Browsing by Author "Vila-Casademunt, Alba"
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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 and validation of risk stratification models for adult spinal deformity surgery.(Journal of neurosurgery. Spine, 2019-06) Pellisé, Ferran; Serra-Burriel, Miquel; Smith, Justin S; Haddad, Sleiman; Kelly, Michael P; Vila-Casademunt, Alba; Sánchez Pérez-Grueso, Francisco Javier; Bess, Shay; Gum, Jeffrey L; Burton, Douglas C; Acaroğlu, Emre; Kleinstück, Frank; Lafage, Virginie; Obeid, Ibrahim; Schwab, Frank; Shaffrey, Christopher I; Alanay, Ahmet; Ames, Christopher; International Spine Study Group; European Spine Study GroupOBJECTIVE:Adult spinal deformity (ASD) surgery has a high rate of major complications (MCs). Public information about adverse outcomes is currently limited to registry average estimates. The object of this study was to assess the incidence of adverse events after ASD surgery, and to develop and validate a prognostic tool for the time-to-event risk of MC, hospital readmission (RA), and unplanned reoperation (RO). METHODS:Two models per outcome, created with a random survival forest algorithm, were trained in an 80% random split and tested in the remaining 20%. Two independent prospective multicenter ASD databases, originating from the European continent and the United States, were queried, merged, and analyzed. ASD patients surgically treated by 57 surgeons at 23 sites in 5 countries in the period from 2008 to 2016 were included in the analysis. RESULTS:The final sample consisted of 1612 ASD patients: mean (standard deviation) age 56.7 (17.4) years, 76.6% women, 10.4 (4.3) fused vertebral levels, 55.1% of patients with pelvic fixation, 2047.9 observation-years. Kaplan-Meier estimates showed that 12.1% of patients had at least one MC at 10 days after surgery; 21.5%, at 90 days; and 36%, at 2 years. Discrimination, measured as the concordance statistic, was up to 71.7% (95% CI 68%-75%) in the development sample for the postoperative complications model. Surgical invasiveness, age, magnitude of deformity, and frailty were the strongest predictors of MCs. Individual cumulative risk estimates at 2 years ranged from 3.9% to 74.1% for MCs, from 3.17% to 44.2% for RAs, and from 2.67% to 51.9% for ROs. CONCLUSIONS:The creation of accurate prognostic models for the occurrence and timing of MCs, RAs, and ROs following ASD surgery is possible. The presented variability in patient risk profiles alongside the discrimination and calibration of the models highlights the potential benefits of obtaining time-to-event risk estimates for patients and clinicians.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.Item Open Access External validation of the adult spinal deformity (ASD) frailty index (ASD-FI)(European Spine Journal, 2018-09-01) Miller, Emily K; Vila-Casademunt, Alba; Neuman, Brian J; Sciubba, Daniel M; Kebaish, Khaled M; Smith, Justin S; Alanay, Ahmet; Acaroglu, Emre R; Kleinstück, Frank; Obeid, Ibrahim; Sánchez Pérez-Grueso, Francisco Javier; Carreon, Leah Y; Schwab, Frank J; Bess, Shay; Scheer, Justin K; Lafage, Virginie; Shaffrey, Christopher I; Pellisé, Ferran; Ames, Christopher P; European Spine Study Group; International Spine Study Group© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Purpose: To assess the ability of the recently developed adult spinal deformity frailty index (ASD-FI) to predict odds of perioperative complications, odds of reoperation, and length of hospital stay after adult spinal deformity (ASD) surgery using a database other than the one used to create the index. Methods: We used the ASD-FI to calculate frailty scores for 266 ASD patients who had minimum postoperative follow-up of 2 years in the European Spine Study Group (ESSG) database. Patients were enrolled from 2012 through 2013. Using ASD-FI scores, we categorized patients as not frail (NF) (< 0.3 points), frail (0.3–0.5 points), or severely frail (SF) (> 0.5 