The adverse inpatient medication event and frailty (AIME-frail) risk prediction model.

BACKGROUND: Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable. AIM: To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients. METHODS: A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA). RESULTS: A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4. CONCLUSION: The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.

Unplanned Rehospitalisation due to Medication Harm following an Acute Myocardial Infarction.

INTRODUCTION: The contribution of medication harm to rehospitalisation and adverse patient outcomes after an acute myocardial infarction (AMI) needs exploration. Rehospitalisation is costly to both patients and the healthcare facility. Following an AMI, patients are at risk of medication harm as they are often older and have multiple comorbidities and polypharmacy. This study aimed to quantify and evaluate medication harm causing unplanned rehospitalisation after an AMI. METHODS: This was a retrospective cohort study of patients discharged from a quaternary hospital post-AMI. All rehospitalisations within 18 months were identified using medical record review and coding data. The primary outcome measure was medication harm rehospitalisation. Preventability, causality, and severity assessments of medication harm were conducted. RESULTS: A total of 1,564 patients experienced an AMI, and 415 (26.5%) were rehospitalised. Eighty-nine patients (5.7% of total population; 6.0% of those discharged) experienced a total of 101 medication harm events. Those with medication harm were older (p = 0.007) and had higher rates of heart failure (p = 0.005), chronic kidney disease (p = 0.046), chronic obstructive pulmonary disease (p = 0.037), and a prior history of ischaemic heart disease (p = 0.005). Gastrointestinal bleeding, acute kidney injury, and hypotension were the most common medication harm events. Forty percent of events were avoidable, and 84% were classed as "serious." Furosemide, antiplatelets, and angiotensin-converting enzyme inhibitors were the most commonly implicated medications. The median time to medication harm rehospitalisation was 79 days (interquartile range: 16-200 days). CONCLUSION: Medication harm causes unplanned rehospitalisation in 5.7% of all AMI patients (1 in 17 patients; 6.0% of those discharged). The majority of harm was serious and occurred within the first 200 days of discharge. This study highlights that measures to attenuate the risk of medication harm rehospitalisation are essential, including post-discharge medication management.

Evaluation of two European risk models for predicting medication harm in an Australian patient cohort.

PURPOSE: To externally evaluate the performance of two European risk prediction models, for identifying patients at high-risk of medication harm, in an Australian hospital setting. METHODS: This was a secondary analysis of a pre-existing cohort study described in a recently published study by Falconer et al. (Br J Clin Pharmacol 87(3):1512-1524, 2021) describing the development of a predictive risk model for inpatient medication harm. We retrospectively extracted relevant variables using the electronic health records of general medical and geriatric patients admitted to a quaternary hospital, in Brisbane, over 6 months from July to December 2017. This dataset was used to externally evaluate the two European models, The Brighton Adverse Drug Reaction Risk (BADRI) model by Tangiisuran et al. and a risk model developed by Trivalle et al. The variables were entered into both models and the patients' risk of medication harm was calculated, and compared with actual patient outcomes. Predictive performance was evaluated by measuring area under the receiver operative characteristic (AuROC) curves. RESULTS: The Australian patient cohort included 1982 patients (median age 74 years), of which 136 (7%) patients experienced ≥ 1 medication harm event(s). External evaluation of the two European models identified that both the BADRI and the Trivalle models had reduced predictive performance in an Australian patient cohort, compared with their original studies (AuROC of 0.63 [95% CI: 0.58-0.68] and 0.60 [95% CI: 0.55-0.65], respectively). CONCLUSION: Neither model demonstrated sufficient discrimination to warrant further evaluation in our local setting. This is likely a result of variations between the development and the validation cohorts, and the change in healthcare systems over time, and highlights the need for an up-to-date and context-specific risk prediction model.

Patient harm from cardiovascular medications.

