Advertisement

Toward Kidney-Specific Causality Assessment Tool

      ABSTRACT

      Purpose

      Current nonspecific causality assessment tools lack the assessment of drug-induced acute kidney injury (DIAKI). We recently published an editorial letter for developing a specific causality assessment tool for DIAKI. The purpose of the present review was to suggest the possible required parameters and outline the path to developing a kidney-specific causality assessment tool (KSCAT).

      Methods

      A stepwise approach for developing a KSCAT is important as this will be first version of this new tool. Thus, as a first step, we performed a screening of previously published articles on nonspecific and liver-specific causality assessment tools to define possible parameters. The suggested parameters for KSCAT fall into 3 categories: (1) drug-related; (2) kidney-related; and (3) terminology. A tri-polar method was then created that consists of definitive adverse drug reactions (ADRS), terminology, and without ADRS to suggest that the new KSCAT be efficient, specific, user friendly, and less time-consuming. Finally, a pyramid model is suggested to offer the perspectives of experts in the fields of pharmacovigilance, pharmacoepidemiology, and nephrology, as well as decision makers, while developing a KSCAT.

      Findings

      Causality assessment tools, either nonspecific or organ-specific, fall into 3 categories: (1) expert judgment; (2) algorithms; and (3) probabilistic methods. None of the current causality assessment tools is sufficient for assessing the causality of kidney-related ADRs and for screening the expanded definition of ADR included in European Union Directive 2010/84/EU.

      Implications

      The causal relationship between drug(s) and DIAKI may be difficult and may not be assessed appropriately with the use of nonspecific tools or approaches. The aim of this article was to reiterate the need for KSCAT development and to propose the associated steps by stating the main principles: namely, the definition of ADR, suggested parameters to be included in the KSCAT, and integration of technology. Our ultimate desire is to invite experts to develop this new tool using an interdisciplinary approach and to benefit from our review in pursuing the next steps. The development of a KSCAT should start with regular and interdisciplinary consortium meetings of experts; the tool should then be tested for its usability, specificity, and practicality; and, finally, it should be used in real-life pharmacovigilance practices, as well as in research by health authorities, regulators, decision-makers, scientists, and clinicians. A KSCAT would support the provision of reliable and reproducible measures of the relationship likelihood in suspected cases of ADR to overcome uncertainty and provide a standardized approach.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Clinical Therapeutics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Wallace MA.
        Anatomy and physiology of the kidney.
        AORN J. 1998; 68: 799-820
        • Finco DR.
        Kidney Function.
        Clinical Biochemistry of Domestic Animals. Fifth Edition. Academic Press, 1997
        • Makris K
        • Spanou L.
        Acute kidney injury: definition, pathophysiology and clinical phenotypes.
        Clin Biochem Rev. 2016; 37: 85-98
        • Thomas ME
        • Blaine C
        • Dawnay A
        • Devonald MA
        • Ftouh S
        • Laing C
        • et al.
        The definition of acute kidney injury and its use in practice.
        Kidney Int. 2015; 87: 62-73
        • Duru M
        • Meydan O
        • Kaya M
        • Gulmez SE.
        Need for causality assessment tool for drug-induced acute kidney injury.
        Clin Ther. 2019; 41: 1894-1897
        • WHO
        Safety of Medicines. A guide to detecting and reporting adverse drug reactions.
        World Health Organization, 2002 ([Available from:)
        • Inman WH.
        Post marketing surveillance of adverse drug reactions in general practice. I: search for new methods.
        Br Med J. 1981; 282: 1131-1132
        • Finney DJ
        Writings on Pharmacovigilance. Selected articles (1963-2003) by David J Finney.
        The Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, 2006
        • Greener M.
        Understanding adverse drug reactions: an overview.
        Nurse Prescribing. 2014; : 12
      1. Directive 2010/84/EU of the European Parliament and of the Council of 15 December 2010 amending, as regards pharmacovigilance, Directive 2001/83/EC on the Community code relating to medicinal products for human use. L 348/74 (2010). [Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32010L0084&from=EN

