A more comprehensive evaluation of quality of care after total hip and knee arthroplasty: combining 4 indicators in an ordered composite outcome

Background and purpose — Most arthroplasty registers give hospital-specific feedback on revision rates after total hip and knee arthroplasties (THA/TKA). However, due to the low number of events per hospital, multiple years of data are required to reliably detect worsening performance, and any single indicator provides only part of the quality of care delivered. Therefore, we developed an ordered composite outcome including revision, readmission, complications, and long length-of-stay (LOS) for a more comprehensive view on quality of care and assessed the ability to reliably differentiate between hospitals in their performance (rankability)with fewer years of data. Methods — All THA and TKA performed between 2017 and 2019 in 20 Dutch hospitals were included. All combinations of the 4 indicators were ranked from best to worst to create the ordinal composite outcome for THA and TKA separately. Between-hospital variation for the composite outcome was compared with individual indicators standardized for case-mix differences, and we calculated the statistical rankability using fixed and random effects models. Results — 22,908 THA and 20,423 TKA were included. Between-hospital variation for the THA and TKA composite outcomes was larger when compared with revision, readmission, and complications, and similar to long LOS. Rankabilities for the composite outcomes were above 80% even with 1 year of data, meaning that largely true hospital differences were detected rather than random variation. Interpretation — The ordinal composite outcome gives a more comprehensive overview of quality of delivered care and can reliably differentiate between hospitals in their performance using 1 year of data, thereby allowing earlier introduction of quality improvement initiatives.

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Development of quality of care indicators for patients undergoing total hip or total knee replacement

BMJ Quality & Safety ◽

10.1136/bmjqs.2009.032524 ◽

2011 ◽

Vol 20 (2) ◽

pp. 153-157 ◽

Cited By ~ 12

Author(s):

N. F. SooHoo ◽

J. R. Lieberman ◽

E. Farng ◽

S. Park ◽

S. Jain ◽

...

Keyword(s):

Quality Of Care ◽

Total Knee Replacement ◽

Knee Replacement ◽

Total Hip ◽

Total Knee

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Development of Quality-of-Care Indicators for Patients Undergoing Total Hip or Total Knee Replacement

Seminars in Arthroplasty ◽

10.1053/j.sart.2009.12.023 ◽

2010 ◽

Vol 21 (1) ◽

pp. 14-18

Author(s):

Nelson F. SooHoo ◽

Jay R. Lieberman ◽

Eugene Farng ◽

Samuel Park ◽

Sushma Jain ◽

...

Keyword(s):

Quality Of Care ◽

Total Knee Replacement ◽

Knee Replacement ◽

Total Hip ◽

Total Knee

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The Consumer Quality Index Hip Knee Questionnaire measuring patients' experiences with quality of care after a total hip or knee arthroplasty

BMC Health Services Research ◽

10.1186/1472-6963-7-60 ◽

2007 ◽

Vol 7 (1) ◽

Cited By ~ 46

Author(s):

Janine H Stubbe ◽

Tanya Gelsema ◽

Diana MJ Delnoij

Keyword(s):

Quality Of Care ◽

Knee Arthroplasty ◽

Quality Index ◽

Total Hip ◽

Consumer Quality Index

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Quality-of-care comparison of stroke: The reliability and robustness of ranking by process or outcome measures

International Journal of Stroke ◽

10.1177/17474930211053139 ◽

2021 ◽

pp. 174749302110531

Author(s):

Jingkun Li ◽

Peng Qu ◽

Chao Wang ◽

Xi Li ◽

Shuang Hou ◽

...

Keyword(s):

Ischemic Stroke ◽

Quality Of Care ◽

Acute Ischemic Stroke ◽

Recombinant Tissue Plasminogen Activator ◽

Care Quality ◽

Case Mix ◽

Hospital Variation ◽

Before And After ◽

Reliability And Robustness

Background and aim Discussion on the most rational types of performance measures for care quality comparisons has received increasing attention. The important consideration is to what extent will the measure detect a genuine difference in the underlying quality. In this study, we aimed to compare the ranking of hospitals on the performance of individual indicators, composite scores (CS, that were calculated by the method of opportunity-based score on patient-level), and in-hospital outcome of acute ischemic stroke across hospitals, and determined the reliability and robustness of the three types of ranking. Methods We analyzed data from 15,090 patients diagnosed with acute ischemic stroke who were treated at 184 large tertiary hospitals from January 2014 to May 2017. We ranked the hospital effects of recombinant tissue plasminogen activator (rt-PA) and CS and independence (modified Rankin Scale ≤2) at discharge based on fixed- and random-effects regression models before and after case-mix adjustment. We assessed the time-robustness of the hospital effects and calculated the rankability by relating the uncertainty within the hospital and the total hospital variation “beyond chance.” Results After case-mix and reliability adjustment, we estimated that 84.03% of the variance in CS between hospitals was due to true quality differences. The uncertainty within hospitals caused a poor (49.51%) rankability in rt-PA and moderate rankability (63.34%) in independence at discharge. The hospital rankings of CS were more robust across years compared with rt-PA and independence. Conclusions Our data indicated that CS is the optimal measure to indicate the quality-of-care variation of acute ischemic stroke between hospitals.

