Kranthi Kiran Jalakam

@inproceedings{Young-Shand2019b,

title = {Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome},

author = {Kathryn Young-Shand and Patrice C. Roy and Syed Sibte Raza Abidi and Michael Dunbar and Janie Astephen Wilson},

year  = {2019},

date = {2019-06-19},

booktitle = {2nd International Combined Meeting of Orthopaedic Research Societies, June 19-22, 2019, Montreal, Canada},

abstract = {Purpose:

Identifying knee osteoarthritis (OA) patient phenotypes is relevant to assessing treatment efficacy, yet biomechanical variability has not been applied to phenotyping. This study aimed to identify demographic and gait related groups (clusters) among total knee arthroplasty (TKA) candidates, and examine inter-cluster differences in gait feature improvement post-TKA.



 





Method:

Knee OA patients scheduled for TKA underwent three-dimensional gait analysis one-week pre and one-year post-TKA, capturing lower-limb external ground reaction forces and kinematics using a force platform and optoelectronic motion capture. Principal component analysis was applied to frontal and sagittal knee angle and moment waveforms (n=135 pre-TKA, n=106 post-TKA), resulting in a new uncorrelated dataset of subject PCscores and PC vectors, describing major modes of variability throughout one gait cycle (0-100%). Demographics (age, gender, body mass index (BMI), gait speed), and gait angle and moment PCscores were standardized and assessed for outliers. One patient exceeding Tukey’s outer (3*IQR) fence was removed. Two-dimensional multidimensional scaling followed by k-medoids clustering was applied to scaled demographics and pre-TKA PCscores [134x15]. Number of clusters (k=2:10) were assessed by silhouette coefficients, s, and stability by Adjusted Rand Indices (ARI) of 100 data subsets. Clusters were validated by examining inter-cluster differences at baseline, and inter-cluster gait changes (PostPCscore–PrePCscore, n=105) by k-way ANOVA and Tukey's honestly significant difference (HSD) criterion.





Results:

Four (k=4) TKA candidate groups yielded optimum clustering metrics (s=0.4, ARI=0.75). Cluster 1 was all-males (male:female=19:0) who walked with faster gait speeds (1>2,3), larger flexion angle magnitudes and stance-phase angle range (PC1 & PC4 1>2,3,4), and more flexion (PC2 1>2,3,4) and adduction moment (PC2 & PC3 1>2,3) range patterns. Cluster 1 had the most dynamic kinematics and kinetic loading/unloading range amongst the clusters, representing a higher-functioning (less “stiff”) male subset. Cluster 2 captured older (2>1,3) males (31:1) with slower gait speeds (2<1,4), and less flexion moment (PC2 2<1,4) and adduction moment (PC2 2<1,4) range, describing an older, stiff-gait male subset. Cluster 3 was mostly (4:36) females with slower gait speeds (3<1,4), higher BMIs (3>4), and lower flexion angle magnitude (PC1 3<1,2,4) and flexion moment range (PC2 3<1,2,4) features. Cluster 3 showed the “stiffest” gait amongst the clusters, representing a more-obese, stiff-gait female subset. Cluster 4 was mostly (2:41) females with faster gait speeds (4>2,3) and less stiff kinematic and kinetic patterns relative to Clusters 2 and 3, representing a higher-functioning female subset. Radiographic severity did not differ between clusters (Kellgren-Lawrence Grade, p=0.9, n=102), and after removing demographics and re-clustering, gender differences remained (p<0.04). Pre-TKA, higher-functioning clusters (1&4) had more dynamic loading/un-loading kinetic patterns. Post-TKA, high-functioning clusters experienced less gait improvement (flexion angle PC2, 1,4<3, p≥0.004; flexion moment PC2, 4<2,3), with some sagittal range patterns decreasing postoperatively.





