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Predicting adverse drug reactions in older adults; a systematic review of the risk prediction models

Authors Stevenson J, Williams JL, Burnham TG, Prevost AT, Schiff R, Erskine SD, Davies JG

Received 3 April 2014

Accepted for publication 15 May 2014

Published 19 September 2014 Volume 2014:9 Pages 1581—1593

DOI https://doi.org/10.2147/CIA.S65475

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 3

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Jennifer M Stevenson,1,2 Josceline L Williams,1,2 Thomas G Burnham,2 A Toby Prevost,3 Rebekah Schiff,4 S David Erskine,2 J Graham Davies1

1Institute of Pharmaceutical Sciences, King’s College London, London, UK; 2Pharmacy Department, St Thomas’ Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, UK; 3Department of Primary Care and Public Health Sciences, King’s College London, London, UK; 4Department of Ageing and Health, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

Abstract: Adverse drug reaction (ADR) risk-prediction models for use in older adults have been developed, but it is not clear if they are suitable for use in clinical practice. This systematic review aimed to identify and investigate the quality of validated ADR risk-prediction models for use in older adults. Standard computerized databases, the gray literature, bibliographies, and citations were searched (2012) to identify relevant peer-reviewed studies. Studies that developed and validated an ADR prediction model for use in patients over 65 years old, using a multivariable approach in the design and analysis, were included. Data were extracted and their quality assessed by independent reviewers using a standard approach. Of the 13,423 titles identified, only 549 were associated with adverse outcomes of medicines use. Four met the inclusion criteria. All were conducted in inpatient cohorts in Western Europe. None of the models satisfied the four key stages in the creation of a quality risk prediction model; development and validation were completed, but impact and implementation were not assessed. Model performance was modest; area under the receiver operator curve ranged from 0.623 to 0.73. Study quality was difficult to assess due to poor reporting, but inappropriate methods were apparent. Further work needs to be conducted concerning the existing models to enable the development of a robust ADR risk-prediction model that is externally validated, with practical design and good performance. Only then can implementation and impact be assessed with the aim of generating a model of high enough quality to be considered for use in clinical care to prioritize older people at high risk of suffering an ADR.

Keyword: aged, stratified care, prognosis, medication-related harm

Introduction

Adverse drug reactions (ADRs) have long been recognized as a potential outcome of taking medicines, and while the severity of such reactions may vary, a significant proportion of ADRs are responsible for hospital admissions.1 Investigators have strived to identify the key factors that increase a person’s risk of suffering an ADR, especially in older adults, a group nearly seven-times more likely to be hospitalized due to an ADR when compared to younger people.2

We know that the changes in drug pharmacokinetic and pharmacodynamic properties that occur as a result of the aging process often lead to an increased susceptibility to ADRs.3 Polypharmacy, a frequently reported risk factor for ADRs,4 is on the increase as people live longer with multiple chronic conditions, so stratifying an older patient’s risk of suffering an ADR might be attractive.

Risk prediction is a routine component of everyday medicine in both specific areas (for example, approaches used to determine stroke risk in patients with atrial fibrillation)5 as well as more generally, to identify patients at risk of hospital admission.6 ADR risk stratification in older adults could assist in case prioritization, supporting clinicians and patients to make informed decisions about treatments and for the delivery of a more efficient health care service.

Accurate risk prediction models are the result of four key stages: development, validation, impact, and implementation.7 It is recognized that often only the first two stages (ie, development and validation) are completed, the methods and outcomes of which are often poorly reported. 7 Furthermore, to be of practical use, these models should use clearly defined easily obtainable data, have good predictive power, be tested in a large sample representative of the target population, and have high reliability and face validity.7 A recent systematic review emphasized that failure to consider risk prediction in a clinical setting can result in poor care.8 With regard to the prediction of medication risk in older adults, as no systematic review of this area has been undertaken, we aim to identify and assess the quality of validated ADR risk-prediction models for use in adults over 65 years of age in order to determine their potential benefit to clinical practice.

Method

Information sources and search

A systematic search for published material was performed, up to November 30, 2012, using standard databases (Embase, Medline, Cochrane Library, BNI, CINAHL, NeLM, IPA) to identify relevant studies as well as those associated with policy documents and unpublished work (Department of Health, King’s Fund, Worldcat, Open Grey, Google Scholar). For the key studies, the bibliographies and citations were reviewed, and an author search was performed, to identify any additional studies.

