randomised vignette study

Background

GPs use their judgement on whether to participate in emergencies; however, little is known about how GPs make their decisions on emergency participation.

Aim

To test whether GPs' participation in emergencies is associated with cause of symptoms, distance to the patient, other patients waiting, and out-of-hours (OOH) clinic characteristics.

Design & setting

An online survey was sent to all GPs in Norway (n = 4701).

Method

GPs were randomised to vignettes describing a patient with acute shortness of breath and asked whether they would participate in a callout. The vignettes varied with respect to cause of symptoms (trauma versus illness), distance to the patient (15 minutes versus 45 minutes), and other patients waiting at the OOH clinic (crowding versus no crowding). The survey included questions about OOH clinic characteristics.

Results

Of the 1013 GPs (22%) who responded, 76% reported that they would participate. The proportion was higher in trauma (83% versus 69%, χ² 24.8, P<0.001), short distances (80% versus 71%, χ² 9.5, P=0.002), and no crowding (81% versus 70% χ² 14.6, P<0.001). Participation was associated with availability of a manned-response vehicle (adjusted odds ratio [OR] 2.06, 95% confidence interval [CI] = 1.25 to 3.41), and team training at the OOH clinic once a year (OR = 1.78, 95% CI = 1.12 to 2.82) or more than once a year (OR = 3.78, 95% CI = 1.64 to 8.68).

Conclusion

GPs were less likely to participate in emergencies when the incident was not owing to trauma, was far away, and when other patients were waiting. A manned-response vehicle and regular team training were associated with increased participation.

Designing an openEHR-Based Pipeline for Extracting and Standardizing Unstructured Clinical Data Using Natural Language Processing

Background  Merging disparate and heterogeneous datasets from clinical routine in a standardized and semantically enriched format to enable a multiple use of data also means incorporating unstructured data such as medical free texts. Although the extraction of structured data from texts, known as natural language processing (NLP), has been researched at least for the English language extensively, it is not enough to get a structured output in any format. NLP techniques need to be used together with clinical information standards such as openEHR to be able to reuse and exchange still unstructured data sensibly.

Objectives  The aim of the study is to automatically extract crucial information from medical free texts and to transform this unstructured clinical data into a standardized and structured representation by designing and implementing an exemplary pipeline for the processing of pediatric medical histories.

Methods  We constructed a pipeline that allows reusing medical free texts such as pediatric medical histories in a structured and standardized way by (1) selecting and modeling appropriate openEHR archetypes as standard clinical information models, (2) defining a German dictionary with crucial text markers serving as expert knowledge base for a NLP pipeline, and (3) creating mapping rules between the NLP output and the archetypes. The approach was evaluated in a first pilot study by using 50 manually annotated medical histories from the pediatric intensive care unit of the Hannover Medical School.

Results  We successfully reused 24 existing international archetypes to represent the most crucial elements of unstructured pediatric medical histories in a standardized form. The self-developed NLP pipeline was constructed by defining 3.055 text marker entries, 132 text events, 66 regular expressions, and a text corpus consisting of 776 entries for automatic correction of spelling mistakes. A total of 123 mapping rules were implemented to transform the extracted snippets to an openEHR-based representation to be able to store them together with other structured data in an existing openEHR-based data repository. In the first evaluation, the NLP pipeline yielded 97% precision and 94% recall.

Conclusion  The use of NLP and openEHR archetypes was demonstrated as a viable approach for extracting and representing important information from pediatric medical histories in a structured and semantically enriched format. We designed a promising approach with potential to be generalized, and implemented a prototype that is extensible and reusable for other use cases concerning German medical free texts. In a long term, this will harness unstructured clinical data for further research purposes such as the design of clinical decision support systems. Together with structured data already integrated in openEHR-based representations, we aim at developing an interoperable openEHR-based application that is capable of automatically assessing a patient's risk status based on the patient's medical history at time of admission.

