Clinical study| Volume 57, P26-32, November 2018

Download started.


Risk stratification in deep brain stimulation surgery: Development of an algorithm to predict patient discharge disposition with 91.9% accuracy

Published:August 28, 2018DOI:


      • An algorithm was developed that could predict discharge disposition (DD) subsequent to DBS surgery with 91.9% accuracy.
      • It is possible to accurately predict the DD of DBS patients using routinely collected preoperative variables.
      • A decision support system was developed to facilitate clinical decision making and improve the informed consent process.
      • These predictive algorithms can improve patient care, clinical decision making and organizational resource planning.


      Clinical decision making is susceptible to biases and can be improved with the application of predictive models and decision support systems (DSS). The purpose of this study was to develop a predictive risk stratification model and DSS that could accurately predict whether a patient was likely to be of high- or low-acuity discharge disposition (DD) status subsequent to DBS surgery. Data were collected for 135 DBS patients by reviewing medical records. Multivariate logistic regression was applied to develop the predictive algorithm. The two significant predictive models showed good fit and were calibrated by using AUROC curve analysis. The model predicting DD in all patients (n = 135) yielded a predictive accuracy of 91.9% (AUROC = 0.825, p < .001). The model predicting DD in Parkinson’s Disease patients (n = 91) yielded a predictive accuracy of 89.0% (AUROC = 0.853, p < .001). Age was a significant predictor of DD across all patients (OR = 1.11, p < .05) and BMI was a significant predictor of DD amongst Parkinson’s Disease patients (OR = 1.16, p < .05). It is possible to accurately predict the DD of DBS patients using routinely collected preoperative variables. The predictive algorithms were applied in the form of a model-driven DSS to assist in improving clinical decision making and patient safety.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Journal of Clinical Neuroscience
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Odekerken V.J.J.
        • van Laar T.
        • Staal M.J.
        • Mosch A.
        • Hoffmann C.F.E.
        • Nijssen P.C.G.
        • et al.
        Subthalamic nucleus versus globus pallidus bilateral deep brain stimulation for advanced Parkinson’s disease (NSTAPS study): a randomised controlled trial.
        Lancet Neurol. 2013; 12: 37-44
        • Schlaepfer T.E.
        • Bewernick B.H.
        • Kayser S.
        • Mädler B.
        • Coenen V.A.
        Rapid effects of deep brain stimulation for treatment-resistant major depression.
        Biol Psychiatry. 2013; 73: 1204-1212
        • Little S.
        • Pogosyan A.
        • Neal S.
        • Zavala B.
        • Zrinzo L.
        • Hariz M.
        • et al.
        Adaptive deep brain stimulation in advanced Parkinson disease.
        Ann Neurol. 2013; 74: 449-457
        • Miocinovic S.
        • Somayajula S.
        • Chitnis S.
        • Vitek J.L.
        History, applications, and mechanisms of deep brain stimulation.
        JAMA Neurol. 2013; 70: 163-171
        • Figee M.
        • Luigjes J.
        • Smolders R.
        • Valencia-Alfonso C.-E.
        • Van Wingen G.
        • De Kwaasteniet B.
        • et al.
        Deep brain stimulation restores frontostriatal network activity in obsessive-compulsive disorder.
        Nat Neurosci. 2013; 16: 386-387
        • Rodriguez-Oroz M.C.
        • Obeso J.A.
        • Lang A.E.
        • Houeto J.-L.
        • Pollak P.
        • Rehncrona S.
        • et al.
        Bilateral deep brain stimulation in Parkinson’s disease: a multicentre study with 4 years follow-up.
        Brain. 2005; 128: 2240-2249
        • Riva-Posse P.
        • Choi K.S.
        • Holtzheimer P.E.
        • McIntyre C.C.
        • Gross R.E.
        • Chaturvedi A.
        • et al.
        Defining critical white matter pathways mediating successful subcallosal cingulate deep brain stimulation for treatment-resistant depression.
        Biol Psychiatry. 2014; 76: 963-969
        • Gratwicke J.
        • Zrinzo L.
        • Kahan J.
        • Peters A.
        • Beigi M.
        • Akram H.
        • et al.
        Bilateral deep brain stimulation of the nucleus basalis of Meynert for Parkinson disease dementia: a randomized clinical trial.
        JAMA Neurol. 2018; 75: 169-178
      1. Blomstedt P, Persson RS, Hariz G-M, Linder J, Fredricks A, Häggström B, et al. Deep brain stimulation in the caudal zona incerta versus best medical treatment in patients with Parkinson’s disease: a randomised blinded evaluation. J Neurol Neurosurg Psychiatry 2018:jnnp-2017.

