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. 2018 Mar 20;13(3):e0194371.
doi: 10.1371/journal.pone.0194371. eCollection 2018.

A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data

Daniel Schwarzkopf  1 , Carolin Fleischmann-Struzek  1 , Hendrik Rüddel  1   2 , Konrad Reinhart  1   2 , Daniel O Thomas-Rüddel  1   2
Affiliations

A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data

Daniel Schwarzkopf et al. PLoS One. .

Abstract

Background: Sepsis is a major cause of preventable deaths in hospitals. Feasible and valid methods for comparing quality of sepsis care between hospitals are needed. The aim of this study was to develop a risk-adjustment model suitable for comparing sepsis-related mortality between German hospitals.

Methods: We developed a risk-model using national German claims data. Since these data are available with a time-lag of 1.5 years only, the stability of the model across time was investigated. The model was derived from inpatient cases with severe sepsis or septic shock treated in 2013 using logistic regression with backward selection and generalized estimating equations to correct for clustering. It was validated among cases treated in 2015. Finally, the model development was repeated in 2015. To investigate secular changes, the risk-adjusted trajectory of mortality across the years 2010-2015 was analyzed.

Results: The 2013 deviation sample consisted of 113,750 cases; the 2015 validation sample consisted of 134,851 cases. The model developed in 2013 showed good validity regarding discrimination (AUC = 0.74), calibration (observed mortality in 1st and 10th risk-decile: 11%-78%), and fit (R2 = 0.16). Validity remained stable when the model was applied to 2015 (AUC = 0.74, 1st and 10th risk-decile: 10%-77%, R2 = 0.17). There was no indication of overfitting of the model. The final model developed in year 2015 contained 40 risk-factors. Between 2010 and 2015 hospital mortality in sepsis decreased from 48% to 42%. Adjusted for risk-factors the trajectory of decrease was still significant.

Conclusions: The risk-model shows good predictive validity and stability across time. The model is suitable to be used as an external algorithm for comparing risk-adjusted sepsis mortality among German hospitals or regions based on administrative claims data, but secular changes need to be taken into account when interpreting risk-adjusted mortality.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Calibration of the model derived in 2013 for predicting hospital mortality in cases with severe sepsis or septic shock treated in 2015.
The gap between predicted and observed mortality (M±SD = 2.7%±0.7%) results from a secular trend with decreasing mortality (compare Fig 2).
Fig 2
Fig 2. Secular trends for hospital mortality between 2010–2015 in cases with severe sepsis or septic shock.
Dashed lines present odds ratios compared to 2010 without controlling for mortality risk-factors, solid lines present odds ratios compared to 2010 while controlling for risk-factors. Results based on a logistic regression using generalized estimating equations.
See this image and copyright information in PMC

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