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Wingfield LR, Salaun A, Khan A, Webb H, Zhu T, Knight S. Clinical Decision Support Systems Used in Transplantation: Are They Tools for Success or an Unnecessary Gadget? A Systematic Review. Transplantation 2024; 108:72-99. [PMID: 37143191 DOI: 10.1097/tp.0000000000004627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Although clinical decision support systems (CDSSs) have been used since the 1970s for a wide variety of clinical tasks including optimization of medication orders, improved documentation, and improved patient adherence, to date, no systematic reviews have been carried out to assess their utilization and efficacy in transplant medicine. The aim of this study is to systematically review studies that utilized a CDSS and assess impact on patient outcomes. A total of 48 articles were identified as meeting the author-derived inclusion criteria, including tools for posttransplant monitoring, pretransplant risk assessment, waiting list management, immunosuppressant management, and interpretation of histopathology. Studies included 15 984 transplant recipients. Tools aimed at helping with transplant patient immunosuppressant management were the most common (19 studies). Thirty-four studies (85%) found an overall clinical benefit following the implementation of a CDSS in clinical practice. Although there are limitations to the existing literature, current evidence suggests that implementing CDSS in transplant clinical settings may improve outcomes for patients. Limited evidence was found using more advanced technologies such as artificial intelligence in transplantation, and future studies should investigate the role of these emerging technologies.
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Affiliation(s)
- Laura R Wingfield
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Achille Salaun
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Aparajita Khan
- Department of Neurosurgery, Stanford University, Stanford, CA
| | - Helena Webb
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Simon Knight
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
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Mao J, Chen Y, Xu L, Chen W, Chen B, Fang Z, Qin W, Zhong M. Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison. Front Pharmacol 2022; 13:1016399. [DOI: 10.3389/fphar.2022.1016399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022] Open
Abstract
Objective: The aim of this study was to identify the important factors affecting cyclosporine (CsA) blood concentration and estimate CsA concentration using seven different machine learning (ML) algorithms. We also assessed the predictability of established ML models and previously built population pharmacokinetic (popPK) model. Finally, the most suitable ML model and popPK model to guide precision dosing were determined.Methods: In total, 3,407 whole-blood trough and peak concentrations of CsA were obtained from 183 patients who underwent initial renal transplantation. These samples were divided into model-building and evaluation sets. The model-building set was analyzed using seven different ML algorithms. The effects of potential covariates were evaluated using the least absolute shrinkage and selection operator algorithms. A separate evaluation set was used to assess the ability of all models to predict CsA blood concentration. R squared (R2) scores, median prediction error (MDPE), median absolute prediction error (MAPE), and the percentages of PE within 20% (F20) and 30% (F30) were calculated to assess the predictive performance of these models. In addition, previously built popPK model was included for comparison.Results: Sixteen variables were selected as important covariates. Among ML models, the predictive performance of nonlinear-based ML models was superior to that of linear regression (MDPE: 3.27%, MAPE: 34.21%, F20: 30.63%, F30: 45.03%, R2 score: 0.68). The ML model built with the artificial neural network algorithm was considered the most suitable (MDPE: −0.039%, MAPE: 25.60%, F20: 39.35%, F30: 56.46%, R2 score: 0.75). Its performance was superior to that of the previously built popPK model (MDPE: 5.26%, MAPE: 29.22%, F20: 33.94%, F30: 51.22%, R2 score: 0.68). Furthermore, the application of the most suitable model and the popPK model in clinic showed that most dose regimen recommendations were reasonable.Conclusion: The performance of these ML models indicate that a nonlinear relationship for covariates may help to improve model predictability. These results might facilitate the application of ML models in clinic, especially for patients with unstable status or during initial dose optimization.