Conclusion:

TKA candidates can be characterized by four clusters, differing by demographics and biomechanical severity features. Post-TKA, functional gains were cluster-specific; stiff-gait clusters experienced more improvement, while higher-functioning clusters experienced less gain and showed some decline. Results suggest the presence of cohorts who may not benefit functionally from TKA. Cluster profiling may support triaging and developing targeted OA treatment strategies, meeting individual function needs.},

keywords = {Clustering, Total knee arthroplasty},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Young-Shand2019,

title = {Clinical and Biomechanical Cluster Classification Before TKA Impacts Functional Outcome},

author = {Kathryn Young-Shand and Patrice C. Roy and Syed Sibte Raza Abidi and Michael Dunbar and Janie Astephen Wilson},

year  = {2019},

date = {2019-02-02},

booktitle = {Orthopaedic Research Society Conference (ORS), February 2-5, 2019  Austin, Texas, USA},

note = {Recipient of the American Academy of Orthopaedic Surgeons (AAOS) Women’s Health Advisory Board (WHAB) Award},

keywords = {Clustering, Total knee arthroplasty},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Abidi:StudHealthTechnolInform:2007,

title = {Automated Interpretation of Optic Nerve Images: A Data Mining Framework for Glaucoma Diagnostic Support.},

author = {Syed Sibte Raza Abidi and Paul Habib Artes and Sanjan Yun and Jin Yu},

issn = {0926-9630},

year  = {2007},

date = {2007-01-01},

booktitle = {Stud Health Technol Inform},

volume = {129},

number = {Pt 2},

pages = {1309-13},

address = {Netherlands},

abstract = {Confocal Scanning Laser Tomography (CSLT) techniques capture high-quality images of the optic disc (the retinal region where the optic nerve exits the eye) that are used in the diagnosis and monitoring of glaucoma. We present a hybrid framework, combining image processing and data mining methods, to support the interpretation of CSLT optic nerve images. Our framework features (a) Zernike moment methods to derive shape information from optic disc images; (b) classification of optic disc images, based on shape information, to distinguish between healthy and glaucomatous optic discs. We apply Multi Layer Perceptrons, Support Vector Machines and Bayesian Networks for feature sub-set selection and image classification; and (c) clustering of optic disc images, based on shape information, using Self-Organizing Maps to visualize sub-types of glaucomatous optic disc damage. Our framework offers an automated and objective analysis of optic nerve images that can potentially support both diagnosis and monitoring of glaucoma.},

keywords = {Classification, Clustering, Data Mining, Decision Support Systems, Optic Nerve},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/cbms/YuAAMH05,

title = {Automated Optic Nerve Analysis for Diagnostic Support in Glaucoma},

author = {Jin Yu and Syed Sibte Raza Abidi and Paul Habib Artes and Andrew R. McIntyre and Malcolm I. Heywood},

url = {http://dx.doi.org/10.1109/CBMS.2005.36},

year  = {2005},

date = {2005-01-01},

booktitle = {18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), 23-24 June 2005, Dublin, Ireland},

pages = {97--102},

crossref = {DBLP:conf/cbms/2005},

keywords = {Classification, Clustering, Data Mining, Decision Support Systems, Glaucoma, Health Data Analytics, Optic Nerve},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/ida/AbidiHG01,

title = {Analyzing Data Clusters: A Rough Sets Approach to Extract Cluster-Defining Symbolic Rules},

author = {Syed Sibte Raza Abidi and Kok Meng Hoe and Alwyn Goh},

url = {http://dx.doi.org/10.1007/3-540-44816-0_25},

year  = {2001},

date = {2001-01-01},

booktitle = {Advances in Intelligent Data Analysis, 4th International Conference, IDA 2001, Cascais, Portugal, September 13-15, 2001, Proceedings},

pages = {248--257},

crossref = {DBLP:conf/ida/2001},

keywords = {Clustering, Data Mining, Health Data Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/icai/OngA99,

title = {Data Mining Using Self-Organizing Kohonen Maps: A Technique for Effective Data Clustering & Visualization},

author = {Jason Ong and Syed Sibte Raza Abidi},

year  = {1999},

date = {1999-01-01},

booktitle = {Proceedings of the International Conference on Artificial Intelligence, IC-AI '99, June 28 - July 1, 1999, Las Vegas, Nevada, USA, Volume 1},

pages = {261--264},

crossref = {DBLP:conf/icai/1999-1},

keywords = {Clustering, Data Mining},

pubstate = {published},

tppubtype = {inproceedings}

}