Development of a Risk Prediction Model With Improved Clinical Utility in Elective Cervical and Lumbar Spine Surgery.
Adult
Aged
Cervical Vertebrae
/ surgery
Cohort Studies
Decompression, Surgical
/ adverse effects
Elective Surgical Procedures
/ adverse effects
Female
Humans
Lumbar Vertebrae
/ surgery
Male
Middle Aged
Models, Theoretical
Postoperative Complications
/ diagnosis
Predictive Value of Tests
Retrospective Studies
Risk Factors
Spinal Diseases
/ diagnosis
Spinal Fusion
/ adverse effects
Surgical Wound Infection
/ diagnosis
Journal
Spine
ISSN: 1528-1159
Titre abrégé: Spine (Phila Pa 1976)
Pays: United States
ID NLM: 7610646
Informations de publication
Date de publication:
01 May 2020
01 May 2020
Historique:
pubmed:
27
11
2019
medline:
17
9
2020
entrez:
27
11
2019
Statut:
ppublish
Résumé
Retrospective cohort. We present a universal model of risk prediction for patients undergoing elective cervical and lumbar spine surgery. Previous studies illustrate predictive risk models as possible tools to identify individuals at increased risk for postoperative complications and high resource utilization following spine surgery. Many are specific to one condition or procedure, cumbersome to calculate, or include subjective variables limiting applicability and utility. A retrospective cohort of 177,928 spine surgeries (lumbar (L) Ln = 129,800; cervical (C) Cn = 48,128) was constructed from the 2012 to 2016 American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database. Cases were identified by Current Procedural Terminology (CPT) codes for cervical fusion, lumbar fusion, and lumbar decompression laminectomy. Significant preoperative risk factors for postoperative complications were identified and included in logistic regression. Sum of odds ratios from each factor was used to develop the Universal Spine Surgery (USS) score. Model performance was assessed using receiver-operating characteristic (ROC) curves and tested on 20% of the total sample. Eighteen risk factors were identified, including sixteen found to be significant outcomes predictors. At least one complication was present among 11.1% of patients, the most common of which included bleeding requiring transfusion (4.86%), surgical site infection (1.54%), and urinary tract infection (1.08%). Complication rate increased as a function of the model score and ROC area under the curve analyses demonstrated fair predictive accuracy (lumbar = 0.741; cervical = 0.776). There were no significant deviations between score development and testing datasets. We present the Universal Spine Surgery score as a robust, easily administered, and cross-validated instrument to quickly identify spine surgery candidates at increased risk for postoperative complications and high resource utilization without need for algorithmic software. This may serve as a useful adjunct in preoperative patient counseling and perioperative resource allocation. 3.
Sections du résumé
STUDY DESIGN
METHODS
Retrospective cohort.
OBJECTIVE
OBJECTIVE
We present a universal model of risk prediction for patients undergoing elective cervical and lumbar spine surgery.
SUMMARY OF BACKGROUND DATA
BACKGROUND
Previous studies illustrate predictive risk models as possible tools to identify individuals at increased risk for postoperative complications and high resource utilization following spine surgery. Many are specific to one condition or procedure, cumbersome to calculate, or include subjective variables limiting applicability and utility.
METHODS
METHODS
A retrospective cohort of 177,928 spine surgeries (lumbar (L) Ln = 129,800; cervical (C) Cn = 48,128) was constructed from the 2012 to 2016 American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database. Cases were identified by Current Procedural Terminology (CPT) codes for cervical fusion, lumbar fusion, and lumbar decompression laminectomy. Significant preoperative risk factors for postoperative complications were identified and included in logistic regression. Sum of odds ratios from each factor was used to develop the Universal Spine Surgery (USS) score. Model performance was assessed using receiver-operating characteristic (ROC) curves and tested on 20% of the total sample.
RESULTS
RESULTS
Eighteen risk factors were identified, including sixteen found to be significant outcomes predictors. At least one complication was present among 11.1% of patients, the most common of which included bleeding requiring transfusion (4.86%), surgical site infection (1.54%), and urinary tract infection (1.08%). Complication rate increased as a function of the model score and ROC area under the curve analyses demonstrated fair predictive accuracy (lumbar = 0.741; cervical = 0.776). There were no significant deviations between score development and testing datasets.
CONCLUSION
CONCLUSIONS
We present the Universal Spine Surgery score as a robust, easily administered, and cross-validated instrument to quickly identify spine surgery candidates at increased risk for postoperative complications and high resource utilization without need for algorithmic software. This may serve as a useful adjunct in preoperative patient counseling and perioperative resource allocation.