        • Ostrem J.L.
        • Ziman N.
        • Galifianakis N.B.
        • Starr P.A.
        • Luciano M.S.
        • Katz M.
        • et al.
        Clinical outcomes using ClearPoint interventional MRI for deep brain stimulation lead placement in Parkinson’s disease.
        J Neurosurg. 2016; 124: 908-916
        • Rumalla K.
        • Smith K.A.
        • Follett K.
        • Nazzaro J.
        • Arnold P.M.
        Rates, Causes, Risk Factors, and Outcomes of Readmission Following Deep Brain Stimulation for Movement Disorders: Analysis of the US Nationwide Readmissions Database.
        Clin Neurol Neurosurg. 2018;
        • Zhou J.J.
        • Chen T.
        • Farber S.H.
        • Shetter A.G.
        • Ponce F.A.
        Open-loop deep brain stimulation for the treatment of epilepsy: a systematic review of clinical outcomes over the past decade (2008–present).
        Neurosurg Focus. 2018; 45: E5
        • Matias C.M.
        • Frizon L.A.
        • Nagel S.J.
        • Lobel D.A.
        • Machado A.G.
        Deep brain stimulation outcomes in patients implanted under general anesthesia with frame-based stereotaxy and intraoperative MRI.
        J Neurosurg. 2018; : 1-7
        • Porter M.E.
        What is value in health care?.
        N Engl J Med. 2010; 363: 2477-2481
        • Porter M.E.
        A strategy for health care reform—toward a value-based system.
        N Engl J Med. 2009; 361: 109-112
        • Boachie-Adjei O.
        • Yagi M.
        • Sacramento-Dominguez C.
        • Akoto H.
        • Cunningham M.E.
        • Gupta M.
        • et al.
        Surgical Risk Stratification Based on Preoperative Risk Factors in Severe Pediatric Spinal Deformity Surgery.
        Spine Deform. 2014; 2: 340-349
        • Bilimoria K.Y.
        • Liu Y.
        • Paruch J.L.
        • Zhou L.
        • Kmiecik T.E.
        • Ko C.Y.
        • 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; 217e3
        • Bekelis K.
        • Desai A.
        • Bakhoum S.F.
        • Missios S.
        A predictive model of complications after spine surgery: The National Surgical Quality Improvement Program (NSQIP) 2005–2010.
        Spine J. 2014; 14: 1247-1255
      2. Scheer J, Smith J, Schwab F, Lafage V, Hart R, Bess RS, et al. Development of Validated Computer-Based Preoperative Predictive Model for Proximal Junction Failure (PJF) or Clinically Significant PJK with 86% Accuracy based on 510 ASD Patients with 2-year Follow-up. Glob Spine J 2016;6:GO304.

        • Buchlak Q.D.
        • Yanamadala V.
        • Leveque J.-C.
        • Edwards A.
        • Nold K.
        • Sethi R.
        The Seattle spine score: Predicting 30-day complication risk in adult spinal deformity surgery.
        J Clin Neurosci. 2017;
        • Fargen K.M.
        • Friedman W.A.
        The science of medical decision making: neurosurgery, errors, and personal cognitive strategies for improving quality of care.
        World Neurosurg. 2014; 82: e21-e29
        • Saposnik G.
        • Redelmeier D.
        • Ruff C.C.
        • Tobler P.N.
        Cognitive biases associated with medical decisions: a systematic review.
        BMC Med Inform Decis Mak. 2016; 16: 138
      3. Croskerry P, Singhal G, Mamede S. Cognitive debiasing 1: origins of bias and theory of debiasing. BMJ Qual Saf 2013;22:ii58-ii64.

