Performance of Comorbidity Measures to Predict Stroke and Death in a Community-Dwelling, Hypertensive Medicaid Population

Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tenn, USA.
Stroke (Impact Factor: 5.72). 08/2008; 39(7):1938-44. DOI: 10.1161/STROKEAHA.107.504688
Source: PubMed


The Charlson and Elixhauser comorbidities are widely used to control for differences in comorbidity in epidemiological studies but have not been validated for outpatient studies of hypertension. This study sought that validation using death and stroke outcomes.
Using Cox models in a retrospective cohort study of 49,479 hypertensive patients, Modified Charlson Index was compared with 6 alternative approaches to assessing comorbidity: individual Charlson comorbidities, Elixhauser comorbidities, prior major cardiovascular disease event, traditional risk factors for cerebrovascular accident, healthcare utilization, and antihypertensive medication utilization. Comorbidity measures were calculated at baseline and for a period before occurrence of the study outcome of interest or study conclusion.
The Charlson comorbidities had the smallest Akaike information criterion value for both the stroke and death outcomes when baseline data were used. The Elixhauser comorbidities had the smallest Akaike information criterion value for both the stroke and death outcomes when follow-up data were used. Modified Charlson Index also predicted stroke and death, but alternative models were more robust.
This study indicates that both the Charlson and Elixhauser comorbidities are valid prediction tools that could enable clinicians and health systems to better assess risk for stroke and death in patients with hypertension. However, the Charlson comorbidities perform better when comorbidities are assessed using baseline data, whereas the Elixhauser comorbidities perform better for short follow-up periods when comorbidities are assessed proximal to events of interest.

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    • "In the validation phase of the CCI, age was also found to be an independent risk factor for death from a comorbid condition. To account for the effects of increasing age, one point can be added to the CCI score for each decade of life over the age of 50 (Tang et al., 2008). Thus, CCI provides an estimated probability of 10-year survival as a function of underlying comorbidities. "
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    ABSTRACT: Phenotyping obstructive sleep apnea syndrome's comorbidity has been attempted for the first time only recently. The aim of our study was to determine phenotypes of comorbidity in obstructive sleep apnea syndrome patients employing a data-driven approach. Data from 1472 consecutive patient records were recovered from our hospital's database. Categorical principal component analysis and two-step clustering were employed to detect distinct clusters in the data. Univariate comparisons between clusters included one-way analysis of variance with Bonferroni correction and chi-square tests. Predictors of pairwise cluster membership were determined via a binary logistic regression model. The analyses revealed six distinct clusters: A, 'healthy, reporting sleeping related symptoms'; B, 'mild obstructive sleep apnea syndrome without significant comorbidities'; C1 : 'moderate obstructive sleep apnea syndrome, obesity, without significant comorbidities'; C2 : 'moderate obstructive sleep apnea syndrome with severe comorbidity, obesity and the exclusive inclusion of stroke'; D1 : 'severe obstructive sleep apnea syndrome and obesity without comorbidity and a 33.8% prevalence of hypertension'; and D2 : 'severe obstructive sleep apnea syndrome with severe comorbidities, along with the highest Epworth Sleepiness Scale score and highest body mass index'. Clusters differed significantly in apnea-hypopnea index, oxygen desaturation index; arousal index; age, body mass index, minimum oxygen saturation and daytime oxygen saturation (one-way analysis of variance P < 0.0001). Binary logistic regression indicated that older age, greater body mass index, lower daytime oxygen saturation and hypertension were associated independently with an increased risk of belonging in a comorbid cluster. Six distinct phenotypes of obstructive sleep apnea syndrome and its comorbidities were identified. Mapping the heterogeneity of the obstructive sleep apnea syndrome may help the early identification of at-risk groups. Finally, determining predictors of comorbidity for the moderate and severe strata of these phenotypes implies a need to take these factors into account when considering obstructive sleep apnea syndrome treatment options.
    Journal of Sleep Research 09/2015; DOI:10.1111/jsr.12344 · 3.35 Impact Factor
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    • "Our finding that the Elixhauser and Charlson indices are the optimal predictors of one-year mortality is also in keeping with other studies of different populations including in-patient populations, community-dwelling older adults, and hypertensive adults [7,9,12,42]. Furthermore, studies that directly compare the Elixhauser to Charlson indices found, as we did, that the Elixhauser index performed better than the Charlson index [6,43,44]. "
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    ABSTRACT: Multiple comorbidity measures have been developed for risk-adjustment in studies using administrative data, but it is unclear which measure is optimal for specific outcomes and if the measures are equally valid in different populations. This research examined the predictive performance of five comorbidity measures in three population-based cohorts. Administrative data from the province of Saskatchewan, Canada, were used to create the cohorts. The general population cohort included all Saskatchewan residents 20+ years, the diabetes cohort included individuals 20+ years with a diabetes diagnosis in hospital and/or physician data, and the osteoporosis cohort included individuals 50+ years with diagnosed or treated osteoporosis. Five comorbidity measures based on health services utilization, number of different diagnoses, and prescription drugs over one year were defined. Predictive performance was assessed for death and hospitalization outcomes using measures of discrimination (c-statistic) and calibration (Brier score) for multiple logistic regression models. The comorbidity measures with optimal performance were the same in the general population (n = 662,423), diabetes (n = 41,925), and osteoporosis (n = 28,068) cohorts. For mortality, the Elixhauser index resulted in the highest c-statistic and lowest Brier score, followed by the Charlson index. For hospitalization, the number of diagnoses had the best predictive performance. Consistent results were obtained when we restricted attention to the population 65+ years in each cohort. The optimal comorbidity measure depends on the health outcome and not on the disease characteristics of the study population.
    BMC Health Services Research 06/2011; 11(1):146. DOI:10.1186/1472-6963-11-146 · 1.71 Impact Factor
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    • "Other studies attempted to quantify the effect of a disease on other * To whom correspondence should be addressed. diseases by introducing comorbidity measures (Kelli et al., 2005; Tang et al., 2008). In a recent study of illness progression, a database was also constructed to summarize statistical correlations between phenotypic diseases from histories of more than 30 million patients in a phenotypic disease network (Hidalgo et al., 2009). "
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    ABSTRACT: In general, diseases are more likely to be comorbid if they share associated genes or molecular interactions in a cellular process. However, there are still a number of pairs of diseases which show relatively high comorbidity but do not share any associated genes or interactions. This observation raises the need for a novel factor which can explain the underlying mechanism of comorbidity. We here consider a feedback loop (FBL) structure ubiquitously found in the human cell signaling network as a key motif to explain the comorbidity phenomenon, since it is well known to have effects on network dynamics. For every pair of diseases, we examined its comorbidity and length of all FBLs involved by the disease-associated genes in the human cell signaling network. We found that there is a negative relationship between comorbidity and length of involved FBLs. This indicates that a disease pair is more likely to comorbid if they are connected with FBLs of shorter length. We additionally showed that such a negative relationship is more obvious when the number of positive involved FBLs is larger than that of negative involved FBLs. Moreover, we observed that the negative relationship between comorbidity and length of involved FBLs holds especially for disease pairs that do not share any disease-associated genes. Finally, we proved all these results through intensive simulations, based on a Boolean network model. Supplementary data are available at BioInformatics online.
    Bioinformatics 02/2011; 27(8):1113-20. DOI:10.1093/bioinformatics/btr082 · 4.98 Impact Factor
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