        • Venulet J.
        The CIBA-GEIGY approach to causality.
        Drug Inf J. 1984; 18: 315-318
        • Lewis JH
        • Larrey D
        • Olsson R
        • Lee WM
        • Frison L
        • Keisu M.
        Utility of the Roussel Uclaf Causality Assessment Method (RUCAM) to analyze the hepatic findings in a clinical trial program: evaluation of the direct thrombin inhibitor ximelagatran.
        Int J Clin Pharmacol Ther. 2008; 46: 327-339
        • Benichou C.
        Criteria of drug-induced liver disorders. Report of an international consensus meeting.
        J Hepatol. 1990; 11: 272-276
        • Danan G
        • Benichou C.
        Causality assessment of adverse reactions to drugs—I. A novel method based on the conclusions of international consensus meetings: application to drug-induced liver injuries.
        J Clin Epidemiol. 1993; 46: 1323-1330
        • Benichou C
        • Danan G
        • Flahault A.
        Causality assessment of adverse reactions to drugs—II. An original model for validation of drug causality assessment methods: case reports with positive rechallenge.
        J Clin Epidemiol. 1993; 46: 1331-1336
        • Agbabiaka TB
        • Savovic J
        • Ernst E.
        Methods for causality assessment of adverse drug reactions: a systematic review.
        Drug Safety. 2008; 31: 21-37
        • Theophile H
        • Arimone Y
        • Miremont-Salame G
        • Moore N
        • Fourrier-Reglat A
        • Haramburu F
        • et al.
        Comparison of three methods (consensual expert judgement, algorithmic and probabilistic approaches) of causality assessment of adverse drug reactions: an assessment using reports made to a French pharmacovigilance centre.
        Drug Safety. 2010; 33: 1045-1054
        • Wiholm BE.
        The Swedish drug-event assessment methods. Special workshop—regulatory.
        Drug Inf J. 1984; 18: 267-269
        • Arimone Y
        • Begaud B
        • Miremont-Salame G
        • Fourrier-Reglat A
        • Molimard M
        • Moore N
        • et al.
        A new method for assessing drug causation provided agreement with experts' judgment.
        J Clin Epidemiol. 2006; 59: 308-314
        • Arimone Y
        • Bidault I
        • Dutertre JP
        • Gerardin M
        • Guy C
        • Haramburu F
        • et al.
        Updating the French method for the causality assessment of adverse drug reactions.
        Therapie. 2013; 68: 69-76
        • Miremont-Salame G
        • Theophile H
        • Haramburu F
        • Begaud B.
        Causality assessment in pharmacovigilance: the French method and its successive updates.
        Therapie. 2016; 71: 179-186
        • Theophile H
        • Dutertre JP
        • Gerardin M
        • Valnet-Rabier MB
        • Bidault I
        • Guy C
        • et al.
        Validation and reproducibility of the updated French Causality Assessment Method: an evaluation by pharmacovigilance centres & pharmaceutical companies.
        Therapie. 2015; 70: 465-476
      2. WHO, The Uppsala Monitoring Center. The use of the WHO-UMC system for standardised case causality assessment [Available from: https://www.who.int/medicines/areas/quality_safety/safety_efficacy/WHOcausality_assessment.pdf?ua=1.

