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Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis

Nature Machine Intelligence volume 5, pages 421–431 (2023)Cite this article

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A preprint version of the article is available at medRxiv.

Abstract

Sepsis is a life-threatening condition with a high in-hospital mortality rate. The timing of antibiotic administration poses a critical problem for sepsis management. Existing work studying antibiotic timing either ignores the temporality of the observational data or the heterogeneity of the treatment effects. Here we propose a novel method (called T4) to estimate treatment effects for time-to-treatment antibiotic stewardship in sepsis. T4 estimates individual treatment effects by recurrently encoding temporal and static variables as potential confounders, and then decoding the outcomes under different treatment sequences. We propose mini-batch balancing matching that mimics the randomized controlled trial process to adjust the confounding. The model achieves interpretability through a global-level attention mechanism and a variable-level importance examination. Meanwhile, we equip T4 with an uncertainty quantification to help prevent overconfident recommendations. We demonstrate that T4 can identify effective treatment timing with estimated individual treatment effects for antibiotic stewardship on two real-world datasets. Moreover, comprehensive experiments on a synthetic dataset exhibit the outstanding performance of T4 compared with the state-of-the-art models on estimation of individual treatment effect.

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Fig. 1: Overall data flow of T4 framework.
Fig. 2: The T4 framework.
Fig. 3: Illustration of the balancing matching.
Fig. 4: Mortality rate comparison of two datasets.
Fig. 5: Case study of a patient for treatment recommendation and model interpretability.

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Data availability

The MIMIC-III dataset is publicly available from PhysioNet (https://mimic.physionet.org/). AmsterdamUMCdb is publicly available from the Amsterdam Medical Data Science website (https://amsterdammedicaldatascience.nl/).

Code availability

The source code for this paper can be downloaded from the GitHub repository at https://github.com/ruoqi-liu/T4 or the Zenodo repository at https://doi.org/10.5281/zenodo.7683025.

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Acknowledgements

This work was funded in part by the National Science Foundation under award number IIS-2145625 and by the National Institutes of Health under award number UL1TR002733. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation or the National Institutes of Health.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Ruoqi Liu & Ping Zhang

  2. Department of Emergency Medicine, The Ohio State University, Columbus, OH, USA

    Katherine M. Hunold & Jeffrey M. Caterino

  3. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA

    Ping Zhang

  4. Translational Data Analytics institute, The Ohio State University, Columbus, OH, USA

    Ping Zhang

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Contributions

P.Z. conceived the project. R.L. and P.Z. developed the method. R.L. conducted the experiments. R.L. and P.Z. analysed the results. R.L., K.M.H., J.M.C. and P.Z. wrote the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ping Zhang.

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Nature Machine Intelligence thanks Deepti Gurdasani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Liu, R., Hunold, K.M., Caterino, J.M. et al. Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis. Nat Mach Intell 5, 421–431 (2023). https://doi.org/10.1038/s42256-023-00638-0

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