County-Level Effects of Prehospital Regionalization of Critically Ill Patients: A Simulation Study

Characteristics, Hospital Course, and Outcomes of Patients Requiring Prolonged Acute Versus Short-Term Mechanical Ventilation in the United States, 2014-2018

OBJECTIVES

Most patients requiring mechanical ventilation only require it for a short term (< 4 d of mechanical ventilation). Those undergoing prolonged acute mechanical ventilation (≥ 4 d mechanical ventilation) represent a select cohort who face significant morbidity, mortality, and resource utilization. Using administrative codes, we identified prolonged acute mechanical ventilation and short-term mechanical ventilation patients and compared their baseline characteristics, hospital events, and hospital outcomes.

DESIGN

Retrospective cohort.

SETTING

Seven-hundred eighty-seven acute care hospitals, United States, contributing data to Premier database, 2014-2018.

PATIENTS

Patients on mechanical ventilation.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Among 691,961 patients meeting the enrollment criteria, 266,374 (38.5%) received prolonged acute mechanical ventilation. At baseline, patients on prolonged acute mechanical ventilation were similar to short-term mechanical ventilation in age (years: 62.0 ± 15.8 prolonged acute mechanical ventilation vs 61.7 ± 17.2 short-term mechanical ventilation), gender (males: 55.6% prolonged acute mechanical ventilation vs 53.9% short-term mechanical ventilation), and race (white: 69.1% prolonged acute mechanical ventilation vs 72.4% short-term mechanical ventilation). The prolonged acute mechanical ventilation group had a higher comorbidity burden than short-term mechanical ventilation (mean Charlson Score 3.5 ± 2.7 vs 3.1 ± 2.7). The prevalence of vasopressors (50.3% vs 36.9%), dialysis (19.4% vs 10.3%), severe sepsis (20.3% vs 10.3%), and septic shock (33.5% vs 15.9%) was higher in prolonged acute mechanical ventilation than short-term mechanical ventilation. Hospital mortality (29.75% vs 21.1%), combined mortality, or discharge to hospice (37.2% vs 25.3%), extubation failure (12.3% vs 6.1%), tracheostomy (21.6% vs 4.5%), development of Clostridium difficile (4.5% vs 1.7%), and incidence density of ventilator-associated pneumonia (2.4/1,000 patient-days vs 0.6/1,000 patient-days) were all higher in the setting of prolonged acute mechanical ventilation than short-term mechanical ventilation. Median (interquartile range) post mechanical ventilation onset length of stay (13 [8-20] vs 4 d [1-8 d]) and hospital costs ($55,014 [$35,051-$88,007] vs $20,120 [$12,071-$34,915] were higher in prolonged acute mechanical ventilation than short-term mechanical ventilation.

CONCLUSIONS

Over one-third of all hospitalized patients on mechanical ventilation require it for greater than or equal to 4 days. Prolonged acute mechanical ventilation patients exhibit a higher burden of both chronic and acute illness and experience higher rates than those on short-term mechanical ventilation of hospital-acquired complications and worse clinical and economic outcomes.

Understanding Emergency Care Delivery Through Computer Simulation Modeling

In 2017, Academic Emergency Medicine convened a consensus conference entitled, "Catalyzing System Change through Health Care Simulation: Systems, Competency, and Outcomes." This article, a product of the breakout session on "understanding complex interactions through systems modeling," explores the role that computer simulation modeling can and should play in research and development of emergency care delivery systems. This article discusses areas central to the use of computer simulation modeling in emergency care research. The four central approaches to computer simulation modeling are described (Monte Carlo simulation, system dynamics modeling, discrete-event simulation, and agent-based simulation), along with problems amenable to their use and relevant examples to emergency care. Also discussed is an introduction to available software modeling platforms and how to explore their use for research, along with a research agenda for computer simulation modeling. Through this article, our goal is to enhance adoption of computer simulation, a set of methods that hold great promise in addressing emergency care organization and design challenges.

Case Volume-Outcomes Associations Among Patients With Severe Sepsis Who Underwent Interhospital Transfer

OBJECTIVES

Case volume-outcome associations bolster arguments to regionalize severe sepsis care, an approach that may necessitate interhospital patient transfers. Although transferred patients may most closely reflect care processes involved with regionalization, associations between sepsis case volume and outcomes among transferred patients are unclear. We investigated case volume-outcome associations among patients with severe sepsis transferred from another hospital.

DESIGN

Serial cross-sectional study using the Nationwide Inpatient Sample.

SETTING

United States nonfederal hospitals, years 2003-2011.

