[Factors affecting in-hospital mortality in patients with sepsis: Development of a risk-adjusted model based on administrative data from German hospitals].

Background: Inpatient administrative data from hospitals is already used nationally and internationally in many areas of internal and public quality assurance in healthcare. For sepsis as the principal condition, only a few published approaches are available for Germany. The aim of this investigation is to identify factors influencing hospital mortality by employing appropriate analytical methods in order to improve the internal quality management of sepsis.

Methods: The analysis was based on data from 754,727 DRG cases of the CLINOTEL hospital network charged in 2015. The association then included 45 hospitals of all supply levels with the exception of university hospitals (range of beds: 100 to 1,172 per hospital). Cases of sepsis were identified via the ICD codes of their principal diagnosis. Multiple logistic regression analysis was used to determine the factors influencing in-hospital lethality for this population. The model was developed using sociodemographic and other potential variables that could be derived from the DRG data set, and taking into account current literature data. The model obtained was validated with inpatient administrative data of 2016 (51 hospitals, 850,776 DRG cases).

Results: Following the definition of the inclusion criteria, 5,608 cases of sepsis (2016: 6,384 cases) were identified in 2015. A total of 12 significant and, over both years, stable factors were identified, including age, severity of sepsis, reason for hospital admission and various comorbidities. The AUC value of the model, as a measure of predictability, is above 0.8 (H-L test p>0.05, R value=0.27), which is an excellent result.

Conclusion: The CLINOTEL model of risk adjustment for in-hospital lethality can be used to determine the mortality probability of patients with sepsis as principal diagnosis with a very high degree of accuracy, taking into account the case mix. Further studies are needed to confirm whether the model presented here will prove its value in the internal quality assurance of hospitals.