Our search strategies for each database included no restrictions and used standard terms based around three key concepts: older people; medication-related problems; and clinical prediction models. The full Embase search strategy is provided in Table S1.

Inclusion criteria and selection

Two researchers (JMS and SDE) independently screened titles, abstracts, and, where necessary, full texts in order to identify studies that potentially satisfied the following inclusion criteria:

  • Majority of patients ≥65 years old
  • Included patients who experienced an adverse drug event (ADE) or ADR but excluded prescription errors
  • A multivariable approach in design and analysis was followed
  • The model had been validated.

Data extraction

Data were extracted (by JMS) to provide details of the population characteristics, study design, process of model development and validation, and performance of the model, as presented in Tables 1 and 2. This was confirmed by secondary reviewers (SDE and ATP) and, where disagreement occurred, this was resolved through discussion.

Table 1 Summary table of population characteristics of included studies
Abbreviations: ACE, angiotensin converting enzyme; ADE, adverse drug event; ADR, adverse drug reaction; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease; CV, cardiovascular; CVD, cerebrovascular disease; F, female; GI, gastrointestinal; GU, genitourinary; HTN, hypertension; IQR, interquartile range; MSK, musculoskeletal; Neuro, neurological comorbidity; NR, not recorded; NSAIDs, non-steroidal anti-inflammatory drug; SD, standard deviation.

Table 2 Summary of quality assessment of included studies
Notes: aInteractions and coding were not dealt with in any of the studies. bAll studies collapsed continuous categorical data into binary outcomes.
Abbreviations: ADE, adverse drug event; ADR, adverse drug reaction; AUROC, area under the receiver operator curve; CI, confidence interval; GI, gastrointestinal.

Quality assessment

All papers were initially reviewed (SDE and JMS) using a standard approach for developing and testing clinical prediction models to satisfy a range of criteria representing four stages: development (identification of candidate predictor variables and model design); validation (testing the performance of the model); impact (measurement of usefulness in the clinical setting); and implementation (widespread acceptance and adoption in clinical practice).7

As no standardized quality assessment for risk-prediction models is available, each study was analyzed using criteria derived from the published literature.811 Candidate predictor variables were grouped into three categories to allow for comparison between studies: demographic factors; medical factors (eg, comorbidities); and medication factors (eg, class of medicine). Event rate was calculated as percentage ADR/ADE rate where it was not reported by the authors in this form. Quality of design and reporting of the studies was compared based on ability to comply with the standard criteria (Table S2). The overall performance of the models was determined by review of their accuracy, discrimination, and calibration through internal or external validation, as described in detail in Table S2.

Results

A total of 13,423 potentially relevant titles were identified from the literature, of which only 549 were associated with adverse outcomes of medicines use. The majority of these (535) were excluded on review of their abstract as they were not associated with the design of a risk prediction model; many of these were observational (see Figure 1). Full papers were requested for the remaining 14 articles for further scrutiny, and four met the inclusion criteria and were subjected to a full evaluation.1215

Figure 1 PRISMA32 flow diagram.
Abbreviations: ADE, adverse drug event; ADR, adverse drug reaction.

Excluded papers

The 535 articles excluded could be categorized into observational studies (325), those in which indicators to support quality prescribing were developed (63 studies; for example Beers’ criteria16), and those applying the prescribing indicators (147 studies) to determine any association between inappropriate medicines and adverse outcomes.

Included papers

Population characteristics

All included studies were conducted in Western Europe, and only in the hospital setting (acute, community, and rehabilitation hospitals) (Table 1).1215 Two studies represented the very elderly (aged over 80 years).13,15 Patient functionality was reported by Onder et al14 Tangiisuran,13 and McElnay et al12 and was measured using patient-perceived health status, Katz Index, and Barthel Index.

The primary outcome in all of the studies was ADR,17 with one study using ADE synonymously15 and another12 including ineffective treatment in an extended definition. The proportion of patients who experienced an ADR/ADE ranged from 6.5% to 39%, with gastrointestinal, cardiovascular, and nervous systems being those most frequently affected. Medications most frequently associated with ADRs/ADEs included psychotropics, anticoagulants, and analgesics.

Quality assessment – overview

Whilst all models included the development and validation phases, none addressed the impact and implementation phases.

