IS IT POSSIBLE TO CALCULATE MINIMUM PROVIDER VOLUMES FOR TOTAL KNEE REPLACEMENT BY MEANS OF ROUTINE DATA ? RESULTS OF A THRESHOLD VALUE ANALYSIS OF GERMAN QUALITY ASSURANCE DATA FOR IN-PATIENT TREATMENT

Abstract

Aim of the study: To calculate minimum-provider-volumes in total knee replacement by means of German routine data for the first time.

Materials and methods: In patients with primary total knee replacement (TKR) the correlation between hospital volume per year and risk of “insufficient mobility” (primary quality indicator) and “wound infection” (secondary quality indicator) was calculated by means of logistic regression models based on the data of 110.349 primary total knee replacements operated in 1.016 German hospitals in 2004.

Results: For both indicators a statistically significant relationship between hospital volume and outcome could be proven. Other risk factors such as age and ASA-status also had a significant influence, but did not appear as important confounders. The risk for the secondary quality indicator “infection” decreased constantly by increasing hospital volume, thus the curve was very flat. This supports the hypothesis that high volume hospitals show up to have a higher quality level than low-volume hospitals. A threshold value of 116 TKR per year (95% CI 90–141) could be calculated. However, the explanation value of the hospital volume was too low to derive a threshold level that clearly discriminates between good and bad quality of care. The relationship between the primary quality indicator “insufficient mobility” and the hospital volume unexpectedly showed a U-shaped distribution. This questions the concept of a minimum provider volume regulation for primary total knee replacement regarding the risk factor “insufficient mobility”. Therefore, in this case no quantitative threshold values were calculated.

Conclusion: This analysis supports the hypothesis of a volume-outcome-relationship in primary total knee replacement. However, a minimum provider volume that clearly discriminates between good and bad quality of care could not be calculated on basis of German quality assurance data.