A clinical prediction rule for uncomplicated ureteral stone: The STONE score; a prospective observational validation cohort study

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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Risk factors, costs and complications of delayed hospital discharge from internal medicine wards at a Canadian academic medical centre: retrospective cohort study

BMC Health Services Research ◽

10.1186/s12913-019-4760-3 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 2

Author(s):

Anthony D. Bai ◽

Cathy Dai ◽

Siddhartha Srivastava ◽

Christopher A. Smith ◽

Sudeep S. Gill

Keyword(s):

Internal Medicine ◽

Risk Factors ◽

Cohort Study ◽

Retrospective Cohort ◽

Hospital Costs ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Delayed Discharge ◽

Patients At Risk

Abstract Background Hospitalized patients are designated alternate level of care (ALC) when they no longer require hospitalization but discharge is delayed while they await alternate disposition or living arrangements. We assessed hospital costs and complications for general internal medicine (GIM) inpatients who had delayed discharge. In addition, we developed a clinical prediction rule to identify patients at risk for delayed discharge. Methods We conducted a retrospective cohort study of consecutive GIM patients admitted between 1 January 2015 and 1 January 2016 at a large tertiary care hospital in Canada. We compared hospital costs and complications between ALC and non-ALC patients. We derived a clinical prediction rule for ALC designation using a logistic regression model and validated its diagnostic properties. Results Of 4311 GIM admissions, 255 (6%) patients were designated ALC. Compared to non-ALC patients, ALC patients had longer median length of stay (30.85 vs. 3.95 days p < 0.0001), higher median hospital costs ($22,459 vs. $5003 p < 0.0001) and more complications in hospital (25.5% vs. 5.3% p < 0.0001) especially nosocomial infections (14.1% vs. 1.9% p < 0.0001). Sensitivity analyses using propensity score and pair matching yielded similar results. In a derivation cohort, seven significant risk factors for ALC were identified including age > =80 years, female sex, dementia, diabetes with complications as well as referrals to physiotherapy, occupational therapy and speech language pathology. A clinical prediction rule that assigned each of these predictors 1 point had likelihood ratios for ALC designation of 0.07, 0.25, 0.66, 1.48, 6.07, 17.13 and 21.85 for patients with 0, 1, 2, 3, 4, 5, and 6 points respectively in the validation cohort. Conclusions Delayed discharge is associated with higher hospital costs and complication rates especially nosocomial infections. A clinical prediction rule can identify patients at risk for delayed discharge.

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Development and validation of a clinical prediction rule to identify suspected breast cancer: a prospective cohort study

BMC Cancer ◽

10.1186/1471-2407-14-743 ◽

2014 ◽

Vol 14 (1) ◽

Cited By ~ 10

Author(s):

Rose Galvin ◽

Doireann Joyce ◽

Eithne Downey ◽

Fiona Boland ◽

Tom Fahey ◽

...

Keyword(s):

Breast Cancer ◽

Cohort Study ◽

Prospective Cohort Study ◽

Prospective Cohort ◽

Prediction Rule ◽

Clinical Prediction Rule ◽

Clinical Prediction ◽

Development And Validation

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A Diagnostic Algorithm Based on a Simple Clinical Prediction Rule for the Diagnosis of Cranial Giant Cell Arteritis

Journal of Clinical Medicine ◽

10.3390/jcm10061163 ◽

2021 ◽

Vol 10 (6) ◽

pp. 1163

Author(s):

Michael Czihal ◽

Christian Lottspeich ◽

Christoph Bernau ◽

Teresa Henke ◽

Ilaria Prearo ◽

...

Keyword(s):

Diagnostic Accuracy ◽

Giant Cell Arteritis ◽

Giant Cell ◽

Validation Cohort ◽

Prediction Rule ◽

Diagnostic Algorithm ◽

Clinical Prediction Rule ◽

Ischemic Optic Neuropathy ◽

Clinical Prediction ◽

Derivation Cohort

Background: Risk stratification based on pre-test probability may improve the diagnostic accuracy of temporal artery high-resolution compression sonography (hrTCS) in the diagnostic workup of cranial giant cell arteritis (cGCA). Methods: A logistic regression model with candidate items was derived from a cohort of patients with suspected cGCA (n = 87). The diagnostic accuracy of the model was tested in the derivation cohort and in an independent validation cohort (n = 114) by receiver operator characteristics (ROC) analysis. The clinical items were composed of a clinical prediction rule, integrated into a stepwise diagnostic algorithm together with C-reactive protein (CRP) values and hrTCS values. Results: The model consisted of four clinical variables (age > 70, headache, jaw claudication, and anterior ischemic optic neuropathy). The diagnostic accuracy of the model for discrimination of patients with and without a final clinical diagnosis of cGCA was excellent in both cohorts (area under the curve (AUC) 0.96 and AUC 0.92, respectively). The diagnostic algorithm improved the positive predictive value of hrCTS substantially. Within the algorithm, 32.8% of patients (derivation cohort) and 49.1% (validation cohort) would not have been tested by hrTCS. None of these patients had a final diagnosis of cGCA. Conclusion: A diagnostic algorithm based on a clinical prediction rule improves the diagnostic accuracy of hrTCS.

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