Decision Tree-based Modelling for Identification of Predictors of Blood Loss and Transfusion Requirement After Adult Spinal Deformity Surgery

Establishing a Benchmark for Iatrogenic Hemodilution and Blood Transfusion in Long-Segment Spine Fusion Surgery

STUDY DESIGN

Single-center retrospective cohort study.

OBJECTIVE

To identify risk factors for transfusion during long-segment thoracolumbar fusion surgery and benchmark cutoffs that could be used by the operative team to guide the use of transfusion.

SUMMARY OF BACKGROUND DATA

Perioperative transfusion for patients undergoing long-segment thoracolumbar fusion surgery is common. To date, no standardized intraoperative and perioperative management of transfusion administration has been defined.

METHODS

Patients who underwent thoracolumbar fusion surgeries of 8 or more levels between 2015 and 2020 were identified. Patient demographics, surgical details, anesthesia and critical care records, and laboratory data were compared between patients who received intraoperative and postoperative blood transfusions and those who did not. Univariate and multivariate propensity-matched analyses were performed to identify independent predictors for blood transfusion, and ordinal analysis was performed to identify possible benchmark cutoffs.

RESULTS

Among 233 patients identified who underwent long-segment fusions, 133 (57.1%) received a blood transfusion. Multivariate propensity-matched logistic regression showed that intravenous (IV) fluid volume was an independent predictor for transfusion (transfusion group 8051 mL vs. non-transfusion group 5070 mL, P <0.01). Patients who received ≥4 L total IV fluids were more likely to undergo transfusion than those who received <4 L (93.2% vs. 50.7%, P <0.01). Those receiving total IV fluids at a rate ≥60 mL/kg (OR 10.45; 95% CI, 2.62-41.72; P <0.01) or intraoperative IV fluids at a rate ≥9 mL/kg/hr (OR 4.46; 95% CI, 1.39-14.32; P <0.01) were more likely to require transfusions.

CONCLUSIONS

IV fluid administration is an independent predictor for blood transfusion after long-segment fusion surgery. Limiting IV fluid administration may prevent iatrogenic hemodilution and decrease transfusion rates. These data can be used to create perioperative protocols with the goal of decreasing transfusion rates when not indicated and allowing earlier administration when indicated.

Constructing a predictive model for high intraoperative excessive bleeding in patients undergoing posterior lumbar decompression and fusion internal fixation surgery during outpatient visits

OBJECTIVE

1. Construct a risk prediction model to predict the factors of high intraoperative bleeding in patients undergoing posterior lumbar decompression and fusion internal fixation surgery during outpatient visits. 2. Implement pre-hospital blood management for surgery patients, to improve clinical outcomes.

DESIGN & METHODS

We collected patients who underwent two-segment and three-segment posterior lumbar decompression and fusion internal fixation surgery in our hospital from 2016 to 2021. A total of 24 preoperative indicators were analyzed, covering medical history, demographic characteristics, segment, operator and laboratory test results. We used a logistic regression model to optimize the model's feature selection. The predictive model was constructed using the multivariable logistic regression method with all included methods, and a nomogram was created to display the model. Activated partial thromboplastin time, surgeon volume, American Society of Anesthesiologists classification, body mass index, and the number of fusion and fixation lumbar segments were used to construct the predictive model. The predictive model's discrimination, calibration, clinical applicability, and rationality were evaluated.

RESULTS

The predictive model's area under the receiver operating characteristic curve is 0.723, with a 95% confidence interval of (0.685-0.760). The training set's decision curve analysis demonstrates that applying this diagnostic curve will increase the net benefit when the threshold probability is between 5% and 40%.

CONCLUSION

This study developed a novel nomogram with relatively good accuracy to assist clinical doctors in assessing the high intraoperative bleeding risk in patients undergoing posterior lumbar decompression and fusion internal fixation surgery during outpatient visits. By evaluating individual risk, surgeons can develop an individualized treatment plan to reduce the risk of intraoperative bleeding for each patient.

High-Dose TXA Is Associated with Less Blood Loss Than Low-Dose TXA without Increased Complications in Patients with Complex Adult Spinal Deformity

BACKGROUND

Tranexamic acid (TXA) is commonly utilized to reduce blood loss in adult spinal deformity (ASD) surgery. Despite its widespread use, there is a lack of consensus regarding the optimal dosing regimen. The aim of this study was to assess differences in blood loss and complications between high, medium, and low-dose TXA regimens among patients undergoing surgery for complex ASD.

