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918 results on '"Kane‐Gill, Sandra L."'

1. Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study

Author

Adiyeke, Esra, Ren, Yuanfang, Shickel, Benjamin, Ruppert, Matthew M., Guan, Ziyuan, Kane-Gill, Sandra L., Murugan, Raghavan, Amatullah, Nabihah, Stottlemyer, Britney A., Tran, Tiffany L., Ricketts, Dan, Horvat, Christopher M, Rashidi, Parisa, Bihorac, Azra, and Ozrazgat-Baslanti, Tezcan

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney damage. Methods: This retrospective cohort study includes adult patients admitted to a non-intensive care unit at the University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of Florida Health (UFH) (n = 127,202). We developed and compared deep learning and conventional machine learning models to predict progression to Stage 2 or higher AKI within the next 48 hours. We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model). We internally and externally validated the models on each site and performed subgroup analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3% (n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under the receiver operating curve values (AUROC) for the UFH test cohort ranged between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80, 0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06]) for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen remained the top three features with the highest influence across the models and health centers. Conclusion: Locally developed models displayed marginally reduced discrimination when tested on another institution, while the top set of influencing features remained the same across the models and sites.

Published
2024

5. Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions

Author

Taneja, Sanya B., Callahan, Tiffany J., Paine, Mary F., Kane-Gill, Sandra L., Kilicoglu, Halil, Joachimiak, Marcin P., and Boyce, Richard D.

Subjects
Computer Science - Artificial Intelligence, Quantitative Biology - Quantitative Methods
Abstract

Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical natural products are co-consumed with pharmaceutical drugs. Understanding mechanisms of NPDIs is key to preventing adverse events. We constructed a knowledge graph framework, NP-KG, as a step toward computational discovery of pharmacokinetic NPDIs. NP-KG is a heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature, constructed with the Phenotype Knowledge Translator framework and the semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through path searches and meta-path discovery to determine congruent and contradictory information compared to ground truth data. The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature. NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify pharmacokinetic interactions involving enzymes, transporters, and pharmaceutical drugs. We envision that NP-KG will facilitate improved human-machine collaboration to guide researchers in future studies of pharmacokinetic NPDIs. The NP-KG framework is publicly available at https://doi.org/10.5281/zenodo.6814507 and https://github.com/sanyabt/np-kg.

Published
2022
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7. Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup

Author

Kashani, Kianoush B., Awdishu, Linda, Bagshaw, Sean M., Barreto, Erin F., Claure-Del Granado, Rolando, Evans, Barbara J., Forni, Lui G., Ghosh, Erina, Goldstein, Stuart L., Kane-Gill, Sandra L., Koola, Jejo, Koyner, Jay L., Liu, Mei, Murugan, Raghavan, Nadkarni, Girish N., Neyra, Javier A., Ninan, Jacob, Ostermann, Marlies, Pannu, Neesh, Rashidi, Parisa, Ronco, Claudio, Rosner, Mitchell H., Selby, Nicholas M., Shickel, Benjamin, Singh, Karandeep, Soranno, Danielle E., Sutherland, Scott M., Bihorac, Azra, and Mehta, Ravindra L.

Published
2023
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8. Acute kidney injury in patients with cirrhosis: Acute Disease Quality Initiative (ADQI) and International Club of Ascites (ICA) joint multidisciplinary consensus meeting

Author

Nadim, Mitra K., Kellum, John A., Forni, Lui, Francoz, Claire, Asrani, Sumeet K., Ostermann, Marlies, Allegretti, Andrew S., Neyra, Javier A., Olson, Jody C., Piano, Salvatore, VanWagner, Lisa B., Verna, Elizabeth C., Akcan-Arikan, Ayse, Angeli, Paolo, Belcher, Justin M., Biggins, Scott W., Deep, Akash, Garcia-Tsao, Guadalupe, Genyk, Yuri S., Gines, Pere, Kamath, Patrick S., Kane-Gill, Sandra L., Kaushik, Manish, Lumlertgul, Nuttha, Macedo, Etienne, Maiwall, Rakhi, Marciano, Sebastian, Pichler, Raimund H., Ronco, Claudio, Tandon, Puneeta, Velez, Juan-Carlos Q., Mehta, Ravindra L., and Durand, François

