scholarly journals Clinical risk scores for the early prediction of severe outcomes in patients hospitalized for COVID-19

2021 ◽  
Author(s):  
Walter Ageno ◽  
◽  
Chiara Cogliati ◽  
Martina Perego ◽  
Domenico Girelli ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Predictive Value ◽  
Clinical Laboratory ◽  
Area Under The Curve ◽  
Orotracheal Intubation ◽  
Risk Scores ◽  
Algorithm Selection ◽  
Derivation Cohort ◽  
Neutrophil Lymphocyte Ratio ◽  
Predictive Risk

AbstractCoronavirus disease of 2019 (COVID-19) is associated with severe acute respiratory failure. Early identification of high-risk COVID-19 patients is crucial. We aimed to derive and validate a simple score for the prediction of severe outcomes. A retrospective cohort study of patients hospitalized for COVID-19 was carried out by the Italian Society of Internal Medicine. Epidemiological, clinical, laboratory, and treatment variables were collected at hospital admission at five hospitals. Three algorithm selection models were used to construct a predictive risk score: backward Selection, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. Severe outcome was defined as the composite of need for non-invasive ventilation, need for orotracheal intubation, or death. A total of 610 patients were included in the analysis, 313 had a severe outcome. The subset for the derivation analysis included 335 patients, the subset for the validation analysis 275 patients. The LASSO selection identified 6 variables (age, history of coronary heart disease, CRP, AST, D-dimer, and neutrophil/lymphocyte ratio) and resulted in the best performing score with an area under the curve of 0.79 in the derivation cohort and 0.80 in the validation cohort. Using a cut-off of 7 out of 13 points, sensitivity was 0.93, specificity 0.34, positive predictive value 0.59, and negative predictive value 0.82. The proposed score can identify patients at low risk for severe outcome who can be safely managed in a low-intensity setting after hospital admission for COVID-19.

scholarly journals Recalibrating the Non-Communicable Diseases Risk Prediction Tools for the Rural Population of Western India

2021 ◽  
Author(s):  
Manoj Kumar Gupta ◽  
Pankaja Raghav ◽  
Tooba Tanvir ◽  
Vaishali Gautam ◽  
Amit Mehto ◽  
...  
Keyword(s):  
Risk Prediction ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Vascular Diseases ◽  
High Sensitivity ◽  
Roc Curves ◽  
Risk Scores ◽  
Western India ◽  
Anthropometric Parameters ◽  
Youden’S Index

Abstract Background: The present study was conducted to recalibrate the effectiveness of Indian Diabetes Risk Scores (IDRS) and Community-Based Assessment Checklist (CBAC) by opportunistically screening for Diabetes Mellitus (DM) and Hypertension (HT) among the patients attending health centres, and to estimate the risk of fatal and non-fatal Cardio-Vascular Diseases (CVDs) using WHO/ISH chartMethods: All the people of age ≥30 years attending the health centers were screened for DM and HT. Weight, height, and waist and hip circumferences were measured and BMI and Waist Hip Ratio (WHR) were calculated. Risk categorization of all participants was done using IDRS, CBAC, and WHO/ISH risk prediction charts. Individuals diagnosed with DM or HT were started on treatment. The data was recorded using Epicollect5 and was analyzed using SPSS v.23 and MedCalc v.19.8. ROC curves were plotted for DM and HT with the IDRS, CBAC score and anthropometric parameters. Sensitivity (SN), specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy and Youden’s index were calculated for different cut-offs of IDRS and CBAC scores.Results: A total of 942 participants were included for the screening, out of them, 6.42 % (95% CI: 4.92-8.20) were diagnosed with DM. Hypertension was detected among 25.7% (95% CI: 22.9-28.5) of the participants. A total of 447 (47.3%) participants were found with IDRS score ≥ 60, and 276 (29.3%) with CBAC score >4. As much as 26.1% were at moderate to higher risk (≥10%) of developing CVDs. Area Under the Curve (AUC) for IDRS in predicting DM was 0.64 (0.58-0.70), with 67.1% SN and 55.2% SP (Youden's Index= 0.22). While the AUC for CBAC was 0.59 (0.53-0.65). For hypertension the both the AUCs were 0.66 (0.62-0.71) and 0.63 (0.59-0.67), respectively.Conclusions: Instead of CBAC, the present study emphasizes the usefulness of IDRS as an excellent tool for screening for both DM and HT. This is the time to expose the hidden part of the NCDs iceberg by having high sensitivity of non-invasive instruments (like IDRS), so, we propose a cut-off value of 50 for the IDRS to screen for diabetes in the rural Indian population.