Effects of Delayed Initiation of End-of-life Care in Terminally Ill Intensive Care Unit Patients

Introduction

Early initiation of end-of-life (EOL) care in terminally ill patients can reduce the administration of unnecessary medications, minimize laboratory and radiological investigations, and avoid procedures that can provoke untoward complications without substantial benefits. This retrospective observational study was performed to compare early vs late initiation of EOL care in terminally ill ICU patients after the recognition of treatment futility.

Materials and methods

The medical records of all patients who were considered to be terminally ill any time after ICU admission between January 2014 and December 2018 were extracted from the ICU database. The patients who were recognized for treatment futility were eligible for inclusion. The patients who were already on EOL care prior to the ICU admission or whose diagnosis was unconfirmed were excluded from the study. The treatment futility was a subjective decision jointly undertaken by the primary physician and the intensivist based upon the disease stage and the available therapeutic options. The commencement of EOL care after recognition of treatment futility was divided into (a) early group (EG)—within 48 hours of decision of treatment futility and (b) late group (LG)—after 48 hours of recognition of treatment futility. Both the groups were compared for (a) ICU mortality, (b) length of ICU stay, (c) number of antibiotic-free days, (d) number of ventilator-free days, (e) number of medical and/or surgical interventions (insertion of central lines, drains, IABP, etc.), (f) number of blood and radiological investigations, and (g) satisfaction level of family members.

Results

Out of 107 terminally ill patients with diagnosis of treatment futility, 64 patients (59.8%) underwent early initiation of EOL against delayed initiation in 43 (40.2%) patients (1.3 ± 0.4 days vs 5.1 ± 1.6 days; p = 0.01). The patients in the late initiation group were younger in age (49 ± 3.6 years vs 66 ± 5.3 years; p = 0.03). The number of antibiotic-free days was higher in the early initiation group (12 ± 5.2 days vs 6 ± 7.5; p = 0.02). The number of medical and surgical interventions was lesser in the early initiation group (3.0 ± 0.7 episodes vs 12 ± 3.9 episodes; p = 0.007). The late initiation of EOL was caused by prognostic dilemma (30.2%), reluctance of the family members (44.1%), ambivalence of the primary physician (18.6%), and hesitancy of the intensivist (6.9%). The satisfaction level of the family members was similar in both the groups.

Conclusion

We conclude that delayed initiation of EOL care in terminally ill ICU patients after recognition of treatment futility can increase the antibiotic usage and medical and/or surgical interventions with no effect on the satisfaction level of the family members.

How to cite this article

Choudhuri AH, Sharma A, Uppal R. Effects of Delayed Initiation of End-of-life Care in Terminally Ill Intensive Care Unit Patients. Indian J Crit Care Med 2020;24(6):404–408.

Time-limited trials: A qualitative study exploring the role of time in decision-making on the Intensive Care Unit

Background

Withholding and withdrawing treatment are deemed ethically equivalent by most Bioethicists, but intensivists often find withdrawing more difficult in practice. This can lead to futile treatment being prolonged. Time-limited trials have been proposed as a way of promoting timely treatment withdrawal whilst giving the patient the greatest chance of recovery. Despite being in UK guidelines, time-limited trials have been infrequently implemented on Intensive Care Units. We will explore the role of time in Intensive Care Unit decision-making and provide a UK perspective on debates surrounding time-limited trials.

Methods

This qualitative study recruited 18 participants (nine doctors, nine nurses) from two Intensive Care Units in North West England for in-depth, one-to-one semi-structured interviews. A thematic analysis was performed of the data.

Results

Our findings show time is utilised by Intensive Care Unit staff in a variety of ways including managing uncertainty when making decisions about a patient’s prognosis or the reversibility of a disease, constructing relationships with patients’ relatives, communicating difficult messages to patients’ relatives, justifying resource allocation decisions to colleagues, and demonstrating compassion towards patients and their families.

Conclusions

Time shifts the balance towards greater certainty in Intensive Care Unit decision-making, by demonstrating futility, and can ease the difficult transition for staff and families from active treatment to palliation. However, this requires clear and open communication, both within the Intensive Care Unit team and with the family, being prioritised when time is used in decision-making.

How to discuss about do-not-resuscitate in the intensive care unit?

