ICU capacity strain and the quality and allocation of critical care

Prescription for crisis: the compounding effect of community drug shortages on Lebanon's healthcare system

The multifaceted crises that Lebanon is facing have led to a shortage of medications in the country's community pharmacies. This shortage has triggered a cascade of adverse effects, rippling throughout the nation's healthcare system. In this report, we examine the causes, which range from economic turmoil to inadequate resource distribution, along with the profound impacts on public health, such as increased length of hospital stays and compromised patient care. The paper also proposes a suite of solutions aimed at mitigating the immediate challenges and paving the way for a more resilient healthcare framework.

Utilization and Outcomes in U.S. ICU Hospitalizations

OBJECTIVES

Despite its importance, detailed national estimates of ICU utilization and outcomes remain lacking. We aimed to characterize trends in ICU utilization and outcomes over a recent 12-year period in the United States.

DESIGN/SETTING

In this longitudinal study, we examined hospitalizations involving ICU care ("ICU hospitalizations") alongside hospitalizations not involving ICU care ("non-ICU hospitalizations") among traditional Medicare beneficiaries using 100% Medicare part A claims data and commercial claims data for the under 65 adult population from 2008 to 2019.

PATIENTS/INTERVENTIONS

There were 18,313,637 ICU hospitalizations and 78,501,532 non-ICU hospitalizations in Medicare, and 1,989,222 ICU hospitalizations and 16,732,960 non-ICU hospitalizations in the commercially insured population.

MEASUREMENTS AND MAIN RESULTS

From 2008 to 2019, about 20% of Medicare hospitalizations and 10% of commercial hospitalizations involved ICU care. Among these ICU hospitalizations, length of stay and ICU length of stay decreased on average. Mortality and hospital readmissions on average also decreased, and they decreased more among ICU hospitalizations than among non-ICU hospitalizations, for both Medicare and commercially insured patients. Both Medicare and commercial populations experienced a growth in shorter ICU hospitalizations (between 2 and 7 d in length), which were characterized by shorter ICU stays and lower mortality. Among these short hospitalizations in the Medicare population, for common clinical diagnoses cared for in both ICU and non-ICU settings, patients were increasingly triaged into an ICU during the study period, despite being younger and having shorter hospital stays.

CONCLUSIONS

ICUs are used in a sizeable share of hospitalizations. From 2008 to 2019, ICU length of stay and mortality have declined, while short ICU hospitalizations have increased. In particular, for clinical conditions often managed both within and outside of an ICU, shorter ICU hospitalizations involving younger patients have increased. Our findings motivate opportunities to better understand ICU utilization and to improve the value of ICU care for patients and payers.

The Association of NICU Strain with Neonatal Mortality and Morbidity

Objective

To examine the association of admission NICU strain with neonatal mortality and morbidity.

Study Design

2008-2021 South Carolina cohort using linked vital statistics and discharge data of 22-44 weeks GA infants, born at hospitals with ≥ level 2 unit and ≥5 births of infants <34 weeks GA/year. The exposure was tertiles of admission NICU strain, defined as the sum of infants <44 weeks GA with a congenital anomaly plus all infants born <33 weeks GA at midnight on the day of birth. We used Poisson generalized linear mixed models to examine the association of exposure to strain with the primary outcome of a composite of mortality and term and preterm morbidities adjusting for patient and hospital characteristics.

Results

We studied 64,647 infants from 30 hospitals. High strain was associated with increased risk of mortality and morbidity adjusting for patient/hospital factors (aIRR 1.07, 95% CI 1.01 - 1.12).

Conclusion

NICU strain is associated with increased adverse outcomes.

In-Hospital Triage

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

The Capacity-to-Serve Model as a Data-Driven Process for Provider Capacity Management in Outpatient Community Mental Health

Despite increasing mental health provider supply shortages, research on capacity planning and management in the field of outpatient community mental healthcare is limited. There is an immediate need for strategies to plan and manage the capacity of existing mental healthcare providers to ensure a balance between demand and resources. To address this need, research on capacity planning and management in healthcare and mental healthcare settings is reviewed. Next, the Capacity-to-Serve Model is introduced and defined as a data-driven process for quantifying and reporting real-time standardized estimates of mental health provider availability based on qualifications, monitoring of outcome targets, and use of the Capacity-to-Serve Ratio and Realizing Capacity Measure. Finally, implications for using the model as an innovative solution for capacity management to meet demand in mental health are addressed. A case example is provided to demonstrate the application of the model. Ultimately, the Capacity-to-Serve Model can standardize capacity reporting of existing provider organizations and networks, both small and large, to support increased access to and supply of mental health services.

Measures and Impact of Caseload Surge During the COVID-19 Pandemic: A Systematic Review

OBJECTIVES

COVID-19 pandemic surges strained hospitals globally. We performed a systematic review to examine measures of pandemic caseload surge and its impact on mortality of hospitalized patients.

DATA SOURCES

PubMed, Embase, and Web of Science.

STUDY SELECTION

English-language studies published between December 1, 2019, and November 22, 2023, which reported the association between pandemic "surge"-related measures and mortality in hospitalized patients.

DATA EXTRACTION

Three authors independently screened studies, extracted data, and assessed individual study risk of bias. We assessed measures of surge qualitatively across included studies. Given multidomain heterogeneity, we semiquantitatively aggregated surge-mortality associations.

DATA SYNTHESIS

Of 17,831 citations, we included 39 studies, 17 of which specifically described surge effects in ICU settings. The majority of studies were from high-income countries ( n = 35 studies) and included patients with COVID-19 ( n = 31). There were 37 different surge metrics which were mapped into four broad themes, incorporating caseloads either directly as unadjusted counts ( n = 11), nested in occupancy ( n = 14), including additional factors (e.g., resource needs, speed of occupancy; n = 10), or using indirect proxies (e.g., altered staffing ratios, alternative care settings; n = 4). Notwithstanding metric heterogeneity, 32 of 39 studies (82%) reported detrimental adjusted odds/hazard ratio for caseload surge-mortality outcomes, reporting point estimates of up to four-fold increased risk of mortality. This signal persisted among study subgroups categorized by publication year, patient types, clinical settings, and country income status.

CONCLUSIONS

Pandemic caseload surge was associated with lower survival across most studies regardless of jurisdiction, timing, and population. Markedly variable surge strain measures precluded meta-analysis and findings have uncertain generalizability to lower-middle-income countries (LMICs). These findings underscore the need for establishing a consensus surge metric that is sensitive to capturing harms in everyday fluctuations and future pandemics and is scalable to LMICs.

Risk factors for converting traditional wards to temporary intensive care units during the COVID-19 pandemic: Insights from nurses' perspectives

BACKGROUND

The surge in critically ill COVID-19 patients caused a shortage of intensive care unit (ICU) beds. Some hospitals temporarily transformed general wards into ICUs to meet this pressing health care demand.

AIM

This study aims to evaluate and analyse the risk factors in temporary ICU from the perspective of nurses. By identifying these factors, the goal is to provide actionable insights and recommendations for effectively establishing and managing temporary ICUs in similar crisis scenarios in the future.

STUDY DESIGN

The study was conducted in China within a public hospital. Specifically, it focused on examining 62 nurses working in a temporary ICU that was converted from an infectious disease ward. The research utilized the Hazard Vulnerability Analysis (HVA) scoring method to identify potential threats, evaluate their probability, estimate their impact on specific organizations or regions and calculate the relative risk associated with such occurrences.

RESULTS

Staff demonstrated the highest risk percentage (32.74%), with Stuff (16.11%), Space (15.19%) and System (11.30%) following suit. The most critical risk factors included insufficient knowledge and decision-making competence in critical care (56.14%), lacking decision-making abilities and skills in renal replacement therapy care (55.37%), inadequate decision-making capacity and relevant skills in respiratory support care (50.64%), limited decision-making capability in circulatory support care (45.73%) and unfamiliarity with work procedures or systems (42.09%).