scholarly journals Predictive Modeling of Poor Outcome in Severe COVID-19: A Single-Center Observational Study Based on Clinical, Cytokine and Laboratory Profiles

2021 ◽  
Vol 10 (22) ◽  
pp. 5431
Author(s):  
Óscar Gorgojo-Galindo ◽  
Marta Martín-Fernández ◽  
María Jesús Peñarrubia-Ponce ◽  
Francisco Javier Álvarez ◽  
Christian Ortega-Loubon ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Predictive Model ◽  
Blood Group ◽  
Clinical Laboratory ◽  
Area Under The Curve ◽  
Abo Blood Group ◽  
Poor Outcome ◽  
Emergency Room Admission ◽  
Poor Outcomes

Pneumonia is the main cause of hospital admission in COVID-19 patients. We aimed to perform an extensive characterization of clinical, laboratory, and cytokine profiles in order to identify poor outcomes in COVID-19 patients. Methods: A prospective and consecutive study involving 108 COVID-19 patients was conducted between March and April 2020 at Hospital Clínico Universitario de Valladolid (Spain). Plasma samples from each patient were collected after emergency room admission. Forty-five serum cytokines were measured in duplicate, and clinical data were analyzed using SPPS version 25.0. Results: A multivariate predictive model showed high hepatocyte growth factor (HGF) plasma levels as the only cytokine related to intubation or death risk at hospital admission (OR = 7.38, 95%CI—(1.28–42.4), p = 0.025). There were no comorbidities included in the model except for the ABO blood group, in which the O blood group was associated with a 14-fold lower risk of a poor outcome. Other clinical variables were also included in the predictive model. The predictive model was internally validated by the receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.94, a sensitivity of 91.7% and a specificity of 95%. The use of a bootstrapping method confirmed these results. Conclusions: A simple, robust, and quick predictive model, based on the ABO blood group, four common laboratory values, and one specific cytokine (HGF), could be used in order to predict poor outcomes in COVID-19 patients.

scholarly journals Differential Diagnostic Utility of Clinical Laboratory Data in Patients with Severe and Non-severe COVID-19

2021 ◽  
Vol In Press (In Press) ◽  
Author(s):  
Ali Hosseinzadeh ◽  
Mohammad Hassan Emamian ◽  
Marzieh Rohani-Rasaf ◽  
Ahmad Khosravi ◽  
Fariba Zare ◽  
...  
Keyword(s):  
Disease Severity ◽  
Clinical Laboratory ◽  
Characteristic Curve ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
International Normalized Ratio ◽  
Therapeutic Effects ◽  
Neutrophil Lymphocyte Ratio ◽  
Laboratory Variables ◽  
Age Range

Background: Coronavirus disease 2019 (COVID-19) is a coronavirus outbreak caused by severe acute respiratory syndrome coronavirus 2 infection. Objectives: This study aimed to investigate the relationship between laboratory variables and COVID-19 severity. Methods: A total of 731 confirmed cases were included in this study. Based on the clinical course of the disease, the patients were divided into non-severe (n = 599) and severe (n = 132) groups. The area under the curve was estimated for each of the significant predictive factors by the receiver operating characteristic curve. Youden’s index was used to determine the optimal cut-off points to predict the severity of COVID-19. Results: Out of 731 patients, 407 (55.56%) cases were male. The mean age value and age range of the patients were 58.37 and 1 - 98 years, respectively. The age (OR = 1.03, 95% CI: 1.02 - 1.05), international normalized ratio (INR) (OR = 2.09, 95% CI: 1.11 - 3.96), lactate dehydrogenase (LDH) (OR = 1.003, 95% CI: 1.001 - 1.1.003), and neutrophil/lymphocyte ratio (NLR) (OR = 1.08, 95% CI: 1.02 - 1.14) were associated with the severity of COVID-19 in the multivariate analyses. The areas under the curve of LDH, NLR, and INR for the diagnosis of disease severity were 0.76, 0.69, and 0.62, respectively. Conclusions: The results of this study revealed that LDH, NLR, and INR could help to discriminate between non-severe and severe COVID-19 cases. Therefore, clinicians can use these variables to improve therapeutic effects and reduce disease severity.

