Discriminative Accuracy of Physician and Nurse Predictions for Survival and Functional Outcomes 6 Months After an ICU Admission

In-Hospital Triage

The intensive care unit (ICU) is a finite and expensive resource with demand not infrequently exceeding capacity. Understanding ICU capacity strain is essential to gain situational awareness. Increased capacity strain can influence ICU triage decisions, which rely heavily on clinical judgment. Having an admission and triage protocol with which clinicians are very familiar can mitigate difficult, inappropriate admissions. This article reviews these concepts and methods of in-hospital triage.

Understanding the provision of goal-concordant care in the intensive care unit: A sequential two-phase qualitative descriptive study

BACKGROUND

Goal-concordant care in intensive care is care that aligns with the patient's expressed goals, values, preferences and beliefs. Communication and shared decision-making are key to ensuring goal-concordant care.

AIMS

The aims of his study were to explore (i) critical care clinicians' perspectives on how patient goals of care were communicated between clinicians, patients, and family in the intensive care unit; (ii) critical care nurses' role in this process; and (iii) how goals of care were used to guide care.

METHOD

Sequential two-phase qualitative descriptive design. Data were collected from February to June 2022 in a level-3 intensive care unit in a private hospital in Melbourne, Australia. In Phase One, individual interviews were conducted with critical care nurse participants (n = 11). In Phase Two, the findings were presented to senior clinical leaders (n = 2) to build a more comprehensive understanding. Data were analysed using Braun and Clarke's six step reflexive thematic analysis.

FINDINGS

There was poor consensus on the term 'goals of care', with some participants referring to daily treatment goals or treatment limitations and others to patients' wishes and expectations beyond the ICU. Critical care nurses perceived themselves as information brokers and patient advocates responsible for ensuring patient goals of care were respected, but engaging in goals-of-care conversations was challenging. A lack of role clarity, poor team communication, and inadequate processes to communicate patient goals impeded goal-concordant care. Senior clinical leaders affirmed these views, emphasising the need to utilise critical care nurses' insight for practical solutions to improve patient care.

CONCLUSIONS

Clarity in both, the term 'goals of care' and the critical care nurses' role in these conversations, are the essential first steps to ensuring patients' values, preferences, and beliefs to guide shared-decision-making and goal-concordant care. Improved verbal and written communication that is inclusive of all members of the treating team is key to addressing these issues.

Time-Limited Trials for Patients With Critical Illness: A Review of the Literature

TOPIC IMPORTANCE

Since the 1990s, time-limited trials have been described as an approach to navigate uncertain benefits and limits of life-sustaining therapies in patients with critical illness. In this review, we aim to synthesize the evidence on time-limited trials in critical care, establish what is known, and highlight important knowledge gaps.

REVIEW FINDINGS

We identified 18 empirical studies and 15 ethical analyses about time-limited trials in patients with critical illness. Observational studies suggest time-limited trials are part of current practice in ICUs in the United States, but their use varies according to unit and physician factors. Some ICU physicians are familiar with, endorse, and have participated in time-limited trials, and some older adults appear to favor time-limited trial strategies over indefinite life-sustaining therapy or care immediately focused on comfort. When time-limited trials are used, they are often implemented incompletely and challenged by systematic barriers (eg, continually rotating ICU staff). Predictive modeling studies support prevailing clinical wisdom that prognostic uncertainty decreases over time in the ICU for some patients. One study prospectively comparing usual ICU care with an intervention designed to support time-limited trials yielded promising preliminary results. Ethical analyses describe time-limited trials as a pragmatic approach within the longstanding discussion about withholding and withdrawing life-sustaining therapies.

SUMMARY

Time-limited trials are endorsed by physicians, align with the priorities of some older adults, and are part of current practice. Substantial efforts are needed to test their impact on patient-centered outcomes, improve their implementation, and maximize their potential benefit.

Quality of life-related and non-quality of life-related issues in ICU survivors and non-ICU-treated controls: a multi-group exploratory factor analysis

BACKGROUND

Quality of life (QoL) is a key outcome measure in healthcare. However, the heterogeneity in its definitions presents challenges in the objective evaluation of improvement. Universal questionnaires, tailored for a broad demographic group, inadequately represent the unique experiences of intensive care unit (ICU) survivors, including a lack of ability to discriminate issues related to QoL from issues that do not.

METHODS

Using a 218-item, 13-domain provisional questionnaire, we assessed 395 adult ICU survivors, with a minimum 72-h stay at one of three Swedish university hospital ICUs, at 6 months to three years post-discharge. Their responses were compared to those of 195 controls, matched for age and sex and randomly recruited from the Swedish Population Registry. By multi-group exploratory factor analysis, we compared dimensionality in QoL perceptions between the two groups, emphasising patterns of correlation to 13 domain-specific QoL questions. Model fit was assessed using information criteria. Internal consistency reliability for each scale was determined using McDonald's omega or Cronbach's alpha. All analyses were conducted using Mplus, applying full information maximum likelihood to handle missing data.

RESULTS

All domains except Cognition had a subset of questions correlating to the domain-specific QoL question in at least the ICU survivor group. The similarity between the two groups varied, with Physical health, Sexual health and Gastrointestinal (GI) functions mainly correlating the same issues to QoL in the two groups. In contrast, Fatigue, Pain, Mental health, activities of daily living, Sleep, Sensory functions and Work life showed considerable differences. In all, about one-fourth of the issues correlated to QoL in the ICU survivor group and about one-tenth of the issues in the control group.

CONCLUSIONS

We found most issues experienced by ICU survivors to be unrelated to quality of life. Our findings indicate that the consequences of post-ICU issues may play a more significant role in affecting QoL than the issues themselves; issues restricting and affecting social life and work life were more related to QoL in ICU survivors than in non-ICU-treated controls. Caution is advised before associating all post-ICU problems with an effect on quality of life.

TRIAL REGISTRATION

ClinicalTrials.gov Ref# NCT02767180; Registered 28 April 2016.

Family engagement in the care of infectious patients in intensive care units: A hybrid concept analysis

AIM

This study aims to define and investigate characteristics, antecedents, and consequences of the concept of family engagement in caring for patients with infectious diseases hospitalised in intensive care units.

DESIGN

This is a three-phase hybrid model study (theoretical, fieldwork, and analytical phase).

METHODS

The York University Guidelines were used in the theoretical phase, and ultimately, 16 pieces of literature related to the subject under study from 2011 to 2021 were reviewed. The content analysis was used for fieldwork phases; eight participants were interviewed. Then, the theoretical and fieldwork findings were compared, integrated, and analysed.

RESULTS

This concept has characteristics such as; awareness, belief, perception, and willingness of the nurse to engage the family; a sense of responsibility, willingness, and sacrifice of the family; the physical or virtual presence of the family; triangular interaction between the nurse, patient, and family; perception and identifying the goals; education and information transfer; team collaboration; delegation of responsibility to the family; decision making; and protection of the family. Antecedents include the availability of infrastructure; patient, family, and nurse conditions; and the quality implementation of engagement. The consequences include positive consequences related to the patient, family, nursing, and society, as well as some negative consequences. This study provided a comprehensive perception of family engagement in the care of patients with infectious diseases in intensive care units and defined it more clearly, showing its characteristics, antecedents, and consequences.

PATIENT OR PUBLIC CONTRIBUTION

Eight participants were interviewed, including five nurses, two family caregivers, and one patient.

The Physician Surprise Question in the Emergency Department: prospective cohort study

OBJECTIVES

This study aims to test the ability of the surprise question (SQ), when asked to emergency physicians (EPs), to predict in-hospital mortality among adults admitted to an emergency room (ER).

METHODS

This prospective cohort study at an academic medical centre included consecutive patients 18 years or older who received care in the ER and were subsequently admitted to the hospital from 20 April 2018 to 20 October 2018. EPs were required to answer the SQ for all patients who were being admitted to hospital. The primary outcome was in-hospital mortality.

RESULTS

The cohort included 725 adults (mean (SD) age, 60 (17) years, 51% men) from 58 128 emergency department (ED) visits. The mortality rates were 20.6% for 30-day all-cause in-hospital mortality and 23.6% for in-hospital mortality. The diagnostic test characteristics of the SQ have a sensitivity of 53.7% and specificity of 87.1%, and a relative risk of 4.02 (95% CI 3.15 to 5.13), p<0.01). The positive and negative predictive values were 57% and 86%, respectively; the positive likelihood ratio was 4.1 and negative likelihood ratio was 0.53; and the accuracy was 79.2%.

CONCLUSIONS

We found that asking the SQ to EPs may be a useful tool to identify patients in the ED with a high risk of in-hospital mortality.

