County-Level Effects of Prehospital Regionalization of Critically Ill Patients

Seymour, Christopher W.; Alotaik, Osama; Wallace, David J.; Elhabashy, Ahmad E.; Chhatwal, Jagpreet; Rea, Thomas D.; Angus, Derek C.; Nichol, Graham; Kahn, Jeremy M.

doi:10.1097/ccm.0000000000001133

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…At the same time, intensive care expansion in the largest hospitals may have unintended consequences, such as reduced quality in small hospitals that lose their critical care capacity (12) and reduced geographic access in regions served by smaller hospitals (13, 14). In silico simulation studies reveal that the former may be less of concern (15), but the relationship between regionalization and reduced quality at small hospitals awaits empiric investigation. There is recent evidence that intensive care provides measureable benefits to the marginal patient (16), making future research to understand the clinical implications of this expansion and centralization ever more relevant in the current health care landscape (17).…”

Section: Discussionmentioning

confidence: 99%

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

Wallace

Seymour

Kahn

2017

Critical Care Medicine

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Objective 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 to 2011. Measurements & 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 intensive care unit beds were in hospitals with 500 or more beds in the highest quartile of occupancy (adjusted OR: 18.9; 95% CI: 14.0 – 25.5; p<0.01) and large teaching hospitals in the highest quartile of occupancy (adjusted OR: 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 intensive care unit occupancy.

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Section: Discussionmentioning

confidence: 99%

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

Wallace

Seymour

Kahn

2017

Critical Care Medicine

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“…For example, in a simulated study of non-trauma, nonarrest critical illness, Seymour et al(46) demonstrated that prehospital regionalization does not drastically increase travel times, overburden referral center ICUs, or deplete non-referral hospitals. Although simulation models suggest that healthcare systems may not bear strain from inter-hospital transfers, studies are needed to identify changes in patient severity of illness and evolution of organ dysfunction during the process of inter-hospital transfer.…”

Section: Discussionmentioning

confidence: 99%

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

Ofoma

Dahdah

Kethireddy

et al. 2017

Critical Care Medicine

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Objective Case volume-outcome associations bolster arguments to regionalize severe sepsis care, an approach that may necessitate inter-hospital 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 non-federal hospitals, years 2003 to 2011. Patients 141,707 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 (LOS) and total charges. Transferred patients accounted for 13.2% of hospitalized severe sepsis cases. In-hospital mortality was 33.2%, with median LOS 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 as compared to the lowest volume hospitals (OR 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) (OR 0.66; 95% CI, 0.55–0.80). Treatment at highest volume hospitals was significantly associated with shorter adjusted LOS (incidence rate ratio 0.86; 95% CI 0.75–0.98), but not costs (% charge difference, 95% CI: (−)18.8, (−)37.9 – (+) 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.

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“…This prehospital risk score complements these condition-specific tools by functioning as a “score for all” among a heterogeneous group of prehospital encounters, and it could be considered for prospective validation. Additional barriers limit regionalization demonstration projects, including uncertainty over which regions to centralize, lack of stakeholder consensus, and the potential impact of new referral patterns on the financial stability of hospitals and healthcare systems [ 24 ]. These challenges will be more feasible to address with a validated triage tool, and careful study of stakeholders’ perspectives on patient referrals and the financial effects of regionalization will be necessary prior to and during any demonstration projects.…”

Section: Discussionmentioning

confidence: 99%

External validation of a prehospital risk score for critical illness

et al. 2016

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BackgroundIdentification 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.MethodsWe 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.ResultsAmong 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).ConclusionsIn an external validation cohort, a prehospital risk score using objective clinical data had moderate discrimination for critical illness during hospitalization.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-016-1408-0) contains supplementary material, which is available to authorized users.

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County-Level Effects of Prehospital Regionalization of Critically Ill Patients

Cited by 7 publications

References 35 publications

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

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

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

External validation of a prehospital risk score for critical illness

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