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5. A Novel Multi-Biomarker Assay for Non-Invasive Quantitative Monitoring of Kidney Injury.

Author

Watson, Drew, Yang, Joshua YC, Sarwal, Reuben D, Sigdel, Tara K, Liberto, Juliane M, Damm, Izabella, Louie, Victoria, Sigdel, Shristi, Livingstone, Devon, Soh, Katherine, Chakraborty, Arjun, Liang, Michael, Lin, Pei-Chen, and Sarwal, Minnie M

Subjects
KIT Assay, biomarker, cfDNA, chronic kidney disease, eGFR, non-invasive, urine, Clinical Sciences
Abstract

The current standard of care measures for kidney function, proteinuria, and serum creatinine (SCr) are poor predictors of early-stage kidney disease. Measures that can detect chronic kidney disease in its earlier stages are needed to enable therapeutic intervention and reduce adverse outcomes of chronic kidney disease. We have developed the Kidney Injury Test (KIT) and a novel KIT Score based on the composite measurement and validation of multiple biomarkers across a unique set of 397 urine samples. The test is performed on urine samples that require no processing at the site of collection and without target sequencing or amplification. We sought to verify that the pre-defined KIT test, KIT Score, and clinical thresholds correlate with established chronic kidney disease (CKD) and may provide predictive information on early kidney injury status above and beyond proteinuria and renal function measurements alone. Statistical analyses across six DNA, protein, and metabolite markers were performed on a subset of residual spot urine samples with CKD that met assay performance quality controls from patients attending the clinical labs at the University of California, San Francisco (UCSF) as part of an ongoing IRB-approved prospective study. Inclusion criteria included selection of patients with confirmed CKD and normal healthy controls; exclusion criteria included incomplete or missing information for sample classification, logistical delays in transport/processing of urine samples or low sample volume, and acute kidney injury. Multivariate logistic regression of kidney injury status and likelihood ratio statistics were used to assess the contribution of the KIT Score for prediction of kidney injury status and stage of CKD as well as assess the potential contribution of the KIT Score for detection of early-stage CKD above and beyond traditional measures of renal function. Urine samples were processed by a proprietary immunoprobe for measuring cell-free DNA (cfDNA), methylated cfDNA, clusterin, CXCL10, total protein, and creatinine. The KIT Score and stratified KIT Score Risk Group (high versus low) had a sensitivity and specificity for detection of kidney injury status (healthy or CKD) of 97.3% (95% CI: 94.6-99.3%) and 94.1% (95% CI: 82.3-100%). In addition, in patients with normal renal function (estimated glomerular filtration rate (eGFR) ≥ 90), the KIT Score clearly identifies those with predisposing risk factors for CKD, which could not be detected by eGFR or proteinuria (p < 0.001). The KIT Score uncovers a burden of kidney injury that may yet be incompletely recognized, opening the door for earlier detection, intervention and preservation of renal function.

Published
2019

12. Potential Roles of Teamwork and Unmet Needs on Surgical Learning Curves of Spinal Robotic Screw Placement

Author

Su,Yu-Feng, Tsai,Tai-Hsin, Kuo,Keng-Liang, Wu,Chieh-Hsin, Tsai,Cheng-Yu, Lu,Yen-Mou, Hwang,Shiuh-Lin, Lin,Pei-Chen, Lieu,Ann-Shung, Lin,Chih-Lung, Chang,Chih-Hui, Su,Yu-Feng, Tsai,Tai-Hsin, Kuo,Keng-Liang, Wu,Chieh-Hsin, Tsai,Cheng-Yu, Lu,Yen-Mou, Hwang,Shiuh-Lin, Lin,Pei-Chen, Lieu,Ann-Shung, Lin,Chih-Lung, and Chang,Chih-Hui

Abstract

Yu-Feng Su,1– 3 Tai-Hsin Tsai,2– 4 Keng-Liang Kuo,2,4 Chieh-Hsin Wu,2,4 Cheng-Yu Tsai,1,2 Yen-Mou Lu,5 Shiuh-Lin Hwang,6 Pei-Chen Lin,7 Ann-Shung Lieu,2 Chih-Lung Lin,1,2,4 Chih-Hui Chang1,2,4 1Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; 2Department of Neurosurgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; 3Division of Neurosurgery, Department of Surgery, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan; 4Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; 5Department of Orthopedics, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; 6Department of Spinal Surgery, Chi-Hsien Spine Hospital, Kaohsiung, Taiwan; 7Department of Oral Hygiene, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, TaiwanCorrespondence: Chih-Hui Chang, Email chchang20@gmail.comBackground: The aim of this study was to investigate the learning curve of robotic spine surgery quantitatively with the well-described power law of practice.Methods: Kaohsiung Medical University Hospital set up a robotic spine surgery team by the neurosurgery department in 2013 and the orthopedic department joined the well-established team in 2014. A total of consecutive 150 cases received robotic assisted spinal surgery. The 150 cases, with 841 transpedicular screws were enrolled into 3 groups: the first 50 cases performed by neurosurgeons, the first 50 cases by orthopedic surgeons, and 50 cases by neurosurgeons after the orthopedic surgeons joined the team. The time per screw and accuracy by each group and individual surgeon were analyzed.Results: The time per screw for each group was 9.56 ± 4.19, 7.29 ± 3.64, and 8.74 ± 5.77 minutes, respectively, with p-value 0.0017. The accuracy was 99.6% (253/254), 99.5% (361/363), and 99.1% (222/224), respectively, with p-value 0.77. Though the first group took time significantly more on per screw placement but

Published
2022

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