Improving Quality Based on the Joint Registry
Phase: NA
Conditions: Registries, Quality Improvement, Hip Arthropathy, Knee Arthropathy, Hospital Performance
Sponsor: Leiden University Medical Center

Grant Application Form Van Rens Foundation version February 2017 
Grant Application Form  
Van Rens Foundation 2017 
1a. Study title (max. 25 words) 
 
 
 
 
1b. General information project leader 
Title 
 
Initials 
 
Name 
 
Position 
 
Institute/organisation 
 
Contact address 
 
Contact email 
 
Contact telephone 
 
 
1c. Other project members involved 
1 
Name 
Position 
Institute 
 
2 
Name 
Position 
Institute 
 
3 
Name 
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Institute 
 
4 
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5 
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Institute 
 
 
Please add the Curriculum Vitae of the project leader to the grant application 
See attached file
 
 
What makes a best performing hospital in hip and knee replacement? Quality 
improvement using joint registry data

Grant Application Form Van Rens Foundation version February 2017 
1d. Project characteristics 
Is this application a resubmission? 
Yes / no  
submitted in 
 
Type of researcher 
Post-doc / PhD / other, specify  
 
Duration in months 
                 months 
Expected starting date (not sooner than January 
1, 2017, not later than March 1, 2017) 
 
 
1e. List of 5 key words  
1. 
2. 
3. 
4. 
5. 
 
1f. Scientific summary (max 1000 words, no tables/figures) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Background 
Several studies have shown variation in hospital performance after hip and knee 
replacement, both in outcomes and costs, thereby suggesting that improvement may 
be possible. In the Dutch joint registry, data are available on 300,000 hip and knee 
arthroplasties since 2007. The variation in outcomes between hospitals is visible in 
their annual report e.g. on the case-mix adjusted 1-year revision rates. However, other 
outcomes (e.g. readmission and length of stay) may be relevant and interrelated so 
that these outcomes need to be considered together and integrated into a composite 
outcome (or hospital performance profile), not yet available for orthopaedic surgery. 
Furthermore, data needs to be available continuously for hospitals to use this 
information effectively to improve quality of care, as shown by audits in other surgical 
fields to be associated with better guideline adherence and improved outcomes. This 
requires orthopaedic surgeons to be educated on how to use this information to figure 
out where and how to improve. 
 
Objectives 
Aim of this study is to gain more insight into the extent of hospital variation in 
outcomes after primary hip and knee replacement as well as in explanatory factors, 
and to test whether a strategy of education combined with more frequent feedback of 
data, results in more effective use of joint registry data, more quality improvement 
activities as well as better outcomes.  
 
Methods 
The study is designed as a randomized controlled trial to test the effectiveness of the 
multifaceted strategy, within the Dutch joint registry to estimate both the extent of 
hospital variation and to monitor the outcomes in the trial. 
Effectiveness
Quality improvement
Performance measures
Hospital variation
March 1, 2018
48 months
PhD MD
No

Grant Application Form Van Rens Foundation version February 2017 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
First we will assess the extent of variation in hospital performance after hip and knee 
replacement for all hospitals in the Netherlands using anonymous LROI data.  All 
patients undergoing a hip or knee replacement in the period 2014-2016 will be 
included, regardless of type of prosthesis. We will use joint registry outcomes 
regarding revision (overall and by cause) as well as PROMs to indicate performance 
(adjusted for case-mix: age, gender, ASA class, BMI, smoking and Charnley 
comorbidity score) and estimate the extent of hospital variation as well as reliability of 
ranking.  
Then we will invite all hospitals performing hip and knee replacement to participate in 
this study. Participation means that they agree their joint registry data to be linked to 
data from other sources (length of stay, readmission, quality indicators) to obtain the 
composite outcome and to be randomized in a study to improve performance based 
on these data. The composite outcome is defined as: survival, no revision within 1 
year or emergency readmission within 30 days, a normal length of stay and an 
increase in PROMs in the upper quartile. Power calculation has shown that 18 
hospitals are needed to detect a difference of 70% versus 80% in the composite 
outcome between randomized groups with 80% power and 95% reliability. For each 
hospital and each outcome we will assess whether the hospital has a better, average 
or worse performance based on the funnel plot, to assess whether the different 
outcomes are correlated so that some hospitals perform good on all outcomes or that 
there are mostly mixed profiles. Potential explanatory factors (e.g. pre- and 
postoperative processes, structural factors like teaching status, experience of 
orthopaedic surgeons and fast track protocols) explaining the variation in performance 
between hospitals, will be assessed through a survey among orthopaedic surgeons 
and interviews. 
Participating hospitals will be randomized to an early versus late group, stratified by 
teaching status as this might influence the time available for quality improvement 
versus production parameters. In the first period, the early group will receive the 
intervention and will be compared with the other group receiving usual care. In the 
second period, the early group should be able to sustain the intervention in daily 
practice (so not actively supported anymore) and will be compared with the late group 
now receiving the intervention. In addition, we will be able to compare the first and 
second period within each group. The intervention will consist of education on how to 
use joint registry data for quality improvement, create more awareness, regular 
feedback on outcomes from the joint registry and other data sources, linking to 
another hospital with better outcomes to exchange information on how this is 
achieved. We will monitor improvement on the composite outcome but also on 
intermediate outcomes (number of quality improvement activities undertaken, 
knowledge about performance among orthopaedic surgeons, number of people 
attending quality meetings etc). 
 
