Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models

Age and Saving Lives in Crisis Standards of Care: A Multicenter Cohort Study of Triage Score Prognostic Accuracy

IMPORTANCE

Current protocols to triage life support use scores that are biased and inaccurate.

OBJECTIVES

To determine if adding age to triage protocols used in disaster scenarios improves the identification of critically ill patients likely to survive.

DESIGN, SETTING, AND PARTICIPANTS

Observational cohort study from March 1, 2020, to March 1, 2022, at 22 hospitals in three networks, divided into derivation (12 hospitals) and validation cohorts (ten hospitals). Participants were critically ill adults (90% COVID-19 positive) who would have needed life support during an overwhelming case surge. Life support was defined as vasoactive medications for shock, invasive or noninvasive mechanical ventilation, or oxygen therapy with Pao2/Fio2 less than 200.

MAIN OUTCOMES AND MEASURES

The primary outcome was death in the intensive care unit. We fit logistic regression models using a modified Sequential Organ Failure Assessment (SOFA) score with and without age in the derivation cohort and assessed predictive performance in the validation cohort using area under the receiver operating characteristic curves (AUCs) and compared observed and predicted mortality.

RESULTS

The final analysis contained 7,660 patients with 16,711 life-support episodes. In the validation cohort, the AUC for age plus SOFA was significantly higher than the AUC for SOFA alone (0.66 vs. 0.54; p < 0.001). SOFA score substantially overpredicted mortality (13% predicted vs. 5% observed) for younger patients (< 40 yr) and underestimated mortality (14% predicted vs. 31% observed) for older patients (> 80 yr). In contrast, age plus SOFA had good calibration overall and across age groups. The addition of age improved but did not eliminate differences between observed and predicted mortality across racial-ethnic groups.

CONCLUSIONS AND RELEVANCE

Age-inclusive triage better identifies ICU survivors than SOFA alone and is more equitable. Incorporating age into prioritization algorithms could save more lives in a crisis scenario.

Bolstering agreement with scarce resource allocation policy using education: a post hoc analysis of a randomized controlled trial

BACKGROUND

The COVID- 19 pandemic prompted rapid development of scarce resource allocation policies (SRAP) in case demand for critical health services eclipsed capacity. We sought to test whether a brief, educational video could improve alignment of participant values and preferences with the tenets of the University of California Health's SRAP in a post hoc analysis of a randomized controlled trial (RCT) conducted during the pandemic.

METHODS

An RCT of an educational video intervention embedded in a longitudinal web-based survey conducted from May to December 2020, analyzed in August 2024. The "explainer" video intervention was approximately 6 min long and provided an overview of the mechanics and ethical principles underpinning the UC Health SRAP, subtitled in six languages. California residents were randomized to view the intervention or not, stratified by age, sex, education, racial identity, and self-reported health care worker status. Non-California residents were assigned to the control group. 1,971 adult participants were enrolled at baseline, and 939 completed follow-up. 770 participants with matched baseline and follow-up responses were analyzed. Self-reported survey assessments of values regarding components of SRAP were scored as the percentage of agreement with the UC Health SRAP as written. Participants responded to items at baseline and follow-up (approximately 10 weeks after baseline), with randomization occurring between administrations.

RESULTS

After the intervention, overall agreement improved by a substantial margin of 5.2% (from 3.8% to 6.6%, P <.001) for the intervention group compared to the control group. Significant changes in agreement with SRAP logistics and health factors were also observed in the intervention group relative to the control, while no significant changes were noted for social factors. Differential intervention effects were observed for certain demographic subgroups.

CONCLUSIONS

A brief educational video effectively explains the complex ethical principles and mechanisms of the SRAP, as well as how to improve the alignment of participant values with the foundational principles of UC Health SRAP. This directly informs practice by providing a framework for educating individuals about the use of these policies during future situations that require crisis standards of care, which can, in turn, enhance agreement and buy-in from affected parties.

TRIAL REGISTRATION

ClinicalTrials.gov registration NCT04373135 (registered 4 May 2020).

A transfer triage tool for COVID-19 mass critical care surges

The objective of this study is to develop and validate a predictive model for mortality among severe COVID-19 patients who are candidates for inter-hospital transfer. A multicenter prospective observational study was conducted between 1 January 2021 and 30 April 2021 (third and fourth pandemic waves) in regional coordination centers of the Emergency Medical Services of eight Spanish autonomous communities. Hospitalized patients with severe COVID-19 transferred to other hospitals were included. Clinical variables from the initial evaluation, the triage score, and in-hospital mortality rates were collected. A Lasso-type regression analysis was performed to fit the mortality predictive model and its performance was evaluated by a leave-one-out cross-validation. Subsequently, the regional mass triage (MATER) score was created. 1,018 transferred patients were included, with a mean age of 62.3 years (SD 12), of whom 65.1% were male and 89.6% were admitted to an Intensive Care Unit. In-hospital mortality was 23.0%. The MATER score included six variables and presented good discrimination ability with an area under the curve of 0.79 (95% CI 0.77-0.81) and a good calibration with a Brier score of 0.135. The MATER score successfully predicted the mortality rate of severe COVID-19 patients and can be helpful in decision-making for triage and transfer prioritization in mass critical care surges.

Minnesota Hospitals' Plans for Implementing Statewide Guidance on Allocation of Scarce Critical Care Resources During the COVID-19 Pandemic

Objectives

To assess hospitals' plans for implementing Minnesota's statewide guidance for allocating scarce critical care resources during the COVID-19 pandemic.

Patients and Methods

Individuals from 23 hospitals across Minnesota were invited to complete a 25-item survey between July 20, 2020, and September 18, 2020 to understand how hospitals in the state intended to operationalize statewide clinical triage instructions for scarce resources (including mechanical ventilation) and written ethics guidance on the allocation of critical care resources in the event crisis standards of care triggered triage.

Results

Of individuals invited from 23 hospitals, 14 hospitals completed the survey (60.9% institutional response rate) and described plans for triage at their respective hospitals. Planned triage team composition and size varied. Hospitals' plans for which individuals should assign a triage score (reflecting patients' illness severity) also differed markedly. Most respondents described plans for staff training to address potential bias in triage.

Conclusion

Despite explicit state guidance to encourage consistency across hospitals, we found considerable heterogeneity in implementation plans. Plans diverged from Minnesota's written ethics guidance on whether to consider race during triage to help mitigate health disparities. Inconsistencies between the state's 2 guidance documents could explain some of these differences. Collaboration between hospitals and committees developing statewide guidance may help identify barriers to effective operationalization. Ongoing review of published guidance and hospital plans can identify issues of clarity and consistency and promote equitable triage.

Race and Ethnicity Disparities in Management and Outcomes of Critically Ill Adults with Acute Respiratory Failure

This article reviews the current evidence base for racial and ethnic disparities related to acute respiratory failure. It discusses the prevailing and most studied mechanisms that underlay these disparities, analytical challenges that face the field, and then uses this discussion to frame future directions to outline next steps for developing disparities-mitigating solutions.

A Clinician's Guide to Understanding Bias in Critical Clinical Prediction Models

This narrative review focuses on the role of clinical prediction models in supporting informed decision-making in critical care, emphasizing their 2 forms: traditional scores and artificial intelligence (AI)-based models. Acknowledging the potential for both types to embed biases, the authors underscore the importance of critical appraisal to increase our trust in models. The authors outline recommendations and critical care examples to manage risk of bias in AI models. The authors advocate for enhanced interdisciplinary training for clinicians, who are encouraged to explore various resources (books, journals, news Web sites, and social media) and events (Datathons) to deepen their understanding of risk of bias.

