Deep Learning Utilizing Suboptimal Spirometry Data to Improve Lung Function and Mortality Prediction in the UK Biobank

BACKGROUND: Spirometry measures lung function by selecting the best of multiple efforts meeting pre-specified quality control (QC), and reporting two key metrics: forced expiratory volume in 1 second (FEV(1)) and forced vital capacity (FVC). We hypothesize that discarded submaximal and QC-failing data meaningfully contribute to the prediction of airflow obstruction and all-cause mortality. METHODS: We evaluated volume-time spirometry data from the UK Biobank. We identified “best” spirometry efforts as those passing QC with the maximum FVC. “Discarded” efforts were either submaximal or failed QC. To create a combined representation of lung function we implemented a contrastive learning approach, Spirogram-based Contrastive Learning Framework (Spiro-CLF), which utilized all recorded volume-time curves per participant and applied different transformations (e.g. flow-volume, flow-time). In a held-out 20% testing subset we applied the Spiro-CLF representation of a participant’s overall lung function to 1) binary predictions of FEV(1)/FVC < 0.7 and FEV(1) Percent Predicted (FEV(1)PP) < 80%, indicative of airflow obstruction, and 2) Cox regression for all-cause mortality. FINDINGS: We included 940,705 volume-time curves from 352,684 UK Biobank participants with 2-3 spirometry efforts per individual (66.7% with 3 efforts) and at least one QC-passing spirometry effort. Of all spirometry efforts, 24.1% failed QC and 37.5% were submaximal. Spiro-CLF prediction of FEV(1)/FVC < 0.7 utilizing discarded spirometry efforts had an Area under the Receiver Operating Characteristics (AUROC) of 0.981 (0.863 for FEV(1)PP prediction). Incorporating discarded spirometry efforts in all-cause mortality prediction was associated with a concordance index (c-index) of 0.654, which exceeded the c-indices from FEV(1) (0.590), FVC (0.559), or FEV(1)/FVC (0.599) from each participant’s single best effort. INTERPRETATION: A contrastive learning model using raw spirometry curves can accurately predict lung function using submaximal and QC-failing efforts. This model also has superior prediction of all-cause mortality compared to standard lung function measurements. FUNDING: MHC is supported by NIH R01HL137927, R01HL135142, HL147148, and HL089856. BDH is supported by NIH K08HL136928, U01 HL089856, and an Alpha-1 Foundation Research Grant. DH is supported by NIH 2T32HL007427-41 EKS is supported by NIH R01 HL152728, R01 HL147148, U01 HL089856, R01 HL133135, P01 HL132825, and P01 HL114501. PJC is supported by NIH R01HL124233 and R01HL147326. SPB is supported by NIH R01HL151421 and UH3HL155806. TY, FH, and CYM are employees of Google LLC