METHODS

A multicenter database was retrospectively analyzed to identify 265 patients with complex ASD. Patients were separated into 3 groups by TXA regimen: (1) low dose (<20-mg/kg loading dose with ≤2-mg/kg/hr maintenance dose), (2) medium dose (20 to 50-mg/kg loading dose with 2 to 5-mg/kg/hr maintenance dose), and (3) high dose (>50-mg/kg loading dose with ≥5-mg/kg/hr maintenance dose). The measured outcomes included blood loss, complications, and red blood cell (RBC) units transfused intraoperatively and perioperatively. The multivariable analysis controlled for TXA dosing regimen, levels fused, operating room time, preoperative hemoglobin, 3-column osteotomy, and posterior interbody fusion.

RESULTS

The cohort was predominantly White (91.3%) and female (69.1%) and had a mean age of 61.6 years. Of the 265 patients, 54 (20.4%) received low-dose, 131 (49.4%) received medium-dose, and 80 (30.2%) received high-dose TXA. The median blood loss was 1,200 mL (interquartile range [IQR], 750 to 2,000). The median RBC units transfused intraoperatively was 1.0 (IQR, 0.0 to 2.0), and the median RBC units transfused perioperatively was 2.0 (IQR, 1.0 to 4.0). Compared with the high-dose group, the low-dose group had increased blood loss (by 513.0 mL; p = 0.022) as well as increased RBC units transfused intraoperatively (by 0.6 units; p < 0.001) and perioperatively (by 0.3 units; p = 0.024). The medium-dose group had increased blood loss (by 491.8 mL; p = 0.006) as well as increased RBC units transfused intraoperatively (by 0.7 units; p < 0.001) and perioperatively (by 0.5 units; p < 0.001) compared with the high-dose group.

CONCLUSIONS

Patients with ASD who received high-dose intraoperative TXA had fewer RBC transfusions intraoperatively, fewer RBC transfusions perioperatively, and less blood loss than those who received low or medium-dose TXA, with no differences in the rates of seizure or thromboembolic complications.

LEVEL OF EVIDENCE

Therapeutic Level III . See Instructions for Authors for a complete description of levels of evidence.

What Are We Transfusing? Evaluating the Quality and Clinical Utility of Intraoperatively Salvaged Red Blood Cells in Spinal Deformity Surgery: A Nonrandomized Controlled Trial

BACKGROUND AND OBJECTIVES

Intraoperative red blood cell (RBC) salvage is frequently used in contemporary spine surgery, despite clinical concern in its efficacy as a surrogate for blood-banked allogeneic packed RBCs (pRBCs). During spine surgery, salvaged RBCs (sRBCs) are exposed to injurious high-heat electrocautery, prolonged stasis, and abrasive pharmaceuticals, potentially making sRBCs a poor blood substitute. We therefore sought to scientifically and objectively define the quality of sRBCs in the context of complex spine surgery.

METHODS

This is a single-center, prospective, nonrandomized controlled trial of patients undergoing posterior-based multilevel thoracolumbar instrumented fusion for spinal deformity with planned use of intraoperative RBC salvage between June 2022 and July 2023. Surgeries were performed by fellowship-trained spinal neurosurgeons and orthopedic surgeons. The participants were split based on transfusion of sRBCs (given sufficient yield) vs no sRBC transfusion. Primary outcomes were RBC electrolyte composition, indices, deformability, and integrity, which were evaluated in comparison blood samples: Baseline, pRBC, and sRBC. Secondary outcomes were related to clinical effects of sRBC transfusion. Morphological assessment used Stimulated Raman Histology and machine learning. Deformability was assessed using ektacytometry.

RESULTS

A total of 174 patients were included. The mean age was 50.2years ±25.4, 58.6% was female, the mean level fused was 10.0 ± 3.9, and 58.0% received sRBCs (median 207.0 mL). sRBCs differed significantly on standard laboratory measures, had a high proportion (30.7%) of shrunken and irregularly spiculated morphologies, and demonstrated abnormal deformability and relaxation kinetics. The hemolysis index was significantly elevated in sRBCs (2.9 ± 1.8) compared with Baseline samples and pRBCs ( P < .01). Transfusion of sRBCs was associated with suboptimal resuscitation and provided no practical clinical benefit.

CONCLUSION

RBCs salvaged during posterior thoracolumbar spine surgery are irreversibly injured, with hemolysis index exceeding Food and Drug Administration and Council of Europe transfusion standards in all samples, questioning their efficacy and safety as a blood substitute.

