Literature DB >> 24218045

Computerized advice on drug dosage to improve prescribing practice.

Florence Gillaizeau1, Ellis Chan, Ludovic Trinquart, Isabelle Colombet, R T Walton, Myriam Rège-Walther, Bernard Burnand, Pierre Durieux.   

Abstract

BACKGROUND: Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health care could be achieved if computer advice improved health outcomes and could be implemented in routine practice in a cost-effective fashion. This is an updated version of an earlier Cochrane systematic review, first published in 2001 and updated in 2008.
OBJECTIVES: To assess whether computerized advice on drug dosage has beneficial effects on patient outcomes compared with routine care (empiric dosing without computer assistance). SEARCH
METHODS: The following databases were searched from 1996 to January 2012: EPOC Group Specialized Register, Reference Manager; Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Ovid; EMBASE, Ovid; and CINAHL, EbscoHost. A "top up" search was conducted for the period January 2012 to January 2013; these results were screened by the authors and potentially relevant studies are listed in Studies Awaiting Classification. The review authors also searched reference lists of relevant studies and related reviews. SELECTION CRITERIA: We included randomized controlled trials, non-randomized controlled trials, controlled before-and-after studies and interrupted time series analyses of computerized advice on drug dosage. The participants were healthcare professionals responsible for patient care. The outcomes were any objectively measured change in the health of patients resulting from computerized advice (such as therapeutic drug control, clinical improvement, adverse reactions). DATA COLLECTION AND ANALYSIS: Two review authors independently extracted data and assessed study quality. We grouped the results from the included studies by drug used and the effect aimed at for aminoglycoside antibiotics, amitriptyline, anaesthetics, insulin, anticoagulants, ovarian stimulation, anti-rejection drugs and theophylline. We combined the effect sizes to give an overall effect for each subgroup of studies, using a random-effects model. We further grouped studies by type of outcome when appropriate (i.e. no evidence of heterogeneity). MAIN
RESULTS: Forty-six comparisons (from 42 trials) were included (as compared with 26 comparisons in the last update) including a wide range of drugs in inpatient and outpatient settings. All were randomized controlled trials except two studies. Interventions usually targeted doctors, although some studies attempted to influence prescriptions by pharmacists and nurses. Drugs evaluated were anticoagulants, insulin, aminoglycoside antibiotics, theophylline, anti-rejection drugs, anaesthetic agents, antidepressants and gonadotropins. Although all studies used reliable outcome measures, their quality was generally low.This update found similar results to the previous update and managed to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care:1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics;2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98);3. it decreased the time to achieve stabilization for oral anticoagulants (SMD -0.56, 95% CI -1.07 to -0.04);4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95% CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40);5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD -0.15, 95% CI -0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care;6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants.For all outcomes, statistical heterogeneity quantified by I(2) statistics was moderate to high. AUTHORS'
CONCLUSIONS: This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics.It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved.However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice.Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 24218045     DOI: 10.1002/14651858.CD002894.pub3

Source DB:  PubMed          Journal:  Cochrane Database Syst Rev        ISSN: 1361-6137


  28 in total

1.  Access to fracture risk assessment by FRAX and linked National Osteoporosis Guideline Group (NOGG) guidance in the UK-an analysis of anonymous website activity.

Authors:  E V McCloskey; H Johansson; N C Harvey; J Compston; J A Kanis
Journal:  Osteoporos Int       Date:  2016-07-20       Impact factor: 4.507

2.  Handling interoccasion variability in model-based dose individualization using therapeutic drug monitoring data.

Authors:  João A Abrantes; Siv Jönsson; Mats O Karlsson; Elisabet I Nielsen
Journal:  Br J Clin Pharmacol       Date:  2019-04-29       Impact factor: 4.335

3.  Definition of variables required for comprehensive description of drug dosage and clinical pharmacokinetics.

Authors:  Anna V Medem; Hanna M Seidling; Hans-Georg Eichler; Jens Kaltschmidt; Michael Metzner; Carina M Hubert; David Czock; Walter E Haefeli
Journal:  Eur J Clin Pharmacol       Date:  2017-02-14       Impact factor: 2.953

Review 4.  Implementing clinical guidelines for chronic obstructive pulmonary disease: barriers and solutions.

Authors:  Jeff D Overington; Yao C Huang; Michael J Abramson; Juliet L Brown; John R Goddard; Rayleen V Bowman; Kwun M Fong; Ian A Yang
Journal:  J Thorac Dis       Date:  2014-11       Impact factor: 2.895

5.  Golden opportunities for clinical decision support in an era of team-based healthcare.

Authors:  Paul R Dexter; Titus Schleyer
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

6.  Impact of a warning CPOE system on the inappropriate pill splitting of prescribed medications in outpatients.

Authors:  Chia-Chen Hsu; Chia-Yu Chou; Chia-Lin Chou; Chin-Chin Ho; Tzeng-Ji Chen; Shu-Chiung Chiang; Min-Shan Wu; Sen-Wen Wang; Chung-Yuan Lee; Yueh-Ching Chou
Journal:  PLoS One       Date:  2014-12-05       Impact factor: 3.240

7.  Cluster Randomised Trials in Cochrane Reviews: Evaluation of Methodological and Reporting Practice.

Authors:  Marty Richardson; Paul Garner; Sarah Donegan
Journal:  PLoS One       Date:  2016-03-16       Impact factor: 3.240

8.  Perceptions and experiences of the implementation, management, use and optimisation of electronic prescribing systems in hospital settings: protocol for a systematic review of qualitative studies.

Authors:  Albert Farre; Danai Bem; Gemma Heath; Karen Shaw; Carole Cummins
Journal:  BMJ Open       Date:  2016-07-08       Impact factor: 2.692

9.  Association between drug-specific indicators of prescribing quality and quality of drug treatment: a validation study.

Authors:  Susanna M Wallerstedt; Björn Belfrage; Johan Fastbom
Journal:  Pharmacoepidemiol Drug Saf       Date:  2015-07-06       Impact factor: 2.890

10.  Evidence-based interventions to reduce adverse events in hospitals: a systematic review of systematic reviews.

Authors:  Marieke Zegers; Gijs Hesselink; Wytske Geense; Charles Vincent; Hub Wollersheim
Journal:  BMJ Open       Date:  2016-09-29       Impact factor: 2.692

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.