Expected outcomes 
Expected outcomes of this study include knowledge on the extent of variation in 
outcomes after primary hip and knee replacement between all hospitals in the 
Netherlands, and the influence of case-mix and explanatory factors. In addition, a 
composite outcome measure will be created which can be used after the study is 
completed for instance within the existing audit by the Dutch Orthopaedic association, 
similar to other scientific associations.  In addition, if the intervention is effective, this 
can be further implemented in other hospitals to further expand the use of the joint 
registry for quality improvement in daily practice.

Grant Application Form Van Rens Foundation version February 2017 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1g. Short lay summary of the project (max 100 words) 
 
 
 
 
 
 
 
 
 
 
 
 
Patient outcomes after hip or knee replacement may vary between hospitals. Part of 
this variation is due to some hospitals treating more complex patients, but part 
cannot be explained and may be due to the quality of care delivered to these 
patients. Therefore hospitals can use information on whether the outcomes in their 
patients are better or worse than in other hospitals to improve their care. The 
present study will gain information on how much variation there is between Dutch 
hospitals, which factors may explain this and test a strategy to use this information 
to improve patient outcomes.

Grant Application Form Van Rens Foundation version February 2017 
Research proposal 
2a. Research question (max 150 words) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2b. Background (max 1000 words) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Hip and knee replacement are frequently performed worldwide [1,2] and the number 
of procedures is expected to increase exponentially in the coming decades, due to 
the ageing of the population and the increasing prevalence of obesity. [3-5] This will 
increase the burden on health care systems and have considerable societal and 
economic consequences. [6] Although these procedures are very effective in 
reducing pain and improving functionality, it becomes increasingly important to 
deliver both high value care for patients and reduce costs while the number of 
procedures increases. In recent years, several studies have shown variation in 
hospital performance after hip and knee replacement both in outcomes and costs 
[7-9], thereby suggesting that there may be room for improvement. To inform 
hospitals and drive quality improvement, data are needed on the extent of variation 
in performance. 
 
In the Netherlands, the nationwide joint registry comprises data from over 300,000 
hip and knee arthroplasties since 2007, with complete coverage of all hospitals 
attained in 2012 and completeness of at least 95%. [10] The joint registry produces 
an annual report in which the variation between hospitals is visible, particularly 
recently when case-mix adjusted funnel plots on 1-year revision rates after hip and 
knee replacement were added. [11] These data are now also available on the 
secured part of the website, where hospitals can see how their performance relates 
to others. However, additional case-mix variables (smoking and BMI) have become 
available but not yet used for case-mix adjustment, whereas these may have 
different risks on revision or complications. [12-14] Furthermore, outcomes are 
aggregated for all types of prosthesis bearings, whereas it may be appropriate to 
report these separately or to adjust for differences in procedure mix between 
hospitals as outcomes may differ. [15] Variation in other outcomes such as long 
length of stay, readmissions or patient reported outcomes has not been routinely 
reported in the Dutch joint registry. Not all of these outcomes are available in the 
joint registry, but are available in other data sources which may be linked. 
Aim of the present study is  
 
1) to gain more insight into variation in hospital performance on different outcomes 
after primary hip and knee replacement, combined into hospital performance profiles 
 
2) the extent to which this variation is explained by differences in patient-mix, type of 
prostheses, pre-and post-operative processes or other factors  
 
3) to test whether an intervention, consisting of education and frequent feedback of 
data results in more effective use of joint registry data, more quality improvement 
activities as well as better outcomes 

Grant Application Form Van Rens Foundation version February 2017 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Hospitals can use information on variation in outcomes as a starting point for quality 
improvement initiatives, particularly if this information shows that their hospital has 
worse outcome than other comparable hospitals. However, a hospital can have a 
good performance on one outcome and needs to improve on another, but as the 
different outcomes are also likely interrelated, these need to be considered 
together. In previous research we have already defined such a composite outcome 
for other specialties [16-18], also known as textbook outcome and used by insurers 
[19], but this is not available for orthopaedic surgery in the Netherlands. So the 
information on different outcomes after hip and knee replacement need to be 
integrated into a composite outcome (or hospital performance profile) but also 
needs to be available continuously for hospitals to use this information effectively to 
improve quality of care, as shown by audits in other surgical fields to be associated 
with better guideline adherence and improved outcomes. [20] This requires 
orthopaedic surgeons to be educated on how to use this information to figure out 
where and how to improve. 

Grant Application Form Van Rens Foundation version February 2017 
 
 
 
 
 
 
 
 
 
 
 
 
 
2c. State of the art of the work field (max 500 words) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Most studies reporting on variation in hospital performance after hip and knee 
replacement are from the United States (US), which were performed particularly in 
the context of the recent introduction of bundled payment for hip and knee 
replacement. [8,9,21] Bundled payment was introduced to reduce variation in costs 
and outcomes between hospitals. Therefore studies performed in such a context 
are likely to have both a better average performance (e.g. on length of stay, or 
readmissions) but also smaller hospital variation as particularly hospitals with worse 
outcomes are forced to improve. In addition, outcomes like length of stay are 
already know to be much lower in the US than Europe, as patients are discharged 
quickly to rehabilitation centres, so that results from US studies may not be 
generalised. Only one European study was found, showing considerable variation in 
return to theatre (RTT) and suggesting this may be used as a quality performance 
measure [22]. However, the question is how this RTT adds to revision surgery 
which is mostly used as a performance measure, as different surgeries including 
revision, dislocation, debridement are now all combined into one reoperation 
variable. Furthermore, all these studies were based on administrative data and 
acknowledged this to be a limitation, whereas the availability of clinical variables 
would allow for better adjustment for case-mix. Research into explanatory factors 
explaining the variation in outcome is scarce. 
 
