Cited by 93

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Agreement between WHO-UMC causality scale and the Naranjo algorithm for causality assessment of adverse drug reactions

Shukla, Ajay K., Jhaj, Ratinder, Misra, Saurav, Ahmed, Shah N., Nanda, Malaya, Chaudhary, Deepa·2021·Journal of Family Medicine and Primary Care

Abstract

Background: The Pharmacovigilance Program of India recommends the use of the World Health Organization-Uppsala Monitoring Centre (WHO-UMC) scale, while many clinicians prefer the Naranjo algorithm for its simplicity. In the present study, we assessed agreement between the two widely used causality assessment scales, that is, the WHO-UMC criteria and the Naranjo algorithm. Materials and Methods: In this study, 842 individual case safety reports were randomly selected from 1000 spontaneously reported forms submitted to the ADR Monitoring Center at a tertiary healthcare Institute in Central India between 2016 and 2018. Two well-trained independent groups performed the causality assessment. One group performed a causality assessment of the 842 ADRs using the WHO-UMC criteria and the other group performed the same using the Naranjo algorithm. The agreement between two ADR causality scales was assessed using the weighted kappa (κ) test. Results: Cohen's kappa coefficient (κ) statistical test was applied between the two scales (WHO-UMC scale and Naranjo algorithm) to find out the agreement between these two scales. “No” agreement was found between the two scales {Kappa statistic with 95% confidence interval = 0.048 ( P < 0.001)}. Conclusion: There was no agreement found between the WHO-UMC criteria and the Naranjo algorithm in our study.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.4103/jfmpc.jfmpc_831_21

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

  • A method for estimating the probability of adverse drug reactions

    Naranjo, C. A., Busto, U., Sellers, E. M., Sandor, P., Ruiz, I., Roberts, E. A., Janecek, E., Domecq, C., & Greenblatt, D. J. (1981) · Clinical Pharmacology and Therapeutics · doi:10.1038/clpt.1981.154 · Cited by 11,914 · cited 1×

    “The most commonly available criteria used worldwide are the Naranjo algorithm [Table 2a and 2b].[9,10] The third category of causality assessment methods is the probabilistic methods.”
    Method
  • Sample-size calculations for Cohen's kappa.

    Cantor, A. B. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.2.150 · Cited by 240 · cited 2×

    “21 (from previous studies), using the formula given by Cantor,[12] the calculated minimum required sample size for the study was 830 ADR cases.”
    MethodInfluential

Flagged first, then by citations

Research works printed but not linked: 16

  1. Mulchandani, 2019, Reporting of adverse drug reactions in India: A review of the current scenario, obstacles and possible solutions, Int J Risk Saf Med, 30, 33, 10.3233/JRS-180025 · DOI
  2. Singh, 2017, Adverse drug reactions at adverse drug reaction monitoring center in Raipur: Analysis of spontaneous reports during 1 year, Indian J Pharmacol, 49, 432, 10.4103/ijp.IJP_781_16 · DOI
  3. Alomar, 2014, Factors affecting the development of adverse drug reactions, Saudi Pharm J, 22, 83, 10.1016/j.jsps.2013.02.003 · DOI
  4. Agbabiaka, 2008, Methods for causality assessment of adverse drug reactions: A systematic review, Drug Saf, 31, 21, 10.2165/00002018-200831010-00003 · DOI · Paper page
  5. Turner, 1984, The Food and Drug Administration algorithm. Special workshop: Regulatory, Drug Inform J, 18, 259, 10.1177/009286158401800311 · DOI · Paper page
  6. Theophile, 2010, Comparison of three methods (consensual expert judgment, algorithmic and probabilistic approaches) of causality assessment of adverse drug reactions: An assessment using reports made to a French Pharmacovigilance center, Drug Saf, 33, 1045, 10.2165/11537780-000000000-00000 · DOI
  7. Mashford, 1984, The Australian method of drug-event assessment. Special workshop – Regulatory, Drug Inf J, 18, 271, 10.1177/009286158401800313 · DOI
  8. Belhekar, 2014, A study of agreement between the Naranjo algorithm and WHO-UMC criteria for causality assessment of adverse drug reactions, Indian J Pharmacol, 46, 117, 10.4103/0253-7613.125192 · DOI
  9. Rana, 2015, Consistency between causality assessments obtained with various scales and their agreement for adverse drug events reported in pediatric population, J Young Pharm, 7, 89, 10.5530/jyp.2015.2.6 · DOI
  10. Behera, 2018, Comparison of different methods for causality assessment of adverse drug reactions, Int J Clin Pharm, 40, 903, 10.1007/s11096-018-0694-9 · DOI
  11. Mahtani, 2018, Catalogue of bias: Observer bias, BMJ Evid Based Med, 23, 23, 10.1136/ebmed-2017-110884 · DOI
  12. Mittal, 2015, Comparison of agreement and rational uses of the WHO and Naranjo adverse event causality assessment tools, J Pharmacol Pharmacother, 6, 91, 10.4103/0976-500X.155486 · DOI
  13. Rehan, 2007, Causality assessment of spontaneously reported adverse drug events: Comparison of WHO-UMC criteria and Naranjo probability scale, Int J Risk Saf Med, 19, 223
  14. Goyal, 2019, Comparison of three methods of causality assessment of adverse drug reactions to antihypertensive drugs, Int Res J Pharm, 10, 195, 10.7897/2230-8407.1003104 · DOI
  15. Sharma, 2017, Inter-rater and intra-rater agreement in causality assessment of adverse drug reactions: A comparative study of WHO-UMC versus Naranjo scale, Int J Res Med Sci, 5, 4389, 10.18203/2320-6012.ijrms20174564 · DOI
  16. Son, 2008, Comparison of the Naranjo and WHO Uppsala monitoring centre criteria for causality assessment of adverse drug reactions, Korean J Med, 74, 181

Cited by

Sorted by citations

Metadata

Authors
Shukla, A. K., Jhaj, R., Misra, S., Ahmed, S. N., Nanda, M., & Chaudhary, D.
Venue
Journal of Family Medicine and Primary Care
Published
2021-09
Volume
10
Issue
9
Pages
3303-3308
ISSN
2249-4863
Cited by
93
References
2
Language
en

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review