International Journal of Clinical Pharmacy

, Volume 40, Issue 4, pp 862–869 | Cite as

Development and validation of an algorithm to identify drug-induced anaphylaxis in the Beijing Pharmacovigilance Database

  • Ying Zhao
  • Haidong Lu
  • Sydney Thai
  • Xiaotong Li
  • John Hui
  • Huilin Tang
  • Suodi Zhai
  • Lulu SunEmail author
  • Tiansheng WangEmail author
Research Article


Background Pharmacovigilance databases are utilized to identify serious adverse drug events (ADEs). In China, very few studies have evaluated the validity of using pharmacovigilance databases to identify drug-induced anaphylaxis (DIA). Objective We aimed to develop and validate an algorithm to identify DIA using the Beijing Pharmacovigilance Database (BPD). Setting ADEs from the BPD mainly spontaneously reported from 94 hospitals in Beijing, China. Method Using the diagnoses, we developed an algorithm to identify potential DIAs from the BPD between January 2004 and December 2014. A sample of 500 patients was randomly selected for chart abstraction. Two physician adjudicators assessed whether DIA occurred using the published clinical criteria as the gold standard. Main outcome measure Positive predictive values (PPVs) and 95% confidence intervals of the algorithm and algorithm criteria components were calculated. Results 500 patients (53.2% female; the mean age 48.2 years) with potential DIA were selected using the algorithm. 444 were adjudicated as having anaphylaxis by physicians. The PPV of the overall algorithm was 88.8% (95% CI 86.0–91.6%). PPV for the algorithm only using specific diagnoses of “anaphylactic shock”, “anaphylactic reaction”, and “anaphylactoid reaction [severe]” was 89.6% (95% CI 86.6–92.4%); this partial algorithm identified 387 (87.2%) DIAs. The diagnosis that identified the most DIAs (83.8%) was “anaphylactic shock”, with a PPV of 91.6% (95% CI 88.9–94.3%). Conclusion The overall algorithm identified a greater number of DIAs than the algorithm that only used specific diagnoses; however, its PPV was slightly lower. We were able to identify DIAs with the algorithm we developed.


Algorithm Anaphylaxis China Pharmacovigilance Pharmacoepidemiology Positive predictive value Validation 



The authors would like to thank the Beijing Pharmacovigilance Database for providing data.


This research is partially supported by the Research Grant 892FY60221022 from School of Pharmaceutical Sciences, Peking University.

Conflicts of interest

The authors have no conflicts of interest to declare.

Supplementary material

11096_2018_594_MOESM1_ESM.docx (35 kb)
Supplementary material 1 (DOCX 35 kb)


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of PharmacyPeking University Third HospitalBeijingChina
  2. 2.Department of PharmacyBeijing Shijitan Hospital, Capital Medical UniversityBeijingChina
  3. 3.Department of Pharmacy Administration and Clinical PharmacyPeking University Health Science CenterBeijingChina
  4. 4.Department of EpidemiologyUniversity of North Carolina Gillings School of Global Public HealthChapel HillUSA
  5. 5.Department of Clinical Pharmacy, School of PharmacyUniversity of California San FranciscoSan FranciscoUSA
  6. 6.Department of EpidemiologyRichard M. Fairbanks School of Public Health, Indiana UniversityIndianapolisUSA

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