Improving medication safety: Development and impact of a multivariate model-based strategy to target high-risk patients

Increasing the number of pharmacist-led medication order reviews using the available workforce: a retrospective study

BACKGROUND

Due to staffing constraints, several hospitals have defined targeting strategies for pharmacist-led medication order review, leaving non-targeted patients exposed to potential harmful drug-related problems (DRPs). Using targeting criteria to stratify medication order review level (level 1 (L1): orders, basic patient characteristics; level 2 (L2) or comprehensive medication order review: orders, patient characteristics, medical records, laboratory results) could make it possible to save time and increase the overall number of medication order reviews. This study aims to define targeting criteria to stratify medication order review level and estimate the time saved for the performance of additional medication order reviews.

METHOD

This retrospective single-centre study included all medication order reviews performed in 2020; DRPs were collected to assess the medication order review level required to detect them. Logistic regressions were performed to define patient characteristics associated with L2. These targeting criteria were applied to the cohort to estimate the time saved and the number of additional medication order reviews which could have been performed using this approach.

RESULTS

2478 DRPs were reported; 54.2% (1343/2748) could have been detected using an L1 medication order review (representing 48.2% of the patients (829/1721)). L2 medication order reviews were significantly associated with age ≥65 years, male, and renal clearance <60 mL/min (OR≥75yo=1.79; OR65-74yo=1.74; ORfemale=0.74; OR30-59mL/min=1.67; OR<30mL/min=2.62; p<0.05). Sex being a confounding factor, only age and renal clearance were used as targeting criteria. The time saved was estimated at 274 hours per year, leading to an additional 1720 medication order reviews (54 hospital beds).

CONCLUSION

The proposed approach would maintain a satisfying level of safety and quality for patients, by performing an L2 medication order review for targeted patients based on age and renal clearance, while improving medication order review coverage with an L1 medication order review for non-targeted patients, using the available workforce.

Prioritizing patients for medication review by emergency department pharmacists: a multi-method study

BACKGROUND

Little is known about how pharmacists think and act in the situation when they need to prioritize patients without prioritization tools.

AIM

To understand how Emergency Department (ED) pharmacists prioritize patients for medication review.

METHOD

A multi-method study with pharmacists working in one ED in Denmark. At the start of the study, non-clinical prioritization rules had been implemented at the ED to ensure that medication histories were available quickly. Qualitative participant observations of all seven pharmacists who work at the ED were carried out. Semi-structured interviews with six of the pharmacists were held near in time to the observations. Observations for each pharmacists' patients were analysed inductively looking for patterns and themes. The interviews were audiotaped, transcribed verbatim and analysed inductively.

RESULTS

The interviews shed light on the kinds of considerations pharmacists processed when prioritizing patients and how they used the ground rules (as set out in the PDSA process). The observations supplemented these data by showing some notable differences between pharmacists' prioritization procedures that linked to their clinical experience. The interviews highlighted the importance of collaboration with physicians and how pharmacists could be better integrated in the ward team.

CONCLUSION

Although the ward-established rules increased efficiency, they were handled differently by the pharmacists according to experience, and were not deemed helpful in detecting the clinically meaningful ED patients. A prioritization tool and sitting near to the physicians at the ward would enable pharmacists to become further integrated into patient care and improve prioritization of patients for medication reviews.

Development and validation of a risk prediction model for medication administration errors among neonates in the neonatal intensive care unit: a study protocol

INTRODUCTION

Medication administration errors (MAEs) are the most common type of medication error. Furthermore, they are more common among neonates as compared with adults. MAEs can result in severe patient harm, subsequently causing a significant economic burden to the healthcare system. Targeting and prioritising neonates at high risk of MAEs is crucial in reducing MAEs. To the best of our knowledge, there is no predictive risk score available for the identification of neonates at risk of MAEs. Therefore, this study aims to develop and validate a risk prediction model to identify neonates at risk of MAEs.