The improvement in cardiopulmonary resuscitation quality has reduced the mortality of individuals treated for cardiac arrest. However, survivors have a high risk of severe brain damage in cases of return of spontaneous circulation. Data suggest that cases of cardiac arrest in critically ill patients with non-shockable rhythms have only a 6% chance of returning of spontaneous circulation, and of these, only one-third recover their autonomy. Should we, therefore, opt for a procedure in which the chance of survival is minimal and the risk of hospital death or severe and definitive brain damage is approximately 70%? Is it worth discussing patient resuscitation in cases of cardiac arrest? Would this discussion bring any benefit to the patients and their family members? Advanced discussions on do-not-resuscitate are based on the ethical principle of respect for patient autonomy, as the wishes of family members and physicians often do not match those of patients. In addition to the issue of autonomy, advanced discussions can help the medical and care team anticipate future problems and, thus, better plan patient care. Our opinion is that discussions regarding the resuscitation of critically ill patients should be performed for all patients within the first 24 to 48 hours after admission to the intensive care unit.

The decision-making process in families of terminal ICU patients

Abstract The objective of this research is to investigate family members’ perceptions as to the end-of-life decision-making process in an ICU. The authors conducted a qualitative descriptive study in which they interviewed six family members of critically ill patients admitted to the ICU of a private hospital. Five categories of analysis emerged from the examination of the material. This study will discuss two of those categories: the decision-making process and the relationship with the medical staff. The results indicate that family members were satisfied with communication with the medical staff, an important aspect for the decision-making process. Within this context, the shared model, prioritization of palliative care and identification of futile treatments prevailed, aimed at ensuring the patient’s comfort and dignity at the end of life. The results also reveal the need for integration of palliative care in ICUs, particularly in end-of-life situations.

Subjective score and outcomes after discharge from the intensive care unit: a prospective observational study

Objective

Intensive care unit (ICU) discharge is a decision process that is usually performed subjectively. We evaluated whether a subjective score (Sabadell score) is associated with hospital outcomes.

Methods

We conducted a prospective cohort study from August 2014 to May 2015 at a tertiary-care private hospital in Brazil. We analyzed 425 patients who were discharged alive from the ICU to the wards. We used univariate and multivariate analysis to identify risk factors associated with a composite endpoint of worse outcomes (later ICU readmission or ward death) during the same hospitalization.

Results

Forty-three patients (10.1%) were readmitted after ICU discharge, and 19 died in the ward. Compared with patients with successful outcomes, those with the composite endpoint were older and more severely ill, had a nonsurgical reason for hospitalization, more frequently came from the ward, were less frequently independent during daily activities, had sepsis, had higher C-reactive protein concentrations at ICU admission, and had higher Sabadell scores at discharge. The multivariate analysis showed that sepsis and the Sabadell score were independently and significantly associated with worse outcomes.

Conclusion

Sepsis at admission and the Sabadell score were predictors of worse hospital outcomes. The Sabadell score might be a promising predictive tool.

CADDIE2-evaluation of a clinical decision-support system for early detection of systemic inflammatory response syndrome in paediatric intensive care: study protocol for a diagnostic study

INTRODUCTION

Systemic inflammatory response syndrome (SIRS) is one of the most critical indicators determining the clinical outcome of paediatric intensive care patients. Clinical decision support systems (CDSS) can be designed to support clinicians in detection and treatment. However, the use of such systems is highly discussed as they are often associated with accuracy problems and 'alert fatigue'. We designed a CDSS for detection of paediatric SIRS and hypothesise that a high diagnostic accuracy together with an adequate alerting will accelerate the use. Our study will (1) determine the diagnostic accuracy of the CDSS compared with gold standard decisions created by two blinded, experienced paediatricians, and (2) compare the system's diagnostic accuracy with that of routine clinical care decisions compared with the same gold standard.