Social Disparities and Critical Illness during the Coronavirus Disease 2019 Pandemic: A Narrative Review

The coronavirus disease 2019 (COVID-19) pandemic raised new considerations for social disparities in critical illness including hospital capacity and access to personal protective equipment, access to evolving therapies, vaccinations, virtual care, and restrictions on family visitation. This narrative review aims to explore evidence about racial/ethnic and socioeconomic differences in critical illness during the COVID-19 pandemic, factors driving those differences and promising solutions for mitigating inequities in the future. We apply a patient journey framework to identify social disparities at various stages before, during, and after patient interactions with critical care services and discuss recommendations for policy and practice.

Investigating ethical tradeoffs in crisis standards of care through simulation of ventilator allocation protocols

INTRODUCTION

Arguments over the appropriate Crisis Standards of Care (CSC) for public health emergencies often assume that there is a tradeoff between saving the most lives, saving the most life-years, and preventing racial disparities. However, these assumptions have rarely been explored empirically. To quantitatively characterize possible ethical tradeoffs, we aimed to simulate the implementation of five proposed CSC protocols for rationing ventilators in the context of the COVID-19 pandemic.

METHODS

A Monte Carlo simulation was used to estimate the number of lives saved and life-years saved by implementing clinical acuity-, comorbidity- and age-based CSC protocols under different shortage conditions. This model was populated with patient data from 3707 adult admissions requiring ventilator support in a New York hospital system between April 2020 and May 2021. To estimate lives and life-years saved by each protocol, we determined survival to discharge and estimated remaining life expectancy for each admission.

RESULTS

The simulation demonstrated stronger performance for age-sensitive protocols. For a capacity of 1 bed per 2 patients, ranking by age bands saves approximately 29 lives and 3400 life-years per thousand patients. Proposed protocols from New York and Maryland which allocated without considering age saved the fewest lives (~13.2 and 8.5 lives) and life-years (~416 and 420 years). Unlike other protocols, the New York and Maryland algorithms did not generate significant disparities in lives saved and life-years saved between White non-Hispanic, Black non-Hispanic, and Hispanic sub-populations. For all protocols, we observed a positive correlation between lives saved and life-years saved, but also between lives saved overall and inequality in the number of lives saved in different race and ethnicity sub-populations.

CONCLUSION

While there is significant variance in the number of lives saved and life-years saved, we did not find a tradeoff between saving the most lives and saving the most life-years. Moreover, concerns about racial discrimination in triage protocols require thinking carefully about the tradeoff between enforcing equality of survival rates and maximizing the lives saved in each sub-population.

A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research

Background

Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies.

Methods

A consortium of critical care physicians and data scientists from eight US healthcare systems developed the Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format that harmonizes a minimum set of ICU Data Elements for use in critical illness research. We created a pipeline to process adult ICU EHR data at each site. After development and iteration, we conducted two proof-of-concept studies with a federated research architecture: 1) an external validation of an in-hospital mortality prediction model for critically ill patients and 2) an assessment of 72-hour temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models.

Results

We converted longitudinal data from 94,356 critically ill patients treated in 2020-2021 (mean age 60.6 years [standard deviation 17.2], 30% Black, 7% Hispanic, 45% female) across 8 health systems and 33 hospitals into the CLIF format, The in-hospital mortality prediction model performed well in the health system where it was derived (0.81 AUC, 0.06 Brier score). Performance across CLIF consortium sites varied (AUCs: 0.74-0.83, Brier scores: 0.06-0.01), and demonstrated some degradation in predictive capability. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality.

Conclusions

CLIF facilitates efficient, rigorous, and reproducible critical care research. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational multi-modal AI models of critical illness, and real-time critical care quality dashboards.

Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer

In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.

The Impact of Health Care Algorithms on Racial and Ethnic Disparities : A Systematic Review

BACKGROUND

There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities.

PURPOSE

To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities.

DATA SOURCES

Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023.

STUDY SELECTION

Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms.

DATA EXTRACTION

Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension.

DATA SYNTHESIS

Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques.

LIMITATION

Results are mostly based on modeling studies and may be highly context-specific.

CONCLUSION

Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes.

PRIMARY FUNDING SOURCE

Agency for Healthcare Quality and Research.

Health Professional vs Layperson Values and Preferences on Scarce Resource Allocation

Importance

COVID-19 prompted rapid development of scarce resource allocation (SRA) policies to be implemented if demand eclipsed health systems' ability to provide critical care. While SRA policies follow general ethical frameworks, understanding priorities of those affected by policies and/or tasked with implementing them is critical.

Objective

To evaluate whether community members and health care profesionals (HCP) agree with SRA protocols at the University of California (UC).

Design, Setting, and Participants

This survey study used social media and community-partnered engagement to recruit participants to a web-based survey open to all participants aged older than 18 years who wished to enroll. This study was fielded between May and September 2020 and queried participants' values and preferences on draft SRA policy tenets. Participants were also encouraged to forward the survey to their networks for snowball sampling. Data were analyzed from July 2020 to January 2024.

Main Outcomes and Measures

Survey items assessed values and preferences, graded on Likert scales. Agreement was tabulated as difference in Likert points between expressed opinion and policy tenets. Descriptive statistics were tested for significance by HCP status. Free text responses were analyzed using applied rapid qualitative analysis.

Results

A total of 1545 participants aged older than 18 years (mean [SD] age 49 [16] years; 1149 female participants [74%], 478 health care practitioners [30%]) provided data on SRA values and preferences. Agreement with UC SRA policy as drafted was moderately high among respondents, ranging from 67% to 83% across domains. Higher agreement with the interim policy was observed for laypersons across all domains except health-related factors. HCPs agreed more strongly on average that resources should not be allocated to those less likely to survive (HCP mean, 3.70; 95% CI, 3.16-3.59; vs layperson mean, 3.38; 95% CI, 3.17-3.59; P = .002), and were more in favor of reallocating life support from patients less likely to those more likely to survive (HCP mean, 6.41; 95% CI, 6.15-6.67; vs layperson mean, 5.40; 95% CI, 5.23-5.58; P < .001). Transparency and trust building themes were common in free text responses and highly rated on scaled items.

Conclusions and Relevance

This survey of SRA policy values found moderate agreement with fundamental principles of such policies. Engagement with communities affected by SRA policy should continue in iterative refinement in preparation for future crises.

Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19

OBJECTIVES

To develop and validate machine learning (ML) models to predict high-flow nasal cannula (HFNC) failure in COVID-19, compare their performance to the respiratory rate-oxygenation (ROX) index, and evaluate model accuracy by self-reported race.

DESIGN

Retrospective cohort study.

SETTING

Four Emory University Hospitals in Atlanta, GA.

PATIENTS

Adult patients hospitalized with COVID-19 between March 2020 and April 2022 who received HFNC therapy within 24 hours of ICU admission were included.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Four types of supervised ML models were developed for predicting HFNC failure (defined as intubation or death within 7 d of HFNC initiation), using routine clinical variables from the first 24 hours of ICU admission. Models were trained on the first 60% (n = 594) of admissions and validated on the latter 40% (n = 390) of admissions to simulate prospective implementation. Among 984 patients included, 317 patients (32.2%) developed HFNC failure. eXtreme Gradient Boosting (XGB) model had the highest area under the receiver-operator characteristic curve (AUROC) for predicting HFNC failure (0.707), and was the only model with significantly better performance than the ROX index (AUROC 0.616). XGB model had significantly worse performance in Black patients compared with White patients (AUROC 0.663 vs. 0.808, p = 0.02). Racial differences in the XGB model were reduced and no longer statistically significant when restricted to patients with nonmissing arterial blood gas data, and when XGB model was developed to predict mortality (rather than the composite outcome of failure, which could be influenced by biased clinical decisions for intubation).