        • Marante KB.
        The challenges of adverse drug reaction evaluation.
        J Pharmacovigilance. 2018; 6
        • Irey NS.
        Teaching monograph. Tissue reactions to drugs.
        Am J Pathol. 1976; 82: 613-647
        • Karch FE
        • Lasagna L.
        Toward the operational identification of adverse drug reactions.
        Clin Pharmacol Ther. 1977; 21: 247-254
        • Dangoumau J
        • Evreux JC
        • Jouglard J.
        Method for determination of undesirable effects of drugs.
        Therapie. 1978; 33: 373-381
        • Begaud B
        • Evreux JC
        • Jouglard J
        • Lagier G
        Imputation of the unexpected or toxic effects of drugs. Actualization of the method used in France.
        Therapie. 1985; 40: 111-118
        • Hutchinson TA
        • Leventhal JM
        • Kramer MS
        • Karch FE
        • Lipman AG
        • Feinstein AR.
        An algorithm for the operational assessment of adverse drug reactions. II. Demonstration of reproducibility and validity.
        JAMA. 1979; 242: 633-638
        • Kramer MS
        • Leventhal JM
        • Hutchinson TA
        • Feinstein AR.
        An algorithm for the operational assessment of adverse drug reactions. I. Background, description, and instructions for use.
        JAMA. 1979; 242: 623-632
        • Leventhal JM
        • Hutchinson TA
        • Kramer MS
        Feinstein AR. An algorithm for the operational assessment of adverse drug reactions. III. Results of tests among clinicians.
        JAMA. 1979; 242: 1991-1994
        • Blanc S
        • Leuenberger P
        • Berger JP
        • Brooke EM
        • Schelling JL.
        Judgments of trained observers on adverse drug reactions.
        Clin Pharmacol Ther. 1979; 25: 493-498
        • Emanueli A
        • Sacchetti G.
        An algorithm for the classification of untoward events in large scale clinical trials.
        Agents Actions Suppl. 1980; 7: 318-322
        • Naranjo CA
        • Busto U
        • Sellers EM
        • Sandor P
        • Ruiz I
        • Roberts EA
        • et al.
        A method for estimating the probability of adverse drug reactions.
        Clin Pharmacol Ther. 1981; 30: 239-245
        • Stephens M.
        Assessment of causality in industrial setting.
        Drug Inf J. 1984; 18: 307-313
        • Castle WM.
        Assessment of causality in industrial settings.
        Drug Inf J. 1984; 18: 297-302
        • Venulet J.
        Aspects of standardization as applied to the assessment of drug-event associations.
        Drug Inf J. 1984; 18: 199-210
        • Venulet J.
        Incomplete information as a limiting factor in causality assessment of adverse drug reactions and its practical consequences.
        Drug Inf J. 1986; 20: 423-431
        • Venulet J
        • Ciucci A
        • Berneker GC.
        Standardized assessment of drug-adverse reaction associations—rationale and experience.
        Int J Clin Pharmacol Ther Toxicol. 1980; 18: 381-388
        • Venulet J
        • Ciucci AG
        • Berneker GC.
        Updating of a method for causality assessment of adverse drug reactions.
        Int J Clin Pharmacol Ther Toxicol. 1986; 24: 559-568
        • Moore N
        • Biour M
        • Paux G
        • Loupi E
        • Begaud B
        • Boismare F
        • et al.
        Adverse drug reaction monitoring: doing it the French way.
        Lancet. 1985; 2: 1056-1058
        • Stricker BHC.
        Diagnosis and causality assessment of drug-induced hepatic injury.
        (editor)in: Dukes MNG Drug-induced hepatic injury. Elsevier, Amsterdam1985: 1-13
        • Danan G
        • Benichou C
        • Begaud B
        • Biour M
        • Couzigou P
        • Evreux JC
        • et al.
        Criteria of imputation of acute hepatitis to a drug. Results of consensus meetings.
        Gastroenterologie clinique et biologique. 1987; 11: 581-585
        • Hoskins RE
        • Mannino S.
        Causality assessment of adverse drug reactions using decision support and informatics tools.
        Pharmacoepidemiol Drug Saf. 1992; 1: 235-249
        • Hsu PH
        • Stoll RW.
        Causality assessment of adverse events in clinical trials: I. How good is the investigator drug causality assessment?.
        Drug Inf J. 1993; 27: 387-394
        • Maria VA
        • Victorino RM.
        Development and validation of a clinical scale for the diagnosis of drug-induced hepatitis.
        Hepatology. 1997; 26: 664-669
        • Koh Y
        • Yap CW
        • Li SC.
        A quantitative approach of using genetic algorithm in designing a probability scoring system of an adverse drug reaction assessment system.
        Int J Med Inform. 2008; 77: 421-430
        • Horn JR
        • Hansten PD
        • Chan LN.
        Proposal for a new tool to evaluate drug interaction cases.
        Ann Pharmacother. 2007; 41: 674-680
        • Mashford ML.
        The Australian method of drug-event assessment. Special workshop—regulatory.
        Drug Inf J. 1984; 18: 271-273
        • Nahler G.
        Bayesian adverse reaction diagnostic instrument (BARDI). Dictionary of Pharmaceutical Medicine.
        Springer, Vienna2009: 13-14
        • Lane DA
        • Kramer MS
        • Hutchinson TA
        • Jones JK
        • Naranjo C.
        The causality of adverse drug reactions using a Bayesian approach.
        Pharmaceut Med. 1987; 2: 265-283
        • Lanctot KL
        • Kwok MCO
        • Naranjo CA.
        Computerized Bayesian evaluation of adverse events.
        Drug Inf J. 1995; 29: 319-325
        • Jones JK.
        Adverse drug reactions in the community health setting: approaches to recognizing, counseling, and reporting.
        Fam Community Health. 1982; 5: 58-67
        • Turner WM.
        The Food and Drug Administration algorithm. Special workshop—regulatory.
        Drug Inf J. 1984; 18: 259-266
        • Watanabe M
        • Shibuya A.
        Validity study of a new diagnostic scale for drug-induced liver injury in Japan—comparison with two previous scales.
        Hepatol Res. 2004; 30: 148-154
        • Gallagher RM
        • Kirkham JJ
        • Mason JR
        • Bird KA
        • Williamson PR
        • Nunn AJ
        • et al.
        Development and inter-rater reliability of the Liverpool adverse drug reaction causality assessment tool.
        PloS One. 2011; 6: e28096
        • Oosterhuis I
        • Zweers P
