scholarly journals Comparison of Clinician Suspicion Versus a Clinical Prediction Rule in Identifying Children at Risk for Intra-abdominal Injuries After Blunt Torso Trauma

2015 ◽  
Vol 22 (9) ◽  
pp. 1034-1041 ◽  
Author(s):  
Prashant Mahajan ◽  
Nathan Kuppermann ◽  
Michael Tunik ◽  
Kenneth Yen ◽  
Shireen M. Atabaki ◽  
...  
Keyword(s):  
At Risk ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Clinical Prediction ◽  
Children At Risk ◽  
Abdominal Injuries ◽  
Torso Trauma

scholarly journals Paramedics assessing Elders at Risk for Independence Loss (PERIL): Derivation, Reliability and Comparative Effectiveness of a Clinical Prediction Rule

CJEM ◽  
2016 ◽  
Vol 18 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Jacques S. Lee ◽  
P. Richard Verbeek ◽  
Michael J. Schull ◽  
Lisa Calder ◽  
Ian G. Stiell ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Clinical Judgment ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Adverse Outcomes ◽  
Clinical Prediction ◽  
Convenience Sample ◽  
Observer Reliability ◽  
Proxy Measure

AbstractObjectivesWe conducted a program of research to derive and test the reliability of a clinical prediction rule to identify high-risk older adults using paramedics’ observations.MethodsWe developed the Paramedics assessing Elders at Risk of Independence Loss (PERIL) checklist of 43 yes or no questions, including the Identifying Seniors at Risk (ISAR) tool items. We trained 1,185 paramedics from three Ontario services to use this checklist, and assessed inter-observer reliability in a convenience sample. The primary outcome, return to the ED, hospitalization, or death within one month was assessed using provincial databases. We derived a prediction rule using multivariable logistic regression.ResultsWe enrolled 1,065 subjects, of which 764 (71.7%) had complete data. Inter-observer reliability was good or excellent for 40/43 questions. We derived a four-item rule: 1) “Problems in the home contributing to adverse outcomes?” (OR 1.43); 2) “Called 911 in the last 30 days?” (OR 1.72); 3) male (OR 1.38) and 4) lacks social support (OR 1.4). The PERIL rule performed better than a proxy measure of clinical judgment (AUC 0.62 vs. 0.56, p=0.02) and adherence was better for PERIL than for ISAR.ConclusionsThe four-item PERIL rule has good inter-observer reliability and adherence, and had advantages compared to a proxy measure of clinical judgment. The ISAR is an acceptable alternative, but adherence may be lower. If future research validates the PERIL rule, it could be used by emergency physicians and paramedic services to target preventative interventions for seniors identified as high-risk.

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

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.

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

BMJ Open ◽  
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.

scholarly journals Prognostic value of the ABCD2 clinical prediction rule: a systematic review and meta-analysis

2011 ◽  
Vol 28 (4) ◽  
pp. 366-376 ◽  
Author(s):  
R. Galvin ◽  
C. Geraghty ◽  
N. Motterlini ◽  
B. D. Dimitrov ◽  
T. Fahey
Keyword(s):  
Systematic Review ◽  
Prognostic Value ◽  
Meta Analysis ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Clinical Prediction

The Risk of Severe Postoperative Pain: Modification and Validation of a Clinical Prediction Rule

2008 ◽  
Vol 107 (4) ◽  
pp. 1330-1339 ◽  
Author(s):  
Kristel J. M. Janssen ◽  
Cor J. Kalkman ◽  
Diederick E. Grobbee ◽  
Gouke J. Bonsel ◽  
Karel G. M. Moons ◽  
...  
Keyword(s):  
Postoperative Pain ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Clinical Prediction ◽  
Severe Postoperative Pain
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