CONCLUSIONS

Our XGB model had better discrimination for predicting HFNC failure in COVID-19 than the ROX index, but had racial differences in accuracy of predictions. Further studies are needed to understand and mitigate potential sources of biases in clinical ML models and to improve their equitability.

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.

Embedding Equity into the Hospital Incident Command System: A Narrative Review

BACKGROUND

Disasters exacerbate health inequities, with historically marginalized populations experiencing unjust differences in health care access and outcomes. Health systems plan and respond to disasters using the Hospital Incident Command System (HICS), an organizational structure that centralizes communication and decision-making. The HICS does not have an equity role or considerations built into its standard structure. The authors conducted a narrative review to identify and summarize approaches to embedding equity into the HICS.

METHODS

The peer-reviewed (PubMed, SCOPUS) and gray literature was searched for articles from high-income countries that referenced the HICS or Incident Command System (ICS) and equity, disparities, or populations that experience inequities in disasters. The primary focus of the search strategy was health care, but the research also included governmental and public health system articles. Two authors used inductive thematic analysis to assess commonalities and refined the themes based on feedback from all authors.

RESULTS

The database search identified 479 unique abstracts; 76 articles underwent full-text review, and 11 were included in the final analysis. The authors found 5 articles through cited reference searching and 13 from the gray literature search, which included websites, organizations, and non-indexed journal articles. Three themes from the articles were identified: including equity specialists in the HICS, modifying systems to promote equity, and sensitivity to the local community.

CONCLUSION

Several efforts to embed equity into the HICS and disaster preparedness and response were discovered. This review provides practical strategies health system leaders can include in their HICS and emergency preparedness plans to promote equity in their disaster response.

Evaluating Prognostic Bias of Critical Illness Severity Scores Based on Age, Sex, and Primary Language in the United States: A Retrospective Multicenter Study

OBJECTIVES

Although illness severity scoring systems are widely used to support clinical decision-making and assess ICU performance, their potential bias across different age, sex, and primary language groups has not been well-studied.

DESIGN SETTING AND PATIENTS

We aimed to identify potential bias of Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) IVa scores via large ICU databases.

SETTING/PATIENTS

This multicenter, retrospective study was conducted using data from the Medical Information Mart for Intensive Care (MIMIC) and eICU Collaborative Research Database. SOFA and APACHE IVa scores were obtained from ICU admission. Hospital mortality was the primary outcome. Discrimination (area under receiver operating characteristic [AUROC] curve) and calibration (standardized mortality ratio [SMR]) were assessed for all subgroups.

INTERVENTIONS

Not applicable.

MEASUREMENTS AND MAIN RESULTS

A total of 196,310 patient encounters were studied. Discrimination for both scores was worse in older patients compared with younger patients and female patients rather than male patients. In MIMIC, discrimination of SOFA in non-English primary language speakers patients was worse than that of English speakers (AUROC 0.726 vs. 0.783, p < 0.0001). Evaluating calibration via SMR showed statistically significant underestimations of mortality when compared with overall cohort in the oldest patients for both SOFA and APACHE IVa, female patients (1.09) for SOFA, and non-English primary language patients (1.38) for SOFA in MIMIC.

CONCLUSIONS

Differences in discrimination and calibration of two scores across varying age, sex, and primary language groups suggest illness severity scores are prone to bias in mortality predictions. Caution must be taken when using them for quality benchmarking and decision-making among diverse real-world populations.

Race and ethnicity in the COVID-19 Critical Care Consortium: demographics, treatments, and outcomes, an international observational registry study

BACKGROUND

Improving access to healthcare for ethnic minorities is a public health priority in many countries, yet little is known about how to incorporate information on race, ethnicity, and related social determinants of health into large international studies. Most studies of differences in treatments and outcomes of COVID-19 associated with race and ethnicity are from single cities or countries.

METHODS

We present the breadth of race and ethnicity reported for patients in the COVID-19 Critical Care Consortium, an international observational cohort study from 380 sites across 32 countries. Patients from the United States, Australia, and South Africa were the focus of an analysis of treatments and in-hospital mortality stratified by race and ethnicity. Inclusion criteria were admission to intensive care for acute COVID-19 between January 14th, 2020, and February 15, 2022. Measurements included demographics, comorbidities, disease severity scores, treatments for organ failure, and in-hospital mortality.

RESULTS

Seven thousand three hundred ninety-four adults met the inclusion criteria. There was a wide variety of race and ethnicity designations. In the US, American Indian or Alaska Natives frequently received dialysis and mechanical ventilation and had the highest mortality. In Australia, organ failure scores were highest for Aboriginal/First Nations persons. The South Africa cohort ethnicities were predominantly Black African (50%) and Coloured* (28%). All patients in the South Africa cohort required mechanical ventilation. Mortality was highest for South Africa (68%), lowest for Australia (15%), and 30% in the US.

CONCLUSIONS

Disease severity was higher for Indigenous ethnicity groups in the US and Australia than for other ethnicities. Race and ethnicity groups with longstanding healthcare disparities were found to have high acuity from COVID-19 and high mortality. Because there is no global system of race and ethnicity classification, researchers designing case report forms for international studies should consider including related information, such as socioeconomic status or migration background. *Note: "Coloured" is an official, contemporary government census category of South Africa and is a term of self-identification of race and ethnicity of many citizens of South Africa.

In-Hospital Mortality Prediction Model for Critically Ill Older Adult Patients Transferred from the Emergency Department to the Intensive Care Unit

Purpose

Studies on the prognosis of critically ill older adult patients admitted to the emergency department (ED) but requiring immediate admission to the intensive care unit (ICU) remain limited. This study aimed to develop an in-hospital mortality prediction model for critically ill older adult patients transferred from the ED to the ICU.

Patients and Methods

The training cohort was taken from the Medical Information Mart for Intensive Care IV (version 2.2) database, and the external validation cohort was taken from the Affiliated Dongyang Hospital of Wenzhou Medical University. In the training cohort, class balance was addressed using Random Over Sampling Examples (ROSE). Univariate and multivariate Cox regression analyses were performed to identify independent risk factors. These were then integrated into the predictive nomogram. In the validation cohort, the predictive performance of the nomogram was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curve, clinical utility decision curve analysis (DCA), and clinical impact curve (CIC).

Results

In the ROSE-balanced training cohort, univariate and multivariate Cox regression analysis identified that age, sex, Glasgow coma scale score, malignant cancer, sepsis, use of mechanical ventilation, use of vasoactive agents, white blood cells, potassium, and creatinine were independent predictors of in-hospital mortality in critically ill older adult patients, and were included in the nomogram. The nomogram showed good predictive performance in the ROSE-balanced training cohort (AUC [95% confidence interval]: 0.792 [0.783-0.801]) and validation cohort (AUC [95% confidence interval]: 0.780 [0.727-0.834]). The calibration curves were well-fitted. DCA and CIC demonstrated that the nomogram has good clinical application value.

Conclusion

This study developed a predictive model for early prediction of in-hospital mortality in critically ill older adult patients transferred from the ED to the ICU, which was validated by external data and has good predictive performance.

Associations of Race With Sedation Depth Among Mechanically Ventilated Adults: A Retrospective Cohort Study

OBJECTIVES

To evaluate the association of race with proportion of time in deep sedation among mechanically ventilated adults.

DESIGN

Retrospective cohort study from October 2017 to December 2019.

SETTING

Five hospitals within a single health system.