CONCLUSIONS

Urgent implementation of tailored training and support for temporary ICU nurses is paramount. Addressing capability and skill-related issues among these nurses supersedes resource availability, infrastructure, equipment and system considerations. Essential interventions must target challenges encompassing nurses' inability to perform critical treatment techniques autonomously and ensure standardized care. These measures are designed to heighten patient safety and elevate care quality during emergencies. These findings offer a viable avenue to mitigate potential moral distress, anxiety and depression among nurses, particularly those transitioning from non-critical care backgrounds. These nurses swiftly assimilate into temporary ICUs, and the study's insights offer practical guidance to alleviate their specific challenges.

RELEVANCE TO CLINICAL PRACTICE

The study on risk factors for converting traditional wards into temporary ICU during the COVID-19 pandemic, especially from the perspective of nurses, provides crucial insights into the challenges and requirements for effectively establishing and managing these emergency settings. The findings highlight several key areas of concern and opportunities for improvement directly related to clinical practice, particularly in situations where there is a rapid need to adapt to increased demands for critical care. By addressing the identified risk factors through enhanced training, support systems, resource management, process improvements and cultivating a culture of adaptability, not only can the quality of care in temporary ICUs be improved, but also can the health care system be better prepared for future emergencies. These actions will help mitigate the risks associated with such conversions, ultimately benefiting patient safety, staff well-being and the overall effectiveness of health care services in crises.

A quality improvement study on how a simulation model can help decision making on organization of ICU wards

BACKGROUND

Intensive Care Unit (ICU) capacity management is essential to provide high-quality healthcare for critically ill patients. Yet, consensus on the most favorable ICU design is lacking, especially whether ICUs should deliver dedicated or non-dedicated care. The decision for dedicated or non-dedicated ICU design considers a trade-off in the degree of specialization for individual patient care and efficient use of resources for society. We aim to share insights of a model simulating capacity effects for different ICU designs. Upon request, this simulation model is available for other ICUs.

METHODS

A discrete event simulation model was developed and used, to study the hypothetical performance of a large University Hospital ICU on occupancy, rejection, and rescheduling rates for a dedicated and non-dedicated ICU design in four different scenarios. These scenarios either simulate the base-case situation of the local ICU, varying bed capacity levels, potential effects of reduced length of stay for a dedicated design and unexpected increased inflow of unplanned patients.

RESULTS

The simulation model provided insights to foresee effects of capacity choices that should be made. The non-dedicated ICU design outperformed the dedicated ICU design in terms of efficient use of scarce resources.

CONCLUSIONS

The choice to use dedicated ICUs does not only affect the clinical outcome, but also rejection- rescheduling and occupancy rates. Our analysis of a large university hospital demonstrates how such a model can support decision making on ICU design, in conjunction with other operation characteristics such as staffing and quality management.

Rearranging the furniture: A blueprint for reappropriating fixed resources to create an emergency department resuscitative care unit

Emergency department (ED) care teams face challenges in providing timely, high-quality care to critically ill patients because of competing patient care priorities and a multitude of system strains, including patient boarding. Patients who are boarding in the ED experience increased morbidity and mortality, and this is particularly true for those who are critically ill. Geography-based models for critical care delivery in the ED range from resuscitation bays to full-fledged ED intensive care units. Studies have shown that such models can improve patient survival without affecting cost. Here, we describe how we reappropriated limited fixed resources to create a critical care resuscitation unit in a busy, urban, academic ED. Our objective is to provide a blueprint for similar models, paying particular attention to operations, clinical care, education, and financial stability.

Increasing severity of medical outcomes and associated substances in cases reported to United States poison centers

INTRODUCTION

Poison centers provide free expert recommendations on the treatment of a wide variety of toxicological emergencies. Prior studies have called attention to the increasing complexity of cases reported to poison centers. We aimed first, to evaluate the trends in medical outcome severity, over a 15-year period in both the adult and pediatric populations. Second, we described the most frequently reported substances associated with major effect or death.

METHODS

This is a retrospective review of exposures reported to the National Poison Data System from 1 January 2007 through 31 December 2021. All closed cases, for human exposures, reported during the study period were included. We assessed trends in frequencies and rates of medical outcomes and level of care received, among the adult (age greater than 19 years) and pediatric (age 19 years and younger) populations by reason for exposure.

RESULTS

During the study period, the number of adult unintentional exposures resulting in major effect (37.4 percent) and death (65.3 percent) increased. The number of adult intentional exposures resulting in death increased by 233.9 percent and those resulting in a major effect increased by 133.1 percent. The rates of exposures resulting in major effect and death increased among both intentional and unintentional adult exposures. The number of pediatric unintentional exposures resulting in a major effect increased by 76.6 percent and the number of pediatric intentional exposures resulting in death and major effect increased by 122.7 and 190.1 percent, respectively. Moderate, major effect, and death rates increased in pediatric unintentional exposures and moderate and major effect rates increased in pediatric intentional exposures.

CONCLUSIONS

We found a worsening severity of medical outcomes in adult and pediatric cases reported to poison centers. Poison centers are increasingly managing complex cases. Monitoring trends in which substances are associated with severe outcomes is imperative for future strategic prevention efforts.

Differences in the association between epidural analgesia and length of stay by surgery type: an observational study

INTRODUCTION

Despite a decline in the use of thoracic epidural analgesia related in part to concerns for delayed discharge, it is unknown whether changes in length of stay (LOS) associated with epidural analgesia vary by surgery type. Therefore, we determined the degree to which the association between epidural analgesia (vs no epidural) and LOS differed by surgery type.

METHODS

We conducted an observational study using data from 1747 patients who had either non-emergent open abdominal, thoracic, or vascular surgery at a single tertiary academic hospital. The primary outcome was hospital LOS and the incidence of a prolonged hospital LOS defined as 21 days or longer. Secondary endpoints included escalation of care, 30-day all-cause readmission, and reason for epidural not being placed. The association between epidural status and dichotomous endpoints was examined using logistic regression.

RESULTS

Among the 1747 patients, 85.7% (1499) received epidural analgesia. 78% (1364) underwent abdominal, 11.5% (200) thoracic, and 10.5% (183) vascular surgeries. After adjustment for differences, receiving epidural analgesia (vs no epidural) was associated with a 45% reduction in the likelihood of a prolonged LOS (p<0.05). This relationship varied by surgery type: abdominal (OR 0.42, 95% CI 0.23 to 0.79, p<0.001), vascular (OR 1.66, 95% CI 0.17 to 16.1, p=0.14), and thoracic (OR 1.07, 95% CI 0.20 to 5.70, p=0.93). Among abdominal surgical patients, epidural analgesia was associated with a median decrease in LOS by 1.4 days and a 37% reduction in the likelihood of 30-day readmission (adjusted OR 0.63, 0.41 to 0.97, p<0.05). Among thoracic surgical patients, epidural analgesia was associated with a median increase in LOS by 3.2 days.

CONCLUSIONS

The relationship between epidural analgesia and LOS appears to be different among different surgical populations.

Hospital surge capacity preparedness in disasters and emergencies: a systematic review

BACKGROUND

Adequate and effective emergency preparedness for hospital surge capacity is a prerequisite to ensuring standard healthcare services for disaster victims. This study aimed to identify, review, and synthesize the preparedness activities for and the barriers to hospital surge capacity in disasters and emergencies.

METHODS

We systematically searched seven databases (PubMed, MEDLINE, CINAHL, Scopus, Embase, Ovid, and PsycINFO). We included all English peer-reviewed studies published in January 2016 and July 2022 on surge capacity preparedness in hospital settings. Two independent researchers screened titles and abstracts, reviewed the full texts, and conducted data extractions using CADIMA software. We assessed the rigor of the included studies using the NIH quality assessment tools for quantitative studies, the Noyes et al. guidelines for qualitative studies, and the MMAT tool for mixed methods studies and summarized findings using the narrative synthesis method. We also used PRISMA reporting guidelines.

RESULTS

From the 2560 studies identified, we finally include 13 peer-reviewed studies: 10 quantitative, one qualitative, and two mixed methods. Five studies were done in the USA, three in Iran (n = 3), and the remaining in Australia, Pakistan, Sweden, Taiwan, and Tanzania. The study identified various ways to increase hospital surge capacity preparedness in all four domains (staff, stuff, space, and system); among them, the use of the Hospital Medical Surge Preparedness Index and the Surge Simulation Tool for surge planning was noteworthy. Moreover, nine studies (69%) recognized several barriers to hospital surge capacity preparedness.