INFLAMMATORY MARKERS IN THE DIAGNOSIS OF COMPLICATED ACUTE APPENDICITIS

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
J Michi Campos ◽  
M Merayo Álvarez ◽  
L García González ◽  
B Carrasco Aguilera ◽  
J L Rodicio Miravalles ◽  
...  
Keyword(s):  
Inflammatory Markers ◽  
Clinical Laboratory ◽  
Complete Blood Count ◽  
Area Under The Curve ◽  
Tertiary Hospital ◽  
Complicated Appendicitis ◽  
Neutrophil Lymphocyte Ratio ◽  
Reactive Protein ◽  
Surgical Emergencies ◽  
Relationship Of

Abstract INTRODUCTION Appendicitis constitutes one of the most frequent surgical emergencies. New inflammatory markers such as the neutrophil-lymphocyte ratio (NLR) have recently emerged, which are added to others such as leukocyte count and C-reactive protein (CRP), and whose role in the diagnosis of appendicitis remains unclear. MATERIAL AND METHODS We conducted an observational, descriptive, longitudinal and retrospective study of 484 adults appendectomized between April 2017 and May 2019 in a tertiary hospital. Sociodemographic, clinical, laboratory, imaging and surgical variables were collected. RESULTS 32.2% of appendicitis were complicated. All patients had a complete blood count and basic biochemistry (98.5% with CRP). Complicated appendicitis had a mean of 14538 leukocytes, 12.4 CRP and 8.7 NLR, and uncomplicated appendicitis had 14667 leukocytes, 5 CRP and 10.7 NLR. When analyzing the relationship of inflammatory markers with the existence or not of complicated appendicitis, CRP yielded an area under the curve (AUC) of 73.1% (95% CI: 0.684-0.779, p < 0.01), while leukocytes and NLR had 52.6% and 55.9%. The CRP cut-off point was determined to be 3.5 which presented a higher discriminative power to predict complicated appendicitis, with a sensitivity and specificity of 70.1% and 65.3% respectively. CONCLUSIONS Of the inflammatory markers studied, only CRP proved to be a valid parameter to help differentiate preoperatively those appendicitis uncomplicated from complicated appendicitis.

scholarly journals Clinical and Laboratory Markers of Brain Abscess in Tetralogy of Fallot (‘BA-TOF’ Score): Results of a Case–Control Study and Implications for Community Surveillance

2021 ◽  
Author(s):  
Vidyasagar Kanneganti ◽  
Sumit Thakar ◽  
Saritha Aryan ◽  
Prayaag Kini ◽  
Dilip Mohan ◽  
...  
Keyword(s):  
Tetralogy Of Fallot ◽  
Brain Abscess ◽  
Predictive Value ◽  
Case Control Study ◽  
Tertiary Care ◽  
Area Under The Curve ◽  
Case Control ◽  
Retrospective Case ◽  
Neutrophil Lymphocyte Ratio ◽  
Control Study

