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Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records

Nature Medicine volume 28, pages 2107–2116 (2022)Cite this article

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Abstract

Idiopathic pulmonary fibrosis (IPF) is a lethal fibrosing interstitial lung disease with a mean survival time of less than 5 years. Nonspecific presentation, a lack of effective early screening tools, unclear pathobiology of early-stage IPF and the need for invasive and expensive procedures for diagnostic confirmation hinder early diagnosis. In this study, we introduce a new screening tool for IPF in primary care settings that requires no new laboratory tests and does not require recognition of early symptoms. Using subtle comorbidity signatures identified from the history of medical encounters of individuals, we developed an algorithm, called the zero-burden comorbidity risk score for IPF (ZCoR-IPF), to predict the future risk of an IPF diagnosis. ZCoR-IPF was trained on a national insurance claims database and validated on three independent databases, comprising a total of 2,983,215 participants, with 54,247 positive cases. The algorithm achieved positive likelihood ratios greater than 30 at a specificity of 0.99 across different cohorts, for both sexes, and for participants with different risk states and history of confounding diseases. The area under the receiver-operating characteristic curve for ZCoR-IPF in predicting IPF exceeded 0.88 and was approximately 0.84 at 1 and 4 years before a conventional diagnosis, respectively. Thus, if adopted, ZCoR-IPF can potentially enable earlier diagnosis of IPF and improve outcomes of disease-modifying therapies and other interventions.

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Fig. 1: Participant characteristics.
Fig. 2: ZCoR-IPF performance for predictions 1 year into the future.

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Data availability

The Truven, UCM and MAYO datasets cannot be made available due to their commercial nature. D.O. and I.C. had access to the Truven and UCM databases, and I.C. was responsible for maintaining the integrity of these datasets. C.G.N., L.J.F. and A.H.L. had access to the MAYO dataset, and A.H.L. was responsible for maintaining the integrity of that dataset.

Code availability

Methodological details needed to evaluate our conclusions are included in the Methods and Supplementary Information. A working software implementation of the pipeline (free for noncommercial evaluations) is available at https://doi.org/10.5281/zenodo.6040418, which includes installation instructions in standard Python environments. To enable fast execution, some more compute-intensive features are disabled in this version. Results from this software are for demonstration purposes only, and must not be interpreted as medical advice, or serve as replacement for such.

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Acknowledgements

This work is funded in part by the Defense Advanced Research Projects Agency under project no.HR00111890043. The claims made in this study do not reflect the position or the policy of the US Government. The UCM dataset is provided by the Clinical Research Data Warehouse (CRDW) maintained by the Center for Research Informatics at the University of Chicago. The Center for Research Informatics is funded by the Biological Sciences Division, the Institute for Translational Medicine/CTSA (National Institutes of Health award no. UL1 TR000430) at the University of Chicago.

Author information

Authors and Affiliations

  1. Department of Medicine, University of Chicago, Chicago, IL, USA

    Dmytro Onishchenko & Ishanu Chattopadhyay

  2. Spencer-Fontayne Corporation, Jersey City, NJ, USA

    Robert J. Marlowe

  3. Mayo Clinic College of Medicine and Science, Rochester, MN, USA

    Che G. Ngufor, Louis J. Faust & Andrew H. Limper

  4. Director, Thoracic Research Unit, Mayo Clinic College of Medicine and Science, Rochester, MN, USA

    Andrew H. Limper

  5. Director, Interstitial Lung Disease Program, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Gary M. Hunninghake

  6. Bruce Webster Professor of Internal Medicine, Medicine, Weill Cornell Medical College, New York, NY, USA

    Fernando J. Martinez

  7. Chief of Division of Pulmonary and Critical Care Medicine at Weill Cornell Medicine and NewYork-Presbyterian Weill Cornell Medical Center, New York, NY, USA

    Fernando J. Martinez

  8. Committee on Genetics, Genomics & Systems Biology, University of Chicago, Chicago, IL, USA

    Ishanu Chattopadhyay

  9. Committee on Quantitative Methods in Social, Behavioral, and Health Sciences, University of Chicago, Chicago, IL, USA

    Ishanu Chattopadhyay

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  1. Dmytro Onishchenko
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Contributions

D.O. implemented the algorithm and ran validation tests. D.O. and I.C. carried out mathematical modeling and algorithm design. R.J.M., F.J.M. and I.C. wrote the paper. F.J.M., G.M.H. and I.C. interpreted results and guided research. C.G.N., L.J.F. and A.H.L. evaluated the tool on the dataset available at the Mayo Clinic. I.C. procured funding for the research.