CONCLUSION

The review provides synthesized evidence of contemporary literature on strategies for and barriers to hospital surge capacity preparedness. Despite the risk of selection bias due to the omission of gray literature, the study findings could help hospital authorities, public health workers, and policymakers to develop effective plans and programs for improving hospital surge capacity preparedness with actions, such as enhancing coordination, new or adapted flows of patients, disaster planning implementation, or the development of specific tools for surge capacity.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022360332.

Measuring the Impact of ICU Strain on Mortality, After-Hours Discharge, Discharge Delay, Interhospital Transfer, and Readmission in Australia With the Activity Index

OBJECTIVES

ICU resource strain leads to adverse patient outcomes. Simple, well-validated measures of ICU strain are lacking. Our objective was to assess whether the "Activity index," an indicator developed during the COVID-19 pandemic, was a valid measure of ICU strain.

DESIGN

Retrospective national registry-based cohort study.

SETTING

One hundred seventy-five public and private hospitals in Australia (June 2020 through March 2022).

SUBJECTS

Two hundred seventy-seven thousand seven hundred thirty-seven adult ICU patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Data from the Australian and New Zealand Intensive Care Society Adult Patient Database were matched to the Critical Health Resources Information System. The mean daily Activity index of each ICU (census total of "patients with 1:1 nursing" + "invasive ventilation" + "renal replacement" + "extracorporeal membrane oxygenation" + "active COVID-19," divided by total staffed ICU beds) during the patient's stay in the ICU was calculated. Patients were categorized as being in the ICU during very quiet (Activity index < 0.1), quiet (0.1 to < 0.6), intermediate (0.6 to < 1.1), busy (1.1 to < 1.6), or very busy time-periods (≥ 1.6). The primary outcome was in-hospital mortality. Secondary outcomes included after-hours discharge from the ICU, readmission to the ICU, interhospital transfer to another ICU, and delay in discharge from the ICU. Median Activity index was 0.87 (interquartile range, 0.40-1.24). Nineteen thousand one hundred seventy-seven patients died (6.9%). In-hospital mortality ranged from 2.4% during very quiet to 10.9% during very busy time-periods. After adjusting for confounders, being in an ICU during time-periods with higher Activity indices, was associated with an increased risk of in-hospital mortality (odds ratio [OR], 1.49; 99% CI, 1.38-1.60), after-hours discharge (OR, 1.27; 99% CI, 1.21-1.34), readmission (OR, 1.18; 99% CI, 1.09-1.28), interhospital transfer (OR, 1.92; 99% CI, 1.72-2.15), and less delay in ICU discharge (OR, 0.58; 99% CI, 0.55-0.62): findings consistent with ICU strain.

CONCLUSIONS

The Activity index is a simple and valid measure that identifies ICUs in which increasing strain leads to progressively worse patient outcomes.

Flow-Sizing Critical Care Resources

OBJECTIVES

To describe the factors affecting critical care capacity and how critical care organizations (CCOs) within academic centers in the U.S. flow-size critical care resources under normal operations, strain, and surge conditions.

DATA SOURCES

PubMed, federal agency and American Hospital Association reports, and previous CCO survey results were reviewed.

STUDY SELECTION

Studies and reports of critical care bed capacity and utilization within CCOs and in the United States were selected.

DATA EXTRACTION

The Academic Leaders in the Critical Care Medicine Task Force established regular conference calls to reach a consensus on the approach of CCOs to "flow-sizing" critical care services.

DATA SYNTHESIS

The approach of CCOs to "flow-sizing" critical care is outlined. The vertical (relation to institutional resources, e.g., space allocation, equipment, personnel redistribution) and horizontal (interdepartmental, e.g., emergency department, operating room, inpatient floors) integration of critical care delivery (ICUs, rapid response) for healthcare organizations and the methods by which CCOs flow-size critical care during normal operations, strain, and surge conditions are described. The advantages, barriers, and recommendations for the rapid and efficient scaling of critical care operations via a CCO structure are explained. Comprehensive guidance and resources for the development of "flow-sizing" capability by a CCO within a healthcare organization are provided.

CONCLUSIONS

We identified and summarized the fundamental principles affecting critical care capacity. The taskforce highlighted the advantages of the CCO governance model to achieve rapid and cost-effective "flow-sizing" of critical care services and provide recommendations and resources to facilitate this capability. The relevance of a comprehensive approach to "flow-sizing" has become particularly relevant in the wake of the latest COVID-19 pandemic. In light of the growing risks of another extreme epidemic, planning for adequate capacity to confront the next critical care crisis is urgent.

Patient and Family Experience With Discharge Directly Home From the Pediatric ICU

BACKGROUND

Children are increasingly discharged directly from the PICU. Transitions have been recognized as a period of increased patient and caregiver stress and risk of adverse events. No study has evaluated patient and caregiver outcomes after direct discharge from the PICU. This study aimed to explore the family's experiences with discharge directly home (DDH) from the PICU.

METHODS

This exploratory mixed-methods study was conducted in the PICU of the Institution is Sainte-Justine Hospital from February to July 2021. We included families of children expected to be DDH within 12 hours. Semistructured interviews were conducted at discharge, followed by telephone interviews 7 and 28 days post-PICU discharge. We measured comfort on a 5-point Likert scale and screened for anxiety using the Generalized Anxiety Disorder-7 tool.

RESULTS

Families of 25 patients were interviewed. Thematic analysis of the interviews revealed several themes, such as feeling stress and anxiety, feeling confident, anticipating home care, and needing support. These findings complemented the quantitative findings; the median comfort score was 4 (comfortable) (interquartile range 4-5) and 8 (interquartile range 4-12) for the Generalized Anxiety Disorder-7 on the day of discharge, with 16 reporting clinically significant anxiety. In the 28-day study period, 2 patients were readmitted and 6 had visited the emergency department.

CONCLUSIONS

Despite feelings of anxiety, many families felt comfortable with DDH from the PICU. Increasing our understanding of the patient and family experiences of discharge from the PICU will help to better support these patients and their families during transition.

Association of Time of Day with Delays in Antimicrobial Initiation among Ward Patients with Hospital-Onset Sepsis

Rationale: Although the mainstay of sepsis treatment is timely initiation of broad-spectrum antimicrobials, treatment delays are common, especially among patients who develop hospital-onset sepsis. The time of day has been associated with suboptimal clinical care in several contexts, but its association with treatment initiation among patients with hospital-onset sepsis is unknown. Objectives: Assess the association of time of day with antimicrobial initiation among ward patients with hospital-onset sepsis. Methods: This retrospective cohort study included ward patients who developed hospital-onset sepsis while admitted to five acute care hospitals in a single health system from July 2017 through December 2019. Hospital-onset sepsis was defined by the Centers for Disease Control and Prevention Adult Sepsis Event criteria. We estimated the association between the hour of day and antimicrobial initiation among patients with hospital-onset sepsis using a discrete-time time-to-event model, accounting for time elapsed from sepsis onset. In a secondary analysis, we fit a quantile regression model to estimate the association between the hour of day of sepsis onset and time to antimicrobial initiation. Results: Among 1,672 patients with hospital-onset sepsis, the probability of antimicrobial initiation at any given hour varied nearly fivefold throughout the day, ranging from 3.0% (95% confidence interval [CI], 1.8-4.1%) at 7 a.m. to 13.9% (95% CI, 11.3-16.5%) at 6 p.m., with nadirs at 7 a.m. and 7 p.m. and progressive decline throughout the night shift (13.4% [95% CI, 10.7-16.0%] at 9 p.m. to 3.2% [95% CI, 2.0-4.0] at 6 a.m.). The standardized predicted median time to antimicrobial initiation was 3.2 hours (interquartile range [IQR], 2.5-3.8 h) for sepsis onset during the day shift (7 a.m.-7 p.m.) and 12.9 hours (IQR, 10.9-14.9 h) during the night shift (7 p.m.-7 a.m.). Conclusions: The probability of antimicrobial initiation among patients with new hospital-onset sepsis declined at shift changes and overnight. Time to antimicrobial initiation for patients with sepsis onset overnight was four times longer than for patients with onset during the day. These findings indicate that time of day is associated with important care processes for ward patients with hospital-onset sepsis. Future work should validate these findings in other settings and elucidate underlying mechanisms to inform quality-enhancing interventions.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

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.

