Real-life impact of clinical prediction rules for venous thromboembolism in primary care: a cross-sectional cohort study

ObjectiveClinical prediction rules (CPRs) followed by D-dimer testing were shown to safely rule out venous thromboembolism (VTE) in about half of all suspected patients in controlled and experienced study settings. Yet, its real-life impact in primary care is unknown. The aim of this study was to determine the real-life impact of CPRs for suspected VTE in primary care.DesignCross-sectional cohort study.SettingPrimary care in the Netherlands.ParticipantsPatients with suspected deep venous thrombosis (n=993) and suspected pulmonary embolism (n=484).InterventionsGeneral practitioners received an educational instruction on how to use CPRs in suspected VTE. We did not rectify incorrect application of the CPR in order to mimic daily clinical care.Main outcome measuresPrimary outcomes were the diagnostic failure rate, defined as the 3-month incidence of VTE in the non-referred group, and the efficiency, defined as the proportion of non-referred patients in the total study population. Secondary outcomes were determinants for and consequences of incorrect application of the CPRs.ResultsIn 267 of the included 1477 patients, VTE was confirmed. When CPRs were correctly applied, the failure rate was 1.51% (95% CI 0.77 to 2.86), and the efficiency was 58.1% (95% CI 55.2 to 61.0). However, the CPRs were incorrectly applied in 339 patients, which resulted in an increased failure rate of 3.31% (95% CI 1.07 to 8.76) and a decreased efficiency of 35.7% (95% CI 30.6 to 41.1). The presence of concurrent heart failure increased the likelihood of incorrect application (adjusted OR 3.26; 95% CI 1.47 to 7.21).ConclusionsCorrect application of CPRs for VTE in primary care is associated with an acceptable low failure rate at a high efficiency. Importantly, in nearly a quarter of patients, the CPRs were incorrectly applied that resulted in a higher failure rate and a considerably lower efficiency.

Download Full-text

Translation of clinical prediction rules for febrile children to primary care practice: an observational cohort study

British Journal of General Practice ◽

10.3399/bjgp15x684373 ◽

2015 ◽

Vol 65 (633) ◽

pp. e224-e233 ◽

Cited By ~ 4

Author(s):

Yvette van Ierland ◽

Gijs Elshout ◽

Marjolein Y Berger ◽

Yvonne Vergouwe ◽

Marcel de Wilde ◽

...

Keyword(s):

Primary Care ◽

Cohort Study ◽

Primary Care Practice ◽

Observational Cohort Study ◽

Care Practice ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Prediction Rules ◽

Observational Cohort

Download Full-text

Clinical prediction rules combining signs, symptoms and epidemiological context to distinguish influenza from influenza-like illnesses in primary care: a cross sectional study

BMC Family Practice ◽

10.1186/1471-2296-12-4 ◽

2011 ◽

Vol 12 (1) ◽

Cited By ~ 30

Author(s):

Barbara Michiels ◽

Isabelle Thomas ◽

Paul Van Royen ◽

Samuel Coenen

Keyword(s):

Primary Care ◽

Cross Sectional Study ◽

Clinical Prediction ◽

Sectional Study ◽

Clinical Prediction Rules ◽

Cross Sectional ◽

Prediction Rules

Download Full-text

Developing an International Register of Clinical Prediction Rules for Use in Primary Care: A Descriptive Analysis

The Annals of Family Medicine ◽

10.1370/afm.1640 ◽

2014 ◽

Vol 12 (4) ◽

pp. 359-366 ◽

Cited By ~ 19

Author(s):

C. Keogh ◽

E. Wallace ◽

K. K. O'Brien ◽

R. Galvin ◽

S. M. Smith ◽

...

Keyword(s):

Primary Care ◽

Descriptive Analysis ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Prediction Rules

Download Full-text

Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish a web-based register

Journal of Clinical Epidemiology ◽

10.1016/j.jclinepi.2010.11.011 ◽

2011 ◽

Vol 64 (8) ◽

pp. 848-860 ◽

Cited By ~ 11

Author(s):

Claire Keogh ◽

Emma Wallace ◽

Kirsty K. O’Brien ◽

Paul J. Murphy ◽

Conor Teljeur ◽

...

Keyword(s):

Primary Care ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Web Based ◽

Prediction Rules

Download Full-text

An Ontology-Driven Approach to Clinical Evidence Modelling Implementing Clinical Prediction Rules

E-Health Technologies and Improving Patient Safety: Exploring Organizational Factors ◽

10.4018/978-1-4666-2657-7.ch016 ◽

2013 ◽

pp. 257-284 ◽

Cited By ~ 1

Author(s):

Derek Corrigan ◽

Lucy Hederman ◽

Haseeb Khan ◽

Adel Taweel ◽

Olga Kostopoulou ◽

...