        • Voges J.
        • Hilker R.
        • Bötzel K.
        • Kiening K.L.
        • Kloss M.
        • Kupsch A.
        • et al.
        Thirty days complication rate following surgery performed for deep-brain-stimulation.
        Mov Disord. 2007; 22: 1486-1489
        • Hamani C.
        • Lozano A.M.
        Hardware-related complications of deep brain stimulation: a review of the published literature.
        Stereotact Funct Neurosurg. 2006; 84: 248-251
        • Hariz M.I.
        Complications of deep brain stimulation surgery.
        Mov Disord. 2002; 17
        • Beric A.
        • Kelly P.J.
        • Rezai A.
        • Sterio D.
        • Mogilner A.
        • Zonenshayn M.
        • et al.
        Complications of deep brain stimulation surgery.
        Stereotact Funct Neurosurg. 2002; 77: 73-78
        • Burdick A.P.
        • Fernandez H.H.
        • Okun M.S.
        • Chi Y.-Y.
        • Jacobson C.
        • Foote K.D.
        Relationship between higher rates of adverse events in deep brain stimulation using standardized prospective recording and patient outcomes.
        Neurosurg Focus. 2010; 29: E4
        • Fenoy A.J.
        • Simpson Jr, R.K.
        Risks of common complications in deep brain stimulation surgery: management and avoidance: Clinical article.
        J Neurosurg. 2014; 120: 132-139
        • Verla T.
        • Marky A.
        • Farber H.
        • Petraglia F.W.
        • Gallis J.
        • Lokhnygina Y.
        • et al.
        Impact of advancing age on post-operative complications of deep brain stimulation surgery for essential tremor.
        J Clin Neurosci. 2015; 22: 872-876
        • Patel D.M.
        • Walker H.C.
        • Brooks R.
        • Omar N.
        • Ditty B.
        • Guthrie B.L.
        Adverse events associated with deep brain stimulation for movement disorders: analysis of 510 consecutive cases.
        Oper Neurosurg. 2015; 11: 190-199
        • Chen T.
        • Mirzadeh Z.
        • Chapple K.
        • Lambert M.
        • Ponce F.A.
        Complication rates, lengths of stay, and readmission rates in “awake” and “asleep” deep brain simulation.
        J Neurosurg. 2016; 127: 360-369
        • Kalakoti P.
        • Ahmed O.
        • Bollam P.
        • Missios S.
        • Wilden J.
        • Nanda A.
        Predictors of unfavorable outcomes following deep brain stimulation for movement disorders and the effect of hospital case volume on outcomes: an analysis of 33, 642 patients across 234 US hospitals using the National (Nationwide) Inpatient Sample from 2002 to 2011.
        Neurosurg Focus. 2015; 38: E4
        • Chen T.
        • Mirzadeh Z.
        • Lambert M.
        • Gonzalez O.
        • Moran A.
        • Shetter A.G.
        • et al.
        Cost of Deep Brain Stimulation Infection Resulting in Explantation.
        Stereotact Funct Neurosurg. 2017; 95: 117-124
        • Sharma M.
        • Ambekar S.
        • Guthikonda B.
        • Wilden J.
        • Nanda A.
        Regional trends and the impact of various patient and hospital factors on outcomes and costs of hospitalization between academic and nonacademic centers after deep brain stimulation surgery for Parkinson’s disease: a United States Nationwide Inpatient Sample analysis from 2006 to 2010.
        Neurosurg Focus. 2013; 35: E2
        • Assmann G.
        • Cullen P.
        • Schulte H.
        Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Münster (PROCAM) study.
        Circulation. 2002; 105: 310-315
        • Cooney M.T.
        • Dudina A.L.
        • Graham I.M.
        Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians.
        J Am Coll Cardiol. 2009; 54: 1209-1227
        • Wang M.Q.
        • Eddy J.M.
        • Fitzhugh E.C.
        Application of odds ratios and logistic models in epidemiology and health research.
        Health Values. 1995; 19: 59-62
        • Hosmer D.W.
        • Lemeshow S.
        • Sturdivant R.X.
        Applied Logistic Regression.
        3rd Edition. John Wiley & Sons, 2013
        • D’Agostino Sr, R.B.
        • Grundy S.
        • Sullivan L.M.
        • Wilson P.
        Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.
        JAMA. 2001; 286: 180-187
        • Few S.
        Information dashboard design.
        O’Reilly Media Inc, Sebastopol2006
        • Kawamoto K.
        • Houlihan C.A.
        • Balas E.A.
        • Lobach D.F.
        Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.
        BMJ. 2005; 330: 765
      4. Sehestedt TH, Olsen M. Traditional Versus New Models of Risk Prediction. Elsevier Inc.; 2015. doi:10.1016/B978-0-12-801387-8.00021-1.

