Potentially Preventable Intensive Care Unit Admissions in the United States, 2006-2015

Individualized prediction of critical illness in older adults: Validation of an elders risk assessment model

BACKGROUND

The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR-based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit.

METHODS

This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit.

RESULTS

A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83-0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98-12.87).

CONCLUSIONS

This simple EHR-based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well-being of older adults at risk of critical illness and its consequences.

Quality of dying and death in intensive care units: family satisfaction

OBJECTIVE

This cohort study identified patterns/classes of surrogates' assessment of their relative's quality of dying and death (QODD) and to evaluate their associations with family satisfaction with intensive care unit (ICU) care.

METHODS

We identified QODD classes through latent class analysis of the frequency component of the QODD questionnaire and examined their differences in summary questions on the QODD and scores of the Family Satisfaction in the ICU questionnaire among 309 bereaved surrogates of ICU decedents.

RESULTS

Four distinct classes (prevalence) were identified: high (41.3%), moderate (20.1%), poor-to-uncertain (21.7%) and worst (16.9%) QODD classes. Characteristics differentiate these QODD classes including physical symptom control, emotional preparedness for death, and amount of life-sustaining treatments (LSTs) received. Patients in the high QODD class had optimal physical symptom control, moderate-to-sufficient emotional preparedness for death and few LSTs received. Patients in the moderate QODD class had adequate physical symptom control, moderate-to-sufficient emotional preparedness for death and the least LSTs received. Patients in the poor-to-uncertain QODD class had inadequate physical symptom control, insufficient-uncertain emotional preparedness for death and some LSTs received. Patients in the worst QODD class had poorest physical symptom control, insufficient-to-moderate emotional preparedness for death and substantial LSTs received. Bereaved surrogates in the worst QODD class scored significantly lower in evaluations of the patient's overall QODD, and satisfaction with ICU care and decision-making process than those in the other classes.

CONCLUSIONS

The identified distinct QODD classes offer potential actionable direction for improving quality of end-of-life ICU care.

Expanding Critical Care Delivery beyond the Intensive Care Unit: Determining the Design and Implementation Needs for a Tele-Critical Care Consultation Service

BACKGROUND

 Unplanned intensive care unit (ICU) admissions from medical/surgical floors and increased boarding times of ICU patients in the emergency department (ED) are common; approximately half of these are associated with adverse events. We explore the potential role of a tele-critical care consult service (TC3) in managing critically ill patients outside of the ICU and potentially preventing low-acuity unplanned admissions and also investigate its design and implementation needs.

METHODS

 We conducted a qualitative study involving general observations of the units, shadowing of clinicians during patient transfers, and interviews with clinicians from the ED, medical/surgical floor units and their ICU counterparts, tele-ICU, and the rapid response team at a large academic medical center in St. Louis, Missouri, United States. We used a hybrid thematic analysis approach supported by open and structured coding using the Consolidated Framework for Implementation Research (CFIR).

RESULTS

 Over 165 hours of observations/shadowing and 26 clinician interviews were conducted. Our findings suggest that a tele-critical care consult (TC3) service can prevent avoidable, lower acuity ICU admissions by offering a second set of eyes via remote monitoring and providing guidance to bedside and rapid response teams in the care delivery of these patients on the floor/ED. CFIR-informed enablers impacting the successful implementation of the TC3 service included the optional and on-demand features of the TC3 service, around-the-clock availability, and continuous access to trained critical care clinicians for avoidable lower acuity (ALA) patients outside of the ICU, familiarity with tele-ICU staff, and a willingness to try alternative patient risk mitigation strategies for ALA patients (suggested by TC3), before transferring all unplanned admissions to ICUs. Conversely, the CFIR-informed barriers to implementation included a desire to uphold physician autonomy by floor/ED clinicians, potential role conflicts with rapid response teams, additional workload for floor/ED nurses, concerns about obstructing unavoidable, higher acuity admissions, and discomfort with audio-visual tools. To amplify these potential enablers and mitigate potential barriers to TC3 implementation, informed by this study, we propose two key characteristics-essential for extending the delivery of critical care services beyond the ICU-underlying a telemedicine critical care consultation model including its virtual footprint and on-demand and optional service features.

CONCLUSION

 Tele-critical care represents an innovative strategy for delivering safe and high-quality critical care services to lower acuity borderline patients outside the ICU setting.

Early Prevention of Critical Illness in Older Adults: Adaptation and Pilot Testing of an Electronic Risk Score and Checklist

OBJECTIVE

Given limited critical care resources and an aging population, early interventions to prevent critical illness are vital. In this work, we measured post-implementation outcomes after introducing a novel electronic scoring system (Elders Risk Assessment-ERA) and a risk-factor checklist, Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN), to detect older patients at high risk of critical illness in a primary care setting.