The present study will add the perspective from another European country, without 
bundled payment or payment for performance present, and use more clinical 
variables for risk-adjustment available in the joint registry. By linking to other data 
sources, case-mix adjustment is likely to be optimized and to give a more 
comprehensive view on quality of care. Innovative elements of this proposal include 
that joint registry data will be used to not only show the variation between hospitals 
in outcomes, but also to investigate explanatory factors and to actively engage 
professionals to use this information for quality improvement in daily clinical practice 
and to test the effectiveness on improving outcomes for patients in a randomized 
study.

Grant Application Form Van Rens Foundation version February 2017 
Methodology 
3a. Methodology – Variables, data sources and data collection methodology (max 
300 words) 
The study is designed as a randomized controlled trial nested within the joint 
registry (LROI). We will first analyse the hospital variation in case-mix adjusted 
outcomes after hip and knee replacement performed in 2014-2016 using all data 
from the joint registry and create a composite ‘textbook’ outcome. Then we will ask 
hospitals to participate in a randomized controlled trial in which they will get more 
regular feedback regarding their performance both on the composite and individual 
outcomes, combined with education on how to use these data to improve their 
quality. The feedback information will be based both on data available in the joint 
registry (e.g. revision) as on data available through linkage with other data sources 
(e.g. readmission). Hospitals are randomized to an early versus late group, with the 
early group receiving regular feedback combined with education in the first period 
followed by a second period in which they continue to receive the regular feedback 
but without the active support and education, to test whether they have been able to 
sustain this in daily clinical practice. The late group receives usual care (i.e. the 
current situation) in the first period, followed by a second period in which they 
receive regular feedback and education (as the early group received in the first 
period). 
 
Available LROI data will be used, linked to hospital supplied data on quality 
indicators and administrative data. Data on potential factors explaining the hospital 
variation in outcomes will be retrospectively collected in participating hospitals, 
using record review among a sample of patients, surveys and interviews among 
professionals. In addition, we will monitor prospectively both the quality 
improvement activities undertaken and the outcomes in participating hospitals using 
data from the LROI and survey data, both before, during and after the intervention.

Grant Application Form Van Rens Foundation version February 2017 
3b. Methodology – Study population (persons, implants, time period, inclusion and 
exclusion criteria) (max 200 words) 
All patients undergoing a hip or knee replacement in the period 2014-2016 are 
included, regardless of type of prosthesis, to assess the extent of variation and 
potential explanatory factors (research question 1 and 2). This period was chosen 
given the additional availability of relevant case-mix factors (smoking and BMI) and 
still allow for sufficient follow-up time for all patients to calculate 1-year revision 
rates. To answer research question 3, we will include more recently treated 
patients, to be able to monitor the effect of quality improvement activities on 
outcomes in the participating hospitals.

Grant Application Form Van Rens Foundation version February 2017 
3c. Methodology – Work plan (max. 1750 words (including tables and figures)) 
All hospitals 
First we will assess the extent of variation in hospital performance after hip and knee 
replacement for all hospitals in the Netherlands using anonymous LROI data. All 
patients undergoing a hip or knee replacement in the period 2014-2016 will be 
included, regardless of type of prosthesis. The hospital variation will be assessed per 
year and for the entire period to increase power. The following outcomes will be used: 
- Overall revision within 1 year 
- Revision for infection within 1 year 
- Revision for loosening within 1 year 
- Revision for dislocation within 1 year 
- Increase in Patient Reported Outcome Measure (PROM) after versus before 
hip/knee replacement (measured by the EQ-5D, HOOS-PS and KOOS-PS) 
 
For the different PROM dimensions (e.g. pain or functionality) we will first calculate 
quartiles of increase in PROM based on all patients from all hospitals, separately for 
hip and knee replacement. Consequently, being in the upper quartile of PROMs 
increase (yes/no) will be calculated as an additional variable for each patient to 
indicate performance, as this is what both patients and doctors strive for. Separate 
case-mix models will be created for each outcome using backwards logistic 
regression, starting with the entire set of patient characteristics (age, gender, ASA 
class, BMI, smoking and Charnley comorbidity score) as independent variables and 
using p<0.10 for inclusion in the model. Using the coefficients from this multivariate 
model, the expected probability on the outcome is calculated for each patient. 
Aggregating these probability at the hospital level results in the expected number of 
events for a hospital, to be compared with the observed number. The risk-adjusted 
outcome rate in the funnel plot will be calculated by dividing the observed number by 
the expected number and multiplying by the average rate across all hospitals. 
 
