HEART FAILURE RISK MONITORING AND HEMOGLOBIN LEVEL PREDICTION FOR HEMODIALYSIS SYSTEMS

Abstract

Disclosed is a system comprising a dialysis machine for performing a dialysis treatment in a patient, and a computer system in communication with the dialysis machine. The computer system uses a trained ML model to generates a result that indicates the risk of the patient for developing heart failure during the dialysis treatment, and shows a warning sign on a display if the result indicates that the patient is at risk of heart failure.

Description

Claims

1. A system, comprising: a dialysis machine for performing a dialysis treatment in a patient; anda computer system in communication with the dialysis machine, the computer system comprising: one or more processors; anda computer readable medium in communication with the one or more processors, the computer readable medium storing instructions that, when executed by the one or more processors, cause the computer system to perform: inputting a plurality of first features and a plurality of second features of the patient to a first trained machine learning (ML) model,wherein the plurality of first features is obtained during the dialysis treatment and the plurality of second features is obtained before the dialysis treatment,wherein the plurality of first features includes a total ultrafiltration volume value and a total ultrafiltration time value received from the dialysis machine, andwherein the plurality of second features includes a predictive value of pulmonary edema based on chest radiographic images, a Charlson comorbidity index value, a value of serum albumin level, a value of mean body surface area, a value of blood potassium level, and a value of predictive dry weight;generating, using the first trained ML model, a result that indicates the risk of the patient for developing heart failure during the dialysis treatment; andif the result indicates that the patient is at risk of heart failure, showing a warning sign on a display.

2. The system of claim 1, wherein the predictive value of pulmonary edema is generated using a second trained ML model with chest radiographic images of the patient as an input.

3. The system of claim 1, further comprising a time-series database which is connected to the computer system and stores real-time streaming intradialysis data received from the dialysis machine.

4. The system of claim 3, wherein at least part of the real-time streaming intradialysis data is shown on the display.

5. The system of claim 1, wherein the first trained ML model is trained using the following data: intradialysis data including arterial blood flow rate, effective blood flow rate, processed blood volume, dialysate flow rate, dialysate sodium level, dialysate sodium profile, dialysate temperature, dialysate bicarbonate level, dialysate conductivity, heparin volume, heparin bolus dose, heparin delivery rate, ultrafiltration rate, ultrafiltration volume, ultrafiltration time, arterial pressure, venous pressure, and transmembrane pressure, andpredialysis data including demographic data, underlying comorbidities, containment medications and laboratory data.

6. The system of claim 1, wherein the value of predictive dry weight using a third trained ML model.

7. The system of claim 6, wherein the third trained ML model is trained using the following data: intradialysis data including arterial blood flow rate, effective blood flow rate, processed blood volume, dialysate flow rate, dialysate sodium level, dialysate sodium profile, dialysate temperature, dialysate bicarbonate level, dialysate conductivity, heparin volume, heparin bolus dose, heparin delivery rate, ultrafiltration rate, ultrafiltration volume, ultrafiltration time, arterial pressure, venous pressure, and transmembrane pressure, andpredialysis data including demographic data, underlying comorbidities, containment medications and laboratory data.

8. One or more computer readable memories storing information to enable a computing device to perform a process comprising: inputting a plurality of first features and a plurality of second features of the patient to a first trained machine learning (ML) model, wherein the plurality of first features is obtained during the dialysis treatment and the plurality of second features is obtained before the dialysis treatment,wherein the plurality of first features includes a total ultrafiltration volume value and a total ultrafiltration time value received from the dialysis machine, andwherein the plurality of second features includes a predictive value of pulmonary edema based on chest radiographic images, a Charlson comorbidity index value, a value of serum albumin level, a value of mean body surface area, a value of blood potassium level, and a value of predictive dry weight;generating, using the first trained ML model, a result that indicates the risk of the patient for developing heart failure during the dialysis treatment; andif the result indicates that the patient is at risk of heart failure, showing a warning sign on a display.

9. The computer readable memories of claim 8, wherein the first trained ML model is trained using the following data: intradialysis data including arterial blood flow rate, effective blood flow rate, processed blood volume, dialysate flow rate, dialysate sodium level, dialysate sodium profile, dialysate temperature, dialysate bicarbonate level, dialysate conductivity, heparin volume, heparin bolus dose, heparin delivery rate, ultrafiltration rate, ultrafiltration volume, ultrafiltration time, arterial pressure, venous pressure, and transmembrane pressure, andpredialysis data including demographic data, underlying comorbidities, containment medications and laboratory data.

10. The computer readable memories of claim 8, wherein the predictive value of pulmonary edema is generated using a second trained ML model with chest radiographic images of the patient as an input.

11. A system, comprising: a dialysis machine for performing a dialysis treatment in a patient; anda computer system in communication with the dialysis machine, the computer system comprising: one or more processors; anda computer readable medium in communication with the one or more processors, the computer readable medium storing instructions that, when executed by the one or more processors, cause the computer system to perform: inputting a plurality of first features and a plurality of second features of the patient to a first trained machine learning (ML) model,wherein the plurality of first features is obtained during the dialysis treatment and the plurality of second features is obtained before the dialysis treatment,wherein the plurality of first features includes arterial blood flow rate, effective blood flow rate, processed blood volume, dialysate flow rate, dialysate sodium level, dialysate sodium profile, dialysate temperature, dialysate bicarbonate level, dialysate conductivity, heparin volume, heparin bolus dose, heparin delivery rate, ultrafiltration rate, ultrafiltration volume, ultrafiltration time, arterial pressure, venous pressure, transmembrane pressure, andwherein the plurality of second features includes serum iron level, total iron binding capacity (TIBC), and transferrin saturation percentage (TSAT);generating, using the first trained ML model, a predicted hemoglobin level of the patient during the dialysis treatment; andshowing the predicted hemoglobin level on a display.

12. The system of claim 11, further comprising a time-series database which is connected to the computer system and stores real-time streaming intradialysis data received from the dialysis machine.

13. The system of claim 12, wherein at least part of the real-time streaming intradialysis data is shown on the display.

14. The system of claim 11, wherein the first trained ML model is trained using the following data: intradialysis data including arterial blood flow rate, effective blood flow rate, processed blood volume, dialysate flow rate, dialysate sodium level, dialysate sodium profile, dialysate temperature, dialysate bicarbonate level, dialysate conductivity, heparin volume, heparin bolus dose, heparin delivery rate, ultrafiltration rate, ultrafiltration volume, ultrafiltration time, arterial pressure, venous pressure, and transmembrane pressure, andpredialysis data including demographic data, underlying comorbidities, containment medications and laboratory data.