Clinical decision model
An embodiment of the invention provides a method for determining a patient-specific probability of disease. 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 the healing rate of an acute traumatic wound 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 the healing rate of an acute traumatic wound.
1. A method for treating a wound on a patient in need thereof, comprising:
determining that the wound is likely to heal following surgical closure based on a patient-specific probability of impaired wound healing, wherein the patient-specific probability of impaired wound healing is generated by:
generating, by a processor, a training database comprising biomarker levels from a plurality of patients having known wound healing outcomes, the biomarker levels being collected from at least one of serum, wound effluent and biopsy tissue, the biomarker levels including gene expression levels for an IL-2 gene, an IL-4 gene, an IL-15 gene, an IFN-γ gene, and a GM-CSF gene;
generating, by the processor, a fully unsupervised Bayesian Belief Network model using data from the training database, the fully unsupervised Bayesian Belief Network model comprising a directed acyclic graph including a plurality of nodes, wherein each of the nodes includes at least two bins with each bin representing a value range of a clinical parameter associated with that node, wherein the data includes an identification of at least one conditional dependence relationship between the known wound healing outcomes and the biomarker levels;
receiving biomarker levels that have been collected for the patient into the fully unsupervised Bayesian Belief Network model; and
calculating, by the processor, the patient-specific probability of impaired wound healing for the patient using the fully unsupervised Bayesian Belief Network model; and
treating the wound in response to the calculated patient-specific probability of impaired wound healing.
2. The method according claim 1 , wherein treating the wound comprises surgically closing the wound.
3. The method according to claim 1 , further comprising:
updating the fully unsupervised Bayesian Belief Network model using the biomarker levels for the patient and the patient-specific probability of impaired wound healing.
4. The method according to claim 1 , wherein the biomarker levels that have been collected for the patient are received into the fully unsupervised Bayesian Belief Network model using a graphical user interface of a computer or an electronic device.
5. The method according to claim 1 , wherein determining that the wound is likely to heal following surgical closure comprises determining that there is a greater than 70% probability that the wound will heal following surgical closure.
6. The method according to claim 1 , wherein the biomarker levels used to generate the patient-specific probability of impaired wound healing further includes gene expression levels for an IL-1α gene, IL-1β gene, IL-3 gene, IL-7 gene, IL-8 gene, IL-10 gene, IL-12(p40) gene, IL-12(p70) gene, IL-13 gene, Eotaxin gene, MIP-1α gene, and TNFα gene.
7. The method according to claim 1 , wherein the biomarker levels used to generate the patient-specific probability of impaired wound healing further includes RNA transcripts of genes of: ACTA2, ACVR1, ADM, ALCAM, ANGPT 1, ANGPT 2, ANGPT 4, BAX, BCL2, BCL2L, 18S, 18S, CAV2, CCL1, CCL11, CCL17, CCL19, CCL 2, CCL 20, CCL22, CCL25, CCL27, CCL28, CCL3, COL3A1, COL4A1, COL4A3, CSSF1, CSF2, CSF3, CTGF, CX3CL1, CXCL1, CXCL10, CXCL11, CXCL12, FGF10, FGF11, FGF12, FGF13, FGF17, FGF2, FGF3, FGF5, FGF7, FGF8, FGF9, FIGF, IFNG, IGF1, IGF2, IGFBP1, IGFBP2, IGFBP3, IGFBP3, IGFBP4, IGFBP5, IGFBP6, IGFBP7, IL10, IL11, IL6, IL7, IL8, IL9, ITGA5, ITGAL, ITGAM, ITGB2, KDR, KITLG, LBP, LTA, MMP7, MMP8, MMP9, MPO, NCAM2, NFKB1, NFKB2, NOS2A, OSMR, PDGFA, PDGFB, PECAM1, SMAD6, SMAD7, SOCS1, SOCS3, SOCS5, STAT3, TEK, TGFA, TGFB1, TGFB2, TGFB3, TGFBR1, DCL2L2, BMP1, BMP15, BMP5, BMP3, BMP4, BMP5, BMP6, BMP7, BMP8A, BMP8B, CALCA, CALCB, CAV1, CCL4,CCL4L1,CCL4L2, CCL5, CCL7, CD14, CD4, CD40, CD4OLG, CD83, CD8A, CD8B, COL18A1, COL1A1, CXCL13, CXCL2, CXCL5, CXCL9, ECGF1, EDN1, EGF, EGR1, EPO, FADD, FAS, FGF1, FLT1, FN1, GAPDH, GDF3, GDF5, MSTN, GDF9, HGF, HMGB1, IAPP, ICAM2, IFNB1, IL12A, IL13, IL15, IL16, IL17A, IL18, ILIA, IL1B, IL2, IL3, IL4, IL5, MAPK14, MET, MMP1, MMP10, MMP11, MMP12, MMP13, MMP14, MMP15, MMP2, MMP24, MMP3, PF4, PLA2G4A, PTGS1, PTGS2, SELE, SELP, SERPINE1, SLPI, SMAD1, SMAD2, SMAD3, SMAD4, TIE1, TIMP1, TIMP2, TIMP3, TNC, TNF, TNFSF10, VCAM1, VEGFB, VEGFC, XCL1, and XCL2.
