# US 2026/0074030 A1 · Prognostic tools for clinical trial enrollment > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0074030 A1 - Document ID: US20260074030A1 - Status: Published application - Kind: A1 - Application number: 19/325,779 - Filed: 2025-09-11 - Published: 2026-03-12 - Earliest claimed: 2024-09-12 - Assignees: Perceiv Research Inc. - Applicants: Perceiv Research Inc. - Inventors: Christian DANSEREAU; Angela TAM; Cesar LAURENT - Web page: https://patentgrep.com/patent/US20260074030A1 ## CPC - G16H 10/20: for electronic clinical trials or questionnaires - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Methods and systems for using prognostic model(s) during a clinical trial enrollment process are disclosed. The method comprises acquiring a trial dataset comprising individual-specific data for a pool of trial individuals; and determining, using a pre-trained model an enrollable set of trial individuals from the pool of trial individuals, wherein: the pre-trained model is a prognostic model that has been trained to forecast a likelihood of progression of a disease or a condition after a pre-determined interval; and the pre-trained model has a dynamic operating point. ## Claims ### Claim 1 A method for determining an operating point for a trained model, wherein the trained model was trained to predict a likelihood of progression of a disease or a condition, the method comprising: acquiring a calibration dataset comprising individual-specific data for a pool of calibration individuals; selecting, based on a biomarker, a set of calibration individuals from the pool of calibration individuals; determining a reference sample size requirement and a reference screen failure rate for the set of calibration individuals; for each of a plurality of candidate operating points of the trained model: determining, using the trained model with a given candidate operating point, a given set of calibration individuals from the pool of calibration individuals that are to be enrolled based on a model-driven selection process, the given candidate operating point being a candidate classification threshold for the trained model for classifying a given calibration individual as being part of an enrolled class or a rejected class; and determining a given sample size requirement and a given screen failure rate for the given set of calibration individuals determined using the given candidate operating point; and selecting the operating point for the trained model amongst the plurality of candidate operating points based on a comparison between at least one of: the reference sample size requirement and respective sample size requirements of the plurality of candidate operating points; and the reference screen failure rate and respective screen failure rates of the plurality of candidate operating points. ### Claim 2 (depends on 1) The method of claim 1, wherein each individual of the pool of calibration individuals satisfies requirements of a clinical trial. ### Claim 3 (depends on 2) The method of claim 2, wherein the operating point is an upper target operating point determined based on a comparison of the reference sample size requirement and the respective sample size requirements of the plurality of candidate operating points, wherein the upper target operating point is configured for reducing a screen failure rate of individuals for the clinical trial. ### Claim 4 (depends on 2) The method of claim 2, wherein the operating point is a lower target operating point determined based on a comparison of the reference screen failure rate and the respective screen failure rates of the plurality of candidate operating points, wherein the lower target operating point is configured for reducing a sample size of individuals for the clinical trial. ### Claim 5 (depends on 1) The method of claim 1, wherein the operating point comprises a range of target operating points determined based on both (i) the reference sample size requirement and the respective sample size requirements of the plurality of candidate operating points, and (ii) the reference screen failure rate and the respective screen failure rates of the plurality of candidate operating points. ### Claim 6 (depends on 1) The method of claim 1, further comprising: acquiring a trial dataset comprising individual-specific data for a pool of trial individuals; and determining, using the trained model and the operating point, a set of individuals to enroll in A clinical trial from the pool of trial individuals. ### Claim 7 A method for training a model to forecast a likelihood of progression of a disease or a condition after a pre-determined interval, the method comprising: receiving a training dataset comprising a plurality of training datapoints, wherein each training datapoint comprises data corresponding to a patient and a label indicating a ground truth outcome for the patient; training the model to predict, for each training datapoint of the plurality of training datapoints, and based on the data corresponding to a respective training datapoint, a predicted outcome for the patient associated with the respective training datapoint; comparing, for each training datapoint of the plurality of training datapoints, the predicted outcome for a respective training datapoint to the ground truth outcome for the respective training datapoint; and adjusting, based on the comparing, the model. ### Claim 8 (depends on 7) The method of claim 7, further comprising determining an operating point for the model, wherein determining the operating point comprises: acquiring a calibration dataset comprising individual-specific data for a pool of calibration individuals; selecting, based on a biomarker, a set of calibration individuals from the pool of calibration individuals; determining a reference sample size requirement and a reference screen failure rate for the set of calibration individuals; for each of a plurality of candidate operating points of the model: determining, using the model with a given candidate operating point, a given set of calibration individuals from the pool of calibration individuals that are to be enrolled based on a model-driven selection process, the given candidate operating point being a candidate classification threshold for the model for classifying a given calibration individual as being part of an enrolled class or a rejected class; and determining a given sample size requirement and a given screen failure rate for the given set of calibration individuals determined