Published
2024
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11. Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup

Author

Zarbock, Alexander, Nadim, Mitra K., Pickkers, Peter, Gomez, Hernando, Bell, Samira, Joannidis, Michael, Kashani, Kianoush, Koyner, Jay L., Pannu, Neesh, Meersch, Melanie, Reis, Thiago, Rimmelé, Thomas, Bagshaw, Sean M., Bellomo, Rinaldo, Cantaluppi, Vicenzo, Deep, Akash, De Rosa, Silvia, Perez-Fernandez, Xose, Husain-Syed, Faeq, Kane-Gill, Sandra L., Kelly, Yvelynne, Mehta, Ravindra L., Murray, Patrick T., Ostermann, Marlies, Prowle, John, Ricci, Zaccaria, See, Emily J., Schneider, Antoine, Soranno, Danielle E., Tolwani, Ashita, Villa, Gianluca, Ronco, Claudio, and Forni, Lui G.

Published
2023
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14. Controversies in acute kidney injury: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference

Author

Ostermann, Marlies, Bellomo, Rinaldo, Burdmann, Emmanuel A, Doi, Kent, Endre, Zoltan H, Goldstein, Stuart L, Kane-Gill, Sandra L, Liu, Kathleen D, Prowle, John R, Shaw, Andrew D, Srisawat, Nattachai, Cheung, Michael, Jadoul, Michel, Winkelmayer, Wolfgang C, Kellum, John A, Participants, Conference, Bagshaw, Sean M, Barreto, Erin F, Bihorac, Azra, Bobek, Ilona, Bouchard, Josée, Cerdá, Jorge, Chakravarthi, Rajasekara, De Rosa, Silvia, Engelman, Daniel T, Forni, Lui G, Hemmilä, Ulla K, Herzog, Charles A, Hoste, Eric A, Huen, Sarah C, Iseki, Kunitoshi, Joannidis, Michael, Kashani, Kianoush B, Koyner, Jay L, Kribben, Andreas, Lameire, Norbert, Levey, Andrew S, Macedo, Etienne, Małyszko, Jolanta, Meersch, Melanie, Mehta, Ravindra L, Mewburn, Irene, Mironova, Olga, Murray, Patrick T, Nadim, Mitra K, Pan, Jenny S, Pannu, Neesh, Peng, Zhiyong, Philips, Barbara, Ponce, Daniela, Ray, Patricio E, Ricci, Zaccaria, Rimmelé, Thomas, Ronco, Claudio, Siew, Edward D, Stevens, Paul E, Tolwani, Ashita J, Tonelli, Marcello, Vaara, Suvi T, van Dam, Marjel, Vijayan, Anitha, Wise, Michael, Wu, Vin-Cent, and Zarbock, Alexander

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Kidney Disease, 7.3 Management and decision making, Management of diseases and conditions, Renal and urogenital, Acute Kidney Injury, Humans, acute kidney disease, acute kidney injury, fluid management, nephrotoxicity, renal replacement therapy, risk stratification, Conference Participants, Urology & Nephrology, Clinical sciences
Abstract

In 2012, Kidney Disease: Improving Global Outcomes (KDIGO) published a guideline on the classification and management of acute kidney injury (AKI). The guideline was derived from evidence available through February 2011. Since then, new evidence has emerged that has important implications for clinical practice in diagnosing and managing AKI. In April of 2019, KDIGO held a controversies conference entitled Acute Kidney Injury with the following goals: determine best practices and areas of uncertainty in treating AKI; review key relevant literature published since the 2012 KDIGO AKI guideline; address ongoing controversial issues; identify new topics or issues to be revisited for the next iteration of the KDIGO AKI guideline; and outline research needed to improve AKI management. Here, we present the findings of this conference and describe key areas that future guidelines may address.

