Abstract
BACKGROUND: The existing comorbidity indexes, like Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Index (ECI), do not take infection factors into account for critically ill patients with immunocompromise, bringing about a decrease of prediction accuracy. Therefore, we attempted to incorporate infection location into the analysis to construct a rapid comorbidity scoring system independent of laboratory tests. METHODS: Data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care III database. A total of 3904 critically ill patients with immunocompromise admitted to ICU were enrolled and assigned into training or validation sets according to the date of ICU admission. The predictive nomogram was constructed in the training set based on logistic regression analysis and then undergone validation in the validation set in comparison with SOFA, CCI and ECI. RESULTS: Factors eligible for the nomogram included patient's age, gender, ethnicity, underlying disease of immunocompromise like metastatic cancer and leukemia, possible infection on admission including pulmonary infection, urinary tract infection and blood infection, and one comorbidity, coagulopathy. The nomogram we developed exhibited better discrimination than SOFA, CCI and ECI with an area under the receiver operating characteristic curve (AUC) of 0.739 (95% CI 0.707-0.771) and 0.746 (95% CI 0.713-0.779) in the training and validation sets, respectively. Combining the nomogram and SOFA could bring a new prediction model with a superior predictive effect in both sets (training set AUC = 0.803 95% CI 0.777-0.828, validation set AUC = 0.818 95% CI 0.783-0.854). The calibration curve exhibited coherence between the nomogram and ideal observation for two cohorts (p>0.05). Decision curve analysis revealed the clinical usefulness of the nomogram in both sets. CONCLUSION: We established a nomogram that could provide an accurate assessment of 30 days ICU mortality in critically ill patients with immunocompromise, which can be employed to evaluate the short-term prognosis of those patients and bring more clinical benefits without dependence on laboratory tests.