METHODS AND ANALYSIS

CADDIE2 is a prospective diagnostic accuracy study taking place at the Department of Pediatric Cardiology and Intensive Care Medicine at the Hannover Medical School; it represents the second step towards our vision of cross-institutional and data-driven decision-support for intensive care environments (CADDIE). The study comprises (1) recruitment of up to 300 patients (start date 1 August 2018), (2) creation of gold standard decisions (start date 1 May 2019), (3) routine SIRS assessments by physicians (starts with recruitment), (4) SIRS assessments by a CDSS (start date 1 May 2019), and (5) statistical analysis with a modified approach for determining sensitivity and specificity and comparing the accuracy results of the different diagnostic approaches (planned start date 1 July 2019).

ETHICS AND DISSEMINATION

Ethics approval was obtained at the study centre (Ethics Committee of Hannover Medical School). Results of the main study will be communicated via publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov NCT03661450; Pre-results.

Student progress decision-making in programmatic assessment: can we extrapolate from clinical decision-making and jury decision-making?

BACKGROUND

Despite much effort in the development of robustness of information provided by individual assessment events, there is less literature on the aggregation of this information to make progression decisions on individual students. With the development of programmatic assessment, aggregation of information from multiple sources is required, and needs to be completed in a robust manner. The issues raised by this progression decision-making have parallels with similar issues in clinical decision-making and jury decision-making.

MAIN BODY

Clinical decision-making is used to draw parallels with progression decision-making, in particular the need to aggregate information and the considerations to be made when additional information is needed to make robust decisions. In clinical decision-making, diagnoses can be based on screening tests and diagnostic tests, and the balance of sensitivity and specificity can be applied to progression decision-making. There are risks and consequences associated with clinical decisions, and likewise with progression decisions. Both clinical decision-making and progression decision-making can be tough. Tough and complex clinical decisions can be improved by making decisions as a group. The biases associated with decision-making can be amplified or attenuated by group processes, and have similar biases to those seen in clinical and progression decision-making. Jury decision-making is an example of a group making high-stakes decisions when the correct answer is not known, much like progression decision panels. The leadership of both jury and progression panels is important for robust decision-making. Finally, the parallel between a jury's leniency towards the defendant and the failure to fail phenomenon is considered.

CONCLUSION

It is suggested that decisions should be made by appropriately selected decision-making panels; educational institutions should have policies, procedures, and practice documentation related to progression decision-making; panels and panellists should be provided with sufficient information; panels and panellists should work to optimise their information synthesis and reduce bias; panellists should reach decisions by consensus; and that the standard of proof should be that student competence needs to be demonstrated.

Behavioural patterns of electrolyte repletion in intensive care units: lessons from a large electronic dataset

Repletion of electrolytes often depends on provider-specific behavior and hospital policy. We examined the pattern of electrolyte repletion across several intensive care units (ICU) in a large healthcare system from 2010–2015. This included 109 723 potassium repletions, 51 833 magnesium repletions, 2 306 calcium repletions, 8 770 phosphate repletions, and 3 128 249 visit-days over 332 018 visits. Potassium, magnesium, and calcium were usually repleted within the institutional reference range. In contrast, the bulk of phosphate repletion was done with pre-repletion serum level below the reference range. The impact of repletion on post-repletion levels was significant but uniformly small. The pre-repletion serum level had a significant inverse correlation with the post-repletion level of each electrolyte. Potassium, magnesium and phosphate follow-up labs were scheduled in 9–10 hours after their repletion. In contrast, calcium was rechecked in less than 20 minutes. Routine repletion of potassium, magnesium and calcium had no effect on the incidence of tachyarrhythmias. We estimated the expense from electrolyte repletion within the reference range was approximately $1.25 million. Absent a specific clinical indication, repleting electrolytes when the serum concentration are within normative values may represent an avenue for cost savings, staff burden unload and potential reduction in frequency of complications in the ICUs.

Outcome in patients perceived as receiving excessive care across different ethical climates: a prospective study in 68 intensive care units in Europe and the USA

Purpose

Whether the quality of the ethical climate in the intensive care unit (ICU) improves the identification of patients receiving excessive care and affects patient outcomes is unknown.

Methods

In this prospective observational study, perceptions of excessive care (PECs) by clinicians working in 68 ICUs in Europe and the USA were collected daily during a 28-day period. The quality of the ethical climate in the ICUs was assessed via a validated questionnaire. We compared the combined endpoint (death, not at home or poor quality of life at 1 year) of patients with PECs and the time from PECs until written treatment-limitation decisions (TLDs) and death across the four climates defined via cluster analysis.

