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 Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245281
Author(s):  
Bianca Magro ◽  
Valentina Zuccaro ◽  
Luca Novelli ◽  
Lorenzo Zileri ◽  
Ciro Celsa ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Brier Score ◽  
Clinical Prediction ◽  
Duration Of Symptoms ◽  
Derivation Cohort ◽  
Independent Risk Factors

Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. Methods and findings We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39–2.12), diabetes (HR 1.21, 95%CI 1.02–1.45), coronary heart disease (HR 1.40 95% CI 1.09–1.80), chronic liver disease (HR 1.78, 95%CI 1.16–2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002–1.0005). The AUC was 0.822 (95%CI 0.722–0.922) in the derivation cohort and 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). Conclusions A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.

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.

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

Abstract TP233: Clinical Predictors of Endovascular Stroke Treatment Among Emergency Department Patients Screened With Multimodal Computed Tomography

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Ken Smythe ◽  
John A Oostema
Keyword(s):  
Emergency Department ◽  
Clinical Characteristics ◽  
Multivariable Analysis ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Clinical Prediction ◽  
Clinical Predictors ◽  
Vessel Occlusion ◽  
Stroke Treatment ◽  
Ct Angiogram

Background: Endovascular therapy (EVT) offers dramatic benefit to selected patients with large vessel occlusion (LVO) ischemic stroke. However, identification of EVT candidates requires advanced imaging and often interfacility transfer. We sought to quantify the yield of such testing as well as identify clinical predictors of EVT candidacy. Methods: This retrospective cohort study identified consecutive Emergency Department (ED) patients with stroke symptoms who underwent CT angiogram and brain perfusion (CTA/P) imaging to assess for EVT candidacy. Demographics, medical history, clinical characteristics, final diagnosis, and outcomes were abstracted. We compared clinical characteristics among those who did and did not undergo EVT. Multivariable logistic regression was used to identify independent clinical predictors of EVT and derive a clinical prediction rule to quantify the probability of EVT. Results: Over a 12-month period, 835 patients underwent CTA/P imaging in the ED. EVT was undertaken for 116 (13.9%) patients; 321 (38.4%) ultimately received a non-stroke diagnosis. Patients who received EVT were older and had higher stroke scores (Table). Patients with an unknown last known well (LKW) time were less likely to receive EVT, however increasing time form LKW to door did not predict EVT (test for trend p=0.976). Multivariable analysis results are presented in the Table. A clinical decision rule based on the regression coefficients demonstrated moderately high discrimination for predicting EVT with an AUC of 0.79 (0.74 to 0.83). Among 102 patients transferred for CTA/P, 24 (24%) had and a score <1, none of whom received EVT. Conclusions: EVT Candidates are common among ED patients screened with CTA/P. Clinical factors can predict the likelihood of EVT candidacy. If validated in other populations, a simple clinical prediction rule may assist in triaging patients in need of urgent transfer to a thrombectomy-capable facility.

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

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.

Derivation of a Simple Clinical Model to Categorize Patients Probability of Pulmonary Embolism: Increasing the Models Utility with the SimpliRED D-dimer

2000 ◽  
Vol 83 (03) ◽  
pp. 416-420 ◽  
Author(s):  
David Anderson ◽  
Marc Rodger ◽  
Jeffrey Ginsberg ◽  
Clive Kearon ◽  
Michael Gent ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Scoring System ◽  
Clinical Symptoms ◽  
Prediction Rule ◽  
Scoring Systems ◽  
Clinical Prediction Rule ◽  
Clinical Prediction ◽  
Clinical Model ◽  
Validation Set ◽  
D Dimer

