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"Comorbidity"
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Predictive Ability of Comorbidity Indices for Surgical Morbidity and Mortality: a Systematic Review and Meta-analysis
Background
Several contemporary risk stratification tools are now being used since the development of the Charlson Comorbidity Index (CCI) in 1987. The purpose of this systematic review and meta-analysis was to compare the utility of commonly used co-morbidity indices in predicting surgical outcomes.
Methods
A comprehensive review was performed to identify studies reporting an association between a pre-operative co-morbidity measurement and an outcome (30-day/in-hospital morbidity/mortality, 90-day morbidity/mortality, and severe complications). Meta-analysis was performed on the pooled data.
Results
A total of 111 included studies were included with a total cohort size 25,011,834 patients. The studies reporting the 5-item Modified Frailty Index (mFI-5) demonstrated a statistical association with an increase in the odds of in-hospital/30-day mortality (OR:1.97,95%CI: 1.55–2.49,
p
< 0.01). The pooled CCI results demonstrated an increase in the odds for in-hospital/30-day mortality (OR:1.44,95%CI: 1.27–1.64,
p
< 0.01). Pooled results for co-morbidity indices utilizing a scale-based continuous predictor were significantly associated with an increase in the odds of in-hospital/30-day morbidity (OR:1.32, 95% CI: 1.20–1.46,
p
< 0.01). On pooled analysis, the categorical results showed a higher odd for in-hospital/30-day morbidity (OR:1.74,95% CI: 1.50–2.02,
p
< 0.01). The mFI-5 was significantly associated with severe complications (Clavien-Dindo ≥ III) (OR:3.31,95% CI:1.13–9.67,
p
< 0.04). Pooled results for CCI showed a positive trend toward severe complications but were not significant.
Conclusion
The contemporary frailty-based index, mFI-5, outperformed the CCI in predicting short-term mortality and severe complications post-surgically. Risk stratification instruments that include a measure of frailty may be more predictive of surgical outcomes compared to traditional indices like the CCI.
Journal Article
Why Summary Comorbidity Measures Such As the Charlson Comorbidity Index and Elixhauser Score Work
BACKGROUND:Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate.
METHODS:We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations.
RESULTS:We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding.
CONCLUSIONS:Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.
Journal Article
Comorbid conditions among children with autism spectrum disorders
\"This book presents the similarities and intersections between Autism Spectrum Disorders and comorbid conditions in children. It describes the prevalence and magnitude of comorbid conditions occurring in conjunction with ASD that complicate diagnosis and can potentially lead to inappropriate treatment and negative outcomes. It addresses the strengths and limitations of age-appropriate assessment measures as well as activity and motor skill measurement methods. Specific comorbid disorders are examined through the review of core symptoms, prognostic and diagnostic issues and treatment options for children on the ASD spectrum. Featured topics include: challenging behaviors in children with ASD; conditions ranging from feeding and gastrointestinal disorders to epilepsy; developmental coordination disorder (DCD); intellectual disability (ID); methods and procedures for measuring comorbid psychological, medical and motor disorders.\"-- Provided by publisher.
BOOK
Adaptation of the Charlson Comorbidity Index for Register-Based Research in Sweden
Comorbidity indices are often used to measure comorbidities in register-based research. We aimed to adapt the Charlson comorbidity index (CCI) to a Swedish setting.
Four versions of the CCI were compared and evaluated by disease-specific experts.
We created a cohesive coding system for CCI to 1) harmonize the content between different international classification of disease codes (ICD-7,8,9,10), 2) delete incorrect codes, 3) enhance the distinction between mild, moderate or severe disease (and between diabetes with and without end-organ damage), 4) minimize duplication of codes, and 5) briefly explain the meaning of individual codes in writing.
This work may provide an integrated and efficient coding algorithm for CCI to be used in medical register-based research in Sweden.
Journal Article
A New Elixhauser-based Comorbidity Summary Measure to Predict In-Hospital Mortality
BACKGROUND:Recently, van Walraven developed a weighted summary score (VW) based on the 30 comorbidities from the Elixhauser comorbidity system. One of the 30 comorbidities, cardiac arrhythmia, is currently excluded as a comorbidity indicator in administrative datasets such as the Nationwide Inpatient Sample (NIS), prompting us to examine the validity of the VW score and its use in the NIS.
METHODS:Using data from the 2009 Maryland State Inpatient Database, we derived weighted summary scores to predict in-hospital mortality based on the full (30) and reduced (29) set of comorbidities and compared model performance of these and other comorbidity summaries in 2009 NIS data.
RESULTS:Weights of our derived scores were not sensitive to the exclusion of cardiac arrhythmia. When applied to NIS data, models containing derived summary scores performed nearly identically (c statistics for 30 and 29 variable-derived summary scores0.804 and 0.802, respectively) to the model using all 29 comorbidity indicators (c=0.809), and slightly better than the VW score (c=0.793). Each of these models performed substantially better than those based on a simple count of Elixhauser comorbidities (c=0.745) or a categorized count (0, 1, 2, or ≥3 comorbidities; c=0.737).
CONCLUSIONS:The VW score and our derived scores are valid in the NIS and are statistically superior to summaries using simple comorbidity counts. Researchers wishing to summarize the Elixhauser comorbidities with a single value should use the VW score or those derived in this study.
Journal Article
Correction: Impact of patients’ age and comorbidities on prostate cancer overdiagnosis in clinical practice
[This corrects the article DOI: 10.1371/journal.pone.0315979.].
Journal Article