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whilst confirming hospital-level effects contributed signifi-
cantly. For process-based QIs, such as PN and MIS, this may
reflect that surgeon preference and access to resources as
opposed to patient or tumor characteristics are driving
treatment decisions. Furthermore, a volume-quality rela-
tionship may explain the variation observed, as supported
by the finding that superior-outlier hospitals were associ-
ated with higher surgical volumes for all QIs aside from the
LOS indicator. This is consistent with previous reports,
demonstrating higher surgical volume and academic
affiliation are associated with the uptake of PN
. Hence, while the underlying mechanism driving
quality variation could not be addressed directly, the
aforementioned factors warrant further investigation as
causal factors. Such data has important implications
regarding decisions surrounding the centralization of RCC
surgery to high volume or academic centers.
According to the Donabedian model, ideal QIs not only
capture variations in care, but further demonstrate con-
struct validity through being associated with known
structural, process, or outcome elements of health care
delivery . For all QIs analyzed, construct validity could
be demonstrated to varying degrees with superior perfor-
mance being associated with greater hospital volume,
academic status, or lower mortality (30-d, 90-d, overall).
We argue that a systematic data-driven approach as
reported here, which determines the construct validity of
putative QIs, should be implemented by policymakers to
prioritize QIs for inclusion in benchmarking strategies.
Composite measures encompassing multiple validated
QIs will likely be required to comprehensively capture
quality-outcome relationships . This is consistent with
the minimal concordance observed between the QIs studied
here ), highlighting that each captures a unique aspect
of RCC quality care. To address this, we developed a
composite measure of RCC surgical quality incorporating all
QIs analyzed in this study, the RC-QS. We included all
individual QIs as each displayed interhospital discrimina-
tion and construct validity by association to a structural or
outcome quality element. Importantly, construct validity
was determined for the RC-QS as a positive score associated
with both structural and outcome measures, including
improved patient survival. Notably, further improvements
to the RC-QS could be adopted through a similar analysis as
represented here as more QIs are validated. As such, we
envision this work as an initial step requiring ongoing and
dynamic improvements towards the creation of a quality-
benchmarking program for RCC surgery, with population-
level databases such as the NCDB serving as practical
vehicles to disseminate the RC-QS.
This study has several limitations. First, NCDB data [42_TD$DIFF]are
retrospective with certain case-mix factors not captured,
including tumor complexity and renal function. These
factors may influence QI performance and were not
controlled for Retrospective data are also
subject to reporting bias, which may particularly affect
PMR. Further, individual surgeon characteristics, referral
patterns, and care networks could not be assessed given
their absence in the NCDB. This is particularly relevant for
the PN and MIS indicators as surgeons may refer patients
due to a lack of experience or resources, contributing to a
poor quality score. Moreover, while quality-mortality
associations were captured, additional important patient
outcomes (eg, in-hospital complications, cancer-specific
survival) could not be assessed given their absence from
the NCDB. Lastly, similar quality assessments utilizing
population level databases outside the USA are required
to confirm the external validity of our results.
5. Conclusions
In conclusion, nationwide variations in RCC surgical quality
exist on a hospital-level. These variations are captured by
the RC-QS, a validated RCC specific composite measure of
quality readily determined from the NCDB. Although
assessments of quality variation between hospitals can
engender controversy, ongoing healthcare delivery im-
provement cannot occur without accurate measure and
feedback. These results support the use of the RC-QS as a
quality benchmarking tool for RCC surgery that provides
audit level feedback to hospitals and policymakers for
quality improvement.
This work was accepted for presentation at the 2017 American
Urological Association and Canadian Urological Association Annual
Meetings.Author contributions: Antonio Finelli had full access to all the
data in the study and takes responsibility for the integrity of the data and
the accuracy of the data analysis.
Study concept and design: Lawson, Saarela, Abouassaly, Finelli.
Acquisition of data: Lawson, Saarela, Abouassaly, Kim, Finelli.
Analysis and interpretation of data: Lawson, Saarela, Abouassaly, Kim,
Breau, Finelli.
Drafting of the manuscript: Lawson, Saarela, Finelli.
Critical revision of the manuscript for important intellectual content:
Lawson, Saarela, Abouassaly, Kim, Breau, Finelli.
Statistical analysis: Lawson, Saarela, Abouassaly, Finelli.
Obtaining funding: Abouassaly, Finelli.
Administrative, technical, or material support: None.
Supervision: Saarela, Abouassaly, Finelli.
Other: None.
Financial disclosures: Antonio Finelli certifies that all conflicts of
interest, including specific financial interests and relationships and
affiliations relevant to the subject matter or materials discussed in the
manuscript (eg, employment/affiliation, grants or funding, consul-
tancies, honoraria, stock ownership or options, expert testimony,
royalties, or patents filed, received, or pending), are the following:
None.
Funding/Support and role of the sponsor: None.
Acknowledgments: This work was supported by funds from the Princess
Margaret Cancer Centre Foundation.
Appendix A. Supplementary data
Supplementary data associated with this article can be
found, in the online version, at
.
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