Predicting Bone Cement Implantation Syndrome Using Artificial Intelligence Methods

Overview

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.

Since the prevalence of hip and knee osteoarthritis increases with age, orthopedic prosthesis operations are widely performed all over the world and in our country. In these surgeries, bone cement is used to ensure adhesion of the prosthesis to the bone. Bone cement implantation syndrome (BCIS) is a fatal complication of cemented bone surgery characterized by systemic hypotension, pulmonary hypertension, arrhythmias, loss of consciousness and cardiac arrest, most commonly occurring during cementing and prosthesis placement, and is increasingly being reported. The syndrome is most commonly seen in cemented hemiarthroplasty after displaced femoral neck fractures, but also occurs in total hip and knee replacement surgery. Despite the publication of safety guidelines to reduce BCIS, it remains a common intraoperative complication with an overall incidence of up to 28% and is a major cause of intraoperative and postoperative morbidity and mortality. The pathophysiology of BCIS is unclear, including hemodynamic instability as a result of changes in pulmonary and systemic vascular resistance, increased intramedullary pressure resulting in the incorporation of polymethyl methacrylate monomers into the circulation causing vasodilation, release of mediators from polymethyl methacrylate, release of fatty acids, and release of mediators from polymethyl methacrylate, Acute right ventricular failure, anaphylaxis, inflammatory and exothermic reaction, and complement activation may include one or a combination of acute right ventricular failure, anaphylaxis, inflammatory and exothermic reaction, and complement activation, which develops when cement and clot particles cause emboli in many organs in the body, especially in the pulmonary system, with increased pulmonary vascular resistance. Donaldson et al. developed a classification system for BCIS severity. Grade 0: no hypotension/hypoxia; Grade 1 moderate hypoxia (SpO2 \< 94%) or hypotension \[systolic blood pressure (SBP) \> 20% decrease from baseline\]; Grade 2 severe hypoxia (SpO2 \< 88%) or hypotension (SBP \> 40% decrease from baseline) or unexpected loss of consciousness; Grade 3 cardiovascular collapse requiring cardiopulmonary resuscitation. Patients with BCIS grades 2 and 3 have been shown to have a 16-fold increase in 30-day postoperative mortality compared to those with BCIS grade 1. Most reports on BCIS focus on deaths and serious problems, and most cases of mild BCIS go unreported. Suspected BCIS should be treated with aggressive resuscitation and supportive care. This risk may prompt some surgeons not to use cement in arthroplasty operations. Although cementless hemiarthroplasty eliminates this risk and saves an average operating time of 20 minutes, it is associated with serious complications. Patients undergoing uncemented hip arthroplasty implants are more likely to experience periprosthetic fractures as well as early revision. Uncemented arthroplasty has a 17-fold greater risk of periprosthetic fracture revision or aseptic revision due to loosening compared to cemented hip arthroplasty. Since additional surgery carries an additional risk of death, skipping the cementation step may also not be the best choice in these patients who are more likely to fall and undergo revision surgery. Early detection of this complication and early intervention is crucial as it will reduce mortality. Prevention of BCIS includes identification of high-risk patients, preoperative optimization of patient risk factors and comorbidities, and good communication with the surgical team. In this way, the patient's comfort and life expectancy can be increased. The increasing number of patients needing and waiting for arthroplasty makes it difficult to identify high-risk patients, optimize patient risk factors and comorbidities preoperatively to prevent this potentially fatal complication. The condition in which BCIS may occur is evaluated according to the results of analyzing the data obtained from retrospective and prospective data sets with complex biostatistical methods. In order to predict the complication, predictive factors associated with it are tried to be determined. Spann et al. (2020) stated in their review study that machine learning can be used in analyzes beyond complex biostatistical methods. At this point, a decision support system created with artificial intelligence modeling will help in the early detection of this complication and thus serve as an important decision-support mechanism for clinicians by increasing the patient's comfort and life expectancy. However, it is not easy to assess at what level this complication will be predicted and reduced without implementing the system.

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