BACKGROUND: Medication harm can lead to hospital admission, prolonged hospital stay and poor patient outcomes. Reducing medication harm is a priority for healthcare organisations worldwide. Recent Australian studies demonstrate cardiovascular (CV) medications are a leading cause of harm. However, they appear to receive less recognition as 'high risk' medications compared with those classified by the medication safety acronym, 'APINCH' (antimicrobials, potassium, insulin, narcotics, chemotherapeutics, heparin). Our aim was to determine the scale and type of medication harm caused by CV medications in healthcare. METHODS: A narrative review of adult (>16 years) medication harm literature identified from PubMed and CINAHL databases was undertaken. Studies with the primary outcome of measuring the incidence of medication harm were included. Harm caused by CV medications was described and ranked against other medication classes at four key stages of a patient's healthcare journey. Where specified, the implicated medications and type of harm were investigated. RESULTS: A total of 75 studies were identified, including seven systematic reviews and three meta-analyses, with most focussing on harm causing hospital admission. CV medications were responsible for approximately 20% of medication harm; however, this proportion increased to 50% in older populations. CV medications were consistently ranked in the top five medication categories causing harm and were often listed as the leading cause. CONCLUSION: CV medications are a leading cause of medication harm, particularly in older adults, and should be the focus of harm mitigation strategies. A practical approach to generate awareness among health professionals is to incorporate 'C' (for CV medications) into the 'APINCH' acronym. PLAIN LANGUAGE SUMMARY: Patient harm from cardiovascular medications: Background: • Harm from medications can cause poor patient outcomes.• Certain medications have been identified as 'high risk' and are known to cause high rates of harm.• 'High risk' medications are included in medication guidelines used by health professionals.• Cardiovascular medications (e.g. blood pressure and cholesterol medications) are important and have many benefits.• Recent studies have found cardiovascular medications to cause high rates of harm.• Cardiovascular medication harm is often under-recognised in clinical practice.• Some guidelines do not consider cardiovascular medications to be 'high risk'.Method: • This review investigated the extent of harm caused by cardiovascular medications in adults across four healthcare settings:(1) at the time of hospital admission;(2) during hospital admission;(3) after hospital; and(4) readmission to hospital.• Harm caused by cardiovascular medications was ranked against other medication classes.• We investigated the type of cardiovascular medications to cause harm and the type of harm caused.Results: • Seventy-five studies were reviewed across 41 countries.• Cardiovascular medications were ranked within the top five medications to cause harm.• Cardiovascular medications were a leading cause of harm in each healthcare setting investigated.• Harm caused by cardiovascular medications was common in older adults (>65 years).• Cardiovascular medications often caused preventable harm.• Medications to treat high blood pressure and abnormal heart rhythms were the most common causes of harm.• We reported kidney injury, electrolyte changes and low blood pressure as common types of harm.Conclusion: • Increased focus on cardiovascular medications in clinical practice is needed.• Health professionals need to carefully prescribe and frequently review cardiovascular medications, especially in older adults.• Patient and health professional discussions should be based on both the benefits and harms of cardiovascular medications.• Cardiovascular medications should be included in all 'high risk' medication guidelines.

The views of New Zealand general practitioners and patients on a proposed risk assessment and communication tool: a qualitative study using Normalisation Process Theory.

