Monitoring Hospital Performance with Statistical Process Control After Total Hip and Knee Arthroplasty: A Study to Determine How Much Earlier Worsening Performance Can Be Detected

• Published in: Journal of bone and joint surgery. American volume 2020-12, Vol.102 (23), p.2087-2094
• Main Authors: van Schie, Peter, van Bodegom-Vos, Leti, van Steenbergen, Liza N., Nelissen, Rob G.H.H., Marang-van de Mheen, Perla J.
• Format: Article
• Language: English
• Subjects: Aged; Arthroplasty, Replacement, Hip - adverse effects; Arthroplasty, Replacement, Hip - standards; Arthroplasty, Replacement, Hip - statistics & numerical data; Arthroplasty, Replacement, Knee - adverse effects; Arthroplasty, Replacement, Knee - standards; Arthroplasty, Replacement, Knee - statistics & numerical data; Female; Hospitals - standards; Hospitals - statistics & numerical data; Humans; Male; Middle Aged; Quality Assurance, Health Care - methods; Quality Assurance, Health Care - statistics & numerical data; Reoperation - statistics & numerical data; Time Factors
• ISSN: 0021-9355; 1535-1386
• DOI: 10.2106/JBJS.20.00005

Given the low early revision rate after total hip arthroplasty (THA) and total knee arthroplasty (TKA), hospital performance is typically compared using 3 years of data. The purpose of this study was to assess how much earlier worsening hospital performance in 1-year revision rates after THA and TKA can be detected. All 86,468 THA and 73,077 TKA procedures performed from 2014 to 2016 and recorded in the Dutch Arthroplasty Register were included. Negative outlier hospitals were identified by significantly higher O/E (observed divided by expected) 1-year revision rates in a funnel plot. Monthly Shewhart p-charts (with 2 and 3-sigma control limits) and cumulative sum (CUSUM) charts (with 3.5 and 5 control limits) were constructed to detect a doubling of revisions (odds ratio of 2), generating a signal when the control limit was reached. The median number of months until generation of a first signal for negative outliers and the number of false signals for non-negative outliers were calculated. Sensitivity, specificity, and accuracy were calculated for all charts and control limit settings using outlier status in the funnel plot as the gold standard. The funnel plot showed that 13 of 97 hospitals had significantly higher O/E 1-year revision rates and were negative outliers for THA and 7 of 98 hospitals had significantly higher O/E 1-year revision rates and were negative outliers for TKA. The Shewhart p-chart with the 3-sigma control limit generated 68 signals (34 false-positive) for THA and 85 signals (63 false-positive) for TKA. The sensitivity for THA and TKA was 92% and 100%, respectively; the specificity was 69% and 51%, respectively; and the accuracy was 72% and 54%, respectively. The CUSUM chart with a 5 control limit generated 18 signals (1 false-positive) for THA and 7 (1 false-positive) for TKA. The sensitivity was 85% and 71% for THA and TKA, respectively; the specificity was 99% for both; and the accuracy was 97% for both. The Shewhart p-chart with a 3-sigma control limit generated the first signal for negative outliers after a median of 10 months (interquartile range [IQR] = 2 to 18) for THA and 13 months (IQR = 5 to 18) for TKA. The CUSUM chart with a 5 control limit generated the first signal after a median of 18 months (IQR = 7 to 22) for THA and 21 months (IQR = 9 to 25) for TKA. Monthly monitoring using CUSUM charts with a 5 control limit enables earlier detection of worsening 1-year revision rates with accuracy so that initiatives to improve