Mapping the landscape of machine learning models used for predicting transfusions in surgical procedures: a scoping review

Massive transfusion of blood products poses challenges in determining the need for transfusion and the appropriate volume of blood products. This review explores the use of machine learning (ML) models to predict transfusion risk during surgical procedure, focusing on the methodology, variables, and software employed to predict transfusion. This scoping review investigates the development and current state of machine learning models for predicting transfusion risk during surgical procedure, aiming to inform physicians about the field's progress and potential directions.The review was conducted using the databases Cochrane, Embase, and PubMed. The search included keywords related to blood transfusion, statistical models, and surgical procedures. Peer-reviewed articles were included, while literature reviews, case reports, and non-human studies were excluded.A total of 40 studies met the inclusion criteria. The most frequently studied biological variables included haemoglobin, platelet count, international normalized ratio (INR), activated partial thromboplastin time (aPTT), fibrinogen, creatinine, white blood cells, and albumin. Clinical variables of importance included age, sex, surgery type, blood pressure, weight, surgery duration, american society of anesthesiology (ASA) status, blood loss, and body mass index (BMI). The software employed varied, with Python, R, SPSS, and SAS being the most commonly used. Logistic regression was the predominant methodology used in 20 studies.Our scoping review highlights the need for improved reporting and transparency in methodology, variables, and software used. Future research should focus on providing detailed descriptions and open access to codes of respective models, promoting reproducibility, and enhancing the clinical relevance of transfusion risk prediction models.

Risk factors for allogeneic red blood cell transfusion in adult spinal deformity surgery

STUDY DESIGN

Retrospective study.

PURPOSE

To investigate the risk factors for allogeneic red blood cell (RBC) transfusion in adult spinal deformity (ASD) surgery.

OVERVIEW OF LITERATURE

Studies have not thoroughly explored the roles of intraoperative hypothermia, autologous blood donation, and hemostatic agent administration, which would provide a better understanding of the risk for perioperative RBC transfusion in ASD surgery.

METHODS

The medical records of 151 patients with ASD who underwent correction surgery between 2012 and 2021 were retrospectively reviewed. Estimated blood loss and perioperative allogeneic transfusion were examined. Patients were categorized into two groups based on whether they received perioperative allogeneic blood transfusion. Logistic regression analysis was employed to investigate the effect of age, sex, blood type, body mass index, American Society of Anesthesiologists' physical status, preoperative hemoglobin level, autologous blood donation, global spine alignment parameters, preoperative use of anticoagulants or antiplatelet medicine and nonsteroidal anti-inflammatory drugs, number of instrumented fusion levels, total operative duration, three-column osteotomy, lateral interbody fusion, pelvic fixation, intraoperative hypothermia, use of gelatin-thrombin based hemostatic agents, and intraoperative tranexamic acid (TXA) with simultaneous exposure by two attending surgeons.

RESULTS

The estimated blood loss was 994.2±754.5 mL, and 71 patients (47.0%) received allogeneic blood transfusion. In the logistic regression analysis, the absence of intraoperative TXA use and simultaneous exposure (odds ratio [OR], 26.3; 95% confidence interval [CI], 7.6-90.9; p<0.001), lack of autologous blood donation (OR, 21.2; 95% CI, 4.4-100.0; p<0.001), and prolonged operative duration (OR, 1.6; 95% CI, 1.3-1.9; p<0.001) were significant independent factors for perioperative allogeneic blood transfusion in ASD surgery.

CONCLUSIONS

Autologous blood storage, intraoperative TXA administration, and simultaneous exposure should be considered to minimize perioperative allogeneic blood transfusion in ASD surgery, particularly in patients with anticipated lengthy surgeries.

Development of a Novel Risk Stratification Score to Predict 30-Day Mortality in Cervical Trauma Patients: CLAAD Score

STUDY DESIGN

Case control.

OBJECTIVE

Traumatic cervical spine injuries are associated with a substantial risk of mortality. The aim of this study is to develop a novel mortality prediction model for patients with cervical trauma who require operative treatment.

SUMMARY OF BACKGROUND DATA

Patients with cervical spine trauma have a high risk of postoperative complications and mortality. There are few reliable systems that can accurately predict mortality after surgery for cervical spine trauma, and those that do exist are typically not specific to cervical trauma.