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Handling missing data in a composite outcome with partially observed components: Application in clustered paediatric routine data.

10.21203/rs.2.15450/v1 ◽

2019 ◽

Author(s):

Susan Gachau ◽

Edmund Njeru Njagi ◽

Nelson Owuor ◽

Paul Mwaniki ◽

Matteo Quartagno ◽

...

Keyword(s):

Quality Of Care ◽

Missing Data ◽

Multiple Imputation ◽

Conventional Method ◽

Routine Data ◽

Composite Outcome ◽

Model Framework ◽

Composite Measures ◽

Partially Observed

Abstract Background: In health care settings, composite measures are used to combine information from multiple quality of care measures into a single summary score. Composite scores provide global insights and trends about complex and multidimensional quality of care processes. However, missing data in subcomponents may hinder the overall reliability of the composite measures in subsequent analysis and inferences. In this study we demonstrate strategies for handling missing data in Paediatric Admission Quality of Care (PAQC) score, a composite outcome which summarizes quality of inpatient paediatric care in low income settings.Methods: We analysed routine data collected in a cluster randomized trial in 12 Kenyan hospitals. Multilevel multiple imputation (MI) within joint model framework was used to fill-in missing values in selected PAQC score subcomponents and partially observed covariates across two levels of hierarchy. We used proportional odds random intercepts and generalized estimating equations (GEE) models to analyse PAQC score before and after multiple imputation. Using a set of simulations scenario, that is, varied proportions of missingness in PAQC score subcomponents of interest under missing at random and missing completely at random mechanisms respectively, we compared the magnitude of bias in parameter estimates obtained under MI and the conventional method in addressing missing data in PAQC score components. Under the conventional method we scored all missing PAQC score components with value 0.Results: Results from observed data showed that multiple imputation of both PAQC score components and covariates yielded more accurate and precise estimates compared to complete case analysis. From the simulation study, the conventional missing data method led to significantly larger biases in estimated proportional log odds of the outcome compared to MI methods. The amount of bias increased with increase in rate of missingness with substantial variation between the missing data mechanisms under the conventional method. Conclusion: In comparison with conventional method, MI produce minimally biased estimates regardless of amount of missing data rate and underlying mechanism. We therefore recommend avoiding the conventional method in favour of multiple imputation; more research is needed to compare different ways of performing multiple imputation at the component and composite outcome level.TRIAL REGISTRATION: US National Institutes of Health-ClinicalTrials.gov identifier (NCT number) NCT02817971 . Registered September 28, 2016-retrospectively registered.

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Perioperative Quality-of-Care Measures for Patients Undergoing Total Hip or Total Knee Replacement

Journal of Long-Term Effects of Medical Implants ◽

10.1615/jlongtermeffmedimplants.v19.i4.10 ◽

2009 ◽

Vol 19 (4) ◽

pp. 249-253 ◽

Cited By ~ 1

Author(s):

Nelson F. SooHoo ◽

Jay R. Lieberman ◽

Eugene Farng ◽

Samuel Park ◽

Sushma Jain ◽

...

Keyword(s):

Quality Of Care ◽

Total Knee Replacement ◽

Knee Replacement ◽

Total Hip ◽

Total Knee

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Examining Hospital Variation on Multiple Indicators of Stroke Quality of Care

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.120.006968 ◽

2020 ◽

Vol 13 (12) ◽

Author(s):

Peter C. Austin ◽

Jiming Fang ◽

Bing Yu ◽

Moira K. Kapral

Keyword(s):

Quality Of Care ◽

Logistic Regression Model ◽

Poor Performance ◽

Provider Profiling ◽

Multiple Indicators ◽

Patient Level ◽

Multivariate Response ◽

Hospital Variation ◽

Number Of Patients

Background: Provider profiling involves comparing the performance of hospitals on indicators of quality of care. Typically, provider profiling examines the performance of hospitals on each quality indicator in isolation. Consequently, one cannot formally examine whether hospitals that have poor performance on one indicator also have poor performance on a second indicator. Methods: We used Bayesian multivariate response random effects logistic regression model to simultaneously examine variation and covariation in multiple binary indicators across hospitals. We considered 7 binary patient-level indicators of quality of care for patients presenting to hospital with a diagnosis of acute stroke. We examined between-hospital variation in these 7 indicators across 86 hospitals in Ontario, Canada. Results: The number of patients eligible for each indicator ranged from 1321 to 14 079. There were 7 pairs of indicators for which there was a strong correlation between a hospital’s performance on each of the 2 indicators. Twenty-nine of the 86 hospitals had a probability higher than 0.90 of having worse performance than average on at least 4 of the 7 indicators. Seven of the 86 of hospitals had a probability higher than 0.90 of having worse performance than average on at least 5 indicators. Fourteen of the 86 of hospitals had a probability higher than 0.50 of having worse performance than average on at least 6 indicators. No hospitals had a probability higher than 0.50 of having worse performance than average on all 7 indicators. Conclusions: These findings suggest that there are a small number of hospitals that perform poorly on at least half of the quality indicators, and that certain indicators tend to cluster together. The described methods allow for targeting quality improvement initiatives at these hospitals.

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