Detection of Drug-Related Problems through a Clinical Decision Support System Used by a Clinical Pharmacy Team

Can the integration of new rules into a clinical decision support system reduce the incidence of acute kidney injury and hyperkalemia among hospitalized older adults: a protocol for a stepped-wedge, cluster-randomized trial (DETECT-IP)

BACKGROUND

Clinical decision support systems (CDSSs) enable the automated, real-time detection of situations associated with a risk of adverse drug events (ADEs). However, the effectiveness of CDSS in reducing ADEs has yet to be demonstrated. We have chosen to focus on the detection of ADE such as hyperkalemia and/or acute kidney injury (AKI), which are common among hospitalized older adults. The present study's primary objective is to use a CDSS to reduce the number of ADEs (such as AKI and/or hyperkalemia) that occur in hospitalized older adults.

METHODS

This is a multicenter, stepped-wedge, cluster-randomized study involving five hospitals. Each hospital will start with a control period (i.e., routine care, during which each center's CDSS is deactivated) and then switch to an intervention period (during which the CDSS is activated). The intervention will be the use of a CDSS and a strategy for managing and transmitting alerts to clinical pharmacists. The rules concerning AKI and hyperkalemia have been drafted and reviewed by a multidisciplinary group. Each rule created in the CDSS is associated with a standardized procedure, based on a review of the literature. Older patients (aged 65 or over) admitted to a participating general medicine ward, a surgical ward, or obstetrics ward will be eligible for inclusion after the provision of verbal informed consent.

DISCUSSION

This study will assess the effectiveness of the CDSS in reducing the incidence of AKI and hyperkalemia. The implementation of the CDSS can assist clinical pharmacists in their daily work and is expected to prevent ADEs.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT05923983. Registered February 02, 2023.

Development of explicit definitions of potentially inappropriate prescriptions for antidiabetic drugs in patients with type 2 diabetes: A multidisciplinary qualitative study

PURPOSE

The management of type 2 diabetes mellitus patients has changed over the past decade, and a large number of antidiabetic drug treatment options are now available. This complexity poses challenges for healthcare professionals and may result in potentially inappropriate prescriptions of antidiabetic drugs in patients with type 2 diabetes mellitus which can be limited using screening tools. The effectiveness of explicit tools such as lists of potentially inappropriate prescriptions has been widely demonstrated. The aim was to set up nominal groups of healthcare professionals from several disciplines and develop a list of explicit definition of potentially inappropriate prescriptions of antidiabetic drugs.

METHODS

In a qualitative, nominal-groups approach, 30 diabetologists, general practitioners, and pharmacists in France developed explicit definitions of potentially inappropriate prescriptions of antidiabetic drugs in patients with type 2 diabetes mellitus. A nominal group technique is a structured method that encourages all the participants to contribute and makes it easier to reach an agreement quickly. Each meeting lasted for two hours.

RESULTS

The three nominal groups comprised 14 pharmacists, 10 diabetologists, and 6 general practitioners and generated 89 explicit definitions. These definitions were subsequently merged and validated by the steering committee and nominal group participants, resulting in 38 validated explicit definitions of potentially inappropriate prescriptions of antidiabetic drugs. The definitions encompassed four contexts: (i) the temporary discontinuation of a medication during acute illness (n = 9; 24%), (ii) dose level adjustments (n = 23; 60%), (iii) inappropriate treatment initiation (n = 3; 8%), and (iv) the need for further monitoring in the management of type 2 diabetes mellitus (n = 3; 8%).

CONCLUSION

The results of our qualitative study show that it is possible to develop a specific list of explicit definitions of potentially inappropriate prescriptions of antidiabetic drugs in patients with type 2 diabetes mellitus by gathering the opinions of healthcare professionals caring for these patients. This list of 38 explicit definitions necessitates additional confirmation by expert consensus before use in clinical practice.