MATERIALS AND METHODS

The National Surgical Quality Improvement Program (NSQIP) database was used to identify patients undergoing surgery for cervical spine trauma. Univariate analyses were performed to identify variables associated with mortality. Variables that were found to be significant in the univariate models were compiled into a multivariable model. The final model was compared with the American Society of Anesthesiologists (ASA), a modified Charlson comorbidity index (mCCI), and the 5-factor modified frailty index (mFI-5) in respect to predicting 30-day mortality after cervical trauma. The score was then externally validated using the Nationwide Inpatient Sample (NIS) database.

RESULTS

Fifty-five (6.7%) of 822 patients did not survive 30 days after surgery. The final multivariable logistic regression model consisted of the following variables: circumferential fusion "C." long "L" fusion (more than 4 levels), anemia "A," age over 60 "A," and dialysis "D." The risk of mortality increased with increasing CLAAD score, with mortality rates of 0.9%, 3.1%, 7.4%, 22.7%, and 14.3% for scores of 0, 1, 2, 3, and 4, respectively. The CLAAD model had an AUC of 0.73 for predicting mortality after cervical trauma.

CONCLUSIONS

The CLAAD score is a simple and effective system that can help identify patients at risk of increased mortality within 30 days of cervical trauma.

LEVEL OF EVIDENCE

Level III.

Personalizing adult spinal deformity surgery through multimodal artificial intelligence

To achieve meaningful, patient-centered outcomes following adult spinal deformity (ASD) surgery, it is crucial to engage in precise preoperative planning, perform excellent intraoperative execution, and ensure careful postoperative management. The field of multimodal artificial intelligence (AI) is rapidly developing and should be integrated into the management of ASD patients. In this context, we outline the current concepts and explore future applications of AI across the ASD care continuum. Cite this article as: Azad TD, Vattipally VN, Ames CP. Personalizing adult spinal deformity surgery through multimodal artificial intelligence. Acta Orthop Traumatol Turc., 2024;58(2):80-82.

Patient-specific Cervical Deformity Corrections With Consideration of Associated Risk: Establishment of Risk Benefit Thresholds for Invasiveness Based on Deformity and Frailty Severity

STUDY DESIGN/SETTING

This was a retrospective cohort study.

BACKGROUND

Little is known of the intersection between surgical invasiveness, cervical deformity (CD) severity, and frailty.

OBJECTIVE

The aim of this study was to investigate the outcomes of CD surgery by invasiveness, frailty status, and baseline magnitude of deformity.

METHODS

This study included CD patients with 1-year follow-up. Patients stratified in high deformity if severe in the following criteria: T1 slope minus cervical lordosis, McGregor's slope, C2-C7, C2-T3, and C2 slope. Frailty scores categorized patients into not frail and frail. Patients are categorized by frailty and deformity (not frail/low deformity; not frail/high deformity; frail/low deformity; frail/high deformity). Logistic regression assessed increasing invasiveness and outcomes [distal junctional failure (DJF), reoperation]. Within frailty/deformity groups, decision tree analysis assessed thresholds for an invasiveness cutoff above which experiencing a reoperation, DJF or not achieving Good Clinical Outcome was more likely.

RESULTS

A total of 115 patients were included. Frailty/deformity groups: 27% not frail/low deformity, 27% not frail/high deformity, 23.5% frail/low deformity, and 22.5% frail/high deformity. Logistic regression analysis found increasing invasiveness and occurrence of DJF [odds ratio (OR): 1.03, 95% CI: 1.01-1.05, P =0.002], and invasiveness increased with deformity severity ( P <0.05). Not frail/low deformity patients more often met Optimal Outcome with an invasiveness index <63 (OR: 27.2, 95% CI: 2.7-272.8, P =0.005). An invasiveness index <54 for the frail/low deformity group led to a higher likelihood of meeting the Optimal Outcome (OR: 9.6, 95% CI: 1.5-62.2, P =0.018). For the frail/high deformity group, patients with a score <63 had a higher likelihood of achieving Optimal Outcome (OR: 4.8, 95% CI: 1.1-25.8, P =0.033). There was no significant cutoff of invasiveness for the not frail/high deformity group.

CONCLUSIONS

Our study correlated increased invasiveness in CD surgery to the risk of DJF, reoperation, and poor clinical success. The thresholds derived for deformity severity and frailty may enable surgeons to individualize the invasiveness of their procedures during surgical planning to account for the heightened risk of adverse events and minimize unfavorable outcomes.

Predictors of Blood Transfusion in Patients Undergoing Lumbar Spinal Fusion

OBJECTIVE

To determine risk factors for perioperative blood transfusion after lumbar fusion surgery.