Defining the Time-limited Trial for Patients with Critical Illness: An Official American Thoracic Society Workshop Report

In critical care, the specific, structured approach to patient care known as a "time-limited trial" has been promoted in the literature to help patients, surrogate decision makers, and clinicians navigate consequential decisions about life-sustaining therapy in the face of uncertainty. Despite promotion of the time-limited trial approach, a lack of consensus about its definition and essential elements prevents optimal clinical use and rigorous evaluation of its impact. The objectives of this American Thoracic Society Workshop Committee were to establish a consensus definition of a time-limited trial in critical care, identify the essential elements for conducting a time-limited trial, and prioritize directions for future work. We achieved these objectives through a structured search of the literature, a modified Delphi process with 100 interdisciplinary and interprofessional stakeholders, and iterative committee discussions. We conclude that a time-limited trial for patients with critical illness is a collaborative plan among clinicians and a patient and/or their surrogate decision makers to use life-sustaining therapy for a defined duration, after which the patient's response to therapy informs the decision to continue care directed toward recovery, transition to care focused exclusively on comfort, or extend the trial's duration. The plan's 16 essential elements follow four sequential phases: consider, plan, support, and reassess. We acknowledge considerable gaps in evidence about the impact of time-limited trials and highlight a concern that if inadequately implemented, time-limited trials may perpetuate unintended harm. Future work is needed to better implement this defined, specific approach to care in practice through a person-centered equity lens and to evaluate its impact on patients, surrogates, and clinicians.

Outcome prediction in dogs admitted through the emergency room: Accuracy of staff prediction and comparison with an illness severity stratification system for hospitalized dogs

OBJECTIVE

To determine whether emergency staff and students can predict patient outcome within 24 hours of admission, comparing the accuracy of clinician prognostication with outcome prediction by Acute Patient Physiologic and Laboratory Evaluation (APPLE)fast scoring and identifying whether experience or mood would be associated with accuracy.

DESIGN

Prospective observational study between April 2020 and March 2021.

SETTING

University teaching hospital.

ANIMALS

One hundred and sixty-one dogs admitted through an Emergency Service were assessed. Where data were available, an APPLEfast score was calculated per patient. An APPLEfast score of >25 was deemed a predictor for mortality.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Emergency staff and students were asked to complete surveys about dogs admitted to the emergency room. All clinicopathological data were available for review, and the animals were available for examination. Data collected included opinions on whether the patient would be discharged from hospital, a mood score, position, and experience in Emergency and Critical Care. One-hundred and twenty-five dogs (77.6%) were discharged; 36 dogs (22.4%) died or were euthanized. Two hundred and sixty-six responses were obtained; 202 responses (75.9%) predicted the correct outcome. Students, interns, residents, faculty, and nurses predicted the correct outcome in 81.4%, 58.3%, 83.3%, 82.1%, and 65.5% of cases, respectively. Of 64 incorrect predictions, 43 (67.2%) predicted death in hospital. APPLEfast scores were obtained in 121 cases, predicting the correct outcome in 83 cases (68.6%). Of 38 cases in which APPLEfast was incorrect, 27 (71.1%) were dogs surviving to discharge. Mean APPLEfast score was 22.9 (± 6.2). There was no difference in outcome prediction accuracy between staff and APPLEfast scores (P = 0.13). Neither experience nor mood score was associated with outcome prediction ability (P = 0.55 and P = 0.74, respectively).

CONCLUSIONS

Outcome prediction accuracy by staff is not significantly different to APPLEfast scoring where a cutoff of >25 is used to predict mortality. When predictions were incorrect, they often predicted nonsurvival.

Predicting Postoperative Hospital Stays Using Nursing Narratives and the Reverse Time Attention (RETAIN) Model: Retrospective Cohort Study

Background

Nursing narratives are an intriguing feature in the prediction of short-term clinical outcomes. However, it is unclear which nursing narratives significantly impact the prediction of postoperative length of stay (LOS) in deep learning models.

Objective

Therefore, we applied the Reverse Time Attention (RETAIN) model to predict LOS, entering nursing narratives as the main input.

Methods

A total of 354 patients who underwent ovarian cancer surgery at the Seoul National University Bundang Hospital from 2014 to 2020 were retrospectively enrolled. Nursing narratives collected within 3 postoperative days were used to predict prolonged LOS (≥10 days). The physician's assessment was conducted based on a retrospective review of the physician's note within the same period of the data model used.

Results

The model performed better than the physician's assessment (area under the receiver operating curve of 0.81 vs 0.58; P=.02). Nursing narratives entered on the first day were the most influential predictors in prolonged LOS. The likelihood of prolonged LOS increased if the physician had to check the patient often and if the patient received intravenous fluids or intravenous patient-controlled analgesia late.

Conclusions

The use of the RETAIN model on nursing narratives predicted postoperative LOS effectively for patients who underwent ovarian cancer surgery. These findings suggest that accurate and interpretable deep learning information obtained shortly after surgery may accurately predict prolonged LOS.

Care intervention on psychological outcomes among patients admitted to intensive care unit: an umbrella review of systematic reviews and meta-analyses

BACKGROUND

Numerous studies have explored care interventions to improve the psychological outcome of intensive care unit (ICU) patients, but inconclusive evidence makes it difficult for decision-makers, managers, and clinicians to get familiar with all available literature and find appropriate interventions. This umbrella review aimed to analyze the relationship between care intervention and psychological outcomes of ICU patients based on existing systematic reviews.

METHODS

An umbrella review of evidence across systematic reviews and meta-analyses published between 1987 and 2023 was undertaken. We systematically searched reviews that examined the association between care intervention and the improvement of adverse psychological outcomes in ICU patients using PubMed, EMBASE, Web of Science, Cochrane Library, and manual reference screening. The measurement tool (AMSTAR 2) was applied to evaluate the methodological quality of included studies. The excess significance bias, between-study heterogeneity expressed by I2, small-study effect, and evidence class were estimated.

RESULTS

A total of 5110 articles were initially identified from the search databases and nine of them were included in the analysis. By applying standardized criteria, only weak evidence was observed in 13 associations, even though most included reviews were of moderate to high methodological quality. These associations pertained to eight interventions (music therapy, early rehabilitation, post-ICU follow-up, ICU diary, information intervention, preoperative education, communication and psychological support, surrogate decision-making) and five psychological outcomes (post-intensive care syndrome, transfer anxiety, post-traumatic stress disorder, anxiety, and depression). Weak or null association was shown among the rest of the associations (e.g., weak association between music therapy and maternal anxiety or stress level).

CONCLUSIONS

The evidence of these eight supporting interventions to improve the adverse psychological outcomes of ICU patients and caregivers was weak. Data from more and better-designed studies with larger sample sizes are needed to establish robust evidence.

Accuracy of physician and nurse predictions for 28-day prognosis in ICU: a single center prospective study

The proportion of correctly predicted prognoses and factors associated with prediction accuracy are unknown. The objective of this study was to explore the accuracy of physician and nurse predictions of 28-day mortality in the ICU. This was a prospective observational single-center study. All medical staff in the ICU have access to patient data, can communicate with patients or clients, and can independently predict the prognosis of patients within 24 h of patient admission. The only question of the questionnaire survey was: What is the patient's outcome on day 28 (alive or death)? There were 2155 questionnaires completed by 18 physicians and 1916 submitted by 15 nurses. In the 312 patients included, the 28-day mortality rates were predicted by physicians and nurses. The overall proportion of correct prognosis prediction was 90.1% for physicians and 64.4% for nurses (P = 0.000). There was no significant difference in the overall correct proportion and average correct proportion among physicians with different seniority levels. The overall correct proportion and average correct proportion increased among nurses with seniority. Physicians in the ICU can moderately predict 28-day mortality in critically ill patients. Nurses with a seniority of less than 10 years in ICU cannot accurately predict 28-day mortality in critically ill patients. However, the accuracy of nurses' prediction of patients' 28-day prognosis increased with their seniority in the ICU.

PREDICT-TBI: Comparison of Physician Predictions with the IMPACT Model to Predict 6-Month Functional Outcome in Traumatic Brain Injury

BACKGROUND

Predicting functional outcome in critically ill patients with traumatic brain injury (TBI) strongly influences end-of-life decisions and information for surrogate decision makers. Despite well-validated prognostic models, clinicians most often rely on their subjective perception of prognosis. In this study, we aimed to compare physicians' predictions with the International Mission on Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic model for predicting an unfavorable functional outcome at 6 months after moderate or severe TBI.

METHODS

PREDICT-TBI is a prospective study of patients with moderate to severe TBI. Patients were admitted to a neurocritical care unit and were excluded if they died or had withdrawal of life-sustaining treatments within the first 24 h. In a paired study design, we compared the accuracy of physician prediction on day 1 with the prediction of the IMPACT model as two diagnostic tests in predicting unfavorable outcome 6 months after TBI. Unfavorable outcome was assessed by the Glasgow Outcome Scale from 1 to 3 by using a structured telephone interview. The primary end point was the difference between the discrimination ability of the physician and the IMPACT model assessed by the area under the curve.