Published
2020

18. Geographic and Temporal Trends in COVID-Associated Acute Kidney Injury in the National COVID Cohort Collaborative

Author

Yoo, Yun J., Wilkins, Kenneth J., Alakwaa, Fadhl, Liu, Feifan, Torre-Healy, Luke A., Krichevsky, Spencer, Hong, Stephanie S., Sakhuja, Ankit, Potu, Chetan K., Saltz, Joel H., Saran, Rajiv, Zhu, Richard L., Setoguchi, Soko, Kane-Gill, Sandra L., Mallipattu, Sandeep K., He, Yongqun, Ellison, David H., Byrd, James B., Parikh, Chirag R., Moffitt, Richard A., and Koraishy, Farrukh M.

Published
2023
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28. Acute kidney disease and renal recovery: consensus report of the Acute Disease Quality Initiative (ADQI) 16 Workgroup.

Author

Chawla, Lakhmir S, Bellomo, Rinaldo, Bihorac, Azra, Goldstein, Stuart L, Siew, Edward D, Bagshaw, Sean M, Bittleman, David, Cruz, Dinna, Endre, Zoltan, Fitzgerald, Robert L, Forni, Lui, Kane-Gill, Sandra L, Hoste, Eric, Koyner, Jay, Liu, Kathleen D, Macedo, Etienne, Mehta, Ravindra, Murray, Patrick, Nadim, Mitra, Ostermann, Marlies, Palevsky, Paul M, Pannu, Neesh, Rosner, Mitchell, Wald, Ron, Zarbock, Alexander, Ronco, Claudio, Kellum, John A, and Acute Disease Quality Initiative Workgroup 16.

Subjects
Acute Disease Quality Initiative Workgroup 16., Humans, Remission Induction, Renal Insufficiency, Chronic, Acute Kidney Injury, Kidney Disease, 2.1 Biological and endogenous factors, Aetiology, Renal and urogenital, Clinical Sciences, Urology & Nephrology
Abstract

Consensus definitions have been reached for both acute kidney injury (AKI) and chronic kidney disease (CKD) and these definitions are now routinely used in research and clinical practice. The KDIGO guideline defines AKI as an abrupt decrease in kidney function occurring over 7 days or less, whereas CKD is defined by the persistence of kidney disease for a period of >90 days. AKI and CKD are increasingly recognized as related entities and in some instances probably represent a continuum of the disease process. For patients in whom pathophysiologic processes are ongoing, the term acute kidney disease (AKD) has been proposed to define the course of disease after AKI; however, definitions of AKD and strategies for the management of patients with AKD are not currently available. In this consensus statement, the Acute Disease Quality Initiative (ADQI) proposes definitions, staging criteria for AKD, and strategies for the management of affected patients. We also make recommendations for areas of future research, which aim to improve understanding of the underlying processes and improve outcomes for patients with AKD.

Published
2017

29. Contemporary considerations for nephrotoxin stewardship: Estimating kidney function and use of novel biomarkers.

Author

Kane‐Gill, Sandra L., Amatullah, Nabihah, Tran, Tiffany, and Karimzadeh, Iman

Subjects
ACUTE kidney failure, MEDICAL personnel, CHRONIC kidney failure, KIDNEY physiology, CYSTATIN C
Abstract

Drug‐associated acute kidney injury (D‐AKI) constitutes approximately 19%–26% of all acute kidney injury (AKI) cases in hospitalized patients. Notably, there is often a deficiency in optimal drug management after D‐AKI diagnosis, with only about half of the patients experiencing discontinuation of nephrotoxins and one‐fifth avoiding further nephrotoxin administration. Nephrotoxin stewardship involves the application of coordinated care management strategies to ensure the safe utilization of nephrotoxins and renally eliminated medications. The purpose of this paper is to review nephrotoxin stewardship strategies and highlight contemporary considerations for estimating kidney function and the use of novel biomarkers as part of the stewardship program. The Chronic Kidney Disease Epidemiology Collaboration (CKDEPI) 2021 serum creatinine formula is now recommended for estimating kidney function for adults in most clinical situations in the United States. It is time to reevaluate how healthcare professionals think of estimating kidney function for drug dosing with the use of the non‐indexed, race‐free, CKDEPI 2021 equation. In addition, widespread use of serum cystatin C (CysC) is anticipated so estimated glomerular filtration rate equations that incorporate serum CysC will need to be further evaluated for drug dosing recommendations and how the use of serum CysC can complement serum creatinine‐based equations. Lastly, novel kidney stress/damage biomarkers should be viewed as a piece of a complex scenario of D‐AKI prediction, diagnosis, and prognosis and should be interpreted along with other diagnostic criteria in relation to the clinical condition of the patient. These contemporary considerations for drug dosing and D‐AKI management are important components of nephrotoxin stewardship. [ABSTRACT FROM AUTHOR]