Results

Of the 4747 eligible clinicians, 2992 (63%) evaluated the ethical climate in their ICU. Of the 321 and 623 patients not admitted for monitoring only in ICUs with a good (n = 12, 18%) and poor (n = 24, 35%) climate, 36 (11%) and 74 (12%), respectively were identified with PECs by at least two clinicians. Of the 35 and 71 identified patients with an available combined endpoint, 100% (95% CI 90.0–1.00) and 85.9% (75.4–92.0) (P = 0.02) attained that endpoint. The risk of death (HR 1.88, 95% CI 1.20–2.92) or receiving a written TLD (HR 2.32, CI 1.11–4.85) in patients with PECs by at least two clinicians was higher in ICUs with a good climate than in those with a poor one. The differences between ICUs with an average climate, with (n = 12, 18%) or without (n = 20, 29%) nursing involvement at the end of life, and ICUs with a poor climate were less obvious but still in favour of the former.

Conclusion

Enhancing the quality of the ethical climate in the ICU may improve both the identification of patients receiving excessive care and the decision-making process at the end of life.

Electronic supplementary material

The online version of this article (10.1007/s00134-018-5231-8) contains supplementary material, which is available to authorized users.

The Influence of Cognitive Bias on Caries Lesion Detection in Preschool Children

We aimed to evaluate whether children’s caries experience exerts an influence on the performance of visual and radiographic methods in detecting nonevident proximal caries lesions in primary molars. Eighty children (3–6 years old) were selected and classified as having a lower (≤3 decayed, missing, or filled surfaces; dmf-s) or higher (> 3 dmf-s) caries experience. Two calibrated examiners then assessed 526 proximal surfaces for caries lesions using visual and radiographic methods. As a reference standard, 2 other examiners checked the surfaces after temporary separation. Noncavitated and cavitated lesion thresholds were considered and Poisson multilevel regression analyses were conducted to evaluate the influence of caries experience on the performance of diagnostic strategies. Accuracy parameters stratified by caries experience were also derived. A statistically significant influence of caries experience was observed only for visual inspection, with more false-positive results in children with a higher caries experience at the noncavitated lesion threshold, and more false results at the cavitated threshold. The detection of noncavitated caries lesions in children with a higher caries experience was overestimated (specificity = 0.696), compared to children with a lower caries experience (specificity = 0.918), probably due to confirmation bias. However, the examiners underestimated the detection of cavitated lesions in children with a higher caries experience (sensitivity = 0.143) compared to lower-caries-experience children (sensitivity = 0.222), possibly because of representativeness bias. The radiographic method was not influenced by children’s caries experience. In conclusion, children’s caries experience influences the performance of visual inspection in detecting proximal caries lesions in primary teeth, evidencing the occurrence of cognitive biases.

Ethical decision-making climate in the ICU: theoretical framework and validation of a self-assessment tool

Background

Literature depicts differences in ethical decision-making (EDM) between countries and intensive care units (ICU).

Objectives

To better conceptualise EDM climate in the ICU and to validate a tool to assess EDM climates.

Methods

Using a modified Delphi method, we built a theoretical framework and a self-assessment instrument consisting of 35 statements. This Ethical Decision-Making Climate Questionnaire (EDMCQ) was developed to capture three EDM domains in healthcare: interdisciplinary collaboration and communication; leadership by physicians; and ethical environment. This instrument was subsequently validated among clinicians working in 68 adult ICUs in 13 European countries and the USA. Exploratory and confirmatory factor analysis was used to determine the structure of the EDM climate as perceived by clinicians. Measurement invariance was tested to make sure that variables used in the analysis were comparable constructs across different groups.

