The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Menendez ME, Neuhaus V, van Dijk CN, Ring D. This item may be available elsewhere in EconPapers: Search for items with the same title. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Windows users should not attempt to download these files with a web browser. The module is made available under terms of the GPL v3 (). Note: This module should be installed from within Stata by typing "ssc install elixhauser". Statistics for 4,623,841 pairs of potentially comorbid medical terms are provided as. Keywords: Elixhauser score mortality comorbidity (search for similar items in EconPapers) A method for comorbidity discovery informed by each patient’s demographic and medical history is introduced. The ICD version of the diagnostic codes in the input database must be specified (a required option). Statistical Software Components from Boston College Department of EconomicsĮlixhauser summarizes a complete set of comorbidity measures from an input database containing Enhanced ICD-9-CM or ICD-10 diagnostic codes, optionally outputting indicators of each elixhauser comorbidity category along with the total number. The comorbidity code lists may be used by future researchers to calculate CCI and EM using records from Read coded databases. It uses 29 or 30 comorbidity measures (depending on the version) based on chronic and acute conditions in the ICD-10. Capturing comorbidities over a prolonged period only modestly improved the predictive value of either index: EM 1-year look-back 0.645 versus 5-year 0.676 versus complete record 0.695 and CCI 0.574 versus 0.591 versus 0.605. Vicki Stagg: Faculty of Medicine, University of Calgary The Elixhauser comorbidity measure is based off of the Charlson comorbidity index. Contact him at 61 or at Opinions expressed are that of the author and do not necessarily represent HCPro, ACDIS, or any of its subsidiaries.ELIXHAUSER: Stata module to calculate Elixhauser index of comorbidity Kennedy is a general internist and certified coder, specializing in clinical effectiveness, medical informatics, and clinical documentation and coding improvement strategies. I suggest developing preoperative documentation templates that capture these conditions in your preoperative and postoperative workflows so that when the observed PSI occurs, it is in proportion to what is expected.Įditor’s note: This article originally appeared in JustCoding. This exercise must be repeated for all the PSIs, keeping in mind that to be coded, they must be explicitly documented when present.Ī list of all ICD-10-CM codes mapping to the Elixhauser model is available online. Note that many conditions not affecting the MS-DRG for payment, such as hyperkalemia or hypokalemia, do affect the expected PSI metric. The trick is knowing what codes drive the coefficients that predict the likelihood of PSI 11 (and others as described in the reference). These follow the Healthcare Cost and Utilization Project (HCUP) model using the Elixhauser Comorbidity software.įor example, in PSI 11 (Postoperative respiratory failure rate), the observed rate is compared to an expected rate determined by certain regression model coefficients. They may fail to realize that the Health and Human Services Agency for Healthcare Research and Quality (AHRQ) has a risk-adjustment methodology that predicts each of these PSIs and is dependent upon the documentation and coding of PSI-sensitive risk factors. Many hospitals find that their patient safety indicator (PSI) ratios remain high despite doing a spectacular job of addressing these events and exclusions.
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