METHODS

The study was conducted at a family medicine clinic in Kasson, MN. The ADAPT-ITT framework was used to modify the CERTAIN checklist for primary care during 2 co-design workshops involving interdisciplinary clinicians, held in April 2023. The ERA score and modified CERTAIN checklist were implemented between May and July 2023 and identify and assess all patients age ≥60 years at risk of critical illness during their primary care visits. Implementation outcomes were evaluated at the end of the study via an anonymous survey and EHR data extraction.

RESULTS

Fourteen clinicians participated in 2 co-design workshops. A total of 19 clinicians participated in a post-pilot survey. All survey items were rated on a 5-point Likert type scale. Mean acceptability of the ERA score and checklist was rated 3.35 (SD = 0.75) and 3.09 (SD = 0.64), respectively. Appropriateness had a mean rating of 3.38 (SD = 0.82) for the ERA score and 3.19 (SD = 0.59) for the checklist. Mean feasibility was rated 3.38(SD = 0.85) and 2.92 (SD = 0.76) for the ERA score and checklist, respectively. The adoption rate was 50% (19/38) among clinicians, but the reach was low at 17% (49/289) of eligible patients.

CONCLUSIONS

This pilot study evaluated the implementation of an intervention that introduced the ERA score and CERTAIN checklist into a primary care practice. Results indicate moderate acceptability, appropriateness, and feasibility of the ERA score, and similar ratings for the checklist, with slightly lower feasibility. While checklist adoption was moderate, reach was limited, indicating inconsistent use.

RECOMMENDATIONS

We plan to use the open-ended resurvey responses to further modify the CERTAIN-FM checklist and implementation process. The ADAPT-ITT framework is a useful model for adapting the checklist to meet the primary care clinician needs.

Racial & ethnic disparities in geographic access to critical care in the United States: A geographic information systems analysis

OBJECTIVE

It is important to identify gaps in access and reduce health outcome disparities, understanding access to intensive care unit (ICU) beds, especially by race and ethnicity, is crucial. Our objective was to evaluate the race and ethnicity-specific 60-minute drive time accessibility of ICU beds in the United States (US).

DESIGN

We conducted a cross-sectional study using road network analysis to determine the number of ICU beds within a 60-minute drive time, and calculated adult intensive care bed ratios per 100,000 adults. We evaluated the US population at the Census block group level and stratified our analysis by race and ethnicity and by urbanicity. We classified block groups into four access levels: no access (0 adult intensive care beds/100,000 adults), below average access (>0-19.5), average access (19.6-32.0), and above average access (>32.0). We calculated the proportion of adults in each racial and ethnic group within the four access levels.

SETTING

All 50 US states and the District of Columbia.

PARTICIPANTS

Adults ≥15 years old.

MAIN OUTCOME MEASURES

Adult intensive care beds/100,000 adults and percentage of adults national and state) within four access levels by race and ethnicity.

RESULTS

High variability existed in access to ICU beds by state, and substantial disparities by race and ethnicity. 1.8% (n = 5,038,797) of Americans had no access to an ICU bed, and 26.8% (n = 73,095,752) had below average access, within a 60-minute drive time. Racial and ethnic analysis showed high rates of disparities (no access/below average access): American Indians/Alaskan Native 12.6%/28.5%, Asian 0.7%/23.1%, Black or African American 0.6%/16.5%, Hispanic or Latino 1.4%/23.0%, Native Hawaiian and other Pacific Islander 5.2%/35.0%, and White 2.1%/29.0%. A higher percentage of rural block groups had no (5.2%) or below average access (41.2%), compared to urban block groups (0.2% no access, 26.8% below average access).

CONCLUSION

ICU bed availability varied substantially by geography, race and ethnicity, and by urbanicity, creating significant disparities in critical care access. The variability in ICU bed access may indicate inequalities in healthcare access overall by limiting resources for the management of critically ill patients.

Unveiling Spatial Associations between COVID-19 Severe Health Index, Racial/Ethnic Composition, and Community Factors in the United States