LEVEL OF EVIDENCE
METHODS
3.
Identifiants
pubmed: 31770338
doi: 10.1097/BRS.0000000000003317
pii: 00007632-202005010-00017
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
E542-E551Références
Yagi M, Hosogane N, Fujita N, et al. Surgical risk stratification based on preoperative risk factors in adult spinal deformity. Spine J 2019; 19:816–826.
Baranek ES, Maier SP 2nd, Matsumoto H, et al. Gross motor function classification system specific growth charts-utility as a risk stratification tool for surgical site infection following spine surgery. J Pediatr Orthop 2019; 39:e298–e302.
Bernstein DN, Keswani A, Chi D, et al. Development and validation of risk-adjustment models for elective, single-level posterior lumbar spinal fusions. J Spine Surg 2019; 5:46–57.
Lakomkin N, Zuckerman SL, Stannard B, et al. Preoperative risk stratification in spine tumor surgery—a comparison of the Modified Charlson Index, Frailty Index, and ASA Score. Spine (Phila Pa 1976) 2019; 44:E782–E787.
Passias PG, Vasquez-Montes D, Poorman GW, et al. Predictive model for distal junctional kyphosis after cervical deformity surgery. Spine J 2018; 18:2187–2194.
Veeravagu A, Li A, Swinney C, et al. Predicting complication risk in spine surgery: a prospective analysis of a novel risk assessment tool. J Neurosurg Spine 2017; 27:81–91.
Bilimoria KY, Liu Y, Paruch JL, et al. Development and evaluation of the universal ACS NSQIP surgical risk calculator: a decision aid and informed consent tool for patients and surgeons. J Am Coll Surg 2013; 217:833–842. e831-833.
Sebastian A, Goyal A, Alvi MA, et al. Assessing the performance of national surgical quality improvement program surgical risk calculator in elective spine surgery: insights from patients undergoing single-level posterior lumbar fusion. World Neurosurg 2019; 126:e323–e329.
Cohen ME, Ko CY, Bilimoria KY, et al. Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus. J Am Coll Surg 2013; 217:336–346. e331.
Liu Y, Cohen ME, Hall BL, et al. Evaluation and enhancement of calibration in the American College of Surgeons NSQIP Surgical Risk Calculator. J Am Coll Surg 2016; 223:231–239.
McMillan MT, Allegrini V, Asbun HJ, et al. Incorporation of procedure-specific risk into the ACS-NSQIP surgical risk calculator improves the prediction of morbidity and mortality after pancreatoduodenectomy. Ann Surg 2017; 265:978–986.
Swets JA. ROC analysis applied to the evaluation of medical imaging techniques. Invest Radiol 1979; 14:109–121.
Vaziri S, Wilson J, Abbatematteo J, et al. Predictive performance of the American College of Surgeons universal risk calculator in neurosurgical patients. J Neurosurg 2018; 128:942–947.
Chung AS, DiGiovanni R, Tseng S, et al. Obstructive sleep apnea in elective spine surgery: national prevalence and inpatient outcomes. Global Spine J 2018; 8:550–556.
Khalsa AS, Eghbali A, Eastlack RK, et al. Resting pain level as a preoperative predictor of success with indirect decompression for lumbar spinal stenosis: a pilot study. Global Spine J 2019; 9:150–154.
Lu Y, Lin CC, Doermann A, et al. Impact of sickle cell anemia on inpatient morbidity after spinal fusion. Clin Spine Surg 2019.
Malik AT, Jain N, Kim J, et al. Chronic obstructive pulmonary disease is an independent predictor for 30-day readmissions following 1- to 2-level posterior lumbar fusions. J Spine Surg 2018; 4:553–559.
Passias PG, Bortz C, Alas H, et al. Alcoholism as a predictor for pseudarthrosis in primary spine fusion: an analysis of risk factors and 30-day outcomes for 52,402 patients from 2005 to 2013. J Orthop 2019; 16:36–40.
Turcotte JJ, Patton CM. Predictors of postoperative complications after surgery for lumbar spinal stenosis and degenerative lumbar spondylolisthesis. J Am Acad Orthop Surg Glob Res Rev 2018; 2:e085.
Campbell PG, Yadla S, Nasser R, et al. Patient comorbidity score predicting the incidence of perioperative complications: assessing the impact of comorbidities on complications in spine surgery. J Neurosurg Spine 2012; 16:37–43.