Perceived Hospital Stress, Severe Acute Respiratory Syndrome Coronavirus 2 Activity, and Care Process Temporal Variance During the COVID-19 Pandemic

OBJECTIVES

The COVID-19 pandemic threatened standard hospital operations. We sought to understand how this stress was perceived and manifested within individual hospitals and in relation to local viral activity.

DESIGN

Prospective weekly hospital stress survey, November 2020-June 2022.

SETTING

Society of Critical Care Medicine's Discovery Severe Acute Respiratory Infection-Preparedness multicenter cohort study.

SUBJECTS

Thirteen hospitals across seven U.S. health systems.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We analyzed 839 hospital-weeks of data over 85 pandemic weeks and five viral surges. Perceived overall hospital, ICU, and emergency department (ED) stress due to severe acute respiratory infection patients during the pandemic were reported by a mean of 43% ( sd , 36%), 32% (30%), and 14% (22%) of hospitals per week, respectively, and perceived care deviations in a mean of 36% (33%). Overall hospital stress was highly correlated with ICU stress (ρ = 0.82; p < 0.0001) but only moderately correlated with ED stress (ρ = 0.52; p < 0.0001). A county increase in 10 severe acute respiratory syndrome coronavirus 2 cases per 100,000 residents was associated with an increase in the odds of overall hospital, ICU, and ED stress by 9% (95% CI, 5-12%), 7% (3-10%), and 4% (2-6%), respectively. During the Delta variant surge, overall hospital stress persisted for a median of 11.5 weeks (interquartile range, 9-14 wk) after local case peak. ICU stress had a similar pattern of resolution (median 11 wk [6-14 wk] after local case peak; p = 0.59) while the resolution of ED stress (median 6 wk [5-6 wk] after local case peak; p = 0.003) was earlier. There was a similar but attenuated pattern during the Omicron BA.1 subvariant surge.

CONCLUSIONS

During the COVID-19 pandemic, perceived care deviations were common and potentially avoidable patient harm was rare. Perceived hospital stress persisted for weeks after surges peaked.

Effect of tracheostomy timing in pediatric patients with traumatic brain injury

BACKGROUND

Traumatic brain injury (TBI) is a prevalent cause of disability and death in the pediatric population, often requiring prolonged mechanical ventilation. Patients with significant TBI or intracranial hemorrhage require advanced airway management to protect against aspiration, hypoxia, and hypercarbia, eventually necessitating tracheostomy. While tracheostomy is much less common in children compared to adults, its prevalence among pediatric populations has been steadily increasing. Although early tracheostomy has demonstrated improved outcomes in adult patients, optimal tracheostomy timing in the pediatric population with TBI remains to be definitively established.

OBJECTIVE

This retrospective cohort analysis aims to evaluate pediatric TBI patients who undergo tracheostomy and to investigate the impact of tracheostomy timing on outcomes.

DESIGN/METHODS

The Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID), collected between in 2016 and 2019, was queried using International Classification of Disease 10th edition (ICD10) codes for patients with traumatic brain injury who had received a tracheostomy. Baseline demographics, insurance status, and procedural day data were analyzed with univariate and multivariate regression analyses. Propensity score matching was performed to estimate the incidence of medical complications and mortality related to early versus late tracheostomy timing (as defined by median = 9 days).

RESULTS

Of the 68,793 patients (mean age = 14, IQR 4-18) who suffered a TBI, 1,956 (2.8%) received a tracheostomy during their hospital stay. TBI patients who were tracheostomized were older (mean age = 16.5 vs 11.4 years), more likely to have injuries classified as severe TBIs and more likely to have accumulated more than one indicator of parenchymal injury as measured by the Composite Stroke Severity Scale (CSSS >1) than non-tracheostomized TBI patients. TBI patients with a tracheostomy were more likely to encounter serious complications such as sepsis, acute kidney injury (AKI), meningitis, or acute respiratory distress syndrome (ARDS). They were also more likely to necessitate an external ventricular drain (EVD) or decompressive hemicraniectomy (DHC) than TBI patients without a tracheostomy. Tracheostomy was also negatively associated with routine discharge. Procedural timing was assessed in 1,867 patients; older children (age >15 years) were more likely to undergo earlier placements (p < 0.001). Propensity score matching (PSM) comparing early versus late placement was completed by controlling for age, gender, and TBI severity. Those who were subjected to late tracheostomy (>9 days) were more likely to face complications such as AKI or deep vein thrombosis (DVT) as well as a host of respiratory conditions such as pulmonary embolism, aspiration pneumonitis, pneumonia, or ARDS. While the timing did not significantly impact mortality across the PSM cohorts, late tracheostomy was associated with increased length of stay (LOS) and ventilator dependence.

CONCLUSIONS

Tracheostomy, while necessary for some patients who have sustained a TBI, is itself associated with several risks that should be assessed in context of each individual patient's overall condition. Additionally, the timing of the intervention may significantly impact the trajectory of the patient's recovery. Early intervention may reduce the incidence of serious complications as well as length of stay and dependence on a ventilator and facilitate a timelier recovery.

Strain on the ICU resources and patient outcomes in the COVID-19 pandemic: A Swedish national registry cohort study

BACKGROUND

The Coronavirus 2019 (COVID-19) pandemic has led to an unprecedented strain on the ICU resources. It is not known how the ICU resources employed in treating COVID-19 patients are related to inpatient characteristics, use of organ support or mortality.

OBJECTIVES

To investigate how the use of ICU resources relate to use of organ support and mortality in COVID-19 patients.

DESIGN

A national register-based cohort study.

SETTING

All Swedish ICUs from March 2020 to November 2021.

PATIENTS

All patients admitted to Swedish ICUs with a primary diagnosis of COVID-19 reported to the national Swedish Intensive Care Register (SIR).

MAIN OUTCOME MEASURES

Organ support (mechanical ventilation, noninvasive ventilation, high-flow oxygen therapy, prone positioning, surgical and percutaneous tracheostomy, central venous catheterisation, continuous renal replacement therapy and intermittent haemodialysis), discharge at night, re-admission, transfer and ICU and 30-day mortality.

RESULTS

Seven thousand nine hundred and sixty-nine patients had a median age of 63 years, and 70% were men. Median daily census was 167% of habitual census, daily new admissions were 20% of habitual census and the median occupancy was 82%. Census and new admissions were associated with mechanical ventilation, OR 1.37 (95% CI 1.28 to 1.48) and OR 1.44 (95% CI 1.13 to 1.84), respectively, but negatively associated with noninvasive ventilation, OR 0.83 (95% CI 0.77 to 0.89) and OR 0.40 (95% CI 0.30 to 52) and high-flow oxygen therapy, OR 0.72 (95% CI 0.67 to 0.77) and OR 0.77 (95% CI 0.61 to 0.97). Occupancy above 90% of available beds was not associated with mechanical ventilation or noninvasive ventilation, but with high-flow oxygen therapy, OR 1.36 (95% CI 1.21 to 1.53). All measures of pressure on resources were associated with transfer to other hospitals, but none were associated with discharge at night, ICU mortality or 30-day mortality.

CONCLUSIONS

Pressure on ICU resources was associated with more invasive respiratory support, indicating that during these times, ICU resources were reserved for sicker patients.

Discharge Directly Home From the PICU: A Retrospective Cohort Study

OBJECTIVE

Healthcare constraints with decreasing bed availability cause strain in acute care units, and patients are more frequently being discharged directly home. Our objective was to describe the population, predictors, and explore PICU readmission rates of patients discharged directly home from PICU, compared with those discharge to the hospital ward, then home.

DESIGN

An observational cohort study.

SETTING

Children admitted to the PICU of CHU Sainte-Justine, between January 2014 and 2020.

