  Background Sepsis is one of the most life-threatening circumstances for
critically ill patients in the US, while a standardized criteria for sepsis
identification is still under development. Disparities in social determinants
of sepsis patients can interfere with the risk prediction performances using
machine learning. Methods Disparities in social determinants, including race,
gender, marital status, insurance types and languages, among patients
identified by six available sepsis criteria were revealed by forest plots.
Sixteen machine learning classifiers were trained to predict in-hospital
mortality for sepsis patients. The performance of the trained model was tested
on the entire randomly conducted test set and each sub-population built based
on each of the following social determinants: race, gender, marital status,
insurance type, and language. Results We analyzed a total of 11,791 critical
care patients from the MIMIC-III database. Within the population identified by
each sepsis identification method, significant differences were observed among
sub-populations regarding race, marital status, insurance type, and language.
On the 5,783 sepsis patients identified by the Sepsis-3 criteria statistically
significant performance decreases for mortality prediction were observed when
applying the trained machine learning model on Asian and Hispanic patients.
With pairwise comparison, we detected performance discrepancies in mortality
prediction between Asian and White patients, Asians and patients of other
races, as well as English-speaking and Spanish-speaking patients. Conclusions
Disparities in proportions of patients identified by various sepsis criteria
were detected among the different social determinant groups. To achieve
accurate diagnosis, a versatile diagnostic system for sepsis is needed to
overcome the social determinant disparities of patients.
