Steven M Handler1, Pui Wen Cheung2, Colleen M Culley3, Subashan Perera4, Sandra L Kane-Gill5, John A Kellum6, Zachary A Marcum7. 1. Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA; Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA; Geriatric Pharmaceutical Outcomes and Geroinformatics Research & Training Program, University of Pittsburgh, Pittsburgh, PA; Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA; Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA. Electronic address: handler@pitt.edu. 2. Department of Medicine, University of Pittsburgh, Pittsburgh, PA. 3. Geriatric Pharmaceutical Outcomes and Geroinformatics Research & Training Program, University of Pittsburgh, Pittsburgh, PA; Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA. 4. Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA; Geriatric Pharmaceutical Outcomes and Geroinformatics Research & Training Program, University of Pittsburgh, Pittsburgh, PA. 5. Geriatric Pharmaceutical Outcomes and Geroinformatics Research & Training Program, University of Pittsburgh, Pittsburgh, PA; Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA; Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA; Department of Pharmacy and Therapeutics, Biomedical Informatics and Critical Care Medicine, School of Pharmacy and Medicine, University of Pittsburgh, Pittsburgh, PA. 6. Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA. 7. Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA; Geriatric Pharmaceutical Outcomes and Geroinformatics Research & Training Program, University of Pittsburgh, Pittsburgh, PA; Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA.
Abstract
OBJECTIVE: Although acute kidney injury (AKI) is well studied in the acute care setting, investigation of AKI in the nursing home (NH) setting is virtually nonexistent. The goal of this study was to determine the incidence of drug-associated AKI using the RIFLE (Risk, Injury, Failure, Loss of kidney function, or End-Stage kidney disease) criteria in NH residents. DESIGN/SETTING/PARTICIPANTS/MEASUREMENTS: We conducted a retrospective study between February 9, 2012, and February 8, 2013, for all residents at 4 UPMC NHs located in southwest Pennsylvania. The TheraDoc™ Clinical Surveillance Software System, which monitors laboratory and medication data and fires alerts when patients have a sufficient increase in serum creatinine, was used for automated case detection. An increase in serum creatinine in the presence of an active medication order identified to potentially cause AKI triggered an alert, and drug-associated AKI was staged according to the RIFLE criteria. Data were analyzed by frequency and distribution of alert type by risk, injury, and failure. RESULTS: Of the 249 residents who had a drug-associated AKI alert fire, 170 (68.3%) were women, and the mean age was 74.2 years. Using the total number of alerts (n = 668), the rate of drug-associated AKI was 0.41 events per 100 resident-days. Based on the RIFLE criteria, there were 191, 70, and 44 residents who were classified as AKI risk, injury, and failure, respectively. The most common medication classes included in the AKI alerts were diuretics, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers (ACEIs/ARBs), and antibiotics. CONCLUSION: Drug-associated AKI was a common cause of potential adverse drug events. The vast majority of cases were related to the use of diuretics, ACEIs/ARBs, and antibiotics. Future studies are needed to better understand patient, provider, and facility risk factors, as well as strategies to enhance the detection and management of drug-associated AKI in the NH.
OBJECTIVE: Although acute kidney injury (AKI) is well studied in the acute care setting, investigation of AKI in the nursing home (NH) setting is virtually nonexistent. The goal of this study was to determine the incidence of drug-associated AKI using the RIFLE (Risk, Injury, Failure, Loss of kidney function, or End-Stage kidney disease) criteria in NH residents. DESIGN/SETTING/PARTICIPANTS/MEASUREMENTS: We conducted a retrospective study between February 9, 2012, and February 8, 2013, for all residents at 4 UPMC NHs located in southwest Pennsylvania. The TheraDoc™ Clinical Surveillance Software System, which monitors laboratory and medication data and fires alerts when patients have a sufficient increase in serum creatinine, was used for automated case detection. An increase in serum creatinine in the presence of an active medication order identified to potentially cause AKI triggered an alert, and drug-associated AKI was staged according to the RIFLE criteria. Data were analyzed by frequency and distribution of alert type by risk, injury, and failure. RESULTS: Of the 249 residents who had a drug-associated AKI alert fire, 170 (68.3%) were women, and the mean age was 74.2 years. Using the total number of alerts (n = 668), the rate of drug-associated AKI was 0.41 events per 100 resident-days. Based on the RIFLE criteria, there were 191, 70, and 44 residents who were classified as AKI risk, injury, and failure, respectively. The most common medication classes included in the AKI alerts were diuretics, angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers (ACEIs/ARBs), and antibiotics. CONCLUSION: Drug-associated AKI was a common cause of potential adverse drug events. The vast majority of cases were related to the use of diuretics, ACEIs/ARBs, and antibiotics. Future studies are needed to better understand patient, provider, and facility risk factors, as well as strategies to enhance the detection and management of drug-associated AKI in the NH.
Authors: Tariq Ali; Izhar Khan; William Simpson; Gordon Prescott; John Townend; William Smith; Alison Macleod Journal: J Am Soc Nephrol Date: 2007-02-21 Impact factor: 10.121
Authors: Jens-Ulrik Stæhr Jensen; Lars Hein; Bettina Lundgren; Morten Heiberg Bestle; Thomas Mohr; Mads Holmen Andersen; Klaus Julius Thornberg; Jesper Løken; Morten Steensen; Zoë Fox; Hamid Tousi; Peter Søe-Jensen; Anne Øberg Lauritsen; Ditte Gry Strange; Nanna Reiter; Katrin Thormar; Paul Christian Fjeldborg; Kim Michael Larsen; Niels-Erik Drenck; Maria Egede Johansen; Lene Ryom Nielsen; Christian Ostergaard; Jesper Kjær; Jesper Grarup; Jens D Lundgren Journal: BMJ Open Date: 2012-03-11 Impact factor: 2.692
Authors: Ravindra L Mehta; John A Kellum; Sudhir V Shah; Bruce A Molitoris; Claudio Ronco; David G Warnock; Adeera Levin Journal: Crit Care Date: 2007 Impact factor: 9.097
Authors: Sandra L Kane-Gill; Joseph T Hanlon; Michael J Fine; Subashan Perera; Colleen M Culley; Stephanie A Studenski; Dave A Nace; Richard D Boyce; Nicholas G Castle; Steven M Handler Journal: Consult Pharm Date: 2016-12-01
Authors: Joseph G Ouslander; Ilkin Naharci; Gabriella Engstrom; Jill Shutes; David G Wolf; Maria Rojido; Ruth Tappen; David Newman Journal: J Am Med Dir Assoc Date: 2016-06-24 Impact factor: 4.669
Authors: Marlies Ostermann; Lakhmir S Chawla; Lui G Forni; Sandra L Kane-Gill; John A Kellum; Jay Koyner; Patrick T Murray; Claudio Ronco; Stuart L Goldstein Journal: Br J Clin Pharmacol Date: 2017-12-01 Impact factor: 4.335
Authors: Eric A J Hoste; Kianoush Kashani; Noel Gibney; F Perry Wilson; Claudio Ronco; Stuart L Goldstein; John A Kellum; Sean M Bagshaw Journal: Can J Kidney Health Dis Date: 2016-02-26