Post on 20-Jan-2021
Predictive Analytics: Making Adult Spinal Deformity Surgery Sustainable
Christopher P Ames MD Professor of Neurosurgery and Orthopaedic Surgery
Director of Spinal Deformity and Spinal Tumor Surgery
University of California San Francisco
Benzel AANS 2019
COI/Disclosures Chris Ames, MD has financial interests to
disclose. Royalty: Biomet Zimmer, Stryker, Depuy Synthes,
K2M, Next Spine, Medicrea, Astura Consulting: Medtronic, Biomet Zimmer,
Depuy Synthes, K2M, Medicrea Research: Titan Spine, Depuy Synthes ISSG Editorial Board: Operative Neurosurgery Grant Funding: SRS Executive Committee: ISSG
How much has implant innovation changed complication
rates and improved outcomes since the first multiaxial screw
was designed?
How much more can spine surgeon technical performance
improve?
Will the next generation be more technically facile than Ed Benzel or Volker Sonntag?
Bounds of human technical performance can be predicted using data analytics
Filippo Radicci (Indiana) predicted exactly the new 100m record of Usain Bolt at 9.63 s and using analytics predicts the ultimate bound of human performance is 8.28 s
Why is disruptive technology needed now?
53 million people over age 65 now and increasing
80 million over 65 by 2050
60% prevalence of spinal deformity (cobb greater than 10 degrees)
32 million people with ASD in US
Economic Burden of Aging Musculoskeletal System
Total Health care cost 3.5 trillion 2017
Musculoskeletal disease cost >800 billion/year
Spinal Deformity $80 billion (2011)
Number of USA ASD Procedures increased by 157% in 10 years
0
50,000
100,000
150,000
200,000
250,000
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Number of discharges with at least one diagnosis of spinal curvature' (ICD‐9 code 737.0 to 737.9)
Children
Adult
Healthcare Costs and Utilization Project (HCUP http://hcupnet.ahrq.gov),
Do not go gentle …
Modern expectations of high function in old age
Complexity IncreasingUtilization of wedge osteotomies
200
300
400
500
600
700
800
2003 2004 2005 2006 2007 2008 2009 2010
# Wedge Osteotomies(77.29 ICD‐9‐CM)
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2003 2004 2005 2006 2007 2008 2009 2010
Wedge Osteotomies by age group
>65
45‐64
18‐44
Increases on 275% in less than 10 years~250 procedures in 2003~700 procedures in 2012
Increase proportion of patients >65yo~20% in 2003~40% in 2012
Surgery improves disability
Disease State PCS; mean NBS
points
MCS; mean NBS
pointsUS Total Population
50 49.9
US Healthy Population
55.4 52.9
ASD 40.9 49.4Back Pain 45.7 47.6Cancer 40.9 47.6Depression 45.4 36.3Diabetes 41.1 47.8Heart Disease 38.9 48.3Hypertension 44.0 49.7Limited Use Arms Legs
39.0 43.0
Lung Disease 38.3 45.6
Spine J 2014
Failures destroy cost effectiveness
Failure Prevention
Double pelvis
Double rods
VCR rod
BMP-2
Ligament repair
Vertebroplasty
2 surgeons
Plastic Surgery
Eliminates provider variability Appropriateness criteria for all surgeons Transparency Multidisciplinary Best practices 3 fold improvement in the worst complications 12 fold decrease in return to surgery in the first three months postop
Collective Intelligence
The 56 person group average better than any individual and came within 3% of total
Only 1 individual “guessed” better
Eliminates outliers
Reduce Complications by Limiting Care
Of course we decrease complications by operating on more robust patients
But, patients who experience major complications still do well
Most disabled patients with high frailty scores improve the most
Approved:Low risk
High Risk butGood Outcome
Older had Greater improvement after PSO in general health
Big Data-Datify the Patient
“Painting true picture of patient with many data points”
FICO Score….Preop Risk Score?
