Objective factors associated with physicians' and nurses' perceptions of intensive care unit capacity strain

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.

Intensivist physician-to-patient ratios and mortality in the intensive care unit

PURPOSE

A high daily census may hinder the ability of physicians to deliver quality care in the intensive care unit (ICU). We sought to determine the relationship between intensivist-to-patient ratios and mortality among ICU patients.

METHODS

We performed a retrospective cohort study of intensivist-to-patient ratios in 29 ICUs in 10 hospitals in the United States from 2018 to 2020. We used meta-data from progress notes in the electronic health record to determine an intensivist-specific caseload for each ICU day. We then fit a multivariable proportional hazards model with time-varying covariates to estimate the relationship between the daily intensivist-to-patient ratio and ICU mortality at 28 days.

RESULTS

The final analysis included 51,656 patients, 210,698 patient days, and 248 intensivist physicians. The average caseload per day was 11.8 (standard deviation: 5.7). There was no association between the intensivist-to-patient ratio and mortality (hazard ratio for each additional patient: 0.987, 95% confidence interval: 0.968-1.007, p = 0.2). This relationship persisted when we defined the ratio as caseload over the sample-wide average (hazard ratio: 0.907, 95% confidence interval: 0.763-1.077, p = 0.26) and cumulative days with a caseload over the sample-wide average (hazard ratio: 0.991, 95% confidence interval: 0.966-1.018, p = 0.52). The relationship was not modified by the presence of physicians-in-training, nurse practitioners, and physician assistants (p value for interaction term: 0.14).

CONCLUSIONS

Mortality for ICU patients appears resistant to high intensivist caseloads. These results may not generalize to ICUs organized differently than those in this sample, such as ICUs outside the United States.

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.

Initial Development of an Automated Platform for Assessing Trainee Performance on Case Presentations

Background

Oral case presentation is a crucial skill of physicians and a key component of team-based care. However, consistent and objective assessment and feedback on presentations during training are infrequent.

Objective

To determine the potential value of applying natural language processing, computer software that extracts meaning from text, to transcripts of oral case presentations as a strategy to assess their quality automatically and objectively.

Methods

We transcribed a collection of simulated oral case presentations. The presentations were from eight critical care fellows and one critical care attending. They were instructed to review the medical charts of 11 real intensive care unit patient cases and to audio record themselves, presenting each case as if they were doing so on morning rounds. We then used natural language processing to convert the transcripts from human-readable text into machine-readable numbers. These numbers represent details of the presentation style and content. The distance between the numeric representation of two different transcripts negatively correlates with the similarity of those two transcripts. We ranked fellows on the basis of how similar their presentations were to the attending's presentations.

Results

The 99 presentations included 260 minutes of audio (mean length: 2.6 ± 1.24 min per case). On average, 23.88 ± 2.65 sentences were spoken, and each sentence had 14.10 ± 0.67 words, 3.62 ± 0.15 medical concepts, and 0.75 ± 0.09 medical adjectives. When ranking fellows on the basis of how similar their presentations were to the attending's presentation, we found a gap between the five fellows with the most similar presentations and the three fellows with the least similar presentations (average group similarity scores of 0.62 ± 0.01 and 0.53 ± 0.01, respectively). Rankings were sensitive to whether presentation style or content information were weighted more heavily when calculating transcript similarity.

Conclusion

Natural language processing enabled the ranking of case presentations on the basis of how similar they were to a reference presentation. Although additional work is needed to convert these rankings, and underlying similarity scores, into actionable feedback for trainees, these methods may support new tools for improving medical education.

Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge

OBJECTIVE

To explore the relationship between novel, time-varying predictors for healthcare delivery strain (eg, counts of patient orders per hour) and imminent discharge and in-hospital mortality.

MATERIALS AND METHODS

We conducted a retrospective cohort study using data from adults hospitalized at 21 Kaiser Permanente Northern California hospitals between November 1, 2015 and October 31, 2020 and the nurses caring for them. Patient data extracted included demographics, diagnoses, severity measures, occupancy metrics, and process of care metrics (eg, counts of intravenous drip orders per hour). We linked these data to individual registered nurse records and created multiple dynamic, time-varying predictors (eg, mean acute severity of illness for all patients cared for by a nurse during a given hour). All analyses were stratified by patients' initial hospital unit (ward, stepdown unit, or intensive care unit). We used discrete-time hazard regression to assess the association between each novel time-varying predictor and the outcomes of discharge and mortality, separately.