BACKGROUND: Communicating risks of medication harm and obtaining informed consent is difficult due to structural barriers, language and cultural practices, bias and a lack of resources appropriately tailored for the health literacy of most patients. A decision support tool was proposed to alert prescribers of risk and provide tailored information for patients to facilitate informed decision-making with patients and their whanau (family) around medication use. Patient and prescriber co-design was used to ensure the tool was designed to best meet the needs of end-users and avoid increasing health inequity. This paper describes the first stage of the co-design process. METHOD: Normalisation Process Theory (NPT) was used to prospectively evaluate the tool. Semi-structured interviews were held with fifteen patients (five Maori, five Pasifika and five NZ European) and nine general practitioners (two Maori and seven European). RESULTS: Three themes were identified, which related to the three NPT concepts most relevant to developing the tool. Theme 1 (coherence: meaning and sense making by participants) explored participants' understanding of prescribing safety, medication harm and risk, which is based on experience. Patients want as much information as possible about their medications and risk, but doctors find it difficult to communicate that information. Theme 2 related to the NPT concept of cognitive participation (commitment and engagement by participants) explored what participants thought about a prescribing decision support tool. Participants were cautiously optimistic, but worried about potential harm arising from its use. They also identified requirements for the tool and features to avoid. Theme 3 describes the collective action required for successful implementation of the tool; namely, culturally safe and trustworthy doctor-patient relationships. CONCLUSION: Patients and general practitioners provided different perspectives when prospectively evaluating the proposed risk assessment and communication tool. This co-design research identified important pre-requisites for the tool and features to avoid and novel ideas for the proposed tool. Overall participants supported the development of the proposed risk assessment and communication tool, but identified the important role that doctor-patient relationships would play to ensure successful implementation. The use of Maori and Pacific languages in the proposed tool may enhance engagement and understanding.

Development and validation of the Adverse Inpatient Medication Event model (AIME).

AIMS: Medication harm has negative clinical and economic consequences, contributing to hospitalisation, morbidity and mortality. The incidence ranges from 4 to 14%, of which up to 50% of events may be preventable. A predictive model for identifying high-risk inpatients can guide a timely and systematic approach to prioritisation. The aim of this study is to develop and internally validate a risk prediction model for prioritisation of hospitalised patients at risk of medication harm. METHODS: A retrospective cohort study was conducted in general medical and geriatric specialties at an Australian hospital over six months. Medication harm was identified using International Classification of Disease (ICD-10) codes and the hospital's incident database. Sixty-eight variables, including medications and laboratory results, were extracted from the hospital's databases. Multivariable logistic regression was used to develop the final risk model. Performance was evaluated using area under the receiver operative characteristic curve (AuROC) and clinical utility was determined using decision curve analysis. RESULTS: The study cohort included 1982 patients with median age 74 years, of which 136 (7%) experienced at least one adverse medication event(s). The model included: length of stay, hospital re-admission within 12 months, venous or arterial thrombosis and/or embolism, ≥ 8 medications, serum sodium < 126 mmol/L, INR > 3, anti-psychotic, antiarrhythmic and immunosuppressant medications, and history of medication allergy. Validation gave an AuROC of 0.70 (95% CI: 0.65-0.74). Decision curve analysis identified that the AIME may be clinically useful to help guide decision making in practice. CONCLUSION: We have developed a predictive model with reasonable performance. Future steps include external validation and impact evaluation.

Preventable medication harm across health care settings: a systematic review and meta-analysis.