Abstract Background Cardiogenic brain abscess (CBA) is the commonest noncardiac cause of morbidity and mortality in cyanotic heart disease (CHD). The clinical diagnosis of a CBA is often delayed due to its nonspecific presentations and the scarce availability of computed tomography (CT) imaging in resource-restricted settings. We attempted to identify parameters that reliably point to the diagnosis of a CBA in patients with Tetralogy of Fallot (TOF). Methods From among 150 children with TOF treated at a tertiary care institute over a 15-year period from 2001 to 2016, 30 consecutive patients with CBAs and 85 age- and sex-matched controls without CBAs were included in this retrospective case–control study. Demographic and clinical features, laboratory investigations, and baseline echocardiographic findings were analyzed for possible correlations with the presence of a CBA. Statistical Analysis Variables demonstrating significant bivariate correlations with the presence of a CBA were further analyzed using multivariate logistic regression (LR) analysis. Various LR models were tested for their predictive value, and the best model was then validated on a hold-out dataset of 25 patients. Results Among the 26 variables tested for bivariate associations with the presence of a CBA, some of the clinical, echocardiographic, and laboratory variables demonstrated significant correlations (p < 0.05). LR analysis revealed elevated neutrophil–lymphocyte ratio and erythrocyte sedimentation rate values and a lower age-adjusted resting heart rate percentile to be the strongest independent biomarkers of a CBA. The LR model was statistically significant, (χ2 = 23.72, p = <0.001), and it fitted the data well. It explained 53% (Nagelkerke R 2) of the variance in occurrence of a CBA, and correctly classified 83.93% of cases. The model demonstrated a good predictive value (area under the curve: 0.80) on validation analysis. Conclusions This study has identified simple clinical and laboratory parameters that can serve as reliable pointers of a CBA in patients with TOF. A scoring model—the ‘BA-TOF’ score—that predicts the occurrence of a CBA has been proposed. Patients with higher scores on the proposed model should be referred urgently for a CT confirmation of the diagnosis. Usage of such a diagnostic aid in resource-limited settings can optimize the pickup rates of a CBA and potentially improve outcomes.

Variables associated with severity in COVID-19 patients

2021 ◽  
pp. 1-15
Author(s):  
Carlos E. Herrera Cartaya ◽  
Julio Betancourt Cervantes ◽  
Agustín Lage Dávila ◽  
Eligio E. Barreto Fiu ◽  
Lizet Sánchez Valdés ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Clinical Laboratory ◽  
Severe Disease ◽  
Clinical Manifestations ◽  
Arterial Oxygen ◽  
Bivariate Analysis ◽  
Neutrophil Lymphocyte Ratio ◽  
Reactive Protein ◽  
Number Of Patients ◽  
Chest X Ray

In COVID-19, a percentage of patients develop severe disease, with high mortality, since has been necessary to study its characteristics to stop the progression of the disease. A retrospective study was carried out in a cohort of 150 adult patients attended at Manuel Fajardo Hospital in Villa Clara, Cuba, from March to June 2020. Demographic, clinical, laboratory, gasometric and radiological variables measured at hospital admission were analyzed, defining two groups of patients according to clinical evolution: severe and non-severe. For the comparison of the groups a bivariate analysis was performed, with the objective of determining those variables with a significant association to severity. Of the total number of patients, 26 (17.3%) evolved to severity and 124 (83.7%) evolved satisfactorily. Severe patients were characterized by advanced age (mean: 83 years) and comorbidities; the most significant being hypertension, diabetes mellitus, heart disease, chronic kidney disease and cancer (p< 0.0001). Polypnea and diarrhea were the clinical manifestations with the highest association with severity (p<0.0001), followed by fever (p=0.0157). The quick SOFA prognostic scale proved to be a useful instrument to evaluate patients at admission. Laboratory variables: neutrophils, lymphocytes, neutrophil/lymphocyte ratio, hemoglobin and lactate dehydrogenase were the most associated with severity (p<0.0001). Leukocytes, lactate, D-dimer, C-reactive protein, glycemia and calcium also showed significant results (p<0.05). Of the gasometric variables, arterial oxygen pressure and saturation were the most significantly associated with severity; as well as the presence of pulmonary infiltrates or consolidation in the chest X-ray (p<0.0001). The study allowed us to identify variables at hospital admission associated with progression to severe forms of the disease.

scholarly journals Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jung-Ting Lee ◽  
Chih-Chia Hsieh ◽  
Chih-Hao Lin ◽  
Yu-Jen Lin ◽  
Chung-Yao Kao
Keyword(s):  
Artificial Intelligence ◽  
Emergency Department ◽  
Hospital Admission ◽  
Subgroup Analysis ◽  
Predictive Value ◽  
Model Building ◽  
Area Under The Curve ◽  
Cutoff Value ◽  
Tertiary Teaching ◽  
Level 3