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Barraclough KA, Metz D, Staatz CE, Gorham G, Carroll R, Majoni SW, Cherian S, Swaminathan R, Holford N. Important lack of difference in tacrolimus and mycophenolic acid pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients. Nephrology (Carlton) 2022; 27:771-779. [PMID: 35727904 DOI: 10.1111/nep.14080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/01/2022] [Accepted: 06/15/2022] [Indexed: 11/26/2022]
Abstract
AIM To examine whether differences in tacrolimus and mycophenolic acid (MPA) pharmacokinetics contribute to the poorer kidney transplant outcomes experienced by Aboriginal Australians. METHODS Concentration-time profiles for tacrolimus and MPA were prospectively collected from 43 kidney transplant recipients: 27 Aboriginal and 16 Caucasian. Apparent clearance (CL/F) and distribution volume (V/F) for each individual were derived from concentration-time profiles combined with population pharmacokinetic priors, with subsequent assessment for between-group difference in pharmacokinetics. In addition, population pharmacokinetic models were developed using the prospective dataset supplemented by previously developed structural models for tacrolimus and MPA. The change in NONMEM objective function was used to assess improvement in goodness of model fit. RESULTS No differences were found between Aboriginal and Caucasian groups or empirical Bayes estimates, for CL/F or V/F of MPA or tacrolimus. However, a higher prevalence of CYP3A5 expressers (26% compared with 0%) and wider between-subject variability in tacrolimus CL/F (SD = 5.00 compared with 3.25 L/h/70 kg) were observed in the Aboriginal group, though these differences failed to reach statistical significance (p = .07 and p = .08). CONCLUSION There were no differences in typical tacrolimus or MPA pharmacokinetics between Aboriginal and Caucasian kidney transplant recipients. This means that Bayesian dosing tools developed to optimise tacrolimus and MPA dosing in Caucasian recipients may be applied to Aboriginal recipients. In turn, this may improve drug exposure and thereby transplant outcomes in this group. Aboriginal recipients appeared to have greater between-subject variability in tacrolimus CL/F and a higher prevalence of CYP3A5 expressers, attributes that have been linked with inferior outcomes.
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Affiliation(s)
- Katherine A Barraclough
- Department of Nephrology, Royal Melbourne Hospital, Melbourne, Victoria, Australia.,School of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - David Metz
- Department of Paediatrics, Monash University, Melbourne, Victoria, Australia.,Department of Nephrology, Royal Children's Hospital, Melbourne, Victoria, Australia.,Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Christine E Staatz
- School of Pharmacy, University of Queensland, Brisbane, Queensland, Australia
| | - Gillian Gorham
- Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia
| | - Robert Carroll
- Department of Nephrology, Central Northern Adelaide Renal Transplantation Services, Adelaide, South Australia, Australia.,Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Sandawana William Majoni
- Menzies School of Health Research, Charles Darwin University, Darwin, Northern Territory, Australia.,Department of Nephrology, Northern Territory Renal Services, Darwin, Northern Territory, Australia.,School of Medicine, Flinders University Northern Territory Medical Program, Darwin, Northern Territory, Australia
| | - Sajiv Cherian
- Renal Services, Alice Springs Hospital, Alice Springs, Northern Territory, Australia
| | | | - Nick Holford
- Pharmacology and Clinical Pharmacology, University of Auckland, Auckland, New Zealand
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Abstract
Drugs are the third leading cause of acute kidney injury (AKI) in critically ill patients. Nephrotoxin stewardship ensures a structured and consistent approach to safe medication use and prevention of patient harm. Comprehensive nephrotoxin stewardship requires coordinated patient care management strategies for safe medication use, ensuring kidney health, and avoiding unnecessary costs to improve the use of nephrotoxins, renally eliminated drugs, and kidney disease treatments. Implementing nephrotoxin stewardship reduces medication errors and adverse drug events, prevents or reduces severity of drug-associated AKI, prevents progression to or worsening of chronic kidney disease, and alleviates financial burden on the health care system.
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Affiliation(s)
- Sandra L Kane-Gill
- Department of Pharmacy and Therapeutics, School of Pharmacy, Center for Critical Care Nephrology, School of Medicine, University of Pittsburgh, PRESBY/SHY Pharmacy Administration Building, 3507 Victoria Street, Mailcode PFG-01-01-01, Pittsburgh, PA 15213, USA.
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Mao JJ, Jiao Z, Yun HY, Zhao CY, Chen HC, Qiu XY, Zhong MK. External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients. Br J Clin Pharmacol 2017; 84:153-171. [PMID: 28891596 DOI: 10.1111/bcp.13431] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 08/08/2017] [Accepted: 09/01/2017] [Indexed: 02/03/2023] Open
Abstract
AIMS Several population pharmacokinetic (popPK) models for ciclosporin (CsA) in adult renal transplant recipients have been constructed to optimize the therapeutic regimen of CsA. However, little is known about their predictabilities when extrapolated to different clinical centres. Therefore, this study aimed to externally evaluate the predictive ability of CsA popPK models and determine the potential influencing factors. METHODS A literature search was conducted and the predictive performance was determined for each selected model using an independent data set of 62 patients (471 predose and 500 2-h postdose concentrations) from our hospital. Prediction-based diagnostics and simulation-based normalized prediction distribution error were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Additionally, potential factors influencing model predictability were investigated. RESULTS Seventeen models extracted from 17 published popPK studies were assessed. Prediction-based diagnostics showed that ethnicity potentially influenced model transferability. Simulation-based normalized prediction distribution error analyses indicated misspecification in most of the models, especially regarding variance. Bayesian forecasting demonstrated that the predictive performance of the models substantially improved with 2-3 prior observations. The predictability of nonlinear Michaelis-Menten models was superior to that of linear compartmental models when evaluating the impact of structural models, indicating the underlying nonlinear kinetics of CsA. Structural model, ethnicity, covariates and prior observations potentially affected model predictability. CONCLUSIONS Structural model is the predominant factor influencing model predictability. Incorporation of nonlinear kinetics in CsA popPK modelling should be considered. Moreover, Bayesian forecasting substantially improved model predictability.