Published
2024
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30. Controversies in acute kidney injury: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference

Author

Bagshaw, Sean M., Barreto, Erin F., Bihorac, Azra, Bobek, Ilona, Bouchard, Josée, Cerdá, Jorge, Chakravarthi, Rajasekara, De Rosa, Silvia, Engelman, Daniel T., Forni, Lui G., Hemmilä, Ulla K., Herzog, Charles A., Hoste, Eric A., Huen, Sarah C., Iseki, Kunitoshi, Joannidis, Michael, Kashani, Kianoush B., Koyner, Jay L., Kribben, Andreas, Lameire, Norbert, Levey, Andrew S., Macedo, Etienne, Małyszko, Jolanta, Meersch, Melanie, Mehta, Ravindra L., Mewburn, Irene, Mironova, Olga, Murray, Patrick T., Nadim, Mitra K., Pan, Jenny S., Pannu, Neesh, Peng, Zhiyong, Philips, Barbara, Ponce, Daniela, Ray, Patricio E., Ricci, Zaccaria, Rimmelé, Thomas, Ronco, Claudio, Siew, Edward D., Stevens, Paul E., Tolwani, Ashita J., Tonelli, Marcello, Vaara, Suvi T., van Dam, Marjel, Vijayan, Anitha, Wise, Michael, Wu, Vin-Cent, Zarbock, Alexander, Ostermann, Marlies, Bellomo, Rinaldo, Burdmann, Emmanuel A., Doi, Kent, Endre, Zoltan H., Goldstein, Stuart L., Kane-Gill, Sandra L., Liu, Kathleen D., Prowle, John R., Shaw, Andrew D., Srisawat, Nattachai, Cheung, Michael, Jadoul, Michel, Winkelmayer, Wolfgang C., and Kellum, John A.

Published
2020
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32. Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference.

Author

Sutherland, Scott M, Chawla, Lakhmir S, Kane-Gill, Sandra L, Hsu, Raymond K, Kramer, Andrew A, Goldstein, Stuart L, Kellum, John A, Ronco, Claudio, Bagshaw, Sean M, and 15 ADQI Consensus Group

Subjects
ADQI Consensus Group, Acute kidney injury, Big data, Electronic health record, Electronic medical record, Prediction, Predictive analytics, Kidney Disease, Prevention, Networking and Information Technology R&D, Renal and urogenital
Abstract

The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.

Published
2016

44. A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping

Author

Camacho, Jhon, Zanoletti-Mannello, Manuela, Landis-Lewis, Zach, Kane-Gill, Sandra L, and Boyce, Richard D

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe implementation of clinical decision support systems (CDSSs) as an intervention to foster clinical practice change is affected by many factors. Key factors include those associated with behavioral change and those associated with technology acceptance. However, the literature regarding these subjects is fragmented and originates from two traditionally separate disciplines: implementation science and technology acceptance. ObjectiveOur objective is to propose an integrated framework that bridges the gap between the behavioral change and technology acceptance aspects of the implementation of CDSSs. MethodsWe employed an iterative process to map constructs from four contributing frameworks—the Theoretical Domains Framework (TDF); the Consolidated Framework for Implementation Research (CFIR); the Human, Organization, and Technology-fit framework (HOT-fit); and the Unified Theory of Acceptance and Use of Technology (UTAUT)—and the findings of 10 literature reviews, identified through a systematic review of reviews approach. ResultsThe resulting framework comprises 22 domains: agreement with the decision algorithm; attitudes; behavioral regulation; beliefs about capabilities; beliefs about consequences; contingencies; demographic characteristics; effort expectancy; emotions; environmental context and resources; goals; intentions; intervention characteristics; knowledge; memory, attention, and decision processes; patient–health professional relationship; patient’s preferences; performance expectancy; role and identity; skills, ability, and competence; social influences; and system quality. We demonstrate the use of the framework providing examples from two research projects. ConclusionsWe proposed BEAR (BEhavior and Acceptance fRamework), an integrated framework that bridges the gap between behavioral change and technology acceptance, thereby widening the view established by current models.

