La bola de cristal. Lectura crítica de reglas de predicción clínica.

Una regla de predicción clínica es una herramienta compuesta por un conjunto de variables de la historia clínica, exploración física y pruebas complementarias básicas, que nos proporciona una estimación de la probabilidad de un evento, nos sugiere un diagnóstico o nos predice una respuesta concreta a un tratamiento. Para conocer el valor de sus conclusiones será importante estudiar su validez interna, la importancia clínica de sus conclusiones y si son aplicables y de utilidad en nuestro entorno clínico. ABSTRACT The crystal ball. Critical appraisal of clinical prediction rules. A clinical prediction rule is a tool composed of a set of variables from clinical history, physical examination and basic complementary tests, which provides us with an estimate of the probability of an event, suggests a diagnosis or predicts a specific response to a treatment. In order to know the value of its conclusions, it will be important to study its internal validity, the clinical relevance of its conclusions and if they are applicable and useful in our clinical environment.

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The Use of a Clinical Prediction Model and D-Dimer (DD) Testing in the Diagnosis of Deep Vein Thrombosis (DVT): A Systematic Review.

Blood ◽

10.1182/blood.v104.11.1052.1052 ◽

2004 ◽

Vol 104 (11) ◽

pp. 1052-1052

Author(s):

Carolyn J. Owen ◽

Steve Doucette ◽

Philip S. Wells

Keyword(s):

High Risk ◽

High Probability ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Clinical Prediction Model ◽

Prediction Rules ◽

Clinical Probability ◽

Probability Estimates ◽

Low Probability

Abstract Background: The diagnosis of DVT can be made by determining pretest probability of disease and using this information in combination with DD testing and ultrasound imaging. A number of studies have evaluated the use of clinical probability but this literature has not been summarized. Purpose: To systematically review trials that evaluated DVT prevalence using clinical prediction rules either with or without DD for the diagnosis of DVT. Data Sources: English and French language studies were identified from a MEDLINE search from 1990 to March 2004 and were supplemented by a review of all relevant bibliographies. Study Selection: Prospective management studies of symptomatic outpatients with suspected DVT in which patients were followed for a minimum of 3 months were selected. Clinical prediction rules had to be employed prior to DD and diagnostic tests. Studies were excluded if patients with a history of prior DVT were enrolled or if insufficient information was presented to calculate the prevalence of DVT for each of the 3 clinical probability estimates (low, moderate and high risk). Data Extraction: Two reviewers assessed each study for inclusion/exclusion criteria and collected data on prevalence and on sensitivity, specificity and likelihood ratios of DD in each of the 3 clinical probability estimates (low, moderate and high risk). Data Synthesis: 14 management studies involving a clinical prediction model in the diagnosis of DVT in over 8000 patients were included, of which 11 utilized DD in the diagnostic algorithm. All studies employed the same clinical prediction rule. The inverse variance weighted average prevalence of DVT in the low, moderate and high probability subgroups were 4.9% (95% CI= 4.2% to 5.7%), 17.4% (95% CI= 16.2% to 18.8%), and 53.6% (95% CI= 51.1% to 56.2%), respectively. The overall weighted prevalence was 18.3% (95% CI= 17.4% to 19.2%). The sensitivity of DD for the diagnosis of DVT in the low, moderate and high probability subgroups were 90.4% (95% CI= 84.7% to 94.2%), 92.0 % (95% CI= 89.1% to 94.2%), 93.6% (95% CI= 91.2% to 94.3%); and the specificities were 69.9% (95% CI= 68.0% to 71.8%), 52.4% (95% CI= 49.8% to 55.0%), and 43.2% (95% CI= 38.8% to 47.6%), respectively. The Mantel-Haenszel pooled estimates for diagnostic odds ratios (DOR) were 17.4 (95%CI=10.4–29.1), 10.2 (95% CI=7.1–14.6), and 10.1 (95% CI=6.9–14.9) in low, moderate and high groups respectively. Conclusion: Accurate estimates of the prevalence of DVT can be achieved using the same clinical prediction rule. Using this rule, it is unlikely that low probability patients have a DVT probability of more than 5%. Specificity of the DD seems to have clinically relevant differences depending on pretest probability but the DORs (which incorporate sensitivity and specificity) are similar. The data suggest that DVT can be excluded if patients are low probability even when DDs of lower sensitivity are employed and that DD testing has lower utility in high probability patients since false positives are common.

