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. 2025 Jun 1;53(6):e1338-e1339.
doi: 10.1097/CCM.0000000000006673. Epub 2025 Jun 3.

The authors reply

Jaap Schuurmans  1 , Santino R Rellum  1 , Denise P Veelo  1 , Alexander P J Vlaar  2   3 , Jimmy Schenk  1   2   4
Affiliations

The authors reply

Jaap Schuurmans et al. Crit Care Med. .
No abstract available

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Conflict of interest statement

Dr. Vlaar acknowledges receiving unrestricted research grants and lecture fees from Edwards Lifesciences and Philips, which were disbursed to the institution. Dr. Veelo discloses receiving research grants from Philips Medical, along with consultancy, lecture, and travel expense fees from Edwards Lifesciences. Dr. Schuurmans received consultancy fees from Edwards Lifesciences, which were paid to the institution. The remaining authors have disclosed that they do not have any potential conflicts of interest.

References

    1. Vistisen ST, Enevoldsen J: Adherence to hypotension prediction index treatment protocols cause more aggressive treatment. Crit Care Med. 2025; 53:e1336–e1337
    1. Schuurmans J, Rellum SR, Schenk J, et al.: Effect of a machine learning-derived early warning tool with treatment protocol on hypotension during cardiac surgery and ICU stay: The Hypotension Prediction 2 (HYPE-2) randomized clinical trial. Crit Care Med. 2025; 53:e328–e340
    1. Rellum SR, Noteboom SH, van der Ster BJP, et al.: The hypotension prediction index versus mean arterial pressure in predicting intraoperative hypotension: A clinical perspective. Eur J Anaesthesiol. 2025 Feb 28. [online first]
    1. Lenharo M: The testing of AI in medicine is a mess. Here’s how it should be done. Nature. 2024; 632:722–724
    1. Kouz K, Hoppe P, Briesenick L, et al.: Intraoperative hypotension: Pathophysiology, clinical relevance, and therapeutic approaches. Indian J Anaesth. 2020; 64:90–96

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