RESULTS

Our dataset consisted of 84 162 161 hourly records from 954 477 hospitalizations. Many novel time-varying predictors had strong associations with the 2 study outcomes. However, most of the predictors did not merely track patients' severity of illness; instead, many of them only had weak correlations with severity, often with complex relationships over time.

DISCUSSION

Increasing availability of process of care data from automated electronic health records will permit better quantification of healthcare delivery strain. This could result in enhanced prediction of adverse outcomes and service delays.

CONCLUSION

New conceptual models will be needed to use these new data elements.

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.

Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study

OBJECTIVE

Electronic health record documentation by intensive care unit (ICU) clinicians may predict patient outcomes. However, it is unclear whether physician and nursing notes differ in their ability to predict short-term ICU prognosis. We aimed to investigate and compare the ability of physician and nursing notes, written in the first 48 hours of admission, to predict ICU length of stay and mortality using 3 analytical methods.

MATERIALS AND METHODS

This was a retrospective cohort study with split sampling for model training and testing. We included patients ≥18 years of age admitted to the ICU at Beth Israel Deaconess Medical Center in Boston, Massachusetts, from 2008 to 2012. Physician or nursing notes generated within the first 48 hours of admission were used with standard machine learning methods to predict outcomes.

RESULTS

For the primary outcome of composite score of ICU length of stay ≥7 days or in-hospital mortality, the gradient boosting model had better performance than the logistic regression and random forest models. Nursing and physician notes achieved area under the curves (AUCs) of 0.826 and 0.796, respectively, with even better predictive power when combined (AUC, 0.839).

DISCUSSION

Models using only nursing notes more accurately predicted short-term prognosis than did models using only physician notes, but in combination, the models achieved the greatest accuracy in prediction.

CONCLUSIONS

Our findings demonstrate that statistical models derived from text analysis in the first 48 hours of ICU admission can predict patient outcomes. Physicians' and nurses' notes are both uniquely important in mortality prediction and combining these notes can produce a better predictive model.

Clinical characteristics and outcomes of critically ill patients with COVID-19 in Kobe, Japan: a single-center, retrospective, observational study

PURPOSE

Coronavirus disease 2019 (COVID-19) has placed a great burden on critical care services worldwide. Data regarding critically ill COVID-19 patients and their demand of critical care services outside of initial COVID-19 epicenters are lacking. This study described clinical characteristics and outcomes of critically ill COVID-19 patients and the capacity of a COVID-19-dedicated intensive care unit (ICU) in Kobe, Japan.

METHODS

This retrospective observational study included critically ill COVID-19 patients admitted to a 14-bed COVID-19-dedicated ICU in Kobe between March 3, 2020 and June 21, 2020. Clinical and daily ICU occupancy data were obtained from electrical medical records. The last follow-up day was June 28, 2020.

RESULTS

Of 32 patients included, the median hospital follow-up period was 27 (interquartile range 19-50) days. The median age was 68 (57-76) years; 23 (72%) were men and 25 (78%) had at least one comorbidity. Nineteen (59%) patients received invasive mechanical ventilation for a median duration of 14 (8-27) days. Until all patients were discharged from the ICU on June 5, 2020, the median daily ICU occupancy was 50% (36-71%). As of June 28, 2020, six (19%) died during hospitalization. Of 26 (81%) survivors, 23 (72%) were discharged from the hospital and three (9%) remained in the hospital.

CONCLUSION

During the first months of the outbreak in Kobe, most critically ill patients were men aged ≥ 60 years with at least one comorbidity and on mechanical ventilation; the ICU capacity was not strained, and the case-fatality rate was 19%.