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Development of a clinical prediction rule to diagnose Pneumocystis jirovecii pneumonia in the World Health Organization’s algorithm for seriously ill HIV-infected patients

Southern African Journal of HIV Medicine ◽

10.4102/sajhivmed.v19i1.851 ◽

2018 ◽

Vol 19 (1) ◽

Cited By ~ 3

Author(s):

Gary Maartens ◽

Annemie Stewart ◽

Rulan Griesel ◽

Andre P. Kengne ◽

Felix Dube ◽

...

Keyword(s):

Oxygen Saturation ◽

Respiratory Rate ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

World Health ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Prediction Rules ◽

Chest X Ray ◽

Seriously Ill

Background: The World Health Organization (WHO) algorithm for the diagnosis of tuberculosis in seriously ill HIV-infected patients recommends that treatment for Pneumocystis jiroveciipneumonia (PJP) should be considered without giving clear guidance on selecting patients for empiric PJP therapy. PJP is a common cause of hospitalisation in HIV-infected patients in resource-poor settings where diagnostic facilities are limited.Methods: We developed clinical prediction rules for PJP in a prospective cohort of HIV-infected inpatients with WHO danger signs and cough of any duration. The reference standard for PJP was > 1000 copies/mL of P. jirovecii DNA on real-time sputum polymerase chain reaction (PCR). Four potentially predictive variables were selected for regression models: dyspnoea, chest X-ray, haemoglobin and oxygen saturation. Respiratory rate was explored as a replacement for oxygen saturation as pulse oximetry is not always available in resource-poor settings.Results: We enrolled 500 participants. After imputation for missing values, there were 56 PJP outcome events. Dyspnoea was not independently associated with PJP. Oxygen saturation and respiratory rate were inversely correlated. Two clinical prediction rules were developed: chest X-ray possible/likely PJP, haemoglobin ≥ 9 g/dL and either oxygen saturation < 94% or respiratory rate. The area under the receiver operating characteristic curve of the clinical prediction rule models was 0.761 (95% CI 0.683–0.840) for the respiratory rate model and 0.797 (95% CI 0.725–0.868) for the oxygen saturation model. Both models had zero probability for PJP for scores of zero, and positive likelihood ratios exceeded 10 for high scores.Conclusion: We developed simple clinical prediction rules for PJP, which, if externally validated, could assist decision-making in the WHO seriously ill algorithm.

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Faculty Opinions recommendation of Derivation and validation of a clinical prediction rule for uncomplicated ureteral stone--the STONE score: retrospective and prospective observational cohort studies.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.718326320.793493330 ◽

2014 ◽

Author(s):

M Hammad Ather

Keyword(s):

Cohort Studies ◽

Ureteral Stone ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Observational Cohort

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Inter‐rater reliability and prospective validation of a clinical prediction rule for SARS‐CoV‐2 infection

Academic Emergency Medicine ◽

10.1111/acem.14309 ◽

2021 ◽

Author(s):

Adam E. Nevel ◽

Jeffrey A. Kline

Keyword(s):

Prediction Rule ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Rater Reliability

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Development and validation of a clinical prediction rule for development of diabetic foot ulceration: an analysis of data from five cohort studies

BMJ Open Diabetes Research & Care ◽

10.1136/bmjdrc-2021-002150 ◽

2021 ◽

Vol 9 (1) ◽

pp. e002150

Author(s):

Francesca M Chappell ◽

Fay Crawford ◽

Margaret Horne ◽

Graham P Leese ◽

Angela Martin ◽

...