SummaryWe have previously demonstrated that a clinical model can be safely used in a management strategy in patients with suspected pulmonary embolism (PE). We sought to simplify the clinical model and determine a scoring system, that when combined with D-dimer results, would safely exclude PE without the need for other tests, in a large proportion of patients. We used a randomly selected sample of 80% of the patients that participated in a prospective cohort study of patients with suspected PE to perform a logistic regression analysis on 40 clinical variables to create a simple clinical prediction rule. Cut points on the new rule were determined to create two scoring systems. In the first scoring system patients were classified as having low, moderate and high probability of PE with the proportions being similar to those determined in our original study. The second system was designed to create two categories, PE likely and unlikely. The goal in the latter was that PE unlikely patients with a negative D-dimer result would have PE in less than 2% of cases. The proportion of patients with PE in each category was determined overall and according to a positive or negative SimpliRED D-dimer result. After these determinations we applied the models to the remaining 20% of patients as a validation of the results. The following seven variables and assigned scores (in brackets) were included in the clinical prediction rule: Clinical symptoms of DVT (3.0), no alternative diagnosis (3.0), heart rate >100 (1.5), immobilization or surgery in the previous four weeks (1.5), previous DVT/PE (1.5), hemoptysis (1.0) and malignancy (1.0). Patients were considered low probability if the score was <2.0, moderate of the score was 2.0 to 6.0 and high if the score was over 6.0. Pulmonary embolism unlikely was assigned to patients with scores <4.0 and PE likely if the score was >4.0. 7.8% of patients with scores of less than or equal to 4 had PE but if the D-dimer was negative in these patients the rate of PE was only 2.2% (95% CI = 1.0% to 4.0%) in the derivation set and 1.7% in the validation set.Importantly this combination occurred in 46% of our study patients. A score of <2.0 and a negative D-dimer results in a PE rate of 1.5% (95% CI = 0.4% to 3.7%) in the derivation set and 2.7% (95% CI = 0.3% to 9.0%) in the validation set and only occurred in 29% of patients. The combination of a score <4.0 by our simple clinical prediction rule and a negative SimpliRED D-Dimer result may safely exclude PE in a large proportion of patients with suspected PE.

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

2021 ◽  
Author(s):  
Michael Czihal ◽  
Christian Lottspeich ◽  
Christoph Bernau ◽  
Theresa Henke ◽  
Ilaria Prearo ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Giant Cell ◽  
Validation Cohort ◽  
Prediction Rule ◽  
Diagnostic Algorithm ◽  
Clinical Prediction Rule ◽  
Temporal Artery ◽  
Ischemic Optic Neuropathy ◽  
Clinical Prediction ◽  
Derivation Cohort

Background: Risk tratification 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 arteriitis (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 to a clinical prediction rule, integrated into a stepwise diagnostic algorithm together with CRP-values and hrTCS-values. Results: The model consisted of 4 clinical variables (age &amp;gt; 70, headache, jaw claudication, 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 (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.

scholarly journals Simple risk scores to predict hospitalization or death in outpatients with COVID-19

2022 ◽  
Author(s):  
Mark Ebell ◽  
Roya Hamadani ◽  
Autumn Kieber-Emmons
Keyword(s):  
Validation Cohort ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Low Risk ◽  
Polymerase Chain Reaction Test ◽  
Risk Scores ◽  
Clinical Prediction ◽  
Positive Polymerase Chain Reaction ◽  
Derivation Cohort ◽  
Temporal Validation

Importance Outpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, in particular whether to hospitalize a patient and if managed as an outpatient, how closely to follow them. Objective To develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging. Design Derivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules. Setting Primary and Express care clinics in a Pennsylvania health system. Participants Patients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19. Main outcomes and measures Classification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC). Results Overall, 7.4% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21) and 5.5% in the late validation cohort (3806 patients presenting 3/1/21 or later) were ultimately hospitalized. We developed and temporally validated three risk scores that all included age, dyspnea, and the presence of comorbidities, adding respiratory rate for the second score and oxygen saturation for the third. All had very good overall accuracy (AUC 0.77 to 0.78) and classified over half of patients in the validation cohort as very low risk with a 1.7% or lower likelihood of hospitalization. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization (2.8%). Conclusions and relevance Simple risk scores applicable to outpatient and telehealth settings can identify patients with very low (1.6% to 1.7%), low (5.2% to 5.9%), moderate (14.7% to 15.6%), and high risk (32.0% to 34.2%) of hospitalization. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.

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