METHODS AND ANALYSIS

This is a prospective direct observational study that will be conducted in five neonatal intensive care units. A minimum sample size of 820 drug preparations and administrations will be observed. Data including patient characteristics, drug preparation-related and administration-related information and other procedures will be recorded. After each round of observation, the observers will compare his/her observations with the prescriber's medication order, hospital policies and manufacturer's recommendations to determine whether MAE has occurred. To ensure reliability, the error identification will be independently performed by two clinical pharmacists after the completion of data collection for all study sites. Any disagreements will be discussed with the research team for consensus. To reduce overfitting and improve the quality of risk predictions, we have prespecified a priori the analytical plan, that is, prespecifying the candidate predictor variables, handling missing data and validation of the developed model. The model's performance will also be assessed. Finally, various modes of presentation formats such as a simplified scoring tool or web-based electronic risk calculators will be considered.

Systematic Review of Risk Factors Assessed in Predictive Scoring Tools for Drug-Related Problems in Inpatients

Drug-related problems (DRP, defined as adverse drug events/reactions and medication errors) are a common threat for patient safety. With the aim to aid improved allocation of specialist resources and to improve detection and prevention of DRP, numerous predictive scoring tools have been proposed. The external validation and evidence for the transferability of these tools still faces limitations. However, the proposed scoring tools include partly overlapping sets of similar factors, which may allow a new approach to estimate the external usability and validity of individual risk factors. Therefore, we conducted this systematic review and analysis. We identified 14 key studies that assessed 844 candidate risk factors for inclusion into predictive scoring tools. After consolidation to account for overlapping terminology and variable definitions, we assessed each risk factor in the number of studies it was assessed, and, if it was found to be a significant predictor of DRP, whether it was included in a final scoring tool. The latter included intake of ≥ 8 drugs, drugs of the Anatomical Therapeutic Chemical (ATC) class N, ≥1 comorbidity, an estimated glomerular filtration rate (eGFR) <30 mL/min and age ≥60 years. The methodological approach and the individual risk factors presented in this review may provide a new starting point for improved risk assessment.

Incidence of adverse drug events in patients hospitalized in the medical wards of a teaching referral hospital in Ethiopia: a prospective observational study

BACKGROUND

Adverse drug events (ADEs) are an important public health problem with considerable clinical and economic costs. However there are limited studies of ADE incidence in adult inpatients in low-income countries, particularly in Ethiopia. Hence, this study aimed to assess the incidence of adverse drug events and associated factors in patients hospitalized in the medical wards of Wolaita Sodo University teaching referral hospital (WSUTRH).

METHODS

A prospective observational study was conducted involving 240 patients admitted to the medical wards of WSUTRH. A checklist was used for data collection, while standard tools were employed for assessing the probability and characterization of ADEs. A multifaceted approach involving daily chart review, patient interview, attendance at ward rounds and/or meetings, and staff reports were employed to collect the data. To identify factors independently associated with ADEs, logistic regression analysis was conducted using Stata version 15.

RESULTS

Patients were followed from ward admission to discharge, accounting for 2200 patient-days of hospital stay. Overall, 976 medications were ordered during the hospital stay. Sixty-four ADEs were identified with an incidence of approximately 27 per 100 admissions and 29 per 1000 patient days. Of the total ADEs, 59% were preventable. Regarding the severity, 2% of the ADEs were severe, while 54% were moderate. The risk of ADEs increased with longer hospital stay (LOHS) (p = 0.021), in patients with blood and immune disease diagnosis (p = 0.001), use of cardiovascular medicines (p = 0.028), and an increase in the number of medications prescribed (p = 0.021).

CONCLUSIONS

In this study, ADEs were identified in about one-quarter of the participants. Longer hospital stays, blood and immune diseases, cardiovascular medicines use, and multiple medication use had increased the likelihood of ADE occurrences. The majority of the ADEs were preventable, indicating the existence of a window of opportunity to ensure patient safety.