Corresponding author

Correspondence to Ishanu Chattopadhyay.

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The authors declare no competing interests.

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Nature Medicine thanks Athol Wells, Harold Collard and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Michael Basson, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Performance under delayed updates to participant records.

a, b, Out-of-sample ROC curves when the patient data is delayed by 4w vs the no-delay condition, for the UCM and the Truven datasets, respectively. 95% confidence bounds about the mean is shown, computed with n=2,053,277 for Truven and n=68,658 for UCM. Note that there is no significant loss of performance with such delayed data. c, d, ZCoR-IPF performance vs a 87-feature baseline model optimized via logistic regression, where these features denote presence/absence of manually-curated risk factors (Supplemental Table 4) and age (over/under 65 years), for the Truven and the UCM datasets, respectively.

Extended Data Fig. 2 Comparison with neural network architectures.

a,b, Out-of-sample AUC achieved in Truven and UCM datasets, respectively, by a range of neural network architectures ranging from simple feed-forward networks, LSTMs and CNNs, to large state of the art models such as the ALEXNET, DENSENET and RESNET, along with 95% confidence intervals about the mean (n=2,053,277 for Truven and n=68,658 for UCM).

Extended Data Fig. 3 Performance with broader target definition.

a,b, Out-of-sample ROC curves for the Truven and the UCM dataset, respectively, comparing the results from the primary analysis with that in the secondary analysis (analysis with broader target definition as specified in Extended Data Table 1). 95% confidence bounds about the mean is shown, computed with n=2,053,277 for Truven and n=68,658 for UCM. c, Negative vs positive likelihood ratios (LR- vs LR+). d, Positive vs negative predictive values. Note that with the broad target definition we can select to operate with LR+ > 30 as well, similar to the target in the primary analysis.

Extended Data Fig. 4 Co-morbidity Spectra.

a,b, Diseases (recorded ICD codes) that increase the odds of the patient being a ‘true positive’ vs a ‘true negative’ for males and females respectively. These odds are broadly similar across the sexes, with over-representation of respiratory disorders.

Extended Data Fig. 5 Expected increase in survival times.

a, Survival function lower bounds at two specificity levels (90 and 95%). b, Cumulative hazard function upper bounds. 95% confidence bounds around the mean shown for both, generated using the Truven dataset (n=2,053,277). c, Variation of the mean survival time as a function of the specificity at which ZCoR-IPF is operated. d, Variation of estimated raw risk as a function of age for screening four years from actual recorded diagnosis of IPF, showing that risk increases almost linearly with age for the patients eventually diagnosed with IPF. e, Degradation of out-of-sample AUC as we attempt to screen earlier, stepping back from the time of current diagnosis (in absence of ZCoR-IPF screening).

Extended Data Table 1 TARGET CODES: DESCRIPTION OF ICD CODES(S) USED TO IDENTIFY IPF DIAGNOSES

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Extended Data Table 2 HIGH RISK COMORBIDITIES WHICH DEFINE OUR HIGH-RISK COHORT

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Extended Data Table 3 FEATURE DEFINITIONS (TOTAL NUMBER OF FEATURES USED: 667)

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Extended Data Table 4 ZCOR-IPF PERFORMANCE FOR DIFFERENT SUBPOPULATIONS AT 99% SPECIFICITY IN MALES

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Extended Data Table 5 ZCOR-IPF PERFORMANCE FOR DIFFERENT SUBPOPULATIONS AT 99% SPECIFICITY IN FEMALES

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Supplementary information

Supplementary Information

Supplementary Note, Tables 1–11 and Figs. 1–3

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Onishchenko, D., Marlowe, R.J., Ngufor, C.G. et al. Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records. Nat Med 28, 2107–2116 (2022). https://doi.org/10.1038/s41591-022-02010-y

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