RESULTS

Of the 93 patients with inclusion and exclusion criteria, 80 patients reached the primary end point. At 6 months, 29 patients (36%) had unfavorable outcome. A total of 31 clinicians participated in the study. Physicians' predictions showed an area under the curve of 0.79 (95% confidence interval 0.68-0.89), against 0.80 (95% confidence interval 0.69-0.91) for the laboratory IMPACT model, with no statistical difference (p = 0.88). Both approaches were well calibrated. Agreement between physicians was moderate (κ = 0.56). Lack of experience was not associated with prediction accuracy (p = 0.58).

CONCLUSIONS

Predictions made by physicians for functional outcome were overall moderately accurate, and no statistical difference was found with the IMPACT models, possibly due to a lack of power. The significant variability between physician assessments suggests prediction could be improved through peer reviewing, with the support of the IMPACT models, to provide a realistic expectation of outcome to families and guide discussions about end-of-life decisions.

AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges

Aim

Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field.

Main text

We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models.

Conclusion

In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.

Outcomes in Patients Perceived as Receiving Excessive Care by ICU Physicians and Nurses: Differences Between Patients < 75 and ≥ 75 Years of Age?

BACKGROUND

The benefit of the ICU for older patients is often debated. There is little knowledge on subjective impressions of excessive care in ICU nurses and physicians combined with objective patient data in real-life cases.

RESEARCH QUESTION

Is there a difference in treatment limitation decisions and 1-year outcomes in patients < 75 and ≥ 75 years of age, with and without concordant perceptions of excessive care by two or more ICU nurses and physicians?

STUDY DESIGN AND METHODS

This was a reanalysis of the prospective observational DISPROPRICUS study, performed in 56 ICUs. Nurses and physicians completed a daily questionnaire about the appropriateness of care for each of their patients during a 28-day period in 2014. We compared the cumulative incidence of patients with concordant perceptions of excessive care, treatment limitation decisions, and the proportion of patients attaining the combined end point (death, poor quality of life, or not being at home) at 1 year across age groups via Cox regression with propensity score weighting and Fisher exact tests.

RESULTS

Of 1,641 patients, 405 (25%) were ≥ 75 years of age. The cumulative incidence of concordant perceptions of excessive care was higher in older patients (13.6% vs 8.5%; P < .001). In patients with concordant perceptions of excessive care, we found no difference between age groups in risk of death (1-year mortality, 83% in both groups; P > .99; hazard ratio [HR] after weighting, 1.11; 95% CI, 0.74-1.65), treatment limitation decisions (33% vs 31%; HR after weighting, 1.11; 95% CI, 0.69-2.17), and reaching the combined end point at 1 year (90% vs 93%; P = .546). In patients without concordant perceptions of excessive care, we found a difference in risk of death (1-year mortality, 41% vs 30%; P < .001; HR after weighting, 1.38; 95% CI, 1.11-1.73) and treatment limitation decisions (11% vs 5%; P < .001; HR, 2.11; 95% CI, 1.37-3.27); however, treatment limitation decisions were mostly documented prior to ICU admission. The risk of reaching the combined end point was higher in the older adults (61.6% vs 52.8%; P < .001).

INTERPRETATION

Although the incidence of perceptions of excessive care is slightly higher in older patients, there is no difference in treatment limitation decisions and 1-year outcomes between older and younger patients once patients are identified by concordant perceptions of excessive care. Additionally, in patients without concordant perceptions, the outcomes are worse in the older adults, pleading against ageism in ICU nurses and physicians.

Use of Mechanical Ventilation Across 3 Countries

Importance

The ability to provide invasive mechanical ventilation (IMV) is a mainstay of modern intensive care; however, whether rates of IMV vary among countries is unclear.

Objective

To estimate the per capita rates of IMV in adults across 3 high-income countries with large variation in per capita intensive care unit (ICU) bed availability.

Design, Setting, and Participants

This cohort study examined 2018 data of patients aged 20 years or older who received IMV in England, Canada, and the US.

Exposure

The country in which IMV was received.

Main Outcomes and Measures

The main outcome was the age-standardized rate of IMV and ICU admissions in each country. Rates were stratified by age, specific diagnoses (acute myocardial infarction, pulmonary embolus, upper gastrointestinal bleed), and comorbidities (dementia, dialysis dependence). Data analyses were conducted between January 1, 2021, and December 1, 2022.

Results

The study included 59 873 hospital admissions with IMV in England (median [IQR] patient age, 61 [47-72] years; 59% men, 41% women), 70 250 in Canada (median [IQR] patient age, 65 [54-74] years; 64% men, 36% women), and 1 614 768 in the US (median [IQR] patient age, 65 [54-74] years; 57% men, 43% women). The age-standardized rate per 100 000 population of IMV was the lowest in England (131; 95% CI, 130-132) compared with Canada (290; 95% CI, 288-292) and the US (614; 95% CI, 614-615). Stratified by age, per capita rates of IMV were more similar across countries among younger patients and diverged markedly in older patients. Among patients aged 80 years or older, the crude rate of IMV per 100 000 population was highest in the US (1788; 95% CI, 1781-1796) compared with Canada (694; 95% CI, 679-709) and England (209; 95% CI, 203-214). Concerning measured comorbidities, 6.3% of admitted patients who received IMV in the US had a diagnosis of dementia (vs 1.4% in England and 1.3% in Canada). Similarly, 5.6% of admitted patients in the US were dependent on dialysis prior to receiving IMV (vs 1.3% in England and 0.3% in Canada).

Conclusions and Relevance

This cohort study found that patients in the US received IMV at a rate 4 times higher than in England and twice that in Canada in 2018. The greatest divergence was in the use of IMV among older adults, and patient characteristics among those who received IMV varied markedly. The differences in overall use of IMV among these countries highlight the need to better understand patient-, clinician-, and systems-level choices associated with the varied use of a limited and expensive resource.

Physician experience improves ability to predict 6-month functional outcome of severe traumatic brain injury

BACKGROUND

The functional prognosis of severe traumatic brain injury (TBI) during the acute phase is often poor and uncertain. We aimed to quantify the elements that shade the degree of uncertainty in prognostic determination of TBI and to better understand the role of clinical experience in prognostic quality.

METHODS

This was an observational, prospective, multicenter study. The medical records of 16 patients with moderate or severe TBI in 2020 were randomly drawn from a previous study and submitted to two groups of physicians: senior and junior. The senior physician group had graduated from a critical care fellowship, and the junior physician group had at least 3 years of anesthesia and critical care residency. They were asked for each patient, based on the reading of clinical data and CT images of the first 24 h, to determine the probability of an unfavorable outcome (Glasgow Outcome Scale < 4) at 6 months between 0 and 100, and their level of confidence. These estimations were compared with the actual evolution.

RESULTS

Eighteen senior physicians and 18 junior physicians in 4 neuro-intensive care units were included in 2021. We observed that senior physicians performed better than junior physicians, with 73% (95% confidence interval (CI) 65-79) and 62% (95% CI 56-67) correct predictions, respectively, in the senior and junior groups (p = 0.006). The risk factors for incorrect prediction were junior group (OR 1.71, 95% CI 1.15-2.55), low confidence in the estimation (OR 1.76, 95% CI 1.18-2.63), and low level of agreement on prediction between senior physicians (OR 6.78, 95% CI 3.45-13.35).

CONCLUSIONS

Determining functional prognosis in the acute phase of severe TBI involves uncertainty. This uncertainty should be modulated by the experience and confidence of the physician, and especially on the degree of agreement between physicians.

Structured Mobilization for Critically Ill Patients: A Pragmatic Cluster-randomized Trial

Rationale: Small trials and professional recommendations support mobilization interventions to improve recovery among critically ill patients, but their real-world effectiveness is unknown. Objective: To evaluate a low-cost, multifaceted mobilization intervention. Methods: We conducted a stepped-wedge cluster-randomized trial across 12 ICUs with diverse case mixes. The primary and secondary samples included patients mechanically ventilated for ⩾48 hours who were ambulatory before admission, and all patients with ICU stays ⩾48 hours, respectively. The mobilization intervention included 1) designation and posting of daily mobilization goals; 2) interprofessional closed-loop communication coordinated by each ICU's facilitator; and 3) performance feedback. Measurements and Main Results: From March 4, 2019 through March 15, 2020, 848 and 1,069 patients were enrolled in the usual care and intervention phases in the primary sample, respectively. The intervention did not increase the primary outcome, patient's maximal Intensive Care Mobility Scale (range, 0-10) score within 48 hours before ICU discharge (estimated mean difference, 0.16; 95% confidence interval, -0.31 to 0.63; P = 0.51). More patients in the intervention (37.2%) than usual care (30.7%) groups achieved the prespecified secondary outcome of ability to stand before ICU discharge (odds ratio, 1.48; 95% confidence interval, 1.02 to 2.15; P = 0.04). Similar results were observed among the 7,115 patients in the secondary sample. The percentage of days on which patients received physical therapy mediated 90.1% of the intervention effect on standing. ICU mortality (31.5% vs. 29.0%), falls (0.7% vs. 0.4%), and unplanned extubations (2.0% vs. 1.8%) were similar between groups (all P > 0.3). Conclusions: A low-cost, multifaceted mobilization intervention did not improve overall mobility but improved patients' odds of standing and was safe. Clinical trial registered with www.clinicaltrials.gov (NCT03863470).

Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration

Hospital early warning systems that use machine learning (ML) to predict clinical deterioration are increasingly being used to aid clinical decision-making. However, it is not known how ML predictions complement physician and nurse judgment. Our objective was to train and validate a ML model to predict patient deterioration and compare model predictions with real-world physician and nurse predictions.

DESIGN

Retrospective and prospective cohort study.

SETTING

Academic tertiary care hospital.

PATIENTS

Adult general internal medicine hospitalizations.

MEASUREMENTS AND MAIN RESULTS

We developed and validated a neural network model to predict in-hospital death and ICU admission in 23,528 hospitalizations between April 2011 and April 2019. We then compared model predictions with 3,374 prospectively collected predictions from nurses, residents, and attending physicians about their own patients in 960 hospitalizations between April 30, and August 28, 2019. ML model predictions achieved clinician-level accuracy for predicting ICU admission or death (ML median F1 score 0.32 [interquartile range (IQR) 0.30-0.34], AUC 0.77 [IQ 0.76-0.78]; clinicians median F1-score 0.33 [IQR 0.30-0.35], AUC 0.64 [IQR 0.63-0.66]). ML predictions were more accurate than clinicians for ICU admission. Of all ICU admissions and deaths, 36% occurred in hospitalizations where the model and clinicians disagreed. Combining human and model predictions detected 49% of clinical deterioration events, improving sensitivity by 16% compared with clinicians alone and 24% compared with the model alone while maintaining a positive predictive value of 33%, thus keeping false alarms at a clinically acceptable level.

CONCLUSIONS

ML models can complement clinician judgment to predict clinical deterioration in hospital. These findings demonstrate important opportunities for human-computer collaboration to improve prognostication and personalized medicine in hospital.

Subjective Assessment of Motor Function by the Bedside Nurses in Mechanically Ventilated Surgical Intensive Care Unit Patients Predicts Tracheostomy

OBJECTIVE

In many institutions, intensive care unit (ICU) nurses assess their patients' muscle function as part of their routine bedside examination. We tested the research hypothesis that this subjective examination of muscle function prior to extubation predicts tracheostomy requirement.

METHODS

Adult, mechanically ventilated patients admitted to 7 ICUs at Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019 were included in this observational study. Assessment of motor function was performed every four hours by ICU nurses. Multivariable logistic regression analysis controlled for acute disease severity, delirium risk assessment through the confusion assessment method for the ICU (CAM-ICU), and pre-defined predictors of extubation failure was applied to examine the association of motor function and tracheostomy within 30 days after extubation.

RESULTS

Within 30 days after extubation, 891 of 9609 (9.3%) included patients required a tracheostomy. The inability to spontaneously move and hold extremities against gravity within 24 h prior to extubation was associated with significantly higher odds of 30-day tracheostomy (adjusted OR 1.56, 95% CI 1.27-1.91, p < 0.001, adjusted absolute risk difference (aARD) 2.8% (p < 0.001)). The effect was magnified among patients who were mechanically ventilated for >7 days (aARD 21.8%, 95% CI 12.4-31.2%, p-for-interaction = 0.015).

CONCLUSIONS

ICU nurses' subjective assessment of motor function is associated with 30-day tracheostomy risk, independent of known risk factors. Muscle function measurements by nursing staff in the ICU should be discussed during interprofessional rounds.

Variability in Clinician Awareness of Intravenous Fluid Administration in Critical Illness: A Prospective Cohort Study

Intravenous (IV) fluids are commonly administered to critically ill children, but clinicians lack effective guidance for the correct dose and duration of therapy resulting in variation of prescribing habits which harm children. It is unknown if clinicians recognize the amount of IV fluid that patients receive. We aimed to determine clinician's accuracy in the identification of the volume of IV fluids patients will receive over the next 24 hours. Prospective cohort study enrolled all patients admitted to the pediatric intensive care unit (PICU) from May to August 2021 at the University of Michigan's C.S. Mott Children's Hospital PICU. For each patient, clinicians estimated the volume of IV fluid that patients will receive in the next 24 hours. The primary outcome was accuracy of the estimation defined as predicted volume of IV fluids versus actual volume administered within 10 mL/kg or 500 mL depending on patient's weight. We tested for differences in accuracy by clinician type using chi-square tests. There were 259 patients for whom 2,295 surveys were completed by 177 clinicians. Clinicians' estimates were accurate 48.8% of the time with a median difference of 10 (1–26) mL/kg. We found that accuracy varied between clinician type: bedside nurses were most accurate at 64.3%, and attendings were least accurate at 30.5%. PICU clinicians have poor recognition of the amount of IV fluids their patients will receive in the subsequent 24-hour period. Estimate accuracy varied by clinician's role and improved over time, which may suggest opportunities for improvement.

Cognition and practice on transitional care during the transfer from intensive care unit to a general ward among health care professionals: A qualitative study

AIM

The aim of this study is to explore the cognition and practice on transitional care during the transfer from intensive care unit to a general ward among health care professionals in China.

BACKGROUND

Due to the significant differences in the medical and humanistic environment at home and abroad, the safety of patients during the transmission from intensive care unit to the general ward is often ignored when their conditions become stable. There are few qualitative studies on the cognition and practice on transitional care during the transfer from intensive care unit to the ward among health care professionals in China.

METHODS

With a qualitative research design, 20 medical and nursing staff in the neurosurgery intensive care unit and ward were interviewed from May 2021 to August 2021. NVivo 11.0 software was utilized for Colaizzi's (1978) method of data analysis.

RESULTS

Based on data analysis, perceptions of transitional care, the influencing factors for transitional care and the recommendations for improving transitional care were obtained.

CONCLUSION

To ensure the continuity of care and improve patient safety during the period from intensive care unit to a general ward in China, we should clarify the expectation for the content of intensive care unit transitional care services, establish the transitional nursing team, guide nursing work, standardize the handover mode and process from intensive care unit to the general ward, promote the communication and coordination of health care professionals and improve the transitional nursing security system from the perspective of institutional level.

IMPLICATIONS FOR NURSING MANAGEMENT

This study can be used as a guide to help health care professionals provide a reference for the comprehensive development of transitional care services and the formulation of targeted intervention measures during the transfer from intensive care unit to a general ward in China.

Identification of elderly patients at risk for 30-day readmission: Clinical insight beyond big data prediction

AIM

This study explores the potential benefit of combining clinicians' risk assessments and the automated 30-day readmission prediction model.

BACKGROUND

Automated readmission prediction models based on electronic health records are increasingly applied as part of prevention efforts, but their accuracy is moderate.

METHODS

This prospective multisource study was based on self-reported surveys of clinicians and data from electronic health records. The survey was performed at 15 internal medicine wards of three general Clalit hospitals between May 2016 and June 2017. We examined the degree of concordance between the Preadmission Readmission Detection Model, clinicians' readmission risk classification and the likelihood of actual readmission. Decision trees were developed to classify patients by readmission risk.

RESULTS

A total of 694 surveys were collected for 371 patients. The disagreement between clinicians' risk assessment and the model was 34.5% for nurses and 33.5% for physicians. The decision tree algorithms identified 22% and 9% (based on nurses and physicians, respectively) of the model's low-medium-risk patients as high risk (accuracy 0.8 and 0.76, respectively).

CONCLUSIONS

Combining the Readmission Model with clinical insight improves the ability to identify high-risk elderly patients.

IMPLICATIONS FOR NURSING MANAGEMENT

This study provides algorithms for the decision-making process for selecting high-risk readmission patients based on nurses' evaluations.

Accuracy of clinicians' ability to predict the need for renal replacement therapy: a prospective multicenter study

PURPOSE

Identifying patients who will receive renal replacement therapy (RRT) during intensive care unit (ICU) stay is a major challenge for intensivists. The objective of this study was to evaluate the performance of physicians in predicting the need for RRT at ICU admission and at acute kidney injury (AKI) diagnosis.

METHODS

Prospective, multicenter study including all adult patients hospitalized in 16 ICUs in October 2020. Physician prediction was estimated at ICU admission and at AKI diagnosis, according to a visual Likert scale. Discrimination, risk stratification and benefit of physician estimation were assessed. Mixed logistic regression models of variables associated with risk of receiving RRT, with and without physician estimation, were compared.

