Predicting the risk of drug–drug interactions in psychiatric hospitals: a retrospective longitudinal pharmacovigilance study

Wolff, Jan; Hefner, Gudrun; Normann, Claus; Kaier, Klaus; Binder, Harald; Domschke, Katharina; Hiemke, Christoph; Marschollek, Michael; Klimke, A.

doi:10.1136/bmjopen-2020-045276

Cited by 11 publications

(6 citation statements)

References 75 publications

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“…Although other studies have shown that the number of diagnoses increases with the number of pDDIs, this may not hold for psychiatric patients in hospital settings 22. Some studies have shown that age is a significant risk factor for pDDIs,2 6 12 while some have not confirmed this association 23 24. Our study showed that age is a protective factor, which can be explained by greater care and more detailed analysis of therapy in elderly patients because they usually have more comorbidities, resulting in more drugs and an increased risk of potential interactions between them 21.…”

Section: Discussioncontrasting

confidence: 50%

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Factors associated with potential drug–drug interactions in psychiatric inpatients

2022

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ObjectiveThe aim of this study was to investigate the prevalence and severity of potential drug–drug interactions (pDDIs) in hospitalised patients with major psychiatric disorders and to identify factors associated with their occurrence.MethodsThe research was designed as an observational, cross-sectional study conducted at the Clinic for Mental Disorders (CMD) ‘Dr. Laza Lazarevic’, Belgrade, Serbia. Medscape, Epocrates and Lexicomp bases were used to detect potential drug interactions among inpatients. Multivariate regression analysis was used to reveal risk and protective factors associated with the number of pDDIs.ResultsThe study included 511 patients, average age 44.63±11.81 years. The average number of pDDIs per patient ranged from 5.9±4.7 (Medscape) to 8.2±5.4 (Epocrates) and 8.5±5.1 (Lexicomp). The following risk factors were identified by all three interaction checkers used: C-reactive protein, number of pharmacological subgroups, number of prescribed drugs, antibiotics, antacids, vitamins, number of associated comorbidities, route, form and dose of the drug.ConclusionsWhen making clinical decisions to reduce drug problems, including DDIs, one should consult several interaction databases, which should be reviewed by a multidisciplinary team consisting of an experienced clinical pharmacist, physician, nurse, and so on.

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Section: Discussioncontrasting

confidence: 50%

“…The risks from DDIs are substantially increased in patients with more comorbidities, and the same was shown by the study of Wolff et al 12. With increased CRP values, antibiotic administration was more frequent, increasing the risk of pDDIs.…”

Section: Discussionmentioning

confidence: 59%

Factors associated with potential drug–drug interactions in psychiatric inpatients

2022

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“…a main diagnosis of F32 � or F33 � , ICD-10. Previous publications from this research project can be found in the reference list [10,12,14,[23][24][25][26].…”

Section: Study Samplementioning

confidence: 99%

Pharmacotherapy, drug-drug interactions and potentially inappropriate medication in depressive disorders

et al. 2021

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Introduction The aim of this study was to describe the number and type of drugs used to treat depressive disorders in inpatient psychiatry and to analyse the determinants of potential drug-drug interactions (pDDI) and potentially inappropriate medication (PIM). Methods Our study was part of a larger pharmacovigilance project funded by the German Innovation Funds. It included all inpatients with a main diagnosis in the group of depressive episodes (F32, ICD-10) or recurrent depressive disorders (F33) discharged from eight psychiatric hospitals in Germany between 1 October 2017 and 30 September 2018 or between 1 January and 31 December 2019. Results The study included 14,418 inpatient cases. The mean number of drugs per day was 3.7 (psychotropic drugs = 1.7; others = 2.0). Thirty-one percent of cases received at least five drugs simultaneously (polypharmacy). Almost one half of all cases received a combination of multiple antidepressant drugs (24.8%, 95% CI 24.1%–25.5%) or a treatment with antidepressant drugs augmented by antipsychotic drugs (21.9%, 95% CI 21.3%–22.6%). The most frequently used antidepressants were selective serotonin reuptake inhibitors, followed by serotonin and norepinephrine reuptake inhibitors and tetracyclic antidepressants. In multivariate analyses, cases with recurrent depressive disorders and cases with severe depression were more likely to receive a combination of multiple antidepressant drugs (Odds ratio recurrent depressive disorder: 1.56, 95% CI 1.41–1.70, severe depression 1.33, 95% CI 1.18–1.48). The risk of any pDDI and PIM in elderly patients increased substantially with each additional drug (Odds Ratio: pDDI 1.32, 95% CI: 1.27–1.38, PIM 1.18, 95% CI: 1.14–1.22) and severity of disease (Odds Ratio per point on CGI-Scale: pDDI 1.29, 95% CI: 1.11–1.46, PIM 1.27, 95% CI: 1.11–1.44), respectively. Conclusion This study identified potential sources and determinants of safety risks in pharmacotherapy of depressive disorders and provided additional data which were previously unavailable. Most inpatients with depressive disorders receive multiple psychotropic and non-psychotropic drugs and pDDI and PIM are relatively frequent. Patients with a high number of different drugs must be intensively monitored in the management of their individual drug-related risk-benefit profiles.

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“…A possible way to mitigate resource constraints in hospitals is the use of modern technologies to make services more cost-effective 4 . For instance, the increasing availability of data allows to support medical decision making in hospitals with information derived from machine learning algorithms 5 , 6 .…”

Section: Introductionmentioning

confidence: 99%

Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data

Wolff

Klimke

Marschollek

et al. 2022

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The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naïve forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44). Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naïve forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, multiple individual forecast horizons could be used simultaneously, such as a yearly model to achieve early forecasts for a long planning period and weekly models to adjust quicker to sudden changes.

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Predicting the risk of drug–drug interactions in psychiatric hospitals: a retrospective longitudinal pharmacovigilance study

Cited by 11 publications

References 75 publications

Factors associated with potential drug–drug interactions in psychiatric inpatients

Factors associated with potential drug–drug interactions in psychiatric inpatients

Pharmacotherapy, drug-drug interactions and potentially inappropriate medication in depressive disorders

Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data

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