PATIENTS

Adult patients who identified race as Black or White who were mechanically ventilated for greater than or equal to 24 hours in one of 12 medical, surgical, cardiovascular, cardiothoracic, or mixed ICUs.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The exposure was White compared with Black race. The primary outcome was the proportion of time in deep sedation during the first 48 hours of mechanical ventilation, defined as Richmond Agitation-Sedation Scale values of -3 to -5. For the primary analysis, we performed mixed-effects linear regression models including ICU as a random effect, and adjusting for age, sex, English as preferred language, body mass index, Elixhauser comorbidity index, Laboratory-based Acute Physiology Score, Version 2, ICU admission source, admission for a major surgical procedure, and the presence of septic shock. Of the 3337 included patients, 1242 (37%) identified as Black, 1367 (41%) were female, and 1002 (30%) were admitted to a medical ICU. Black patients spent 48% of the first 48 hours of mechanical ventilation in deep sedation, compared with 43% among White patients in unadjusted analysis. After risk adjustment, Black race was significantly associated with more time in early deep sedation (mean difference, 5%; 95% CI, 2-7%; p < 0.01).

CONCLUSIONS

There are disparities in sedation during the first 48 hours of mechanical ventilation between Black and White patients across a diverse set of ICUs. Future work is needed to determine the clinical significance of these findings, given the known poorer outcomes for patients who experience early deep sedation.

Simulation of New York City's Ventilator Allocation Guideline During the Spring 2020 COVID-19 Surge

Importance

The spring 2020 surge of COVID-19 unprecedentedly strained ventilator supply in New York City, with many hospitals nearly exhausting available ventilators and subsequently seriously considering enacting crisis standards of care and implementing New York State Ventilator Allocation Guidelines (NYVAG). However, there is little evidence as to how NYVAG would perform if implemented.

Objectives

To evaluate the performance and potential improvement of NYVAG during a surge of patients with respect to the length of rationing, overall mortality, and worsening health disparities.

Design, Setting, and Participants

This cohort study included intubated patients in a single health system in New York City from March through July 2020. A total of 20 000 simulations were conducted of ventilator triage (10 000 following NYVAG and 10 000 following a proposed improved NYVAG) during a crisis period, defined as the point at which the prepandemic ventilator supply was 95% utilized.

Exposures

The NYVAG protocol for triage ventilators.

Main Outcomes and Measures

Comparison of observed survival rates with simulations of scenarios requiring NYVAG ventilator rationing.

Results

The total cohort included 1671 patients; of these, 674 intubated patients (mean [SD] age, 63.7 [13.8] years; 465 male [69.9%]) were included in the crisis period, with 571 (84.7%) testing positive for COVID-19. Simulated ventilator rationing occurred for 163.9 patients over 15.0 days, 44.4% (95% CI, 38.3%-50.0%) of whom would have survived if provided a ventilator while only 34.8% (95% CI, 28.5%-40.0%) of those newly intubated patients receiving a reallocated ventilator survived. While triage categorization at the time of intubation exhibited partial prognostic differentiation, 94.8% of all ventilator rationing occurred after a time trial. Within this subset, 43.1% were intubated for 7 or more days with a favorable SOFA score that had not improved. An estimated 60.6% of these patients would have survived if sustained on a ventilator. Revising triage subcategorization, proposed improved NYVAG, would have improved this alarming ventilator allocation inefficiency (25.3% [95% CI, 22.1%-28.4%] of those selected for ventilator rationing would have survived if provided a ventilator). NYVAG ventilator rationing did not exacerbate existing health disparities.

Conclusions and Relevance

In this cohort study of intubated patients experiencing simulated ventilator rationing during the apex of the New York City COVID-19 2020 surge, NYVAG diverted ventilators from patients with a higher chance of survival to those with a lower chance of survival. Future efforts should be focused on triage subcategorization, which improved this triage inefficiency, and ventilator rationing after a time trial, when most ventilator rationing occurred.

Linking Sepsis with chronic arterial hypertension, diabetes mellitus, and socioeconomic factors in the United States: A scoping review

RATIONALE

Sepsis is a syndrome of life-threatening organ dysfunction caused by a dysregulated host immune response to infection. Social risk factors including location and poverty are associated with sepsis-related disparities. Understanding the social and biological phenotypes linked with the incidence of sepsis is warranted to identify the most at-risk populations. We aim to examine how factors in disadvantage influence health disparities related to sepsis.

METHODS

A scoping review was performed for English-language articles published in the United States from 1990 to 2022 on PubMed, Web of Science, and Scopus. Of the 2064 articles found, 139 met eligibility criteria and were included for review.

RESULTS

There is consistency across the literature of disproportionately higher rates of sepsis incidence, mortality, readmissions, and associated complications, in neighborhoods with socioeconomic disadvantage and significant poverty. Chronic arterial hypertension and diabetes mellitus also occur more frequently in the same geographic distribution as sepsis, suggesting a potential shared pathophysiology.

CONCLUSIONS

The distribution of chronic arterial hypertension, diabetes mellitus, social risk factors associated with socioeconomic disadvantage, and sepsis incidence, are clustered in specific geographical areas and linked by endothelial dysfunction. Such population factors can be utilized to create equitable interventions aimed at mitigating sepsis incidence and sepsis-related disparities.

Availability of information needed to evaluate algorithmic fairness - A systematic review of publicly accessible critical care databases

BACKGROUND

Machine learning (ML) may improve clinical decision-making in critical care settings, but intrinsic biases in datasets can introduce bias into predictive models. This study aims to determine if publicly available critical care datasets provide relevant information to identify historically marginalized populations.

METHOD

We conducted a review to identify the manuscripts that report the training/validation of ML algorithms using publicly accessible critical care electronic medical record (EMR) datasets. The datasets were reviewed to determine if the following 12 variables were available: age, sex, gender identity, race and/or ethnicity, self-identification as an indigenous person, payor, primary language, religion, place of residence, education, occupation, and income.

RESULTS

7 publicly available databases were identified. Medical Information Mart for Intensive Care (MIMIC) reports information on 7 of the 12 variables of interest, Sistema de Informação de Vigilância Epidemiológica da Gripe (SIVEP-Gripe) on 7, COVID-19 Mexican Open Repository on 4, and eICU on 4. Other datasets report information on 2 or fewer variables. All 7 databases included information about sex and age. Four databases (57%) included information about whether a patient identified as native or indigenous. Only 3 (43%) included data about race and/or ethnicity. Two databases (29%) included information about residence, and one (14%) included information about payor, language, and religion. One database (14%) included information about education and patient occupation. No databases included information on gender identity and income.

CONCLUSION

This review demonstrates that critical care publicly available data used to train AI algorithms do not include enough information to properly look for intrinsic bias and fairness issues towards historically marginalized populations.

Triage Procedures for Critical Care Resource Allocation During Scarcity

Importance

During the COVID-19 pandemic, many US states issued or revised pandemic preparedness plans guiding allocation of critical care resources during crises. State plans vary in the factors used to triage patients and have faced criticism from advocacy groups due to the potential for discrimination.

Objective

To analyze the role of comorbidities and long-term prognosis in state triage procedures.

Design, Setting, and Participants

This cross-sectional study used data gathered from parallel internet searches for state-endorsed pandemic preparedness plans for the 50 US states, District of Columbia, and Puerto Rico (hereafter referred to as states), which were conducted between November 25, 2021, and June 16, 2023. Plans available on June 16, 2023, that provided step-by-step instructions for triaging critically ill patients were categorized for use of comorbidities and prognostication.

Main Outcomes and Measures

Prevalence and contents of lists of comorbidities and their stated function in triage and instructions to predict duration of postdischarge survival.