Practical Considerations of PRN Medicines Management: An Integrative Systematic Review

Background and objectives: Highly widespread use of pro re nata (PRN) medicines in various healthcare settings is a potential area for improper medication prescription and administration leading to patient harm. This study aimed to summarize and integrate the findings of all relevant individual studies regarding the practical considerations of PRN medicines management including strategies and interventions by healthcare professionals for safe prescription, dispensing, administration, monitoring, and deprescription of PRN medicines in healthcare settings. Methods: An integrative systematic review on international databases were performed. Electronic databases including Web of Knowledge, Scopus, PubMed (including MEDLINE), and Cinahl were searched to retrieve articles published until end of May 2021. Original qualitative, quantitative, and mixed methods studies written in English were included with a focus on PRN medicines management in healthcare settings. Research synthesis using the narrative method was performed to summarise the results of included studies. Results: Thirty-one studies on PRN medicines in healthcare settings by different healthcare providers were included after the screening of the databases based on eligibility criteria. They were published from 1987 to 2021. The majority of studies were from Australia, the United States, Canada, and the United Kingdom and were conducted in psychiatric settings. Given variations in their purposes, methods, and outcomes, the research synthesis was conducted narratively based on diversities and similarities in findings. Eight categories were developed by the authors as follows: "PRN indications and precautionary measures," "requirements of PRN prescription," "interventions for PRN administration," "monitoring and follow up interventions," "deprescription strategies," "healthcare professionals' role," "participation of patients and families," and "multidisciplinary collaboration." Each category consists of several items and describes what factors should be considered by healthcare professionals for PRN medicines management. Conclusion: The review findings provide insights on the practical considerations of PRN medicines management in clinical practice. The suggested list of considerations in our review can be used by healthcare professionals for optimal PRN medicines management and safeguarding patient care.

Risk factors for adverse drug reactions associated with clopidogrel therapy

This study aimed to investigate the possible influence of genetic and non-genetic factors on the incidence of clopidogrel adverse drug reactions (ADRs) in cardiology patients, including the most important CYP2C19 alleles, namely *2 and *17, as well as compliance, dose, drug interactions, and clinical factors. A total of 102 clopidogrel-treated adult Caucasian patients hospitalized at the Cardiology Department of the Clinical Center of Montenegro were enrolled in the study. Data on clinical outcomes of interest were obtained by intensive monitoring ADRs during hospitalization and one year after hospital discharge. Genotyping for CYP2C19*2 and *17 was conducted using the real-time polymerase chain reaction method. ADRs were characterized using the Rawlins and Thompson classification and the World Health Organization criteria. Causality was assessed using the Naranjo probability scale. ADRs to clopidogrel were observed in 9 of 102 patients (8.8%). The observed frequencies of CYP2C19*2 and *17 were 13.2 and 25.5%, respectively. Our study, which is the first to report the frequency of CYP2C19 polymorphism in the Montenegrin population, as well as to link the pharmacovigilance of clopidogrel with CYP2C19 gene variability, shows that the incidence of ADRs of clopidogrel in cardiac patients is high and depends on CYP2C19 polymorphisms, comedication/drug interactions, and gastrointestinal comorbidity.

Development of a decision tree for the pharmacy-led consultation of elderly patients with haematological malignancies

INTRODUCTION

Elderly patients with haematological malignancies are a population at risk of iatrogenic for whom these activities could optimize therapeutic management. However, the limitation of human resources requires optimization of the process in order to improve the efficiency of pharmaceutical activities. The objective was to build a decision tree to optimize the pharmaceutical consultation in these population within a multidisciplinary team in haematology.

METHOD

Pharmaceutical consultations were proposed to elderly subjects with haematological malignancies followed up in a haematology day hospitalization at the University Hospital of Limoges. Risk factors for prescribing risky drugs in this population were determined by logistic regression models. A decision tree was constructed based on these results and by agreement between pharmacist, geriatrician and hematologist.

