Performance of StatSensor Point-of-Care Device for Measuring Creatinine in Patients With Chronic Kidney Disease and Postkidney Transplantation

Nataatmadja, Melissa; Fung, Angela W.S.; Jacobson, Beryl; Ferera, Jack; Bernstein, Eva; Komenda, Paul; Mattman, André; Seccombe, David W.; Levin, Adeera

doi:10.1177/2054358120970716

Cited by 8 publications

(1 citation statement)

References 23 publications

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“…Usually, this diagnostic procedure is carried out during a planned hospital visit once every three months; the urea-based kt/V dialysis index characterizing the dose of the dialysis is calculated and, if necessary, a peritoneal equilibration test is conducted [34]. To provide a faster and more regular determination of the dose for dialysis, POCT devices for the rapid measurement of urea and creatinine plasma levels in patients on dialysis are also actively being developed [35,36].…”

Section: Introductionmentioning

confidence: 99%

Optical Chemical Sensor Based on Fast-Protein Liquid Chromatography for Regular Peritoneal Protein Loss Assessment in End-Stage Renal Disease Patients on Continuous Ambulatory Peritoneal Dialysis

Kuznetsov¹,

Frorip²,

Sünter³

et al. 2022

Chemosensors

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Point-of-care testing (POCT) devices are becoming increasingly popular in the medical community as an alternative to conventional laboratory testing, especially for home treatments or other forms of outpatient care. Multiple-use chemical sensors with minimal requirements for disposables are among the most practical and cost-effective POC diagnostic instruments, especially in managing chronic conditions. An affordable, simple, and easy-to-use optical sensor based on fast protein liquid chromatography with direct UV absorption detection was developed for the rapid determination of the total protein concentration in effluent peritoneal dialysate and for the assessment of protein losses in end-stage renal disease (ESRD) patients on constant ambulatory peritoneal dialysis (CAPD). The sensor employs non-disposable PD-10 desalting columns for the separation of molecules with different molecular weights and a deep UV LED (maximum at 285 nm) as a light source for optical detection. The analytic procedure is relatively simple, takes 10–15 min, and potentially can be performed by patients themselves or nursing staff without laboratory training. Preliminary clinical trials on a group of 23 patients on CAPD revealed a good concordance between the protein concentrations in dialysate samples measured with the sensor and an automated biochemical analyzer; the mean relative error was about 10%, which is comparable with routine clinical laboratory methods.

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Section: Introductionmentioning

confidence: 99%

Optical Chemical Sensor Based on Fast-Protein Liquid Chromatography for Regular Peritoneal Protein Loss Assessment in End-Stage Renal Disease Patients on Continuous Ambulatory Peritoneal Dialysis

Kuznetsov¹,

Frorip²,

Sünter³

et al. 2022

Chemosensors

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Selective Partitioned Regression for Accurate Kidney Health Monitoring

Whelan,

Elsayed,

Bellofiore

et al. 2024

Ann Biomed Eng

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The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings.

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Development and evaluation of an electrochemical biosensor for creatinine quantification in a drop of whole human blood

Menéndez¹,

Amor-Gutiérrez²,

Garcı́a

et al. 2023

Clinica Chimica Acta

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Performance of StatSensor Point-of-Care Device for Measuring Creatinine in Patients With Chronic Kidney Disease and Postkidney Transplantation

Cited by 8 publications

References 23 publications

Optical Chemical Sensor Based on Fast-Protein Liquid Chromatography for Regular Peritoneal Protein Loss Assessment in End-Stage Renal Disease Patients on Continuous Ambulatory Peritoneal Dialysis

Optical Chemical Sensor Based on Fast-Protein Liquid Chromatography for Regular Peritoneal Protein Loss Assessment in End-Stage Renal Disease Patients on Continuous Ambulatory Peritoneal Dialysis

Selective Partitioned Regression for Accurate Kidney Health Monitoring

Development and evaluation of an electrochemical biosensor for creatinine quantification in a drop of whole human blood

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