Results

Overall, 32 state-promulgated pandemic preparedness plans included triage procedures specific enough to guide triage in clinical practice. Twenty of these (63%) included lists of comorbidities that excluded (11 of 20 [55%]) or deprioritized (8 of 20 [40%]) patients during triage; one state's list was formulated to resolve ties between patients with equal triage scores. Most states with triage procedures (21 of 32 [66%]) considered predicted survival beyond hospital discharge. These states proposed different prognostic time horizons; 15 of 21 (71%) were numeric (ranging from 6 months to 5 years after hospital discharge), with the remaining 6 (29%) using descriptive terms, such as long-term.

Conclusions and Relevance

In this cross-sectional study of state-promulgated critical care triage policies, most plans restricted access to scarce critical care resources for patients with listed comorbidities and/or for patients with less-than-average expected postdischarge survival. This analysis raises concerns about access to care during a public health crisis for populations with high burdens of chronic illness, such as individuals with disabilities and minoritized racial and ethnic groups.

Bias in artificial intelligence algorithms and recommendations for mitigation

The adoption of artificial intelligence (AI) algorithms is rapidly increasing in healthcare. Such algorithms may be shaped by various factors such as social determinants of health that can influence health outcomes. While AI algorithms have been proposed as a tool to expand the reach of quality healthcare to underserved communities and improve health equity, recent literature has raised concerns about the propagation of biases and healthcare disparities through implementation of these algorithms. Thus, it is critical to understand the sources of bias inherent in AI-based algorithms. This review aims to highlight the potential sources of bias within each step of developing AI algorithms in healthcare, starting from framing the problem, data collection, preprocessing, development, and validation, as well as their full implementation. For each of these steps, we also discuss strategies to mitigate the bias and disparities. A checklist was developed with recommendations for reducing bias during the development and implementation stages. It is important for developers and users of AI-based algorithms to keep these important considerations in mind to advance health equity for all populations.

Racial Disparities in Length of Stay Among Severely Ill Patients Presenting With Sepsis and Acute Respiratory Failure

Importance

Although racial and ethnic minority patients with sepsis and acute respiratory failure (ARF) experience worse outcomes, how patient presentation characteristics, processes of care, and hospital resource delivery are associated with outcomes is not well understood.

Objective

To measure disparities in hospital length of stay (LOS) among patients at high risk of adverse outcomes who present with sepsis and/or ARF and do not immediately require life support and to quantify associations with patient- and hospital-level factors.

Design, Setting, and Participants

This matched retrospective cohort study used electronic health record data from 27 acute care teaching and community hospitals across the Philadelphia metropolitan and northern California areas between January 1, 2013, and December 31, 2018. Matching analyses were performed between June 1 and July 31, 2022. The study included 102 362 adult patients who met clinical criteria for sepsis (n = 84 685) or ARF (n = 42 008) with a high risk of death at the time of presentation to the emergency department but without an immediate requirement for invasive life support.

Exposures

Racial or ethnic minority self-identification.

Main Outcomes and Measures

Hospital LOS, defined as the time from hospital admission to the time of discharge or inpatient death. Matches were stratified by racial and ethnic minority patient identity, comparing Asian and Pacific Islander patients, Black patients, Hispanic patients, and multiracial patients with White patients in stratified analyses.

Results

Among 102 362 patients, the median (IQR) age was 76 (65-85) years; 51.5% were male. A total of 10.2% of patients self-identified as Asian American or Pacific Islander, 13.7% as Black, 9.7% as Hispanic, 60.7% as White, and 5.7% as multiracial. After matching racial and ethnic minority patients to White patients on clinical presentation characteristics, hospital capacity strain, initial intensive care unit admission, and the occurrence of inpatient death, Black patients experienced longer LOS relative to White patients in fully adjusted matches (sepsis: 1.26 [95% CI, 0.68-1.84] days; ARF: 0.97 [95% CI, 0.05-1.89] days). Length of stay was shorter among Asian American and Pacific Islander patients with ARF (-0.61 [95% CI, -0.88 to -0.34] days) and Hispanic patients with sepsis (-0.22 [95% CI, -0.39 to -0.05] days) or ARF (-0.47 [-0.73 to -0.20] days).

Conclusions and Relevance

In this cohort study, Black patients with severe illness who presented with sepsis and/or ARF experienced longer LOS than White patients. Hispanic patients with sepsis and Asian American and Pacific Islander and Hispanic patients with ARF both experienced shorter LOS. Because matched differences were independent of commonly implicated clinical presentation-related factors associated with disparities, identification of additional mechanisms that underlie these disparities is warranted.

Circulating extracellular vesicles are associated with the clinical outcomes of sepsis

Introduction

Sepsis is associated with endothelial cell (EC) dysfunction, increased vascular permeability and organ injury, which may lead to mortality, acute respiratory distress syndrome (ARDS) and acute renal failure (ARF). There are no reliable biomarkers to predict these sepsis complications at present. Recent evidence suggests that circulating extracellular vesicles (EVs) and their content caspase-1 and miR-126 may play a critical role in modulating vascular injury in sepsis; however, the association between circulating EVs and sepsis outcomes remains largely unknown.

Methods

We obtained plasma samples from septic patients (n=96) within 24 hours of hospital admission and from healthy controls (n=45). Total, monocyte- or EC-derived EVs were isolated from the plasma samples. Transendothelial electrical resistance (TEER) was used as an indicator of EC dysfunction. Caspase-1 activity in EVs was detected and their association with sepsis outcomes including mortality, ARDS and ARF was analyzed. In another set of experiments, total EVs were isolated from plasma samples of 12 septic patients and 12 non-septic critical illness controls on days 1, and 3 after hospital admission. RNAs were isolated from these EVs and Next-generation sequencing was performed. The association between miR-126 levels and sepsis outcomes such as mortality, ARDS and ARF was analyzed.

Results

Septic patients with circulating EVs that induced EC injury (lower transendothelial electrical resistance) were more likely to experience ARDS (p<0.05). Higher caspase-1 activity in total EVs, monocyte- or EC-derived EVs was significantly associated with the development of ARDS (p<0.05). MiR-126-3p levels in EC EVs were significantly decreased in ARDS patients compared with healthy controls (p<0.05). Moreover, a decline in miR-126-5p levels from day 1 to day 3 was associated with increased mortality, ARDS and ARF; while decline in miR-126-3p levels from day 1 to day 3 was associated with ARDS development.

Conclusions

Enhanced caspase-1 activity and declining miR-126 levels in circulating EVs are associated with sepsis-related organ failure and mortality. Extracellular vesicular contents may serve as novel prognostic biomarkers and/or targets for future therapeutic approaches in sepsis.

The impact of ethnic background on ICU care and outcome in sepsis and septic shock - A retrospective multicenter analysis on 17,949 patients

BACKGROUND

Previous studies have been inconclusive about racial disparities in sepsis. This study evaluated the impact of ethnic background on management and outcome in sepsis and septic shock.

METHODS

This analysis included 17,146 patients suffering from sepsis and septic shock from the multicenter eICU Collaborative Research Database. Generalized estimated equation (GEE) population-averaged models were used to fit three sequential regression models for the binary primary outcome of hospital mortality.

RESULTS

Non-Hispanic whites were the predominant group (n = 14,124), followed by African Americans (n = 1,852), Hispanics (n = 717), Asian Americans (n = 280), Native Americans (n = 146) and others (n = 830). Overall, the intensive care treatment and hospital mortality were similar between all ethnic groups. This finding was concordant in patients with septic shock and persisted after adjusting for patient-level variables (age, sex, mechanical ventilation, vasopressor use and comorbidities) and hospital variables (teaching hospital status, number of beds in the hospital).