METHODS

After institutional review board approval, a retrospective cohort study of adult patients who underwent lumbar fusion at a single, urban tertiary academic center was retrospectively retrieved. Our primary outcome, blood transfusion, was collected via chart query. A receiver operating characteristic curve was used to evaluate the regression model. A P-value < 0.05 was considered statistically significant.

RESULTS

Of the 3,842 patients, 282 (7.3%) required a blood transfusion. For patients undergoing posterolateral decompression and fusion, predictors of transfusion included age (P < 0.001) and more levels fused (P < 0.001). A higher preoperative hemoglobin level (P < 0.001) and revision surgery (P = 0.005) were protective of blood transfusion. For patients undergoing transforaminal lumbar interbody fusion, greater Elixhauser comorbidity index (P < 0.001), longer operative time (P = 0.040), and more levels fused (P = 0.030) were independent predictors of the need for blood transfusion. Patients with a higher body mass index (P = 0.012) and preoperative hemoglobin level (P < 0.001) had a reduced likelihood of receiving a transfusion. For circumferential fusion, greater age (P = 0.006) and longer operative times (P = 0.015) were independent predictors of blood transfusion, while a higher preoperative hemoglobin level (P < 0.001) and male sex (P = 0.002) were protective.

CONCLUSIONS

Our analysis identified older age, lower body mass index, greater Elixhauser comorbidity index, longer operative duration, more levels fused, and lower preoperative hemoglobin levels as independent predictors of requiring a blood transfusion following lumbar spinal fusion. Different surgical approaches were not found to be associated with transfusion.

Prediction of red blood cell transfusion after orthopedic surgery using an interpretable machine learning framework

Objective

Postoperative red blood cell (RBC) transfusion is widely used during the perioperative period but is often associated with a high risk of infection and complications. However, prediction models for RBC transfusion in patients with orthopedic surgery have not yet been developed. We aimed to identify predictors and constructed prediction models for RBC transfusion after orthopedic surgery using interpretable machine learning algorithms.

Methods

This retrospective cohort study reviewed a total of 59,605 patients undergoing orthopedic surgery from June 2013 to January 2019 across 7 tertiary hospitals in China. Patients were randomly split into training (80%) and test subsets (20%). The feature selection method of recursive feature elimination (RFE) was used to identify an optimal feature subset from thirty preoperative variables, and six machine learning algorithms were applied to develop prediction models. The Shapley Additive exPlanations (SHAP) value was employed to evaluate the contribution of each predictor towards the prediction of postoperative RBC transfusion. For simplicity of the clinical utility, a risk score system was further established using the top risk factors identified by machine learning models.

Results

Of the 59,605 patients with orthopedic surgery, 19,921 (33.40%) underwent postoperative RBC transfusion. The CatBoost model exhibited an AUC of 0.831 (95% CI: 0.824-0.836) on the test subset, which significantly outperformed five other prediction models. The risk of RBC transfusion was associated with old age (>60 years) and low RBC count (<4.0 × 10¹²/L) with clear threshold effects. Extremes of BMI, low albumin, prolonged activated partial thromboplastin time, repair and plastic operations on joint structures were additional top predictors for RBC transfusion. The risk score system derived from six risk factors performed well with an AUC of 0.801 (95% CI: 0.794-0.807) on the test subset.

Conclusion

By applying an interpretable machine learning framework in a large-scale multicenter retrospective cohort, we identified novel modifiable risk factors and developed prediction models with good performance for postoperative RBC transfusion in patients undergoing orthopedic surgery. Our findings may allow more precise identification of high-risk patients for optimal control of risk factors and achieve personalized RBC transfusion for orthopedic patients.

An Artificial Neural Network Model for the Prediction of Perioperative Blood Transfusion in Adult Spinal Deformity Surgery

Prediction of blood transfusion after adult spinal deformity (ASD) surgery can identify at-risk patients and potentially reduce its utilization and the complications associated with it. The use of artificial neural networks (ANNs) offers the potential for high predictive capability. A total of 1173 patients who underwent surgery for ASD were identified in the 2017–2019 NSQIP databases. The data were split into 70% training and 30% testing cohorts. Eighteen patient and operative variables were used. The outcome variable was receiving RBC transfusion intraoperatively or within 72 h after surgery. The model was assessed by its sensitivity, positive predictive value, F1-score, accuracy (ACC), and area under the curve (AUROC). Average patient age was 56 years and 63% were female. Pelvic fixation was performed in 21.3% of patients and three-column osteotomies in 19.5% of cases. The transfusion rate was 50.0% (586/1173 patients). The best model showed an overall ACC of 81% and 77% on the training and testing data, respectively. On the testing data, the sensitivity was 80%, the positive predictive value 76%, and the F1-score was 78%. The AUROC was 0.84. ANNs may allow the identification of at-risk patients, potentially decrease the risk of transfusion via strategic planning, and improve resource allocation.

