Dataset Information

Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare.

ABSTRACT:

Background

Computer-modelling approaches have the potential to predict the interactions between different antipsychotics and provide guidance for polypharmacy.

Aims

To evaluate the accuracy of the quantitative systems pharmacology platform to predict parkinsonism side-effects in patients prescribed antipsychotic polypharmacy.

Methods

Using anonymized data from South London and Maudsley NHS Foundation Trust electronic health records we applied quantitative systems pharmacology, a neurophysiology-based computer model of humanized neuronal circuits, to predict the risk for parkinsonism symptoms in patients with schizophrenia prescribed two concomitant antipsychotics. The performance of the quantitative systems pharmacology model was compared with the performance of simple parameters such as: combination of affinity constants (1/K_sum); sum of D₂R occupancies (D₂R) and chlorpromazine equivalent dose.

Results

We identified 832 patients with schizophrenia who were receiving two antipsychotics for six or more months, between 1 January 2007 and 31 December 2014. The area under the receiver operating characteristic (AUROC) curve for the quantitative systems pharmacology model was 0.66 ( p = 0.01), while AUROCs for D₂R, 1/K_sum and chlorpromazine equivalent dose were 0.52 ( p = 0.350), 0.53 ( p = 0.347) and 0.52 ( p = 0.330) respectively.

Conclusion

Our results indicate that quantitative systems pharmacology has the potential to predict the risk of parkinsonism associated with antipsychotic polypharmacy from minimal source information, and thus might have potential decision-support applicability in clinical settings.

SUBMITTER: Kadra G

PROVIDER: S-EPMC6238161 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare.

Kadra Giouliana G Spiros Athan A Shetty Hitesh H Iqbal Ehtesham E Hayes Richard D RD Stewart Robert R Geerts Hugo H

Journal of psychopharmacology (Oxford, England) 20180920 11

<h4>Background</h4>Computer-modelling approaches have the potential to predict the interactions between different antipsychotics and provide guidance for polypharmacy.<h4>Aims</h4>To evaluate the accuracy of the quantitative systems pharmacology platform to predict parkinsonism side-effects in patients prescribed antipsychotic polypharmacy.<h4>Methods</h4>Using anonymized data from South London and Maudsley NHS Foundation Trust electronic health records we applied quantitative systems pharmacolo ...[more]

PMID: 30232932

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Project description:BackgroundPolypharmacy is a type of treatment that involves the concurrent use of multiple medications. Drugs may interact when they are used simultaneously. So, understanding and mitigating polypharmacy side effects are critical for patient safety and health. Since the known polypharmacy side effects are rare and they are not detected in clinical trials, computational methods are developed to model polypharmacy side effects.ResultsWe propose a neural network-based method for polypharmacy side effects prediction (NNPS) by using novel feature vectors based on mono side effects, and drug-protein interaction information. The proposed method is fast and efficient which allows the investigation of large numbers of polypharmacy side effects. Our novelty is defining new feature vectors for drugs and combining them with a neural network architecture to apply for the context of polypharmacy side effects prediction. We compare NNPS on a benchmark dataset to predict 964 polypharmacy side effects against 5 well-established methods and show that NNPS achieves better results than the results of all 5 methods in terms of accuracy, complexity, and running time speed. NNPS outperforms about 9.2% in Area Under the Receiver-Operating Characteristic, 12.8% in Area Under the Precision-Recall Curve, 8.6% in F-score, 10.3% in Accuracy, and 18.7% in Matthews Correlation Coefficient with 5-fold cross-validation against the best algorithm among other well-established methods (Decagon method). Also, the running time of the Decagon method which is 15 days for one fold of cross-validation is reduced to 8 h by the NNPS method.ConclusionsThe performance of NNPS is benchmarked against 5 well-known methods, Decagon, Concatenated drug features, Deep Walk, DEDICOM, and RESCAL, for 964 polypharmacy side effects. We adopt the 5-fold cross-validation for 50 iterations and use the average of the results to assess the performance of the NNPS method. The evaluation of the NNPS against five well-known methods, in terms of accuracy, complexity, and running time speed shows the performance of the presented method for an essential and challenging problem in pharmacology. Datasets and code for NNPS algorithm are freely accessible at https://github.com/raziyehmasumshah/NNPS .

Project description:ObjectivesThe aim of this study was to demonstrate the existence of systematic associations in drug prescription that lead to the establishment of patterns of polypharmacy, and the clinical interpretation of the associations found in each pattern.MethodsA cross-sectional study was conducted based on information obtained from electronic medical records and the primary care pharmacy database in 2008. An exploratory factor analysis of drug dispensing information regarding 79,089 adult patients was performed to identify the patterns of polypharmacy. The analysis was stratified by age and sex.ResultsSeven patterns of polypharmacy were identified, which may be classified depending on the type of disease they are intended to treat: cardiovascular, depression-anxiety, acute respiratory infection (ARI), chronic obstructive pulmonary disease (COPD), rhinitis-asthma, pain, and menopause. Some of these patterns revealed a clear clinical consistency and included drugs that are prescribed together for the same clinical indication (i.e., ARI and COPD patterns). Other patterns were more complex but also clinically consistent: in the cardiovascular pattern, drugs for the treatment of known risk factors-such as hypertension or dyslipidemia-were combined with other medications for the treatment of diabetes or established cardiovascular pathology (e.g., antiplatelet agents). Almost all of the patterns included drugs for preventing or treating potential side effects of other drugs in the same pattern.ConclusionsThe present study demonstrated the existence of non-random associations in drug prescription, resulting in patterns of polypharmacy that are sound from the pharmacological and clinical viewpoints and that exist in a significant proportion of the population. This finding necessitates future longitudinal studies to confirm some of the proposed causal associations. The information discovered would further the development and/or adaptation of clinical patient guidelines to patients with multimorbidity who are taking multiple drugs.

Dataset Information

Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare.

Background

Aims

Methods

Results

Conclusion

Publications

Predicting parkinsonism side-effects of antipsychotic polypharmacy prescribed in secondary mental healthcare.

Similar Datasets

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