RESULTS

Female gender (aOR[CI95%] = 1.71 [1.14-2.57]), polypharmacy (aOR[CI95%] = 1.89 [1.14-3.13]), hyper-polypharmacy (aOR[CI95%] = 5.73 [3.03-10.84]) and moderate cholinergic load (aOR[CI95%] = 2.15 [1.04-4.45]) were risk factors for the prescription of inappropriate medicine. Female gender (aOR[CI95%] = 1.55 [1.02-2.35]) and hyper-polypharmacy (aOR[CI95%] = 6.19 [1-1.28]) were risk factors for prescribing anticholinergic drugs or anticoagulants; in contrast, frailty status was a protective factor for prescribing anticholinergics (aOR[CI95%] = 0.51 [0.33-0.81]). Prioritization of pharmaceutical consultations is based on frailty status, prescription of a target drug and polypharmacy.

DISCUSSION

Pharmaceutical consultations during the day hospitalization of elderly subjects with hematological diseases allow to propose therapeutic optimizations. The prioritization proposed in our study would increase the efficiency of pharmaceutical activities in order to improve quality and safety throughout the care pathway of these patients.

Older patients' engagement in hospital medication safety behaviours

BACKGROUND

Increasing age is associated with more medication errors in hospitalised patients. Patient engagement is a strategy to reduce medication harm.

AIMS

To measure older patients' preferences for and reported medication safety behaviours, identify the relationship between preferred and reported medication safety behaviours and identify whether perceptions of medication safety behaviours differ between groups of young-old, middle-old and old-old patients (65-74 years, 75-84 years, and ≥ 85 years).

METHODS

A survey, which included the Inpatient Medication Safety Involvement Scale (IMSIS) was administered to 200 older patients from medical settings, at one hospital. Data were analysed using descriptive statistics, Spearman's rho and the Kruskal-Wallis test.

RESULTS

Patients reported a desire to ask questions (59.5% n = 119) and check with healthcare professionals if they perceived that a medication was wrong (86.5% n = 173) or forgotten (87.0% n = 174). Patients did not have particular preferences, which differed from their experiences in terms of viewing the medication administration chart and self-administering medications. Preferred and reported behaviours correlated positively (r = 0.46-0.58, n = 200, p ≤ 0.001). Young-old patients preferred notifying healthcare professionals of perceived medication errors more than middle-old and old-old patients (p ≤ 0.05).

CONCLUSIONS

Older patients may prefer verbal medication safety behaviours like asking questions and notifying healthcare professionals of medication errors, over viewing medication charts and self-administering medications. The young-old group wanted to identify perceived medication errors more than other age groups. Older patients are willing to engage in medication safety behaviours, and healthcare professionals and organisations need to embrace this engagement in an effort to reduce medication harm.

Clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems: A systematic review of clinical prediction models and risk assessment tools

AIMS

This study aimed to review systematically all available prediction tools identifying adult hospitalized patients at risk of drug-related problems, and to synthesize the evidence on performance and clinical usefulness.

METHODS

PubMed, Scopus, Web of Science, Embase, and CINAHL databases were searched for relevant studies. Titles, abstracts and full-text studies were sequentially screened for inclusion by two independent reviewers. The Prediction Model Risk of Bias Assessment Tool (PROBAST) and the Revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklists were used to assess risk of bias and applicability of prediction tools. A narrative synthesis was performed.

RESULTS

A total of 21 studies were included, 14 of which described the development of new prediction tools (four risk assessment tools and ten clinical prediction models) and six studies were validation based and one an impact study. There were variations in tool development processes, outcome measures and included predictors. Overall, tool performance had limitations in reporting and consistency, with the discriminatory ability based on area under the curve receiver operating characteristics (AUROC) ranging from poor to good (0.62-0.81), sensitivity and specificity ranging from 57.0% to 89.9% and 30.2% to 88.0%, respectively. The Medicines Optimisation Assessment tool and Assessment of Risk tool were prediction tools with the lowest risk of bias and low concern for applicability. Studies reporting external validation and impact on patient outcomes were scarce.