[Use of a pharmaceutical decision support system in the valuation of hospital stays: Evaluation through 3 examples in collaboration with the department of medical information]

Pharmacy decision support systems (PDSS) help clinical pharmacists to prevent and detect adverse drug events. The coding of hospital stays by the department of medical information (DMI) requires expertise, as it determines hospital revenues and the epidemiological data transmitted via the French national hospital database. The aim was to study the interest and feasibility of using a PDSS, in collaboration with the DMI, to help with the coding of hospital stays. Over 5 months, three rules were implemented in the PDSS to detect gout, Parkinson's disease and oro-pharyngeal candidiasis. The PDSS alerts were analyzed by a pharmacy resident and then forwarded to the DMI, who analyzed the stays to see whether or not the coding for the disease corresponding to the alert was present. The absence of coding was evaluated and tracked, along with the resulting change in severity and valuation. Three hundred and ninety-nine alerts from the PDSS were analyzed and sent to the DMI, representing 211 stays and 309 uniform hospital standardized discharge abstract (UHSDA) in the fields of medicine, surgery and obstetrics. Two hundred and eight (67.3%) UHSDA did not have the coding corresponding to the alert. For the majority of these UHSDAs, apart from diagnostic precision, there was no impact on the valuation of stays. For 4 UHSDAs, the addition of the diagnosis code led to an increase in the value of the stay and the severity of the homogeneous patient groups. The total revaluation corresponding to this modification was €5416. The use of PDSS has helped in the precision of diagnosis coding and the valuation of stays. This result must be weighed against the time invested in analyzing alerts and associated coding. An improvement in disease detection and data processing is needed to be feasible in practice, given the more than 227,600 RSS performed per year at our facility.

Development of machine-learning models using pharmacy inquiry database for predicting dose-related inquiries in a tertiary teaching hospital

BACKGROUND

Drug-related problems (DRPs) are a significant concern in healthcare. Pharmacists play a vital role in detecting and resolving DRPs to improve patient safety. A pharmacy inquiry program was established in a tertiary teaching hospital to document inquiries about physicians' orders, aimed at preventing potential DRPs or providing medication information during order reviews.

OBJECTIVE

We aimed to develop machine-learning models using a pharmacy inquiry database to predict dose-related inquiries based on prescriptions and patient information.

METHODS

This retrospective study analyzed 20,393 pharmacy inquiries collected between January 2018 and February 2023. Data included prescription information (drug ingredient, dose, unit, and frequency), patient characteristics (age, sex, weight, and department), and renal function. The inquiries were categorized into two classes: dose-related inquiries (e.g., wrong dose and inappropriate regimen) and non-dose-related inquiries (e.g., inappropriate drug form and administration route). Six machine-learning models were developed: logistic regression, support vector classifier, decision tree, random forest, extreme gradient boosting, and categorical boosting. To evaluate the performance of the models, the area under the receiver operating characteristic curve and the accuracy were compared.

RESULTS

The CatBoost model achieved the highest performance (sensitivity: 0.92; accuracy: 0.79). The SHapley Additive exPlanations values highlighted the importance of features in the model predictions, drug ingredients, units, and renal function, in that order. Notably, lower renal function positively contributed to the prediction of dose-related inquiries. Additionally, the subsequent feature importance among drug ingredients showed that drugs such as acetylsalicylic acid, famotidine, metformin, and spironolactone strongly influenced the prediction of dose-related inquiries.

CONCLUSION

Machine-learning models that use pharmacy inquiry data can effectively predict dose-related inquiries. Further external validation and refinement of the models are required for broader applications in healthcare settings. These findings provide valuable guidance for healthcare professionals and highlight the potential of machine learning in pharmacists' decision-making.

One Hundred Explicit Definitions of Potentially Inappropriate Prescriptions of Antibiotics in Hospitalized Older Patients: The Results of an Expert Consensus Study

BACKGROUND

In geriatrics, explicit criteria for potentially inappropriate prescriptions (PIPs) are useful for optimizing drug use.

OBJECTIVE

To produce an expert consensus on explicit definitions of antibiotic-PIPs for hospitalized older patients.

METHODS

We conducted a Delphi survey involving French experts on antibiotic stewardship in hospital settings. During the survey's rounds, the experts gave their opinion on each explicit definition, and could suggest new definitions. Definitions with a 1-to-9 Likert score of between 7 and 9 from at least 75% of the participants were adopted. The results were discussed during consensus meetings after each round.

RESULTS

Of the 155 invited experts, 128 (82.6%) participated in the whole survey: 59 (46%) infectious diseases specialists, 45 (35%) geriatricians, and 24 (19%) other specialists. In Round 1, 65 explicit definitions were adopted and 21 new definitions were suggested. In Round 2, 35 other explicit definitions were adopted. The results were validated during consensus meetings (with 44 participants after Round 1, and 54 after Round 2).

CONCLUSIONS

The present study is the first to have provided a list of explicit definitions of potentially inappropriate antibiotic prescriptions for hospitalized older patients. It might help to disseminate key messages to prescribers and reduce inappropriate prescriptions of antibiotics.