公开(公告)号：US20220137069A1

公开(公告)日：2022-05-05

申请号：US17575202

申请日：2022-01-13

Applicant: Roche Diagnostics Operations, Inc.

Inventor： Aikaterini GEORGOPOULOU ,  Feliz GRUENEWALD ,  Martin HUND ,  Martin KLAMMER ,  Heike WEGMEYER

IPC: G01N33/68

Abstract: The present invention relates to methods of assessing whether a patient has endometriosis or is at risk of developing endometriosis, to methods of selecting a patient for therapy, and method of monitoring a patient suffering from endometriosis or being treated for endometriosis by determining the amount or concentration of S100A12 in a sample of the patient, and comparing the determined amount or concentration to a reference.

公开(公告)号：US20250099000A1

公开(公告)日：2025-03-27

申请号：US18728195

申请日：2023-01-24

Applicant: Roche Diagnostics Operations, Inc.

Inventor： Carsten DANZER ,  Martin Josef EMONS ,  Martin KLAMMER ,  Nicolas Seungoon SILLITOE ,  Riccardo TRIUNFO

IPC: A61B5/20 ,  A61B5/00 ,  G16H10/60 ,  G16H50/20

Abstract: A computer-implemented method is provided, which determines at a prediction time tp, a likelihood of kidney failure of a patient within an amount of time Δt. The method comprises receiving input data, the input data comprising a recent creatinine level cR or recent eGFR eGFRR, and one or more of the following: (a) an initial creatinine level c0 and either: a time t0 at which the initial creatinine level c0 was measured, or a time interval ΔT0=tp−t0; (b) an initial estimated glomerular filtration rate (eGFR) eGFR0 and either: a time t0 at which the initial eGFR was determined, or a time interval ΔT0=tp−t0; (c) for a plurality of past creatinine level measurements ci measured at a respective times ti, a statistical parameter derived from a linear regression of the plurality of past creatinine level measurements; and (d) for a plurality of past eGFR values eGFRi determined at respective times ti, a statistical parameter derived from a linear regression of the plurality of past eGFR values; and applying a machine-learning model to the input data to generate an output indicating the likelihood of kidney failure within the given amount of time Δt. Corresponding training methods and systems are also provided.