Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients.

Background: Accurate assessment of oxygen delivery relative to oxygen demand is crucial in the care of a critically ill patient. The central venous oxygen saturation (Svo) enables an estimate of cardiac output yet obtaining these clinical data requires invasive procedures and repeated blood sampling. Interpretation remains subjective and vulnerable to error. Recognition of patient's evolving clinical status as well as the impact of therapeutic interventions may be delayed.

Objective: The predictive analytics algorithm, inadequate delivery of oxygen (IDo) index, was developed to noninvasively estimate the probability of a patient's Svo to fall below a preselected threshold.

Derivation Cohort: A retrospective multicenter cohort study was conducted using data temporally independent from the design and development phase of the IDo index.

Validation Cohort: A total of 20,424 Svo measurements from 3,018 critically ill neonates, infants, and children were retrospectively analyzed. Collected data included vital signs, ventilator data, laboratory data, and demographics.

Prediction Model: The ability of the IDo index to predict Svo below a preselected threshold (30%, 40%, or 50%) was evaluated for discriminatory power, range utilization, and robustness.

Results: Area under the receiver operating characteristic curve (AUC) was calculated for each index threshold. Datasets with greater amounts of available data had larger AUC scores. This was observed across each configuration. For the majority of thresholds, Svo values were observed to be significantly lower as the IDo index increased.

Conclusions: The IDo index may inform decision-making in pediatric cardiac critical care settings by providing a continuous, noninvasive assessment of oxygen delivery relative to oxygen demand in a specific patient. Leveraging predictive analytics to guide timely patient care, including support for escalation or de-escalation of treatments, may improve care delivery for patients and clinicians.