The CORE score, developed by a UCLA research team, aims to provide a more accurate assessment of a patient’s risk of mortality after surgery by accounting for chronic illnesses. The score was designed to address the limitations of existing tools such as the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI), which were not specifically tailored for surgical populations and often lack detailed information about pre-existing health conditions. By incorporating data from the 2019 National Inpatient Sample (NIS) and using machine learning algorithms, the researchers were able to develop a more nuanced risk evaluation tool.
The CORE score was developed using data from 699,155 patients undergoing 62 different operations across 14 specialties. The researchers used International Classification of Diseases, 10th Revision (ICD-10) codes to identify chronic diseases and sorted them into Clinical Classifications Software Refined (CCSR) groups. Logistic regression was then used to predict in-hospital mortality, and the resulting coefficients were used to calculate the CORE score, which ranges from zero (lowest risk) to 100 (highest risk). This new scoring system allows surgeons to better adjust for patients’ pre-existing conditions and more accurately determine mortality risk.
Health services and outcomes research using retrospective databases has become a significant aspect of surgical research. While researchers have been successful in identifying quality issues and disparities in healthcare, the lack of appropriate tools can make it challenging to determine if poor outcomes are independent of pre-existing conditions. The CORE score addresses this issue by providing a more comprehensive assessment of patient risk, allowing for more accurate analysis of surgical outcomes using large databases.
Dr. Nikhil Chervu, the lead author of the study and a resident physician in the UCLA Department of Surgery, emphasized the importance of incorporating the CORE score into future research to further validate its use and improve the analysis of surgical outcomes. The advent of novel statistical software and methodology has enabled researchers to leverage large databases to answer questions about healthcare quality, disparities, and outcomes. By incorporating tools like the CORE score, researchers can ensure that population comparisons are more accurate and meaningful, ultimately leading to improved patient care.
By utilizing machine learning algorithms and data from the NIS, the UCLA research team was able to create a more precise risk evaluation tool that takes into account the impact of chronic illnesses on surgical outcomes. The CORE score provides surgeons with a valuable tool to better assess a patient’s risk of mortality after surgery and adjust treatment plans accordingly. Incorporating this scoring system into future research can help further validate its effectiveness and improve the analysis of surgical outcomes using large databases.
Overall, the development of the CORE score represents a significant advancement in the field of surgical research, allowing for more nuanced and accurate assessments of patient risk. By addressing the limitations of existing tools and incorporating detailed information about pre-existing health conditions, the CORE score provides a valuable tool for surgeons and researchers to improve patient care and outcomes. Further validation and incorporation of this scoring system into future research efforts will help advance the understanding of surgical outcomes and ultimately lead to better patient outcomes.
