Relative mortality analysis: A new tool to evaluate clinical performance in trauma centers

Napoli, Nicholas J.; Barnhardt, William; Kotoriy, Madeline E.; Young, Jeffrey S.; Barnes, Laura E.

doi:10.1080/24725579.2017.1325948

Cited by 3 publications

(6 citation statements)

References 21 publications

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“…Note that each bin is expressed graphically on the RMM curve using the midpoint Ps for that bin. 5 The RMM also outputs a numerical value of survival margin as an expression of the area that falls between anticipated and observed mortality curves. This value is determined on a scale ranging from -1 to +1, as demonstrated in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…This value is determined on a scale ranging from -1 to +1, as demonstrated in Table 1. 5 Confidence intervals for each bin were calculated to maintain 95 per cent confidence (P < 0.05). The standard error of the RMM was determined to be the sum of all standard errors for each bin.…”

Section: Methodsmentioning

confidence: 99%

“…The RMM was designed at our institution with methods detailed in Napoli et al 5 The RMM applies a method of nonlinear binning in which bin intervals can be adjusted based on Ps population distribution. This aspect of dynamic bin sizing allows for enhanced resolution while maintaining statistical confidence.…”

Section: Methodsmentioning

confidence: 99%

“…4 A potential drawback to this metric, however, rests in its inability to account for a subpopulation that overly dominates the population on which the analysis is being carried out. 5 Specifically, the W score cannot accurately assess the full spectrum of care in a trauma population that is heavily dominated by patients within a narrow Ps range. This poses a problem, as the patient population of most trauma centers is largely made up of patients of low acuity (Ps > 0.94).…”

mentioning

confidence: 99%

“…Our institution developed the RMM to account for this, using the same TRISS methodology, but in a way that allows greater resolution through comparison of subpopulations with comparable anticipated mortality. 5 The RMM allows a numerical and graphical illustration of survival margin, which we define as the difference between anticipated and observed mortality across all probabilities of survival. This tool has the potential to allow a trauma center to pinpoint specific subpopulations that are underperforming and track their improvement over time.…”

mentioning

confidence: 99%

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Trauma Survival Margin Analysis: A Dissection of Trauma Center Performance through Initial Lactate

Kassar

Eklund

Barnhardt

et al. 2016

The American Surgeon™

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Measurement of trauma center performance presently relies on W-score calculation and comparison to national data sets. A limitation to this practice is a skewing of the W score, as it determines overall performance of a trauma population that is often heavily weighted by patients of low acuity. The University of Virginia relative mortality metric (RMM) was formulated to provide higher resolution in identifying areas of performance improvement within subpopulations of a trauma center using traditional Trauma Injury Severity Score methodology. Lactic acidosis has been established as a risk factor for mortality in the setting of trauma. This study aims to compare survival margin, defined as the area between actual and predicted mortality curves, in patients with either normal or elevated initial lactate. W score and RMM were calculated and compared in these cohorts. Whereas the W score suggested increased survival within the high initial lactate group, the RMM demonstrated the expected finding of increased survival margin in the normal lactate cohort. The RMM is a potentially valuable tool for trauma centers to monitor and improve performance. In addition, these findings validate the use of lactate as a triage and risk adjustment tool in the trauma setting.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Trauma Survival Margin Analysis: A Dissection of Trauma Center Performance through Initial Lactate

Kassar

Eklund

Barnhardt

et al. 2016

The American Surgeon™

Self Cite

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NAPS Fusion: A framework to overcome experimental data limitations to predict human performance and cognitive task outcomes

et al. 2023

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Relative Mortality Analysis Of The “Golden Hour”: A Comprehensive Acuity Stratification Approach To Address Disagreement In Current Literature

Schroeder¹,

Napoli²,

Barnhardt³

et al. 2018

Prehospital Emergency Care

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These results suggest that previous studies failed to support the "golden hour" not due to a lack of patients significantly impacted by prehospital time within their trauma populations, but instead due to limitations in their efforts to account for patient acuity. As a result, these studies inappropriately rejected the "golden hour," leading to the current disagreement in literature regarding the relationship between prehospital time and trauma patient mortality. The Relative Mortality Analysis was shown to overcome the limitations of these studies and demonstrated that the "golden hour" was significant for patients who were not low acuity (PoS >91%) or severely high acuity (PoS <23%).

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Relative mortality analysis: A new tool to evaluate clinical performance in trauma centers

Cited by 3 publications

References 21 publications

Trauma Survival Margin Analysis: A Dissection of Trauma Center Performance through Initial Lactate

Trauma Survival Margin Analysis: A Dissection of Trauma Center Performance through Initial Lactate

NAPS Fusion: A framework to overcome experimental data limitations to predict human performance and cognitive task outcomes

Relative Mortality Analysis Of The “Golden Hour”: A Comprehensive Acuity Stratification Approach To Address Disagreement In Current Literature

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