        • Akins P.T.
        • Harris J.
        • Alvarez J.L.
        • Chen Y.
        • Paxton E.W.
        • Bernbeck J.
        • et al.
        Risk factors associated with 30-day readmissions after instrumented spine surgery in 14,939 patients.
        Spine (Phila Pa 1976). 1976; 2015: 1022-1032
        • Murphy M.E.
        • Maloney P.R.
        • McCutcheon B.A.
        • Rinaldo L.
        • Shepherd D.
        • Kerezoudis P.
        • et al.
        Predictors of Discharge to a Nonhome Facility in Patients Undergoing Lumbar Decompression Without Fusion for Degenerative Spine Disease.
        Neurosurgery. 2017;
        • McClendon Jr, J.
        • Smith T.R.
        • Thompson S.E.
        • Sugrue P.A.
        • O’Shaughnessy B.A.
        • Ondra S.L.
        • et al.
        The impact of body mass index on hospital stay and complications after spinal fusion.
        Neurosurgery. 2014; 74: 42-50
        • Jiang J.
        • Teng Y.
        • Fan Z.
        • Khan S.
        • Xia Y.
        Does obesity affect the surgical outcome and complication rates of spinal surgery? A meta-analysis.
        Clin Orthop Relat Res. 2014; 472: 968-975
        • Magaziner J.
        • Simonsick E.M.
        • Kashner T.M.
        • Hebel J.R.
        • Kenzora J.E.
        Predictors of functional recovery one year following hospital discharge for hip fracture: a prospective study.
        J Gerontol. 1990; 45: M101-M107
        • Ng T.-P.
        • Niti M.
        • Tan W.-C.
        • Cao Z.
        • Ong K.-C.
        • Eng P.
        Depressive symptoms and chronic obstructive pulmonary disease: effect on mortality, hospital readmission, symptom burden, functional status, and quality of life.
        Arch Intern Med. 2007; 167: 60-67
        • Poole L.
        • Leigh E.
        • Kidd T.
        • Ronaldson A.
        • Jahangiri M.
        • Steptoe A.
        The combined association of depression and socioeconomic status with length of post-operative hospital stay following coronary artery bypass graft surgery: Data from a prospective cohort study.
        J Psychosom Res. 2014; 76: 34-40
        • Menendez M.E.
        • Neuhaus V.
        • Bot A.G.J.
        • Ring D.
        • Cha T.D.
        Psychiatric disorders and major spine surgery: epidemiology and perioperative outcomes.
        Spine (Phila Pa 1976). 1976; 2014: E111-E122
        • Nerland U.S.
        • Jakola A.S.
        • Giannadakis C.
        • Solheim O.
        • Weber C.
        • Nygaard Ø.P.
        • et al.
        The risk of getting worse: predictors of deterioration after decompressive surgery for lumbar spinal stenosis: a multicenter observational study.
        World Neurosurg. 2015; 84: 1095-1102
        • Chapin L.
        • Ward K.
        • Ryken T.
        Preoperative Depression, Smoking, and Employment Status are Significant Factors in Patient Satisfaction After Lumbar Spine Surgery.
        J Spinal Disord Tech. 2015;
        • Acosta Jr, F.L.
        • McClendon Jr, J.
        • O’Shaughnessy B.A.
        • Koller H.
        • Neal C.J.
        • Meier O.
        • et al.
        Morbidity and mortality after spinal deformity surgery in patients 75 years and older: complications and predictive factors: clinical article.
        J Neurosurg Spine. 2011; 15: 667-674
        • Chitale R.
        • Campbell P.G.
        • Yadla S.
        • Whitmore R.G.
        • Maltenfort M.G.
        • Ratliff J.K.
        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
        • Gupta H.
        • Ramanan B.
        • Gupta P.K.
        • Fang X.
        • Polich A.
        • Modrykamien A.
        • et al.
        Impact of COPD on postoperative outcomes: results from a national database.