points). Multivariable logistic regression, adjusted for preoperative and surgical factors such as operative time and blood loss, was performed to determine the relationship between ASD-FI category and odds of major complications, odds of reoperation, and length of hospital stay. Results: We categorized 135 patients (51%) as NF, 90 patients (34%) as frail, and 41 patients (15%) as SF. Overall mean ASD-FI score was 0.29 (range 0–0.8). The adjusted odds of experiencing a major intraoperative or postoperative complication (OR 4.5, 95% CI 2.0–10) or having a reoperation (OR 3.9, 95% CI 1.7–8.9) were higher for SF patients compared with NF patients. Mean hospital stay was 2.1 times longer (95% CI 1.8–2.4) for SF patients compared with NF patients. Conclusions: Greater patient frailty, as measured by the ASD-FI, is associated with longer hospital stays and greater odds of major complications and reoperation. Graphical abstract: These slides can be retrieved under Electronic Supplementary Material.[Figure not available: see fulltext.].Item Open Access Quality metrics in adult spinal deformity surgery over the last decade: a combined analysis of the largest prospective multicenter data sets.(Journal of neurosurgery. Spine, 2021-10) Pellisé, Ferran; Serra-Burriel, Miquel; Vila-Casademunt, Alba; Gum, Jeffrey L; Obeid, Ibrahim; Smith, Justin S; Kleinstück, Frank S; Bess, Shay; Pizones, Javier; Lafage, Virginie; Pérez-Grueso, Francisco Javier S; Schwab, Frank J; Burton, Douglas C; Klineberg, Eric O; Shaffrey, Christopher I; Alanay, Ahmet; Ames, Christopher P; International Spine Study Group (ISSG) and European Spine Study Group (ESSG)Objective
The reported rate of complications and cost of adult spinal deformity (ASD) surgery, associated with an exponential increase in the number of surgeries, cause alarm among healthcare payers and providers worldwide. The authors conjointly analyzed the largest prospective available ASD data sets to define trends in quality-of-care indicators (complications, reinterventions, and health-related quality of life [HRQOL] outcomes) since 2010.Methods
This is an observational prospective longitudinal cohort study. Patients underwent surgery between January 2010 and December 2016, with > 2 years of follow-up data. Demographic, surgical, radiological, and HRQOL (i.e., Oswestry Disability Index, SF-36, Scoliosis Research Society-22r) data obtained preoperatively and at 3, 6, 12, and 24 months after surgery were evaluated. Trends and changes in indicators were analyzed using local regression (i.e., locally estimated scatterplot smoothing [LOESS]) and adjusted odds ratio (OR).Results
Of the 2286 patients included in the 2 registries, 1520 underwent surgery between 2010 and 2016. A total of 1151 (75.7%) patients who were treated surgically at 23 centers in 5 countries met inclusion criteria. Patient recruitment increased progressively (2010-2011 vs 2015-2016: OR 1.64, p < 0.01), whereas baseline clinical characteristics (age, American Society of Anesthesiologists class, HRQOL scores, sagittal deformity) did not change. Since 2010 there has been a sustained reduction in major and minor postoperative complications observed at 90 days (major: OR 0.59; minor: OR 0.65; p < 0.01); at 1 year (major: OR 0.52; minor: 0.75; p < 0.01); and at 2 years of follow-up (major: OR 0.4; minor: 0.80; p < 0.01) as well as in the 2-year reintervention rate (OR 0.41, p < 0.01). Simultaneously, there has been a slight improvement in the correction of sagittal deformity (i.e., pelvic incidence-lumbar lordosis mismatch: OR 1.11, p = 0.19) and a greater gain in quality of life (i.e., Oswestry Disability Index 26% vs 40%, p = 0.02; Scoliosis Research Society-22r, self-image domain OR 1.16, p = 0.13), and these are associated with a progressive reduction of surgical aggressiveness (number of fused segments: OR 0.81, p < 0.01; percent pelvic fixation: OR 0.66, p < 0.01; percent 3-column osteotomies: OR 0.63, p < 0.01).Conclusions
