Browsing by Author "Pérez-Grueso, Francisco Javier Sánchez"
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Item Open Access Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme that Predicts Quality and Value.(Spine, 2019-07) Ames, Christopher P; Smith, Justin S; Pellisé, Ferran; Kelly, Michael; Alanay, Ahmet; Acaroğlu, Emre; Pérez-Grueso, Francisco Javier Sánchez; Kleinstück, Frank; Obeid, Ibrahim; Vila-Casademunt, Alba; Shaffrey, Christopher I; Burton, Douglas; Lafage, Virginie; Schwab, Frank; Shaffrey, Christopher I; Bess, Shay; Serra-Burriel, Miquel; European Spine Study Group, International Spine Study GroupSTUDY DESIGN:Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases. OBJECTIVE:To apply artificial intelligence (AI)-based hierarchical clustering as a step toward a classification scheme that optimizes overall quality, value, and safety for ASD surgery. SUMMARY OF BACKGROUND DATA:Prior ASD classifications have focused on radiographic parameters associated with patient reported outcomes. Recent work suggests there are many other impactful preoperative data points. However, the ability to segregate patient patterns manually based on hundreds of data points is beyond practical application for surgeons. Unsupervised machine-based clustering of patient types alongside surgical options may simplify analysis of ASD patient types, procedures, and outcomes. METHODS:Two prospective cohorts were queried for surgical ASD patients with baseline, 1-year, and 2-year SRS-22/Oswestry Disability Index/SF-36v2 data. Two dendrograms were fitted, one with surgical features and one with patient characteristics. Both were built with Ward distances and optimized with the gap method. For each possible n patient cluster by m surgery, normalized 2-year improvement and major complication rates were computed. RESULTS:Five hundred-seventy patients were included. Three optimal patient types were identified: young with coronal plane deformity (YC, n = 195), older with prior spine surgeries (ORev, n = 157), and older without prior spine surgeries (OPrim, n = 218). Osteotomy type, instrumentation and interbody fusion were combined to define four surgical clusters. The intersection of patient-based and surgery-based clusters yielded 12 subgroups, with major complication rates ranging from 0% to 51.8% and 2-year normalized improvement ranging from -0.1% for SF36v2 MCS in cluster [1,3] to 100.2% for SRS self-image score in cluster [2,1]. CONCLUSION:Unsupervised hierarchical clustering can identify data patterns that may augment preoperative decision-making through construction of a 2-year risk-benefit grid. In addition to creating a novel AI-based ASD classification, pattern identification may facilitate treatment optimization by educating surgeons on which treatment patterns yield optimal improvement with lowest risk. LEVEL OF EVIDENCE:4.Item Open Access Development of Deployable Predictive Models for Minimal Clinically Important Difference Achievement Across the Commonly Used Health-related Quality of Life Instruments in Adult Spinal Deformity Surgery.(Spine, 2019-08) Ames, Christopher P; Smith, Justin S; Pellisé, Ferran; Kelly, Michael P; Gum, Jeffrey L; Alanay, Ahmet; Acaroğlu, Emre; Pérez-Grueso, Francisco Javier Sánchez; Kleinstück, Frank S; Obeid, Ibrahim; Vila-Casademunt, Alba; Burton, Douglas C; Lafage, Virginie; Schwab, Frank J; Shaffrey, Christopher I; Bess, Shay; Serra-Burriel, Miquel; European Spine Study Group, International Spine Study GroupStudy design
Retrospective analysis of prospectively-collected, multicenter adult spinal deformity (ASD) databases.Objective
To predict the likelihood of reaching minimum clinically important differences in patient-reported outcomes after ASD surgery.Summary of background data
ASD surgeries are costly procedures that do not always provide the desired benefit. In some series only 50% of patients achieve minimum clinically important differences in patient-reported outcomes (PROs). Predictive modeling may be useful in shared-decision making and surgical planning processes. The goal of this study was to model the probability of achieving minimum clinically important differences change in PROs at 1 and 2 years after surgery.Methods
Two prospective observational ASD cohorts were queried. Patients with Scoliosis Research Society-22, Oswestry Disability Index , and Short Form-36 data at preoperative baseline and at 1 and 2 years after surgery were included. Seventy-five variables were used in the training of the models including demographics, baseline PROs, and modifiable surgical parameters. Eight predictive algorithms were trained at four-time horizons: preoperative or postoperative baseline to 1 year and preoperative or postoperative baseline to 2 years. External validation was accomplished via an 80%/20% random split. Five-fold cross validation within the training sample was performed. Precision was measured as the mean average error (MAE) and R values.Results
Five hundred seventy patients were included in the analysis. Models with the lowest MAE were selected; R values ranged from 20% to 45% and MAE ranged from 8% to 15% depending upon the predicted outcome. Patients with worse preoperative baseline PROs achieved the greatest mean improvements. Surgeon and site were not important components of the models, explaining little variance in the predicted 1- and 2-year PROs.Conclusion
We present an accurate and consistent way of predicting the probability for achieving clinically relevant improvement after ASD surgery in the largest-to-date prospective operative multicenter cohort with 2-year follow-up. This study has significant clinical implications for shared decision making, surgical planning, and postoperative counseling.Level of evidence
4.Item Open Access Development of predictive models for all individual questions of SRS-22R after adult spinal deformity surgery: a step toward individualized medicine.(European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2019-09) Ames, Christopher P; Smith, Justin S; Pellisé, Ferran; Kelly, Michael; Gum, Jeffrey L; Alanay, Ahmet; Acaroğlu, Emre; Pérez-Grueso, Francisco Javier Sánchez; Kleinstück, Frank S; Obeid, Ibrahim; Vila-Casademunt, Alba; Shaffrey, Christopher I; Burton, Douglas C; Lafage, Virginie; Schwab, Frank J; Shaffrey, Christopher I; Bess, Shay; Serra-Burriel, Miquel; European Spine Study Group; International Spine Study GroupPurpose
Health-related quality of life (HRQL) instruments are essential in value-driven health care, but patients often have more specific, personal priorities when seeking surgical care. The Scoliosis Research Society-22R (SRS-22R), an HRQL instrument for spinal deformity, provides summary scores spanning several health domains, but these may be difficult for patients to utilize in planning their specific care goals. Our objective was to create preoperative predictive models for responses to individual SRS-22R questions at 1 and 2 years after adult spinal deformity (ASD) surgery to facilitate precision surgical care.Methods
Two prospective observational cohorts were queried for ASD patients with SRS-22R data at baseline and 1 and 2 years after surgery. In total, 150 covariates were used in training machine learning models, including demographics, surgical data and perioperative complications. Validation was accomplished via an 80%/20% data split for training and testing, respectively. Goodness of fit was measured using area under receiver operating characteristic (AUROC) curves.Results
In total, 561 patients met inclusion criteria. The AUROC ranged from 56.5 to 86.9%, reflecting successful fits for most questions. SRS-22R questions regarding pain, disability and social and labor function were the most accurately predicted. Models were less sensitive to questions regarding general satisfaction, depression/anxiety and appearance.Conclusions
To the best of our knowledge, this is the first study to explicitly model the prediction of individual answers to the SRS-22R questionnaire at 1 and 2 years after deformity surgery. The ability to predict individual question responses may prove useful in preoperative counseling in the age of individualized medicine. These slides can be retrieved under Electronic Supplementary Material.