Associations Between Dust Storms and Intensive Care Unit Admissions in the United States, 2000-2015

Anthropogenic climate change is influencing the incidence of dust storms and associated human exposure to coarse particulate matter (PM2.5-10) in the United States. Studies have found adverse health consequences related to dust exposure. These consequences include respiratory disease exacerbations and premature mortality, resulting in increased health care utilization. However, the impact of dust storms on critical care demand has not been studied in the United States. We seek to quantify the relationship between dust storms and surges in critical care demand by investigating the association between dust storms and intensive care unit (ICU) admissions at nearby hospitals from 2000 to 2015. ICU data were acquired from Premier, Inc. and encompass 15-20% of all ICU admissions in the United States. Dust storm, meteorology, and air pollutant data were downloaded from the U.S. National Weather Service, the U.S. National Climatic Data Center, and the U.S. Environmental Protection Agency websites, respectively. Associations between ICU admission and dust storms, controlling for temperature, dew point temperature, ambient PM2.5 and ozone, as well as seasonally varying confounders, were estimated using a distributed lag conditional Poisson model with overdispersion. We found a 4.8% (95% CI: 0.4, 9.4; p = 0.033) increase in total ICU admissions on the day of the dust storm (Lag 0) and a 9.2% (95% CI: 1.8, 17.0; p = 0.013) and 7.5% (95% CI: 0.3, 15.2; p = 0.040) increase in respiratory admissions at Lags 0 and 5. North American dust storms are associated with increases in same day and lagged demand for critical care services at nearby hospitals.

Fortalecer la coordinación de enfermería para mejorar el cuidado hospitalario

Introducción: La coordinación adecuada para organizar la atención de enfermería a nivel hospitalario es decisiva frente al servicio que se ofrece a los sujetos de cuidado y al mejoramiento permanente de la institución. Objetivo: Describir qué dice la literatura mundial sobre cómo fortalecer el rol de coordinación de enfermería a nivel hospitalario. Materiales y Métodos: Búsqueda integradora de literatura bajo parámetros de Ganong, con publicaciones arbitradas, observación de seis años, sin límites geográficos, en idiomas inglés, español y portugués.  Resultados: De acuerdo con la literatura surgen tres categorías que incluyen: características del rol de coordinador de enfermería; impacto de esta coordinación en los resultados frente al sujeto de cuidado; y estrategias para fortalecer el rol de coordinación dentro de la institución. La evidencia que respalda el desarrollo de la coordinación de enfermería en el hospital es débil, con una mayoría de estudios de tipo descriptivo. Discusión: La coordinación de enfermería debe articular la dirección y la operación en el hospital y para ello requiere conocer y acompañar la directriz institucional a mediano y largo plazo y manejar estrategias de motivación y acompañamiento para facilitar su logro. Conclusiones:  En una institución hospitalaria, la enfermera coordinadora debe mantener el norte en medio de la cotidianeidad de la asistencia; acompañar y motivar al personal a su cargo mediante competencias de liderazgo, trabajo en equipo y conocimiento clínico e institucional.  Requiere para el ejercicio de su rol, proyectar el cambio y mantener un pensamiento integrador. Como citar este artículo: Laverde Contreras Olga Lucia, Sánchez Herrera Beatriz. Fortalecer la coordinación de enfermería para mejorar el cuidado hospitalario. Revista Cuidarte. 2020; 11(2): e826. http://dx.doi.org/10.15649/cuidarte.826

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.

Associations of Intensive Care Unit Capacity Strain with Disposition and Outcomes of Patients with Sepsis Presenting to the Emergency Department

RATIONALE

Intensive care unit (ICU) capacity strain refers to the potential limits placed on an ICU's ability to provide high-quality care for all patients who may need it at a given time. Few studies have investigated how fluctuations in ICU capacity strain might influence care outside the ICU.

OBJECTIVES

To determine whether ICU capacity strain is associated with initial level of inpatient care and outcomes for emergency department (ED) patients hospitalized for sepsis.

METHODS

We performed a retrospective cohort study of patients with sepsis admitted from the ED to a medical ward or ICU at three hospitals within the University of Pennsylvania Health System between 2012 and 2015. Patients were excluded if they required life support therapies, defined as invasive or noninvasive ventilatory support or vasopressors, at the time of admission. The exposures were four measures of ICU capacity strain at the time of the ED disposition decision: ICU occupancy, ICU turnover, ICU census acuity, and ward occupancy. The primary outcome was the decision to admit to a ward or to an ICU. Secondary analyses assessed the association of ICU capacity strain with in-hospital outcomes, including mortality.