Model development

Study design

During the development phase, all except Onder et al14 used a prospective case-cohort design method, where events accrued over the study period. Onder et al extracted 3 years of data from a historical database, whereas data were extracted over 1–6 months in the other studies. Patient medical notes, in-patient charts, and electronic records were reviewed in the prospective studies.12,13,15 In addition, McElnay12 asked a sample of the patients about aspects of their medicines, while Trivalle et al15 used patient self-reporting as a trigger for further analysis. The validation phase was conducted prospectively for all studies except for that of Trivalle et al where bootstrapping was used.

Participant recruitment

The criteria for inclusion and exclusion as well as any loss to follow-up were clearly described in all studies, although reporting of patient selection was poor (Table 2). An unknown number of patients were excluded by Onder et al due to incomplete data.14

Candidate predictors

The handling of candidate predictor variables was generally poor. In all studies, the description of the variables was inadequate; where Trivalle et al15 did not report the potential candidate variables, McElnay et al12 Tangiisuran13 and Onder et al14 used variables with unclear definitions, eg, “previous ADR”. Despite being labeled as a “bad idea”,18 dichotomization of continuous candidate predictor variables (eg, four or more comorbidities, more than eight medications, previous ADR) was common practice, and may explain the failure to consider conformity to the linear gradient in all1214 but the Trivalle et al study.15 Interactions were poorly addressed, as was the coding of variables. Insufficient detail in the results made it difficult to establish whether tests that were mentioned in the methods had been implemented; eg, McElnay et al12 reported testing for interactions and colinearity, but this was not followed through to the results. Predictor-variable measurement was blinded for outcome in the development phase in three of the four studies.12,13,15

Outcome

The occurrence of an ADE/ADR was the primary outcome measure for all studies. A validated assessment of causality, in the form of the Naranjo algorithm19 or Hallas criteria,20 was adopted by all but Trivalle et al who used their own checklist.15 The outcome was recorded in the form of continuous categorical data (ie, unlikely, possible, probable, definite) then collapsed to produce a binary outcome. Possible, probable, and definite were combined as a positive outcome. Blinding to the outcome occurred in all four studies during the validation phase.

Statistical power

The poor description of potential candidate predictor variables made it impossible to determine if the studies were adequately powered (Table 2).

Selection of predictor variables

The method of selection of predictor variables for inclusion within the multivariable analysis was described in all of the studies (Table 2). Tangiisuran13 provided the most detailed description, whilst Trivalle et al15 provided the least detailed description. Mixed methods (using the literature, expert opinion, and univariate analysis) were used by Tangiisuran.13 Onder et al appeared to have used univariate analysis alone.14 There was variation in the significance levels used to retain a predictor variable.1215

Model performance and validation

The area under the receiver operator curve was used to assess discrimination in three of the four studies, and was 0.70–0.74 for the development phase.1315 Sensitivity and specificity were reported by Tangiisuran,13 Onder et al14 and McElnay et al.12 Calibration was only reported by Tangiisuran,13 for which Hosmer-Lemeshow was satisfactory but Nagelkerke21 was low.

All models underwent the subsequent stage of validation using a second dataset. Internal validation was reported by McElnay et al12 and Trivalle15 in the form of split sample and bootstrapping, retrospectively. External validation was performed by Onder et al14 and Tangiisuran13 in the same European cohort. Another research group (O’Connor et al22) subsequently applied the model developed by Onder et al14 providing additional external validation (Table 1). Area under the receiver operator curve in the validation phase ranged from 0.623 to 0.73 (Table 3). The number of patients involved in the external validation ranged from 204 to 483.1215,22 Only the study by O’Connor et al22 met the recommended minimum number of events (100 events and nonevents).

Table 3 Summary of final ADR risk-prediction models
Abbreviations: ADR, adverse drug reaction; AUROC, area under the receiver operator curve; CI, confidence interval; COAD, chronic obstructive airways disease; OR, odds ratio.

Score development

Predictor variables within the final models (Table 3) were attributed a points-based score, which was simplified for practical application.1315 McElnay et al did not proceed to this stage due to the poor performance of their model.12 The score developed by Onder et al14 was on a points-based system derived from the odds ratio. There was no assessment to determine if any of the predictive ability was lost in this simplification. Tangiisuran13 assigned one point to each predictor variable based on the “variable coefficient being of the same magnitude”. It is unclear how Trivalle et al15 assigned the values to each predictor variable.