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Affiliation(s)
- Jun-Jun Mao
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Zheng Jiao
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Hwi-Yeol Yun
- College of Pharmacy, Chungnam National University, Daejeon, South Korea
| | - Chen-Yan Zhao
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Han-Chao Chen
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao-Yan Qiu
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Ming-Kang Zhong
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
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Wang J, Zheng H, Wang K, Wang Z, Ding Y. Population pharmacokinetics of arginine glutamate in healthy Chinese volunteers. Xenobiotica 2017; 48:809-817. [PMID: 28925806 DOI: 10.1080/00498254.2017.1370745] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
1. The present study developed population pharmacokinetic models of arginine and glutamate in healthy Chinese volunteers. Two nonlinear mixed-effect models were developed using NONMEM® software (ICON Development Solutions, Ellicott City, MD) to describe the pharmacokinetic properties and to assess the relevant parameters as well as the inter-individual variability. The potential covariates were screened using stepwise approach and the stability and predictive capability of the models were performed using bootstrap and visual predictive check. 2. The concentration time curves of arginine and glutamate were best described by a first-order elimination two-compartment model and a nonlinear elimination one-compartment model, respectively. The final parameter estimation of arginine for CL was 44.1 L/h. Q, V1 and V2 were 23 L/h, 20.3 L and 46 L, respectively. The final parameter estimation of glutamate for Vmax and Km were 18.8 mg/h and 77.2 mg/L, respectively. V for low dose and high dose was 23.1 L and 36.3 L, respectively. 3. For arginine, weight was significant covariate on the apparent distribution volume of peripheral compartment. The gain in weight remarkably increases V2. For glutamate, dose as a significant covariate on the apparent distribution volume was included, subjects received high dose (20 g) have remarkably higher V compared to subjects received low dose (10 g).
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Affiliation(s)
- Jing Wang
- a Department of Pharmacy , Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology , Wuhan , China and
| | - Heng Zheng
- a Department of Pharmacy , Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology , Wuhan , China and
| | - Kun Wang
- b Department of Pharmacometrics , Center for Drug Clinical Research, Shanghai University of Traditional Chinese Medicine , Shanghai , China
| | - Zheng Wang
- a Department of Pharmacy , Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology , Wuhan , China and
| | - Yufeng Ding
- a Department of Pharmacy , Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology , Wuhan , China and
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Niazkhani Z, Pirnejad H, Rashidi Khazaee P. The impact of health information technology on organ transplant care: A systematic review. Int J Med Inform 2017; 100:95-107. [DOI: 10.1016/j.ijmedinf.2017.01.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 12/01/2016] [Accepted: 01/19/2017] [Indexed: 01/02/2023]
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Improved Tacrolimus Target Concentration Achievement Using Computerized Dosing in Renal Transplant Recipients--A Prospective, Randomized Study. Transplantation 2016; 99:2158-66. [PMID: 25886918 DOI: 10.1097/tp.0000000000000708] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND Early after renal transplantation, it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization using population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians. METHODS A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first 8 weeks after transplantation. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target, 3-7 μg/L) and high-risk (8-12 μg/L) recipients were analyzed separately. RESULTS Eighty renal transplant recipients were randomized, and 78 were included in the analysis (computerized dosing (n = 39): 32 standard risk/7 high-risk, conventional dosing (n = 39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians, 90% [95% confidence interval {95% CI}, 84-95%] vs 78% [95% CI, 76-82%], respectively, P < 0.001) and in high-risk patients (medians, 77% [95% CI, 71-80%] vs 59% [95% CI, 40-74%], respectively, P = 0.04). CONCLUSIONS Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.
