Diagnostic Modeling to Identify Unrecognized Inpatient Hypercapnia Using Health Record Data

Hypercapnic respiratory failure (an accumulation of carbon dioxide, CO2, in the blood) is often missed in clinical practice. Arterial blood gas is the standard diagnostic test, but it is painful and not routine. When clinicians fail to make the diagnosis, it is often because an arterial blood gas was not obtained. This ‘partial verification’ of CO2 levels presents a challenge for machine learning algorithms. We assessed the accuracy of two machine learning methods using demographics and routine lab work to estimate the likelihood that a patient has hypercapnic respiratory failure at hospital admission. Hospitalized patients who received an arterial blood gas sample constituted the training (n = 111,015) and geographic validation (n = 20,834) sets. Acceptance of “silver standard” diagnostic criteria and weighting observations by their modeled likelihood of receiving arterial blood gas sampling were used to assess the stability of findings in the presence of partial verification. Both regularized logistic regression and random-forest-based models resulted in acceptable performance (area under the curve: 0.763 and 0.758 respectively), with minimal changes in the auxiliary analyses. This work suggests that routinely available health record data can stratify the likelihood of hypercapnic respiratory failure among hospitalized adults, and findings may generalize to patients who have not received arterial blood gas sampling in clinical practice.