Literature DB >> 3897864

Clinical prediction rules. Applications and methodological standards.

J H Wasson, H C Sox, R K Neff, L Goldman.   

Abstract

The objective of clinical prediction rules is to reduce the uncertainty inherent in medical practice by defining how to use clinical findings to make predictions. Clinical prediction rules are derived from systematic clinical observations. They can help physicians identify patients who require diagnostic tests, treatment, or hospitalization. Before adopting a prediction rule, clinicians must evaluate its applicability to their patients. We describe methodological standards that can be used to decide whether a prediction rule is suitable for adoption in a clinician's practice. We applied these standards to 33 reports of prediction rules; 42 per cent of the reports contained an adequate description of the prediction rules, the patients, and the clinical setting. The misclassification rate of the rule was measured in only 34 per cent of reports, and the effects of the rule on patient care were described in only 6 per cent of reports. If the objectives of clinical prediction rules are to be fully achieved, authors and readers need to pay close attention to basic principles of study design.

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Year:  1985        PMID: 3897864     DOI: 10.1056/NEJM198509263131306

Source DB:  PubMed          Journal:  N Engl J Med        ISSN: 0028-4793            Impact factor:   91.245


  182 in total

1.  An optimization model for sequential decision-making applied to risk prediction after liver resection and transplantation.

Authors:  G Tusch
Journal:  Proc AMIA Symp       Date:  1999

2.  Severity prediction rules in community acquired pneumonia: a validation study.

Authors:  W S Lim; S Lewis; J T Macfarlane
Journal:  Thorax       Date:  2000-03       Impact factor: 9.139

3.  Clinical decision rules in the emergency department.

Authors:  I G Stiell
Journal:  CMAJ       Date:  2000-11-28       Impact factor: 8.262

4.  Validation of an instrument for injury data collection in rugby union.

Authors:  A McManus
Journal:  Br J Sports Med       Date:  2000-10       Impact factor: 13.800

5.  Searching for clinical prediction rules in MEDLINE.

Authors:  B J Ingui; M A Rogers
Journal:  J Am Med Inform Assoc       Date:  2001 Jul-Aug       Impact factor: 4.497

6.  Reference standards, judges, and comparison subjects: roles for experts in evaluating system performance.

Authors:  George Hripcsak; Adam Wilcox
Journal:  J Am Med Inform Assoc       Date:  2002 Jan-Feb       Impact factor: 4.497

7.  Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE.

Authors:  Sharon S -L Wong; Nancy L Wilczynski; R Brian Haynes; Ravi Ramkissoonsingh
Journal:  AMIA Annu Symp Proc       Date:  2003

8.  Incorporation of physiological trend and interaction effects in neonatal severity of illness scores: an experiment using a variant of the Richardson score.

Authors:  Michael Kuzniewicz; David Draper; Gabriel J Escobar
Journal:  Intensive Care Med       Date:  2007-06-19       Impact factor: 17.440

Review 9.  Risk assessment models to estimate cancer probabilities.

Authors:  Constance M Johnson; Derek Smolenski
Journal:  Curr Oncol Rep       Date:  2007-11       Impact factor: 5.075

10.  Predicting outcome in very low birthweight infants using an objective measure of illness severity and cranial ultrasound scanning.

Authors:  P W Fowlie; W O Tarnow-Mordi; C R Gould; D Strang
Journal:  Arch Dis Child Fetal Neonatal Ed       Date:  1998-05       Impact factor: 5.747

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