Results
25%
44%
62%
0%
10%
20%
30%
40%
50%
60%
70%
Not Frail Pre-Frail Frail
Major Complication Incidence
Pearson Chi2 = 29.7 Pr = 0.000
Frailty is a Predictive ROS 1) Bladder incontinence☐ Yes☐ No
2) Bowel incontinence☐ Yes☐ No
3) Leg weakness☐ Yes☐ No
4) Loss of Balance☐ Yes☐ No
5) Do you currently smoke?☐ Yes☐ No
6) Are you currently on disability?☐ Yes☐ No
7) Current height and weight (BMI)☐ <18.5☐ 18.5-30☐ >30
8-18) Medical History (check all that apply):☐ Cancer☐ Heart Disease☐ Diabetes☐ Hypertension☐ Liver disease☐ Lung disease☐ Kidney disease☐ Osteoporosis☐ Peripheral vascular disease☐ Prior DVT/PE/Stroke (blood clot)☐ Greater than 3 medical problems
19) Would you say your current health is:☐ the same or better than last year☐ worse than this time last year
20) Would you say your current health is:☐ Excellent or Good☐ Fair or Poor
How much difficulty do you have with each of the following activities:
21) Climbing 1 flight of stairs☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
22) Driving a car☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
23) Getting dressed☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
24) Getting in and out of bed☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
25) Walking 100 yards☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
26) Get around the house without an assistive device☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
27) Performing light activity (vacuuming, playing golf)☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
28) Bathing yourself☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
29) Normal work or schoolwork or housework☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
30) Lift medium weight objects☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable to do
31) Travel more than 1 hour☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable t
32) Perform all personal care☐ Moderate/Little/No difficulty☐ Extreme difficulty/Require assistance or assistive device/Unable t
How often in the last month have you experienced the following:
33) Feeling downhearted and depressed☐ All or most of the time☐ Some, little or none of the time
34) Feeling so down in the dumps you cannot cheer up no matter what you☐ All or most of the time☐ Some, little or none of the time
35) Feeling tired/exhausted☐ All or most of the time☐ Some, little or none of the time
36) Feeling worn out/used up☐ All or most of the time☐ Some, little or none of the time
37) Difficulty remembering things you used to have no trouble with☐ All or most of the time☐ Some, little or none of the time
38) Feeling like your thinking is slow or clouded☐ All or most of the time☐ Some, little or none of the time
39) What is your current level of activity?☐ Bedridden or primarily no activity☐ Light to full sports/activities
40) How is your social life?☐ My social life is restricted to my home or non-existent☐ My social life is normal or mildly restricted
Research to ImplementationSpine Frailty is now in UCSF
EHR
Augmented Intelligence EHR work flows
Datify the Procedure…. Mirza ASD-S ASD-R
R2 EBL 0.22 0.28 0.34 p value
EBL 0.0012 <0.001 <0.001
R2 Op time
0.18 0.26 0.34
p value OP time
0.007 0.0002 < 0.0001
Neurosurgery 2017
Ok for RISK but ….What drives OUTCOMES?
Previous work has sought answers in correlations
Outcome driven by alignment
But what does the new Information Age and AI tell us??
There is much more
Baseline SVA vs ODI
All pts, op and nonop, n=1622 R2 = .19
2yr SVA fused to Pelvis vs ODI
All 2yr follow up Op pts, n= 502 R2 = .04
Predictive Analytics
Pt
Apical FusionT10-pelvisT3-pelvis PSO
Frailty
+
All data fields analyzed separately 25% MCID pain10% MCID appearance50% revision 5% medical complication90% play tennis
60% MCID pain80% MCID appearance10% revision25% major complication5% play tennis
ComplicationAvoidance
First Generation Models-Q/O
3 successful binary output models constructed Proximal junctional kyphosis/failure *Spine 2016
Major intra/periop complications *JNS Spine 2017
Oswestry Disability Index (ODI) minimal clinical important difference *Spine Deformity 2018
Methods
5 different bootstrapped decision trees
Internal validation 70:30 data split for training/testing
Accuracy, and the area under a receiver operator characteristic (ROC) calculated
Second generation models-Q/V
Pseudoarthrosis (with/without biologics as modifiable variable) 91% accuracy
LOS model –first attempt at a continuous output model (from yes/no to 3,4, 5,6 days etc) 75% accuracy
Cost effectiveness model: what if we used our MCID model for patient selection ?