8. The method according to claim 1 , wherein the biomarker levels used to generate the patient-specific probability of impaired wound healing further includes translation products of genes of: ACTA2, ACVR1, ADM, ALCAM, ANGPT 1, ANGPT 2, ANGPT 4, BAX, BCL2, BCL2L, 18S, 18S, CAV2, CCL1, CCL11, CCL17, CCL19, CCL 2, CCL 20, CCL22, CCL25, CCL27, CCL28, CCL3, COL3A1, COL4A1, COL4A3, CSSF1, CSF2, CSF3, CTGF, CX3CL1, CXCL1, CXCL10, CXCL11, CXCL12, FGF10, FGF11, FGF12, FGF13, FGF17, FGF2, FGF3, FGF5, FGF7, FGF8, FGF9, FIGF, IFNG, IGF1, IGF2, IGFBP1, IGFBP2, IGFBP3, IGFBP3, IGFBP4, IGFBP5, IGFBP6, IGFBP7, IL10, IL11, IL6, IL7, IL8, IL9, ITGA5, ITGAL, ITGAM, ITGB2, KDR, KITLG, LBP, LTA, MMP7, MMP8, MMP9, MPO, NCAM2, NFKB1, NFKB2, NOS2A, OSMR, PDGFA, PDGFB, PECAM1, SMAD6, SMAD7, SOCS1, SOCS3, SOCS5, STAT3, TEK, TGFA, TGFB1, TGFB2, TGFB3, TGFBR1, DCL2L2, BMP1, BMP15, BMP5, BMP3, BMP4, BMP5, BMP6, BMP7, BMP8A, BMP8B, CALCA, CALCB, CAV1, CCL4,CCL4L1,CCL4L2, CCL5, CCL7, CD14, CD4, CD40, CD4OLG, CD83, CD8A, CD8B, COL18A1, COL1A1, CXCL13, CXCL2, CXCL5, CXCL9, ECGF1, EDN1, EGF, EGR1, EPO, FADD, FAS, FGF1, FLT1, FN1, GAPDH, GDF3, GDF5, MSTN, GDF9, HGF, HMGB1, IAPP, ICAM2, IFNB1, IL12A, IL13, IL15, IL16, IL17A, IL18, ILIA, IL1B, IL2, IL3, IL4, IL5, MAPK14, MET, MMP1, MMP10, MMP11, MMP12, MMP13, MMP14, MMP15, MMP2, MMP24, MMP3, PF4, PLA2G4A, PTGS1, PTGS2, SELE, SELP, SERPINE1, SLPI, SMAD1, SMAD2, SMAD3, SMAD4, TIE1, TIMP1, TIMP2, TIMP3, TNC, TNF, TNFSF10, VCAM1, VEGFB, VEGFC, XCL1, and XCL2.
9. The method according to claim 1 , wherein the biomarker levels used to generate the patient-specific probability of impaired wound healing further includes RNA transcripts of genes of IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12(p40), IL-12(p70), IL-13, and IL-15.
10. The method according to claim 1 , wherein the biomarker levels used to generate the patient-specific probability of impaired wound healing further includes translation products of genes of IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12(p40), IL-12(p70), IL-13, and IL-15.