PATIENTS

Patients less than 18 years old, who survived their PICU stay, and were discharged directly home or to an inpatient ward. Patients discharged directly home were compared with patients discharged to the ward using descriptive statistics. Logistic regression was used to identify factors associated with home discharge. Propensity scores were used to compare PICU readmission rates in patients discharged directly home to those discharged to the ward.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Among the 5,531 admissions included, 594 (10.7%) were discharged directly home from the PICU. Patients who were more severe ill (odds ratio [OR], 0.93; 95% CI, 0.90-0.97), had invasive ventilation (OR, 0.70; 95% CI, 0.53-0.92), or had vasoactive agents (OR, 0.70; 95% CI, 0.53-0.92) were less likely to be discharged directly home. Diagnoses associated with discharge directly home were acute intoxication, postoperative ear-nose-throat care, and shock states. There was no difference in the rate of readmission to PICU at 2 (relative risk [RR], 0.20 [95% CI, 0.02-1.71]) and 28 days (RR, 1.20 [95% CI, 0.61-3.36]) between propensity matched patients discharged to the ward for 2 or less days, compared with those discharged directly home.

CONCLUSION

Discharge directly home from the PICU is increasing locally. The population includes less severely ill patients with rapidly resolving diagnoses. Rates of PICU readmission between patients discharged directly home from the PICU versus to ward are similar, but safety of the practice requires ongoing evaluation.

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.

The association of strained ICU capacity with hospital patient racial and ethnic composition and federal relief during the COVID-19 pandemic

OBJECTIVE

To identify the association between strained intensive care unit (ICU) capacity during the COVID-19 pandemic and hospital racial and ethnic patient composition, federal pandemic relief, and other hospital characteristics.

DATA SOURCES

We used government data on hospital capacity during the pandemic and Provider Relief Fund (PRF) allocations, Medicare claims and enrollment data, hospital cost reports, and Social Vulnerability Index data.

STUDY DESIGN

We conducted cross-sectional bivariate analyses relating strained capacity and PRF award per hospital bed with hospital patient composition and other characteristics, with and without adjustment for hospital referral region (HRR).

DATA COLLECTION

We linked PRF data to CMS Certification Numbers based on hospital name and location. We used measures of racial and ethnic composition generated from Medicare claims and enrollment data. Our sample period includes the weeks of September 18, 2020 through November 5, 2021, and we restricted our analysis to short-term, general hospitals with at least one intensive care unit (ICU) bed. We defined "ICU strain share" as the proportion of ICU days occurring while a given hospital had an ICU occupancy rate ≥ 90%.

PRINCIPAL FINDINGS

After adjusting for HRR, hospitals in the top tercile of Black patient shares had higher ICU strain shares than did hospitals in the bottom tercile (30% vs. 22%, p < 0.05) and received greater PRF amounts per bed ($118,864 vs. $92,407, p < 0.05). Having high versus low ICU occupancy relative to pre-pandemic capacity was associated with a modest increase in PRF amounts per bed after adjusting for HRR ($107,319 vs. $96,627, p < 0.05), but there were no statistically significant differences when comparing hospitals with high versus low ICU occupancy relative to contemporaneous capacity.

CONCLUSIONS

Hospitals with large Black patient shares experienced greater strain during the pandemic. Although these hospitals received more federal relief, funding was not targeted overall toward hospitals with high ICU occupancy rates.

Impact of ICU strain on outcomes

PURPOSE OF REVIEW

Acute surge events result in health capacity strain, which can result in deviations from normal care, activation of contingencies and decisions related to resource allocation. This review discusses the impact of health capacity strain on patient centered outcomes.

RECENT FINDINGS

This manuscript discusses the lack of validated metrics for ICU strain capacity and a need for understanding the complex interrelationships of strain with patient outcomes. Recent work through the coronavirus disease 2019 pandemic has shown that acute surge events are associated with significant increase in hospital mortality. Though causal data on the differential impact of surge actions and resource availability on patient outcomes remains limited the overall signal consistently highlights the link between ICU strain and critical care outcomes in both normal and surge conditions.

SUMMARY

An understanding of ICU strain is fundamental to the appropriate clinical care for critically ill patients. Accounting for stain on outcomes in critically ill patients allows for minimization of variation in care and an ability of a given healthcare system to provide equitable, and quality care even in surge scenarios.

ICU Admission Tool for Congenital Heart Catheterization (iCATCH): A Predictive Model for High Level Post-Catheterization Care and Patient Management

OBJECTIVES

Currently, there are no prediction tools available to identify patients at risk of needing high-complexity care following cardiac catheterization for congenital heart disease. We sought to develop a method to predict the likelihood a patient will require intensive care level resources following elective cardiac catheterization.

DESIGN

Prospective single-center study capturing important patient and procedural characteristics for predicting discharge to the ICU. Characteristics significant at the 0.10 level in the derivation dataset (July 1, 2017 to December 31, 2019) were considered for inclusion in the final multivariable logistic regression model. The model was validated in the testing dataset (January 1, 2020 to December 31, 2020). The novel pre-procedure cardiac status (PCS) feature, collection started in January 2019, was assessed separately in the final model using the 2019 through 2020 dataset.

SETTING

Tertiary pediatric heart center.

PATIENTS

All elective cases coming from home or non-ICU who underwent a cardiac catheterization from July 2017 to December 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A total of 2,192 cases were recorded in the derivation dataset, of which 11% of patients ( n = 245) were admitted to the ICU, while 64% ( n = 1,413) were admitted to a medical unit and 24% ( n = 534) were discharged home. In multivariable analysis, the following predictors were identified: 1) weight less than 5 kg and 5-9.9 kg, 2) presence of systemic illness, 3) recent cardiac intervention less than 90 days, and 4) ICU Admission Tool for Congenital Heart Catheterization case type risk categories (1-5), with C -statistics of 0.79 and 0.76 in the derivation and testing cohorts, respectively. The addition of the PCS feature fit into the final model resulted in a C -statistic of 0.79.

CONCLUSIONS

The creation of a validated pre-procedural risk prediction model for ICU admission following congenital cardiac catheterization using a large volume, single-center, academic institution will improve resource allocation and prediction of capacity needs for this complex patient population.

SWEAT ICU-An Observational Study of Physician Workload and the Association of Physician Outcomes in Academic ICUs

The optimal staffing model for physicians in the ICU is unknown. Patient-to-intensivist ratios may offer a simple measure of workload and be associated with patient mortality and physician burnout. To evaluate the association of physician workload, as measured by the patient-to-intensivist ratio, with physician burnout and patient mortality.

DESIGN

Cross-sectional observational study.

SETTING

Fourteen academic centers in the United States from August 2020 to July 2021.

SUBJECTS

We enrolled ICU physicians and collected data on adult ICU patients under the physician's care on the single physician-selected study day for each physician.

MEASUREMENTS and MAIN RESULTS

The primary exposure was workload (self-reported number of patients' physician was responsible for) modeled as high (>14 patients) and low (≤14 patients). The primary outcome was burnout, measured by the Well-Being Index. The secondary outcome measure was 28-day patient mortality. We calculated odds ratio for burnout and patient outcomes using a multivariable logistic regression model and a binomial mixed effects model, respectively. We enrolled 122 physicians from 62 ICUs. The median patient-to-intensivist ratio was 12 (interquartile range, 10-14), and the overall prevalence of burnout was 26.4% (n = 32). Intensivist workload was not independently associated with burnout (adjusted odds ratio, 0.74; 95% CI, 0.24-2.23). Of 1,322 patients, 679 (52%) were discharged alive from the hospital, 257 (19%) remained hospitalized, and 347 (26%) were deceased by day 28; 28-day outcomes were unknown for 39 of patients (3%). Intensivist workload was not independently associated with 28-day patient mortality (adjusted odds ratio, 1.33; 95% CI, 0.92-1.91).

CONCLUSIONS

In our cohort, approximately one in four physicians experienced burnout on the study day. There was no relationship be- tween workload as measured by patient-to-intensivist ratio and burnout. Factors other than the number of patients may be important drivers of burnout among ICU physicians.

Cost-effectiveness of an Emergency Department-Based Intensive Care Unit

IMPORTANCE

Value in health care is quality per unit cost (V = Q/C), and an emergency department-based intensive care unit (ED-ICU) model has been associated with improved quality. To assess the value of this care delivery model, it is essential to determine the incremental direct cost of care.