RESULTS

Among 77,142 hospital admissions from the ED, 3,067 patients met the study's eligibility criteria. The ICU capacity strain metrics varied between and within study hospitals over time. In unadjusted analyses, ICU occupancy, ICU turnover, ICU census acuity, and ward occupancy were all negatively associated with ICU admission. In the fully adjusted model including patient-level covariates, only ICU occupancy remained associated with ICU admission (odds ratio, 0.87; 95% confidence interval, 0.79-0.96; P = 0.005), such that a 10% increase in ICU occupancy (e.g., one additional patient in a 10-bed ICU) was associated with a 13% decrease in the odds of ICU admission. Among the subset of patients admitted initially from the ED to a medical ward, ICU occupancy at the time of admission was associated with increased odds of hospital mortality (odds ratio, 1.61; 95% confidence interval, 1.21-2.14; P = 0.001).

CONCLUSIONS

The odds that patients in the ED with sepsis who do not require life support therapies will be admitted to the ICU are reduced when those ICUs experience high occupancy but not high levels of other previously explored measures of capacity strain. Patients with sepsis admitted to the wards during times of high ICU occupancy had increased odds of hospital mortality.

Intensive Care Role in Disaster Management Critical Care Clinics

The "daily disasters" within the ebb and flow of routine critical care provide a foundation of preparedness for the less-frequent, larger events that affect most health care organizations at some time. Although large disasters can overwhelm, those who strengthen processes and habits through daily practice will be the best prepared to manage them.

Intensive Care Unit Capacity Strain and Outcomes of Critical Illness in a Resource-Limited Setting: A 2-Hospital Study in South Africa

OBJECTIVE

To measure the association of intensive care unit (ICU) capacity strain with processes of care and outcomes of critical illness in a resource-limited setting.

METHODS

We performed a retrospective cohort study of 5332 patients referred to the ICUs at 2 public hospitals in South Africa using the country's first published multicenter electronic critical care database. We assessed the association between multiple ICU capacity strain metrics (ICU occupancy, turnover, census acuity, and referral burden) at different exposure time points (ICU referral, admission, and/or discharge) with clinical and process of care outcomes. The association of ICU capacity strain at the time of ICU admission with ICU length of stay (LOS), the primary outcome, was analyzed with a multivariable Cox proportional hazard model. Secondary outcomes of ICU triage decision (with strain at ICU referral), ICU mortality (with strain at ICU admission), and ICU LOS (with strain at ICU discharge), were analyzed with linear and logistic multivariable regression.

RESULTS

No measure of ICU capacity strain at the time of ICU admission was associated with ICU LOS, the primary outcome. The ICU occupancy at the time of ICU admission was associated with increased odds of ICU mortality (odds ratio = 1.07, 95% confidence interval: 1.02-1.11; P = .004), a secondary outcome, such that a 10% increase in ICU occupancy would be associated with a 7% increase in the odds of ICU mortality.

CONCLUSIONS

In a resource-limited setting in South Africa, ICU capacity strain at the time of ICU admission was not associated with ICU LOS. In secondary analyses, higher ICU occupancy at the time of ICU admission, but not other measures of capacity strain, was associated with increased odds of ICU mortality.

Perspectives on strained intensive care unit capacity: A survey of critical care professionals

Background

Strained intensive care unit (ICU) capacity represents a supply-demand mismatch in ICU care. Limited data have explored health care worker (HCW) perceptions of strain.

Methods

Cross-sectional survey of HCW across 16 Alberta ICUs. A web-based questionnaire captured data on demographics, strain definition, and sources, impact and strategies for management.

Results

658 HCW responded (33%; 95%CI, 32–36%), of which 452 were nurses (69%), 128 allied health (19%), 45 physicians (7%) and 33 administrators (5%). Participants (agreed/strongly agreed: 94%) reported that strain was best defined as “a time-varying imbalance between the supply of available beds, staff and/or resources and the demand to provide high-quality care for patients who may become or who are critically ill”; while some recommended defining “high-quality care”, integrating “safety”, and families in the definition. Participants reported significant contributors to strain were: “inability to discharge ICU patients due to lack of available ward beds” (97%); “increases in the volume” (89%); and “acuity and complexity of patients requiring ICU support” (88%). Strain was perceived to “increase stress levels in health care providers” (98%); and “burnout in health care providers” (96%). The highest ranked strategies were: “have more consistent and better goals-of-care conversations with patients/families outside of ICU” (95%); and “increase non-acute care beds” (92%).

