Hypotension Prediction Index Based Perioperative Protocolized Hemodynamic Management in Geriatric Orthopedic Surgery

NCT05274412 | Unknown

• 1 other identifier: 202112163RIND
• Study Type: interventional
• Target: 280
• Locations: 1 country
• Sites: 1

Brief Summary

Blood pressure optimization has been considered as a crucial factor to avoid perioperative vital organ hypoperfusion, and perioperative hypotension has been addressed as a risk factor for complications and adverse clinical outcomes. Hypotension prediction index (HPI) is an novel machine-learning derived parameters, and was developed to predict the risk of future hypotension.Series of clinical studies have verified its clinical efficacy in avoiding perioperative hypotension. Major orthopedic surgeries, such as spine surgery, joint surgery, long bone fracture surgery, are quite common in elder people, who are vulnerable to perioperative adverse outcomes.Thus the investigator design this study to testify the clinical efficacy of implementing HPI in perioperative goal-directed hemodynamic therapy in elder patients receiving major orthopedic surgery.

Conditions

Orthopedic Disorder; Hemodynamic Instability

Outcome Measures

Primary Outcomes (1)

• Perioperative acute kidney injury

  Acute kidney injury will be assessed according to the KDIGO guideline. Serum creatinine will be examined on the day before surgery and postoperative day 1.

  24 hours

Other Outcomes (1)

• perioperative neurocognitive disorder

  30 day

Study Arms (2)

Control group

ACTIVE COMPARATOR

Participants in this group will receive protocolized hemodynamic management based on advanced hemodynamic monitoring and dynamic parameters.Keep pulse pressure variation \>12%; keep cardiac index \>2L/min/cm\^2; keep mean arterial pressure \> 65mmHg.

Other: ordinary goal-directed hemodynamic therapy

HPI group

EXPERIMENTAL

Participants in this group will receive protocolized hemodynamic management based on advanced hemodynamic monitoring, hypotension prediction index (HPI), and dynamic parameters.Keep HPI \<85; pulse pressure variation \>12%; keep cardiac index \>2L/min/cm\^2; keep mean arterial pressure \> 65mmHg.

Other: hypotension prediction index(HPI)

Interventions

hypotension prediction index(HPI) OTHER

Implementing hypotension prediction index (HPI) in perioperative goal-directed hemodynamic therapy. Keep HPI \< 85; pulse pressure variation \>12%; keep cardiac index \>2L/min/cm\^2; keep mean arterial pressure \> 65mmHg.

HPI group

ordinary goal-directed hemodynamic therapy OTHER

Keep pulse pressure variation \>12%; keep cardiac index \>2L/min/cm\^2; keep mean arterial pressure \> 65mmHg.

Control group

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• adults elder or equal to 60 years old receiving scheduled orthopedic surgery

You may not qualify if:

• Patients with a history of arrhythmia, congestive heart failure, preoperative neurocognitive disorder, cerebrovascular event, chronic obstructive pulmonary disease, chronic kidney disease, ongoing infectious disease, will be excluded.

Sponsors & Collaborators

• National Taiwan University Hospital: lead

Study Sites (1)

National Taiwan University Hospital

Taipei, Taiwan

RECRUITING

Related Publications (4)

• Monk TG, Bronsert MR, Henderson WG, Mangione MP, Sum-Ping ST, Bentt DR, Nguyen JD, Richman JS, Meguid RA, Hammermeister KE. Association between Intraoperative Hypotension and Hypertension and 30-day Postoperative Mortality in Noncardiac Surgery. Anesthesiology. 2015 Aug;123(2):307-19. doi: 10.1097/ALN.0000000000000756.

  PMID: 26083768 BACKGROUND

• Wesselink EM, Kappen TH, Torn HM, Slooter AJC, van Klei WA. Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review. Br J Anaesth. 2018 Oct;121(4):706-721. doi: 10.1016/j.bja.2018.04.036. Epub 2018 Jun 20.

  PMID: 30236233 BACKGROUND

• Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology. 2018 Oct;129(4):663-674. doi: 10.1097/ALN.0000000000002300.

  PMID: 29894315 RESULT

• Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, Schenk J, Terwindt LE, Hollmann MW, Vlaar AP, Veelo DP. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020 Mar 17;323(11):1052-1060. doi: 10.1001/jama.2020.0592.

  PMID: 32065827 RESULT

MeSH Terms

Conditions

Musculoskeletal Diseases

Study Officials

• Chen-Tse Lee, MD

  National Taiwan University Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Chen-Tse Lee, MD

CONTACT

0972653169; lctbrian314@gmail.com

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: PARTICIPANT, INVESTIGATOR, OUTCOMES ASSESSOR
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 1, 2022
• First Posted: March 10, 2022
• Study Start: March 23, 2022
• Primary Completion: March 1, 2024
• Study Completion: April 1, 2024
• Last Updated: March 29, 2023

Record last verified: 2022-03

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)