RESULTS

Six hundred and forty-nine patients were included, 270 (41.6%) developed AKI and 77 (11.8%) received RRT. At ICU admission and at AKI diagnosis, a model including physician prediction, the experience of the physician, SOFA score, serum creatinine and diuresis to determine need for RRT performed better than a model without physician estimation with an area under the ROC curve of 0.90 [95% CI 0.86-0.94, p < 0.008 (at ICU admission)] and 0.89 [95% CI 0.83-0.93, p = 0.0014 (at AKI diagnosis)]. In multivariate analysis, physician prediction was strongly associated with the need for RRT, independently of creatinine levels, diuresis, SOFA score and the experience of the doctor who made the prediction.

CONCLUSION

As physicians are able to stratify patients at high risk of RRT, physician judgement should be taken into account when designing new randomized studies focusing on RRT initiation during AKI.

Complications and Failure to Rescue After Inpatient Pediatric Surgery

OBJECTIVE

To describe the frequency and patterns of postoperative complications and FTR after inpatient pediatric surgical procedures and to evaluate the association between number of complications and FTR.

SUMMARY AND BACKGROUND

FTR, or a postoperative death after a complication, is currently a nationally endorsed quality measure for adults. Although it is a contributing factor to variation in mortality, relatively little is known about FTR after pediatric surgery.

METHODS

Cohort study of 200,554 patients within the National Surgical Quality Improvement Program-Pediatric database (2012-2016) who underwent a high (≥ 1%) or low (< 1%) mortality risk inpatient surgical procedures. Patients were stratified based on number of postoperative complications (0, 1, 2, or ≥3) and further categorized as having undergone either a low- or high-risk procedure. The association between the number of postoperative complications and FTR was evaluated with multivariable logistic regression.

RESULTS

Among patients who underwent a low- (89.4%) or high-risk (10.6%) procedures, 14.0% and 12.5% had at least 1 postoperative complication, respectively. FTR rates after low- and high-risk procedures demonstrated step-wise increases as the number of complications accrued (eg, low-risk- 9.2% in patients with ≥3 complications; high-risk-36.9% in patients with ≥ 3 complications). Relative to patients who had no complications, there was a dose-response relationship between mortality and the number of complications after low-risk [1 complication - odds ratio (OR) 3.34 (95% CI 2.62-4.27); 2 - OR 10.15 (95% CI 7.40-13.92); ≥3-27.48 (95% CI 19.06-39.62)] and high-risk operations [1 - OR 3.29 (2.61-4.16); 2-7.24 (5.14-10.19); ≥3-20.73 (12.62-34.04)].

CONCLUSIONS

There is a dose-response relationship between the number of postoperative complications after inpatient surgery and FTR, ever after common, "minor" surgical procedures. These findings suggest FTR may be a potential quality measure for pediatric surgical care.

Prognosis Predictions by Families, Physicians, and Nurses of Patients with Severe Acute Brain Injury: Agreement and Accuracy

BACKGROUND

Effective shared decision-making relies on some degree of alignment between families and the medical team regarding a patient's likelihood of recovery. Patients with severe acute brain injury (SABI) are often unable to participate in decisions, and therefore family members make decisions on their behalf. The goal of this study was to evaluate agreement between prognostic predictions by families, physicians, and nurses of patients with SABI regarding their likelihood of regaining independence and to measure each group's prediction accuracy.

METHODS

This observational cohort study, conducted from 01/2018 to 07/2020, was based in the neuroscience and medical/cardiac intensive care units of a single center. Patient eligibility included a diagnosis of SABI-specifically stroke, traumatic brain injury, or hypoxic ischemic encephalopathy-and a Glasgow Coma Scale ≤ 12 after hospital day 2. At enrollment, families, physicians, and nurses were asked separately to predict a patient's likelihood of recovering to independence within 6 months on a 0-100 scale, regardless of whether a formal family meeting had occurred. True outcome was based on modified Rankin Scale assessment through a family report or medical chart review. Prognostic agreement was measured by (1) intraclass correlation coefficient; (2) mean group prediction comparisons using paired Student's t-tests; and (3) prevalence of concordance, defined as an absolute difference of less than 20 percentage points between predictions. Accuracy for each group was measured by calculating the area under a receiver operating characteristic curve (C statistic) and compared by using DeLong's test.

RESULTS

Data were collected from 222 patients and families, 45 physicians, and 103 nurses. Complete data on agreement and accuracy were available for 187 and 177 patients, respectively. The intraclass correlation coefficient, in which 1 indicates perfect correlation and 0 indicates no correlation, was 0.49 for physician-family pairs, 0.40 for family-nurse pairs, and 0.66 for physician-nurse pairs. The difference in mean predictions between families and physicians was 23.5 percentage points (p < 0.001), 25.4 between families and nurses (p < 0.001), and 1.9 between physicians and nurses (p = 0.38). Prevalence of concordance was 39.6% for family-physician pairs, 30.0% for family-nurse pairs, and 56.2% for physician-nurse pairs. The C statistic for prediction accuracy was 0.65 for families, 0.82 for physicians, and 0.76 for nurses. The p values for differences in C statistics were < 0.05 for family-physician and family-nurse groups and 0.18 for physician-nurse groups.

CONCLUSIONS

For patients with SABI, agreement in predictions between families, physicians, and nurses regarding likelihood of recovery is poor. Accuracy appears higher for physicians and nurses compared with families, with no significant difference between physicians and nurses.

Ethische Fragen im Gesundheitswesen als Gegenstand interprofessionellen Lernens: Überblick zur Situation in Deutschland und Projektbericht

Interprofessionelles Lernen von Auszubildenden der Pflegeberufe sowie Medizinstudierenden bietet vielfältige Chancen für die zukünftige Zusammenarbeit mit dem Ziel einer qualitativ hochwertigen Versorgung von Patient*innen. Expert*innengremien fordern daher eine frühzeitige Integration von interprofessionellen Lehr- und Lernformaten, um effektive und nachhaltige Verbesserungen in der Praxis erreichen zu können. In Deutschland wird interprofessionelle Lehre in der grundständigen Ausbildung der zwei Professionen in wachsendem Umfang in ausgewählten Fächern – obligat oder fakultativ – eingesetzt. Der Bereich der Ethik im Gesundheitswesen wird dabei bislang jedoch kaum berücksichtigt. Der Beitrag untersucht die Situation interprofessioneller Ethiklehre in Deutschland und beleuchtet deren Möglichkeiten und Grenzen vor dem Hintergrund eines Pilotprojektes.

Comparison of the accuracy of residents, senior physicians and surrogate decision-makers for predicting hospital mortality of critically ill patients

OBJECTIVE

To compare the predictive performance of residents, senior intensive care unit physicians and surrogates early during intensive care unit stays and to evaluate whether different presentations of prognostic data (probability of survival versus probability of death) influenced their performance.

METHODS

We questioned surrogates and physicians in charge of critically ill patients during the first 48 hours of intensive care unit admission on the patient's probability of hospital outcome. The question framing (i.e., probability of survival versus probability of death during hospitalization) was randomized. To evaluate the predictive performance, we compared the areas under the ROC curves (AUCs) for hospital outcome between surrogates and physicians' categories. We also stratified the results according to randomized question framing.

RESULTS

We interviewed surrogates and physicians on the hospital outcomes of 118 patients. The predictive performance of surrogate decisionmakers was significantly lower than that of physicians (AUC of 0.63 for surrogates, 0.82 for residents, 0.80 for intensive care unit fellows and 0.81 for intensive care unit senior physicians). There was no increase in predictive performance related to physicians' experience (i.e., senior physicians did not predict outcomes better than junior physicians). Surrogate decisionmakers worsened their prediction performance when they were asked about probability of death instead of probability of survival, but there was no difference for physicians.

CONCLUSION

Different predictive performance was observed when comparing surrogate decision-makers and physicians, with no effect of experience on health care professionals' prediction. Question framing affected the predictive performance of surrogates but not of physicians.

Differences in goals of care discussion outcomes among healthcare professionals: an observational cross-sectional study

BACKGROUND

Goals of care discussions ensure patients receive the care that they want. Recent studies have recognized the opportunity for allied health professionals, such as nurses, in facilitating goals of care discussions. However, the outcomes of such interventions are not well studied.

AIM

To compare the outcomes of goals of care discussions led by physicians and nurses.

DESIGN

This is a retrospective cohort study of patients admitted to an Internal Medicine unit from January 2018 to August 2019. A comprehensive chart review was performed on a random sample of patients. Patient's decision to accept or refuse cardiopulmonary resuscitation was recorded and analyzed. Analysis was stratified by patients' comorbidity burden and illness severity.

SETTING/PARTICIPANTS

The study took place at a tertiary care center and included 200 patients. Patients aged ⩾ 18 were included. Patients who have had pre-existing goals of care documentation were excluded.

RESULTS

About 52% of the goals of care discussions were completed by nurses and 48% by physicians. Patients were more likely to accept cardiopulmonary resuscitation in nurse-led discussions compared to physician-led ones (80.8% vs 61.4%, p = 0.003). Multiple regression showed that patients with higher comorbidity burden (OR 0.71, 95% CI: 0.62-0.82), more severe illness (OR 0.89, 95% CI 0.88-0.99), and physician-led goals of care discussions (OR 0.30, 95% CI: 0.15-0.62) were less likely to accept cardiopulmonary resuscitation.

