Derivation and Validation of a Clinical Prediction Score for Isolation of Inpatients With Suspected Pulmonary Tuberculosis

2008 ◽  
Vol 29 (10) ◽  
pp. 927-932 ◽  
Author(s):  
Kara S. Rakoczy ◽  
Stuart H. Cohen ◽  
Hien H. Nguyen
Keyword(s):  
Validation Cohort ◽  
Multivariable Analysis ◽  
Prediction Rule ◽  
Prediction Score ◽  
Risk Scores ◽  
Clinical Prediction ◽  
Foreign Born ◽  
Factors Associated ◽  
Suspected Tuberculosis ◽  
Immunocompromised State

Background.The use of a clinical prediction score to improve the practice of instituting airborne-transmission precautions in patients with suspected tuberculosis holds promise for increasing appropriate isolation and decreasing unnecessary isolation. The objective of this study was to derive and validate a clinical prediction score for patients with suspected tuberculosis.Methods.We used a case—control study design to evaluate differences between patients with a diagnosis of tuberculosis and those placed under airborne precautions who had negative culture results. We developed risk scores based on a multivariable analysis of independently significant factors associated with tuberculosis. Subsequently, we evaluated the sensitivity and specificity of the score in a separate (validation) cohort of patients.Results.Within our population, we found 4 clinical factors associated with tuberculosis: chronic symptoms (odds ratio [OR], 10.2 [95% confidence interval {CI}, 2.95-35.4]), upper lobe disease on chest radiograph (OR, 5.27 [95% CI, 1.6-17.23]), foreign-born status (OR, 7.01 [95% CI, 2.1-23.8]), and immunocompromised state other than human immunodeficiency virus infection (OR, 8.14 [95% CI, 2.08-31.8]). Shortness of breath (OR, 0.13 [95% CI, 0.04-0.45]) was found to be associated with non-tuberculosis diagnoses and considered a negative predictor in the model. Using a cut-off point to maximize sensitivity, we applied the prediction rule to the validation cohort, resulting in a sensitivity of 97% and a specificity of 42%.Conclusion.The tuberculosis prediction rule derived from our patient population could improve utilization of airborne precautions. Clinical prediction rules continue to show their utility for improvement in isolation practices in different demographic areas.

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/.

scholarly journals Peran Metode Noninvasif dalam Mendeteksi Varises Esofagus Signifikan pada Anak dengan Hipertensi Portal

Sari Pediatri ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 182
Author(s):  
William Jayadi Iskandar ◽  
Hanifah Oswari
Keyword(s):  
Risk Score ◽  
Full Text ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Prediction Score ◽  
Cochrane Library ◽  
Clinical Prediction

Latar belakang. Esofagogastroduodenoskopi (EGD) penting dilakukan pada anak dengan hipertensi portal untuk mendeteksi varises esofagus signifikan (derajat II, III, atau stigmata perdarahan), tetapi prosedur ini invasif dan traumatik.Tujuan. Mengetahui kemampuan metode noninvasif dibandingkan EGD dalam menentukan varises esofagus signifikan pada anak dengan hipertensi portal.Metode. Penelusuran literatur melalui Pubmed, Scopus, dan Cochrane Library dilakukan pada tanggal 25 Juni 2019. Kriteria inklusi adalah subyek anak hingga berusia 18 tahun, dipublikasi dalam 5 tahun terakhir, berbahasa Inggris, dan tersedia full text. Kriteria eksklusi adalah subyek pascaoperasi atau tidak membahas metode noninvasif. Artikel terpilih kemudian dinilai secara kritis.Hasil. Tiga buah artikel penelitian ditemukan, terdiri atas sebuah telaah sistematik dan dua buah penelitian observasional. Metode noninvasif yang memiliki sensitivitas tinggi adalah clinical prediction rule (80%), varices prediction rule (80%), dan risk score (85,7%). Metode yang memiliki spesifisitas tinggi adalah King’s variceal prediction score (72,7%).Kesimpulan. Metode noninvasif dapat digunakan untuk memilih prioritas pasien anak dengan hipertensi portal yang perlu dilakukan EGD untuk menentukan varises esofagus signifikan.

scholarly journals Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248438
Author(s):  
Jeffrey A. Kline ◽  
Carlos A. Camargo ◽  
D. Mark Courtney ◽  
Christopher Kabrhel ◽  
Kristen E. Nordenholz ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Emergency Care ◽  
Point Of Care ◽  
Unmet Need ◽  
Prediction Rule ◽  
Negative Likelihood Ratio ◽  
Prediction Score ◽  
Measured Temperature ◽  
Clinical Prediction ◽  
Patient Reported

Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79–0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8–96.3%), specificity of 20.0% (19.0–21.0%), negative likelihood ratio of 0.22 (0.19–0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.