CONCLUSION

Most prediction tools have limitations in development and validation processes, as well as scarce evidence of clinical usefulness. Future studies should attempt to either refine currently available tools or apply a rigorous process capturing evidence of acceptance, usefulness, performance and outcomes.

Predicting inpatient pharmacy order interventions using provider action data

Objective

The widespread deployment of electronic health records (EHRs) has introduced new sources of error and inefficiencies to the process of ordering medications in the hospital setting. Existing work identifies orders that require pharmacy intervention by comparing them to a patient’s medical records. In this work, we develop a machine learning model for identifying medication orders requiring intervention using only provider behavior and other contextual features that may reflect these new sources of inefficiencies.

Materials and Methods

Data on providers’ actions in the EHR system and pharmacy orders were collected over a 2-week period in a major metropolitan hospital system. A classification model was then built to identify orders requiring pharmacist intervention. We tune the model to the context in which it would be deployed and evaluate global and local feature importance.

Results

The resultant model had an area under the receiver-operator characteristic curve of 0.91 and an area under the precision-recall curve of 0.44.

Conclusions

Providers’ actions can serve as useful predictors in identifying medication orders that require pharmacy intervention. Careful model tuning for the clinical context in which the model is deployed can help to create an effective tool for improving health outcomes without using sensitive patient data.

Patient Factors Associated with Pharmaceutical Interventions for Inpatients at a Brazilian Teaching Hospital

Background

Pharmaceutical interventions aim to correct or prevent a drug-related problem (DRP) that might lead to negative clinical consequences and increase health care costs.

Objective

To identify variables associated with the provision of pharmaceutical interventions by clinical pharmacists during hospitalization.

Methods

In this retrospective cohort study, adult inpatients of the medical ward of the University Hospital of the University of São Paulo in São Paulo, Brazil, were followed from admission to discharge. Logistic regression models were used to evaluate the association between occurrence of at least 1 pharmaceutical intervention and the following baseline characteristics: sex, age, Charlson comorbidity index, renal failure, electrolyte imbalance, hemoglobin, platelet count, and use of a nasoenteric tube, as well as the number, second-level Anatomical Therapeutic Chemical (ATC) code, and administration route of prescribed medications.

Results

A total of 148 patients were included in the study, of whom 75 (50.7%) were men. The mean age was 62.8 (95% confidence interval [CI] 59.9-65.8) years, and the mean length of the hospital stay was 10.7 (95% CI 8.4-13.1) days. Analgesics (ATC code N02), the most common type of medication, were prescribed to 144 (97.3%) of the patients. Pharmaceutical interventions were performed for only 49 (33.1%) of the patients. One out of every 4 of these interventions was intended to obtain information not provided in the prescription, to allow the prescription to be completed and dispensing to proceed. According to the multivariate analysis, the odds ratio (OR) of occurrence of at least 1 pharmaceutical intervention increased for patients with electrolyte imbalance (OR 2.68, 95% CI 1.09-6.63; p = 0.033), patients using 5 to 8 medications (OR 8.73, 95% CI 1.07-71.36; p = 0.043), patients using 9 or more medications (OR 10.39, 95% CI 1.28-84.05; p = 0.028), and patients using at least 1 systemic antibacterial (ATC code J01; OR 2.76, 95% CI 1.30-5.84; p = 0.008).

Conclusions

The findings of this study could allow the identification, at the time of admission and possibly before the occurrence of a DRP, of patients at higher risk of requiring a pharmaceutical intervention later during their hospital stay. To optimize patient care, clinical pharmacists should closely follow inpatients with electrolyte imbalance, polypharmacy, and/or use of systemic antibacterials.

A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error

Objective

To improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks.