        CHEST J. 2013; 143: 1599-1606
        • Moghavem N.
        • Morrison D.
        • Ratliff J.K.
        • Hernandez-Boussard T.
        Cranial neurosurgical 30-day readmissions by clinical indication.
        J Neurosurg. 2015; 123: 189-197
        • Rolston J.D.
        • Han S.J.
        • Lau C.Y.
        • Berger M.S.
        • Parsa A.T.
        Frequency and predictors of complications in neurological surgery: national trends from 2006 to 2011.
        J Neurosurg. 2014; 120: 736-745
        • Gigerenzer G.
        • Gaissmaier W.
        Heuristic decision making.
        Annu Rev Psychol. 2011; 62: 451-482
        • Dovidio J.F.
        • Fiske S.T.
        Under the radar: how unexamined biases in decision-making processes in clinical interactions can contribute to health care disparities.
        Am J Public Health. 2012; 102: 945-952
        • Hilbert M.
        Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making.
        Psychol Bull. 2012; 138: 211
        • Stone J.
        • Moskowitz G.B.
        Non-conscious bias in medical decision making: what can be done to reduce it?.
        Med Educ. 2011; 45: 768-776
        • Dawson N.V.
        • Arkes H.R.
        Systematic errors in medical decision making.
        J Gen Intern Med. 1987; 2: 183-187
        • Chapman G.B.
        • Elstein A.S.
        Cognitive processes and biases in medical decision making.
        Cambridge University Press, Cambridge2000
        • Sethi R.K.
        • Pong R.P.
        • Leveque J.-C.
        • Dean T.C.
        • Olivar S.J.
        • Rupp S.M.
        The Seattle Spine Team approach to adult deformity surgery: a systems-based approach to perioperative care and subsequent reduction in perioperative complication rates.
        Spine Deform. 2014; 2: 95-103
        • Buchlak Q.D.
        • Yanamadala V.
        • Leveque J.-C.
        • Sethi R.
        Complication avoidance with pre-operative screening: insights from the Seattle spine team.
        Curr Rev Musculoskelet Med. 2016;9.;
        • Kassin M.T.
        • Owen R.M.
        • Perez S.D.
        • Leeds I.
        • Cox J.C.
        • Schnier K.
        • et al.
        Risk factors for 30-day hospital readmission among general surgery patients.
        J Am Coll Surg. 2012; 215: 322-330
        • Spatz E.S.
        • Krumholz H.M.
        • Moulton B.W.
        The new era of informed consent: getting to a reasonable-patient standard through shared decision making.
        JAMA. 2016; 315: 2063-2064
        • Siu J.M.
        • Rotenberg B.W.
        • Franklin J.H.
        • Sowerby L.J.
        Multimedia in the informed consent process for endoscopic sinus surgery: A randomized control trial.
        Laryngoscope. 2016; 126: 1273-1278
        • Saigal R.
        • Clark A.J.
        • Scheer J.K.
        • Smith J.S.
        • Bess S.
        • Mummaneni P.V.
        • et al.
        Adult spinal deformity patients recall fewer than 50% of the risks discussed in the informed consent process preoperatively and the recall rate worsens significantly in the postoperative period.
        Spine (Phila Pa 1976). 1976; 2015: 1079-1085
        • Kubu C.S.
        • Frazier T.
        • Cooper S.E.
        • Machado A.
        • Vitek J.
        • Ford P.J.
        Patients’ shifting goals for deep brain stimulation and informed consent.
        Neurology. 2018; 91: e472-e478
      5. Corda DM, Dexter F, Pasternak JJ, Trentman TL, Brull SJ, Nottmeier EW. Patients’ perspective on full disclosure and informed consent regarding postoperative visual loss associated with spinal surgery in the prone position. Mayo Clin. Proc., vol. 86, Elsevier; 2011, p. 865–8.

