Dataset Information

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

ABSTRACT:

Objectives

The aim was to use routine data available at a patient's admission to the hospital to predict polypharmacy and drug-drug interactions (DDI) and to evaluate the prediction performance with regard to its usefulness to support the efficient management of benefits and risks of drug prescriptions.

Design

Retrospective, longitudinal study.

Setting

We used data from a large multicentred pharmacovigilance project carried out in eight psychiatric hospitals in Hesse, Germany.

Participants

Inpatient episodes consecutively discharged between 1 October 2017 and 30 September 2018 (year 1) or 1 January 2019 and 31 December 2019 (year 2).

Outcome measures

The proportion of rightly classified hospital episodes.

Methods

We used gradient boosting to predict respective outcomes. We tested the performance of our final models in unseen patients from another calendar year and separated the study sites used for training from the study sites used for performance testing.

Results

A total of 53 909 episodes were included in the study. The models' performance, as measured by the area under the receiver operating characteristic, was 'excellent' (0.83) and 'acceptable' (0.72) compared with common benchmarks for the prediction of polypharmacy and DDI, respectively. Both models were substantially better than a naive prediction based solely on basic diagnostic grouping.

Conclusion

This study has shown that polypharmacy and DDI can be predicted from routine data at patient admission. These predictions could support an efficient management of benefits and risks of hospital prescriptions, for instance by including pharmaceutical supervision early after admission for patients at risk before pharmacological treatment is established.

SUBMITTER: Wolff J

PROVIDER: S-EPMC8043005 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

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

Wolff Jan J Hefner Gudrun G Normann Claus C Kaier Klaus K Binder Harald H Domschke Katharina K Hiemke Christoph C Marschollek Michael M Klimke Ansgar A

BMJ open 20210409 4

<h4>Objectives</h4>The aim was to use routine data available at a patient's admission to the hospital to predict polypharmacy and drug-drug interactions (DDI) and to evaluate the prediction performance with regard to its usefulness to support the efficient management of benefits and risks of drug prescriptions.<h4>Design</h4>Retrospective, longitudinal study.<h4>Setting</h4>We used data from a large multicentred pharmacovigilance project carried out in eight psychiatric hospitals in Hesse, Germa ...[more]

PMID: 33837103

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Project description:Background and aimsDrug-drug interactions (DDIs) are a significant health issue that may adversely affect the health and well-being of patients. This study assesses and compares potential DDI (pDDI) patterns, severity, and associated risk factors in government and private hospitals in Dhaka, Bangladesh.MethodsA total of 188 and 206 prescriptions were collected from various government and private hospitals' outdoor departments, respectively, by capturing pictures of the prescriptions. Bivariate analyses were performed through STATA 15. MedScape drug interaction checker was applied to identify pDDIs, while their consequences were obtained from DrugBank and MedScape.ResultsPrivate hospitals had more pDDIs containing prescriptions than government hospitals (62.62% and 57.97%, respectively). The mean pDDIs cases were 3.29 in the private hospitals, while at government hospitals they were 3.02. Among the detected pDDIs, pharmacodynamic pDDIs were predominat, accounting for 63.32% and 66.23% of total events in government and private hospitals, respectively. Severity-wise, both types of hospitals had almost equal amounts of serious (10.34% vs. 9.18%), moderate (68.96% vs. 59.61%), and minor (20.06% vs. 21.79%) pDDIs. Polypharmacy was crucial in pDDI cases, responsible for 70.21% and 81.83% of pDDIs in government and private hospitals, respectively. Multiple comorbidities with pDDIs were more common in private hospitals (24.46% vs. 18.93%), while government hospitals displayed a higher frequency of pDDIs with one comorbidity (24.75% vs. 17.55%). Non-mention of comorbidities was correlated with both types of hospitals (p ≤ 0.01) in pDDIs cases. Furthermore, considerable amounts of pDDIs in prescription error categories were detected. Both types of hospitals had a prevalence of antihypertensive, antidiabetic, psychotic, and antiplatelet-related pDDIs.ConclusionThe two kinds of hospitals exhibited similar pDDI patterns, while their associations were random with the risk variables. When prescribing pharmacokinetics and pharmacodynamics pDDIs, physicians should evaluate the risk-benefit ratio.

Project description: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.

Dataset Information

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

Objectives

Design

Setting

Participants

Outcome measures

Methods

Results

Conclusion

Publications

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

Similar Datasets

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