AbstractTimely assessment to accurately prioritize patients is crucial for emergency department (ED) management. Urgent (i.e., level-3, on a 5-level emergency severity index system) patients have become a challenge since under-triage and over-triage often occur. This study was aimed to develop a computational model by artificial intelligence (AI) methodologies to accurately predict urgent patient outcomes using data that are readily available in most ED triage systems. We retrospectively collected data from the ED of a tertiary teaching hospital between January 1, 2015 and December 31, 2019. Eleven variables were used for data analysis and prediction model building, including 1 response, 2 demographic, and 8 clinical variables. A model to predict hospital admission was developed using neural networks and machine learning methodologies. A total of 282,971 samples of urgent (level-3) visits were included in the analysis. Our model achieved a validation area under the curve (AUC) of 0.8004 (95% CI 0.7963–0.8045). The optimal cutoff value identified by Youden's index for determining hospital admission was 0.5517. Using this cutoff value, the sensitivity was 0.6721 (95% CI 0.6624–0.6818), and the specificity was 0.7814 (95% CI 0.7777–0.7851), with a positive predictive value of 0.3660 (95% CI 0.3586–0.3733) and a negative predictive value of 0.9270 (95% CI 0.9244–0.9295). Subgroup analysis revealed that this model performed better in the nontraumatic adult subgroup and achieved a validation AUC of 0.8166 (95% CI 0.8199–0.8212). Our AI model accurately assessed the need for hospitalization for urgent patients, which constituted nearly 70% of ED visits. This model demonstrates the potential for streamlining ED operations using a very limited number of variables that are readily available in most ED triage systems. Subgroup analysis is an important topic for future investigation.

scholarly journals The 2CAN Score

Stroke ◽  
2018 ◽  
Vol 49 (12) ◽  
pp. 2866-2871 ◽  
Author(s):  
Philip Chang ◽  
Ilana Ruff ◽  
Scott J. Mendelson ◽  
Fan Caprio ◽  
Deborah L. Bergman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Predictive Value ◽  
Validation Cohort ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Clinical Score ◽  
Final Diagnosis ◽  
Operating Characteristics ◽  
Derivation Cohort ◽  
Academic Center

Background and Purpose— A quarter of acute strokes occur in patients hospitalized for another reason. A stroke recognition instrument may be useful for non-neurologists to discern strokes from mimics such as seizures or delirium. We aimed to derive and validate a clinical score to distinguish stroke from mimics among inhospital suspected strokes. Methods— We reviewed consecutive inpatient stroke alerts in a single academic center from January 9, 2014, to December 7, 2016. Data points, including demographics, stroke risk factors, stroke alert reason, postoperative status, neurological examination, vital signs and laboratory values, and final diagnosis, were collected. Using multivariate logistic regression, we derived a weighted scoring system in the first half of patients (derivation cohort) and validated it in the remaining half of patients (validation cohort) using receiver operating characteristics testing. Results— Among 330 subjects, 116 (35.2%) had confirmed stroke, 43 (13.0%) had a neurological mimic (eg, seizure), and 171 (51.8%) had a non-neurological mimic (eg, encephalopathy). Four risk factors independently predicted stroke: clinical deficit score (clinical deficit score 1: 1 point; clinical deficit score ≥2: 3 points), recent cardiac procedure (1 point), history of atrial fibrillation (1 point), and being a new patient (<24 hours from admission: 1 point). The score showed excellent discrimination in the first 165 patients (derivation cohort, area under the curve=0.93) and remaining 165 patients (validation cohort, area under the curve=0.88). A score of ≥2 had 92.2% sensitivity, 69.6% specificity, 62.2% positive predictive value, and 94.3% negative predictive value for identifying stroke. Conclusions— The 2CAN score for recognizing inpatient stroke performs well in a single-center study. A future prospective multicenter study would help validate this score.

scholarly journals Busto COVID-19 Score Identify Low Risk Patients. External Validation.