OBJECTIVE

To determine the association of an ED-ICU with inflation-adjusted change in mean direct cost of care, net revenue, and direct margin per ED patient encounter.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective economic analysis evaluated the cost of care delivery to patients in the ED before and after deployment of the Joyce and Don Massey Family Foundation Emergency Critical Care Center, an ED-ICU, on February 16, 2015, at a large academic medical center in the US with approximately 75 000 adult ED visits per year. The pre-ED-ICU cohort was defined as all documented ED visits by patients 18 years or older with a complete financial record from September 8, 2012, through June 30, 2014 (660 days); the post-ED-ICU cohort, all visits from July 1, 2015, through April 21, 2017 (660 days). Fiscal year 2015 was excluded from analysis to phase in the new care model. Statistical analysis was performed March 1 through December 30, 2021.

EXPOSURES

Implementation of an ED-ICU.

MAIN OUTCOMES AND MEASURES

Inflation-adjusted direct cost of care, net revenue, and direct margin per patient encounter in the ED.

RESULTS

A total of 234 884 ED visits during the study period were analyzed, with 115 052 patients (54.7% women) in the pre-ED-ICU cohort and 119 832 patients (54.5% women) in the post-ED-ICU cohort. The post-ED-ICU cohort was older (mean [SD] age, 49.1 [19.9] vs 47.8 [19.6] years; P < .001), required more intensive respiratory support (2.2% vs 1.1%; P < .001) and more vasopressor use (0.5% vs 0.2%; P < .001), and had a higher overall case mix index (mean [SD], 1.7 [2.0] vs 1.5 [1.7]; P < .001). Implementation of the ED-ICU was associated with similar inflation-adjusted total direct cost per ED encounter (pre-ED-ICU, mean [SD], $4875 [$15 175]; post-ED-ICU, $4877 [$17 400]; P = .98). Inflation-adjusted net revenue per encounter increased by 7.0% (95% CI, 3.4%-10.6%; P < .001), and inflation-adjusted direct margin per encounter increased by 46.6% (95% CI, 32.1%-61.2%; P < .001).

CONCLUSIONS AND RELEVANCE

Implementation of an ED-ICU was associated with no significant change in inflation-adjusted total direct cost per ED encounter. Holding delivery costs constant while improving quality demonstrates improved value via the ED-ICU model of care.

Association of Unit Census with Delays in Antimicrobial Initiation among Ward Patients with Hospital-acquired Sepsis

Rationale: Patients with hospital-acquired sepsis (HAS) experience higher mortality and delayed care compared with those with community-acquired sepsis. Capacity strain, the extent to which demand for hospital resources exceeds availability, thus impacting patient care, is a possible mechanism underlying antimicrobial delays for HAS but has not been studied. Objectives: Assess the association of ward census with the timing of antimicrobial initiation among ward patients with HAS. Methods: This retrospective cohort study included adult patients hospitalized at five acute care hospitals between July 2017 and December 2019 who developed ward-onset HAS, distinguished from community-acquired sepsis by onset after 48 hours of hospitalization. The primary exposure was ward census, measured as the number of patients present in each ward at each hour, standardized by quarter and year. The primary outcome was time from sepsis onset to antimicrobial initiation. We used quantile regression to assess the association between ward census at sepsis onset and time to antimicrobial initiation among patients with HAS defined by Centers for Disease Control and Prevention Adult Sepsis Event criteria. We adjusted for hospital, year, quarter, age, sex, race, ethnicity, severity of illness, admission diagnosis, and service type. Results: A total of 1,672 hospitalizations included at least one ward-onset HAS episode. Median time to antimicrobial initiation after HAS onset was 4.1 hours (interquartile range, 0.4-22.3). Marginal adjusted time to antimicrobial initiation ranged from 3.6 hours (95% confidence interval [CI], 2.4-4.8 h) to 6.8 hours (95% CI, 5.3-8.4 h) at census levels 2 standard deviations (SDs) below and above the ward-specific mean, respectively. Each 1-SD increase in ward census at sepsis onset, representing a median of 2.4 patients, was associated with an increase in time to antimicrobial initiation of 0.80 hours (95% CI, 0.32-1.29 h). In sensitivity analyses, results were consistent across severity of illness and electronic health record-based sepsis definitions. Conclusions: Time to antimicrobial initiation increased with increasing census among ward patients with HAS. These findings suggest that delays in care for HAS may be related to ward capacity strain as measured by census. Additional work is needed to validate these findings and identify potential mechanisms operating through clinician behavior and care delivery processes.

Does high workload reduce the quality of healthcare? Evidence from rural Senegal

There is a widely held perception that staff shortages in low and middle-income countries (LMICs) lead to excessive workloads, which in turn worsen the quality of healthcare. Yet there is little evidence supporting these claims. We use data from standardised patient visits in Senegal and determine the effect of workload on the quality of primary care by exploiting quasi-random variation in workload. We find that despite a lack of staff, average levels of workload are low. Even at times when workload is high, there is no evidence that provider effort or quality of care are significantly reduced. Our data indicate that providers operate below their production possibility frontier and have sufficient capacity to attend more patients without compromising quality. This contradicts the prevailing discourse that staff shortages are a key reason for poor quality primary care in LMICs and suggests that the origins likely lie elsewhere.

Does Unprecedented ICU Capacity Strain, As Experienced During the COVID-19 Pandemic, Impact Patient Outcome?

OBJECTIVES

To determine whether patients admitted to an ICU during times of unprecedented ICU capacity strain, during the COVID-19 pandemic in the United Kingdom, experienced a higher risk of death.

DESIGN

Multicenter, observational cohort study using routine clinical audit data.

SETTING

Adult general ICUs participating the Intensive Care National Audit & Research Centre Case Mix Programme in England, Wales, and Northern Ireland.

PATIENTS

One-hundred thirty-thousand six-hundred eighty-nine patients admitted to 210 adult general ICUs in 207 hospitals.

INTERVENTIONS

Multilevel, mixed effects, logistic regression models were used to examine the relationship between levels of ICU capacity strain on the day of admission (typical low, typical, typical high, pandemic high, and pandemic extreme) and risk-adjusted hospital mortality.

MEASUREMENTS AND MAIN RESULTS

In adjusted analyses, compared with patients admitted during periods of typical ICU capacity strain, we found that COVID-19 patients admitted during periods of pandemic high or pandemic extreme ICU capacity strain during the first wave had no difference in hospital mortality, whereas those admitted during the pandemic high or pandemic extreme ICU capacity strain in the second wave had a 17% (odds ratio [OR], 1.17; 95% CI, 1.05-1.30) and 15% (OR, 1.15; 95% CI, 1.00-1.31) higher odds of hospital mortality, respectively. For non-COVID-19 patients, there was little difference in trend between waves, with those admitted during periods of pandemic high and pandemic extreme ICU capacity strain having 16% (OR, 1.16; 95% CI, 1.08-1.25) and 30% (OR, 1.30; 95% CI, 1.14-1.48) higher overall odds of acute hospital mortality, respectively.

CONCLUSIONS

For patients admitted to ICU during the pandemic, unprecedented levels of ICU capacity strain were significantly associated with higher acute hospital mortality, after accounting for differences in baseline characteristics. Further study into possible differences in the provision of care and outcome for COVID-19 and non-COVID-19 patients is needed.

Association of patient-to-intensivist ratio with hospital mortality in Australia and New Zealand

Purpose

The impact of intensivist workload on intensive care unit (ICU) outcomes is incompletely described and assessed across healthcare systems and countries. We sought to examine the association of patient-to-intensivist ratio (PIR) with hospital mortality in Australia/New Zealand (ANZ) ICUs.

Methods

We conducted a retrospective study of adult admissions to ANZ ICUs (August 2016–June 2018) using two cohorts: “narrow”, based on previously used criteria including restriction to ICUs with a single daytime intensivist; and “broad”, refined by individual ICU daytime staffing information. The exposure was average daily PIR and the outcome was hospital mortality. We used summary statistics to describe both cohorts and multilevel multivariable logistic regression models to assess the association of PIR with mortality. In each, PIR was modeled using restricted cubic splines to allow for non-linear associations. The broad cohort model included non-PIR physician and non-physician staffing covariables.