The extent of variation between hospitals will be expressed as the median rate with 
the interquartile range, as well as the number of outliers (both above and below the 
95% confidence interval). Exact Poisson 95% confidence intervals will be calculated. 
The reliability of ranking hospitals (rankability) will also be calculated (see 3d - 
statistics). As a separate step, we will add different prosthesis characteristics to the 
model (e.g. cemented or uncemented, manufacturer etc.) as these may be 
determinants of the outcomes and explain part of the variation, but can be changed 
and are thus part of possible quality improvement initiatives. We will assess whether 
adding these variables will change both the overall model fit (assessed by the 
C-statistic) and the number of outliers to indicate possible consequences for hospitals. 
 
Participating hospitals 
All hospitals in the Netherlands will be approached to participate using an online 
survey to the head of the orthopaedic department, combined with news feeds on the 
LROI secured site and the general Dutch Orthopaedic Association site with a direct 
link. Participation means that they agree their LROI data to be linked to data from 
other sources to obtain the composite outcome and to be randomized in a study to 
improve performance based on these data. LROI data will be linked to the following 
additional data sources: 
- Administrative data, routinely collected in all hospitals, containing admission data to 
calculate length of stay and readmissions following primary hip and knee replacement

Grant Application Form Van Rens Foundation version February 2017 
If possible, cost data will also be requested. 
- Annual quality indicators partly based on LROI data but with data added by the 
hospital e.g. on the number of surgeons performing the hip and knee replacements 
and percentage of surgical site infections. 
 
Linkage will be done according to the following procedure (see also 7c for more 
detail). After agreement for participation, we will approach the LROI to select the 
appropriate hospitals and patients from the total dataset into a new dataset. Each 
hospital can convert the anonymous patient identifiers to the local unique patient 
number for their own patients, which can be used to add the following variables: 
- Admission and discharge data in hospital 
- Any subsequent admissions for this patient, whether this was an emergency 
admission or not, and the primary diagnosis for these admissions 
- Date of death or patient still alive 
 
Each hospital will send this additional information linked to the anonymous patient 
identifier back to the coordinating centre. The new dataset will be used to conduct 
the same analyses as for all hospitals to assess whether results are similar. 
Differences between participating and non-participating hospitals in outcomes and 
patient characteristics will be tested using chi-square tests for categorical variables 
and t-tests for continuous variables. As described above, case-mix models will be 
built to adjust hospital variation for the additional outcomes: 
- All readmissions within 30 days 
- Emergency readmissions within 30 days 
- Long length-of-stay, defined as a length of stay in the upper quartile 
We will create the following composite outcome: survival, no revision within 1 year 
or emergency readmission within 30 days, a normal length of stay and an increase 
in PROMs in the upper quartile. This is indicated for each patient as a yes/no 
variable and aggregated to the hospital level to calculate the observed number. 
Using similar procedures as described above, a case-mix model can be build and 
funnel plots can be created. For each hospital and each outcome we will assess 
whether the hospital has a better, average or worse performance based on the 
funnel plot, to assess whether the different outcomes are correlated so that some 
hospitals perform good on all outcomes or that there are mostly mixed profiles. 
 
Potential other factors (in addition to patient and prosthesis characteristics) 
explaining the variation in performance between hospitals, will be based on 
literature review and expert advice and depend on the outcome considered. For 
instance, for differences in revision for infection we will consider factors such as 
antibiotic prophylaxis, both timing and general policy (single vs multiple shot). These 
factors will be assessed in the survey among all orthopaedic surgeons performing 
hip and knee replacement in the participating hospitals, and added as hospital level 
explanatory factor in the statistical model. In addition to questions on pre- and 
postoperative processes, some more general structural characteristics will be 
assessed such as teaching status, experience of orthopaedic surgeons and fast 
track protocols. We will also conduct interviews to assess in a qualitative way how 
hospitals act based on feedback information provided from the joint registry as well 
as  to provide context regarding factors explaining difference in performance. 
Multilevel logistic regression models will be used with some variables assessed at

Grant Application Form Van Rens Foundation version February 2017 
the hospital level (e.g. from the survey or teaching status) and taking into account 
patients clustering within these hospitals. 
 
Randomized trial – using data on variation to improve hospital performance 
A pragmatic randomized controlled trial will be conducted with hospitals randomized 
to an early versus late group, stratified by teaching status as this might influence the 
time available for quality improvement versus production parameters. Randomization 
will be done using a computer generated randomization table in a 1:1 ratio. When 
agreeing to participate, the hospital supplies a list with names and email addresses of 
orthopaedic surgeons who perform hip and knee replacements to be used to send 
information and schedule meetings. The intervention will consist of the following 
components: 
- Create more awareness about actual performance: all orthopaedic surgeons 
performing hip and knee replacement will receive their own LROI account to be able 
to look at their performance. In the current situation, only the head of the department 
and a datamanager have access.  
- Education on how to use the information from the LROI to identify in which area a 
specific hospital can improve. 
- Add feedback data from other sources as well as from the survey, combined with 
education on how to use this information for improvement. This will be send quarterly 
by email to the intervention group to prevent contamination bias for the control group 
(receiving this intervention at a later point in time). 
- Link a hospital in need for improvement on a certain outcome (defined as outside the 
95% confidence interval of the funnel plot) to another similar hospital to enable 
learning from each other by exchanging in more detail how they treat these patients 
and achieve a better performance on this outcome. As it is likely that most hospitals 
will have mixed profiles, this will probably go both ways i.e. that hospital 1 learns from 
hospital 2 on a certain outcome but vice versa on another outcome. To facilitate this 
learning, meetings will be organized at respective hospital sites or linked to the 
bi-annual meetings of the Dutch Orthopaedic Association. The local datamanagers of 
hospitals will be included in these meetings as they will be the contact for monitoring 
the effect on outcomes and to support the orthopaedic surgeons in correctly 
interpreting the LROI data. 
- Prepare for sustainment: discuss at the meetings how this can be incorporated in 
daily clinical practice. 
 