Materials and Methods

Data from electronic health records were collated over a period of 18 months. Inferred scores at a patient level (probability of a patient’s set of active orders to require a pharmacist review) were calculated using a hybrid approach (machine learning and a rule-based expert system). A clinical pharmacist analyzed randomly selected prescription orders over a 2-week period to corroborate our findings. Predicted scores were compared with the pharmacist’s review using the area under the receiving-operating characteristic curve and area under the precision-recall curve. These metrics were compared with existing tools: computerized alerts generated by a clinical decision support (CDS) system and a literature-based multicriteria query prioritization technique. Data from 10 716 individual patients (133 179 prescription orders) were used to train the algorithm on the basis of 25 features in a development dataset.

Results

While the pharmacist analyzed 412 individual patients (3364 prescription orders) in an independent validation dataset, the areas under the receiving-operating characteristic and precision-recall curves of our digital system were 0.81 and 0.75, respectively, thus demonstrating greater accuracy than the CDS system (0.65 and 0.56, respectively) and multicriteria query techniques (0.68 and 0.56, respectively).

Discussion

Our innovative digital tool was notably more accurate than existing techniques (CDS system and multicriteria query) at intercepting potential prescription errors.

Conclusions

By primarily targeting high-risk patients, this novel hybrid decision support system improved the accuracy and reliability of prescription checks in a hospital setting.

Mapping of drug-related problems among older adults conciliating medical and pharmaceutical approaches

PURPOSE

To lay the fundamentals of drug-related problems (DRPs) in older adults, and to organize them according to a logical process conciliating medical and pharmaceutical approaches, to better identify the causes and consequences of DRPs.

MATERIALS AND METHODS

A narrative overview.

RESULTS

The causes of DRPs may be intentional or unintentional. They lie in poor prescription, poor adherence, medication errors (MEs) and substance use disorders (SUD). Poor prescription encompasses sub-optimal or off-label drug choice; this choice is either intentional or unintentional, often within a polypharmacy context and not taking sufficiently into account the patient's clinical condition. Poor adherence is often the consequence of a complicated administration schedule. This review shows that MEs are not the most frequent causes of DRPs. SUD are little studied in older adults and needs to be more investigated because the use of psychoactive substances among older people is frequent. Prescribers, pharmacists, nurses, patients, and caregivers all play a role in different causes of DRPs. The potential deleterious outcomes of DRPs result from adverse drug reactions and therapeutic failures. These can lead to a negative benefit-risk ratio for a given treatment regimen.

DISCUSSION/CONCLUSION

Interdisciplinary pharmacotherapy programs show significant clinical impacts in preventing or resolving adverse drug events and, suboptimal responses. New technologies also seem to be interesting solutions to prevent MEs. Better communication between healthcare professionals, patients and their caregivers would ensure greater safety and effectiveness of treatments.

Development and validation of a score to assess risk of medication errors detected during medication reconciliation process at admission in internal medicine unit: SCOREM study

BACKGROUND

Medication errors (ME) can be reduced through preventive strategies such as medication reconciliation. Such strategies are often limited by human resources and need targeting high risk patients.

AIMS

To develop a score to identify patients at risk of ME detected during medication reconciliation in a specific population from internal medicine unit.

METHODS

Prospective observational study conducted in an internal medicine unit of a French University Hospital from 2012 to 2016. Adult hospitalised patients were eligible for inclusion. Medication reconciliation was conducted by a pharmacist and consisted in comparing medication history with admission prescription to identify MEs. Risk factors of MEs were analysed using multivariate stepwise logistic regression model. A risk score was constructed using the split-sample approach. The split was done at random (using a fixed seed) to define a development data set (N = 1256) and a validation sample (N = 628). A regression coefficient-base scoring system was used adopting the beta-Sullivan approach (Sullivan's scoring).