        • Rumke H
        • Muller-Hansma A
        • van Puijenbroek EP.
        A tailor-made approach for causality assessment for ADR reports on drugs and vaccines.
        Pharmacoepidemiol Drug Saf. 2019; 28: 544-550
        • Mascoloa A
        • Scavonea C
        • Sessaa M
        • di Mauroa G
        • Cimmarutaa D
        • Orlandoc V
        • et al.
        Can causality assessment fulfill the new European definition of adverse drug reaction? A review of methods used in spontaneous reporting.
        Pharmacologic Res. 2017; 123: 122-129
        • Talbot J
        • Aronson JK.
        Stephens’ Detection and Evaluation of Adverse Drug Reactions Principles and Practice.
        Sixth Edition. Wiley-Blackwell, 2012
        • Pichler WJ
        • Tilch J.
        The lymphocyte transformation test in the diagnosis of drug hypersensitivity.
        Allergy. 2004; 59: 809-820
        • Joh K
        • Aizawa S
        • Yamaguchi Y
        • Inomata I
        • Shibasaki T
        • Sakai O
        • et al.
        Drug-induced hypersensitivity nephritis: lymphocyte stimulation testing and renal biopsy in 10 cases.
        Am J Nephrol. 1990; 10: 222-230
        • Davies EA
        • O'Mahony MS.
        Adverse drug reactions in special populations—the elderly.
        Br J Clin Pharmacol. 2015; 80: 796-807
        • Corsonello A
        • Pedone C
        • Corica F
        • Mussi C
        • Carbonin P
        • Antonelli Incalzi R
        • et al.
        Concealed renal insufficiency and adverse drug reactions in elderly hospitalized patients.
        Arch Intern Med. 2005; 165: 790-795
        • Awdishu L
        • Mehta RL.
        The 6R's of drug induced nephrotoxicity.
        BMC Nephrol. 2017; 18: 124
        • Gahl WA.
        Chemical individuality: concept and outlook.
        J Inherit Metab Dis. 2008; 31: 630-640
        • Awada Z
        • Zgheib NK.
        Pharmacogenovigilance: a pharmacogenomics pharmacovigilance program.
        Pharmacogenomics. 2014; 15: 845-856
        • Sardas S.
        Pharmacogenovigilance—an idea whose time has come.
        Curr Pharmacogenomics Pers Med. 2010; 8: 1-3
        • Bégaud B.
        Dictionary of Pharmacoepidemiology.
        John Wiley & Sons, Ltd, 2000
        • Dieterle F
        • Perentes E
        • Cordier A
        • Roth DR
        • Verdes P
        • Grenet O
        • et al.
        Urinary clusterin, cystatin C, beta2-microglobulin and total protein as markers to detect drug-induced kidney injury.
        Nature Biotechnol. 2010; 28: 463-469
        • Da Y
        • Akalya K
        • Murali T
        • Vathsala A
        • Tan CS
        • Low S
        • et al.
        Serial quantification of urinary protein biomarkers to predict drug-induced acute kidney injury.
        Curr Drug Metab. 2019; 20: 656-664
        • Guo J
        • Guan Q
        • Liu X
        • Wang H
        • Gleave ME
        • Nguan CY
        • et al.
        Relationship of clusterin with renal inflammation and fibrosis after the recovery phase of ischemia-reperfusion injury.
        BMC Nephrol. 2016; 17: 133
        • Newman DJ.
        • Cystatin C.
        Ann Clin Biochemistry. 2002; 39: 89-104
        • Yamamoto T
        • Noiri E
        • Ono Y
        • Doi K
        • Negishi K
        • Kamijo A
        • et al.
        Renal L-type fatty acid–binding protein in acute ischemic injury.
        J Am Soc Nephrol. 2007; 18: 2894-2902
        • Noiri E
        • Doi K
        • Negishi K
        • Tanaka T
        • Hamasaki Y
        • Fujita T
        • et al.
        Urinary fatty acid-binding protein 1: an early predictive biomarker of kidney injury.
        Am J Physiol Renal Physiol. 2009; 296: F669-F679
        • Mehta RL
        • Awdishu L
        • Davenport A
        • Murray PT
        • Macedo E
        • Cerda J
        • et al.
        Phenotype standardization for drug-induced kidney disease.
        Kidney Int. 2015; 88: 226-234
        • Moledina DG
        • Perazella MA.
        Treatment of drug-induced acute tubulointerstitial nephritis: the search for better evidence.
        Clin J Am Soc Nephrol. 2018; 13: 1785-1787
        • Kashani K
        • Al-Khafaji A
        • Ardiles T
        • Artigas A
        • Bagshaw SM
        • Bell M
        • et al.
        Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury.
        Crit Care. 2013; 17: R25
        • Guzzi LM
        • Bergler T
        • Binnall B
        • Engelman DT
        • Forni L
        • Germain MJ
        • et al.
        Clinical use of [TIMP-2]*[IGFBP7] biomarker testing to assess risk of acute kidney injury in critical care: guidance from an expert panel.
        Crit Care. 2019; 23: 225
        • Han WK
        • Bailly V
        • Abichandani R
        • Thadhani R
        • Bonventre JV.
        Kidney injury molecule-1 (KIM-1): a novel biomarker for human renal proximal tubule injury.
        Kidney Int. 2002; 62: 237-244
        • Vaidya VS
        • Ferguson MA
        • Bonventre JV.
        Biomarkers of acute kidney injury.
        Annu Rev Pharmacol Toxicol. 2008; 48: 463-493
        • Haase M
        • Bellomo R
        • Devarajan P
        • Schlattmann P
        • Haase-Fielitz A
        • NGAL Meta-analysis Investigator Group
        Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: a systematic review and meta-analysis.
        Am J Kidney Dis. 2009; 54: 1012-1024
        • Lumlertgul N
        • Amprai M
        • Tachaboon S
        • Dinhuzen J
        • Peerapornratana S
        • Kerr SJ
        • et al.
        Urine neutrophil gelatinase-associated lipocalin (NGAL) for prediction of persistent AKI and major adverse kidney events.
        Sci Rep. 2020; 10: 8718
        • Lin X
        • Yuan J
        • Zhao Y
        • Zha Y.
        Urine interleukin-18 in prediction of acute kidney injury: a systemic review and meta-analysis.
        J Nephrol. 2015; 28: 7-16
        • Delanaye P
        • Cavalier E
        • Pottel H.
        Serum creatinine: not so simple!.
        Nephron. 2017; 136: 302-308
      3. Guideline on good pharmacovigilance practices (GVP). Module VI—collection, management and submission of reports of suspected adverse reactions to medicinal products (Rev 2), (2017). [Available from https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-good-pharmacovigilance-practices-gvp-module-vi-collection-management-submission-reports_en.pdf.