During the trial, we will conduct quarterly measurement on intermediate outcomes 
showing whether the intervention reaches the target group (process evaluation). The 
following measurements will take place: 
- Knowledge among orthopaedic surgeons on their recent performance and how that 
relates to others 
- Number of quality improvement activities undertaken directed at improving a certain 
outcome. 
- Planned improvement activities (e.g. record review to figure out why performance is 
not as good as in other centers) together with who is responsible and by which time  
- Number of people attending the meetings, number of times a specific account has 
accessed the LROI site to ensure that information has reached the target group. 
- Survey among orthopaedic surgeons at the end of the intervention period on 
knowledge learned and the extent to which they think this is sustainable in daily 
practice. We will analyse the data using statistical process control techniques, based

Grant Application Form Van Rens Foundation version February 2017 
3d. Methodology – Statistics / power calculation (max 500 words) 
For the first part of the study, funnel plots will be used to assess the variation 
between hospitals in outcomes adjusted for case-mix. Expected probabilities for 
each outcome and patient will be calculated using logistic regression models and all 
available patient characteristics (age at primary procedure, gender, ASA class, BMI, 
smoking and comorbidity (Charnley score)) as independent variables. These are 
aggregated per hospital to be able to compare observed with expected numbers. 
Exact Poisson 95% confidence intervals will be calculated. We will also calculate 
the reliability of ranking hospitals for each outcome, a measure to indicate which 
part of the total variation is due to real hospital differences as opposed to chance. 
[23-25] This will be done for different outcomes as these might have different 
explanatory factors (e.g. revision for infection versus revision for dislocation) and for 
different types of prosthesis besides overall as it may direct towards using a 
different type of prosthesis if it would be associated with better outcomes. 
 
For the randomized controlled trial, at least 18 participating hospitals (9 in each 
arm) are needed to be able to detect a difference in performance on the composite 
outcome of 70% versus 80% with 80% power and 95% reliability (assuming an 
intra-hospital correlation of 0.02 and a median of 100 procedures per hospital, 
separately for hip and knee replacement). Based on previous multicentre studies 
conducted by our group [26] and the presence of the research collaborative CORE 
now at the Dutch Orthopaedic Association expected to further facilitate multicentre 
studies, inclusion of this number of hospitals is feasible (9 hospitals have already 
agreed or shown interest at this point). We will then perform the same analyses as 
in the first part to check whether participating hospitals may be a selection of 
hospitals, looking at both patient characteristics, extent of variation and structural 
factors like teaching status. Additional collected data for these hospitals (e.g. on 
pre-and postoperative processes) will be added to test whether these explain part of 
the hospital variation (e.g. different antibiotic prophylaxis policy might explain part of 
differences in revision for infection).
on quarterly data (monthly if feasible), particularly suitable to signal whether 
outcomes have improved. This will be done both for the outcomes from the LROI 
data and the intermediate outcomes within the group receiving the intervention.  
In addition we will compare the outcomes between the early group and the late 
group (control group because they receive usual care in first period) to test the 
effectiveness of the intervention, using the appropriate regression techniques. In the 
second period we will test the sustainability of this approach in daily practice by 
comparing the outcomes within the early group with the first period, and compare 
with the late group to test whether outcomes in the sustainability phase are similar 
as when actively supported. 

Grant Application Form Van Rens Foundation version February 2017 
3e. Methodology – Limitations of study design, data sources and analytical methods 
(max 300 words) 
3f. Methodology – Timeline (max 500 words) 
Year 1: Analysis variation all hospitals using anonymous LROI data, invite hospitals 
to participate, survey on explanatory factors, request additional hospital data and 
create queries to regularly extract this data 
 
Year 2: Analysis variation participating hospitals, linkage data, create composite 
outcome, preparation RCT
Even though a randomized controlled trial is the highest level of evidence 
attainable, some limitations of the overall project can be noted. First, showing the 
magnitude of the variation in outcomes between hospitals may be hampered by 
insufficient case-mix adjustment or large random variation. Insufficient case-mix 
adjustment will remain an issue because we can never obtain all relevant variables, 
but the available variables are likely to be the most relevant ones. Large random 
variation is particularly an issue for low frequent outcomes, which is why we have 
chosen to combine several outcomes in a composite outcome with the advantage of 
increasing power. In addition we will calculate the reliability of ranking hospitals to 
signal the ability to identify hospital variation from random noise (signal to noise 
ratio). A limitation for the randomized controlled trial might be that the intervention 
period in which professionals receive education and frequent feedback, may be too 
short to show results in patient outcomes. For this reason we have also included a 
process evaluation containing intermediate outcomes such as the number of quality 
improvement activities undertaken, which are likely to result in improved patient 
outcomes in the long run. Finally, a limitation may be that orthopaedic surgeons 
sometimes work in different hospitals, that both participate in the randomized 
controlled trial but are assigned to different arms. We will check for this possibility 
even though chances are low, by comparing names and email addresses of 
orthopaedic surgeons within participating hospitals.