CONCLUSIONS

There was a significant difference between the outcomes of goals of care discussions led by nurses and physicians. Patients were more likely to accept aggressive resuscitative measures in nurse-led goals of care discussions. Further research efforts are needed to identify the factors contributing to this discrepancy, and to devise ways of improving goals of care discussion delivery.

Patient, Caregiver, and Oncologist Predictions of Quality of Life in Advanced Cancer: Accuracy and Associations with End-of-Life Care and Caregiver Bereavement

OBJECTIVE

Informed treatment decision-making necessitates accurate prognostication,including predictions about quality of life. We examined whether oncologists, patients with advanced cancer, and caregivers accurately predict patients' future quality of life and whether these predictions are prospectively associated with end-of-life care and bereavement.

METHODS

We secondary analyses of clinical trial data. Patients with advanced cancer (n=156), caregivers (n=156), and oncologists (n=38) predicted patient quality of life 3 months into the future. Patients subsequently rated their quality of life 3 months later. Medical record data documented chemotherapy and emergency department (ED)/inpatient visits in the 30 days before death (n=79 decedents). Caregivers self-reported on depression, anxiety, grief, purpose, 21 and regret 7-months post-mortem. In mixed-effects models, patient, caregiver, and oncologist quality-of-life predictions at study entry were used to predict end-of-life care and caregiver outcomes, controlling for patients' quality of life at 3-month follow-up, demographic and clinical characteristics, and nesting within oncologists.

RESULTS

Caregivers (P<.0001) and oncologists (P=.001) predicted lower quality of life than what patients actually experienced. Among decedents, 24.0% received chemotherapy and 54.5% had an ED/inpatient visit. When caregivers' predictions were more negative, patients were less likely to receive chemotherapy (P=.028) or have an ED/inpatient visit (P=.033), and caregivers reported worse depression (P=.002), anxiety (P=.019), and grief (P=.028) and less purpose in life (P<.001) 7-months post-mortem.

CONCLUSION

When caregivers have more negative expectations about patients' quality of life, patients receive less intensive end-of-life care, and caregivers report worse bereavement This article is protected by copyright. All rights reserved.

Prediction of intensive care unit admission (>24h) after surgery in elective noncardiac surgical patients using machine learning algorithms

Background

To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources.

Method

Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, adaptive boosting, and gradient boosting machine. Four secondary analyses were conducted to improve the interpretability of the results.

Results

A total of 2702 (2.0%) patients were admitted to the intensive care unit postoperatively. The gradient boosting machine model attained the highest area under the receiver operating characteristic curve of 0.90. The machine learning models predicted intensive care unit admission better than the American Society of Anesthesiologists Physical Status (area under the receiver operating characteristic curve: 0.68). The gradient boosting machine recognized several features as highly significant predictors for postoperatively intensive care unit admission. By applying subgroup analysis and secondary analysis, we found that patients with operations on the digestive, respiratory, and vascular systems had higher probabilities for intensive care unit admission.

Conclusion

Compared with conventional American Society of Anesthesiologists Physical Status and logistic regression model, the gradient boosting machine could improve the performance in the prediction of intensive care unit admission. Machine learning models could be used to improve the discrimination and identify the need for intensive care unit admission after surgery in elective noncardiac surgical patients, which could help manage the surgical risk.

Surrogate and Physician Decision Making for Mechanically Ventilated Patients According to Expected Patient Outcome

Background: Surrogates and physicians may differ in their priorities and perspectives when making decisions for incapacitated, critically ill patients. Objectives: To determine the extent to which surrogate and physician decisions to sustain life support are associated with their expectations for patient outcomes. Setting/Subjects: Surrogates and physicians of 100 mechanically ventilated patients at an academic, tertiary care medical center in the United States were surveyed. Measurements: Linear regression was used to determine if participant expectations for patient survival, good quality of life, and confidence in these expectations were associated with their agreement that mechanical ventilation should be continued if required for patient survival. Results: Surrogates were more likely than physicians to expect that patients would be alive in three months (91% interquartile range [IQR 70-95%] vs. 65% [IQR 43-77%], p < 0.001) and have good quality of life in three months (71% [IQR 50-90%] vs. 40% [IQR 19-50%], p < 0.001). Surrogates who were most confident in their prognostic abilities were also the most optimistic for good patient outcomes. As such, expectations for patient survival and good quality of life were not associated with level agreement that mechanical ventilation should be continued among confident surrogates, (R² = 0.03, p = 0.13) and (R² = 0.01, p = 0.53), respectively. In contrast, among physicians, confidence was not synonymous with optimism. Instead, the significant associations between expectations for patient survival and good quality of life with the agreement that mechanical ventilation should be continued were strengthened when physicians were confident, (R² = 0.34, p < 0.01) and (R² = 0.47, p < 0.001), respectively. Conclusion: Surrogates and physicians have different approaches to incorporating their expectations for patient prognosis and their confidence in these expectations when they are making decisions for incapacitated critically ill patients.

The simple observational critical care studies: estimations by students, nurses, and physicians of in-hospital and 6-month mortality

Background

Prognostic assessments of the mortality of critically ill patients are frequently performed in daily clinical practice and provide prognostic guidance in treatment decisions. In contrast to several sophisticated tools, prognostic estimations made by healthcare providers are always available and accessible, are performed daily, and might have an additive value to guide clinical decision-making. The aim of this study was to evaluate the accuracy of students’, nurses’, and physicians’ estimations and the association of their combined estimations with in-hospital mortality and 6-month follow-up.

Methods

The Simple Observational Critical Care Studies is a prospective observational single-center study in a tertiary teaching hospital in the Netherlands. All patients acutely admitted to the intensive care unit were included. Within 3 h of admission to the intensive care unit, a medical or nursing student, a nurse, and a physician independently predicted in-hospital and 6-month mortality. Logistic regression was used to assess the associations between predictions and the actual outcome; the area under the receiver operating characteristics (AUROC) was calculated to estimate the discriminative accuracy of the students, nurses, and physicians.

Results

In 827 out of 1,010 patients, in-hospital mortality rates were predicted to be 11%, 15%, and 17% by medical students, nurses, and physicians, respectively. The estimations of students, nurses, and physicians were all associated with in-hospital mortality (OR 5.8, 95% CI [3.7, 9.2], OR 4.7, 95% CI [3.0, 7.3], and OR 7.7 95% CI [4.7, 12.8], respectively). Discriminative accuracy was moderate for all students, nurses, and physicians (between 0.58 and 0.68). When more estimations were of non-survival, the odds of non-survival increased (OR 2.4 95% CI [1.9, 3.1]) per additional estimate, AUROC 0.70 (0.65, 0.76). For 6-month mortality predictions, similar results were observed.

Conclusions

Based on the initial examination, students, nurses, and physicians can only moderately predict in-hospital and 6-month mortality in critically ill patients. Combined estimations led to more accurate predictions and may serve as an example of the benefit of multidisciplinary clinical care and future research efforts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03809-w.

Outcomes of critically ill patients according to the perception of intensivists on the appropriateness of intensive care unit admission

Background

It is important for intensivists to determine which patient may benefit from intensive care unit (ICU) admission. We aimed to assess the outcomes of patients perceived as non-beneficially or beneficially admitted to the ICU and evaluate whether their prognosis was consistent with the intensivists’ perception.

Methods

A prospective observational study was conducted on patients admitted to the medical ICU of a tertiary referral center between February and April 2014. The perceptions of four intensivists at admission (day 1) and on day 3 were investigated as non-beneficial admission, beneficial admission, or indeterminate state.

Results

A total of 210 patients were enrolled. On days 1 and 3, 22 (10%) and 23 (11%) patients were judged as having non-beneficial admission; 166 (79%) and 159 (79%), beneficial admission; and 22 (10%) and 21 (10%), indeterminate state, respectively. The ICU mortality rates of each group on day 1 were 59%, 23%, and 59%, respectively; their 6-month mortality rates were 100%, 48%, and 82%, respectively. The perceptions of non-beneficial admission or indeterminate state were the significant predictors of ICU mortality (day 3; odds ratio [OR], 4.049; 95% confidence interval [CI], 1.892–8.664; P<0.001) and 6-month mortality (day 1: OR, 4.983; 95% CI, 1.260–19.703; P=0.022; day 3: OR, 4.459; 95% CI, 1.162–17.121; P=0.029).

Conclusions

The outcomes of patients perceived as having non-beneficial admission were extremely poor. The intensivists’ perception was important in predicting patients’ outcomes and was more consistent with long-term prognosis than with immediate outcomes. The intensivists’ role can be reflected in limited ICU resource utilization.

