Individually Significant Risk Factors Do Not Provide an Accurate Clinical Prediction Rule for Infant Underimmunization in One Disadvantaged Urban Area

2006 ◽  
Vol 6 (3) ◽  
pp. 165-172 ◽  
Author(s):  
Simon J. Hambidge ◽  
Stephanie L. Phibbs ◽  
Arthur J. Davidson ◽  
Charles W. LeBaron ◽  
Vijayalaxmi Chandramouli ◽  
...  
Keyword(s):  
Risk Factors ◽  
Urban Area ◽  
Significant Risk ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Clinical Prediction

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

2019 ◽  
Vol 19 (1) ◽  
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.

Developing a Clinical Prediction Rule for First Hospital-OnsetClostridium difficileInfections: A Retrospective Observational Study

2016 ◽  
Vol 37 (8) ◽  
pp. 896-900 ◽  
Author(s):  
Anne Press ◽  
Benson Ku ◽  
Lauren McCullagh ◽  
Lisa Rosen ◽  
Safiya Richardson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Characteristic Curve ◽  
Multivariable Analysis ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Clinical Prediction ◽  
Derivation Cohort ◽  
Tertiary Healthcare ◽  
Validation Set ◽  
Hospital Acquired

BACKGROUNDThe healthcare burden of hospital-acquiredClostridium difficileinfection (CDI) demands attention and calls for a solution. Identifying patients’ risk of developing a primary nosocomial CDI is a critical first step in reducing the development of new cases of CDI.OBJECTIVETo derive a clinical prediction rule that can predict a patient’s risk of acquiring a primary CDI.DESIGNRetrospective cohort study.SETTINGLarge tertiary healthcare center.PATIENTSTotal of 61,482 subjects aged at least 18 admitted over a 1-year period (2013).INTERVENTIONNone.METHODSPatient demographic characteristics, evidence of CDI, and other risk factors were retrospectively collected. To derive the CDI clinical prediction rule the patient population was divided into a derivation and validation cohort. A multivariable analysis was performed in the derivation cohort to identify risk factors individually associated with nosocomial CDI and was validated on the validation sample.RESULTSAmong 61,482 subjects, CDI occurred in 0.46%. CDI outcome was significantly associated with age, admission in the past 60 days, mechanical ventilation, dialysis, history of congestive heart failure, and use of antibiotic medications. The sensitivity and specificity of the score, in the validation set, were 82.0% and 75.7%, respectively. The area under the receiver operating characteristic curve was 0.85.CONCLUSIONThis study successfully derived a clinical prediction rule that will help identify patients at high risk for primary CDI. This tool will allow physicians to systematically recognize those at risk for CDI and will allow for early interventional strategies.Infect Control Hosp Epidemiol2016;37:896–900

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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