Prioritisation of Adverse Drug Events Leading to Hospital Admission and Occurring during Hospitalisation: A RAND Survey

(1) Adverse drug events (ADEs) are a common cause of emergency department visits and occur frequently during hospitalisation. Instruments that facilitate the detection of the most relevant ADEs could lead to a more targeted and efficient use of limited resources in research and practice. (2) We conducted two consensus processes based on the RAND/UCLA appropriateness method, in order to prioritise ADEs leading to hospital admission (panel 1) and occurring during hospital stay (panel 2) for inclusion in future ADE measurement instruments. In each panel, the experts were asked to assess the “overall importance” of each ADE on a four-point Likert scale (1 = not important to 4 = very important). ADEs with a median rating of ≥3 without disagreement were defined as “prioritised“. (3) The 13 experts in panel 1 prioritised 38 out of 65 ADEs, while the 12 experts in panel 2 prioritised 34 out of 63 ADEs. The highest rated events were acute kidney injury and hypoglycaemia (both panels), as well as Stevens–Johnson syndrome in panel 1 and rhabdomyolysis in panel 2. (4) The survey led to a set of ADEs for which there was consensus that they were of particular importance as presentations of acute medication-related harm, thereby providing a focus for further medication safety research and clinical practice.

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.

Knowledge of the Use of Herbal Medicines among Community Pharmacists and Reporting Their Adverse Drug Reactions

Introduction:

Community pharmacist’s knowledge about the uses of herbal medicines and its adverse drug reactions reporting can contribute in better therapeutic outcomes and patient safety. Objectives: To evaluate community pharmacists’ knowledge about the use of herbal medicines and its adverse drug reactions reporting in Kedah state, Malaysia.

Methods:

A cross-sectional, questionnaire-based study was conducted among 103 pharmacists from 74 different community pharmacies to assess their knowledge about the use of herbal medicines and its adverse drug reaction reporting by using a pre-validate knowledge questionnaire consisting of 12 questions related to it. The pharmacists’ responses were measured at a 3-point Likert scale (Poor=1, Moderate=2, and Good=3) and data was entered in SPSS version 22. The minimum and maximum possible scores for knowledge questionnaires were 12 and 36 respectively. Quantitative data was analyzed by using One Way ANOVA and Paired t-test whereas Chi-square and Fisher exact test were used for qualitative data analysis. A p-value of less than 0.05 was considered statistically significant for all the analyses.

Results:

About 92% of the pharmacist had good knowledge regarding the use of herbal medicines and its adverse drug reaction reporting with a mean knowledge score of 32.88±3.16. One-way ANOVA determined a significant difference of employment setting (p<0.043) and years of experience (<0.008) with mean knowledge scores of Pharmacists. Pharmacists’ knowledge was significantly associated with their years of experience with the Chi-square test.

Conclusion:

Pharmacists exhibit good knowledge regarding the use of herbal medicines and its adverse drug reaction reporting. However, with an increasing trend of herbal medicine use and its adverse drug reaction reporting it recalls the empowerment of experienced pharmacists with training programs in this area for better clinical outcomes.

Predicting medication-associated altered mental status in hospitalized patients: Development and validation of a risk model

PURPOSE

This study presents a medication-associated altered mental status (AMS) risk model for real-time implementation in inpatient electronic health record (EHR) systems.

METHODS

We utilized a retrospective cohort of patients admitted to 2 large hospitals between January 2012 and October 2013. The study population included admitted patients aged ≥18 years with exposure to an AMS risk-inducing medication within the first 5 hospitalization days. AMS events were identified by a measurable mental status change documented in the EHR in conjunction with the administration of an atypical antipsychotic or haloperidol. AMS risk factors and AMS risk-inducing medications were identified from the literature, drug information databases, and expert opinion. We used multivariate logistic regression with a full and backward eliminated set of risk factors to predict AMS. The final model was validated with 100 bootstrap samples.

RESULTS

During 194,156 at-risk days for 66,875 admissions, 262 medication-associated AMS events occurred (an event rate of 0.13%). The strongest predictors included a history of AMS (odds ratio [OR], 9.55; 95% confidence interval [CI], 5.64-16.17), alcohol withdrawal (OR, 3.34; 95% CI, 2.18-5.13), history of delirium or psychosis (OR, 3.25; 95% CI, 2.39-4.40), presence in the intensive care unit (OR, 2.53; 95% CI, 1.89-3.39), and hypernatremia (OR, 2.40; 95% CI, 1.61-3.56). With a C statistic of 0.85, among patients scoring in the 90th percentile, our model captured 159 AMS events (60.7%).

CONCLUSION

The risk model was demonstrated to have good predictive ability, with all risk factors operationalized from discrete EHR fields. The real-time identification of higher-risk patients would allow pharmacists to prioritize surveillance, thus allowing early management of precipitating factors.

Development and validation of a dynamic inpatient risk prediction model for clinically significant hypokalemia using electronic health record data

Purpose

The purpose of this study was to develop a dynamic risk prediction model for inpatient hypokalemia and evaluate its predictive performance.

