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AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients
Sri Harsha Boppana, Divyansh Tyagi, Sachin Sravan Kumar Komati, Sri Lasya Boppana, Ritwik Raj, Cyrus David Mintz. PLoS ONE, 2025-06-05
Topic: Risk, prevention and care in population studies
The optimized XGBoost model demonstrated superior performance, achieving an AUC of 0.85. Key hyperparameters included min_child_weight = 1, max_depth = 5, gamma = 0.3, subsample = 0.9, colsample_bytree = 0.7, reg_alpha = 0.0007, learning_rate = 0.14, and n_estimators = 123. The model exhibited an accuracy of 0.926, recall of 0.945, precision of 0.934, and an F1-score of 0.939, depicting a higher level of predictive accuracy & balance between sensitivity and specificity. This study proposes a strong XGBoost-based model to predict POD in older surgical patients, demonstrating the potential of Machine Learning (ML) in clinical risk assessment. With the help of the model's balanced performance indicators and high accuracy, physicians may identify high-risk patients and promptly execute interventions in clinical settings. Subsequent investigations ought to concentrate on integration into clinical workflows and external validation.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Boppana, S. H., Tyagi, D., Komati, S. S. K., Boppana, S. L., Raj, R., & Mintz, C. D. (2025). AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients. PLoS ONE. https://doi.org/10.1371/journal.pone.0322032
Vancouver
Boppana SH, Tyagi D, Komati SSK, Boppana SL, Raj R, Mintz CD. AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients. PLoS ONE. 2025. doi:10.1371/journal.pone.0322032. PMID: 40471913.
BibTeX
@article{boppana2025-40471913,
author = {Boppana, Sri Harsha and Tyagi, Divyansh and Komati, Sachin Sravan Kumar and Boppana, Sri Lasya and Raj, Ritwik and Mintz, Cyrus David},
title = {{AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients}},
journal = {PLoS ONE},
year = {2025},
doi = {10.1371/journal.pone.0322032},
pmid = {40471913},
url = {https://doi.org/10.1371/journal.pone.0322032}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
Key findings
- The optimized XGBoost model demonstrated superior performance, achieving an AUC of 0.85.
- Key hyperparameters included min_child_weight = 1, max_depth = 5, gamma = 0.3, subsample = 0.9, colsample_bytree = 0.7, reg_alpha = 0.0007, learning_rate = 0.14, and n_estimators = 123.
- The model exhibited an accuracy of 0.926, recall of 0.945, precision of 0.934, and an F1-score of 0.939, depicting a higher level of predictive accuracy & balance between sensitivity and specificity.
- This study proposes a strong XGBoost-based model to predict POD in older surgical patients, demonstrating the potential of Machine Learning (ML) in clinical risk assessment.
- With the help of the model's balanced performance indicators and high accuracy, physicians may identify high-risk patients and promptly execute interventions in clinical settings.
Why it matters
This study developed an XGBoost machine learning model that predicted postoperative delirium in older surgical patients with an AUC of 0.85 and high accuracy, recall, precision, and F1-score. The authors say physicians may use it to identify high-risk patients and promptly execute interventions, but it concerns delirium prediction, not treating, curing, or preventing Alzheimer's disease.
Study details
- Participants17,000
- Evidence strengthLow
How this study scored 50/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (PLoS ONE) |
| Journal's citation record | 4 / 4 | PLoS ONE: journal h-index 594 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 4 / 4 | 17,000 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 2 / 7 | Cited 10 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 3.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 3 / 6 | Cited 7.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 10 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Cyrus David Mintz: h-index 18 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Johns Hopkins University: institution h-index 2,306 (OpenAlex) |
| Multicenter collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 40471913) |
| Free to read | 2 / 2 | Free to read |
| Total | 50 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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