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 ◽  
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 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 > 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 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 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 Derivation and External Validation of a Simple Prediction Rule for the Development of Respiratory Failure in Hospitalized Patients With Influenza

2022 ◽  
Author(s):  
Blanca Ayuso ◽  
Antonio Lalueza ◽  
Estibaliz Arrieta ◽  
Eva Maria Romay ◽  
Álvaro Marchán-López ◽  
...  
Keyword(s):  
Respiratory Failure ◽  
Lactate Dehydrogenase ◽  
Respiratory Rate ◽  
Validation Cohort ◽  
Influenza Infection ◽  
External Validation ◽  
Prediction Rule ◽  
Influenza Viruses ◽  
Chronic Obstructive ◽  
Derivation Cohort

Abstract BACKGROUND: Influenza viruses cause seasonal epidemics worldwide with a significant morbimortality burden. Clinical spectrum of Influenza is wide, being respiratory failure (RF) one of its most severe complications. This study aims to elaborate a clinical prediction rule of RF in hospitalized Influenza patients.METHODS: a prospective cohort study was conducted during two consecutive Influenza seasons (December 2016 - March 2017 and December 2017 - April 2018) including hospitalized adults with confirmed A or B Influenza infection. A prediction rule was derived using logistic regression and recursive partitioning, followed by internal cross-validation. External validation was performed on a retrospective cohort in a different hospital between December 2018 - May 2019. RESULTS: Overall, 707 patients were included in the derivation cohort and 285 in the validation cohort. RF rate was 6.8% and 11.6%, respectively. Chronic obstructive pulmonary disease, immunosuppression, radiological abnormalities, respiratory rate, lymphopenia, lactate dehydrogenase and C-reactive protein at admission were associated with RF. A four category-grouped seven point-score was derived including radiological abnormalities, lymphopenia, respiratory rate and lactate dehydrogenase. Final model area under the curve was 0.796 (0.714-0.877) in the derivation cohort and 0.773 (0.687-0.859) in the validation cohort (p<0.001 in both cases). The predicted model showed an adequate fit with the observed results (Fisher’s test p>0.43). CONCLUSION: we present a simple, discriminating, well-calibrated rule for an early prediction of the development of RF in hospitalized Influenza patients, with proper performance in an external validation cohort. This tool can be helpful in patient´s stratification during seasonal Influenza epidemics.

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 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

Predicting postresection hydrocephalus in pediatric patients with posterior fossa tumors

2009 ◽  
Vol 3 (5) ◽  
pp. 378-385 ◽  
Author(s):  
Jay Riva-Cambrin ◽  
Allan S. Detsky ◽  
Maria Lamberti-Pasculli ◽  
Michael A. Sargent ◽  
Derek Armstrong ◽  
...  
Keyword(s):  
High Risk ◽  
Posterior Fossa ◽  
Validation Cohort ◽  
Tumor Resection ◽  
Prediction Rule ◽  
Low Risk ◽  
Third Ventriculostomy ◽  
Posterior Fossa Tumors ◽  
Derivation Cohort ◽  
Preoperative Prediction

Object Approximately 30% of children with posterior fossa tumors exhibit hydrocephalus after tumor resection. Recent literature has suggested that prophylactic endoscopic third ventriculostomy diminishes the risk of this event. Because the majority of patients will not have postoperative hydrocephalus, a preoperative clinical prediction rule that identifies patients at high or low risk for postresection hydrocephalus would be helpful to optimize the care of these children. Methods The authors evaluated a derivation cohort of 343 consecutive children with posterior fossa tumors who underwent treatment between 1989 and 2003. Multivariate methods were used on these data to generate the Canadian Preoperative Prediction Rule for Hydrocephalus. The rule's estimated risk of postresection hydrocephalus was compared with risk observed in 111 independent patients in the validation cohort. Results Variables identified as significant in predicting postresection hydrocephalus were age < 2 years (score of 3), papilledema (score of 1), moderate to severe hydrocephalus (score of 2), cerebral metastases (score of 3), and specific estimated tumor pathologies (score of 1). Patients with scores ≥ 5 were deemed as high risk. Predicted probabilities for the high- and low-risk groups were 0.73 and 0.25, respectively, from the derivation cohort, and 0.59 and 0.14 after prevalence adjustment compared with the observed values of 0.42 and 0.17 in the validation cohort. Conclusions A patient's score on the Preoperative Prediction Rule for Hydrocephalus will allow improved patient counseling and surgical planning by identifying patients at high risk of developing postresection hydrocephalus. These patients might selectively be exposed to the risks of preresection CSF diversion to improve outcome.

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.

scholarly journals Paramedics assessing Elders at Risk for Independence Loss (PERIL): Derivation, Reliability and Comparative Effectiveness of a Clinical Prediction Rule

CJEM ◽  
2016 ◽  
Vol 18 (2) ◽  
pp. 121-132 ◽  
Author(s):  
Jacques S. Lee ◽  
P. Richard Verbeek ◽  
Michael J. Schull ◽  
Lisa Calder ◽  
Ian G. Stiell ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Clinical Judgment ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Adverse Outcomes ◽  
Clinical Prediction ◽  
Convenience Sample ◽  
Observer Reliability ◽  
Proxy Measure

AbstractObjectivesWe conducted a program of research to derive and test the reliability of a clinical prediction rule to identify high-risk older adults using paramedics’ observations.MethodsWe developed the Paramedics assessing Elders at Risk of Independence Loss (PERIL) checklist of 43 yes or no questions, including the Identifying Seniors at Risk (ISAR) tool items. We trained 1,185 paramedics from three Ontario services to use this checklist, and assessed inter-observer reliability in a convenience sample. The primary outcome, return to the ED, hospitalization, or death within one month was assessed using provincial databases. We derived a prediction rule using multivariable logistic regression.ResultsWe enrolled 1,065 subjects, of which 764 (71.7%) had complete data. Inter-observer reliability was good or excellent for 40/43 questions. We derived a four-item rule: 1) “Problems in the home contributing to adverse outcomes?” (OR 1.43); 2) “Called 911 in the last 30 days?” (OR 1.72); 3) male (OR 1.38) and 4) lacks social support (OR 1.4). The PERIL rule performed better than a proxy measure of clinical judgment (AUC 0.62 vs. 0.56, p=0.02) and adherence was better for PERIL than for ISAR.ConclusionsThe four-item PERIL rule has good inter-observer reliability and adherence, and had advantages compared to a proxy measure of clinical judgment. The ISAR is an acceptable alternative, but adherence may be lower. If future research validates the PERIL rule, it could be used by emergency physicians and paramedic services to target preventative interventions for seniors identified as high-risk.

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