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Abstract
The enduring success of lung transplantation is built on the use of immunosuppressive drugs to stop the immune system from rejecting the newly transplanted lung allograft. Most patients receive a triple-drug maintenance immunosuppressive regimen consisting of a calcineurin inhibitor, an antiproliferative and corticosteroids. Induction therapy with either an antilymphocyte monoclonal or an interleukin-2 receptor antagonist are prescribed by many centres aiming to achieve rapid inhibition of recently activated and potentially alloreactive T lymphocytes. Despite this generic approach acute rejection episodes remain common, mandating further fine-tuning and augmentation of the immunosuppressive regimen. While there has been a trend away from cyclosporine and azathioprine towards a preference for tacrolimus and mycophenolate mofetil, this has not translated into significant protection from the development of chronic lung allograft dysfunction, the main barrier to the long-term success of lung transplantation. This article reviews the problem of lung allograft rejection and the evidence for immunosuppressive regimens used both in the short- and long-term in patients undergoing lung transplantation.
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Gillaizeau F, Chan E, Trinquart L, Colombet I, Walton RT, Rège-Walther M, Burnand B, Durieux P. Computerized advice on drug dosage to improve prescribing practice. Cochrane Database Syst Rev 2013:CD002894. [PMID: 24218045 DOI: 10.1002/14651858.cd002894.pub3] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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.
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Affiliation(s)
- Florence Gillaizeau
- French Cochrane Center, Hôpital Hôtel-Dieu, 1 place du Parvis Notre-Dame, Paris, France, 75004
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Tawadrous D, Shariff SZ, Haynes RB, Iansavichus AV, Jain AK, Garg AX. Use of clinical decision support systems for kidney-related drug prescribing: a systematic review. Am J Kidney Dis 2011; 58:903-14. [PMID: 21944664 DOI: 10.1053/j.ajkd.2011.07.022] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2011] [Accepted: 07/22/2011] [Indexed: 02/07/2023]
Abstract
BACKGROUND Clinical decision support systems (CDSSs) have the potential to improve kidney-related drug prescribing by supporting the appropriate initiation, modification, monitoring, or discontinuation of drug therapy. STUDY DESIGN Systematic review. We identified studies by searching multiple bibliographic databases (eg, MEDLINE and EMBASE), conference proceedings, and reference lists of all included studies. SETTING & POPULATION CDSSs used in hospital or outpatient settings for acute kidney injury and chronic kidney disease, including end-stage renal disease (chronic dialysis patients or transplant recipients). SELECTION CRITERIA FOR STUDIES Studies prospectively using CDSSs to aid in kidney-related drug prescribing. INTERVENTION Computerized or manual CDSSs. OUTCOMES Clinician prescribing and patient-important outcomes as reported by primary study investigators. CDSS characteristics, such as whether the system was computerized, and system setting. RESULTS We identified 32 studies. In 17 studies, CDSSs were computerized, and in 15 studies, they were manual pharmacist-based systems. Systems intervened by prompting for drug dosing adjustments in relation to the level of decreased kidney function (25 studies) or in response to serum drug concentrations or a clinical parameter (7 studies). They were used most in academic hospital settings. For computerized CDSSs, clinician prescribing outcomes (eg, frequency of appropriate dosing) were considered in 11 studies, with all 11 reporting statistically significant improvements. Similarly, manual CDSSs that incorporated clinician prescribing outcomes showed statistically significant improvements in 6 of 8 studies. Patient-important outcomes (eg, adverse drug events) were considered in 7 studies of computerized CDSSs, with statistically significant improvements in 2 studies. For manual CDSSs, 6 studies measured patient-important outcomes and 5 reported statistically significant improvements. Cost-savings also were reported, mostly for manual CDSSs. LIMITATIONS Studies were heterogeneous in design and often limited by the evaluation method used. Benefits of CDSSs may be reported selectively in this literature. CONCLUSION CDSSs are available for many dimensions of kidney-related drug prescribing, and results are promising. Additional high-quality evaluations will guide their optimal use.