Methods

A retrospective cohort included all admissions aged 18 years and above from 2 large tertiary hospitals in Florida over a 22-month period. Hypokalemia was defined as a potassium value of less than 3 mmol/L, and subsequent initiation of potassium supplements. Twenty-five risk factors (RF) identified from literature were operationalized using discrete electronic health record (EHR) data elements. For each of the first 5 hospital days, we modeled the probability of developing hypokalemia at the subsequent hospital day using logistic regression. Predictive performance of our model was validated with 100 bootstrap datasets and evaluated by the C statistic and Hosmer-Lemeshow goodness-of-fit test.

Results

A total of 4511 hypokalemia events occurred over 263 436 hospital days (1.71%). Validated C statistics of the prediction model ranged from 0.83 (Day 1 model) to 0.86 (Day 3), while p-values for the Hosmer-Lemeshow test spanned from 0.005 (Day 1) to 0.27 (Day 4 and 5). For the Day 3 prediction, 9.9% of patients with risk scores in the 90th percentile developed hypokalemia and accounted for 60.4% of all hypokalemia events. After controlling for baseline potassium values, strong predictors included diabetic ketoacidosis, increased mineralocorticoid activity, polyuria, use of kaliuretics, use of potassium supplements and watery stool.

Conclusion

This is the first risk prediction model for hypokalemia. Our model achieved excellent discrimination and adequate calibration ability. Once externally validated, this risk assessment tool could use real-time EHR information to identify individuals at the highest risk for hypokalemia and support proactive interventions by pharmacists.

Patient prioritization for pharmaceutical care in hospital: A systematic review of assessment tools

BACKGROUND

Clinical pharmacy services improve patient safety, outcomes, and care quality; however, UK clinical pharmacy services face limited resources, insufficient capacity, and patients who present with increasingly complex medication regimes and morbidities. These indicate a need for the prioritization of pharmacy services. Several prioritization tools have been developed; however, there has been no comprehensive review of such tools to date.

OBJECTIVE

A systematic review was conducted to provide a structured overview and description of existing assessment tools with a focus on study quality, themes, tool validity, risk factors, and high-risk drug classes.

METHODS

Systematic searches for English-language publications (from 1990 to September 2017) were conducted in Embase, Medline, Scopus, International Pharmaceutical Abstracts, and Web of Science. Papers in the inpatient setting and in which the tool users were pharmacists or pharmacy technicians were included. Data on each study (e.g. aim and design) and the structure of tools (e.g. risk factors) from each included study were extracted by 2 independent reviewers. A descriptive analysis was conducted to summarize these tools along with a thematic analysis of study findings. The quality of each paper was assessed using the Hawker method.

RESULTS

Nineteen studies involving 17 risk assessment tools were included. Most tools were developed in Europe (76.5%) and published in the last 5 years (82%). Most tools (88%) were designed to identify patients at greatest risk of adverse drug reactions, adverse drug events, or medication errors and to guide appropriate pharmaceutical care. Ten out of 17 tools (59%) were validated. None showed a measurable impact on prescription errors or adverse drug events. Keys themes identified from the studies were the positive impact of risk assessment tools on both patient care and provision of pharmacy services as well as the limitations of risk assessment tools.

CONCLUSIONS

Current assessment tools are heterogeneous in their content, targeting diverse patient groups and clinical settings making generalization difficult. However, an underlying theme of all studies was that tools appear to achieve their aim in directing pharmaceutical care to where it is needed most which might provide reassurance and incentive for greater adoption and development of tools across clinical pharmacy services. However, further research is required to measure objectively the impact of tools on patient outcomes and on workforce efficiency so that comparisons can be made between tools.

Measurement of selected preventable adverse drug events in electronic health records: Toward developing a complexity score

PURPOSE

The defining of a select number of high-priority preventable adverse drug events (pADEs) for measurement in the electronic health record (EHR) and the estimation of pADE incidences in two tertiary care facilities are described.

METHODS

This study was part of a larger effort aimed at developing an automated electronic health record (EHR)-based complexity-score (C-score) that ranks hospitalized patients according to their risk for pADEs for clinical intervention. We developed measures for 16 high-priority pADEs often deemed preventable using discrete clinical and administrative EHR data. For each pADE we specified inclusion and exclusion criteria that were used to define risk populations for each specific pADE. The incidence of each type of pADE was then measured during a designated follow-up period considering all adult admissions to 2 large academic tertiary care hospitals, who were eligible for the pADE-specific risk populations during any of their first 5 hospital days.

RESULTS

Utilizing the data from 83,787 admissions who were at risk for at least one pADE during at least one of their first five hospital days, we found that 27,193 admissions (32.5%) developed at least one pADE. Uncontrolled postsurgical pain, uncontrolled pneumonia, and drug-associated hypotension had the highest incidences with the following number of days with pADE per number of patients at risk: 13,484 of 19,640; 527 of 1,530; and 13,394 of 43,630, while drug-associated falls (446 of 75,036), drug-associated acute mental status changes (262 of 66,875) and venous thromboembolism (214 of 74,283) had the lowest incidence rates.

CONCLUSION

EHR-based definitions of clinically important pADEs were developed, and the incidence of the pADEs was estimated. These definitions will be advanced for the creation of prediction models to develop a C-score for identifying patients at risk for pADEs to prioritize pharmacist intervention.