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Research article
Narayan Sharma
Instute of Nursing Science, University of Basel
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8698-5469
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Background When chronic conditions are associated with outcomes such as mortality, comorbidity measures are essential both to describe patient health status and to adjust for potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, as optimal comorbidity weightings remain undetermined. The present study aimed to derive a set of new population-based Elixhauser comorbidity weightings, then to validate and compare their mortality predictivity against those of the Charlson and Elixhauser-based van Walraven weightings estimates in a population-based cohort.
Methods Retrospective analysis was conducted with routine Swiss general hospital (102 hospitals) data (2012–2017) for 6.09 million inpatient cases. To derive the population-based weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results for Part 1 alongside the established weighting systems used for Part 2. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Generalized additive models were weighted and adjusted for age, gender and hospital types.
Results Overall, the population-based weights’ c-statistic (0.867, 95% CI: 0.865–0.868) was consistently higher than Elixhauser-van Walraven’s (0.863, 95% CI: 0.862–0.864) and Charlson’s (0.850, 95% CI: 0.849–0.851) in the derivation and validation groups and net reclassification improvement of new weights offers improved predictive performance of 0.4% on the Elixhauser-van Walraven and 6.1% on the Charlson weightings.
Conclusions All weightings were validated with the national dataset and the new population-based weightings model improved the prediction of in-hospital mortality. The newly derive weights support patient population-based analysis of health outcomes.
Keywords
comorbidity indices; weights; risk adjustment; inpatient data; in-hospital mortality
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CURRENT STATUS: Published
View the published article at BMC Health Services Research.
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Submission checks complete
On 08 Dec, 2020
Editorial decision: Accept
On 03 Dec, 2020
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Review #1 received
Received 15 Nov, 2020
Editorial decision: Minor revision
On 15 Nov, 2020
Review #2 received
Received 04 Nov, 2020
Reviewer #2 agreed
On 31 Oct, 2020
Editor assigned
On 26 Oct, 2020
Reviewers invited
Invitations sent on 26 Oct, 2020
Reviewer #1 agreed
On 26 Oct, 2020
Submission checks complete
On 25 Oct, 2020
Editor invited
On 25 Oct, 2020
No comments provided
Editorial decision: Major revision
On 26 Sep, 2020
Review #2 received
Received 25 Sep, 2020
Review #1 received
Received 14 Sep, 2020
Reviewer #2 agreed
On 03 Sep, 2020
Reviewer #1 agreed
On 02 Sep, 2020
Editor assigned
On 01 Sep, 2020
Reviewers invited
Invitations sent on 01 Sep, 2020
Submission checks complete
On 31 Aug, 2020
Editor invited
On 31 Aug, 2020
Version 1
Posted 14 Jul, 2020
No comments provided
Review #2 received
Received 31 Jul, 2020
Editorial decision: Major revision
On 31 Jul, 2020
Review #3 received
Received 31 Jul, 2020
Review #1 received
Received 27 Jul, 2020
Review #4 received
Received 25 Jul, 2020
Review #5 received
Received 24 Jul, 2020
Reviewer #5 agreed
On 14 Jul, 2020
Reviewer #4 agreed
On 13 Jul, 2020
Reviewer #3 agreed
On 10 Jul, 2020
Editor assigned
On 08 Jul, 2020
Reviewers invited
Invitations sent on 08 Jul, 2020
Reviewer #1 agreed
On 08 Jul, 2020
Reviewer #2 agreed
On 08 Jul, 2020
Submission checks complete
On 07 Jul, 2020
Editor invited
On 07 Jul, 2020
First submitted
On 26 Jun, 2020
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Health Services Research.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Narayan Sharma
Instute of Nursing Science, University of Basel
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8698-5469
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Health Services Research.
No community comments so far
Submission checks complete
On 08 Dec, 2020
Editorial decision: Accept
On 03 Dec, 2020
No comments provided
Review #1 received
Received 15 Nov, 2020
Editorial decision: Minor revision
On 15 Nov, 2020
Review #2 received
Received 04 Nov, 2020
Reviewer #2 agreed
On 31 Oct, 2020
Editor assigned
On 26 Oct, 2020
Reviewers invited
Invitations sent on 26 Oct, 2020
Reviewer #1 agreed
On 26 Oct, 2020
Submission checks complete
On 25 Oct, 2020
Editor invited
On 25 Oct, 2020
No comments provided
Editorial decision: Major revision
On 26 Sep, 2020
Review #2 received
Received 25 Sep, 2020
Review #1 received
Received 14 Sep, 2020
Reviewer #2 agreed
On 03 Sep, 2020
Reviewer #1 agreed
On 02 Sep, 2020
Editor assigned
On 01 Sep, 2020
Reviewers invited
Invitations sent on 01 Sep, 2020
Submission checks complete
On 31 Aug, 2020
Editor invited
On 31 Aug, 2020
Version 1
Posted 14 Jul, 2020
No comments provided
Review #2 received
Received 31 Jul, 2020
Editorial decision: Major revision
On 31 Jul, 2020
Review #3 received
Received 31 Jul, 2020
Review #1 received
Received 27 Jul, 2020
Review #4 received
Received 25 Jul, 2020
Review #5 received
Received 24 Jul, 2020
Reviewer #5 agreed
On 14 Jul, 2020
Reviewer #4 agreed
On 13 Jul, 2020
Reviewer #3 agreed
On 10 Jul, 2020
Editor assigned
On 08 Jul, 2020
Reviewers invited
Invitations sent on 08 Jul, 2020
Reviewer #1 agreed
On 08 Jul, 2020
Reviewer #2 agreed
On 08 Jul, 2020
Submission checks complete
On 07 Jul, 2020
Editor invited
On 07 Jul, 2020
First submitted
On 26 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Health Services Research.
Background When chronic conditions are associated with outcomes such as mortality, comorbidity measures are essential both to describe patient health status and to adjust for potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, as optimal comorbidity weightings remain undetermined. The present study aimed to derive a set of new population-based Elixhauser comorbidity weightings, then to validate and compare their mortality predictivity against those of the Charlson and Elixhauser-based van Walraven weightings estimates in a population-based cohort.
Methods Retrospective analysis was conducted with routine Swiss general hospital (102 hospitals) data (2012–2017) for 6.09 million inpatient cases. To derive the population-based weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results for Part 1 alongside the established weighting systems used for Part 2. Charlson and van Walraven weightings were applied to Charlson and Elixhauser comorbidity indices. Generalized additive models were weighted and adjusted for age, gender and hospital types.
Results Overall, the population-based weights’ c-statistic (0.867, 95% CI: 0.865–0.868) was consistently higher than Elixhauser-van Walraven’s (0.863, 95% CI: 0.862–0.864) and Charlson’s (0.850, 95% CI: 0.849–0.851) in the derivation and validation groups and net reclassification improvement of new weights offers improved predictive performance of 0.4% on the Elixhauser-van Walraven and 6.1% on the Charlson weightings.
Conclusions All weightings were validated with the national dataset and the new population-based weightings model improved the prediction of in-hospital mortality. The newly derive weights support patient population-based analysis of health outcomes.
Figure 1
This is a list of supplementary files associated with this preprint. Click to download.
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