Impact and implementation

The impact and implementation of these models have not been published, perhaps reflecting their poor to modest performance. McElnay et al recognized the limitation of their level of performance,12 and both Tangiisuran and Onder et al called for further external validation of their models.13,14 However, Trivalle et al15 concluded that their model could be applied in clinical practice alongside other tools, eg, Mini Mental State Exam. It is also worth considering some of the difficulties highlighted by O’Connor et al22 in the application of Onder et al’s14 model that are due to unclear definition of predictor variables.14,22 Similar challenges are likely to arise when applying results from Tangiisuran, Trivalle et al and McElnay et al given the poorly defined predictor variables.12,13,15 The use of variables such as length of stay would also make prospective risk stratification impossible.

Discussion

Our review suggests that the four models identified, which were designed to predict the risk of older patients suffering an ADR, are not yet suitable for use in clinical practice. While only two (Tangiisuran and Onder et al) were externally validated, their ability to discriminate between those who had experienced an ADR and those who had not was only modest.13,14 This could result in a failure to identify patients at high risk of experiencing an ADR. Furthermore, none were subjected to the investigational rigor required when producing a risk-prediction model; in particular, none reported the findings of impact and implementation stages, thus widening the gap between research potential and clinical application. Pressures within health care systems are driving a need for robust clinical risk-prediction models to inform care provision, but, to be useful, these models must be of high statistical quality and be clinically relevant.

All four studies had limitations commonly reported in the prognostic research literature.7 Three failed to provide sufficient information relating to events-per-variable ratio12,14,15 and one was insufficiently powered (Tangiisuran),13 so the risk of a type II error (false negative finding) was more likely.23 All studies dichotomized their predictor variables (eg, when categorizing the number of medicines) and outcomes (eg, collapsing a continuous ADR causality scale), despite this practice being suboptimal.18,23 The use of unrepresentative samples and the management of missing data were also problematic, regardless of whether a retrospective or prospective design was used. In addition, there was often a lack of reporting of candidate predictor variables, which could hinder replication by others.24

So, if the current risk prediction models have shortcomings, what can we do to limit older adults experiencing ADEs? Although research investigating medication risk in older adults is widespread, the 535 titles identified in our initial search were often associated with other, mainly system-based, approaches to managing risk, and a substantial proportion were observational in nature. This body of evidence documents the complexity of medication risk in older adults and highlights the multidimensional nature of this field, which includes: clinical aspects, such as the changes in drug handling demonstrated in older age; social risk factors, especially during the transfer of care between different settings; and high-risk medicines, where the risk of medicines are considered but not always balanced against the potential benefits. Furthermore, the difficulty in determining whether a patient has experienced an ADR is challenging given the progressive nature of aging, where functional decline and loss of independence are common. Unfortunately, as older adults are often excluded from clinical trials, this limits our understanding of medicine risk in this population, and can result in inappropriate extrapolation of clinical guidelines, often based on research in younger patients.

So, is there a place for risk models in this care setting? A more common strategy is to adopt a systems approach to medicines use where pharmacological appropriateness is monitored, usually by applying a list of prescribing indicators: for example, Beer’s criteria.16 The recognized limitations of such an approach are that it is time-consuming if used in routine care and can be viewed as one-dimensional. This focus on specific medicines often restricts, due to formulary and licensing issues, value in an international context. Perhaps the way forward is a hybrid whereby risk models bring a multidimensional perspective to guide clinical intervention, delivered as part of an integrated system built around the principles of medication safety. If models can map this complex interplay between clinical, social, and medication-related variables to stratify an individual’s risk of a future ADE, they may become a useful decision support tool for clinicians and patients to be used alongside systems-based approaches. This approach could help prioritize interventions for those patients at highest risk. Ultimately, the variables associated with medication risk, eg, polypharmacy and renal impairment, are inherent in clinical decisions and form part of a clinician’s intuitive risk assessment when prescribing medicines. Furthermore, clinicians often modify decisions based on individual variability, whereas a statistical model may not be able to accommodate the clinical nuances and overcome the gerontological phenomenon of age heterogeneity.25 While risk prediction models are not intended to replace clinicians’ decisions, they should not stratify patients less accurately than clinicians. It would be helpful if future work could compare a clinician’s risk stratification against that of an ADR risk-prediction model. This work would help inform the clinical relevance of the model and contribute to the impact and implementation research that is thus far lacking. In the meantime, useful strategies that clinicians may adopt to prevent ADRs occurring are: ensuring that reliable medicines reconciliation is undertaken; avoiding the prescribing cascade (where a drug is prescribed to manage the problem caused by another); and the routine optimization of drug use in line with renal and liver function.

