Predicting Bone Cement Implantation Syndrome Using Artificial Intelligence Methods

NCT06777160 | Recruiting

• 1 other identifier: 499
• Study Type: observational
• Target: 250
• Locations: 1 country
• Sites: 1

Brief Summary

This study aims to predict the development of bone cement syndrome, which may develop due to polymethylmethacrylate cement used to adhere the prosthesis to the bone in arthroplasty surgeries, which may cause intraoperative and postoperative mortality and morbidity, using artificial intelligence methods and to provide a sustainable life comfort to patients in the postoperative period with the standardization predicted in the long term.

Conditions

Bone Cement Implantation Syndrome

Keywords

CEMENT REACTION; ARTIFICIAL INTELLIGENCE; ARTHROPLASTY

Outcome Measures

Primary Outcomes (4)

• collection of data-1

  General examination findings of the patient in the perioperative period, ASA score, whether the patient is female whether male or female, body mass index, age, whether mobility is decreased, whether assisted living, basic life parameters (operation blood pressure, pulse, respiratory rate, saturation, temperature, consciousness, orientation, etc.), the cause of the fracture (trauma, arthrosis, infection, necrosis, osteolysis, intertrochanteric fracture), type of surgery (total hip replacement, partial hip replacement, total knee replacement or revision surgery), medical history and medical history (cardiovascular disease, kidney disease, diabetes, stroke, peripheral vascular disease, arteriosclerosis, pulmonary hypertension, angina pectoris, congestive heart failure, COPD, cancer, dementia, arrhythmia, nicotine dependence, and the presence of previous myocardial infarction.

  2 months

• collection of data-2

  β-adrenergic blockers, diuretics, antiplatelet drugs, organic nitrates, calcium antagonists, ACE inhibitors, ARBs, acetylsalicylic acid, insulin, and warfarin and statin use), radiological evaluations (chest radiography), laboratory parameters (D-Dimer, IMA, IL-6, C-Peeptit, HbA1c, glucose, insulin, Ig E, NT Pro BNP, homocysteine, serotonin, adrenaline, noradrenaline, D vit, CK, CKMB, troponin, myoglobin, urea, uric acid, creatinine, AST, ALT, BIL, ALB, GGT, ALP, LDH, PT, PTT, INR, Sedim, CRP, fibrinogen, procalcitonin, TFT, HDL, LDL, TG, cholesterol, electrolytes and calcium, Mg, CA125, CA15-3, CA19 -9, afp, CEA, hemogram, 5HT-2A receptor, GFR) and anaesthesia and surgery-related parameters (type of anaesthesia; general anaesthesia, local anesthesia) anaesthesia, volume of epidural anaesthetic used, type of local anaesthetic used, opioid use, duration of surgery, duration of anaesthesia, total blood loss

  2 months

• collection of data-3

  IMA measured at certain intervals after cementing. troponin-T measured at intervals, duration of hypotension, duration of hypoxia, medullary lavage, use of cement gun, vacuum cement Mixing and cementing pressure have been reported to be risk factors for the development of BCIS; these data will be collected throughout the study. When and which parameters will be performed will be determined by experienced physicians, and their results will be analysed according to their clinical experience. are interpreted by the algorithms they create.

  2 months

• artificial intelligence modeling

  Artificial Intelligence model process steps The collected data will be made ready for artificial intelligence algorithms by going through the data preprocessing stage. In the feature extraction stage, simple feature extraction algorithms, kurtosis, skewness, local maximum, local minimum, hyperparameters, and principal component analysis processes will be applied. In the classification stage, a dataset is assigned to one of the predetermined classes that are different from each other. Classification algorithms learn which data to assign to which class from the given training set. Then it tries to assign the test data to the correct classes. Logistic regression, linear discriminant analysis, decision trees, Naive Bayes, the K-nearest neighbour algorithm, support vector machines, and random forest algorithms will be used in the classification phase. In the evaluation phase, the best model will be selected by creating an error matrix and interpreting the accuracy value of the model.

  1 months

Study Arms (1)

Patients who underwent arthroplasty and cement was used

For the operation of artificial intelligence modeling; First of all, the data to be entered into the system will be prepared. Some of the data will be entered manually (such as examination findings), some will be entered through the hospital information management system, and some will be converted into numerical form and entered manually (radiological reports). While data is being entered, critical criteria and panic values of each data will be introduced into the system. In this way, users will be warned when the system is activated.

Procedure: Collecting data from patients who underwent arthroplasty and cement was used

Interventions

Collecting data from patients who underwent arthroplasty and cement was used PROCEDURE

Collecting data that may change in cement reaction before, during and after surgery from patients who underwent arthroplasty and cement use and taught it to artificial intelligence.

Patients who underwent arthroplasty and cement was used

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This prospective study was conducted after obtaining the informed written consent of the patients and the ethics committee of Atatürk University Medical Faculty Research Hospital. on patients aged ≥18 years who underwent cemented total knee arthroplasty (TKA), cemented partial hip arthroplasty and cemented total hip arthroplasty It will be done.

You may qualify if:

• Total knee arthrplasties
• Partial hip arthrplasty using cement
• Total hip arthrplasty using cement
• Patients ≥18 years old.

You may not qualify if:

• Patients who do not want to participate in the study

Sponsors & Collaborators

• Ataturk University: lead

Study Sites (1)

Ataturk University Faculty of Medicine

Erzurum, 25240, Turkey (Türkiye)

RECRUITING

Central Study Contacts

Muhammed Çağatay ENGIN, Associate professor

CONTACT

05323301949; mcagatayengin@hotmail.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Target Duration: 12 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: associate professor doctor

Study Record Dates

• First Submitted: January 5, 2025
• First Posted: January 15, 2025
• Study Start: September 8, 2024
• Primary Completion: March 20, 2025
• Study Completion: April 15, 2025
• Last Updated: January 15, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)