2021 ◽  
Author(s):  
Foieni Fabrizio ◽  
Beltrami Laura Maria Giovanna ◽  
Sala Girolamo ◽  
Ughi Nicola ◽  
Del Gaudio Francesca ◽  
...  
Keyword(s):  
Hospital Admission ◽  
Retrospective Cohort ◽  
Clinical Laboratory ◽  
External Validation ◽  
White Blood Cells ◽  
Area Under The Curve ◽  
Low Risk ◽  
Reactive Protein ◽  
Risk Patients ◽  
Admission Variables

Abstract Background: Coronavirus disease of 2019 (COVID-19) is associated with severe acute respiratory failure. Early identifcation of low-risk COVID-19 patients is crucial, discharging safely patients to home and optimizing the use of available resources. Methods: We aimed to external validate a simple score for the prediction of low-risk outcomes. A retrospective cohort study of patients hospitalized for COVID-19 was carried out by the Busto Hospital and Niguarda hospital. Epidemiological, clinical, laboratory, and treatment variables were collected at hospital admission. Variables included in this retrospective cohort were analized to validate the Busto COVID-19 score as a Clinical Risk Score able to individuate low risk COVID-19 patients. Among COVID-19 patients admitted to the hospital, severe outcome was defned as the composite of the admission to the Intensive Care Unit or death. Results: The development cohort included 427 consecutive patients. The mean (SD) age of patients among the cohort was 60.5 years; 273 (63%) were men. As potential predictors, Busto COVID-19 score variables include: lung ultrasound abnormality, age, total white blood cells count , C-reactive protein value, pO2/FiO2 ratio, lactates value, arterial hypertension and fever from 5 days or more and resulted in the best performing score with an area under the curve in the derivation sample of 0.88 and 0.71 in the external sample. Conclusions: The proposed score can identify patients at low risk for severe outcome who can be safely managed in a low-intensity setting after hospital admission for COVID-19.

scholarly journals Estimating Risk of Mechanical Ventilation and Mortality Among Adult COVID-19 patients Admitted to Mass General Brigham: The VICE and DICE Scores

2020 ◽  
Author(s):  
Christopher J Nicholson ◽  
Luke Wooster ◽  
Haakon H Sigurslid ◽  
Rebecca F Li ◽  
Wanlin Jiang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Massachusetts General Hospital ◽  
Laboratory Data ◽  
Risk Scores ◽  
C Reactive Protein ◽  
Entire Cohort ◽  
Derivation Cohort ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Reactive Protein

Background: Risk stratification of COVID-19 patients upon hospital admission is key for their successful treatment and efficient utilization of hospital resources. Objective: To evaluate the risk factors associated with ventilation need and mortality. Design, setting and participants: We established a retrospective cohort of COVID-19 patients from Mass General Brigham hospitals. Demographic, clinical, and admission laboratory data were obtained from electronic medical records of patients admitted to hospital with laboratory-confirmed COVID-19 before May 19th, 2020. Using patients admitted to Massachusetts General Hospital (MGH, derivation cohort), multivariable logistic regression analyses were used to construct the Ventilation in COVID Estimator (VICE) and Death in COVID Estimator (DICE) risk scores. Measurements: The primary outcomes were ventilation status and death. Results: The entire cohort included 1042 patients (median age, 64 years; 56.8% male). The derivation and validation cohorts for the risk scores included 578 and 464 patients, respectively. We found seven factors to be independently predictive for ventilation requirement (diabetes mellitus, dyspnea, alanine aminotransferase, troponin, C-reactive protein, neutrophil-lymphocyte ratio, and lactate dehydrogenase), and 10 factors to be predictors of in-hospital mortality (age, sex, diabetes mellitus, chronic statin use, albumin, C-reactive protein, neutrophil-lymphocyte ratio, mean corpuscular volume, platelet count, and procalcitonin). Using these factors, we constructed the VICE and DICE risk scores, which performed with C-statistics of at least 0.8 in our cohorts. Importantly, the chronic use of a statin was associated with protection against death due to COVID-19. The VICE and DICE score calculators have been placed on an interactive website freely available to the public (https://covid-calculator.com/). Limitations: One potential limitation is the modest sample sizes in both our derivation and validation cohorts. Conclusion: The risk scores developed in this study may help clinicians more appropriately determine which COVID-19 patients will need to be managed with greater intensity.

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