The best available data show a robust global improvement in quality metrics in ASD surgery over the last decade. Surgical complications and reoperations have been reduced by half, while improvement in disability increased and correction rates were maintained, in patients with similar baseline characteristics.Item Open Access Surgeons' risk perception in ASD surgery: The value of objective risk assessment on decision making and patient counselling.(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, 2022-05) Pellisé, Ferran; Vila-Casademunt, Alba; Núñez-Pereira, Susana; Haddad, Sleiman; Smith, Justin S; Kelly, Michael P; Alanay, Ahmet; Shaffrey, Christopher; Pizones, Javier; Yilgor, Çaglar; Obeid, Ibrahim; Burton, Douglas; Kleinstück, Frank; Fekete, Tamas; Bess, Shay; Gupta, Munish; Loibl, Markus; Klineberg, Eric O; Sánchez Pérez-Grueso, Francisco J; Serra-Burriel, Miquel; Ames, Christopher P; European Spine Study Group, International Spine Study GroupBackground
Surgeons often rely on their intuition, experience and published data for surgical decision making and informed consent. Literature provides average values that do not allow for individualized assessments. Accurate validated machine learning (ML) risk calculators for adult spinal deformity (ASD) patients, based on 10 year multicentric prospective data, are currently available. The objective of this study is to assess surgeon ASD risk perception and compare it to validated risk calculator estimates.Methods
Nine ASD complete (demographics, HRQL, radiology, surgical plan) preoperative cases were distributed online to 100 surgeons from 22 countries. Surgeons were asked to determine the risk of major complications and reoperations at 72 h, 90 d and 2 years postop, using a 0-100% risk scale. The same preoperative parameters circulated to surgeons were used to obtain ML risk calculator estimates. Concordance between surgeons' responses was analyzed using intraclass correlation coefficients (ICC) (poor < 0.5/excellent > 0.85). Distance between surgeons' and risk calculator predictions was assessed using the mean index of agreement (MIA) (poor < 0.5/excellent > 0.85).Results
Thirty-nine surgeons (74.4% with > 10 years' experience), from 12 countries answered the survey. Surgeons' risk perception concordance was very low and heterogeneous. ICC ranged from 0.104 (reintervention risk at 72 h) to 0.316 (reintervention risk at 2 years). Distance between calculator and surgeon prediction was very large. MIA ranged from 0.122 to 0.416. Surgeons tended to overestimate the risk of major complications and reintervention in the first 72 h and underestimated the same risks at 2 years postop.Conclusions
This study shows that expert surgeon ASD risk perception is heterogeneous and highly discordant. Available validated ML ASD risk calculators can enable surgeons to provide more accurate and objective prognosis to adjust patient expectations, in real time, at the point of care.Item Open Access Utilization of Predictive Modeling to Determine Episode of Care Costs and to Accurately Identify Catastrophic Cost Nonwarranty Outlier Patients in Adult Spinal Deformity Surgery: A Step Toward Bundled Payments and Risk Sharing.(Spine, 2020-03) Ames, Christopher P; Smith, Justin S; Gum, Jeffrey L; Kelly, Michael; Vila-Casademunt, Alba; Burton, Douglas C; Hostin, Richard; Yeramaneni, Samrat; Lafage, Virginie; Schwab, Frank J; Shaffrey, Christopher I; Bess, Shay; Pellisé, Ferran; Serra-Burriel, Miquel; European Spine Study Group and International Spine Study GroupStudy design
Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) database.Objective
The aim of this study was to evaluate the rate of patients who accrue catastrophic cost (CC) with ASD surgery utilizing direct, actual costs, and determine the feasibility of predicting these outliers.Summary of background data
Cost outliers or surgeries resulting in CC are a major concern for ASD surgery as some question the sustainability of these surgical treatments.Methods