RESULTS

Pharmacists detected 740 MEs in 368/1884 (19.5%) patients related to medication reconciliation. Female gender, number of treatments >7, admission from emergency department and during night or weekend were significantly associated with a higher risk of MEs. Risk score was constructed by attributing 1 or 2 points to these variables. Patients with a score ≥3 (OR [95% CI] 3.10 [1.15-8.37]) out of 5 (OR [95% CI] 8.11 [2.89-22.78]) were considered at high risk of MEs.

CONCLUSIONS

Risk factors identified in our study may help prioritising patients admitted in internal medicine units who may benefit the most from medication reconciliation (ClinicalTrials.gov number NCT03422484).

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.

Adverse Drug Events and Contributing Factors Among Hospitalized Adult Patients at Jimma Medical Center, Southwest Ethiopia: A Prospective Observational Study

Background

Adverse drug events (ADEs) are common complications of clinical care resulting in significant morbidity, mortality, and high clinical expenditure. Population-level estimates of inpatient ADEs are limited in Ethiopia.

Objective

This study aimed to assess the incidence, contributing factors, severity, and preventability of ADEs among hospitalized adult patients at Jimma Medical Center, Ethiopia.

Methods

A prospective observational study design was conducted among hospitalized adult patients at tertiary hospital in Ethiopia. A structured data collection tool was prepared from relevant literatures for data collection. Data were analyzed using statistical software. Logistic regression was performed to identify factors contributing to ADE occurrence. P values < 0.05 were considered statistically significant.

Results

A total of 319 patients were included with follow-up period of 5667 person-days. About 50.5% were women. The mean (SD) age of patients was 43 (17.6) years. One hundred sixteen ADEs were identified with the incidence of 36.4 (95% CI, 30.1-43.6) per 100 admissions and 20.5 (95% CI, 16.9-24.6) per 1000 person-days. Antituberculosis agents (adjusted odds ratio [aOR] = 2.52; 95% CI, 1.06-5.98; P = 0.036), disease of the circulatory system (aOR = 2.67; 95% CI, 1.46-4.89; P = 0.001), disease of the digestive system (aOR = 2.84; 95% CI, 1.45-5.57; P = 0.002), being on medication during admission (aOR = 3.09; 95% CI, 1.77-5.41; P < 0.001), and hospital stay more than 2 weeks (aOR = 3.93; 95% CI, 1.39-11.12; P = 0.010) were independent predictors of ADE occurrence.

Conclusions

One in every 4 patients admitted to the hospital experienced ADEs during their hospital stay. Most ADEs were moderate in severity. About two-thirds of the ADEs identified were deemed probably or definitely preventable. Therefore, it is high time to reinforce large-scale efforts to redesign safer, higher quality health care systems to adequately tackle the problem.

Susceptibility to adverse drug reactions

The pharmacological effects of a drug depend on its concentration at the site of action, and therefore on the concentration in blood and on the dose. The relationship between the concentration or dose and the corresponding effect can usually be represented mathematically as a rectangular hyperbola; when effect is plotted against log concentration or log dose, the curve is sigmoidal. Inevitably, the effect size and the doses causing benefit and harm will differ among individuals, since they are biological phenomena: some individuals are more likely than others to suffer harm at any given dose. Some harmful effects can occur at much lower doses than those used in therapeutics; that is, the log dose-response curve for harm lies far to the left of the log dose-response curve for benefit. Those who suffer such reactions are hypersusceptible. When the dose-response curves for harm and therapeutic effect are in the same range, dose cannot separate the harmful effects from the therapeutic effects, and adverse reactions are collateral. Toxic effects occur when harmful doses are above the doses needed for benefit. In this review we consider factors that influence a subject's susceptibility to adverse drug reactions. Determinants of susceptibility include Immunological, Genetic, demographic (Age and Sex), Physiological and Exogenous factors (drug-drug interactions, for example), and Diseases and disorders such as renal failure, giving the mnemonic I GASPED. Some susceptibility factors are discrete (for example, all-or-none) and some are continuous; susceptibility can therefore be discrete or continuous; and the factors can interact to determine a person's overall susceptibility to harm.