CONCLUSION

We could not detect ethnic disparities in the management and outcomes of critically ill septic patients and patients suffering from septic shock. Disparate outcomes among critically ill septic patients of different ethnicities are a public health, rather than a critical care challenge.

Change Management Strategies Towards Dismantling Race-Based Structural Barriers in Radiology

Political momentum for antiracist policies grew out of the collective trauma highlighted during the COVID pandemic. This prompted discussions of root cause analyses for differences in health outcomes among historically underserved populations, including racial and ethnic minorities. Dismantling structural racism in medicine is an ambitious goal that requires widespread buy-in and transdisciplinary collaborations across institutions to establish systematic, rigorous approaches that enable sustainable change. Radiology is at the center of medical care and renewed focus on equity, diversity, and inclusion (EDI) provides an opportune window for radiologists to facilitate an open forum to address racialized medicine to catalyze real and lasting change. The framework of change management can help radiology practices create and maintain this change while minimizing disruption. This article discusses how change management principles can be leveraged by radiology to lead EDI interventions that will encourage honest dialogue, serve as a platform to support institutional EDI efforts, and lead to systemic change.

Prediction of in-hospital mortality among intensive care unit patients using modified daily Laboratory-based Acute Physiology Scores, version 2 (LAPS2)

Background

Mortality prediction for intensive care unit (ICU) patients frequently relies on single acuity measures based on ICU admission physiology without accounting for subsequent clinical changes.

Objectives

Evaluate novel models incorporating modified admission and daily, time-updating Laboratory-based Acute Physiology Scores, version 2 (LAPS2) to predict in-hospital mortality among ICU patients.

Research design

Retrospective cohort study.

Subjects

All ICU patients in five hospitals from October 2017 through September 2019.

Measures

We used logistic regression, penalized logistic regression, and random forest models to predict in-hospital mortality within 30 days of ICU admission using admission LAPS2 alone in patient-level and patient-day-level models, or admission and daily LAPS2 at the patient-day level. Multivariable models included patient and admission characteristics. We performed internal-external validation using four hospitals for training and the fifth for validation, repeating analyses for each hospital as the validation set. We assessed performance using scaled Brier scores (SBS), c-statistics, and calibration plots.

Results

The cohort included 13,993 patients and 120,101 ICU days. The patient-level model including the modified admission LAPS2 without daily LAPS2 had an SBS of 0.175 (95% CI 0.148-0.201) and c-statistic of 0.824 (95% CI 0.808-0.840). Patient-day-level models including daily LAPS2 consistently outperformed models with modified admission LAPS2 alone. Among patients with <50% predicted mortality, daily models were better calibrated than models with modified admission LAPS2 alone.

Conclusions

Models incorporating daily, time-updating LAPS2 to predict mortality among an ICU population perform as well or better than models incorporating modified admission LAPS2 alone.

The lived experience of people with disabilities during the COVID-19 pandemic on Twitter: Content analysis

Objective

People with disabilities (PWDs) are at greater risk of COVID-19 infection, complications, and death, and experience more difficulty accessing care. We analyzed Twitter tweets to identify important topics and investigate health policies' effects on PWDs.

Methods

Twitter's application programming interface was used to access its public COVID-19 stream. English-language tweets from January 2020 to January 2022 containing a combination of keywords related to COVID-19, disability, discrimination, and inequity were collected and refined to exclude duplicates, replies, and retweets. The remaining tweets were analyzed for user demographics, content, and long-term availability.

Results

The collection yielded 94,814 tweets from 43,296 accounts. During the observation period, 1068 (2.5%) accounts were suspended and 1088 (2.5%) accounts were deleted. Account suspension and deletion among verified users tweeting about COVID-19 and disability were 0.13% and 0.3%, respectively. Emotions were similar among active, suspended, and deleted users, with general negative and positive emotions most common followed by sadness, trust, anticipation, and anger. The overall average sentiment for the tweets was negative. Ten of the 12 topics identified (96.8%) related to pandemic effects on PWDs; "politics that rejects and leaves the disabled, elderly, and children behind" (48.3%) and "efforts to support PWDs in the COVID crisis" (31.8%) were most common. The sample of tweets by organizations (43.9%) was higher for this topic than for other COVID-19-related topics the authors have investigated.

Conclusions

The primary discussion addressed how pandemic politics and policies disadvantage PWDs, older adults, and children, and secondarily expressed support for these populations. The increased level of Twitter use by organizations suggests a higher level of organization and advocacy within the disability community than in other groups. Twitter may facilitate recognition of increased harm to or discrimination against specific populations such as people living with disability during national health events.

Risk of hospitalization in a sample of COVID-19 patients with and without chronic obstructive pulmonary disease

BACKGROUND AND OBJECTIVE

Patients with chronic obstructive pulmonary disease (COPD) may have worse coronavirus disease-2019 (COVID-19)-related outcomes. We compared COVID-19 hospitalization risk in patients with and without COPD.

METHODS

This retrospective cohort study included patients ≥40 years, SARS-CoV-2 positive, and with Kaiser Permanente Northern California membership ≥1 year before COVID-19 diagnosis (electronic health records and claims data). COVID-19-related hospitalization risk was assessed by sequentially adjusted logistic regression models and stratified by disease severity. Secondary outcome was death/hospice referral after COVID-19.

RESULTS AND DISCUSSION

Of 19,558 COVID-19 patients, 697 (3.6%) had COPD. Compared with patients without COPD, COPD patients were older (median age: 69 vs 53 years); had higher Elixhauser Comorbidity Index (5 vs 0) and more median baseline outpatient (8 vs 4), emergency department (2 vs 1), and inpatient (2 vs 1) encounters. Unadjusted analyses showed increased odds of hospitalization with COPD (odds ratio [OR]: 3.93; 95% confidence interval [CI]: 3.40-4.60). After full risk adjustment, there were no differences in odds of hospitalization (OR: 1.14, 95% CI: 0.93-1.40) or death/hospice referral (OR: 0.96, 95% CI: 0.72-1.27) between patients with and without COPD. Primary/secondary outcomes did not differ by COPD severity, except for higher odds of hospitalization in COPD patients requiring supplemental oxygen versus those without COPD (OR: 1.84, 95% CI: 1.02-3.33).

CONCLUSIONS

Except for hospitalization among patients using supplemental oxygen, no differences in odds of hospitalization or death/hospice referral were observed in the COVID-19 patient sample depending on whether they had COPD.

Artificial intelligence-based clinical decision support in pediatrics

Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.

Overt and Occult Hypoxemia in Patients Hospitalized With COVID-19

Progressive hypoxemia is the predominant mode of deterioration in COVID-19. Among hypoxemia measures, the ratio of the Pao2 to the Fio2 (P/F ratio) has optimal construct validity but poor availability because it requires arterial blood sampling. Pulse oximetry reports oxygenation continuously (ratio of the Spo2 to the Fio2 [S/F ratio]), but it is affected by skin color and occult hypoxemia can occur in Black patients. Oxygen dissociation curves allow noninvasive estimation of P/F ratios (ePFRs) but remain unproven.

OBJECTIVES

Measure overt and occult hypoxemia using ePFR.

DESIGN SETTING AND PARTICIPANTS

We retrospectively studied COVID-19 hospital encounters (n = 5,319) at two academic centers (University of Virginia [UVA] and Emory University).

