Development and Validation of ‘Prediction of Adverse Drug Reactions in Older Inpatients (PADROI)’ Risk Assessment Tool

Abstract

Background Adverse drug reactions (ADR) detection and prediction methods in hospitalized older adults remain imprecise. The identification of the risk factors for ADRs in this group of patients is crucial to develop plausible prediction models. Objective This study aimed at developing and validating a “Prediction of ADR in Older Inpatients (PADROI)” risk assessment tool in hospitalized older adults. Methods and Materials We had previously conducted a derivational study that aimed to determine the risk factors of ADRs in hospitalized older adults. We developed the PADROI model as a potential ADR risk assessment tool incorporating 8 predictors each given a score by rounding off the respective adjusted odds ratios (AORs) to the nearest whole number. Subsequently, we conducted another prospective cohort among adults aged 60 years and older admitted to Gynecology and Obstetrics, Medical, Oncology, Surgery, and Psychiatry wards at Mbarara Regional Referral Hospital (MRRH) from July 5 to September 17, 2021. Results A total of 124 participants, 70 females and 54 males aged 60–95 years, were included in this validation cohort; 62 of them experienced 90 ADRs. When applied to the derivational cohort, the area under receiver operating characteristic curve (AUROC) for the PADROI model was shown to be 0.896 (0.869–0.923; at 95% CI). In the validation study, AUROC of PADROI was 0.917 (0.864–0.971 at 95% CI; p < 0.001). Overall, PADROI correctly predicted 91.7% of those who experienced an ADR. Conclusion Using the adjusted odds ratios from our derivational cohort, we developed an ADR prediction tool (PADROI) that achieved an excellent AUROC (0.917), high sensitivity (87.1%) and specificity (90.3%). The current model demonstrated a high potential for clinical applicability which can be strengthened if similar results are reproduced in larger and multi-centered studies.

Development and validation of a predictive tool for adverse drug reactions in neonates under intensive care

“…Existing ADR predictive models are usually focused on hospitalized adults and the elderly. [11][12][13][14][15] Typically, the development of predictive clinical tools for ADR is based on collecting variables at the time of hospitalization. These variables are used to develop a score that identifies patients who, throughout their hospital stay, are at increased risk of ADR.…”

“…Existing ADR predictive models are usually focused on hospitalized adults and the elderly 11–15 . Typically, the development of predictive clinical tools for ADR is based on collecting variables at the time of hospitalization.…”

“…Identification of neonates at higher risk for ADR occurrence would provide anticipatory care and prevent or minimize potential harm. However, studies related to predictive models of ADRs are scarce and all involve adult and elderly patients 11–15 . Given the lack of predictive tools for ADRs targeting the neonatal population, the objective of the present study was to develop and validate a tool capable of predicting ADRs in neonatal intensive care units (NICUs).…”

Opportunities and challenges of pharmacovigilance in special populations: a narrative review of the literature

“…99 Common tools for identifying PIMs include the Medication Appropriateness Index (MAI), HEDIS DAE, Beer's criteria, STOPP/ START criteria and EU-7-PIM list [Figure 3(c)]. [106][107][108][109] Wang et al 110 used Beer's criteria, STOPP/START criteria, and the EU-7-PIM list to evaluate the use of drugs in 560 elderly inpatients and found that Beer's criteria best predicted avoidable ADRs. However, for the elderly in Africa (Nigeria and South Africa), PIMs identified using Beer's criteria did not correlate to ADRs in hospitalized patients.…”

“…116 There are differences in pharmacotherapy in countries in different levels of development. For example, Yadesa et al 107 applied the Prediction of ADR in Old Patients (PADROI) model as a risk assessment tool for elderly inpatients (⩾60 years old) in low-income countries.…”