ABSTRACT
ImportanceBreath analysis has been explored as a non-invasive means to detect COVID-19. However, the impact of the emerging variants such as Omicron on the exhaled breath profile and hence the accuracy of breath analysis is unknown. ObjectiveTo evaluate the diagnostic accuracies of breath analysis on detecting COVID-19 patients in periods where Delta and Omicron were most prevalent. Design, Setting, and ParticipantsA convenience cohort of patients testing positive and negative for COVID-19 using reverse transcriptase polymerase chain reaction (RT-PCR) were studied and included 167 COVID and non-COVID patients recruited between April 2021 and May 2022, which covers the period when Delta (and other variants prior to Delta) was the dominant variant (April - December 2021) and when Omicron was the dominant variant (January - May 2022). The breath from those patients were collected and analyzed for volatile organic compounds (VOCs) with a newly developed portable gas chromatography-based breath analyzer. Diagnostic patterns and algorithms were developed. ResultsA total of 205 breath samples were analyzed from 167 COVID and non-COVID patients. The RT-PCR was conducted within 18 hours of the breath analysis to confirm the COVID status of the patients. Among 94 COVID positive samples, 41 samples were collected from the patients in 2021 who were assumed to be infected by the Delta variant (or other variants occurring in 2021) and 53 samples from the patients in 2022 who were assumed to be infected by the Omicron variant (BA.1 and BA.2). Breath analysis using a set of 4 VOC biomarkers was able to distinguish between COVID (Delta and other variants in 2021) and non-COVID with an overall accuracy of 94.7%. However, the accuracy dropped significantly to 82.1% when the same set of biomarkers were applied to the Omicron variant with and 21 out of 53 COVID positive being misidentified. A new set of 4 VOC biomarkers were found to distinguish the Omicron variant and non-COVID, which yielded an overall accuracy of 90.9%. Breath analysis was also found to be able to distinguish between COVID (for all the variants occurring between April 2021 and May 2022) and non-COVID with an overall accuracy of 90.2%, and between the Omicron variant and the earlier variants (Delta and other variants occurring in 2021) with an overall accuracy of 91.5%. Conclusions and RelevanceBreath analysis of VOCs using point of care gas chromatography may be a promising diagnostic modality for detection of COVID and similar diseases that result in VOC production. However, similar to other diagnostic modalities such as rapid antigen testing, challenges are posed by the dynamic emergence of viral variants. The results of this study warrant additional investment and evaluation on how to overcome these challenges and to exploit breath analysis to improve the diagnosis and care of patients. Key PointsO_ST_ABSQuestionC_ST_ABSCan volatile organic compounds (VOCs) in exhaled breath provide diagnostic information on COVID-19? Will variants such as Omicron B.1.1.529 and others affect the accuracy in breath analysis? FindingsA set of 4 VOC biomarkers were found to distinguish between Delta (and the variants occurring in 2021) from non-COVID. The Omicron variant (occurring in 2022) significantly affects VOC profiles requiring the search for a new set of VOC biomarkers to distinguish between Omicron and non-COVID. MeaninThese findings demonstrate the ability of breath analysis to distinguish between COVID and non-COVID, but also reveal the significant difference in the exhaled breath profile between COVID-19 patients during the period when Delta was most prevalent and when Omicron was most prevalent.
ABSTRACT
IntroductionThe 2019 coronavirus (COVID-19) has led to unprecedented strain on healthcare facilities across the United States. Accurately identifying patients at an increased risk of deterioration may help hospitals manage their resources while improving the quality of patient care. Here we present the results of an analytical model, PICTURE (Predicting Intensive Care Transfers and Other UnfoReseen Events), to identify patients at a high risk for imminent intensive care unit (ICU) transfer, respiratory failure, or death with the intention to improve prediction of deterioration due to COVID-19. We compare PICTURE to the Epic Deterioration Index (EDI), a widespread system which has recently been assessed for use to triage COVID-19 patients. MethodsThe PICTURE model was trained and validated on a cohort of hospitalized non-COVID-19 patients using electronic health record data from 2014-2018. It was then applied to two hold-out test sets: non-COVID-19 patients from 2019 and patients testing positive for COVID-19 in 2020. PICTURE results were aligned to the EDI for head-to-head comparison via Area Under the Receiver Operator Curve (AUROC) and Area Under the Precision Recall Curve (AUPRC). We compared the models ability to predict an adverse event (defined as ICU transfer, mechanical ventilation use, or death) at two levels of granularity: (1) maximum score across an encounter with a minimum lead time before the first adverse event and (2) predictions at every observation with instances in the last 24 hours before the adverse event labeled as positive. PICTURE and the EDI were also compared on the encounter level using different lead times extending out to 24 hours. Shapley values were used to provide explanations for PICTURE predictions. ResultsPICTURE successfully delineated between high- and low-risk patients and consistently outperformed the EDI in both of our cohorts. In non-COVID-19 patients, PICTURE achieved an AUROC (95% CI) of 0.819 (0.805 - 0.834) and AUPRC of 0.109 (0.089 - 0.125) on the observation level, compared to the EDI AUROC of 0.762 (0.746 - 0.780) and AUPRC of 0.077 (0.062 - 0.090). On COVID-19 positive patients, PICTURE achieved an AUROC of 0.828 (0.794 - 0.869) and AUPRC of 0.160 (0.089 - 0.199), while the EDI scored an AUROC of 0.792 (0.754 - 0.835) and AUPRC of 0.131 (0.092 - 0.159). The most important variables influencing PICTURE predictions in the COVID-19 cohort were a rapid respiratory rate, a high level of oxygen support, low oxygen saturation, and impaired mental status (Glasgow coma score). ConclusionThe PICTURE model is more accurate in predicting adverse patient outcomes for both general ward patients and COVID-19 positive patients in our cohorts compared to the EDI. The ability to consistently anticipate these events may be especially valuable when considering a potential incipient second wave of COVID-19 infections. PICTURE also has the ability to explain individual predictions to clinicians by ranking the most important features for a prediction. The generalizability of the model will require testing in other health care systems for validation.