Chitale R, Campbell PG, Yadla S, et al. International classification of disease clinical modification 9 modeling of a patient comorbidity score predicts incidence of perioperative complications in a nationwide inpatient sample assessment of complications in spine surgery. J Spinal Disord Tech 2015; 28:126–133.
Ratliff JK, Balise R, Veeravagu A, et al. Predicting occurrence of spine surgery complications using “big data” modeling of an Administrative Claims Database. J Bone Joint Surg Am 2016; 98:824–834.
Buchlak QD, Yanamadala V, Leveque JC, et al. The Seattle spine score: predicting 30-day complication risk in adult spinal deformity surgery. J Clin Neurosci 2017; 43:247–255.
Diebo BG, Segreto FA, Jalai CM, et al. Baseline mental status predicts happy patients after operative or non-operative treatment of adult spinal deformity. J Spine Surg 2018; 4:687–695.
Passias PG, Bortz CA, Segreto FA, et al. Development of a modified cervical deformity frailty index: a streamlined clinical tool for preoperative risk stratification. Spine (Phila Pa 1976) 2019; 44:169–176.
Yagi M, Michikawa T, Hosogane N, et al. The 5-Item modified frailty index is predictive of severe adverse events in patients undergoing surgery for adult spinal deformity. Spine (Phila Pa 1976) 2019; 44:E1083–E1091.
Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ 2005; 173:489–495.
Yagi M, Fujita N, Okada E, et al. Impact of frailty and comorbidities on surgical outcomes and complications in adult spinal disorders. Spine (Phila Pa 1976) 2018; 43:1259–1267.
Ali R, Schwalb JM, Nerenz DR, et al. Use of the modified frailty index to predict 30-day morbidity and mortality from spine surgery. J Neurosurg Spine 2016; 25:537–541.
Shaw K, Chen J, Sheppard W, et al. Use of the subcutaneous lumbar spine (SLS) index as a predictor for surgical complications in lumbar spine surgery. Spine J 2018; 18:2181–2186.
Bekelis K, Desai A, Bakhoum SF, et al. A predictive model of complications after spine surgery: the National Surgical Quality Improvement Program (NSQIP) 2005-2010. Spine J 2014; 14:1247–1255.
Ellis DJ, Mallozzi SS, Mathews JE, et al. The relationship between preoperative expectations and the short-term postoperative satisfaction and functional outcome in lumbar spine surgery: a systematic review. Global Spine J 2015; 5:436–452.
Mancuso CA, Duculan R, Cammisa FP, et al. Fulfillment of patients’ expectations of lumbar and cervical spine surgery. Spine J 2016; 16:1167–1174.
Mancuso CA, Reid MC, Duculan R, et al. Improvement in pain after lumbar spine surgery: the role of preoperative expectations of pain relief. Clin J Pain 2017; 33:93–98.
Witiw CD, Mansouri A, Mathieu F, et al. Exploring the expectation-actuality discrepancy: a systematic review of the impact of preoperative expectations on satisfaction and patient reported outcomes in spinal surgery. Neurosurg Rev 2018; 41:19–30.
Lubelski D, Alentado V, Nowacki AS, et al. Preoperative nomograms predict patient-specific cervical spine surgery clinical and quality of life outcomes. Neurosurgery 2018; 83:104–113.
Bovonratwet P, Webb ML, Ondeck NT, et al. Discrepancies in the definition of “outpatient” surgeries and their effect on study outcomes related to ACDF and lumbar discectomy procedures: a retrospective analysis of 45,204 cases. Clin Spine Surg 2018; 31:E152–E159.
Basques BA, McLynn RP, Fice MP, et al. Results of database studies in spine surgery can be influenced by missing data. Clin Orthop Relat Res 2017; 475:2893–2904.
McLynn RP, Diaz-Collado PJ, Ottesen TD, et al. Risk factors and pharmacologic prophylaxis for venous thromboembolism in elective spine surgery. Spine J 2018; 18:970–978.
Rock AK, Opalak CF, Workman KG, et al. Safety outcomes following spine and cranial neurosurgery: evidence from the National Surgical Quality Improvement Program. J Neurosurg Anesthesiol 2018; 30:328–336.
Sebastian A, Huddleston P 3rd, Kakar S, et al. Risk factors for surgical site infection after posterior cervical spine surgery: an analysis of 5,441 patients from the ACS NSQIP 2005–2012. Spine J 2016; 16:504–509.
Wang X, Hu Y, Zhao B, et al. Predictive validity of the ACS-NSQIP surgical risk calculator in geriatric patients undergoing lumbar surgery. Medicine (Baltimore) 2017; 96:e8416.