Results

The narrow cohort of 27,380 patients across 67 ICUs (predicted mortality: median 1.2% [IQR 0.4–1.4%]; mean 5.9% [sd 13.2%]) had a median PIR of 10.1 (IQR 7–14). The broad cohort of 91,206 patients across 73 ICUs (predicted mortality: 1.9% [0.6–6.5%]; 7.6% [14.9%]) had a median PIR of 7.8 (IQR 5.8–10.2). We found no association of PIR with mortality in either the narrow (PIR 1st spline term odds ratio [95% CI]: 1 [0.94, 1.06], Wald testing of spline terms p = 0.61) or the broad (1.02 [0.97, 1.07], p = 0.4) cohort.

Conclusion

We found no association of PIR with hospital mortality across ANZ ICUs. The low cohort predicted mortality may limit external validity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-021-06575-z.

Assessment of a novel marker of ICU strain, the ICU Activity Index, during the COVID-19 pandemic in Victoria, Australia

Objectives: To validate a real-time Intensive Care Unit (ICU) Activity Index as a marker of ICU strain from daily data available from the Critical Health Resource Information System (CHRIS), and to investigate the association between this Index and the need to transfer critically ill patients during the coronavirus disease 2019 (COVID-19) pandemic in Victoria, Australia. Design: Retrospective observational cohort study. Setting: All 45 hospitals with an ICU in Victoria, Australia. Participants: Patients in all Victorian ICUs and all critically ill patients transferred between Victorian hospitals from 27 June to 6 September 2020. Main outcome measure: Acute interhospital transfer of one or more critically ill patients per day from one site to an ICU in another hospital. Results: 150 patients were transported over 61 days from 29 hospitals (64%). ICU Activity Index scores were higher on days when critical care transfers occurred (median, 1.0 [IQR, 0.4-1.7] v 0.6 [IQR, 0.3-1.2]; P < 0.001). Transfers were more common on days of higher ICU occupancy, higher numbers of ventilated or COVID-19 patients, and when more critical care staff were unavailable. The highest ICU Activity Index scores were observed at hospitals in north-western Melbourne, where the COVID-19 disease burden was greatest. After adjusting for confounding factors, including occupancy and lack of available ICU staff, a rising ICU Activity Index score was associated with an increased risk of a critical care transfer (odds ratio, 4.10; 95% CI, 2.34-7.18; P < 0.001). Conclusions: The ICU Activity Index appeared to be a valid marker of ICU strain during the COVID-19 pandemic. It may be useful as a real-time clinical indicator of ICU activity and predict the need for redistribution of critical ill patients.

Interventions to Reduce Hospital Length of Stay in High-risk Populations: A Systematic Review

IMPORTANCE

Many strategies to reduce hospital length of stay (LOS) have been implemented, but few studies have evaluated hospital-led interventions focused on high-risk populations. The Agency for Healthcare Research and Quality (AHRQ) Learning Health System panel commissioned this study to further evaluate system-level interventions for LOS reduction.

OBJECTIVE

To identify and synthesize evidence regarding potential systems-level strategies to reduce LOS for patients at high risk for prolonged LOS.

EVIDENCE REVIEW

Multiple databases, including MEDLINE and Embase, were searched for English-language systematic reviews from January 1, 2010, through September 30, 2020, with updated searches through January 19, 2021. The scope of the protocol was determined with input from AHRQ Key Informants. Systematic reviews were included if they reported on hospital-led interventions intended to decrease LOS for high-risk populations, defined as those with high-risk medical conditions or socioeconomically vulnerable populations (eg, patients with high levels of socioeconomic risk, who are medically uninsured or underinsured, with limited English proficiency, or who are hospitalized at a safety-net, tertiary, or quaternary care institution). Exclusion criteria included interventions that were conducted outside of the hospital setting, including community health programs. Data extraction was conducted independently, with extraction of strength of evidence (SOE) ratings provided by systematic reviews; if unavailable, SOE was assessed using the AHRQ Evidence-Based Practice Center methods guide.

FINDINGS

Our searches yielded 4432 potential studies. We included 19 systematic reviews reported in 20 articles. The reviews described 8 strategies for reducing LOS in high-risk populations: discharge planning, geriatric assessment, medication management, clinical pathways, interdisciplinary or multidisciplinary care, case management, hospitalist services, and telehealth. Interventions were most frequently designed for older patients, often those who were frail (9 studies), or patients with heart failure. There were notable evidence gaps, as there were no systematic reviews studying interventions for patients with socioeconomic risk. For patients with medically complex conditions, discharge planning, medication management, and interdisciplinary care teams were associated with inconsistent outcomes (LOS, readmissions, mortality) across populations. For patients with heart failure, clinical pathways and case management were associated with reduced length of stay (clinical pathways: mean difference reduction, 1.89 [95% CI, 1.33 to 2.44] days; case management: mean difference reduction, 1.28 [95% CI, 0.52 to 2.04] days).

CONCLUSIONS AND RELEVANCE

This systematic review found inconsistent results across all high-risk populations on the effectiveness associated with interventions, such as discharge planning, that are often widely used by health systems. This systematic review highlights important evidence gaps, such as the lack of existing systematic reviews focused on patients with socioeconomic risk factors, and the need for further research.

Association of a Novel Index of Hospital Capacity Strain with Admission to Intensive Care Units

Rationale: Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations.Objectives: We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes.Methods: Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression.Results: Among high-acuity patients with sepsis (n = 90,150) and acute respiratory failure (ARF; n = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%] at the highest strain index decile; P < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all P ≥ 0.13). Strain was also not meaningfully associated with patient characteristics.Conclusions: Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.

A mathematical model for predicting length of postoperative intensive care requirement following cardiac surgery in an Indian hospital

Intensive care unit (ICU) is a critical resource in a hospital, especially in developing countries such as India. The length of ICU stay after a cardiac surgery is an important variable for effective use of this critical resource. In this context, a predictive model can help a hospital to make optimum use of its ICU occupancy. A study was thus conducted on ICU patients and data gather over a 1-year period in a hospital in India. The critical factors for prolonged ICU stay (more than 72 h) were identified using univariate and multivariate logistic regression and a predictive index was built based on model development set. The predictive index was tested on a validation set and the mean length of ICU stay appeared to increase with an increase in the risk score. In addition, the risk score was tested in case of mortality. Efficient use of the ICU facility is possible with the help of this predictive index.

Memory is key in capturing COVID-19 epidemiological dynamics

SARS-CoV-2 virus has spread over the world rapidly creating one of the largest pandemics ever. The absence of immunity, presymptomatic transmission, and the relatively high level of virulence of the COVID-19 infection led to a massive flow of patients in intensive care units (ICU). This unprecedented situation calls for rapid and accurate mathematical models to best inform public health policies. We develop an original parsimonious discrete-time model that accounts for the effect of the age of infection on the natural history of the disease. Analysing the ongoing COVID-19 in France as a test case, through the publicly available time series of nationwide hospital mortality and ICU activity, we estimate the value of the key epidemiological parameters and the impact of lock-down implementation delay. This work shows that including memory-effects in the modelling of COVID-19 spreading greatly improves the accuracy of the fit to the epidemiological data. We estimate that the epidemic wave in France started on Jan 20 [Jan 12, Jan 28] (95% likelihood interval) with a reproduction number initially equal to 2.99 [2.59, 3.39], which was reduced by the national lock-down started on Mar 17 to 24 [21, 27] of its value. We also estimate that the implementation of the latter a week earlier or later would have lead to a difference of about respectively -13k and +50k hospital deaths by the end of lock-down. The present parsimonious discrete-time framework constitutes a useful tool for now- and forecasting simultaneously community incidence and ICU capacity strain.

Effect of an emergency department intensive care unit on medical intensive unit admissions and care: A retrospective cohort study

OBJECTIVE

Evaluate the impact of an emergency critical care center (EC3) on the admissions of critically ill patients to a critical care medicine unit (CCMU) and their outcomes.

METHODS

This was a retrospective before/after cohort study in a tertiary university teaching hospital. To improve the care of critically ill patients in the emergency department (ED), a 9-bed EC3 was opened in the ED in February 2015. All critically ill patients in the emergency department must receive intensive support in EC3 before being considered for admission to the CCMU for further treatment. Patients from the emergency department account for a significant proportion of the patients admitted to the CCMU. The proportions of patients admitted to the CCMU from the ED were analyzed 1 year before and 1 year after the opening of the EC3. We also compared the admission data, demographic data, APACHE III scores and patient outcomes among patients admitted from ED to the CCMU in the year before and the year after the opening of the EC3.