AI MSK clinical applications: spine imaging

Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions.

Emerging Technologies in the Treatment of Adult Spinal Deformity

Outcomes for adult spinal deformity continue to improve as new technologies become integrated into clinical practice. Machine learning, robot-guided spinal surgery, and patient-specific rods are tools that are being used to improve preoperative planning and patient satisfaction. Machine learning can be used to predict complications, readmissions, and generate postoperative radiographs which can be shown to patients to guide discussions about surgery. Robot-guided spinal surgery is a rapidly growing field showing signs of greater accuracy in screw placement during surgery. Patient-specific rods offer improved outcomes through higher correction rates and decreased rates of rod breakage while decreasing operative time. The objective of this review is to evaluate trends in the literature about machine learning, robot-guided spinal surgery, and patient-specific rods in the treatment of adult spinal deformity.

Artificial intelligence-enhanced intraoperative neurosurgical workflow: state of the art and future perspectives

BACKGROUND

Artificial Intelligence (AI) and Machine Learning (ML) augment decision-making processes and productivity by supporting surgeons over a range of clinical activities: from diagnosis and preoperative planning to intraoperative surgical assistance. We reviewed the literature to identify current AI platforms applied to neurosurgical perioperative and intraoperative settings and describe their role in multiple subspecialties.

METHODS

A systematic review of the literature was conducted following the PRISMA guidelines. PubMed, EMBASE, and Scopus databases were searched from inception to December 31, 2020. Original articles were included if they: presented AI platforms implemented in perioperative, intraoperative settings and reported ML models' performance metrics. Due to the heterogeneity in neurosurgical applications, a qualitative synthesis was deemed appropriate. The risk of bias and applicability of predicted outcomes were assessed using the PROBAST tool.

RESULTS

41 articles were included. All studies evaluated a supervised learning algorithm. A total of 10 ML models were described; the most frequent were neural networks (n = 15) and tree-based models (n = 13). Overall, the risk of bias was medium-high, but applicability was considered positive for all studies. Articles were grouped into 4 categories according to the subspecialty of interest: neuro-oncology, spine, functional and other. For each category, different prediction tasks were identified.

CONCLUSIONS

In this review, we summarize the state-of-art applications of AI for the intraoperative augmentation of neurosurgical workflows across multiple subspecialties. ML models may boost surgical team performances by reducing human errors and providing patient-tailored surgical plans, but further and higher-quality studies need to be conducted.

Assessment of Clinical Decision Support System Efficiency in Spinal Neurosurgery for Personalized Minimally Invasive Technologies Used on Lumbar Spine

The aim of the study was to assess clinical decision support system (CDSS) in spinal surgery for personalized minimally invasive technologies on lumbar spine.

Materials and Methods

The prospective study involved 59 patients operated on using CDSS based on a personalized surgical algorithm considering patient-specific parameters of lumbar segments. Among them, 11 patients underwent total disk replacement (TDR), 25 and 23 patients had minimally invasive (MI-TLIF) and open (O-TLIF) dorsal rigid stabilization, respectively, according to an original technology. The comparative analysis was carried out using retrospective findings of 196 patients operated on involving TDR (n=42), MI-TLIF (n=79), and O-TLIF (n=75). The efficiency of CDSS medical algorithms was assessed by pain syndrome in the lumbar spine and lower limbs, as well as by patients' functional status on discharge according to ODI, 3 and 6 months after the operation.

Results

The comparison by gender characteristics and anthropometric data revealed no significant intergroup differences among the groups under study (p>0.05). Intergroup analysis of functional status by ODI, pain intensity in lower limbs and lumbar spine showed better clinical outcomes in patients operated using CDSS compared to a retrospective group (p<0.05): 6 months after TDR and O-TLIF, and 3 months after MI-TLIF.

Conclusion

The study findings demonstrated high efficiency of CDSS developed for personalized surgical treatment of patients with degenerative lumbar spine diseases taking into consideration individual biometric parameters of lumbar segments.