Grant Application Form Van Rens Foundation version February 2017 
 
Year 3: In first 6 months feedback and education in early hospitals (organisation of 
meetings, frequent feedback, data collection on number of quality improvement 
initiatives and outcomes), in last 6 months in late hospitals. 
 
Year 4: Evaluation of effectiveness of intervention, writing publications 

Grant Application Form Van Rens Foundation version February 2017 
Outcome 
4a. Expected outcome / end product and impact (max 200 words) 
4b. Plans for communicating results and dissemination (max 200 words) 
Results from this study are particularly relevant for those professionals caring for hip 
and knee replacement patients, but also for datamanagers who might become 
increasingly important to help monitoring the hospital specific outcomes and to 
signal any changes that occur. We therefore aim to also disseminate our results by 
organizing a workshop alongside the scientific conferences of the Dutch 
Orthopaedic association which non-participating hospitals can attend to also learn 
about how to effectively use joint registry data for quality improvement. In this 
workshop we hope to involve professionals from participating hospitals as our 
champion experts, thereby creating a growing quality improvement network among 
orthopaedic surgeons, nurses and datamanagers. On the external joint registry 
website, an extract of study results will be made available for patients and insurers.
Outcomes of this study include knowledge on the extent of variation in outcomes 
after primary hip and knee replacement between all hospitals in the Netherlands, 
and the influence of case-mix and explanatory factors. In addition, a composite 
outcome measure will be created which can be used after the study is completed for 
instance within the existing audit by the Dutch Orthopaedic association, similar to 
other scientific associations.  The method to be used to link the joint registry data to 
other data sources can be implemented nationwide, as well as the strategies to 
improve quality of care on specific outcomes (if shown to be effective in the current 
study). Other end products include education material and formats for feedback of 
data which will be made available to the joint registry after completion of the study. 
As such, the study is likely to expand use of joint registry data and consolidate its 
use in routine clinical practice to patients.

Grant Application Form Van Rens Foundation version February 2017 
4c. Contribution of the project to the quality of orthopaedic care (max 300 words) 
5. Structure and cooperation research group (max 200 words)
Within the LUMC and the present research group, the departments of Orthopaedic 
Surgery and Medical Decision Making have successfully collaborated on a large 
number of multicentre studies and cohorts (eg LISBOA study, pragmatic RCT in 21 
hospitals, LOAS, Vespa and Paprika cohort). In addition, Prof Nelissen is the 
founder of the Dutch joint registry and still active in both national and international 
joint registries. Dr Marang-van de Mheen is one of the editors of BMJ Quality & 
Safety, has ample experience in working in large (international) consortia trying to 
reliably compare hospital outcomes (e.g. dr Foster Global Comparators), running 
large multicentre studies and large databases, as well as connecting routinely used 
data to quality improvement initiatives. Furthermore, extensive knowledge and 
experience in implementation research among orthopaedic surgeons is available 
(van Bodegom-Vos & Hofstede), thereby being able to create effective strategies for 
quality improvement by professionals. Besides the 5 orthopaedic departments 
already involved in writing this application, another 4 have already indicated their 
interest to participate. As such, the present study will be able to benefit both from a 
large existing network of hospitals as well as from extensive knowledge on registry 
data, reliably comparing hospital outcomes and quality of care.
The project will both show the extent of variation in outcomes between hospitals, 
and thereby the potential for quality improvement. Whether this is attainable in 
practice will depend on the importance of case-mix and explanatory factors. In 
addition, for hospitals participating in the randomized controlled trial the project will 
directly contribute to their activities to improve quality of care. 