        • Argyropoulos A
        • Townley S
        • Upton PM
        • Dickinson S
        • Pollard AS.
        Identifying on admission patients likely to develop acute kidney injury in hospital.
        BMC Nephrol. 2019; 20: 56
        • Roberts G
        • Phillips D
        • McCarthy R
        • Bolusani H
        • Mizen P
        • Hassan M
        • et al.
        Acute kidney injury risk assessment at the hospital front door: what is the best measure of risk?.
        Clin Kidney J. 2015; 8: 673-680
        • McMahon GM
        • Zeng X
        • Waikar SS.
        A risk prediction score for kidney failure or mortality in rhabdomyolysis.
        JAMA Intern Med. 2013; 173: 1821-1828
        • Tziakas D
        • Chalikias G
        • Stakos D
        • Apostolakis S
        • Adina T
        • Kikas P
        • et al.
        Development of an easily applicable risk score model for contrast-induced nephropathy prediction after percutaneous coronary intervention: a novel approach tailored to current practice.
        Int J Cardiol. 2013; 163: 46-55
        • Gosling R
        • Iqbal J.
        Predicting contrast induced nephropathy in patients undergoing percutaneous coronary intervention.
        J Thorac Dis. 2019; 11: 2672-2674
        • Jeon N
        • Staley B
        • Henriksen C
        • Lipori GP
        • Winterstein AG.
        Development and validation of an automated algorithm for identifying patients at higher risk for drug-induced acute kidney injury.
        Am J Health Syst Pharm. 2019; 76: 654-666
        • Ventola CL.
        Big data and pharmacovigilance: data mining for adverse drug events and interactions.
        P T. 2018; 43: 340-351
        • Oosterhuis I
        • Taavola H
        • Tregunno PM
        • Mas P
        • Gama S
        • Newbould V
        • et al.
        Characteristics, quality and contribution to signal detection of spontaneous reports of adverse drug reactions via the WEB-RADR mobile application: a descriptive cross-sectional study.
        Drug Safety. 2018; 41: 969-978
        • Schmider J
        • Kumar K
        • LaForest C
        • Swankoski B
        • Naim K
        • Caubel PM.
        Innovation in pharmacovigilance: use of artificial intelligence in adverse event case processing.
        Clin Pharmacol Ther. 2019; 105: 954-961