Clinical decision support to aid watchful waiting of cardiovascular disease

Abstract

Provided herein are methods and systems for making patient-specific therapy recommendations of a lipid-lowering therapy for patients with known or suspected cardiovascular disease, such as atherosclerosis.

What is claimed is: 
     
         1 . A computer-implemented method comprising:
 receiving non-invasively obtained data related to a plaque from a patient;   inputting the non-invasively obtained data from the patient to an estimation model;   generating, as an output of the estimation model, virtual 'omics data that include estimated pathway activations or molecule levels, or both, of the patient by applying the estimation model to the non-invasively obtained data from the patient;   updating an in silico systems biology model using the generated virtual 'omics data to generate an in silico patient-specific systems biology model, wherein
 (i) the in silico systems biology model comprises a set of networks, wherein each network comprises a plurality of nodes, each node representing a baseline level of a molecule or pathway, and a plurality of edges between pairs of nodes, each edge representing a node-node interaction, 
 (ii) at least two of the nodes of the plurality of nodes in each network represent molecules or pathways whose levels are affected by an atherosclerotic cardiovascular disease, and 
 (iii) at least one network of the set of networks includes a disease-associated pathway activation or molecule level, or both, for each of the nodes in the network; 
   perturbing the in silico patient-specific systems biology model to simulate, for the patient, a therapeutic effect of one or more pharmaceutical agents singly or in combination; and   providing an output indicating a level of improvement in the atherosclerotic cardiovascular disease for the patient and a recommendation supporting a clinical decision to monitor patient status without introducing new treatment if the patient's atherosclerosis is at stable levels.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the recommendation informs a decision that leads to a clinical action. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the recommendation enables a healthcare provider to tailor a therapy for the patient. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein the at least one network of the set of networks includes nodes corresponding, respectively, to one or more of a glycosylated low-density lipoprotein (glyLDL), an oxidized LDL (oxLDL), a minimally-modified LDL (mmLDL), a very-low-density lipoprotein (VLDL), a byproduct of a triglyceride, a byproduct of a high-density lipoprotein, IL-1, IL1β, IL-6, TNF, IL12/23, IL17, IL18, interleukin, another cytokine molecule, MTOR, SGLT2, GLP1, NFκβ1, ICAM1, or VCAM1. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein the non-invasively obtained data comprises imaging data. 
     
     
         6 . The computer-implemented method of  claim 5 , wherein the non-invasively obtained imaging data is obtained by computed tomography (CT), dual energy computed tomography (DECT), spectral computed tomography (spectral CT), computed tomography angiography (CTA), cardiac computed tomography angiography (CCTA), magnetic resonance imaging (MRI), multi-contrast magnetic resonance imaging (multi-contrast MRI), ultrasound (US), positron emission tomography (PET), intra-vascular ultrasound (IVUS), optical coherence tomography (OCT), near-infrared radiation spectroscopy (NIRS), or single-photon emission tomography (SPECT) diagnostic images or any combination thereof. 
     
     
         7 . The computer-implemented method of  claim 1 , wherein the estimated pathway activations or molecule levels, or both, comprise an alteration in a level of a gene, a transcript, a protein, or a metabolite or dysregulation of a pathway. 
     
     
         8 . The computer-implemented method of  claim 7 , wherein the at least one network of the set of networks further comprises edges representing protein-protein interactions, gene-gene interactions, gene-transcript interactions, transcript-protein interactions, protein-metabolite interactions, and/or protein-gene interactions. 
     
     
         9 . The computer-implemented method of  claim 8 , wherein interactions represent any one of translation, activation, inhibition, indirect effect, state change, binding, dissociation, phosphorylation, dephosphorylation, glycosylation, ubiquitination, and methylation as a result of an interaction between two molecules. 
     
     
         10 . The computer-implemented method of  claim 1 , wherein the dyslipidemia management agent is an agent controlling hypertriglyceridemia or hypercholesterolemia, an agent raising atheroprotective lipoproteins, or other dyslipidemia control medication. 
     
     
         11 . The computer-implemented method of  claim 1 , wherein the dyslipidemia management agent is a statin. 
     
     
         12 . The computer-implemented method of  claim 11 , wherein the statin is atorvastatin. 
     
     
         13 . The computer-implemented method of  claim 1 , wherein the dyslipidemia management agent is an intensive lipid-lowering agent. 
     
     
         14 . The computer-implemented method of  claim 1 , wherein the dyslipidemia management agent is a proprotein convertase subtilisin kexin type 9 (PCSK9) inhibitor, a Lp(a)-lowering therapy, or a cholesteryl ester transfer protein (CETP) inhibitor. 
     
     
         15 . The computer-implemented method of  claim 1 , wherein the dyslipidemia management agent is a hypertriglyceridemia lowering agent or a hypercholesterolemia lowering agent. 
     
     
         16 . The computer-implemented method of  claim 15 , wherein the hypercholesterolemia lowering agent is a statin, ezetimibe, bile acid sequestrant, adenosine triphosphate-citrate lyase (ACL) inhibitor, fibrate, niacin, omega-3 fatty acid ethyl ester, or omega-3 polyunsaturated fatty acids (PUFA). 
     