MAIN OUTCOMES AND MEASURES

We measured primary outcomes (death or ICU transfer within 24 hr), ePFR, conventional hypoxemia measures, baseline predictors (age, sex, race, comorbidity), and acute predictors (National Early Warning Score [NEWS] and Sequential Organ Failure Assessment [SOFA]). We updated predictors every 15 minutes. We assessed predictive validity using adjusted odds ratios (AORs) and area under the receiver operating characteristic curves (AUROCs). We quantified disparities (Black vs non-Black) in empirical cumulative distributions using the Kolmogorov-Smirnov (K-S) two-sample test.

RESULTS

Overt hypoxemia (low ePFR) predicted bad outcomes (AOR for a 100-point ePFR drop: 2.7 [UVA]; 1.7 [Emory]; p < 0.01) with better discrimination (AUROC: 0.76 [UVA]; 0.71 [Emory]) than NEWS (0.70 [both sites]) or SOFA (0.68 [UVA]; 0.65 [Emory]) and similar to S/F ratio (0.76 [UVA]; 0.70 [Emory]). We found racial differences consistent with occult hypoxemia. Black patients had better apparent oxygenation (K-S distance: 0.17 [both sites]; p < 0.01) but, for comparable ePFRs, worse outcomes than other patients (AOR: 2.2 [UVA]; 1.2 [Emory]; p < 0.01).

CONCLUSIONS AND RELEVANCE

The ePFR was a valid measure of overt hypoxemia. In COVID-19, it may outperform multi-organ dysfunction models. By accounting for biased oximetry as well as clinicians' real-time responses to it (supplemental oxygen adjustment), ePFRs may reveal racial disparities attributable to occult hypoxemia.

Fair admission risk prediction with proportional multicalibration

Fair calibration is a widely desirable fairness criteria in risk prediction contexts. One way to measure and achieve fair calibration is with multicalibration. Multicalibration constrains calibration error among flexibly-defined subpopulations while maintaining overall calibration. However, multicalibrated models can exhibit a higher percent calibration error among groups with lower base rates than groups with higher base rates. As a result, it is possible for a decision-maker to learn to trust or distrust model predictions for specific groups. To alleviate this, we propose proportional multicalibration, a criteria that constrains the percent calibration error among groups and within prediction bins. We prove that satisfying proportional multicalibration bounds a model's multicalibration as well its differential calibration, a fairness criteria that directly measures how closely a model approximates sufficiency. Therefore, proportionally calibrated models limit the ability of decision makers to distinguish between model performance on different patient groups, which may make the models more trustworthy in practice. We provide an efficient algorithm for post-processing risk prediction models for proportional multicalibration and evaluate it empirically. We conduct simulation studies and investigate a real-world application of PMC-postprocessing to prediction of emergency department patient admissions. We observe that proportional multicalibration is a promising criteria for controlling simultaneous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.

The Ethics of Implementing Emergency Resource Allocation Protocols

AbstractWe explore the various ethical challenges that arise during the practical implementation of an emergency resource allocation protocol. We argue that to implement an allocation plan in a crisis, a hospital system must complete five tasks: (1) formulate a set of general principles for allocation, (2) apply those principles to the disease at hand to create a concrete protocol, (3) collect the data required to apply the protocol, (4) construct a system to implement triage decisions with those data, and (5) create a system for managing the consequences of implementing the protocol, including the effects on those who must carry out the plan, the medical staff, and the general public. Here we illustrate the complexities of each task and provide tentative solutions, by describing the experiences of the Coronavirus Ethics Response Group, an interdisciplinary team formed to address the ethical issues in pandemic resource planning at the University of Rochester Medical Center. While the plan was never put into operation, the process of preparing for emergency implementation exposed ethical issues that require attention.

Ethical Aspects of Personalized Research and Management of Systemic Inflammatory Response Syndrome (SIRS) in Children

Systemic inflammatory response syndrome (SIRS) is a life-threatening condition with nonspecific symptoms. Because of that, defining a targeted therapy against SIRS in children and adults remains a challenge. The identification of diagnostic patterns from individualized immuneprofiling can lead to development of a personalized therapy. The aim of this study was to identify and analyze ethical issues associated with personalized research and therapy for SIRS in pediatric populations. We conducted an ethical analysis based on a principled approach according to Beauchamp and Childress' four bioethical principles. Relevant information for the research objectives was extracted from a systematic literature review conducted in the scientific databases PubMed, Embase and Web of Science. We searched for pertinent themes dealing with at least one of the four bioethical principles: "autonomy", "non-maleficence", "beneficence" and "justice". 48 publications that met the research objectives were included in the thorough analysis, structured and discussed in a narrative synthesis. From the analysis of the results, it has emerged that traditional paradigms of patient's autonomy and physician paternalism need to be reexamined in pediatric research. Standard information procedures and models of informed consent should be reconsidered as they do not accommodate the complexities of pediatric omics research.

Characterizing Equity of Intensive Care Unit Admissions for Sepsis and Acute Respiratory Failure

Rationale: Patients who identify as from racial or ethnic minority groups who have sepsis or acute respiratory failure (ARF) experience worse outcomes relative to nonminority patients, but processes of care accounting for disparities are not well-characterized. Objectives: Determine whether reductions in intensive care unit (ICU) admission during hospital-wide capacity strain occur preferentially among patients who identify with racial or ethnic minority groups. Methods: This retrospective cohort among 27 hospitals across the Philadelphia metropolitan area and Northern California between 2013 and 2018 included adult patients with sepsis and/or ARF who did not require life support at the time of hospital admission. An updated model of hospital-wide capacity strain was developed that permitted determination of relationships between patient race, ethnicity, ICU admission, and strain. Results: After adjustment for demographics, disease severity, and study hospital, patients who identified as Asian or Pacific Islander had the highest adjusted ICU admission odds relative to patients who identified as White in both the sepsis and ARF populations (odds ratio, 1.09; P = 0.006 and 1.26; P < 0.001). ICU admission was also elevated for patients with ARF who identified as Hispanic (odds ratio, 1.11; P = 0.020). Capacity strain did not modify differences in ICU admission for patients who identified with a minority group in either disease population (all interactions, P > 0.05). Conclusions: Systematic differences in ICU admission patterns were observed for patients that identified as Asian, Pacific Islander, and Hispanic. However, ICU admission was not restricted from these groups, and capacity strain did not preferentially reduce ICU admission from patients identifying with minority groups. Further characterization of provider decision-making can help contextualize these findings as the result of disparate decision-making or a mechanism of equitable care.

Race and resource allocation: an online survey of US and UK adults' attitudes toward COVID-19 ventilator and vaccine distribution

OBJECTIVE

This study aimed to assess US/UK adults' attitudes towards COVID-19 ventilator and vaccine allocation.

DESIGN

Online survey including US and UK adults, sampled to be representative for sex, age, race, household income and employment. A total of 2580 participated (women=1289, age range=18 to 85 years, Black American=114, BAME=138).

INTERVENTIONS

Participants were asked to allocate ventilators or vaccines in scenarios involving individuals or groups with different medical risk and additional risk factors.

RESULTS

Participant race did not impact vaccine or ventilator allocation decisions in the USA, but did impact ventilator allocation attitudes in the UK (F(4,602)=6.95, p<0.001). When a racial minority or white patient had identical chances of survival, 14.8% allocated a ventilator to the minority patient (UK BAME participants: 24.4%) and 68.9% chose to toss a coin. When the racial minority patient had a 10% lower chance of survival, 12.4% participants allocated them the ventilator (UK BAME participants: 22.1%). For patients with identical risk of severe COVID-19, 43.6% allocated a vaccine to a minority patient, 7.2% chose a white patient and 49.2% chose a coin toss. When the racial minority patient had a 10% lower risk of severe COVID-19, 23.7% participants allocated the vaccine to the minority patient. Similar results were seen for obesity or male sex as additional risk factors. In both countries, responses on the Modern Racism Scale were strongly associated with attitudes toward race-based ventilator and vaccine allocations (p<0.0001).