PATIENTS

One hundred forty-one thousand seven hundred seven patients (weighted national estimate of 717,732) with severe sepsis transferred from another acute care hospital.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We examined associations between quintiles of annual hospital severe sepsis case volume for the receiving hospital and in-hospital mortality among transferred patients with severe sepsis. Secondary outcomes included hospital length of stay and total charges. Transferred patients accounted for 13.2% of hospitalized severe sepsis cases. In-hospital mortality was 33.2%, with median length of stay 11 days (interquartile range, 5-22), and median total charge $70,722 (interquartile range, $30,591-$159,013). Patients transferred to highest volume hospitals had higher predicted mortality risk, greater number of acutely dysfunctional organs, and lower adjusted in-hospital mortality when compared with the lowest-volume hospitals (odds ratio, 0.80; 95% CI, 0.67-0.90). In stratified analysis (p < 0.001 for interaction of case volume by organ failure), mortality benefit associated with case volume was limited to patients with single organ dysfunction (n = 48,607, 34.3% of transfers) (odds ratio, 0.66; 95% CI, 0.55-0.80). Treatment at highest volume hospitals was significantly associated with shorter adjusted length of stay (incidence rate ratio, 0.86; 95% CI, 0.75-0.98) but not costs (% charge difference, 95% CI: [-]18.8, [-]37.9 to [+]0.3).

CONCLUSIONS

Hospital mortality was lowest among patients with severe sepsis who were transferred to high-volume hospitals; however, case volume benefits for transferred patients may be limited to patients with lower illness severity.

Hospital-Level Changes in Adult ICU Bed Supply in the United States

OBJECTIVES

Although the number of intensive care beds in the United States is increasing, little is known about the hospitals responsible for this growth. We sought to better characterize national growth in intensive care beds by identifying hospital-level factors associated with increasing numbers of intensive care beds over time.

DESIGN

We performed a repeated-measures time series analysis of hospital-level intensive care bed supply using data from Centers for Medicare and Medicaid Services.

SETTING

All United States acute care hospitals with adult intensive care beds over the years 1996-2011.

PATIENTS

None.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We described the number of beds, teaching status, ownership, intensive care occupancy, and urbanicity for each hospital in each year of the study. We then examined the relationship between increasing intensive care beds and these characteristics, controlling for other factors. The study included 4,457 hospitals and 55,865 hospital-years. Overall, the majority of intensive care bed growth occurred in teaching hospitals (net, +13,471 beds; 72.1% of total growth), hospitals with 250 or more beds (net, +18,327 beds; 91.8% of total growth), and hospitals in the highest quartile of occupancy (net, +10,157 beds; 54.0% of total growth). In a longitudinal multivariable model, larger hospital size, teaching status, and high intensive care occupancy were associated with subsequent-year growth. Furthermore, the effects of hospital size and teaching status were modified by occupancy: the greatest odds of increasing ICU beds were in hospitals with 500 or more beds in the highest quartile of occupancy (adjusted odds ratio, 18.9; 95% CI, 14.0-25.5; p < 0.01) and large teaching hospitals in the highest quartile of occupancy (adjusted odds ratio, 7.3; 95% CI, 5.3-9.9; p < 0.01).

CONCLUSIONS

Increasingly, intensive care bed expansion in the United States is occurring in larger hospitals and teaching centers, particularly following a year with high ICU occupancy.

External validation of a prehospital risk score for critical illness

Background

Identification of critically ill patients during prehospital care could facilitate early treatment and aid in the regionalization of critical care. Tools to consistently identify those in the field with or at higher risk of developing critical illness do not exist. We sought to validate a prehospital critical illness risk score that uses objective clinical variables in a contemporary cohort of geographically and temporally distinct prehospital encounters.

Methods

We linked prehospital encounters at 21 emergency medical services (EMS) agencies to inpatient electronic health records at nine hospitals in southwestern Pennsylvania from 2010 to 2012. The primary outcome was critical illness during hospitalization, defined as an intensive care unit stay with delivery of organ support (mechanical ventilation or vasopressor use). We calculated the prehospital risk score using demographics and first vital signs from eligible EMS encounters, and we tested the association between score variables and critical illness using multivariable logistic regression. Discrimination was assessed using the AUROC curve, and calibration was determined by plotting observed versus expected events across score values. Operating characteristics were calculated at score thresholds.

Results

Among 42,550 nontrauma, non-cardiac arrest adult EMS patients, 1926 (4.5 %) developed critical illness during hospitalization. We observed moderate discrimination of the prehospital critical illness risk score (AUROC 0.73, 95 % CI 0.72–0.74) and adequate calibration based on observed versus expected plots. At a score threshold of 2, sensitivity was 0.63 (95 % CI 0.61–0.75), specificity was 0.73 (95 % CI 0.72–0.73), negative predictive value was 0.98 (95 % CI 0.98–0.98), and positive predictive value was 0.10 (95 % CI 0.09–0.10). The risk score performance was greater with alternative definitions of critical illness, including in-hospital mortality (AUROC 0.77, 95 % CI 0.7 –0.78).

Conclusions

In an external validation cohort, a prehospital risk score using objective clinical data had moderate discrimination for critical illness during hospitalization.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-016-1408-0) contains supplementary material, which is available to authorized users.