A novel primary care clinical prediction rule for early detection of osteoporosis

2011 ◽  
Vol 17 (2) ◽  
pp. 175 ◽  
Author(s):  
Cheryl Kimber ◽  
Karen Grimmer-Somers
Keyword(s):  
Primary Care ◽  
Health Care Providers ◽  
Bone Health ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Wrist Fracture ◽  
Clinical Suspicion ◽  
Care Providers ◽  
Clinical Prediction ◽  
Risk Patients

The effects of osteoporosis (OP) can be significantly slowed if disease is detected early. We report on a clinical risk prediction rule developed from patient histories taken in an orthopaedic outpatient clinic, before confirmatory testing for OP. Data were extracted from routine audits of consecutive records of patients with recent wrist fracture, comprising demographic details, medications, past and current disease, and fracture details. Clinical prediction rule elements were tested against clinical suspicion of OP. The clinical prediction elements comprised sex and age risk, medications that predispose patients to OP and/or falls, previous fractures and disease/medical conditions that are known OP risks. The best cut point (6.5) demonstrated 100% sensitivity with clinical suspicion of OP. Patient history information is often available before OP is clinically suspected or a definitive diagnosis is made. Our clinical prediction rule will be useful in primary care settings where objective measures of bone health are not readily available. It will raise OP awareness amongst health care providers and patients, particularly those not previously suspected of having OP. It will assist in identifying at-risk patients early and commencing them on appropriate management, without waiting for definitive bone health tests.

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 Ruling out coronary heart disease in primary care: external validation of a clinical prediction rule

2012 ◽  
Vol 62 (599) ◽  
pp. e415-e421 ◽  
Author(s):  
Jörg Haasenritter ◽  
Stefan Bösner ◽  
Paul Vaucher ◽  
Lilli Herzig ◽  
Monika Heinzel-Gutenbrunner ◽  
...  
Keyword(s):  
Primary Care ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
External Validation ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Clinical Prediction

scholarly journals Development of a clinical prediction rule for sepsis in primary care: protocol for the TeSD-IT study

2020 ◽  
Author(s):  
Feike J. Loots ◽  
Rogier Hopstaken ◽  
Kevin Jenniskens ◽  
Geert W.J. Frederix ◽  
Alma C. van de Pol ◽  
...  
Keyword(s):  
Primary Care ◽  
The Netherlands ◽  
Expert Panel ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Secondary Outcome ◽  
Hospital Treatment ◽  
Diagnostic Study ◽  
Clinical Prediction ◽  
Blood Samples

Abstract Background Early recognition and treatment of sepsis is crucial to prevent detrimental outcomes. General practitioners (GPs) are often the first healthcare providers to encounter seriously-ill patients. The aim of this study is to assess the value of clinical information and additional tests to develop a clinical prediction rule to support early diagnosis and management of sepsis by GPs. Methods We will perform a diagnostic study in the setting of out-of-hours home visits in four GP cooperatives in the Netherlands. Acutely ill adult patients suspected of a serious infection will be screened for eligibility by the GP. The following candidate predictors will be prospectively recorded: 1) age; 2) body temperature; 3) systolic blood pressure; 4) heart rate; 5) respiratory rate; 6) peripheral oxygen saturation; 7) mental status; 8) history of rigors and 9) rate of progression. After clinical assessment by the GP, blood samples will be collected in all patients to measure C-reactive protein, lactate and procalcitonin. All patients will receive care as usual. The primary outcome is presence or absence of sepsis within 72 hours after inclusion, according to an expert panel. The need for hospital treatment for any indication will be assessed by the expert panel as a secondary outcome. Multivariable logistic regression will be used to design an optimal prediction model first, and subsequently derive a simplified clinical prediction rule that enhances feasibility of using the model in daily clinical practice. Bootstrapping will be performed for internal validation of both the optimal model and simplified prediction rule. Performance of both models will be compared to existing clinical prediction rules for sepsis. Discussion This study will enable us to develop a clinical prediction rule for the recognition of sepsis in a high-risk primary care setting to aid in the decision which patients have to be immediately referred to a hospital and who can be safely treated at home. As clinical signs and blood samples will be obtained prospectively, near complete data will be available for analyses. External validation will be needed before implementation in routine care and to determine in which pre-hospital settings care can be improved using the prediction rule. Trial registration The study is registered in the Netherlands Trial Registry (registration number NTR7026).

scholarly journals Development of a clinical prediction rule for sepsis in primary care: protocol of the TeSD-IT study

2020 ◽  
Author(s):  
Feike J. Loots ◽  
Rogier Hopstaken ◽  
Kevin Jenniskens ◽  
Geert W.J. Frederix ◽  
Alma C. van de Pol ◽  
...  
Keyword(s):  
Primary Care ◽  
The Netherlands ◽  
Expert Panel ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Secondary Outcome ◽  
Hospital Treatment ◽  
Diagnostic Study ◽  
Clinical Prediction ◽  
Blood Samples

Abstract Background Early recognition and treatment of sepsis is crucial to prevent detrimental outcomes. General practitioners (GPs) are often the first healthcare providers to encounter seriously-ill patients. The aim of this study is to assess the value of clinical information and additional tests to develop a clinical prediction rule (CPR) to support early diagnosis and management of sepsis by GPs.Methods We will perform a diagnostic study in the setting of out-of-hours home visits in four GP cooperatives in the Netherlands. Acutely ill adult patients suspected of a serious infection are screened for eligibility by the GP. The following candidate predictors are prospectively recorded: 1) age; 2) body temperature; 3) systolic blood pressure; 4) heart rate; 5) respiratory rate; 6) peripheral oxygen saturation; 7) altered mental status; 8) rigors and 9) rapid illness progression. After the clinical assessment of the GP, blood samples are collected in all patients to measure C-reactive protein, lactate and procalcitonin. All patients receive care as usual. The primary outcome is presence or absence of sepsis within 72 hours of inclusion, according to an expert panel. The need for hospital treatment for any indication will be assessed by the expert panel as a secondary outcome. Multivariable logistic regression is used to design an optimal prediction model first, and subsequently derive a simplified CPR that enhances feasibility of using the model in daily clinical practice. Bootstrapping will be performed for internal validation of both the optimal model and simplified CPR. Performance of both models will be compared to existing CPRs for sepsis.Discussion This study will enable us to develop a CPR for the recognition of sepsis in a high-risk primary care setting to aid in the decision which patients have to be immediately referred to a hospital and who can be safely treated at home. As clinical signs and blood samples will be retrieved prospectively in all participants, near complete data will be available for analyses. External validation is needed before the CPR is implemented in routine care and to determine in which pre-hospital settings care can be improved using the CPR.Trial registration The study is registered in the Netherlands Trial Registry (registration number NTR7026).

Sign in / Sign up

Export Citation Format

Share Document