Keyword(s):

Primary Care ◽

Evidence Based Medicine ◽

Clinical Evidence ◽

Evidence Based ◽

Clinical Prediction ◽

Clinical Prediction Rules ◽

Prediction Rules ◽

Support Provision ◽

Primary Care Clinicians ◽

Based Medicine

Diagnostic error is a major threat to patient safety in the context of the primary care setting. Evidence-based medicine has been advocated as one part of a solution. The ability to effectively apply evidence-based medicine implies the use of information systems by providing efficient access to the latest peer-reviewed evidence-based information sources. A fundamental challenge in applying information technology to a diagnostic clinical domain is how to formally represent known clinical knowledge as part of an underlying evidence repository. Clinical prediction rules (CPRs) can provide the basis for a formal representation of knowledge. The TRANSFoRm project defines the architectural components required to deliver a solution by providing an ontology driven clinical evidence service to support provision of diagnostic tools, designed to be maintained and updated from electronic sources of research data, to assist primary care clinicians during the patient consultation through delivery of up to date evidence based diagnostic rules.

Download Full-text

The Effect of Amoxicillin in Adult Patients Presenting to Primary Care with Acute Cough Predicted to Have Pneumonia or a Combined Viral-Bacterial Infection

Antibiotics ◽

10.3390/antibiotics10070817 ◽

2021 ◽

Vol 10 (7) ◽

pp. 817

Author(s):

Robin Bruyndonckx ◽

Beth Stuart ◽

Paul Little ◽

Niel Hens ◽

Margareta Ieven ◽

...

Keyword(s):

Primary Care ◽

Bacterial Infection ◽

Controlled Trial ◽

Symptom Duration ◽

Antibiotic Prescribing ◽

Clinical Prediction ◽

Acute Cough ◽

Clinical Prediction Rules ◽

Prediction Rules ◽

Combined Infection

While most cases of acute cough are self-limiting, antibiotics are prescribed to over 50%. This proportion is inappropriately high given that benefit from treatment with amoxicillin could only be demonstrated in adults with pneumonia (based on chest radiograph) or combined viral–bacterial infection (based on modern microbiological methodology). As routine use of chest radiographs and microbiological testing is costly, clinical prediction rules could be used to identify these patient subsets. In this secondary analysis of data from a multicentre randomised controlled trial in adults presenting to primary care with acute cough, we used prediction rules for pneumonia or combined infection and assessed the effect of amoxicillin in patients predicted to have pneumonia or combined infection on symptom duration, symptom severity and illness deterioration. In total, 2056 patients that fulfilled all inclusion criteria were randomised, 1035 to amoxicillin, 1021 to placebo. Neither patients with a predicted pneumonia nor patients with a predicted combined infection were significantly more likely to benefit from amoxicillin. While the studied clinical prediction rules may help primary care clinicians to reduce antibiotic prescribing for low-risk patients, they did not identify adult acute cough patients that would benefit from amoxicillin treatment.

Download Full-text

Clinical prediction rules for childhood UTIs: a cross-sectional study in ambulatory care

BJGP Open ◽

10.3399/bjgpo.2021.0171 ◽

2022 ◽

pp. BJGPO.2021.0171

Author(s):

Hanne Ann Boon ◽

Jan Y Verbakel ◽

Tine De Burghgraeve ◽

Ann Van den Bruel

Keyword(s):

Area Under The Curve ◽

High Specificity ◽

Cross Sectional Study ◽

Clinical Prediction ◽

Sectional Study ◽

Clinical Prediction Rules ◽

Cross Sectional ◽

Prediction Rules ◽

Low Sensitivity ◽

Tract Infections

BackgroundDiagnosing childhood urinary tract infections (UTI) is challenging.AimValidate clinical prediction rules (UTIcalc, DUTY, Gorelick) for paediatric UTIs in primary care.Design & settingPost-hoc analysis of a cross-sectional study in 39 general practices and 2 emergency departments (Belgium, March 2019 to March 2020).MethodPhysicians recruited acutely ill children ≤18 years and sampled urine systematically for culture. Per rule, we performed an apparent validation; calculated sensitivities and specificities with 95%CI per threshold in the target group. For the DUTY coefficient-based algorithm, we performed a logistic calibration and calculated the Area Under the Curve with 95%CI.ResultsOf 834 children ≤18 years recruited, there were 297 children <5 years. The UTIcalc and Gorelick score had high to moderate sensitivity and low specificity (UTIcalc ≥2%) 75%; and 16% respectively; Gorelick (≥2 variables) 91%; and 8%. In contrast, the DUTY score ≥5 points had low sensitivity (8%), but high specificity (99%). Urine samples would be obtained in 72% vs 38% (UTIcalc), 92% vs 38% (Gorelick) or 1% vs 32% (DUTY) of children, compared to routine care. The number of missed infections per score was 1/4 (UTIcalc), 2/23 (Gorelick) and 24/26 (DUTY). The UTIcalc+ dipstick model had high sensitivity and specificity (100%; and 91%); resulting in no missed cases and 59% (95%CI 49%–68%) of antibiotics prescribed inappropriately.ConclusionIn this study, the UTIcalc and Gorelick score were useful for ruling out UTI but resulted in high urine sampling rates. The DUTY score had low sensitivity, meaning that 92% of UTIs would be missed.

Download Full-text