Projectdetails

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Effect of implementing the hypotension prediction index on intra-operative hypotension

Keywords:
anesthesia hypotension hemodynamic monitoring

Researchers:
J.J. Vos
prof. dr. T.W.L. Scheeren
I.N. de Keijzer

Nature of the research:
In this study the machine-learning based hypotension prediction index (HPI) algorithm will be implemented in the clinical routine in order to assess if this novel technology will be able to reduce the incidence of intra-operative hypotension in adult patients undergoing major surgery.

Fields of study:
anesthesiology surgery intensive care

Background / introduction
In patients undergoing major surgery, intraoperative hypotension (IOH) may ensue, resulting from many different factors including for example the use of anesthetic drugs and surgical blood loss. IOH has been a widely studied phenomenon the past decades. Various definitions have been used to identify IOH. The incidence varies widely depending on which definition is used. E.g., when systolic blood pressure is below 70 mmHg for at least 5 minutes, its incidence is around 5%, while the incidence would increase to 99% when a decrease of >10% of the systolic blood pressure is used.
IOH is associated with adverse postoperative outcomes: In non-cardiac surgery, IOH is associated with acute kidney injury (AKI), myocardial injury and death. A study in 33,330 patients showed that below a mean arterial pressure(MAP) of 55 mmHg the risk for AKI, myocardial injury and cardiac complications (cardiac arrest, heart failure) all increased. Moreover, even short durations of MAP <55mmH are associated with AKI and myocardial injury. Another study, primarily focused on mortality, showed that when MAP decreases from 80 mmHg to 50 mmHg the mortality rates tripled. A recent systematic review showed increased risks for AKI, myocardial injury and mortality as MAP decreases. Thus, the severity and duration of IOH has an important association with postoperative organ damage.
The hypotension prediction index (HPI) is a novel algorithm, based on machine-learning, and uses the arterial pressure waveform to assess the chance of impending hypotension in the next 5 minutes – while the patient is still hemodynamically stable at that moment. The monitor can also give additional ‘clues’ on the origin of IOH. Hence, HPI may allow the treating anesthesiologist to provide the appropriate therapy to prevent hypotension from occurring.
In this study, HPI monitoring is introduced in clinical routine in adult patients undergoing major surgery. The aim of the study is to assess the incidence of IOH when HPI is actively used by the treating anesthesiologist for hemodynamic monitoring and optimization.
Research question / problem definition
Does HPI reduce the incidence of intra-operative hypotension in adult patients undergoing major non-cardiac surgery?
Workplan
The student will assist in screening patient eligibility for the study, will participate in retrieving informed consent for the study, and will be responsible for actual data-collection in the operating room. The student will also be responsible for overall data-management.
References
https://www.ncbi.nlm.nih.gov/pubmed/29894315
https://www.ncbi.nlm.nih.gov/pubmed/31582098
https://www.ncbi.nlm.nih.gov/pubmed/31974829
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