Grant Application Form Van Rens Foundation version February 2017 
6. References (max 1 page) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1. Kremers HM, Larson DR, Crowson CR, et al. Prevalence of total hip and knee 
replacement in the United States. J Bone Joint Surg 2015;97:1386-97. 
2. OECD. Hip and Knee replacement. In: Health at a Glance: Europe 2012. OECD 
publishing, 2012 page 86-7. 
3. Kurtz S, Ong K, Lau E, Mowat F, Halpern M. Projections of primary and revision hip and 
knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg 2007;89:780-5. 
4. Kurtz SM, Ong KL, Lau E et al. International survey or primary and revision total knee 
replacement. Int Orthop 2011;35:1783-9. 
5. Kurtz SM, Lau E, Ong K, Zhao K, Kelly M, Bozic KJ. Future young patient demand for 
primary and revision joint replacement: national projections from 2010-2030. Clin Orthop 
Relat Res 2009;467:2606-12. 
6. Kurtz SM, Ong KL, Lau E, Bozic KJ. Impact of the economic downturn on total joint 
replacement demand in the United States: update projections to 2021. J Bone Joint Surg 
2014;96:724-30. 
7. Bozic KJ, Grosso LM, Lin Z, et al. Variation in hospital-level risk-standardized 
complication rates following elective primary total hip and knee arthroplasty. J Bone Joint 
Surg Am 2014;96:640-7. 
8. Fry DE, Pine M, Nedza SM, Locke DG, Reband AM, Pine G. Risk-adjusted hospital 
outcomes in medicare total joint replacement surgical procedures. JBJS 2017;99:10-8. 
9. Weeks WB, Kotzbauer GR, Weinstein JN. Using publicly available data to construct a 
transparent measure of health care value: a method and initial results. Milbank Quart 
2016;94:314-33. 
10. Van Steenbergen LN, Denissen GAW, Spooren A, van Rooden SM, van Oosterhout FJ, 
Morrenhof JW, Nelissen RGHH. More than 95% completeness of reported procedures in the 
population-based Dutch Arthroplasty Register. Acta Orthoped 2015;86:498-505. 
11. Dutch Arthroplasty Register (LROI). Annual report 2015. Den Bosch, 2016. Available at: 
www.lroi-rapportage.nl  
12. Wagner ER, Kamath AF, Fruth KM, Harmsen WS, Berry DJ. Effect of body mass index 
on complications and reoperation after total hip arthroplasty. J Bone Joint Surg 
2016;98:169-79. 
13. Si HB, Zeng Y, Shen B, et al. The influence of body mass index on the outcomes of 
primary total knee arthroplasty (review). Knee Surg Sports Traumatol Arthrosc 
2015;23:1824-32. 
14. Singh JA. Smoking and outcomes after knee and hip arthroplasty: a systematic review. J 
Rheumatol 2011;38:1824-34. 
15. Varnum C. Outcomes of different bearings in total hip arthroplasty – implant survival, 
revision causes and patient-reported outcome. Dan Med J 2017;64 pii:B5350. 
16. Kolfschoten NE, Kievit J, Gooiker GA, van Leersum NJ, Snijders HS, Eddes EH, 
Tollenaar RAEM, Wouters MWJM, Marang-van de Mheen PJ. Focusing on desired 
outcomes of care after colon cancer resections; hospital variations in ‘textbook outcome’. 
Eur J Surg Oncol 2013;39:156-63. 
17. Dijs-Elsinga J, Otten W, Versluijs M, Smeets H, Kievit J, Vree R, van der Made WJ, 
Marang-van de Mheen PJ. Choosing a hospital for surgery: the importance of information on 
quality of care. Med Decis Making 2010,30:544-55.  
18. Marang-van de Mheen PJ, Dijs-Elsinga J, Otten W, Versluijs M, Smeets H, Vree R, van 
der Made WJ, Kievit J. The relative importance of quality of care information when choosing 
a hospital for surgical treatment: a hospital choice experiment. Med Decis Making 
2011;31:816-27. 
19. Busweiler LA, Schouwenburg MG, van Berge Henegouwen MI, et al. Textbook outcome 
as a composite measure in oesophagogastric cancer surgery. Br J Surg 2017; Epub Feb 27 
20. Van Leersum NJ, Snijders HS, Henneman D, et al. The Dutch surgical colorectal audit. 
Eur J Surg Oncol 2013;39:1000-6. 

Grant Application Form Van Rens Foundation version February 2017 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
21. Dummit LA, KahveciogluD, Marrufo G, et al. Association between hospital participation in a 
medicare bundled payment initiative and payment and quality outcomes for lower extremity 
joint replacement episodes. JAMA 2016;316:1267-78. 
22. Bottle A, Aylin P, Loeffler M. Return to theatre for elective hip and knee replacements. 
What is the importance of patient factors, surgeon and hospital? Bone J J 2014;96-B:1663-8. 
23. Van Dishoeck AM, Lingsma HF, Mackenbach JP, Steyerberg EW. Random variation and 
rankability of hospitals using outcome indicators. BMJ Qual Saf 2011;20:869-74. 
24. Van Dishoeck AM, Koek MB, Steyerberg EW, van Benthem BH, Vos MC, Lingsma HF. 
Use of surgical site infection rates to rank hospital performance across several types of 
surgery. Br J Surg 2013;100:628-36. 
25. Henneman D, van Bommel AC, Snijders A, et al. Ranking and rankability of hospital 
postoperative mortality rates in colorectal cancer surgery. Ann Surg 2014;259:844-9. 
26. Voorn VMA, , Marang-van de Mheen PJ, So-Osman C, Kaptein AA, van der Hout A, 
Akker-van Marle ME, Koopman-Gemert AWMM, Dahan A, Nelissen RGHH, Vliet Vlieland 
TPMM, van Bodegom-Vos L. De-implementation of expensive blood saving measures in hip 
and knee arthroplasties: study protocol for the LISBOA II cluster randomized trial. Impl Science 
2014;9:48.

Grant Application Form Van Rens Foundation version February 2017 
Data storage 
7a. Information governance (max 100 words) 
 
 
 
 
 
 
 
 
 
 
 
7b. Patient identifiable data (max 100 words) 
 
 
 
 
 
 
 
 
 
 
 
7c. Linking to patient identifiable data from other sources (max 100 words) 
 
 
 
 
 
 
 
 
 
 
 
Budget  
8a. Co-financing (max 400 words) 
  
 
 
 
 
 
 
 
 
 
 
Co-financing will be requested to cover the material costs in this project (estimated 
at 20.000 euro). Material costs include data retrieval from participating hospitals, 
building a dedicated database for participating hospitals, organising meetings in 
hospitals during the trial and travel costs to visit the hospitals. If not found, these will 
be covered by department of the project leader or by the participating hospitals (e.g. 
hosting the meetings).
Data will be linked to other sources using the procedure as described above in more 
detail. Linkage will be done within participating hospitals so that any patient 
identifiable information will remain there and only anonymous data will be send to 
the coordinating centre to be analysed. The procedure will also be presented to the 
Medical Ethics committee for approval to comply with relevant Dutch laws.
Any patient identifiable data will remain in individual hospitals or in the joint registry.
The study will be presented to the Medical Ethics committee for a waiver, but in this 
way the procedure for governance of data will be checked. Data will be stored on 
the secured server of the LUMC in anonymised format, with the key to patient 
identifiable information remaining in individual hospitals. Only researchers involved 
in the project will have access to the data. The handling of data will comply with 
Dutch  law on privacy of patients and hospitals.