        • Kondziolka D.S.
        • Pirris S.M.
        • Lunsford L.D.
        Improving the informed consent process for surgery.
        Neurosurgery. 2006; 58: 1184-1189
        • Krupp W.
        • Spanehl O.
        • Laubach W.
        • Seifert V.
        Informed consent in neurosurgery: patients’ recall of preoperative discussion.
        Acta Neurochir (Wien). 2000; 142: 233-239
        • Park J.
        • Son W.
        • Park K.-S.
        • Kang D.-H.
        • Lee J.
        • Oh C.W.
        • et al.
        Educational and interactive informed consent process for treatment of unruptured intracranial aneurysms.
        J Neurosurg. 2017; 126: 825-830
        • Main B.G.
        • McNair A.G.K.
        • Huxtable R.
        • Donovan J.L.
        • Thomas S.J.
        • Kinnersley P.
        • et al.
        Core information sets for informed consent to surgical interventions: baseline information of importance to patients and clinicians.
        BMC Med Ethics. 2017; 18: 29
        • Krumholz H.M.
        • Chen J.
        • Wang Y.
        • Radford M.J.
        • Chen Y.-T.
        • Marciniak T.A.
        Comparing AMI mortality among hospitals in patients 65 years of age and older evaluating methods of risk adjustment.
        Circulation. 1999; 99: 2986-2992
        • Goodney P.P.
        • Stukel T.A.
        • Lucas F.L.
        • Finlayson E.V.A.
        • Birkmeyer J.D.
        Hospital volume, length of stay, and readmission rates in high-risk surgery.
        Ann Surg. 2003; 238: 161-167
        • Ghaferi A.A.
        • Birkmeyer J.D.
        • Dimick J.B.
        Variation in hospital mortality associated with inpatient surgery.
        N Engl J Med. 2009; 361: 1368-1375
        • Sethi R.
        • Buchlak Q.D.
        • Yanamadala V.
        • Anderson M.L.
        • Baldwin E.A.
        • Mecklenburg R.S.
        • et al.
        A systematic multidisciplinary initiative for reducing the risk of complications in adult scoliosis surgery.
        J Neurosurg Spine. 2017;26.;
      6. Yanamadala V, Kim Y, Buchlak QD, Wright AK, Babington J, Friedman A, et al. Multidisciplinary Evaluation Leads to the Decreased Utilization of Lumbar Spine Fusion: An Observational Cohort Pilot Study. Spine (Phila Pa 1976) 2017.

        • Lim S.
        • Parsa A.T.
        • Kim B.D.
        • Rosenow J.M.
        • Kim J.Y.S.
        Impact of resident involvement in neurosurgery: an analysis of 8748 patients from the 2011 American College of Surgeons National Surgical Quality Improvement Program database.
        J Neurosurg. 2015; 122: 962-970
        • Hunink M.G.M.
        • Weinstein M.C.
        • Wittenberg E.
        • Drummond M.F.
        • Pliskin J.S.
        • Wong J.B.
        • et al.
        Decision making in health and medicine: integrating evidence and values.
        Cambridge University Press, 2014