A One-Nearest-Neighbor Approach to Identify the Original Time of Infection Using Censored Baboon Sepsis Data*

Abstract

Objective Sepsis therapies have proven to be elusive due to the difficulty of translating biologically sound and effective interventions in animal models to humans. A part of this problem originates from the fact that septic patients present at various times after the onset of sepsis while the exact time of infection is controlled in animal models. We sought to determine whether data mining longitudinal physiological data in a non-human primate model of E. coli-induced sepsis could help inform the time of onset of infection. Design and Setting A nearest-neighbors approach was used to back cast the time of onset of infection in animal models of sepsis. Animal data was censored to simulate prospective monitoring at any moment along the septic infection. This was compared against an uncensored database to find the most similar animal in order to estimate the infection onset time. Leave-one-out cross-validation was utilized for validation. Biomarker selection was performed based on criteria of estimation accuracy and/or ease-of-measurement. Subjects Retrospective data from 33 septic baboons (Papio ursinus) subjected to E.coli infusion. Validation was performed using 14 pigs that were subjected to surgically-induced peritonitis with feces and 22 pigs that were subjected to LPS infusion. Measurements and Main Results The presence of uniquely changing biomarkers during septic infection enabled the estimation of infection onset time in the datasets. Various combinations of temporal biomarkers such as white blood cell, oxygen content, mean arterial pressure, and heart rate yielded estimation accuracies of up to 97.8%. The use of temporal vital signs and a single measurement of serum biomarkers yielded highly accurate estimates without the need for invasive measurements. Validation in the pig data revealed similar results despite the heterogeneity of multiple experimental cohorts. This suggests that the method may be effective if sufficiently similar subjects are present in the database. Conclusions One nearest neighbor analysis showed promise in accurately identifying the onset of infection given a database of known infection times and of sufficient breadth. We suggest that this approach is ready for evaluation within the clinical setting using human data.

Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

“…However, biomedical systems are stochastic, and incorporating randomness into clinical modeling is crucial for their validity. Zhang et al (35) attempted a "back in time" approach in a primate study using a mathematical cluster modeling technique known as "nearest Identifying core pathways in an individual is crucial for understanding septic mechanisms and applying custom therapeutic decisions.…”

“…However, biomedical systems are stochastic, and incorporating randomness into clinical modeling is crucial for their validity. Zhang et al (35) attempted a “back in time” approach in a primate study using a mathematical cluster modeling technique known as “nearest neighbor.” Two pig models were used to validate the methodology: one with surgically induced peritonitis and the other using an lipopolysaccharide infusion-induced approach. This study did not incorporate other immunological information in addition to biomarker assay levels.…”

Survival Time Prediction of Breast Cancer Patients Using Feature Selection Algorithm Crystall

“…The previous study demonstrated that TriVote achieved very good and stable classification performances with its chosen OMIC features on both transcriptomic and methylomic datasets [62]. The Python package TriVote calculated the 10-fold cross validation classification performances of four representative binary classifiers, i.e., Nearest Neighbor (NN) [93], Support Vector Machine (SVM) [94,95], Naive Bayes (NBayes) [49,96], and Decision Tree (DTree) [97]. Both prediction accuracy and feature number are important for an OMIC-based prediction panel [37,98].…”