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

被引:41
作者
Ames, Christopher P. [1 ]
Smith, Justin S. [2 ]
Pellise, Ferran [3 ]
Kelly, Michael P. [4 ]
Gum, Jeffrey L. [5 ]
Alanay, Ahmet [6 ]
Acaroglu, Emre [7 ]
Sanchez Perez-Grueso, Francisco Javier [8 ]
Kleinstuck, Frank S. [9 ]
Obeid, Ibrahim [10 ]
Vila-Casademunt, Alba [11 ]
Burton, Douglas C. [12 ]
Lafage, Virginie [13 ]
Schwab, Frank J. [13 ]
Shaffrey, Christopher I. [2 ]
Bess, Shay [14 ]
Serra-Burriel, Miquel [15 ]
机构
[1] Univ Calif San Francisco, Dept Neurosurg, San Francisco, CA USA
[2] Univ Virginia, Med Ctr, Dept Neurosurg, Charlottesville, VA USA
[3] Hosp Valle De Hebron, Spine Surg Unit, Barcelona, Spain
[4] Washington Univ, Dept Orthopaed Surg, St Louis, MO 63110 USA
[5] Norton Leatherman Spine Ctr, Louisville, KY USA
[6] Acibadem Univ, Dept Orthoped & Traumatol, Buyukdere Cad, Istanbul, Turkey
[7] Ankara ARTES Spine Ctr, Ankara, Turkey
[8] Hosp Univ La Paz, Spine Surg Unit, Madrid, Spain
[9] Schulthess Klin, Spine Ctr Div, Dept Orthoped & Neurosurg, Zurich, Switzerland
[10] Bordeaux Univ Hosp, Spine Surg Unit, Bordeaux, France
[11] Vall dHebron Inst Res VHIR Barcelona, Barcelona, Spain
[12] Univ Kansas, Med Ctr, Dept Orthopaed Surg, Kansas City, KS 66103 USA
[13] Hosp Special Surg, Dept Orthopaed Surg, 535 E 70th St, New York, NY 10021 USA
[14] Presbyterian St Lukes Rocky Mt Hosp Children, Denver Denver Int Spine Ctr, Denver, CO USA
[15] Univ Pompeu Fabra, Ctr Res Hlth & Econ, Barcelona, Spain
关键词
adult spinal deformity surgery; MCID; predictive modeling; prognosis; shared decision-making; OSWESTRY DISABILITY INDEX; MEASUREMENT ERROR; QUESTIONNAIRE; APPEARANCE; IMPACT; CARE;
D O I
10.1097/BRS.0000000000003031
中图分类号
R74 [神经病学与精神病学];
学科分类号
100204 [神经病学];
摘要
Study 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-2 values. Results. Five hundred seventy patients were included in the analysis. Models with the lowest MAE were selected; R-2 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.
引用
收藏
页码:1144 / 1153
页数:10
相关论文
共 44 条
[1]
Well-posedness of measurement error models for self-reported data [J].
An, Yonghong ;
Hu, Yingyao .
JOURNAL OF ECONOMETRICS, 2012, 168 (02) :259-269
[2]
Refinement of the SRS-22 health-related quality of life questionnaire function domain [J].
Asher, MA ;
Lai, SM ;
Glattes, C ;
Burton, DC ;
Alanay, A ;
Bago, J .
SPINE, 2006, 31 (05) :593-597
[3]
Improving acute kidney injury diagnostics using predictive analytics [J].
Basu, Rajit K. ;
Gist, Katja ;
Wheeler, Derek S. .
CURRENT OPINION IN CRITICAL CARE, 2015, 21 (06) :473-478
[4]
Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients [J].
Bates, David W. ;
Saria, Suchi ;
Ohno-Machado, Lucila ;
Shah, Anand ;
Escobar, Gabriel .
HEALTH AFFAIRS, 2014, 33 (07) :1123-1131
[5]
Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[6]
MEASUREMENT ERROR IN SELF-REPORTED HEALTH VARIABLES [J].
BUTLER, JS ;
BURKHAUSER, RV ;
MITCHELL, JM ;
PINCUS, TP .
REVIEW OF ECONOMICS AND STATISTICS, 1987, 69 (04) :644-650
[7]
Carreon LY., 2017, Spine Deform, V5, P139, DOI [DOI 10.1016/J.JSPD.2016.11.001, 10.1016/j.jspd.2016.11.001]
[8]
XGBoost: A Scalable Tree Boosting System [J].
Chen, Tianqi ;
Guestrin, Carlos .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :785-794
[9]
Chen Tianqi., XGBoost eXtreme Gradient Boosting
[10]
Cichosz SL, 2015, J DIABETES SCI TECHN, V10, P1