RESULT

The establishment of the EC3 was associated with a decreased proportion of patients admitted to the CCMU from the ED (OR 0.73 95% CI 0.63-0.84, p < 0.01), a decrease in the proportion of patients with sepsis admitted from the ED (OR 0.68, 95% CI, 0.54-0.87, p < 0.01) and a decrease in the proportion of patients with gastrointestinal bleeding admitted from the ED (OR 0.49, 95% CI 0.28-0.84, p < 0.05). Following the establishment of the EC3, patients admitted to the CCMU had a higher APACHE III score in 2015 (74.85 ± 30.42 vs 72.39 ± 29.64, p = 0.015). Fewer low-risk patients were admitted to the CCMU for monitoring following the opening of the EC3 (112 [6.8%] vs. 181 [9.3%], p < 0.01). Propensity score matching analysis showed that the opening of the EC3 was associated with improved 60-day survival (HR 0.84, 95% CI 0.70-0.99, p = 0.046).

CONCLUSION

Following the opening of the EC3, the proportion of CCMU admissions from the ED decreased. The EC3 may be most effective at reducing the admission of lower-acuity patients with GI bleeding and possibly sepsis. The EC3 may be associated with improved survival in ED patients.

Association between ICU interruptions and physicians trainees' electronic health records efficiency

The intensive care unit (ICU) is a stressful and complex environment in due to its dynamic nature and severity of admitted patients. EHR interface design can be cumbersome and lead to prolonged times to complete tasks. This paper investigated the relationship between a prominent EHR interface design and interruptions with physician's efficiency during patient chart review at ICU Pre-Rounds. We conducted a live observation of ICU physicians in a 30-bed MICU at a tertiary, southeastern medical center. Directly after the observation sessions, the physicians completed a modified System Usability Scale (SUS) survey. A total of 52 EHR patient chart reviews were observed at the MICU Pre-rounds. There was statistically significant positive correlation between time spent to review patient EHR with both number of scrolling(p-value<0.0001) across EHR interface; and with number of visited EHR  screens (p-value=0.0444). There was positive correlation between number of interruptions with time spent to review patient EHR during ICU prerounds. EHR design and the occurrence of interruptions lead to reduced physician-EHR efficiency levels. We report that the number of scrolling and visited screens executed by physicians to gather the required information was associated with increased screen time and consequently decreased physician efficiency.

Critical emergency medicine and the resuscitative care unit

Critical emergency medicine is the medical field concerned with management of critically ill patients in the emergency department (ED). Increased ED stay due to intensive care unit (ICU) overcrowding has a negative impact on patient care and outcome. It has been proposed that implementation of critical care services in the ED can negate this effect. Two main Critical Emergency Medicine models have been proposed, the "resource intensivist" and "ED-ICU" models. The resource intensivist model is based on constant presence of an intensivist in the traditional ED setting, while the ED-ICU model encompasses the notion of a separate ED-based unit, with monitoring and therapeutic capabilities similar to those of an ICU. Critical emergency medicine has the potential to improve patient care and outcome; however, establishment of evidence-based protocols and a multidisciplinary approach in patient management are of major importance.

Criticality: A New Concept of Severity of Illness for Hospitalized Children

OBJECTIVES

To validate the conceptual framework of "criticality," a new pediatric inpatient severity measure based on physiology, therapy, and therapeutic intensity calibrated to care intensity, operationalized as ICU care.

DESIGN

Deep neural network analysis of a pediatric cohort from the Health Facts (Cerner Corporation, Kansas City, MO) national database.

SETTING

Hospitals with pediatric routine inpatient and ICU care.

PATIENTS

Children cared for in the ICU (n = 20,014) and in routine care units without an ICU admission (n = 20,130) from 2009 to 2016. All patients had laboratory, vital sign, and medication data.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A calibrated, deep neural network used physiology (laboratory tests and vital signs), therapy (medications), and therapeutic intensity (number of physiology tests and medications) to model care intensity, operationalized as ICU (versus routine) care every 6 hours of a patient's hospital course. The probability of ICU care is termed the Criticality Index. First, the model demonstrated excellent separation of criticality distributions from a severity hierarchy of five patient groups: routine care, routine care for those who also received ICU care, transition from routine to ICU care, ICU care, and high-intensity ICU care. Second, model performance assessed with statistical metrics was excellent with an area under the curve for the receiver operating characteristic of 0.95 for 327,189 6-hour time periods, excellent calibration, sensitivity of 0.817, specificity of 0.892, accuracy of 0.866, and precision of 0.799. Third, the performance in individual patients with greater than one care designation indicated as 88.03% (95% CI, 87.72-88.34) of the Criticality Indices in the more intensive locations was higher than the less intense locations.

CONCLUSIONS

The Criticality Index is a quantification of severity of illness for hospitalized children using physiology, therapy, and care intensity. This new conceptual model is applicable to clinical investigations and predicting future care needs.

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace

BACKGROUND

Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace activity, but little is known about the ability of these data to predict RN strain.

OBJECTIVES

The purpose of this study was to assess the utility of ambient workplace data, defined as time-stamped transaction records and log file data produced by non-electronic health record care delivery tools (e.g., nurse call systems, communication devices), as an information channel for automated sensing of RN strain.

METHODS

In this exploratory retrospective study, ambient data for a 1-year time period were exported from electronic nurse call, medication dispensing, time and attendance, and staff communication systems. Feature sets were derived from these data for supervised machine learning models that classified work shifts by unplanned overtime. Models for three timeframes -8, 10, and 12 hours-were created to assess each model's ability to predict unplanned overtime at various points across the work shift.

RESULTS

Classification accuracy ranged from 57 to 64% across three analysis timeframes. Accuracy was lowest at 10 hours and highest at shift end. Features with the highest importance include minutes spent using a communication device and percent of medications delivered via a syringe.

CONCLUSION

Ambient data streams can serve as information channels that contain signals related to unplanned overtime as a proxy indicator of RN strain as early as 8 hours into a work shift. This study represents an initial step toward enhanced detection of RN strain and proactive prevention of missed or delayed patient care.

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.

OBJECTIVE

To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.

DESIGN

Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.

SETTING

3 hospitals in an academic health system.

PATIENTS

All people living in the greater Philadelphia region.

MEASUREMENTS

The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators.

RESULTS

Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators.

LIMITATIONS

Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction.

CONCLUSION

Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic.

PRIMARY FUNDING SOURCE

University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.

Utilization of a Resuscitative Care Unit for Initial Triage, Management, and Disposition of Minor Intracranial Hemorrhage

Management of minor intracranial hemorrhage typically involves ICU admission. ICU capacity is increasingly strained, resulting in increased emergency department boarding of critically ill patients. Our objectives were to implement a novel protocol using our emergency department–based resuscitative care unit for management of management of minor intracranial hemorrhage patients in the emergency department setting, to provide timely and appropriate critical care, and to decrease inpatient ICU utilization.

A Conceptual and Adaptable Approach to Hospital Preparedness for Acute Surge Events Due to Emerging Infectious Diseases

At the time this article was written, the World Health Organization had declared a global pandemic due to the novel coronavirus disease 2019, the first pandemic since 2009 H1N1 influenza A. Emerging respiratory pathogens are a common trigger of acute surge events—the extreme end of the healthcare capacity strain spectrum in which there is a dramatic increase in care demands and/or decreases in care resources that trigger deviations from normal care delivery processes, reliance on contingencies and external resources, and, in the most extreme cases, nonroutine decisions about resource allocation. This article provides as follows: 1) a conceptual introduction and approach to healthcare capacity strain including the etiologies of patient volume, patient acuity, special patient care demands, and resource reduction; 2) a framework for considering key resources during an acute surge event—the “four Ss” of preparedness: space (beds), staff (clinicians and operations), stuff (physical equipment), and system (coordination); and 3) an adaptable approach to and discussion of the most common domains that should be addressed during preparation for and response to acute surge events, with an eye toward combating novel respiratory viral pathogens.