Interpretation

Strain is perceived as common. HCW believe precipitants represent a mix of patient-related and operational factors. Strain is thought to have negative implications for quality of care, HCW well-being and workplace environment. Most indicated strategies “outside” of ICU settings were priorities for managing strain.

Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay

OBJECTIVES

Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, random forest, and elastic net regression models, with and without unstructured clinical text data, to predict a binary composite outcome of in-hospital death or ICU length of stay greater than or equal to 7 days using data from the first 48 hours of hospitalization.

DESIGN

Retrospective cohort study with split sampling for model training and testing.

SETTING

A single urban academic hospital.

PATIENTS

All hospitalized patients who required ICU care at the Beth Israel Deaconess Medical Center in Boston, MA, from 2001 to 2012.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Among eligible 25,947 hospital admissions, we observed 5,504 (21.2%) in which patients died or had ICU length of stay greater than or equal to 7 days. The gradient boosting machine model had the highest discrimination without (area under the receiver operating characteristic curve, 0.83; 95% CI, 0.81-0.84) and with (area under the receiver operating characteristic curve, 0.89; 95% CI, 0.88-0.90) text-derived variables. Both gradient boosting machines and random forests outperformed logistic regression without text data (p < 0.001), whereas all models outperformed logistic regression with text data (p < 0.02). The inclusion of text data increased the discrimination of all four model types (p < 0.001). Among those models using text data, the increasing presence of terms "intubated" and "poor prognosis" were positively associated with mortality and ICU length of stay, whereas the term "extubated" was inversely associated with them.

CONCLUSIONS

Variables extracted from unstructured clinical text from the first 48 hours of hospital admission using natural language processing techniques significantly improved the abilities of logistic regression and other machine learning models to predict which patients died or had long ICU stays. Learning health systems may adapt such models using open-source approaches to capture local variation in care patterns.

Indicators of intensive care unit capacity strain: a systematic review

BACKGROUND

Strained intensive care unit (ICU) capacity represents a fundamental supply-demand mismatch in ICU resources. Strain is likely to be influenced by a range of factors; however, there has been no systematic evaluation of the spectrum of measures that may indicate strain on ICU capacity.

METHODS

We performed a systematic review to identify indicators of strained capacity. A comprehensive peer-reviewed search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Web of Science Core Collection was performed along with selected grey literature sources. We included studies published in English after 1990. We included studies that: (1) focused on ICU settings; (2) included description of a quality or performance measure; and (3) described strained capacity. Retrieved studies were screened, selected and extracted in duplicate. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Analysis was descriptive.

RESULTS

Of 5297 studies identified in our search; 51 fulfilled eligibility. Most were cohort studies (n = 39; 76.5%), five (9.8%) were case-control, three (5.8%) were cross-sectional, two (3.9%) were modeling studies, one (2%) was a correlational study, and one (2%) was a quality improvement project. Most observational studies were high quality. Sixteen measures designed to indicate strain were identified 110 times, and classified as structure (n = 4, 25%), process (n = 7, 44%) and outcome (n = 5, 31%) indicators, respectively. The most commonly identified indicators of strain were ICU acuity (n = 21; 19.1% [process]), ICU readmission (n = 18; 16.4% [outcome]), after-hours discharge (n = 15; 13.6% [process]) and ICU census (n = 13; 11.8% [structure]). There was substantial heterogeneity in the operational definitions used to define strain indicators across studies.

CONCLUSIONS

We identified and characterized 16 indicators of strained ICU capacity across the spectrum of healthcare quality domains. Future work should aim to evaluate their implementation into practice and assess their value for evaluating strategies to mitigate strain.

SYSTEMATIC REVIEW REGISTRATION

This systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931 ).

What are the ethical aspects surrounding the collegial decisional process in limiting and withdrawing treatment in intensive care?

The decision to limit or withdraw life-support treatment is an integral part of the job of a physician working in the intensive care unit, and of the approach to care. However, this decision is influenced by a number of factors. It is widely accepted that a medical decision that will ultimate lead to end-of-life in the intensive care unit (ICU) must be shared between all those involved in the care process, and should give precedence to the patient's wishes (either directly expressed by the patient or in written form, such as advance directives), and taking into account the opinion of the patient's family, including the surrogate if the patient is no longer capable of expressing themselves. A number of questions still remain unanswered regarding how decisions to limit or withdraw treatment are taken in daily practice, especially when this decision can be anticipated. We discuss here the collegial procedure for decision-making, in particular in the context of recent French legislation on end-of-life issues. We describe how collegial decision-making procedures should be carried out, and what points are covered in shared discussions regarding decisions to limit or withdraw life-sustaining therapies.