• World Neurosurgery 2018• Clinical Spine Surgery 2018• Neurosurgical Focus 2018
Results: QALY
Surgical Decisions according to model vs Surgical Decisions by Surgeon – Simulation
Greater Qaly Gain using model
2019 : Results in Combined Dataset patients from 17 hospitals
ISSG and ESSG Data Time frame: 2008-2015 >1600 patients > 2000 patient years 17 sites, 11 US, 2 Spain, 2 Turkey, 1 France and 1
Switzerland 35 surgeons R (Miquel Serra PhD) Kernel Analytics for Web Deployment and Machine
Learning
Complications, Reop, ReadmissionAccepted JNS Spine 2019
Individual informed consent
Individual cumulative risk estimates for MC at 2y ranged from 3.9%-74.1%
Surgical invasiveness (LIV-pelvic fixation, length of fusion, prior surgery), age, sagittal deformity, patient frailty (walking and lifting capacity) and blood loss most strongly predict MC
* Pellise et al ISSG ESSG analytics collaboration SRS 2019 submitted
Dynamics of Complications Prediction
Before Surgery After SurgeryBlood & Time
Discharge
Patient Characteristics
Surgery Characteristics
Hospital
Surgeon
64% 70% 73%
65% 71% 75%
79%74%70%
80%79%76%
Identify pts at risk Of bounce back
** predictive analytics-driven interventions directed at high-risk individuals reduced emergency room and specialist visits
Major Complication Risk Calculator 2018v1
Patient-related factors, >1/3 of which are potentially modifiable, account for 55% of the predictive model weight.
Surgeon and site represent 4-10% for MC, but are most relevant for READMIT and UNPLAN
Baseline & outcome heterogeneity, n=2,207, 4078.57 observation-years
Outcomes—Are YOU average?
* Only 5% of patients had “average improvement”
Can we Predict Outcome?
75 variables were used in the training of the models including demographic data, comorbidities, frailty, modifiable surgical variables, baseline health-related quality of life, coronal and sagittal radiographic parameters, hospital and surgeon
8 different prediction algorithms were trained with 3-time horizons, baseline-1year, baseline-2years and 1year-2years
SRS 22R, DOMAINS AND TOTAL, ODI, SF-36 PCS and MCS
* Spine 2019
Top Outcome Predictors:
Up to 82.5 % predictive power
Preop scores most important
Surgeon and Site 1.8% of variation
Calculator Output SRS 22
BUT, Patients don’t want to know how their SRS Total will improve!
Will I walk better?
Will I be able to return to work?
Will my pain improve?
Will my mood improve?
Will I feel better about how I look?
Will I really be satisfied with surgical treatment?
Individual SRS 22 responses with wait for surgery simulations
75-85% Predictive Power
The Age of Artificial Intelligence Driven Decision Support
Every patient is different and represents a unique combination of frailty, disability, mental health …
Every surgeon is different and every surgery plan is unique ….
How the Machine sees it
HOW THE SURGEON SEES IT
Frailty= .5ODI 42CCI 4ASA 3BMI 25BMD -1.5Cc: low back painMed: norco
SVA 12cmPI-LL 28PT 35TK 55CSVA 0
>100 variables
A.I. Nearest Neighbor Recall Clusters and R/B
Spine 2019
New AI driven ASD Classification
Outcome and Complications
Based on Clustering of Surgical Types and Patient Types
Rather than a classification based on R2 to one parameter class which varies by age
Results: Outcomes Grid
52
Results: Efficiency Plots
Preop Risk vs Benefit Plotting
53
Preoperative Prediction of Cost and Catastrophic Cost in Adult Spine Deformity Surgery: Feasibility Analysis of Predictive Analytics to Establish 90 day
bundled payments Models Predicted 90 day dollar
cost with 70.1 % accuracy
Out of the total variance explained, 22.63% was only explained by site and surgeon fixed-effects
The top 4 predictors of cost by order were; surgeon, number of levels fused, IBF, site
CC > $ 100,000 was predicted preoperatively with a 90.41% accuracy
Predicting ASD Surgeries That Exceed Medicare Allowable Payment Thresholds
AUC 94.48%)
56.8% increased likelihood getting reimbursed more than the cost of surgery (iEOC<MA) if done at an academic center.
SRS 2019
Where is ASD? & the ladder
56
The Ladder of Causation – J. Pearl
ASD surgery, PubMed literature 1950-2018, n=6,621 articles
Clustering of patients v2 – “Young coronal” vs “worst patients”
n=19316%27.55-6.641.69
48.8217.263.47
44.96
47.14
n=11847%65.57
140.4971.2438.4257.272.39
43.25
25.31
Clinical Trials
N=729 N=35
Clustering of patients – Effectsize & Power for trials
Pseudoarthrosis Trends ISSG—Average or Benchmarking
THANK YOU !!!