Charlson comorbidity index tool

The point values we assigned to the new instrument were the same as those suggested by the original CCI instrument, with a few exceptions. For renal disease, we changed the 2 points for a single category to 1 for mild-to-moderate disease and to 3 for severe renal disease. We tested the ICD-9 and ICD scoring systems in the Medicare Advantage population of Humana a national health and wellness organization , including only individuals who were continuously enrolled in Humana Medicare Advantage in at least 1 of 3 consecutive month periods.

Those with dual eligibility for Medicaid and Medicare were also excluded. The initial two month time windows were the last 1-year periods in which ICD-9 codes were used exclusively in the United States ie, October September and October September The third time window was the earliest 1-year window where ICD codes were used exclusively ie, October September The CCI scoring was based on diagnosis codes in claims for services received during these time periods.

Of these 19 conditions, 11 received 1 point. Especially serious conditions or severe levels of a condition received more points eg, 1 point for diabetes without chronic complications and 2 points for diabetes with chronic complications. Appendix I Table SI-2 online displays the 6 hierarchy categories. In each category, only the more severe condition should contribute to the CCI score when codes for both conditions are listed on an individual's claims record; for example, if an individual has cerebrovascular disease 1 point and hemiplegia or paraplegia 2 points , only the 2 points for hemiplegia or paraplegia are counted.

The full set of condition-specific tabular comparisons is shown in Appendix I Tables SI-3a to SI-3s online and is designed to allow the replication of the new scheme. We used 4 sets of analyses to assess the performance of the scoring systems. First, we computed the prevalence of each of the 19 CCI condition categories for all 3 periods and assessed for consistency.

Second, as a preliminary validation of the updated CCI instrument, we assessed the association between the CCI score and the current-year hospital admissions marked by discharge dates and the association with near-term day mortality.

The relationship between CCI score and admissions was evaluated by using a linear regression model to predict the mean admissions per , which was adjusted for sex reference, female and race reference, white , using 13 binary variables for CCI scoring reference value, 2.

This approach allowed for an assessment of whether the relationship between the CCI score and utilization was linear. We constructed a robust Poisson regression model 11 to assess the relationship between a CCI score and mortality in the 3-month period after the end of the analytic time window October-September and before the first enrollment month of a new plan year January. We chose to use a robust Poisson regression model to predict near-term mortality, which allows for the direct modeling of relative risks.

As in the utilization model, we included sex and race as covariates in addition to the CCI score represented as 13 binary variables for the CCI score; reference value, 2.

The mean CCI score or disease prevalence within the subpopulations with diagnoses were related to key changes in the new instruments. As noted earlier, the Deyo system ascribes the same score to all individuals with HIV infection regardless of whether the patient has AIDS; it does not include the codes for secondary diabetes; and it does not take the severity of renal disease into account.

Finally, we constructed 3 logistic regression models for the prediction of near-term mortality, using CCI score as the independent variable, and the area under the receiver operating characteristics AUC-ROC curve was calculated for each model.

Our new code sets were reviewed by a senior physician AR , who is familiar with administrative data and with Humana's enhancement of the Diabetes Complications Severity Index. These 3 researchers then resolved any remaining inconsistencies. In an iterative process, the prevalence of the 19 conditions was compared between ICD-9 and ICD populations to confirm the performance of the code, and modifications were made in response to any discrepancies.

In several of the analyses, the new ICD-9 and ICD scoring systems yielded comparable findings across similar populations. The populations grew from 1,, to 2,, from the first October September to the third October September panel. The demographics were very similar between the 3 panels, with a near-identical distribution of the age-sex subgroups.

From the first to the third panel, the populations were on average 0. The number and proportion of individuals with each of the 19 conditions, after application of the 6 condition hierarchies, is shown in Appendix II Table SII-2 online ; these data reveal a similar morbidity profile across the 3 populations. There was some shifting of classification within hierarchies with the transition from ICD-9 to ICD , notably with a greater proportion of diabetes complications and metastatic cancer present in the ICD era.

With the age adjustment, the minimum CCI score was 2 and the mode was 3. The 2 approaches to validation yielded expected results. The same pattern persisted, again with nonoverlapping CIs, after adjustment for race and sex Table 2.

These data suggest a near-linear relationship with a slightly increasing slope from low to high CCI score level.

The 3 utilization models explained Figure 2 shows the CCI score prevalence line graph and the corresponding mortality risk bar chart by panel. The line plots demonstrate a Pareto-like right-skewed distribution of each population across the morbidity spectrum and a near-linear, monotonic increase in risk from the lowest to the highest CCI score level. After adjustment for sex and race, the same pattern persisted for relative risk estimates, except for a slight overlap between CCI score levels 13 and 14 in the ICD panel Table 4.

Nonoverlapping CIs showed these differences to be significant. In other words, compared with the Deyo system, the CDMF ICD-9 system differentiated more sharply between individuals with mild-to-moderate renal disease and severe renal disease: a difference of 2. Compared with the Deyo system, the CDMF ICD-9 system identified slightly more individuals with diabetes and was slightly more likely to identify individuals as having severe diabetes, but the CIs overlapped considerably.

The third panel represented the first 12 months after the adoption in the United States of the ICD codes system. Email Address. Password Show. Or create a new account it's free. Forgot Password? Sign In Required. To save favorites, you must log in.

Creating an account is free, easy, and takes about 60 seconds. Log In Create Account. The principal investigators of the study request that you use the official version of the modified score here. Predicts year survival in patients with multiple comorbidities.

When to Use. Myocardial infarction. Exertional or paroxysmal nocturnal dyspnea and has responded to digitalis, diuretics, or afterload reducing agents. Peripheral vascular disease. History of a cerebrovascular accident with minor or no residua and transient ischemic attacks. The Charlson comorbidity index CCI , first developed in to assess 1-year mortality by reviewing hospital charts and validated in a cohort study of nearly patients affected by breast cancer.

Since then, CCI has become widely used scoring system to predict outcomes of variety of medical conditions and malignancies. Since age has been determined to be correlated with survival, the CCI was modified by Charlson et al.

This modification, the age-adjusted Charlson comorbidity index ACCI , includes the age of the patient as a correction variable of the final score of the CCI with the addition of one point for every decade over 40 years.

In the current study by Shanbhag et al. This was a single-center, retrospective study that included patients aged between 18 and 90 years with moderate—severe COVID patients. ACCI was calculated for all patients; the need for invasive mechanical ventilation, days of hospital stay, and in-hospital mortality were noted from the electronic data base.

The primary outcome of the study was the ability of ACCI to predict in-hospital mortality whereas secondary outcomes was the ability of ACCI to predict length of hospital stay and requirement of invasive mechanical ventilation.

The results showed that out of patients, 91 patients The authors have concluded that ACCI was able to predict the need for mechanical ventilation and in-hospital mortality reliably. The results of the study, however, should be interpreted with caution for the following reasons:. Ideally the outcomes should have been measured by using the optimum cutoff determined by ROC curve with best sensitivity and specificity. And one of these cutoffs would probably give a better result than the other.

The current study is a single-center retrospective study. Select End organ damage Uncomplicated No. Select Metastatic Yes No. Select Moderate to severe Mild No. The Charlson comorbidity scoring system The CCI index predicts the ten year mortality for patients presenting one or more of the conditions in the model.

CCI interpretation Each of the conditions listed above are awarded 1, 2, 3 or 6 points depending on the mortality risk associated with each of the comorbidities. The ten year survival equals 0.