The global burden of ASD was huge compared with other self-reported chronic conditions in the general population of eight industrialized countries. The impact of ASD on HRQL warrants the same research and health policy attention as other important chronic diseases.

The Spanish version of the SRS-22 Patient Questionnaire demonstrated adequate internal consistency for the majority of domains and excellent reproducibility. These results suggest that the process of adaptation has produced an instrument that is apparently equivalent to the original and suitable for clinical research.

BackgroundOutcome assessment in idiopathic scoliosis should probably include patients' perception of their trunk deformity in addition to self-image. This can be accomplished with the Walter Reed Visual Assessment Scale (WRVAS). Nevertheless, this instrument has some shortcomings: the drawings are abstract and some figures do not relate to the corresponding radiological deformity. These considerations prompted us to design the Trunk Appearance Perception Scale (TAPS).MethodsPatients with idiopathic scoliosis and no prior surgical treatment were included. Each patient completed the TAPS and SRS-22 questionnaire and underwent a complete radiographic study of the spine. The magnitude of the upper thoracic, main thoracic, and thoracolumbar/lumbar structural curves were recorded. The TAPS includes 3 sets of figures that depict the trunk from 3 viewpoints: looking toward the back, looking toward the head with the patient bending over and looking toward the front. Drawings are scored from 1 (greatest deformity) to 5 (smallest deformity), and a mean score is obtained.ResultsA total of 186 patients (86% females), with a mean age of 17.8 years participated. The mean of the largest curve (CMAX) was 40.2°. The median of TAPS sum score was 3.6. The floor effect was 1.6% and ceiling effect 3.8%. Cronbach's alpha coefficient was 0.89; the ICC for the mean sum score was 0.92. Correlation coefficient of the TAPS mean sum and CMAX was -0.55 (P < 0.01). Correlation coefficients between TAPS mean sum score and SRS-22 scales were all statistically significant, ranging from 0.45 to 0.52 (P < 0.05).ConclusionsThe TAPS is a valid instrument for evaluating the perception patients have of their trunk deformity. It shows excellent distribution of scores, internal consistency, and test-retest reliability, and has good capacity to differentiate the severity of the disease. It is simple and easy to complete and score, the figures are natural, and a new frontal view is included.

The GAP score is a new pelvic-incidence-based proportional method of analyzing the sagittal plane that predicts mechanical complications in patients undergoing surgery for adult spinal deformity. Setting surgical goals according to the GAP score may decrease the prevalence of mechanical complications.

OBJECTIVEThe subtraction of lumbar lordosis (LL) from the pelvic incidence (PI) offers an estimate of the LL required for a given PI value. Relative LL (RLL) and the lordosis distribution index (LDI) are PI-based individualized measures. RLL quantifies the magnitude of lordosis relative to the ideal lordosis as defined by the magnitude of PI. LDI defines the magnitude of lower arc lordosis in proportion to total lordosis. The aim of this study was to compare RLL and PI − LL for their ability to predict postoperative complications and their correlations with health-related quality of life (HRQOL) scores.METHODSInclusion criteria were ≥ 4 levels of fusion and ≥ 2 years of follow-up. Mechanical complications were proximal junctional kyphosis/proximal junctional failure, distal junctional kyphosis/distal junctional failure, rod breakage, and implant-related complications. Correlations between PI − LL, RLL, PI, and HRQOL were analyzed using the Pearson correlation coefficient. Mechanical complication rates in PI − LL, RLL, LDI, RLL, and LDI interpreted together, and RLL subgroups for each PI − LL category were compared using chi-square tests and the exact test. Predictive models for mechanical complications with RLL and PI − LL were analyzed using binomial logistic regressions.RESULTSTwo hundred twenty-two patients (168 women, 54 men) were included. The mean age was 52.2 ± 19.3 years (range 18–84 years). The mean follow-up was 28.8 ± 8.2 months (range 24–62 months). There was a significant correlation between PI − LL and PI (r = 0.441, p < 0.001), threatening the use of PI − LL to quantify spinopelvic mismatch for different PI values. RLL was not correlated with PI (r = −0.093, p > 0.05); therefore, it was able to quantify divergence from ideal lordosis for all PI values. Compared with PI − LL, RLL had stronger correlations with HRQOL scores (p < 0.05). Discrimination performance was better for the model with RLL than for PI − LL. The agreement between RLL and PI − LL was high (κ = 0.943, p < 0.001), moderate (κ = 0.455, p < 0.001), and poor (κ = −0.154, p = 0.343), respectively, for large, average, and small PI sizes. When analyzed by RLL, each PI − LL category was further divided into distinct groups of patients who had different mechanical complication rates (p < 0.001).CONCLUSIONSUsing the formula of PI − LL may be insufficient to quantify normolordosis for the whole spectrum of PI values when applied as an absolute numeric value in conjunction with previously reported population-based average thresholds of 10° and 20°. Schwab PI − LL groups were found to constitute an inhomogeneous group of patients. RLL offers an individualized quantification of LL for all PI sizes. Compared with PI − LL, RLL showed a greater association with both mechanical complications and HRQOL. The use of RLL and LDI together, instead of PI − LL, for surgical planning may result in lower mechanical complication rates and better long-term HRQOL.

The Spanish version of the SRS-22 is valid. It has a factorial structure similar to that of the original questionnaire. Moreover, it relates to known severity characteristics of the disease, distinguishes among scoliosis patient groups, and shows concordant values with another valid instrument for measuring self-perceived health.

Greater patient frailty, as measured by the ASD-FI, is associated with longer hospital stays and greater odds of major complications and reoperation. These slides can be retrieved under Electronic Supplementary Material.

Study 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