     
         17 . The computer-implemented method of  claim 1 , wherein the agent that affects the inflammatory cascade is an anti-inflammatory or pro-resolving drug. 
     
     
         18 . The computer-implemented method of  claim 17 , wherein the anti-inflammatory or pro-resolving drug is an inhibitor of IL-1. 
     
     
         19 . The computer-implemented method of  claim 18 , wherein the inhibitor of IL-1 is canakinumab. 
     
     
         20 . The computer-implemented method of  claim 17 , wherein the anti-inflammatory or pro-resolving drug inhibits TNF activity. 
     
     
         21 . The computer-implemented method of  claim 17 , wherein the anti-inflammatory or pro-resolving drug inhibits IL12/23. 
     
     
         22 . The computer-implemented method of  claim 17 , wherein the anti-inflammatory or pro-resolving drug inhibits IL17. 
     
     
         23 . The computer-implemented method of  claim 1 , wherein the agent that affects the inflammatory cascade is an inhibitor of a pro-inflammatory cytokine induced on danger signaling. 
     
     
         24 . The computer-implemented method of  claim 1 , wherein the agent that affects the inflammatory cascade is a pro-resolvin. 
     
     
         25 . The computer-implemented method of  claim 24 , wherein the pro-resolvin is an omega-3 fatty acid. 
     
     
         26 . The computer-implemented method of  claim 1 , wherein the glycemic treatment agent is metformin. 
     
     
         27 . The computer-implemented method of  claim 1 , wherein the plaque comprises atherosclerotic plaque. 
     
     
         28 . A system comprising:
 a memory configured to store instructions; and   one or more processors configured to execute the instructions to perform operations comprising:
 inputting, to an estimation model, non-invasively obtained data related to a plaque from a patient; 
 generating virtual 'omics data that include estimated pathway activations or molecule levels, or both, of the patient, by applying the estimation model to the non-invasively obtained data from the patient; 
 updating an in silico systems biology model using the generated virtual 'omics data to generate an in silico patient-specific systems biology model, wherein
 (i) the in silico systems biology model comprises a set of networks, wherein each network comprises a plurality of nodes, each node representing a baseline level of a molecule or pathway, and a plurality of edges between pairs of nodes, each edge representing a node-node interaction, 
 (ii) at least two of the nodes of the plurality of nodes in each network represent molecules or pathways whose levels are affected by an atherosclerotic cardiovascular disease, and 
 (iii) at least one network of the set of networks includes a disease-associated pathway activation or molecule level, or both, for each of the nodes in the network; 
 
 perturbing the in silico patient-specific systems biology model to simulate, for the patient, a therapeutic effect of one or more pharmaceutical agents singly or in combination; and 
 providing an output indicating a level of improvement in the atherosclerotic cardiovascular disease for the patient and a recommendation supporting a clinical decision to monitor patient status without introducing new treatment if the patient's atherosclerosis is at stable levels. 
   
     
     
         29 . The system of  claim 28 , wherein the estimated pathway activations or molecule levels, or both, comprise an alteration in a level of a gene, a transcript, a protein, or a metabolite or dysregulation of a pathway. 
     
     
         30 . The system of  claim 29 , wherein the at least one network of the set of networks further comprises edges representing protein-protein interactions, gene-gene interactions, gene-transcript interactions, transcript-protein interactions, protein-metabolite interactions, and/or protein-gene interactions. 
     
     
         31 . The system of  claim 30 , wherein interactions represent any one of translation, activation, inhibition, indirect effect, state change, binding, dissociation, phosphorylation, dephosphorylation, glycosylation, ubiquitination, and methylation as a result of an interaction between two molecules. 
     
     
         32 . The system of  claim 28 , wherein the plaque comprises atherosclerotic plaque. 
     
     
         33 . One or more non-transitory computer-readable media storing instructions that are executable by one or more processing devices, and upon such execution cause the one or more processing devices to perform operations comprising:
 inputting, to an estimation model, non-invasively obtained data related to a plaque from a patient;   generating virtual 'omics data that include estimated pathway activations or molecule levels, or both, of the patient, by applying the estimation model to the non-invasively obtained data related from the patient;   updating an in silico systems biology model using the generated virtual 'omics data to generate an in silico patient-specific systems biology model, wherein
 (i) the in silico systems biology model comprises a set of networks, wherein each network comprises a plurality of nodes, each node representing a baseline level of a molecule or pathway, and a plurality of edges between pairs of nodes, each edge representing a node-node interaction, 
 (ii) at least two of the nodes of the plurality of nodes in each network represent proteins whose levels are affected by an atherosclerotic cardiovascular disease, and 
 (iii) at least one network of the set of networks includes a disease-associated pathway activation or molecule level, or both, for each of the nodes in the network; 
   perturbing the in silico patient-specific systems biology model to simulate, for the patient, a therapeutic effect of one or more pharmaceutical agents singly or in combination; and   providing an output indicating a level of improvement in the atherosclerotic cardiovascular disease for the patient and a recommendation supporting a clinical decision to monitor patient status without introducing new treatment if the patient's atherosclerosis is at stable levels.   
     
     
         34 . The one or more non-transitory computer-readable media of  claim 33 , wherein the plaque comprises atherosclerotic plaque.