Algorithmic prognostication in critical care: a promising but unproven technology for supporting difficult decisions

PURPOSE OF REVIEW

Patients, surrogate decision makers, and clinicians face weighty and urgent decisions under uncertainty in the ICU, which could be aided by risk prediction. Although emerging artificial intelligence/machine learning (AI/ML) algorithms could reduce uncertainty surrounding these life and death decisions, certain criteria must be met to ensure their bedside value.

RECENT FINDINGS

Although ICU severity of illness scores have existed for decades, these tools have not been shown to predict well or to improve outcomes for individual patients. Novel AI/ML tools offer the promise of personalized ICU care but remain untested in clinical trials. Ensuring that these predictive models account for heterogeneity in patient characteristics and treatments, are not only specific to a clinical action but also consider the longitudinal course of critical illness, and address patient-centered outcomes related to equity, transparency, and shared decision-making will increase the likelihood that these tools improve outcomes. Improved clarity around standards and contributions from institutions and critical care departments will be essential.

SUMMARY

Improved ICU prognostication, enabled by advanced ML/AI methods, offer a promising approach to inform difficult and urgent decisions under uncertainty. However, critical knowledge gaps around performance, equity, safety, and effectiveness must be filled and prospective, randomized testing of predictive interventions are still needed.

Shared decision-making in the ICU from the perspective of physicians, nurses and patients: a qualitative interview study

OBJECTIVE

To identify views, experiences and needs for shared decision-making (SDM) in the intensive care unit (ICU) according to ICU physicians, ICU nurses and former ICU patients and their close family members.

DESIGN

Qualitative study.

SETTING

Two Dutch tertiary centres.

PARTICIPANTS

19 interviews were held with 29 participants: seven with ICU physicians from two tertiary centres, five with ICU nurses from one tertiary centre and nine with former ICU patients, of whom seven brought one or two of their close family members who had been involved in the ICU stay.

RESULTS

Three themes, encompassing a total of 16 categories, were identified pertaining to struggles of ICU physicians, needs of former ICU patients and their family members and the preferred role of ICU nurses. The main struggles ICU physicians encountered with SDM include uncertainty about long-term health outcomes, time constraints, feeling pressure because of having final responsibility and a fear of losing control. Former patients and family members mainly expressed aspects they missed, such as not feeling included in ICU treatment decisions and a lack of information about long-term outcomes and recovery. ICU nurses reported mainly opportunities to strengthen their role in incorporating non-medical information in the ICU decision-making process and as liaison between physicians and patients and family.

CONCLUSIONS

Interviewed stakeholders reported struggles, needs and an elucidation of their current and preferred role in the SDM process in the ICU. This study signals an essential need for more long-term outcome information, a more informal inclusion of patients and their family members in decision-making processes and a more substantial role for ICU nurses to integrate patients' values and needs in the decision-making process.

Outcomes of ICU patients with and without perceptions of excessive care: a comparison between cancer and non-cancer patients

Background

Whether Intensive Care Unit (ICU) clinicians display unconscious bias towards cancer patients is unknown. The aim of this study was to compare the outcomes of critically ill patients with and without perceptions of excessive care (PECs) by ICU clinicians in patients with and without cancer.

Methods

This study is a sub-analysis of the large multicentre DISPROPRICUS study. Clinicians of 56 ICUs in Europe and the United States completed a daily questionnaire about the appropriateness of care during a 28-day period. We compared the cumulative incidence of patients with concordant PECs, treatment limitation decisions (TLDs) and death between patients with uncontrolled and controlled cancer, and patients without cancer.

Results

Of the 1641 patients, 117 (7.1%) had uncontrolled cancer and 270 (16.4%) had controlled cancer. The cumulative incidence of concordant PECs in patients with uncontrolled and controlled cancer versus patients without cancer was 20.5%, 8.1%, and 9.1% (p < 0.001 and p = 0.62, respectively). In patients with concordant PECs, we found no evidence for a difference in time from admission until death (HR 1.02, 95% CI 0.60–1.72 and HR 0.87, 95% CI 0.49–1.54) and TLDs (HR 0.81, 95% CI 0.33–1.99 and HR 0.70, 95% CI 0.27–1.81) across subgroups. In patients without concordant PECs, we found differences between the time from admission until death (HR 2.23, 95% CI 1.58–3.15 and 1.66, 95% CI 1.28–2.15), without a corresponding increase in time until TLDs (NA, p = 0.3 and 0.7) across subgroups.

Conclusions

The absence of a difference in time from admission until TLDs and death in patients with concordant PECs makes bias by ICU clinicians towards cancer patients unlikely. However, the differences between the time from admission until death, without a corresponding increase in time until TLDs, suggest prognostic unawareness, uncertainty or optimism in ICU clinicians who did not provide PECs, more specifically in patients with uncontrolled cancer. This study highlights the need to improve intra- and interdisciplinary ethical reflection and subsequent decision-making at the ICU.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-021-00895-5.

An environmental scan of visitation policies in Canadian intensive care units during the first wave of the COVID-19 pandemic

Purpose

In response to the rapid spread of SARS-CoV-2, hospitals in Canada enacted temporary visitor restrictions to limit the spread of COVID-19 and preserve personal protective equipment supplies. This study describes the extent, variation, and fluctuation of Canadian adult intensive care unit (ICU) visitation policies before and during the first wave of the COVID-19 pandemic.

Methods

We conducted an environmental scan of Canadian hospital visitation policies throughout the first wave of the pandemic. We conducted a two-phased study analyzing both quantitative and qualitative data.

Results

We collected 257 documents with reference to visitation policies (preCOVID, 101 [39%]; midCOVID, 71 [28%]; and lateCOVID, 85 [33%]). Of these 257 documents, 38 (15%) were ICU-specific and 70 (27%) referenced the ICU. Most policies during the midCOVID/lateCOVID pandemic period allowed no visitors with specific exceptions (e.g., end-of-life). Framework analysis revealed five overarching themes: 1) reasons for restricted visitation policies; 2) visitation policies and expectations; 3) exceptions to visitation policy; 4) patient and family-centred care; and 5) communication and transparency.

Conclusions

During the first wave of the COVID-19 pandemic, most Canadian hospitals had public-facing visitor restriction policies with specific exception categories, most commonly for patients at end-of-life, patients requiring assistance, or COVID-19 positive patients (varying from not allowed to case-by-case). Further studies are needed to understand the consistency with which visitation policies were operationalized and how they may have impacted patient- and family-centred care.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12630-021-02049-4.

Accuracy of Clinicians' Ability to Predict the Need for Intensive Care Unit Readmission

Rationale: Determining when an intensive care unit (ICU) patient is ready for discharge to the ward is a complex daily challenge for any ICU care team. Patients who experience unplanned readmissions to the ICU have increased mortality, length of stay, and cost compared with those not readmitted during their hospital stay. The accuracy of clinician prediction for ICU readmission is unknown.Objectives: To determine the accuracy of ICU physicians and nurses for predicting ICU readmissionsMethods: We conducted a prospective study in the medical ICU of an academic hospital from October 2015 to September 2017. After daily rounding for patients being transferred to the ward, ICU clinicians (nurses, residents, fellows, and attendings) were asked to report the likelihood of readmission within 48 hours (using a 1-10 scale, with 10 being "extremely likely"). The accuracy of the clinician prediction score (1-10) was assessed for all clinicians and by clinician type using sensitivity, specificity, and area under the curve (AUC) for the receiver operating characteristic curve for predicting the primary outcome, which was ICU readmission within 48 hours of ICU discharge.Results: A total of 2,833 surveys was collected for 938 ICU-to-ward transfers, of which 40 (4%) were readmitted to the ICU within 48 hours of transfer. The median clinician likelihood of readmission score was 3 (interquartile range, 2-4). When physician and nurse likelihood scores were combined, the median clinician likelihood score had an AUC of 0.70 (95% confidence interval [CI], 0.62-0.78) for predicting ICU readmission within 48 hours. Nurses were significantly more accurate than interns at predicting 48-hour ICU readmission (AUC, 0.73 [95% CI, 0.64-0.82] vs. AUC, 0.60 [95% CI, 0.49-0.71]; P = 0.03). All other pairwise comparisons were not significantly different for predicting ICU readmission within 48 hours (P > 0.05 for all comparisons).Conclusions: We found that all clinicians surveyed in our ICU, regardless of the level of experience or clinician type, had only fair accuracy for predicting ICU readmission. Further research is needed to determine if clinical decision support tools would provide prognostic value above and beyond clinical judgment for determining who is ready for ICU discharge.

Definition and incidence of hypotension in intensive care unit patients, an international survey of the European Society of Intensive Care Medicine

INTRODUCTION

Although hypotension in ICU patients is associated with adverse outcome, currently used definitions are unknown and no universally accepted definition exists.

METHODS

We conducted an international, peer-reviewed survey among ICU physicians and nurses to provide insight in currently used definitions, estimations of incidence, and duration of hypotension.

