Original Investigation
Pathogenesis and Treatment of Kidney Disease
Use of Clinical Decision Support Systems for Kidney-Related Drug Prescribing: A Systematic Review

https://doi.org/10.1053/j.ajkd.2011.07.022Get rights and content

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.

Section snippets

Design and Study Selection

We conducted this systematic review in accordance with a prespecified protocol and reported results according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.14 To facilitate CDSS identification, we classified systems by the inferencing mechanism that controlled the system's output; computerized systems driven by computerized logic and manual systems by human logic. However, both manual and computerized CDSSs use predefined guidelines (algorithms) to

Study Selection

We screened 13,785 citations, reviewed 404 articles in full text, and identified 32 studies that met our criteria for review.17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48 The study selection flow diagram is shown in Fig 1. Chance-corrected agreement between the 2 independent reviewers for assessment of full-text study eligibility was excellent (Īŗ, 0.80; 95% confidence interval, 0.69-0.91). We successfully contacted

Discussion

Our systematic review summarizes 32 unique computerized and manual CDSSs that aid kidney-related drug prescribing. Both computerized and manual CDSSs primarily adjust drug prescribing in reference to the level of decreased kidney function, with other systems doing so in response to serum drug concentrations or a clinical parameter (eg, hemoglobin level). Certainly, beneficial outcomes of CDSS use may be reported selectively in the literature (ie, the file drawer effect for those that proved

Acknowledgements

We thank Mr Dariusz Gozdzik, Dr Michael Beyea, Mr Stephen Woo, and Ms Patricia Hizo-Abes for their contributions.

Support: Ms Shariff is supported by the Canadian Institutes of Health Research (CIHR) Doctoral Research Award. Dr Garg is supported by a Clinician Scientist Award from the CIHR.

Financial Disclosure: The authors declare that they have no other relevant financial interests.

References (57)

  • D.C. Miskulin et al.

    Computerized decision support for EPO dosing in hemodialysis patients

    Am J Kidney Dis

    (2009)
  • D. Richardson et al.

    Optimizing erythropoietin therapy in hemodialysis patients

    Am J Kidney Dis

    (2001)
  • R. Rosenthal

    The file drawer problem and tolerance for null results

    Psychol Bull

    (1979)
  • L.E. Boulware et al.

    Identification and referral of patients with progressive CKD: a national study

    Am J Kidney Dis

    (2006)
  • A.R. Nissenson et al.

    Opportunities for improving the care of patients with chronic renal insufficiency: current practice patterns

    J Am Soc Nephrol

    (2001)
  • P. Durieux

    Electronic medical alertsā€”so simple, so complex

    N Engl J Med

    (2005)
  • J. Lomas et al.

    Do practice guidelines guide practice?The effect of a consensus statement on the practice of physicians

    N Engl J Med

    (1989)
  • A.G. Ellrodt et al.

    Measuring and improving physician compliance with clinical practice guidelinesA controlled interventional trial

    Ann Intern Med

    (1995)
  • A.X. Garg et al.

    Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review

    JAMA

    (2005)
  • D.L. Hunt et al.

    Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review

    JAMA

    (1998)
  • R. Walton et al.

    Computer support for determining drug dose: systematic review and meta-analysis

    BMJ

    (1999)
  • K. Kawamoto et al.

    Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success

    BMJ

    (2005)
  • J. Cohen

    A coefficient of agreement for nominal scale

    Educat Psychol Measure

    (1960)
  • M.E. Johnston et al.

    Effects of computer-based clinical decision support systems on clinician performance and patient outcomeA critical appraisal of research

    Ann Intern Med

    (1994)
  • A.L. Alvarez et al.

    Assessment of a pharmaceutical interventional programme in patients on medications with renal risk

    Farm Hosp

    (2009)
  • A. Asberg et al.

    Computer-assisted cyclosporine dosing performs better than traditional dosing in renal transplant recipients: results of a pilot study

    Ther Drug Monit

    (2010)
  • G.M. Chertow et al.

    Guided medication dosing for inpatients with renal insufficiency

    JAMA

    (2001)
  • J.F. Connelly

    Adjusting dosage intervals of intermittent intravenous ranitidine according to creatinine clearance: a cost-minimization analysis

    Hosp Pharm

    (1994)
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    Originally published online September 26, 2011.

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