Grant Application Form Van Rens Foundation version February 2017 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8b. Budget 
 
 
Proportion 
(fte) 
Starting 
date 
Finishing 
date 
Duration (in 
months) 
Budget 
Personnel1 
Junior 
researcher / 
PhD / post-doc 
/ other, specify 
 
 
 
 
 
 
Junior 
researcher / 
PhD / post-doc 
/ other, specify 
 
 
 
 
 
 
Congress 
visit2 
 
 
1 Max. 1.0 fte in total; 2  Including administration fee, travel and hotel expenses, excluding NOV 
congress. 
 
 
4.000
31.673
48
0.1 project 
coordination
226.068
48
0.9 junior 
researcher

Grant Application Form Van Rens Foundation version February 2017 
8c. Motivation of requested budget (max 300 words) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8d. Contact person financial administration (max 100 words) 
 
 
 
 
 
 
 
 
 
  
 
 
 
Dhr. Frans van den Broek 
Email: fvdbroek@lumc.nl  
Phone: 071-5298298 
The project will be carried out by a junior researcher who preferably has a MD and 
will enter a PhD trajectory. In addition, given the multicentre character of the study 
and linkage of data to other sources, both epidemiological and project management 
expertise is required to coordinate the project. Conference costs to present results 
at an international conference are included. 
 
Part of the required project coordination costs will be contributed by the department, 
so that the total budget requested is 240.000 euro.

Grant Application Form Van Rens Foundation version February 2017 
Review and conflicts of interest 
9a. Suggested (international) referees (minimal 5)1 
 
Name 
Position and organisation 
Email address 
1. 
 
 
 
 
 
2. 
 
 
 
 
 
3. 
 
 
 
 
 
4. 
 
 
 
 
 
5. 
 
 
 
 
 
6. 
 
 
 
 
 
7. 
 
 
 
 
 
8. 
 
 
 
 
 
Comments (max 100 words) 
 
 
 
 
 
 
 
 
 
 
1 Please provide at least 5 (international) possible referees who are not  directly involved in this or 
comparable research projects within your research group. Prevent ‘hot shots’, since they are generally 
too busy to judge your project. This information will only be used internal and not send to external 
referees.  
 
9b. Conflicts of interest (max 100 words) 
 
 
 
 
 
 
 
 
 
 
All project members declare to have no conflicts of interest.
sten.rasmussen@rn.dk
Orthopaedic surgeon and associate professor, Aalborg University Hospital
Sten Rasmussen
andrew.carr@ndorms.ox.ac.uk
Nuffield Professor of Orthopaedics, Director of the Oxford Musculoskeletal BRU, Dir
Andrew Carr
a.gordon@sheffield.ac.uk
Consultant Orthopaedic surgeon, Sheffield teaching hospitals
Andrew Gordon
Ton.Tran@monashhealth.org
Director of Orthopaedic Surgery, Monash Health
Ton Tran
abp@clin.au.dk
Post doctoral scientist, Department of Clinical Epidemiology, University of Aarhus
Alma Pedersen

Grant Application Form Van Rens Foundation version February 2017 
Specification of data application LROI 
Specification of data application 
Joint 
Procedure type 
Patient demographics 
Hip 
 
Primary 
 
Gender 
 
Knee 
 
Revision 
 
Age at procedure 
 
Shoulder (available 
since 2014) 
 
Linked Primary-
revision 
 
Smoking (available since 2014) 
 
Elbow (available since 
2014) 
 
 
 
BMI (available since 2014) 
 
Ankle (available since 
2014) 
 
 
 
ASA Grade 
 
 
 
 
 
Charnley score (hip/knee) 
(available since 2014) 
 
 
 
 
 
Walch class (shoulder) 
(available since 2014) 
 
 
 
 
 
Previous operations of affected 
joint 
 
 
Procedure details 
Implant data 
 
 
Year of procedure 
 
Manufacturer 
 
 
 
Side 
 
Name of implant 
 
 
 
Indication for primary 
procedure 
 
Material of implant 
 
 
 
Approach 
 
Type of implant 
 
 
 
Type of prosthesis 
 
 
 
 
 
Fixation 
 
 
 
 
 
Articulation 
 
 
 
 
 
Reason for revision 
 
 
 
 
 
 
Data is provided on the level of detail needed to answer the research question. Data will not 
contain any patient identifiable data and is made untraceable to physician(s) and hospital(s). 
Traceability of data on the level of the physician or hospital will only be performed after 
approval of the concerning hospital(s) or physician(s). 
 
Please fill in the form in English and save the definitive Application Form Van Rens Foundation 
as a PDF file. Please sent this grant application form (as PDF) including the Curriculum Vitae of 
the project leader (as PDF) and a motivation letter (as PDF) to vanrensfonds@orthopeden.org. 
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Anonymous hospital number!!
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