Results

Of 3610 nurses and 1137 physicians providing ICU bedside care, 2275 (63.1%) and 717 (62.9%) participated respectively. Statistical analyses revealed that a shortened 32-item version of the EDMCQ scale provides a factorial valid measurement of seven facets of the extent to which clinicians perceive an EDM climate: self-reflective and empowering leadership by physicians; practice and culture of open interdisciplinary reflection; culture of not avoiding end-of-life decisions; culture of mutual respect within the interdisciplinary team; active involvement of nurses in end-of-life care and decision-making; active decision-making by physicians; and practice and culture of ethical awareness. Measurement invariance of the EDMCQ across occupational groups was shown, reflecting that nurses and physicians interpret the EDMCQ items in a similar manner.

Conclusions

The 32-item version of the EDMCQ might enrich the EDM climate measurement, clinicians’ behaviour and the performance of healthcare organisations. This instrument offers opportunities to develop tailored ICU team interventions.

Blood transfusion in elderly patients with chronic anemia: a qualitative analysis of the general practitioners' attitudes

Background

Blood transfusion in chronic anemia is not covered by guidelines specific to older adults. When they consider that this treatment is necessary in elderly patients, French general practitioners (GPs) contact a hospital specialist to plan a transfusion.

Methods

Twenty French GPs were questioned individually regarding their approach to blood transfusion using semi-structured interviews. Each interview was recorded, typed up verbatim and then coded using an inductive procedure by theme, in a cross-over design (two researchers) in two phases: analysis and summary, followed by grouping of the recorded comments.

Results

The criteria for transfusion were hemoglobin level < 8 g/dL and cardiac comorbidities. Some geriatric issues, such as cognitive disorder or dependence, were considered, either as aspects of frailty favoring transfusion or as markers of reduced life expectancy that limit care. Falls and fear of an unpleasant death from anemia prompted GPs to order blood transfusion. The patient’s family provided guidance, but the patient was not routinely consulted. The specialists were rarely asked to participate in decision making. GPs’ perceptions were ambivalent: they considered transfusion to be extraordinary and magical, but also pointless since its effects are transient.

Conclusion

The decision to give a transfusion to an elderly patient with chronic anemia is deemed complex, but GPs seem to take it alone, sometimes guided by the patient’s family. The drawing up of an advance care plan could help involve the patient in decision making.

Electronic supplementary material

The online version of this article (doi:10.1186/s12875-017-0647-8) contains supplementary material, which is available to authorized users.

Development, Validation, and Implementation of a Medical Judgment Metric

Background: Medical decision making is a critical, yet understudied, aspect of medical education. Aims: To develop the Medical Judgment Metric (MJM), a numerical rubric to quantify good decisions in practice in simulated environments; and to obtain initial preliminary evidence of reliability and validity of the tool. Methods: The individual MJM items, domains, and sections of the MJM were built based on existing standardized frameworks. Content validity was determined by a convenient sample of eight experts. The MJM instrument was pilot tested in four medical simulations with a team of three medical raters assessing 40 participants with four levels of medical experience and skill. Results: Raters were highly consistent in their MJM scores in each scenario (intraclass correlation coefficient 0.965 to 0.987) as well as their evaluation of the expected patient outcome (Fleiss’s Kappa 0.791 to 0.906). For each simulation scenario, average rater cut-scores significantly predicted expected loss of life or stabilization (Cohen’s Kappa 0.851 to 0.880). Discussion: The MJM demonstrated preliminary evidence of reliability and validity.

Artificial Neural Network for Total Laboratory Automation to Improve the Management of Sample Dilution

Diluting a sample to obtain a measure within the analytical range is a common task in clinical laboratories. However, for urgent samples, it can cause delays in test reporting, which can put patients' safety at risk. The aim of this work is to show a simple artificial neural network that can be used to make it unnecessary to predilute a sample using the information available through the laboratory information system. Particularly, the Multilayer Perceptron neural network built on a data set of 16,106 cardiac troponin I test records produced a correct inference rate of 100% for samples not requiring predilution and 86.2% for those requiring predilution. With respect to the inference reliability, the most relevant inputs were the presence of a cardiac event or surgery and the result of the previous assay. Therefore, such an artificial neural network can be easily implemented into a total automation framework to sensibly reduce the turnaround time of critical orders delayed by the operation required to retrieve, dilute, and retest the sample.