The impact of interhospital transfer on mortality benchmarking at Level III and IV trauma centers: A step toward shared mortality attribution in a statewide system

Holena, Daniel N.; Kaufman, Elinore J.; Hatchimonji, Justin S.; Smith, Brian P.; Xiong, Ruiying; Wasser, Thomas; Delgado, M. Kit; Wiebe, Douglas J.; Carr, Brendan G.; Reilly, Patrick

doi:10.1097/ta.0000000000002491

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…A principal criticism of current benchmarking methodology is inadequate risk adjustment that unfairly penalizes certain trauma centers. 19,20 Ioannides and colleagues 21 evaluated risk-prediction in acute conditions presenting to the emergency department. They showed the addition of race and poverty variables to their risk-adjustment model moved centers that treated predominantly underserved patients out of the lowest performing decile relative to other hospitals.…”

Section: Discussionmentioning

confidence: 99%

Social determinants of health and patient-level mortality prediction after trauma

Phelos

Kass

Deeb

et al. 2021

J Trauma Acute Care Surg

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BACKGROUND:Social determinants of health (SDOH) impact patient outcomes in trauma. Census data are often used to account for SDOH; however, there is no consensus on which variables are most important. Social vulnerability indices offer the advantage of combining multiple constructs into a single variable. Our objective was to determine if incorporation of SDOH in patient-level risk-adjusted outcome modeling improved predictive performance. METHODS:We evaluated two social vulnerability indices at the zip code level: Distressed Community Index (DCI) and National Risk Index (NRI). Individual variable combinations from Agency for Healthcare Research and Quality's SDOH data set were used for comparison. Patients were obtained from the Pennsylvania Trauma Outcomes Study 2000 to 2020. These measures were added to a validated base mortality prediction model with comparison of area under the curve and Bayesian information criterion. We performed center benchmarking using risk-standardized mortality ratios to evaluate change in rank and outlier status based on SDOH. Geospatial analysis identified geographic variation and autocorrelation. RESULTS:There were 449,541 patients included. The DCI and NRI were associated with an increase in mortality (adjusted odds ratio, 1.02; 95% confidence interval, 1.01-1.03 per 10% percentile rank increase; p < 0.01, respectively). The DCI, NRI, and seven Agency for Healthcare Research and Quality variables also improved base model fit but discrimination was similar. Two thirds of centers changed mortality ranking when accounting for SDOH compared with the base model alone. Outlier status changed in 7% of centers, most representing an improvement from worse-than-expected to nonoutlier or nonoutlier to better-than-expected. There was significant geographic variation and autocorrelation of the DCI and NRI (DCI; Moran's I 0.62, p = 0.01; NRI; Moran's I 0.34, p = 0.01). CONCLUSION:Social determinants of health are associated with an individual patient's risk of mortality after injury. Accounting for SDOH may be important in risk adjustment for trauma center benchmarking.

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Section: Discussionmentioning

confidence: 99%

Social determinants of health and patient-level mortality prediction after trauma

Phelos

Kass

Deeb

et al. 2021

J Trauma Acute Care Surg

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“…As discussed earlier, it is possible that inter‐hospital variations are due, at least in part, to residual confounding due to risk factors on which we had no information (eg, pupil reactivity for neurotrauma) or imperfect measurement of included risk factors (eg, inter‐hospital variation in injury coding, in the evaluation of physiological parameters or in reporting comorbidities). Indication bias is also a potential limit because physicians’ therapeutic decisions are directly related to patients’ prognosis, which is not fully quantified by the variables available in the registry 58 . This may make comparisons between levels of care difficult to interpret.…”

Section: Discussionmentioning

confidence: 99%

Inter‐hospital variation in surgical intensity for trauma admissions: A multicentre cohort study

Patton

Moore

Farhat

et al. 2020

Int. J. Clin. Pract.

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Background: Guidelines for injury care are increasingly moving away from surgical management towards less invasive procedures but there is a knowledge gap on how these recommendations are influencing practice. We aimed to assess inter-hospital variation in surgical intensity for injury admissions and evaluate the correlation between hospital surgical intensity and mortality/complications. Methods: We included adults admitted for major trauma between 2006 and 2016 in a Canadian provincial trauma system. Analyses were stratified for orthopaedic (n = 16 887), neurological (n = 12 888) and torso injuries (n = 9816). Surgical intensity was quantified with the number of surgical procedures <72 hours. Inter-hospital variation was assessed with the intra-class correlation coefficient (ICC). We assessed the correlation between the risk-adjusted mean number of surgical procedures and risk-adjusted incidence of mortality and complications using Pearson correlation coefficients (r). Results: Moderate inter-hospital variation was observed for orthopaedic surgery (ICC = 14.0%) whereas variation was low for torso surgery (ICC = 2.7%) and neurosurgery (ICC = 0.8%). Surgical intensity was negatively correlated with hospital mortality for torso injury (r = −.32, P = .02) and neurotrauma (r = −.65, P = .08). A strong positive correlation was observed with hospital complications for orthopaedic injuries (r = .36, P = .006) whereas the opposite was observed for neurotrauma (r = −.71, P = .05). Conclusions: Results should be interpreted with caution as they may be a result of residual confounding. However, they may suggest that there are opportunities for quality improvement in surgical care for injury admissions, particularly for orthopaedic injuries. Moving forward, we should aim to prospectively evaluate adherence to guidelines on non-operative management and their impact on mortality and morbidity.

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“…Over time, both EMS and hospital‐based emergency care have evolved significantly, but the origins of this ad hoc, decentralized system still affect emergency care today. Because of this, the Institute of Medicine (IOM) (now National Academy of Medicine), the National Quality Forum, and scholars have called on policymakers to adopt a population‐based perspective for emergency care for the purposes of payment, resource planning, and outcome measurement 3–15 . Yet there are few theoretical frameworks for understanding population‐based networks of emergency care.…”

Section: Introductionmentioning

confidence: 99%

A Conceptual Framework for Optimizing the Equity of Hospital‐Based Emergency Care: The Structure of Hospital Transfer Networks

et al. 2023

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r Current pay-for-performance and other payment policies ignore hospital transfers for emergency conditions, which may exacerbate disparities.r No conceptual framework currently exists that offers a patientcentered, population-based perspective for the structure of hospital transfer networks.r The hospital transfer network equity-quality framework highlights the external and internal factors that determine the structure of hospital transfer networks, including structural inequity and racism.Context: Emergency care includes two key components: initial stabilization and transfer to a higher level of care. Significant work has focused on ensuring that local facilities can stabilize patients. However, less is understood about transfers for definitive care. To better understand how transfer network structure impacts population health and equity in emergency care, we propose

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The impact of interhospital transfer on mortality benchmarking at Level III and IV trauma centers: A step toward shared mortality attribution in a statewide system

Cited by 7 publications

References 12 publications

Social determinants of health and patient-level mortality prediction after trauma

Social determinants of health and patient-level mortality prediction after trauma

Inter‐hospital variation in surgical intensity for trauma admissions: A multicentre cohort study

A Conceptual Framework for Optimizing the Equity of Hospital‐Based Emergency Care: The Structure of Hospital Transfer Networks

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