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

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

Journal of Clinical Medicine ◽

10.3390/jcm10061163 ◽

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.

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

10.20944/preprints202102.0001.v1 ◽

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 &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.

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

PLoS ONE ◽

10.1371/journal.pone.0245281 ◽

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.

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