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.

scholarly journals Clinical Prediction Rule for Patient Outcome after In-Hospital CPR: A New Model, Using Characteristics Present at Hospital Admission, to Identify Patients Unlikely to Benefit from CPR after In-Hospital Cardiac Arrest

2015 ◽  
Vol 9 ◽  
pp. PCRT.S28338
Author(s):  
Satyam Merja ◽  
Ryan H. Lilien ◽  
Hilary F. Ryder
Keyword(s):  
Clinical Judgment ◽  
Validation Cohort ◽  
Cardiopulmonary Arrest ◽  
Prediction Rule ◽  
Retrospective Chart Review ◽  
Accurate Information ◽  
Clinical Prediction ◽  
Linear Discriminant ◽  
Derivation Cohort ◽  
Probability Of Survival

Background Physicians and patients frequently overestimate likelihood of survival after in-hospital cardiopulmonary resuscitation. Discussions and decisions around resuscitation after in-hospital cardiopulmonary arrest often take place without adequate or accurate information. Methods We conducted a retrospective chart review of 470 instances of resuscitation after in-hospital cardiopulmonary arrest. Individuals were randomly assigned to a derivation cohort and a validation cohort. Logistic Regression and Linear Discriminant Analysis were used to perform multivariate analysis of the data. The resultant best performing rule was converted to a weighted integer tool, and thresholds of survival and nonsurvival were determined with an attempt to optimize sensitivity and specificity for survival. Results A 10-feature rule, using thresholds for survival and nonsurvival, was created; the sensitivity of the rule on the validation cohort was 42.7% and specificity was 82.4%. In the Dartmouth Score (DS), the features of age (greater than 70 years of age), history of cancer, previous cardiovascular accident, and presence of coma, hypotension, abnormal PaO2, and abnormal bicarbonate were identified as the best predictors of nonsurvival. Angina, dementia, and chronic respiratory insufficiency were selected as protective features. Conclusions Utilizing information easily obtainable on admission, our clinical prediction tool, the DS, provides physicians individualized information about their patients’ probability of survival after in-hospital cardiopulmonary arrest. The DS may become a useful addition to medical expertise and clinical judgment in evaluating and communicating an individual's probability of survival after in-hospital cardiopulmonary arrest after it is validated by other cohorts.

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

2019 ◽  
Vol 19 (3) ◽  
pp. 91-95 ◽  
Author(s):  
Arash Safaie ◽  
Mojdeh Mirzadeh ◽  
Ehsan Aliniagerdroudbari ◽  
Sepideh Babaniamansour ◽  
Alireza Baratloo
Keyword(s):  
Cohort Study ◽  
Validation Cohort ◽  
Ureteral Stone ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Clinical Prediction

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 Added value of clinical prediction rules for bacteremia in hemodialysis patients: An external validation study

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0247624
Author(s):  
Sho Sasaki ◽  
Yoshihiko Raita ◽  
Minoru Murakami ◽  
Shungo Yamamoto ◽  
Kentaro Tochitani ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Characteristic Curve ◽  
Tertiary Care ◽  
External Validation ◽  
Prediction Rule ◽  
Blood Cultures ◽  
Added Value ◽  
Clinical Prediction ◽  
Basic Model ◽  
Net Reclassification Improvement

Introduction Having developed a clinical prediction rule (CPR) for bacteremia among hemodialysis (HD) outpatients (BAC-HD score), we performed external validation. Materials & methods Data were collected on maintenance HD patients at two Japanese tertiary-care hospitals from January 2013 to December 2015. We enrolled 429 consecutive patients (aged ≥ 18 y) on maintenance HD who had had two sets of blood cultures drawn on admission to assess for bacteremia. We validated the predictive ability of the CPR using two validation cohorts. Index tests were the BAC-HD score and a CPR developed by Shapiro et al. The outcome was bacteremia, based on the results of the admission blood cultures. For added value, we also measured changes in the area under the receiver operating characteristic curve (AUC) using logistic regression and Net Reclassification Improvement (NRI), in which each CPR was added to the basic model. Results In Validation cohort 1 (360 subjects), compared to a Model 1 (Basic Model) AUC of 0.69 (95% confidence interval [95% CI]: 0.59–0.80), the AUC of Model 2 (Basic model + BAC-HD score) and Model 3 (Basic model + Shapiro’s score) increased to 0.8 (95% CI: 0.71–0.88) and 0.73 (95% CI: 0.63–0.83), respectively. In validation cohort 2 (96 subjects), compared to a Model 1 AUC of 0.81 (95% CI: 0.68–0.94), the AUCs of Model 2 and Model 3 increased to 0.83 (95% CI: 0.72–0.95) and 0.85 (95% CI: 0.76–0.94), respectively. NRIs on addition of the BAC-HD score and Shapiro’s score were 0.3 and 0.06 in Validation cohort 1, and 0.27 and 0.13, respectively, in Validation cohort 2. Conclusion Either the BAC-HD score or Shapiro’s score may improve the ability to diagnose bacteremia in HD patients. Reclassification was better with the BAC-HD score.

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