RESULTS

Out of 1394 respondents (1055 physicians (76%) and 339 nurses (24%)), 1207 (82%) completed the questionnaire. In all patient categories, hypotension definitions were predominantly based on an absolute MAP of 65 mmHg, except for the neuro(trauma) category (75 mmHg, p < 0.001), without differences between answers from physicians and nurses. Hypotension incidence was estimated at 55%, and time per day spent in hypotension at 15%, both with nurses reporting higher percentages than physicians (estimated mean difference 5%, p = 0.01; and 4%, p < 0.001).

CONCLUSIONS

An absolute MAP threshold of 65 mmHg is most frequently used to define hypotension in ICU patients. In neuro(trauma) patients a higher threshold was reported. The majority of ICU patients are estimated to endure hypotension during their ICU admission for a considerable amount of time, with nurses reporting a higher estimated incidence and time spent in hypotension than physicians.

Harming patients by provision of intensive care treatment: is it right to provide time-limited trials of intensive care to patients with a low chance of survival?

Time-limited trials of intensive care have arisen in response to the increasing demand for intensive care treatment for patients with a low chance of surviving their critical illness, and the clinical uncertainty inherent in intensive care decision-making. Intensive care treatment is reported by most patients to be a significantly unpleasant experience. Therefore, patients who do not survive intensive care treatment are exposed to a negative dying experience. Time-limited trials of intensive care treatment in patients with a low chance of surviving have both a small chance of benefiting this patient group and a high chance of harming them by depriving them of a good death. A 'rule of rescue' for the critically unwell does not justify time-limiting a trial of intensive care treatment and overlooks the experiential costs that intensive care patients face. Offering time-limited trials of intensive care to all patients, regardless of their chance of survival, overlooks the responsibility of resource-limited intensive care clinicians for suffering caused by their actions. A patient-specific risk-benefit analysis is vital when deciding whether to offer intensive care treatment, to ensure that time-limited trials of intensive care are not undertaken for patients who have a much higher chance of being harmed, rather than benefited by the treatment. The virtue ethics concept of human flourishing has the potential to offer additional ethical guidance to resource-limited clinicians facing these complex decisions, involving the balancing of a quantifiable survival benefit against the qualitative suffering that intensive care treatment may cause.

Critical Care Nurses' Qualitative Reports of Experiences With Physician Behaviors, Nursing Issues, and Other Obstacles in End-of-Life Care

BACKGROUND

Critical care nurses (CCNs) frequently provide end of life (EOL) care in intensive care units (ICUs). Obstacles to EOL care in ICUs exist and have been previously published along with reports from CCNs. Further data exploring obstacles faced during ICU EOL care may increase awareness of common EOL obstacles. Research focusing on obstacles related to physician behaviors and nursing issues (and others) may provide improvement of care.

OBJECTIVE

The aim of this study was to gather first-hand data from CCNs regarding obstacles related to EOL care.

METHODS

A random, geographically dispersed sample of 2000 members of the American Association of Critical-Care Nurses was surveyed. Responses from an item asking CCNs to tell us of the obstacles they experience providing EOL care to dying patients were analyzed.

RESULTS

There were 104 participants who provided 146 responses to this item reflecting EOL obstacles. These obstacles were divided into 11 themes; 6 physician-related obstacles and 5 nursing- and other related obstacles. Major EOL ICU barrier themes were inadequate physician communication, physicians giving false hope, poor nurse staffing, and inadequate EOL care education for nurses.

DISCUSSION AND CONCLUSION

Poor physician communication was the main obstacle noted by CCNs during ICU EOL care, followed by physicians giving false hope. Heavy patient workloads with inadequate staffing were also a major barrier in CCNs providing EOL care.

National Trends and Variation of Functional Status Deterioration in the Medically Critically Ill

OBJECTIVES

Physical and psychologic deficits after an ICU admission are associated with lower quality of life, higher mortality, and resource utilization. This study aimed to examine the prevalence and secular changes of functional status deterioration during hospitalization among nonsurgical critical illness survivors over the past decade.

DESIGN

We performed a retrospective longitudinal cohort analysis.

SETTING

Analysis performed using the Cerner Acute Physiology and Chronic Health Evaluation outcomes database which included manually abstracted data from 236 U.S. hospitals from 2008 to 2016.

PATIENTS

We included nonsurgical adult ICU patients who survived their hospitalization and had a functional status documented at ICU admission and hospital discharge. Physical functional status was categorized as fully independent, partially dependent, or fully dependent.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Functional status deterioration occurred in 38,116 patients (29.3%). During the past decade, functional status deterioration increased in each disease category, as well as overall (prevalence rate ratio, 1.15; 95% CI, 1.13-1.17; p < 0.001). Magnitude of functional status deterioration also increased over time (odds ratio, 1.03; 95% CI, 1.03-1.03; p < 0.001) with hematological, sepsis, neurologic, and pulmonary disease categories having the highest odds of severe functional status deterioration.

CONCLUSIONS

Following nonsurgical critical illness, the prevalence of functional status deterioration and magnitude increased in a nationally representative cohort, despite efforts to reduce ICU dysfunction over the past decade. Identifying the prevalence of functional status deterioration and primary etiologies associated with functional status deterioration will elucidate vital areas for further research and targeted interventions. Reducing ICU debilitation for key disease processes may improve ICU survivor mortality, enhance quality of life, and decrease healthcare utilization.

Observational study of agreement between attending and trainee physicians on the surprise question: "Would you be surprised if this patient died in the next 12 months?"

BACKGROUND

Optimal end-of-life care requires identifying patients that are near the end of life. The extent to which attending physicians and trainee physicians agree on the prognoses of their patients is unknown. We investigated agreement between attending and trainee physician on the surprise question: "Would you be surprised if this patient died in the next 12 months?", a question intended to assess mortality risk and unmet palliative care needs.

METHODS

This was a multicentre prospective cohort study of general internal medicine patients at 7 tertiary academic hospitals in Ontario, Canada. General internal medicine attending and senior trainee physician dyads were asked the surprise question for each of the patients for whom they were responsible. Surprise question response agreement was quantified by Cohen's kappa using Bayesian multilevel modeling to account for clustering by physician dyad. Mortality was recorded at 12 months.

RESULTS

Surprise question responses encompassed 546 patients from 30 attending-trainee physician dyads on academic general internal medicine teams at 7 tertiary academic hospitals in Ontario, Canada. Patients had median age 75 years (IQR 60-85), 260 (48%) were female, and 138 (25%) were dependent for some or all activities of daily living. Trainee and attending physician responses agreed in 406 (75%) patients with adjusted Cohen's kappa of 0.54 (95% credible interval 0.41 to 0.66). Vital status was confirmed for 417 (76%) patients of whom 160 (38% of 417) had died. Using a response of "No" to predict 12-month mortality had positive likelihood ratios of 1.84 (95% CrI 1.55 to 2.22, trainee physicians) and 1.51 (95% CrI 1.30 to 1.72, attending physicians), and negative likelihood ratios of 0.31 (95% CrI 0.17 to 0.48, trainee physicians) and 0.25 (95% CrI 0.10 to 0.46, attending physicians).

CONCLUSION

Trainee and attending physician responses to the surprise question agreed in 54% of cases after correcting for chance agreement. Physicians had similar discriminative accuracy; both groups had better accuracy predicting which patients would survive as opposed to which patients would die. Different opinions of a patient's prognosis may contribute to confusion for patients and missed opportunities for engagement with palliative care services.

Physicians' predictions of long-term survival and functional outcomes do not influence the decision to admit patients with advanced disease to intensive care: A prospective study

BACKGROUND

Long-term survival and functional outcomes should influence admission decisions to intensive care, especially for patients with advanced disease.

AIM

To determine whether physicians' predictions of long-term prognosis influenced admission decisions for patients with and without advanced disease.

DESIGN

A prospective study was conducted. Physicians estimated patient survival with intensive care and with care on the ward, and the probability of 4 long-term outcomes: leaving hospital alive, survival at 6 months, recovery of functional status, and recovery of cognitive status. Patient mortality at 28 days was recorded. We built multivariate logistic regression models using admission to the intensive care unit (ICU) as the dependent variable.

SETTING/PARTICIPANTS

ICU consultations for medical inpatients at a Swiss tertiary care hospital were included.

RESULTS

Of 201 evaluated patients, 105 (52.2%) had an advanced disease and 140 (69.7%) were admitted to the ICU. The probability of admission was strongly associated with the expected short-term survival benefit for patients with or without advanced disease. In contrast, the predicted likelihood that the patient would leave the hospital alive, would be alive 6 months later, would recover functional status, and would recover initial cognitive capacity was not associated with the decision to admit a patient to the ICU. Even for patients with advanced disease, none of these estimated outcomes influenced the admission decision.

CONCLUSIONS

ICU admissions of patients with advanced disease were determined by short-term survival benefit, and not by long-term prognosis. Advance care planning and developing decision-aid tools for triage could help limit potentially inappropriate admissions to intensive care.