Keyword(s):

Cohort Studies ◽

Secondary Care ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Initial Assessment ◽

Validation Dataset ◽

Benefit Analysis ◽

Clinical Prediction ◽

Net Benefit ◽

Foot Ulceration

IntroductionThe aim of the study was to develop and validate a clinical prediction rule (CPR) for foot ulceration in people with diabetes.Research design and methodsDevelopment of a CPR using individual participant data from four international cohort studies identified by systematic review, with validation in a fifth study. Development cohorts were from primary and secondary care foot clinics in Europe and the USA (n=8255, adults over 18 years old, with diabetes, ulcer free at recruitment). Using data from monofilament testing, presence/absence of pulses, and participant history of previous ulcer and/or amputation, we developed a simple CPR to predict who will develop a foot ulcer within 2 years of initial assessment and validated it in a fifth study (n=3324). The CPR’s performance was assessed with C-statistics, calibration slopes, calibration-in-the-large, and a net benefit analysis.ResultsCPR scores of 0, 1, 2, 3, and 4 had a risk of ulcer within 2 years of 2.4% (95% CI 1.5% to 3.9%), 6.0% (95% CI 3.5% to 9.5%), 14.0% (95% CI 8.5% to 21.3%), 29.2% (95% CI 19.2% to 41.0%), and 51.1% (95% CI 37.9% to 64.1%), respectively. In the validation dataset, calibration-in-the-large was −0.374 (95% CI −0.561 to −0.187) and calibration slope 1.139 (95% CI 0.994 to 1.283). The C-statistic was 0.829 (95% CI 0.790 to 0.868). The net benefit analysis suggested that people with a CPR score of 1 or more (risk of ulceration 6.0% or more) should be referred for treatment.ConclusionThe clinical prediction rule is simple, using routinely obtained data, and could help prevent foot ulcers by redirecting care to patients with scores of 1 or above. It has been validated in a community setting, and requires further validation in secondary care settings.

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Developing a clinical prediction rule for repeated consultations with functional somatic symptoms in primary care: a cohort study

BMJ Open ◽

10.1136/bmjopen-2020-040730 ◽

2021 ◽

Vol 11 (1) ◽

pp. e040730

Author(s):

Gea A Holtman ◽

Huibert Burger ◽

Robert A Verheij ◽

Hans Wouters ◽

Marjolein Y Berger ◽

...

Keyword(s):

Primary Care ◽

Cohort Study ◽

Somatic Symptoms ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Model Discrimination ◽

Clinical Prediction ◽

Internal Validation ◽

Predictive Values ◽

Healthy Activity

ObjectivesPatients who present in primary care with chronic functional somatic symptoms (FSS) have reduced quality of life and increased health care costs. Recognising these early is a challenge. The aim is to develop and internally validate a clinical prediction rule for repeated consultations with FSS.Design and settingRecords from the longitudinal population-based (‘Lifelines’) cohort study were linked to electronic health records from general practitioners (GPs).ParticipantsWe included patients consulting a GP with FSS within 1 year after baseline assessment in the Lifelines cohort.Outcome measuresThe outcome is repeated consultations with FSS, defined as ≥3 extra consultations for FSS within 1 year after the first consultation. Multivariable logistic regression, with bootstrapping for internal validation, was used to develop a risk prediction model from 14 literature-based predictors. Model discrimination, calibration and diagnostic accuracy were assessed.Results18 810 participants were identified by database linkage, of whom 2650 consulted a GP with FSS and 297 (11%) had ≥3 extra consultations. In the final multivariable model, older age, female sex, lack of healthy activity, presence of generalised anxiety disorder and higher number of GP consultations in the last year predicted repeated consultations. Discrimination after internal validation was 0.64 with a calibration slope of 0.95. The positive predictive value of patients with high scores on the model was 0.37 (0.29–0.47).ConclusionsSeveral theoretically suggested predisposing and precipitating predictors, including neuroticism and stressful life events, surprisingly failed to contribute to our final model. Moreover, this model mostly included general predictors of increased risk of repeated consultations among patients with FSS. The model discrimination and positive predictive values were insufficient and preclude clinical implementation.

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