Mostrando las entradas con la etiqueta PRWEB. Mostrar todas las entradas
Mostrando las entradas con la etiqueta PRWEB. Mostrar todas las entradas

02 noviembre, 2011

Evidence-Based Medicine in the EMR Era

Students working with an artificial patient (F...Image via Wikipedia

Evidence-Based Medicine in the EMR Era

Jennifer Frankovich, M.D., Christopher A. Longhurst, M.D., and Scott M. Sutherland, M.D.
November 2, 2011 (10.1056/NEJMp1108726)

Many physicians take great pride in the practice of evidence-based medicine. Modern medical education emphasizes the value of the randomized, controlled trial, and we learn early on not to rely on anecdotal evidence. But the application of such superior evidence, however admirable the ambition, can be constrained by trials' strict inclusion and exclusion criteria — or the complete absence of a relevant trial. For those of us practicing pediatric medicine, this reality is all too familiar. In such situations, we are used to relying on evidence at Levels III through V — expert opinion — or resorting to anecdotal evidence. What should we do, though, when there aren't even meager data available and we don't have a single anecdote on which to draw?
We recently found ourselves in such a situation as we admitted to our service a 13-year-old girl with systemic lupus erythematosus (SLE). Our patient's presentation was complicated by nephrotic-range proteinuria, antiphospholipid antibodies, and pancreatitis. Although anticoagulation is not standard practice for children with SLE even when they're critically ill, these additional factors put our patient at potential risk for thrombosis, and we considered anticoagulation. However, we were unable to find studies pertaining to anticoagulation in our patient's situation and were therefore reluctant to pursue that course, given the risk of bleeding. A survey of our pediatric rheumatology colleagues — a review of our collective Level V evidence, so to speak — was equally fruitless and failed to produce a consensus.
Without clear evidence to guide us and needing to make a decision swiftly, we turned to a new approach, using the data captured in our institution's electronic medical record (EMR) and an innovative research data warehouse. The platform, called the Stanford Translational Research Integrated Database Environment (STRIDE), acquires and stores all patient data contained in the EMR at our hospital and provides immediate advanced text searching capability.1 Through STRIDE, we could rapidly review data on an SLE cohort that included pediatric patients with SLE cared for by clinicians in our division between October 2004 and July 2009. This “electronic cohort” was originally created for use in studying complications associated with pediatric SLE and exists under a protocol approved by our institutional review board.
Of the 98 patients in our pediatric lupus cohort, 10 patients developed thrombosis, documented in the EMR, while they were acutely ill. The prevalence was higher among patients who had persistent nephrotic-range proteinuria and pancreatitis (see tableResults of Electronic Search of Patient Medical Records (for a Cohort of 98 Pediatric Patients with Lupus) Focused on Risk Factors for Thrombosis Relevant to Our 13-Year-Old Patient with Systemic Lupus Erythematosus.). As compared with our patients with lupus who did not have these risk factors, the risk of thrombosis was 14.7 (95% confidence interval [CI], 3.3 to 96) among patients with persistent nephrosis and 11.8 (95% CI, 3.8 to 27) among those with pancreatitis. This automated cohort review was conducted in less than 4 hours by a single clinician. On the basis of this real-time, informatics-enabled data analysis, we made the decision to give our patient anticoagulants within 24 hours after admission.
Our case is but one example of a situation in which the existing literature is insufficient to guide the clinical care of a patient. But it illustrates a novel process that is likely to become much more standard with the widespread adoption of EMRs and more sophisticated informatics tools. Although many other groups have highlighted the secondary use of EMR data for clinical research,2,3 we have now seen how the same approach can be used to guide real-time clinical decisions. The rapid electronic chart review and analysis were not only feasible, but also more helpful and accurate than physician recollection and pooled colleague opinion. Such real-time availability of data to guide decision making has already transformed other industries,4 and the growing prevalence of EMRs along with the development of sophisticated tools for real-time analysis of deidentified data sets will no doubt advance the use of this data-driven approach to health care delivery. We look forward to a future in which health information systems help physicians learn from every patient at every visit and close the feedback loop for clinical decision making in real time.
Did we make the correct decision for our patient? Thrombosis did not develop, and the patient did not have any sequelae related to her anticoagulation; truthfully, though, we may never really know. We will, however, know that we made the decision on the basis of the best data available — acting, as the fictional detective Nero Wolfe would say, “in the light of experience as guided by intelligence.”5 In the practice of medicine, one can't do better than that.
Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.
This article (10.1056/NEJMp1108726) was published on November 2, 2011, at NEJM.org.

