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Defining a new 6-factor Charlson Comorbidity Index utilizing data points available in the American College of
J Christopher Polanco-Santana1, João P G Kasakewitch2, Kaique Filardi3
1Department of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, United States.
Background:
The Charlson Comorbidity Index (CCI) is widely used in surgical research to summarize patients' baseline comorbidities. However, large surgical databases, such as the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP), lack key CCI components, limiting its applicability. Developing a modified CCI (mCCI) using available ACS-NSQIP variables could improve baseline risk stratification for patients undergoing pancreatoduodenectomy (PD). This study aimed to define such an mCCI and evaluate its performance compared with that of the conventional CCI.
Methods:
This was a 2-phase retrospective study that included patients who underwent PD. In the derivation phase, our institutional ACS-NSQIP database (2015-2021) was used to construct the mCCI, with scores reweighted to the observed 14-point maximum. The Spearman rank correlation was used to evaluate the relationship between CCI and mCCI. The nationwide ACS-NSQIP database (2022) was used for external validation. Unadjusted logistic regression models were constructed to predict discharge disposition and postoperative complications. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC)/receiver operating characteristic curve analysis with AUCs compared with the DeLong test.
Results:
A total of 333 institutional and 4867 national patients who underwent PD were included. In the derivation cohort, the mCCI was strongly correlated with the CCI (r = 0.85; P <.001). For discharge disposition, both indices yielded an AUC of 0.74. For postoperative complications, AUCs were 0.50 for CCI and 0.55 for mCCI. Similar trends were observed in external validation. No significant differences were found in the discriminatory capacities between the models.
Conclusion:
mCCI is a reasonable alternative to account for baseline comorbidities in patients undergoing PD using the ACS-NSQIP database. Further studies should refine weighting schemes across diverse populations to optimize mCCI performance.
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