A Time-Motion Study of ICU Workflow and the Impact of Strain

OBJECTIVE

Understanding ICU workflow and how it is impacted by ICU strain is necessary for implementing effective improvements. This study aimed to quantify how ICU physicians spend time and to examine the impact of ICU strain on workflow.

DESIGN

Prospective, observational time-motion study.

SETTING

Five ICUs in two hospitals at an academic medical center.

SUBJECTS

Thirty attending and resident physicians.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

In 137 hours of field observations, the most time-84 hours (62% of total observation time)-was spent on professional communication. Reviewing patient data and documentation occupied a combined 52 hours (38%), whereas direct patient care and education occupied 24 hours (17%) and 13 hours (9%), respectively. The most frequently used tool was the computer, used in tasks that occupied 51 hours (37%). Severity of illness of the ICU on day of observation was the only strain factor that significantly impacted work patterns. In a linear regression model, increase in average ICU Sequential Organ Failure Assessment was associated with more time spent on direct patient care (β = 4.3; 95% CI, 0.9-7.7) and education (β = 3.2; 95% CI, 0.7-5.8), and less time spent on documentation (β = -7.4; 95% CI, -11.6 to -3.2) and on tasks using the computer (β = -7.8; 95% CI, -14.1 to -1.6). These results were more pronounced with a combined strain score that took into account unit census and Sequential Organ Failure Assessment score. After accounting for ICU type (medical vs surgical) and staffing structure (resident staffed vs physician assistant staffed), results changed minimally.

CONCLUSION

Clinicians spend the bulk of their time in the ICU on professional communication and tasks involving computers. With the strain of high severity of illness and a full unit, clinicians reallocate time from documentation to patient care and education. Further efforts are needed to examine system-related aspects of care to understand the impact of workflow and strain on patient care.

Healthcare Provider Perceptions of Causes and Consequences of ICU Capacity Strain in a Large Publicly Funded Integrated Health Region: A Qualitative Study

OBJECTIVES

Discrepancy in the supply-demand relationship for critical care services precipitates a strain on ICU capacity. Strain can lead to suboptimal quality of care and burnout among providers and contribute to inefficient health resource utilization. We engaged interprofessional healthcare providers to explore their perceptions of the sources, impact, and strategies to manage capacity strain.

DESIGN

Qualitative study using a conventional thematic analysis.

SETTING

Nine ICUs across Alberta, Canada.

SUBJECTS

Nineteen focus groups (n = 122 participants).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Participants' perspectives on strain on ICU capacity and its perceived impact on providers, families, and patient care were explored. Participants defined "capacity strain" as a discrepancy between the availability of ICU beds, providers, and ICU resources (supply) and the need to admit and provide care for critically ill patients (demand). Four interrelated themes of contributors to strain were characterized (each with subthemes): patient/family related, provider related, resource related, and health system related. Patient/family-related subthemes were "increasing patient complexity/acuity," along with patient-provider communication issues ("paucity of advance care planning and goals-of-care designation," "mismatches between patient/family and provider expectations," and "timeliness of end-of-life care planning"). Provider-related factor subthemes were nursing workforce related ("nurse attrition," "inexperienced workforce," "limited mentoring opportunities," and "high patient-to-nurse ratios") and physician related ("frequent turnover/handover" and "variations in care plan"). Resource-related subthemes were "reduced service capability after hours" and "physical bed shortages." Health system-related subthemes were "variable ICU utilization," "preferential "bed" priority for other services," and "high ward bed occupancy." Participants perceived that strain had negative implications for patients ("reduced quality and safety of care" and "disrupted opportunities for patient- and family-centered care"), providers ("increased workload," "moral distress," and "burnout"), and the health system ("unnecessary, excessive, and inefficient resource utilization").

CONCLUSIONS

Engagement with frontline critical care providers is essential for understanding their experiences and perspectives regarding strained capacity and for the development of sustainable strategies for improvement.