CONCLUSIONS

Although living in countries with high racial inequality during a pandemic, most US and UK adults in our survey allocated ventilators and vaccines preferentially to those with the highest chance of survival or highest chance of severe illness. Race of recipient led to vaccine prioritisation in cases where risk of illness was similar.

Social determinants of recovery

PURPOSE OF REVIEW

The purpose of this review is to examine evidence describing the influence of social determinants on recovery following hospitalization with critical illness. In addition, it is meant to provide insight into the several mechanisms through which social factors influence recovery as well as illuminate approaches to addressing these factors at various levels in research, clinical care, and policy.

RECENT FINDINGS

Social determinants of health, ranging from individual factors like social support and socioeconomic status to contextual ones like neighborhood deprivation, are associated with disability, cognitive impairment, and mental health after critical illness. Furthermore, many social factors are reciprocally related to recovery wherein the consequences of critical illness such as financial toxicity and caregiver burden can put essential social needs under strain turning them into barriers to recovery.

SUMMARY

Recovery after hospitalization for critical illness may be influenced by many social factors. These factors warrant attention by clinicians, health systems, and policymakers to enhance long-term outcomes of critical illness survivors.

What the American Journal of Critical Care Junior Peer Reviewers Were Reading During Year 2 of the Program

The American Journal of Critical Care's Junior Peer Reviewer program aims to mentor novice reviewers in the peer review process. To grow their critical appraisal skills, the participants take part in discussion sessions in which they review articles published in other journals. Here we summarize the articles reviewed during the second year of the program, which again focused on the care of critically ill patients with COVID-19. This article aims to share these reviews and the reviewers' thoughts regarding the relevance, design, and applicability of the findings from the selected studies. High rates of delirium associated with COVID-19 may be impacted by optimizing sedation strategies and allowing safe family visitation. Current methodology in crisis standards of care may result in inequity and further research is needed. The use of extracorporeal carbon dioxide removal to facilitate super low tidal volume ventilation does not improve 90-day mortality outcomes. Continued research to better understand the natural history of COVID-19 and interventions useful for improving outcomes is imperative.

Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

Background

The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU.

Methods

We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure.

Results

The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of -0.04 [-0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of -0.19 [-0.27; -0.10] and slope of 0.89 [0.84; 0.94] for the random forest model.

Discussion

We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

Elevated serum level of human epididymal protein 4 (HE4) predicts poor prognosis in the critically ill with sepsis: a prospective observational cohort study

OBJECTIVE

Human epididymal protein 4 (HE4) has been widely used as an important clinical tumor biomarker for epithelial ovarian cancer. HE4 has recently been suggested to be an inflammatory biomarker and we hypothesized that the serum HE4 level upon intensive care unit (ICU) admission might predict prognosis in septic patients. We hypothesized that serum HE4 level upon intensive care unit (ICU) admission could predict prognosis in septic patients.

METHODS

Serum levels of HE4, procalcitonin (PCT), C-reactive protein (CRP), IL-6 and IL-8 were quantified, and sequential organ failure assessment (SOFA) scores were recorded on day one of admission to ICU. The area under the receiver operating characteristic (ROC) curve (AUC) analysis of HE4, IL-6, PCT and SOFA at ICU admission for 28-day mortality was used to evaluate the ability of HE4 in predicting 28-day mortality of sepsis. Multivariate regression analysis was used to identify the independent risk factors for 28-day mortality.

RESULTS

A total of 1289 patients were recruited, and 117 patients were included for final analysis. On day of ICU admission, septic patients had significantly higher levels of serum HE4 than those with infection without sepsis, those with ovarian cancer, or healthy controls. Compared with septic survivors, septic non-survivors presented with significantly higher serum HE4 concentrations. Serum levels of HE4 correlated with disease severity scores and cytokine levels (IL-6 and IL-8). Upon ICU admission, the AUC for HE4 level association with 28-day mortality was 0.881, higher than the AUC for SOFA (0.713), IL-6 (0.589), and PCT (0.567). A regression analysis showed that HE4 was an independent mortality predictor.

CONCLUSION

HE4 can predict poor prognosis in septic patients, which may help to identify a group of septic patients at high risk of death.

Epidemiology of Organ Failure Before and During COVID-19 Pandemic Surge Conditions

BACKGROUND

Understanding the distribution of organ failure before and during the COVID-19 pandemic surge can provide a deeper understanding of how the pandemic strained health care systems and affected outcomes.

OBJECTIVE

To assess the distribution of organ failure in 3 New York City hospitals during the COVID-19 pandemic.

METHODS

A retrospective cohort study of adult admissions across hospitals from February 1, 2020, through May 31, 2020, was conducted. The cohort was stratified into those admitted before March 17, 2020 (prepandemic) and those admitted on or after that date (SARS-CoV-2-positive and non-SARS-CoV-2). Sequential Organ Failure Assessment scores were computed every 2 hours for each admission.

RESULTS

A total of 1 794 975 scores were computed for 20 704 admissions. Before and during the pandemic, renal failure was the most common type of organ failure at admission and respiratory failure was the most common type of hospital-onset organ failure. The SARS-CoV-2-positive group showed a 231% increase in respiratory failure compared with the prepandemic group. More than 65% of hospital-onset organ failure in the prepandemic group and 83% of hospital-onset respiratory failure in the SARS-CoV-2-positive group occurred outside intensive care units. The SARS-CoV-2-positive group showed a 341% increase in multiorgan failure compared with the prepandemic group. Compared with the prepandemic and non-SARS-CoV-2 patients, SARS-CoV-2-positive patients had significantly higher mortality for the same admission and maximum organ failure score.

CONCLUSION

Most hospital-onset organ failure began outside intensive care units, with a marked increase in multiorgan failure during pandemic surge conditions and greater hospital mortality for the severity of organ failure.

How Common SOFA and Ventilator Time Trial Criteria Would Have Performed During the COVID-19 Pandemic: An Observational Simulated Cohort Study

OBJECTIVES

To evaluate how key aspects of New York State Ventilator Allocation Guidelines (NYSVAG)-Sequential Organ Failure Assessment score criteria and ventilator time trials -might perform with respect to the frequency of ventilator reallocation and survival to hospital discharge in a simulated cohort of coronavirus disease (COVID-19) patients.

METHODS

Single center retrospective observational and simulation cohort study of 884 critically ill COVID-19 patients undergoing ventilator allocation per NYSVAG.

RESULTS

In total, 742 patients (83.9%) would have had their ventilator reallocated during the 11-day observation period, 280 (37.7%) of whom would have otherwise survived to hospital discharge if provided with a ventilator. Only 65 (18.1%) of the observed surviving patients would have survived by NYSVAG. Extending ventilator time trials from 2 to 5 days resulted in a 49.2% increase in simulated survival to discharge.

CONCLUSIONS

In the setting of a protracted respiratory pandemic, implementation of NYSVAG or similar protocols could lead to a high degree of ventilator reallocation, including withdrawal from patients who might otherwise survive. Longer ventilator time trials might lead to improved survival for COVID-19 patients given their protracted respiratory failure. Further studies are needed to understand the survival of patients receiving reallocated ventilators to determine whether implementation of NYSVAG would improve overall survival.

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study

OBJECTIVE

To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.

DESIGN

Retrospective cohort study.

SETTING

One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21.

PARTICIPANTS

33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19.

MAIN OUTCOME MEASURES

An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early.

RESULTS

9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge.

CONCLUSION

A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.