There are limited efforts to incorporate different predisposing factors into prediction models that account for population racial/ethnic composition in exploring the burden of high COVID-19 Severe Health Risk Index (COVID-19 SHRI) scores. This index quantifies the risk of severe COVID-19 symptoms among a county's population depending on the presence of some chronic conditions. These conditions, as identified by the Centers for Disease Control and Prevention (CDC), include Chronic Obstructive Pulmonary Disease (COPD), heart disease, high blood pressure, diabetes, and obesity. Therefore, the objectives of this study were (1) to investigate potential population risk factors preceding the COVID-19 pandemic that are associated with the COVID-19 SHRI utilizing non-spatial regression models and (2) to evaluate the performance of spatial regression models in comparison to non-spatial regression models. The study used county-level data for 3107 United States counties, utilizing publicly available datasets. Analyses were carried out by constructing spatial and non-spatial regression models. Majority White and majority Hispanic counties showed lower COVID-19 SHRI scores when compared to majority Black counties. Counties with an older population, low income, high smoking, high reported insufficient sleep, and a high percentage of preventable hospitalizations had higher COVID-19 SHRI scores. Counties with better health access and internet coverage had lower COVID-19 SHRI scores. This study helped to identify the county-level characteristics of risk populations to help guide resource allocation efforts. Also, the study showed that the spatial regression models outperformed the non-spatial regression models. Racial/ethnic inequalities were associated with disparities in the burden of high COVID-19 SHRI scores. Therefore, addressing these factors is essential to decrease inequalities in health outcomes. This work provides the baseline typology to further explore many social, health, economic, and political factors that contribute to different health outcomes.

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.

Course and predictors of posttraumatic stress-related symptoms among family members of deceased ICU patients during the first year of bereavement

Background/Objective

Death in intensive care units (ICUs) may increase bereaved family members’ risk for posttraumatic stress disorder (PTSD). However, posttraumatic stress-related symptoms (hereafter as PTSD symptoms) and their precipitating factors were seldom examined among bereaved family members and primarily focused on associations between PTSD symptoms and patient/family characteristics. We aimed to investigate the course and predictors of clinically significant PTSD symptoms among family members of deceased ICU patients by focusing on modifiable quality indicators for end-of-life ICU care.

Method

In this longitudinal observational study, 319 family members of deceased ICU patients were consecutively recruited from medical ICUs from two Taiwanese medical centers. PTSD symptoms were assessed at 1, 3, 6, and 13 months post-loss using the Impact of Event Scale-Revised (IES-R). Family satisfaction with end-of-life care in ICUs was assessed at 1 month post-loss. End-of-life care received in ICUs was documented over the patient’s ICU stay. Predictors for developing clinically significant PTSD symptoms (IES-R score ≥ 33) were identified by multivariate logistic regression with generalized estimating equation modeling.

Results

The prevalence of clinically significant PTSD symptoms decreased significantly over time (from 11.0% at 1 month to 1.6% at 13 months post-loss). Longer ICU stays (adjusted odds ratio [95% confidence interval] = 1.036 [1.006, 1.066]), financial insufficiency (3.166 [1.159, 8.647]), and reported use of pain medications (3.408 [1.230, 9.441]) by family members were associated with a higher likelihood of clinically significant PTSD symptoms among family members during bereavement. Stronger perceived social support (0.937 [0.911, 0.965]) and having a Do-Not-Resuscitate (DNR) order issued before the patient’s death (0.073 [0.011, 0.490]) were associated with a lower likelihood of clinically significant PTSD symptoms. No significant association was observed for family members’ satisfaction with end-of-life care (0.988 [0.944, 1.034]) or decision-making in ICUs (0.980 [0.944, 1.018]).

Conclusions

The likelihood of clinically significant PTSD symptoms among family members decreased significantly over the first bereavement year and was lower when a DNR order was issued before death. Enhancing social support and facilitating a DNR order may reduce the trauma of ICU death of a beloved for family members at risk for developing clinically significant PTSD symptoms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03719-x.

Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients

Recent health reforms have created incentives for cardiologists and accountable care organizations to participate in value-based care models for heart failure (HF). Accurate risk stratification of HF patients is critical to efficiently deploy interventions aimed at reducing preventable utilization. The goal of this paper was to compare deep learning approaches with traditional logistic regression (LR) to predict preventable utilization among HF patients. We conducted a prognostic study using data on 93,260 HF patients continuously enrolled for 2-years in a large U.S. commercial insurer to develop and validate prediction models for three outcomes of interest: preventable hospitalizations, preventable emergency department (ED) visits, and preventable costs. Patients were split into training, validation, and testing samples. Outcomes were modeled using traditional and enhanced LR and compared to gradient boosting model and deep learning models using sequential and non-sequential inputs. Evaluation metrics included precision (positive predictive value) at k, cost capture, and Area Under the Receiver operating characteristic (AUROC). Deep learning models consistently outperformed LR for all three outcomes with respect to the chosen evaluation metrics. Precision at 1% for preventable hospitalizations was 43% for deep learning compared to 30% for enhanced LR. Precision at 1% for preventable ED visits was 39% for deep learning compared to 33% for enhanced LR. For preventable cost, cost capture at 1% was 30% for sequential deep learning, compared to 18% for enhanced LR. The highest AUROCs for deep learning were 0.778, 0.681 and 0.727, respectively. These results offer a promising approach to identify patients for targeted interventions.