SOURCE INFORMATION

From the Division of Rheumatology (J.F.), the Division of Systems Medicine (C.A.L.), and the Division of Nephrology (S.M.S.), Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA.

REFERENCES

  1. 1
    Lowe HJFerris TAHernandez PMWeber SC. STRIDE -- an integrated standards-based translational research informatics platform. AMIA Annu Symp Proc 2009;14:391-395
  2. 2
    Prokosch HUGanslandt T. Perspectives for medical informatics: reusing the electronic medical record for clinical research. Methods Inf Med 2009;48:38-44
    Web of Science | Medline
  3. 3
    Gunn PWHansen MLKaelber DC. Underdiagnosis of pediatric hypertension -- an example of a new era of clinical research enabled by electronic medical records. AMIA Annu Symp Proc 2007;11:966-966
  4. 4
    Halevy A, Norvig P, Pereira F. The Unreasonable Effectiveness of Data. IEEE Intelligent Systems, March/April 2009:8-12.
  5. 5
    Stout R. In the best families. New York: Viking Press, 1950:71.


13 octubre, 2011

Estimating treatment effects for individual patients based on the results of randomised clinical trials


BMJ 2011; 343:d5888 doi: 10.1136/bmj.d5888 (Published 3 October 2011)
Cite this as: BMJ 2011; 343:d5888
  • Research

Estimating treatment effects for individual patients based on the results of randomised clinical trials

Free via Creative Commons: OPEN ACCESS
  1. Johannes A N Dorresteijn, epidemiologist and medical doctor1,
  2. Frank L J Visseren, professor of vascular medicine, epidemiologist, and internist1
  3. Paul M Ridker, Eugene Braunwald professor of medicine, epidemiologist, and cardiologist2
  4. Annemarie M J Wassink, internist and postdoctoral researcher1
  5. Nina P Paynter, assistant professor of epidemiology2
  6. Ewout W Steyerberg, professor of medical decision making, and methodologist3
  7. Yolanda van der Graaf, professor of epidemiology and imaging4
  8. Nancy R Cook, associate professor of biostatistics and epidemiology2
Author Affiliations
  1. 1Department of Vascular Medicine, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, Netherlands
  2. 2Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
  3. 3Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands
  4. 4Julius Center for Health Sciences and Primary Care, Utrecht, Netherlands
  1. Correspondence to: F L J Visseren F.L.J.Visseren@umcutrecht.nl
  • Accepted 12 August 2011

Abstract

Objectives To predict treatment effects for individual patients based on data from randomised trials, taking rosuvastatin treatment in the primary prevention of cardiovascular disease as an example, and to evaluate the net benefit of making treatment decisions for individual patients based on a predicted absolute treatment effect.
Setting As an example, data were used from the Justification for the Use of Statins in Prevention (JUPITER) trial, a randomised controlled trial evaluating the effect of rosuvastatin 20 mg daily versus placebo on the occurrence of cardiovascular events (myocardial infarction, stroke, arterial revascularisation, admission to hospital for unstable angina, or death from cardiovascular causes).
Population 17 802 healthy men and women who had low density lipoprotein cholesterol levels of less than 3.4 mmol/L and high sensitivity C reactive protein levels of 2.0 mg/L or more.
Methods Data from the Justification for the Use of Statins in Prevention trial were used to predict rosuvastatin treatment effect for individual patients based on existing risk scores (Framingham and Reynolds) and on a newly developed prediction model. We compared the net benefit of prediction based rosuvastatin treatment (selective treatment of patients whose predicted treatment effect exceeds a decision threshold) with the net benefit of treating either everyone or no one.
Results The median predicted 10 year absolute risk reduction for cardiovascular events was 4.4% (interquartile range 2.6-7.0%) based on the Framingham risk score, 4.2% (2.5-7.1%) based on the Reynolds score, and 3.9% (2.5-6.1%) based on the newly developed model (optimal fit model). Prediction based treatment was associated with more net benefit than treating everyone or no one, provided that the decision threshold was between 2% and 7%, and thus that the number willing to treat (NWT) to prevent one cardiovascular event over 10 years was between 15 and 50.
Conclusions Data from randomised trials can be used to predict treatment effect in terms of absolute risk reduction for individual patients, based on a newly developed model or, if available, existing risk scores. The value of such prediction of treatment effect for medical decision making is conditional on the NWT to prevent one outcome event.
Trial registration number Clinicaltrials.gov NCT00239681.