Development and validation of a modified SOFA score for mortality prediction in candidemia patients
Xiaofei Liu, Ranran Ding, Guangming Yang, Yuling Qiao, Zhen Ma, Yaping Feng, Feng Qu, Qiang Meng

TL;DR
This study improves the SOFA score to better predict mortality in patients with candidemia, using clinical data and machine learning.
Contribution
A modified SOFA score (mSOFA_3) is developed and validated for candidemia mortality prediction.
Findings
The mSOFA_3 model achieved an AUC of 0.826 in internal validation and 0.813 in the test cohort.
Kaplan-Meier analysis confirmed the model's ability to stratify patients into high- and low-risk groups.
The model integrates clinical biomarkers and SOFA components for improved accuracy.
Abstract
Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk stratification is crucial for timely intervention and could improve patient outcomes. This study aimed to enhance the predictive performance of the sequential organ failure assessment (SOFA) score by developing a modified SOFA (mSOFA) score, which is specifically designed for candidemia patients. Using data from MIMIC-III, MIMIC-IV, and ICU-JN databases, we identified key prognostic variables through LASSO regression and integrated into the mSOFA_3 model. The model incorporated respiratory_SOFA, coagulation_SOFA, and circulatory_SOFA along with clinical biomarkers, including lactate, albumin, and blood urea nitrogen. The mSOFA_3 model demonstrated superior predictive performance across multiple machine learning algorithms, with the logistic…
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Taxonomy
TopicsMachine Learning in Healthcare · Sepsis Diagnosis and Treatment · Dialysis and Renal Disease Management