Effect of ICU strain on timing of limitations in life-sustaining therapy and on death

PURPOSE

The effect of capacity strain in an ICU on the timing of end-of-life decision-making is unknown. We sought to determine how changes in strain impact timing of new do-not-resuscitate (DNR) orders and of death.

METHODS

Retrospective cohort study of 9891 patients dying in the hospital following an ICU stay ≥72 h in Project IMPACT, 2001-2008. We examined the effect of ICU capacity strain (measured by standardized census, proportion of new admissions, and average patient acuity) on time to initiation of DNR orders and time to death for all ICU decedents using fixed-effects linear regression.

RESULTS

Increases in strain were associated with shorter time to DNR for patients with limitations in therapy (predicted time to DNR 6.11 days for highest versus 7.70 days for lowest quintile of acuity, p = 0.02; 6.50 days for highest versus 7.77 days for lowest quintile of admissions, p < 0.001), and shorter time to death (predicted time to death 7.64 days for highest versus 9.05 days for lowest quintile of admissions, p < 0.001; 8.28 days for highest versus 9.06 days for lowest quintile of census, only in closed ICUs, p = 0.006). Time to DNR order significantly mediated relationships between acuity and admissions and time to death, explaining the entire effect of acuity, and 65 % of the effect of admissions. There was no association between strain and time to death for decedents without a limitation in therapy.

CONCLUSIONS

Strains in ICU capacity are associated with end-of-life decision-making, with shorter times to placement of DNR orders and death for patients admitted during high-strain days.

Intensive care unit capacity strain and adherence to prophylaxis guidelines

PURPOSE

The purpose of the study is to examine the relationship between different measures of capacity strain and adherence to prophylaxis guidelines in the intensive care unit (ICU).

MATERIALS AND METHODS

We conducted a retrospective cohort study within the Project IMPACT database. We used multivariable logistic regression to examine relationships between ICU capacity strain and appropriate usage of venous thromboembolism prophylaxis (VTEP) and stress ulcer prophylaxis (SUP).

RESULTS

Of 776,905 patient-days eligible for VTEP, appropriate therapy was provided on 68%. Strain as measured by proportion of new admissions (odds ratio [OR], 0.91; 95% confidence interval [CI], 0.90-0.91) and census (OR, 0.97; 95% CI, 0.97-0.98) was associated with decreased odds of receiving VTEP. With increasing strain as measured by new admissions, the degradation of VTEP utilization was more severe in ICUs with closed (OR, 0.85; 95% CI, 0.83-0.88) than open (OR, 0.91; 95% CI, 0.91-0.92) staffing models (interaction P<.001). Of 185425 patient-days eligible for SUP, 48% received appropriate therapy. Administration of SUP was not significantly influenced by any measure of strain.

CONCLUSIONS

Rising capacity strain in the ICU reduces the odds that patients will receive appropriate VTEP but not SUP. The variability among different types of ICUs in the extent to which strain degraded VTEP use suggests opportunities for systems improvement.

Epidemiology and outcomes in patients with severe sepsis admitted to the hospital wards

PURPOSE

The purpose of this study was to detail the trajectory and outcomes of patients with severe sepsis admitted from the emergency department to a non-intensive care unit (ICU) setting and identify risk factors associated with adverse outcomes.

MATERIAL AND METHODS

This was a single-center retrospective cohort study conducted at a tertiary, academic hospital in the United States between 2005 and 2009. The primary outcome was a composite outcome of ICU transfer within 48 hours of admission and/or 28-day mortality.

RESULTS

Of 1853 patients admitted with severe sepsis, 841 (45%) were admitted to a non-ICU setting, the rate increased over time (P < .001), and 12.5% of these patients were transferred to the ICU within 48 hours and/or died within 28 days. In multivariable models, age (P < .001), an oncology diagnosis (P < .001), and illness severity as measured by Acute Physiologic and Chronic Health Evaluation II (P = .04) and high (≥4 mmol/L) initial serum lactate levels (P = .005) were associated with the primary outcome.

CONCLUSIONS

Patients presenting to the emergency department with severe sepsis were frequently admitted to a non-ICU setting, and the rate increased over time. Of 8 patients admitted to the hospital ward, one was transferred to the ICU within 48 hours and/or died within 28 days of admission. Factors present at admission were identified that were associated with adverse outcomes.