Bayesian clinical decision model for determining probability of transplant glomerulopathy
An embodiment of the invention provides a method for determining a patient-specific probability of transplant glomerulopathy. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of transplant glomerulopathy is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant glomerulopathy.
1. A method for determining a patient-specific probability of transplant glomerulopathy, said method including:
collecting clinical parameters from a plurality of patients to create a training database, the clinical parameters including biomarker levels from biopsy tissue;
creating a fully unsupervised Bayesian Belief Network model using data from the training database;
validating the fully unsupervised Bayesian Belief Network model;
collecting the clinical parameters for an individual patient;
receiving the clinical parameters for the individual patient into the fully unsupervised Bayesian Belief Network model;
outputting the patient-specific probability of transplant glomerulopathy from the fully unsupervised Bayesian Belief Network model to a graphical user interface for use by a clinician; and
updating the fully unsupervised Bayesian Belief Network model using the clinical parameters for the individual patient and the patient-specific probability of transplant glomerulopathy.
2. The method according to claim 1 , wherein the biomarker levels include gene expression levels for an ICAM-1 gene, IL-10 gene, CCL-3 gene, CD-86 gene, CCL-2 gene, CXCL-11 gene, CD-80 gene, GNLY gene, and PRF-1 gene.
3. The method according to claim 2 , wherein the biomarker levels further include gene expression levels for a CD40LG gene, IFNG gene, CD-28 gene, CXCL-10 gene, CCR-5 gene, CD-40 gene, CTLA-4 gene, TNF gene, CXCL-9 gene, CX3CR-1 gene, FOXP-3 gene, EDN-1 gene, CD-4 gene, TBX-21 gene, FASLG gene, C-3 gene, CD3E gene, CXCR-3 gene, and CCL-5 gene.
4. The method according to claim 1 , wherein the biomarker levels include gene expression levels for the VCAM1 gene, MMP9 gene, Banff C4d gene, MMP7 gene, and LAMC2 gene.
5. The method according to claim 4 , wherein the biomarker levels further include gene expression levels for a TNC gene, S100A4 gene, NPHS1 gene, NPHS2 gene, AFAP gene, PDGF8 gene, SERPINH1 gene, TIMP4 gene, TIMP3 gene, VIM gene, SERPINE1 gene, TIMP1 gene, FN1 gene, ANGPT2 gene, TGFB1 gene, ACTA2 gene, TIMP2 gene, COL4A2 gene, MMP2 gene, COL1A1 gene, COL3A1 gene, GREM1 — 2 gene, SPARC gene, IGF1 gene, SMAD3 gene, HSPG2 gene, FN1 gene, ANGPT2 gene, TGFB1 gene, ACTA2 gene, THBS1 gene, CTNNB1 gene, FGF2 gene, TJP1 gene, FAT gene, CDH1 gene, SMAD7 gene, CD2AP gene, CDH3 gene, CTGF gene, ACTN4 gene, SPP1 gene, AGRN gene, VEGF gene, and BMP7 gene.
6. The method according to claim 1 , wherein said creating of the fully unsupervised Bayesian Belief Network model includes creating the fully unsupervised Bayesian Belief Network model without human-developed decision support rules.
7. The method according to claim 1 , further including estimating an accuracy of the patient-specific probability of transplant glomerulopathy, the accuracy including at least one of model sensitivity, model specificity, positive and negative predictive values, and overall accuracy.