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Affiliation(s)
- Davy Tawadrous
- Schulich School of Medicine, University of Western Ontario, London, Ontario, Canada
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Chang J, Ronco C, Rosner MH. Computerized decision support systems: improving patient safety in nephrology. Nat Rev Nephrol 2011; 7:348-55. [PMID: 21502973 PMCID: PMC5048740 DOI: 10.1038/nrneph.2011.50] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Incorrect prescription and administration of medications account for a substantial proportion of medical errors in the USA, causing adverse drug events (ADEs) that result in considerable patient morbidity and enormous costs to the health-care system. Patients with chronic kidney disease or acute kidney injury often have impaired drug clearance as well as polypharmacy, and are therefore at increased risk of experiencing ADEs. Studies have demonstrated that recognition of these conditions is not uniform among treating physicians, and prescribed drug doses are often incorrect. Early interventions that ensure appropriate drug dosing in this group of patients have shown encouraging results. Both computerized physician order entry and clinical decision support systems have been shown to reduce the rate of ADEs. Nevertheless, these systems have been implemented at surprisingly few institutions. Economic stimulus and health-care reform legislation present a rare opportunity to refine these systems and understand how they could be implemented more widely. Failure to explore this technology could mean that the opportunity to reduce the morbidity associated with ADEs is missed.
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Affiliation(s)
- Jamison Chang
- Division of Nephrology, University of Virginia Health System, Box 80013, 1215 Lee Street, Charlottesville, VA 22908, USA
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Amundsen R, Christensen H, Zabihyan B, Åsberg A. Cyclosporine A, but Not Tacrolimus, Shows Relevant Inhibition of Organic Anion-Transporting Protein 1B1-Mediated Transport of Atorvastatin. Drug Metab Dispos 2010; 38:1499-504. [DOI: 10.1124/dmd.110.032268] [Citation(s) in RCA: 111] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
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Shojania KG, Jennings A, Mayhew A, Ramsay CR, Eccles MP, Grimshaw J. The effects of on-screen, point of care computer reminders on processes and outcomes of care. Cochrane Database Syst Rev 2009; 2009:CD001096. [PMID: 19588323 PMCID: PMC4171964 DOI: 10.1002/14651858.cd001096.pub2] [Citation(s) in RCA: 271] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The opportunity to improve care by delivering decision support to clinicians at the point of care represents one of the main incentives for implementing sophisticated clinical information systems. Previous reviews of computer reminder and decision support systems have reported mixed effects, possibly because they did not distinguish point of care computer reminders from e-mail alerts, computer-generated paper reminders, and other modes of delivering 'computer reminders'. OBJECTIVES To evaluate the effects on processes and outcomes of care attributable to on-screen computer reminders delivered to clinicians at the point of care. SEARCH STRATEGY We searched the Cochrane EPOC Group Trials register, MEDLINE, EMBASE and CINAHL and CENTRAL to July 2008, and scanned bibliographies from key articles. SELECTION CRITERIA Studies of a reminder delivered via a computer system routinely used by clinicians, with a randomised or quasi-randomised design and reporting at least one outcome involving a clinical endpoint or adherence to a recommended process of care. DATA COLLECTION AND ANALYSIS Two authors independently screened studies for eligibility and abstracted data. For each study, we calculated the median improvement in adherence to target processes of care and also identified the outcome with the largest such improvement. We then calculated the median absolute improvement in process adherence across all studies using both the median outcome from each study and the best outcome. MAIN RESULTS Twenty-eight studies (reporting a total of thirty-two comparisons) were included. Computer reminders achieved a median improvement in process adherence of 4.2% (interquartile range (IQR): 0.8% to 18.8%) across all reported process outcomes, 3.3% (IQR: 0.5% to 10.6%) for medication ordering, 3.8% (IQR: 0.5% to 6.6%) for vaccinations, and 3.8% (IQR: 0.4% to 16.3%) for test ordering. In a sensitivity analysis using the best outcome from each study, the median improvement was 5.6% (IQR: 2.0% to 19.2%) across all process measures and 6.2% (IQR: 3.0% to 28.0%) across measures of medication ordering. In the eight comparisons that reported dichotomous clinical endpoints, intervention patients experienced a median absolute improvement of 2.5% (IQR: 1.3% to 4.2%). Blood pressure was the most commonly reported clinical endpoint, with intervention patients experiencing a median reduction in their systolic blood pressure of 1.0 mmHg (IQR: 2.3 mmHg reduction to 2.0 mmHg increase). AUTHORS' CONCLUSIONS Point of care computer reminders generally achieve small to modest improvements in provider behaviour. A minority of interventions showed larger effects, but no specific reminder or contextual features were significantly associated with effect magnitude. Further research must identify design features and contextual factors consistently associated with larger improvements in provider behaviour if computer reminders are to succeed on more than a trial and error basis.
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Affiliation(s)
- Kaveh G Shojania
- Director, University of Toronto Centre for Patient Safety, Sunnybrook Health Sciences Centre, Room D474, 2075 Bayview Avenue, Toronto, Ontario, Canada, M4N 3M5
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