Evidence map›Paper›PMID 42325441›Full record

ArticleIEEE journal of translational engineering in health and medicine2026

Accuracy of Quantifying Hypotension During Surgery Using Physiological Sensor Data.

Martin Jacobsson, Arman Valadkhani, Greg Winski, Max Bell

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Article in IEEE journal of translational engineering in health and medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Martin JacobssonDepartment of Biomedical Engineering and Health SystemsKTH Royal Institute of Technology 100 44 Stockholm Sweden.ORCID https://orcid.org/0000-0002-8359-5745
Arman ValadkhaniPerioperative Medicine and Intensive Care (PMI)Karolinska University Hospital 171 76 Stockholm Sweden.ORCID https://orcid.org/0000-0001-6611-5781
Greg WinskiPerioperative Medicine and Intensive Care (PMI)Karolinska University Hospital 171 76 Stockholm Sweden.ORCID https://orcid.org/0000-0002-1832-5423
Max BellPerioperative Medicine and Intensive Care (PMI)Karolinska University Hospital 171 76 Stockholm Sweden.ORCID https://orcid.org/0000-0001-7464-0324

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveDuring surgery it is common to measure the arterial blood pressure. One important reason is to monitor for hypotension, a too low blood pressure, which is known to be harmful. Since post-surgical complications correlate with hypotension, it is common to calculate the number of hypotensive events, their cumulative length, or their cumulative area under threshold. To make the data collection process manageable, parameter data is often stored only once every 15 seconds and then used to derive the amount of hypotension. METHODS AND PROCEDURES: In this study, we calculate the errors introduced in quantizing hypotension based on different amount of downsampling from 419 surgical patients at the open abdomen surgery unit at Karolinska University Hospital. Experiments with error mitigation are also tested. Results are compared with using the continuous blood pressure waveform data.

resultsFor a downsampling to once every 15 seconds, the relative bias was 27.0% for the number of hypotensive events, 30.0% for the cumulative hypotensive length, and 10.8% for the cumulative area under threshold. Error mitigation approaches often do not improve the errors.

conclusionThese results highlight the necessity of using high frequency data and that it is important to be aware of the errors when interpreting older studies involving quantification of hypotension and based on downsampled data.

Indexed as

Blood Pressure DeterminationHypotensionMonitoring, IntraoperativeSignal Processing, Computer-AssistedBlood PressureHumansArterial blood pressuredownsamplinghypotensionmean arterial blood pressure

Identifiers

PMID42325441
PMCPMC13278748

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