BACKGROUND: Mitigating or reducing the risk of medication harm is a global policy priority. But evidence reflecting preventable medication harm in medical care and the factors that derive this harm remain unknown. Therefore, we aimed to quantify the prevalence, severity and type of preventable medication harm across medical care settings. METHODS: We performed a systematic review and meta-analysis of observational studies to compare the prevalence of preventable medication harm. Searches were carried out in Medline, Cochrane library, CINAHL, Embase and PsycINFO from 2000 to 27 January 2020. Data extraction and critical appraisal was undertaken by two independent reviewers. Random-effects meta-analysis was employed followed by univariable and multivariable meta-regression. Heterogeneity was quantified using the I2 statistic, and publication bias was evaluated. PROSPERO: CRD42020164156. RESULTS: Of the 7780 articles, 81 studies involving 285,687 patients were included. The pooled prevalence for preventable medication harm was 3% (95% confidence interval (CI) 2 to 4%, I2 = 99%) and for overall medication harm was 9% (95% CI 7 to 11%, I2 = 99.5%) of all patient incidence records. The highest rates of preventable medication harm were seen in elderly patient care settings (11%, 95% 7 to 15%, n = 7), intensive care (7%, 4 to 12%, n = 6), highly specialised or surgical care (6%, 3 to 11%, n = 13) and emergency medicine (5%, 2 to 12%, n = 12). The proportion of mild preventable medication harm was 39% (28 to 51%, n = 20, I2 = 96.4%), moderate preventable harm 40% (31 to 49%, n = 22, I2 = 93.6%) and clinically severe or life-threatening preventable harm 26% (15 to 37%, n = 28, I2 = 97%). The source of the highest prevalence rates of preventable harm were at the prescribing (58%, 42 to 73%, n = 9, I2 = 94%) and monitoring (47%, 21 to 73%, n = 8, I2 = 99%) stages of medication use. Preventable harm was greatest in medicines affecting the 'central nervous system' and 'cardiovascular system'. CONCLUSIONS: This is the largest meta-analysis to assess preventable medication harm. We conclude that around one in 30 patients are exposed to preventable medication harm in medical care, and more than a quarter of this harm is considered severe or life-threatening. Our results support the World Health Organisation's push for the detection and mitigation of medication-related harm as being a top priority, whilst highlighting other key potential targets for remedial intervention that should be a priority focus for future research.

Medication-related harm in older adults following hospital discharge: development and validation of a prediction tool.

OBJECTIVES: To develop and validate a tool to predict the risk of an older adult experiencing medication-related harm (MRH) requiring healthcare use following hospital discharge. DESIGN, SETTING, PARTICIPANTS: Multicentre, prospective cohort study recruiting older adults (≥65 years) discharged from five UK teaching hospitals between 2013 and 2015. PRIMARY OUTCOME MEASURE: Participants were followed up for 8 weeks in the community by senior pharmacists to identify MRH (adverse drug reactions, harm from non-adherence, harm from medication error). Three data sources provided MRH and healthcare use information: hospital readmissions, primary care use, participant telephone interview. Candidate variables for prognostic modelling were selected using two systematic reviews, the views of patients with MRH and an expert panel of clinicians. Multivariable logistic regression with backward elimination, based on the Akaike Information Criterion, was used to develop the PRIME tool. The tool was internally validated. RESULTS: 1116 out of 1280 recruited participants completed follow-up (87%). Uncertain MRH cases ('possible' and 'probable') were excluded, leaving a tool derivation cohort of 818. 119 (15%) participants experienced 'definite' MRH requiring healthcare use and 699 participants did not. Modelling resulted in a prediction tool with eight variables measured at hospital discharge: age, gender, antiplatelet drug, sodium level, antidiabetic drug, past adverse drug reaction, number of medicines, living alone. The tool's discrimination C-statistic was 0.69 (0.66 after validation) and showed good calibration. Decision curve analysis demonstrated the potential value of the tool to guide clinical decision making compared with alternative approaches. CONCLUSIONS: The PRIME tool could be used to identify older patients at high risk of MRH requiring healthcare use following hospital discharge. Prior to clinical use we recommend the tool's evaluation in other settings.

Do the 2015 Beers Criteria predict medication-related harm in older adults? Analysis from a multicentre prospective study in the United Kingdom.

PURPOSE: To investigate whether inappropriate prescribing, defined by the Beers Criteria, is associated with medication-related harm (MRH), hospital admission, and mortality in older adults in England. METHODS: A multicentre, prospective cohort study recruited 1280 patients (median age 82 years) at hospital discharge. Patients were followed-up in the community by pharmacists for 8 weeks to identify MRH (harm from adverse drug reactions, non-adherence, and medication errors) and hospital admissions. One-year mortality was determined using hospital records. Potentially inappropriate medications (PIMs) were determined using the 2015 version of the Beers criteria. Logistic regression was used to investigate the relationship between patients prescribed PIMs and adverse outcomes. RESULTS: Two hundred and seventy-six patients (22%) were prescribed one or more PIMs at hospital discharge. The main PIM classes prescribed at hospital discharge were benzodiazepines and related drugs (30%) and antidepressants (27%). 1116 out of 1280 patients completed follow-up and 413 (37%) experienced MRH. In 51 cases (12%), MRH was attributable to a PIM. There was no significant relationship between patients prescribed PIMs and overall MRH, hospital readmission or all-cause one-year mortality. Multiple PIMs at discharge was independently associated with an increased risk of ADR (OR 2.32, 95% CI 1.03-5.23). CONCLUSION: The prescribing of PIMs is common at hospital discharge of older adults in England. The 2015 Beers criteria have a limited clinical value to predict adverse outcomes following hospital discharge in this setting.

