Prediction models for post-induction hypotension in patients undergoing general anesthesia: a systematic review and meta-analysis
Lu Meng, Kanru Zhao, Long Shen, Yuelai Yang · 2026
Read Prediction models for post-induction hypotension in patients undergoing general anesthesia: a systematic review and meta-analysis by Lu Meng, Kanru Zhao, Long Shen, Yuelai Yang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13593812.txt.
Description
Lu Meng and three co-authors review tools that predict a drop in blood pressure after anesthesia starts. Their 2026 review covers patients under general anesthesia, where this drop is common and harms outcomes. Many groups have built prediction models, often with machine learning, but quality varies.
The team searched six databases for studies that built or tested such models. Two reviewers screened the work and judged bias risk with a standard tool. They pooled model accuracy across studies and combined odds ratios for shared risk factors. The review gives clinicians a clearer view of which models can be trusted and what to improve.
Catalog record
| CREATOR | Lu Meng, Kanru Zhao, Long Shen, Yuelai Yang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |