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Naik, S.

Mayo Clinic

Columbia University

Citations 1

Harmonized Assessment and Quality Control of Quantitative Measures of Lung Structure on Pre-pandemic Cardiac and Lung Computed Tomography (CT) Scans for Large-scale Investigation of Risk of Long COVID-19: The Collaborative Cohort of Cohorts for COVID-19 Re
Naik, Seema , Smith, B.M. , Cheng, T.-K., Lynch, David A. , Matsushita, Kunihiro , O'Driscoll, O., Ortega, Victor E. , Washko, George R. , Laine, Andrew F. , Oelsner, Elizabeth C. , Oelsner, Elizabeth C , Naik, S., Allen, Norrina B. , Balte, Pallavi , Blaha, Michael Joseph , Budoff, Matthew J. , Carr, J. Jeffrey , Carson, April P. , Cheng, T.-K., Estépar, Raúl San Jośe , Hoffman, Eric A. , Humphries, Stephen M. , Kanaya, Alka M , Kandula, Namratha R. , O’Connor, George T. , Pottinger, Tess D. , Schwartz, David A. , Wenzel, Sally E. , Xanthakis, Vanessa , Angelini, Elsa D. , Barr, R. Graham

American Journal of Respiratory and Critical Care Medicine | May 11, 2025 | Volume: 211 | Issue: Supplement_1 | Pages: A7658-A7658

10.1164/ajrccm.2025.211.abstracts.a7658 Get It

Abstract RATIONALE: Susceptibility to severe COVID-19 and long COVID may be influenced by pre-existing lung structure. ECG-gated, non-contrast cardiac CT scans, originally acquired for assessing coronary artery calcium, are available in multiple longitudinal cohort studies and have been previously validated against lung CT scans for densitometry; however, protocol variation across cohorts may hinder direct comparison. To address this, we developed a quality control (QC) rubric to grade cardiac CT scans for standardized quantitative lung measures across cohorts. METHODS: C4R assessed SARS-CoV-2 infection and outcomes in 14 NHLBI cohorts, of which 10 (six community-based and four longitudinal case-control studies) included CT scans (12,459 participants with lung and 13,752 with cardiac CTs). Logic rules were applied to categorize scans (Table): Grade “A-B” scans had 2-3.5mm slice thickness, at least 20 axial slices, no lung cropping in axial view and lung and airway volumes greater than the cohort-specific 1st percentile. “C-D” scans had one lung obscured by cropping around the heart, so quantitative measures were restricted to the lung in complete view. “F-H” scans were deemed unusable, suffering from low slice count (<20), severe image cropping, low lung or airway volumes (< 1st%ile). Visual inspection was performed on a sample from each category to ensure accuracy. Percent of lung voxels in low attenuation areas < -950 HU (LAA950), high attenuation areas (-600 to -250 HU, HAA) and 15th percentile HU value (perc15) were measured. Within-participant variability was quantified using the intraclass correlation coefficient (ICC). RESULTS: We performed QC on cardiac CT scans from an initial sample of 6,275 participants from three C4R cohorts (ARIC, CARDIA, MASALA), imaged with GE or Siemens scanners. Of these, 5,125 (81.7%) were graded “A-B”, 496 (7.9%) were “C-D” and 654 (10.4%) were “F-H”. The percentage of “F-H” scans varied widely across cohorts [<1% to 26%] due to image cropping. Among the 5,621 cardiac CT scans graded A-D, the mean±SD for LAA950, HAA and perc15 respectively were 1.1±2.1%, 7.2±4.7% and -887±33 HU. Repeated imaging was available for 107 participants (time interval 5.1±0.7y). For paired scans in grades A-B (n=101/107), the within-participant correlation for LAA950, HAA and perc15 were 0.91, 0.38 and 0.74 respectively. CONCLUSION: Initial application of QC to cardiac CT scans identified scans with high reproducibility, with the aim to provide valid quantitative lung metrics on over 20,000 highly characterized participants in C4R.