Clinical prediction rule

About: Clinical prediction rule is a research topic. Over the lifetime, 745 publications have been published within this topic receiving 35026 citations.

Derivation of a Simple Clinical Model to Categorize Patients Probability of Pulmonary Embolism: Increasing the Models Utility with the SimpliRED D-dimer

Abstract: We have previously demonstrated that a clinical model can be safely used in a management strategy in patients with suspected pulmonary embolism (PE). We sought to simplify the clinical model and determine a scoring system, that when combined with D-dimer results, would safely exclude PE without the need for other tests, in a large proportion of patients. We used a randomly selected sample of 80% of the patients that participated in a prospective cohort study of patients with suspected PE to perform a logistic regression analysis on 40 clinical variables to create a simple clinical prediction rule. Cut points on the new rule were determined to create two scoring systems. In the first scoring system patients were classified as having low, moderate and high probability of PE with the proportions being similar to those determined in our original study. The second system was designed to create two categories, PE likely and unlikely. The goal in the latter was that PE unlikely patients with a negative D-dimer result would have PE in less than 2% of cases. The proportion of patients with PE in each category was determined overall and according to a positive or negative SimpliRED D-dimer result. After these determinations we applied the models to the remaining 20% of patients as a validation of the results. The following seven variables and assigned scores (in brackets) were included in the clinical prediction rule: Clinical symptoms of DVT (3.0), no alternative diagnosis (3.0), heart rate >100 (1.5), immobilization or surgery in the previous four weeks (1.5), previous DVT/PE (1.5), hemoptysis (1.0) and malignancy (1.0). Patients were considered low probability if the score was 4.0. 7.8% of patients with scores of less than or equal to 4 had PE but if the D-dimer was negative in these patients the rate of PE was only 2.2% (95% CI = 1.0% to 4.0%) in the derivation set and 1.7% in the validation set. Importantly this combination occurred in 46% of our study patients. A score of

Clinical prediction rules. Applications and methodological standards.

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.

Clinical prediction rules. A review and suggested modifications of methodological standards.

Abstract: Background. —Clinical prediction rules are decision-making tools for clinicians, containing variables from the history, physical examination, or simple diagnostic tests. Objective. —To review the quality of recently published clinical prediction rules and to suggest methodological standards for their development and evaluation. Data Sources. —Four general medical journals were manually searched for clinical prediction rules published from 1991 through 1994. Study Selection. —Four hundred sixty potentially eligible reports were identified, of which 30 were clinical prediction rules eligible for study. Most methodological standards could only be evaluated in 29 studies. Data Abstraction. —Two investigators independently evaluated the quality of each report using a standard data sheet. Disagreements were resolved by consensus. Data Synthesis. —The mathematical technique was used to develop the rule, and the results of the rule were described in 100% (29/29) of the reports. All the rules but 1 (97% [28/29]) were felt to be clinically sensible. The outcomes and predictive variables were clearly defined in 83% (24/29) and 59% (17/29) of the reports, respectively. Blind assessment of outcomes and predictive variables occurred in 41% (12/29) and 79% (23/29) of the reports, respectively, and the rules were prospectively validated in 79% (11/14). Reproducibility of predictive variables was assessed in only 3% (1/29) of the reports, and the effect of the rule on clinical use was prospectively measured in only 3% (1/30). Forty-one percent (12/29) of the rules were felt to be easy to use. Conclusions. —Although clinical prediction rules comply with some methodological criteria, for other criteria, better compliance is needed.

Users' Guides to the Medical Literature: XXII: How to Use Articles About Clinical Decision Rules

Abstract: Clinical experience provides clinicians with an intuitive sense of which findings on history, physical examination, and investigation are critical in making an accurate diagnosis, or an accurate assessment of a patient’s fate. A clinical decision rule (CDR) is a clinical tool that quantifies the individual contributions that various components of the history, physical examination, and basic laboratory results make toward the diagnosis, prognosis, or likely response to treatment in a patient. Clinical decision rules attempt to formally test, simplify, and increase the accuracy of clinicians’ diagnostic and prognostic assessments. Existing CDRs guide clinicians, establish pretest probability, provide screening tests for common problems, and estimate risk. Three steps are involved in the development and testing of a CDR: creation of the rule, testing or validating the rule, and assessing the impact of the rule on clinical behavior. Clinicians evaluating CDRs for possible clinical use should assess the following components: the method of derivation; the validation of the CDR to ensure that its repeated use leads to the same results; and its predictive power. We consider CDRs that have been validated in a new clinical setting to be level 1 CDRs and most appropriate for implementation. Level 1 CDRs have the potential to inform clinical judgment, to change clinical behavior, and to reduce unnecessary costs, while maintaining quality of care and patient satisfaction. JAMA. 2000;284:79-84 www.jama.com

Derivation and Validation of a Prognostic Model for Pulmonary Embolism

Abstract: Rationale: An objective and simple prognostic model for patients with pulmonary embolism could be helpful in guiding initial intensity of treatment.Objectives: To develop a clinical prediction rule that accurately classifies patients with pulmonary embolism into categories of increasing risk of mortality and other adverse medical outcomes.Methods: We randomly allocated 15,531 inpatient discharges with pulmonary embolism from 186 Pennsylvania hospitals to derivation (67%) and internal validation (33%) samples. We derived our prediction rule using logistic regression with 30-day mortality as the primary outcome, and patient demographic and clinical data routinely available at presentation as potential predictor variables. We externally validated the rule in 221 inpatients with pulmonary embolism from Switzerland and France.Measurements: We compared mortality and nonfatal adverse medical outcomes across the derivation and two validation samples.Main Results: The prediction rule is based on 11 simple patient ...