Chest CT scan predictors of intensive care unit admission in hospitalized pregnant women with COVID-19: a case–control study

Abstract

Purpose

To investigate the role of chest computed tomography (CT) scan in the prediction of admission of pregnant women with COVID-19 into intensive care unit (ICU).

Methods

This was a single-center retrospective case–control study. We included pregnant women diagnosed with COVID-19 by reverse transcriptase polymerase chain reaction between February 2020 and July 2021, requiring hospital admission due to symptoms, who also had a CT chest scan at presentation. Patients admitted to the ICU (case group) were compared with patients who did not require ICU admission (control group). The CT scans were reported by an experienced radiologist, blinded to the patient’s course and outcome, aided by an artificial intelligence software. Total CT scan score, chest CT severity score (CT-SS), total lung volume (TLV), infected lung volume (ILV), and infected-to-total lung volume ratio (ILV/TLV) were calculated. Receiver operating characteristic curves were constructed to test the sensitivity and specificity of each parameter.

Results

8/28 patients (28.6%) required ICU admission. These also had lower TLV, higher ILV, and ILV/TLV. The area under the curve (AUC) for these three parameters was 0.789, 0.775, and 0.763, respectively. TLV, ILV, and ILV/TLV had good sensitivity (62.5%, 87.5%, and 87.5%, respectively) and specificity (84.2%, 70%, and 73.7%, respectively) for predicting ICU admission at the following selected thresholds: 2255 mL, 319 mL, and 14%, respectively. The performance of CT-SS, CT scan score, and ILV/TLV in predicting ICU admission was comparable.

Conclusion

TLV, ILV, and ILV/TLV as measured by an artificial intelligence software on chest CT, may predict ICU admission in hospitalized pregnant women, symptomatic for COVID-19.

Keywords:

• Artificial intelligence
• COVID-19
• CT scan
• intensive care
• pregnancy

Author contributions

Conceptualization (D.A.B., F.D.L.); Data curation (D.A.B., F.D.L.); Formal analysis (D.A.B., F.D.L., A.C., J.C.J, M.M.C.); Funding acquisition (J.C.J., M.M.C.); Investigation (D.A.B., F.D.L., A.C.); Methodology (D.A.B., F.D.L., A.C.); Project administration (J.C.J., M.M.C.); Resources; Software (D.A.B., F.D.L.); Supervision (J.C.J); Validation (J.C.J., M.M.C.); Visualization (D.A.B., M.D.R.); Writing – original draft (D.A.B., F.D.L., A.C); Writing – review & editing (J.C.J., M.M.C.).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets analyzed during the current study are available from the corresponding author upon reasonable request.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.