FIP Perspectives: Realising global patient safety goals requires an integrated approach with pharmacy at the core.

Patient safety is becoming increasingly recognised as a top priority for action that requires a collective and coordinated response. The leading cause of harm or injury in health care systems is medication errors. As medicines experts, the pharmacy workforce plays a key role in minimising medication errors and mitigating the global challenge of patient safety. Pharmacists' involvement in ensuring patient safety is crucial. Pharmacists are charged with the responsibility to ensure that when a patient receives and uses a medicine, it will not cause harm or death. The International Pharmaceutical Federation (FIP) recognises the critical role the pharmacy plays in realising global, regional and national patient safety goals; and works with its partners, stakeholders and members around the world to advocate for the role of pharmacy in achieving this global patient safety agenda and to envision a world of safe access to medicines and care.

Defining and classifying terminology for medication harm: a call for consensus.

PURPOSE: The multiplicity in terms and definitions of medication-related harm has been a long-standing challenge for health researchers, clinicians, and regulatory bodies. The purpose of this narrative review was to report the diversity of terms; compare definitions, classifications, and models describing medication harm; and suggest which may be useful in both clinical practice and the research setting. METHODS: A narrative review of key studies defining and/or classifying medication harm terminology was undertaken. RESULTS: This review found that numerous terms are used to describe medication harm, and that there is a lack of consistency in current definitions, classifications, and applications. This lack of consistency applied across clinical jurisdictions and regulatory terminologies. A number of limitations in current definitions and classifications were identified. These included the exclusion of key types of medication harm events, ambiguous wording, and a lack of clarity and consensus on subclassifications. In general, there was some overlap in key models from the literature and these were presented to describe similarities and differences. CONCLUSION: Without uniformity quantifying, comparing, combining, or extrapolating medication harm data, such as a rate of harm in a specific population, is a challenge for those involved in medication safety and pharmacovigilance. There is a pressing need for further discussion and international consensus on this topic. Adoption of standard descriptors by practitioner groups, regulatory and policy organisations would foster quality improvement and patient safety.

How hospital pharmacists prioritise patients at high-risk for medication harm.

BACKGROUND: Medication harm is experienced by up to 30% of hospitalised patients, of which 7% experience severe harm. Pharmacist review can mitigate this harm. However, in increasingly busy hospitals, with high patient throughput, and scarce resources, there is a need to prioritise patients. Current methods are cumbersome, include many risk factors and are not evaluated in the clinical setting. OBJECTIVES: To determine key criteria used by hospital pharmacists and investigate perspectives related to patient prioritisation for potential medication harm in the hospital setting. METHODS: This study used two methods; focus groups and a cross-sectional survey of Australian hospital pharmacists. Focus groups were used to identify criteria and perspectives related to prioritisation and were analysed thematically. Criteria from focus groups, and a systematic review, were used to design the survey. The survey was distributed via the Society of Hospital Pharmacists of Australia. The top 10 prioritisation criteria, and associated sub-criteria selected by over 50% of respondents were ranked. Combination of criteria used most frequently on a day-to-day basis were identified. RESULTS: Twenty clinical pharmacists participated in four, one-hour, audio recorded focus groups. Using inductive thematic analysis of transcripts three themes were identified; 1) prioritisation criteria, 2) barriers to, and 3) facilitators of patient prioritisation, with five sub-themes and 26 codes. Pharmacists identified a number of barriers such as a lack of relevant handover information. Organisational demands, such as patient discharge and medications supply also influenced priority and could act as barriers to a pharmacist enacting their prioritisation plan. A total of 231 pharmacists completed the survey. High priority criteria included, renal impairment, use of high-risk medications and therapeutic drug monitoring. CONCLUSION: Pharmacists described prioritisation as a multifactorial process with a focus on high-risk medications and renal impairment. These findings will inform the development of a predictive risk score for patient prioritisation.