Development of a multivariable prediction model for identification of patients at risk for medication transfer errors at ICU discharge

INTRODUCTION

Discharge from the intensive care unit (ICU) is a high-risk process, leading to numerous potentially harmful medication transfer errors (PH-MTE). PH-MTE could be prevented by medication reconciliation by ICU pharmacists, but resources are scarce, which renders the need for predicting which patients are at risk for PH-MTE. The aim of this study was to develop a prognostic multivariable model in patients discharged from the ICU to predict who is at increased risk for PH-MTE after ICU discharge, using predictors of PH-MTE that are readily available at the time of ICU discharge.

MATERIAL AND METHODS

Data for this study were derived from the Transfer ICU Medication reconciliation study, which included ICU patients and scored MTE at discharge of the ICU. The potential harm of every MTE was estimated with a validated score, where after MTE with potential for harm were indicated as PH-MTE. Predictors for PH-MTE at ICU discharge were identified using LASSO regression. The c statisticprovided a measure of the overall discriminative ability of the prediction model and the prediction model was internally validated by bootstrap resampling. Based on sensitivity and specificity, the cut-off point of the prediction model was determined.

RESULTS

The cohort contained 258 patients and six variables were identified as predictors for PH-MTE: length of ICU admission, number of home medications and patient taking one of the following medication groups at home: vitamin/mineral supplements, cardiovascular medication, psycholeptic/analeptic medication and medication for obstructive airway disease. The c of the final prediction model was 0.73 (95%CI 0.67-0.79) and decreased to 0.62 according to bootstrap resampling. At a cut-off score of two the prediction model yielded a sensitivity of 70% and a specificity of 61%.

CONCLUSIONS

A multivariable prediction model was developed to identify patients at risk for PH-MTE after ICU discharge. The model contains predictors that are available on the day of ICU discharge. Once external validation and evaluation of this model in daily practice has been performed, its incorporation into clinical practice could potentially allow institutions to identify patients at risk for PH-MTE after ICU discharge, on the day of ICU discharge, thus allowing for efficient, patient-specific allocation of clinical pharmacy services.

TRIAL REGISTRATION

Dutch trial register: NTR4159, 5 September 2013, retrospectively registered.

Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists' input to prevent medication-related problems

Background

Medicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services, there is a need to increase efficiency while maintaining patient safety.

Objective

To develop a prediction tool, the Medicines Optimisation Assessment Tool (MOAT), to target patients most in need of pharmacists’ input in hospital.

Methods

Patients from adult medical wards at two UK hospitals were prospectively included into this cohort study. Data on medication-related problems (MRPs) were collected by pharmacists at the study sites as part of their routine daily clinical assessments. Data on potential risk factors, such as number of comorbidities and use of ‘high-risk’ medicines, were collected retrospectively. Multivariable logistic regression modelling was used to determine the relationship between risk factors and the study outcome: preventable MRPs that were at least moderate in severity. The model was internally validated and a simplified electronic scoring system developed.

Results

Among 1503 eligible admissions, 610 (40.6%) experienced the study outcome. Eighteen risk factors were preselected for MOAT development, with 11 variables retained in the final model. The MOAT demonstrated fair predictive performance (concordance index 0.66) and good calibration. Two clinically relevant decision thresholds (ie, the minimum predicted risk probabilities to justify pharmacists’ input) were selected, with sensitivities of 90% and 66% (specificity 30% and 61%); these equate to positive predictive values of 47% and 54%, respectively. Decision curve analysis suggests that the MOAT has potential value in clinical practice in guiding decision-making.

Conclusion

The MOAT has potential to predict those patients most at risk of moderate or severe preventable MRPs, experienced by 41% of admissions. External validation is now required to establish predictive accuracy in a new group of patients.