Abstract: TH-PO042
Using an Ensemble Model to Improve ESA Prescription in Hemodialysis (HD)
Session Information
- AI, Digital Health, Data Science - I
November 02, 2023 | Location: Exhibit Hall, Pennsylvania Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Augmented Intelligence, Digital Health, and Data Science
- 300 Augmented Intelligence, Digital Health, and Data Science
Authors
- Chiu, Yi-Wen, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung, Taiwan
- Lin, Ming-Yen, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung, Taiwan
- Yen, Hong-Ren, National Sun Yat-sen University, Kaohsiung, Taiwan
- Hsu, Chan, National Sun Yat-sen University, Kaohsiung, Taiwan
- Ku, Chan-Tung, National Sun Yat-sen University, Kaohsiung, Taiwan
- Kang, Yihuang, National Sun Yat-sen University, Kaohsiung, Taiwan
Background
How the ensemble model trained by new indexes can improve the ESA prescription is unclear.
Methods
We have compiled a dataset from 2013-2020, including bi-monthly ESA prescriptions, Iron doses, Hb, and relevant data of HD patients in our hospital. We divided all records into six categories, status 1 to 6, based on subsequent hemoglobin(sHb), using cut-off values of 10.8 and 11.2 and direction of change (not decreasing: 1-3 vs. decreasing:4-6). Indexes I, II, and III were used to evaluate how the ESA dose deviations of model recommendations theoretically impacted sHb. Index I assesses how model deviations keep sHb near 11 g/dL (positive index), while II and III are away from 11 g/dL (negative index). (Figure 1) Besides the traditional ESA-prescription algorithm (TEA, Figure 2), we have trained various models, including a meta-learning model (MLM).
Results
25,632 records of 315 ESKD patients were included, with 71.9% of the Hb levels between 10-12mg/dL. Besides TEA, we selected a generalized linear mixed-effect tree model (GLMM) and a weighted random forests model (WRF) for sHb and ESA dose prediction. The overall results are shown in Table 1. Our WRF and MLM can almost preserve the proper prescriptions (Index I), and avoid those improvable or unacceptable recommendations (Index II & III). The MLM can theoretically improve 65.4% and 43.8% of prescriptions with sHb >11.5 and <10.5, respectively. (undetermined)
Conclusion
The ensemble model trained by new indexes can theoretically recommend more proper ESA dosages.
| Total n=25,632 | sHb between 10.8-11.2 Status 2, n=2,727; Status 5, n=2,550 | sHb below 10.8 Status 1, n=3,859; Status 6, n=5,767 | sHb above 11.2 Status 3, n=7,345; Status 4, n=3,384 | |||||||||||||||||
| Index (Score assigned to record) | I (1) | Undetermined (0) | Unaccepted (-2) | II (-1) | II unaccepted (-2) | Undetermined (0) | III (-1) | III unaccepted (-2) | undetermined (0) | Total score | ||||||||||
| Model/Status | 2 | 5 | 2 | 5 | 2 | 5 | 1 | 6 | 1 | 6 | 1 | 6 | 3 | 4 | 3 | 4 | 3 | 4 | Positive | Negative |
| TEA, % | 91.4 | 74.5 | 6.2 | 21.8 | 2.4 | 3.7 | 2.2 | 25.5 | 0.3 | 1.7 | 97.5 | 72.8 | 2.4 | 0.2 | 0.3 | 0.03 | 97.6 | 99.8 | 1530 | -434 |
| GLMM, % | 71.8 | 57.0 | 18.6 | 27.1 | 9.6 | 15.8 | 1.2 | 13.1 | 1.3 | 2.8 | 97.5 | 85.1 | 11.2 | 4.7 | 4.9 | 3.4 | 88.9 | 95.3 | 789 | -581 |
| WRF, % | 95.3 | 97.5 | 3.6 | 2.3 | 0.9 | 0.2 | 4.2 | 9.4 | 0 | 0.03 | 95.8 | 90.6 | 3.7 | 1.9 | 0.2 | 0 | 96.1 | 98.1 | 2518 | -312 |
| MLM,% | 95.5 | 97.7 | 3.2 | 2.2 | 1.3 | 0.2 | 3.3 | 7.6 | 0 | 0.03 | 96.7 | 92.4 | 3.2 | 1.6 | 0.3 | 0 | 96.5 | 98.4 | 2510 | -244 |
2-week prescripiton as example
Funding
- Clinical Revenue Support