Incidence of Medication-Related Harm in Older Adults After Hospital Discharge: A Systematic Review.

OBJECTIVES: To determine the incidence, severity, and preventability of and risk factors for medication-related harm (MRH) in community-dwelling older adults after hospital discharge. DESIGN: Systematic review. SETTING: A search of Medline, EMBASE, CINAHL, and the Cochrane Library was undertaken without time restrictions. PARTICIPANTS: Older adults (average age ≥65) participating in observational studies investigating postdischarge adverse drug reactions (ADRs) or adverse drug events (ADEs) within a defined follow-up period. MEASUREMENTS: One author screened abstracts of all articles to exclude obviously irrelevant articles. Two authors independently screened the remaining articles for inclusion. Two authors independently extracted data, including study characteristics, MRH incidence, and risk factors; a third reviewer critically appraised and verified the data. Disagreements were resolved through discussion. RESULTS: From 584 potentially relevant articles, 8 studies met our inclusion criteria: 5 North American and 3 European. Most of the included studies were of moderate quality. There was a wide range in MRH incidence, from 0.4% to 51.2% of participants, and 35% to 59% of MRH was preventable. MRH incidence within 30 days after discharge ranged from 167 to 500 events per 1,000 individuals discharged (17-51% of individuals). There is substantial methodological heterogeneity across multiple domains of the studies, including ADR and ADE definitions, characteristics of recruited populations, follow-up duration after discharge, and data collection. CONCLUSION: MRH is common after hospital discharge in older adults, but methodological inconsistencies between studies and a paucity of data on risk factors limits clear understanding of the epidemiology. There is a need for international consensus on conducting and reporting MRH studies. Data from large, multicenter studies examining a range of biopsychosocial risk factors could provide insight into this important area of safety.

Incidence and cost of medication harm in older adults following hospital discharge: a multicentre prospective study in the UK.

AIMS: Polypharmacy is increasingly common in older adults, placing them at risk of medication-related harm (MRH). Patients are particularly vulnerable to problems with their medications in the period following hospital discharge due to medication changes and poor information transfer between hospital and primary care. The aim of the present study was to investigate the incidence, severity, preventability and cost of MRH in older adults in England postdischarge. METHODS: An observational, multicentre, prospective cohort study recruited 1280 older adults (median age 82 years) from five teaching hospitals in Southern England, UK. Participants were followed up for 8 weeks by senior pharmacists, using three data sources (hospital readmission review, participant telephone interview and primary care records), to identify MRH and associated health service utilization. RESULTS: Overall, 413 participants (37%) experienced MRH (556 MRH events per 1000 discharges), of which 336 (81%) cases were serious and 214 (52%) potentially preventable. Four participants experienced fatal MRH. The most common MRH events were gastrointestinal (n = 158, 25%) or neurological (n = 111, 18%). The medicine classes associated with the highest risk of MRH were opiates, antibiotics and benzodiazepines. A total of 328 (79%) participants with MRH sought healthcare over the 8-week follow-up. The incidence of MRH-associated hospital readmission was 78 per 1000 discharges. Postdischarge MRH in older adults is estimated to cost the National Health Service £396 million annually, of which £243 million is potentially preventable. CONCLUSIONS: MRH is common in older adults following hospital discharge, and results in substantial use of healthcare resources.