While conducting this systematic review, we could not assess for publication bias using conventional methods such as funnel plots due to the small number of studies available.26 Publication bias in favor of positive results has been raised as a significant problem in the area of cancer risk-prediction research, and it is likely to be present in this area in which negative results remain unpublished.27 The proposal to develop reporting guidelines that stipulate registration of all risk-prediction research should go some way in reducing future reporting bias.28 These guidelines could also be applied to protocols and manuscripts when designing or publishing risk-prediction research, and may be a more suitable tool for quality assessment in the future.29 In the absence of a consensus guideline, we used an amalgamation of standards for reporting risk-prediction research to carry out this review. This approach should reduce the likelihood of any important quality measures being excluded. In the future, recommendations developed by the Cochrane Prognosis Methods Group and the Prognosis Research Strategy Partnership should assist investigators in combating the challenges present when conducting risk-prediction research.23,28,29

Conclusion

Risk stratification is attractive, especially in older patients where the population is growing and placing an increased demand on the health care service, a service that is woefully underprepared for the projected global growth to over 2 billion people over the age of 60 years by 2050.30 We identified four ADR risk-prediction models with poor to modest performance and raised questions about their overall quality, a finding not uncommon in the area of risk-prediction research. If these models are to be embraced as part of routine clinical care, further work needs to be conducted so that external validity can be assured and a practical approach upheld. Only then can implementation and impact be assessed with the view to adoption as part of a systems approach within routine clinical care.

Disclosure

The authors report no conflicts of interest in this work.


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Supplementary materials

Table S1 Embase search strategy indicating the order in which the terms were entered and how they were combined
Notes: aThe numbers demonstrate how search terms have been combined ie, all of the terms for the risk tool were combined in Step 9 of the search. Then these combined terms were combined with those from all those relating to medication related problems ie, Step 22 and with terms relating to elderly ie, Step 29. This resulted in a combined search of the terms listed in Steps 9 and 22 and 29.
Abbreviations: exp, explode all trees; mp, multiple posting.

Table S2 Criteria to consider when evaluating the quality of risk prediction models
Note: aCriteria derived from the published literature.811
Abbreviations: ADR, adverse drug reaction; AUROC, area under the receiver operator curve.


References

1.

Siontis GC, Tzoulaki I, Siontis KC, Ioannidis JP. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ. 2012;344:e3318.

2.

Nelson EA, Dannefer D. Age heterogeneity: fact or fiction? The fate of diversity in gerontological research. Gerontologist. 1992;32:17–23.

3.

Hingorani AD, Windt DA, Riley RD, et al. Prognosis research strategy (PROGRESS) 4: stratified medicine research. BMJ. 2013;346:e5793.

4.

Macaskill P, Walter SD, Irwig L. A comparison of methods to detect publication bias in meta-analysis. Stat Med. 2001;20:641–654.

5.

Trivalle C, Burlaud A, Ducimetière P; The IMEPAG Group 1. Risk factors for adverse drug events in hospitalised elderly patients: a geriatric score. Eur Geriatr Med. 2011;2:284–289.

6.

Field A. Discovering Statistics Using IBM SPSS Statistics. 4th edition. London: Sage; 2013.

7.

Onder G, Petrovic M, Tangiisuran B, et al. Development and validation of a score to assess risk of adverse drug reactions among in-hospital patients 65 years or older: The GerontoNet ADR Risk Score. Arch Intern Med. 2010;170:1142–1148.

8.

Yourman LC, Lee SJ, Schonberg MA, Widera EW, Smith AK. Prognostic indicies for older adults. A systematic review. JAMA. 2012;307:182–192.

9.

Bagley SC, White H, Golomb BA. Logistic regression in the medical literature: standards for use and reporting, with particular attention to one medical domain. J Clin Epidemiol. 2001;54:979–985.

10.

Critical Appraisal Skills Programme for Clinical Prediction Rules [homepage on the Internet]. Available from: http://www.casp-uk.net. Accessed November 9, 2011.

11.

Steyerberg EW. Clinical Prediction Models: a practical approach to development, validation and updating. New York: Springer; 2010.

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