Published
2020
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49. Perceived Motivating Factors and Barriers for the Completion of Postgraduate Training Among American Pharmacy Students Prior to Beginning Advanced Pharmacy Practice Experiences

Author

Hammond, Drayton A., Oyler, Douglas R., Devlin, John W., Painter, Jacob T., Bolesta, Scott, Swanson, Joseph M., Bailey, Brett J., Branan, Trisha, Barletta, Jeffrey F., Dunn, Brianne, Haney, Jason S., Juang, Paul, Kane-Gill, Sandra L., Kiser, Tyree H., Shafeeq, Hira, Skaar, Debra, Smithburger, Pamela, and Taylor, Jodi

Published
2017
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50. Vancomycin With Concomitant Piperacillin/Tazobactam vs. Cefepime or Meropenem Associated Acute Kidney Injury in General Ward Patients: A Multicenter Propensity Score-Matched Study.

Author

Komerdelj, Ivan A., Buckley, Mitchell S., D'Alessio, Paul A., Ziadat, Delia S., Kobic, Emir, Rangan, Pooja, Agarwal, Sumit K., Tinta, Nicole C., Yerondopoulos, Melanie J., and Kane-Gill, Sandra L.

Subjects
ANTIBIOTICS, MEROPENEM, RESEARCH, COMBINATION drug therapy, BETA lactam antibiotics, HOSPITAL patients, VANCOMYCIN, RETROSPECTIVE studies, DISEASE incidence, CEFEPIME, RISK assessment, COMPARATIVE studies, HOSPITAL wards, ROOMS, DESCRIPTIVE statistics, ACUTE kidney failure, LONGITUDINAL method, PROBABILITY theory
Abstract

Background: Concurrent administration of vancomycin and piperacillin/tazobactam (VAN+PTZ) may increase the risk of acute kidney injury (AKI) in hospitalized patients. Comprehensive characterization of VAN+PTZ associated AKI and recovery patterns remains lacking in previous reports. Objective: To compare the incidence of AKI associated with VAN+PTZ compared to either cefepime (CEF) or meropenem (MER) with VAN in adult general ward patients. Methods: A multicenter, retrospective, propensity score cohort study was conducted in non-critically ill adult patients. Included patients were concurrently administered VAN+PTZ or VAN+CEF/MER. Patients developing AKI ≤48 hours following combination therapy were excluded. The primary endpoint was to compare the incidence of AKI between study groups. Multivariable Cox regression modeling in predicting AKI was also conducted. Results: A total of 3199 patients met inclusion criteria and were evaluated. The incidence of AKI in VAN+PTZ and VAN+CEF/MER groups were 16.4% and 8.7%, respectively (P <.001). The onset to AKI was 1.8 days earlier with VAN+PTZ compared to VAN+CEF/MER (P <.001). Multivariable prediction model showed concomitant VAN+PTZ was identified as an independent risk factor of developing AKI (HR 2.34, 1.82-3.01, P <.001). The VAN+PTZ group experienced significantly higher rates of severe AKI (stage II or III) compared to the VAN+CEF/MER group (P =.002). No differences in the AKI recovery patterns were found between study groups. Conclusions: Concomitant VAN+PTZ in adult general ward patients was independently associated with an increased risk of AKI overall. More severe AKI was also associated with VAN+PTZ. [ABSTRACT FROM AUTHOR]

Published
2024
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