using the given candidate operating point; and selecting the operating point for the model amongst the plurality of candidate operating points based on a comparison between at least one of: the reference sample size requirement and respective sample size requirements of the plurality of candidate operating points; and the reference screen failure rate and respective screen failure rates of the plurality of candidate operating points. ### Claim 9 (depends on 8) The method of claim 8, further comprising: acquiring a trial dataset comprising individual-specific data for a pool of trial individuals; and determining, using the model and the operating point, a set of individuals to enroll in A clinical trial from the pool of trial individuals. ### Claim 10 A method for an enrollment selection process in a clinical trial, the method comprising: acquiring a trial dataset comprising individual-specific data for a pool of trial individuals; and determining, using a pre-trained model an enrollable set of trial individuals from the pool of trial individuals, wherein: the pre-trained model is a prognostic model that has been trained to forecast a likelihood of progression of a disease or a condition after a pre-determined interval; and the pre-trained model has a dynamic operating point. ### Claim 11 (depends on 10) The method of claim 10, wherein the pre-trained model is configured to reduce a sample size requirement of the enrollable set of trial individuals in comparison to a sample size requirement of the enrollable set of trial individuals when a solely biomarker-driven process is used to determine the enrollable set of trial individuals. ### Claim 12 (depends on 10) The method of claim 10, wherein the pre-trained model is configured to reduce a screen failure rate of the enrollable set of trial individuals in comparison to a screen failure rate of the enrollable set of trial individuals when a solely biomarker-driven process is used to determine the enrollable set of trial individuals. ### Claim 13 (depends on 10) The method of claim 10, wherein the dynamic operating point is a target operating point selected from a plurality of candidate operating points, the selection of the target operating point being based on at least one of (i) a sample size requirement for the clinical trial and (ii) a screen failure rate for the clinical trial. ### Claim 14 (depends on 10) The method of claim 10, wherein the method further comprises executing a calibration phase of the pre-trained model for determining a target operating point amongst a plurality of candidate operating points, and wherein executing the calibration phase comprises: acquiring a calibration dataset comprising individual-specific data for a pool of calibration individuals, the pool of calibration individuals matching requirements of the clinical trial; determining a reference set of calibration individuals from the pool of calibration individuals that are to be enrolled based on a biomarker-driven selection process applied onto the individual-specific data; determining a reference sample size requirement and a reference screen failure rate for the reference set of calibration individuals; for each of the plurality of candidate operating points of the pre-trained model: determining, using the pre-trained model with a given candidate operating point, a given set of calibration individuals from the pool of calibration individuals that are to be enrolled based on a model-driven selection process, the given candidate operating point being a candidate classification threshold for the pre-trained model for classifying a given calibration individual as being part of an enrolled class and a rejected class; and determining a given sample size requirement and a given screen failure rate for the given set of calibration individuals determined using the given candidate operating point; and determining the target operating point for the pre-trained model amongst the plurality of candidate operating points based on a comparison between at least one of: (i) the reference sample size requirement and respective sample size requirements of the plurality of candidate operating points; and (ii) the reference screen failure rate and respective screen failure rates of the plurality of candidate operating points. ### Claim 15 (depends on 14) The method of claim 14, wherein the target operating point is an upper target operating point determined based on a comparison of the reference sample size requirement and the respective sample size requirements of the plurality of candidate operating points, the upper target operating point for reducing a screen failure rate of an enrolled set of trial individuals for a same sample size requirement of the enrolled set of trial individuals if a biomarker-driven process has been used to determine the enrolled set of trial individuals. ### Claim 16 (depends on 14) The method of claim 14, wherein the target operating point is a lower target operating point determined based on a comparison of the reference screen failure rate and the respective screen failure rates of the plurality of candidate operating points, the lower target operating point for reducing a sample size requirement of an enrolled set of trial individuals for a same screen failure rate of the enrolled set of trial individuals if a biomarker-driven process has been used to determine the enrolled set of trial individuals. ### Claim 17 (depends on 14) The method of claim 14, wherein the target operating point is a range of target operating points determined based on both (i) the reference sample size requirement and the respective sample size requirements of the plurality of candidate operating points, and (ii) the reference screen failure rate and the respective screen failure rates of the plurality of candidate operating points, and wherein the method further comprises determining an enrolled set of trial individuals using the pre-trained model with an operating point within the range of target operating points. ### Claim 18 (depends on 14) The method of claim 14, wherein the individual-specific data comprises multimodal data. ### Claim 19 (depends on 14) The method of claim 14, wherein the individual-specific data comprises biomarker data. ### Claim 20 (depends on 10) The method of any one of claim 10, wherein the pre-trained model is at least one of: a Bayesian model, a support vector machine, a linear regression model, a random forest model, a deep learning model, an ensemble-based model.