Generalized linear regression models were used to explain the determinants of direct costs. Regression tree and random forest models were used to predict which patients would have CC (>$100,000).Results
A total of 210 ASD patients were included (mean age of 59.3 years, 83% women). The mean index episode of care direct cost was $70,766 (SD = $24,422). By 90 days and 2 years following surgery, mean direct costs increased to $74,073 and $77,765, respectively. Within 90 days of the index surgery, 11 (5.2%) patients underwent 13 revisions procedures, and by 2 years, 26 (12.4%) patients had undergone 36 revision procedures. The CC threshold at the index surgery and 90-day and 2-year follow-up time points was exceeded by 11.9%, 14.8%, and 19.1% of patients, respectively. Top predictors of cost included number of levels fused, surgeon, surgical approach, interbody fusion (IBF), and length of hospital stay (LOS). At 90 days and 2 years, a total of 80.6% and 64.0% of variance in direct cost, respectively, was explained in the generalized linear regression models. Predictors of CC were number of fused levels, surgical approach, surgeon, IBF, and LOS.Conclusion
The present study demonstrates that direct cost in ASD surgery can be accurately predicted. Collectively, these findings may not only prove useful for bundled care initiatives, but also may provide insight into means to reduce and better predict cost of ASD surgery outside of bundled payment plans.Level of evidence
3.Item Open Access Validation of Adult Spinal Deformity Surgical Outcome Prediction Tools in Adult Symptomatic Lumbar Scoliosis.(Spine, 2023-01) Wondra, James P; Kelly, Michael P; Greenberg, Jacob; Yanik, Elizabeth L; Ames, Christopher P; Pellise, Ferran; Vila-Casademunt, Alba; Smith, Justin S; Bess, Shay; Shaffrey, Christopher I; Lenke, Lawrence G; Serra-Burriel, Miquel; Bridwell, Keith HStudy design
A post hoc analysis.Objective
Advances in machine learning (ML) have led to tools offering individualized outcome predictions for adult spinal deformity (ASD). Our objective is to examine the properties of these ASD models in a cohort of adult symptomatic lumbar scoliosis (ASLS) patients.Summary of background data
ML algorithms produce patient-specific probabilities of outcomes, including major complication (MC), reoperation (RO), and readmission (RA) in ASD. External validation of these models is needed.Methods
Thirty-nine predictive factors (12 demographic, 9 radiographic, 4 health-related quality of life, 14 surgical) were retrieved and entered into web-based prediction models for MC, unplanned RO, and hospital RA. Calculated probabilities were compared with actual event rates. Discrimination and calibration were analyzed using receiver operative characteristic area under the curve (where 0.5=chance, 1=perfect) and calibration curves (Brier scores, where 0.25=chance, 0=perfect). Ninety-five percent confidence intervals are reported.Results
A total of 169 of 187 (90%) surgical patients completed 2-year follow up. The observed rate of MCs was 41.4% with model predictions ranging from 13% to 68% (mean: 38.7%). RO was 20.7% with model predictions ranging from 9% to 54% (mean: 30.1%). Hospital RA was 17.2% with model predictions ranging from 13% to 50% (mean: 28.5%). Model classification for all three outcome measures was better than chance for all [area under the curve=MC 0.6 (0.5-0.7), RA 0.6 (0.5-0.7), RO 0.6 (0.5-0.7)]. Calibration was better than chance for all, though best for RA and RO (Brier Score=MC 0.22, RA 0.16, RO 0.17).Conclusions
ASD prediction models for MC, RA, and RO performed better than chance in a cohort of adult lumbar scoliosis patients, though the homogeneity of ASLS affected calibration and accuracy. Optimization of models require samples with the breadth of outcomes (0%-100%), supporting the need for continued data collection as personalized prediction models may improve decision-making for the patient and surgeon alike.