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Research ArticleORIGINAL RESEARCH

Development and Validation of the COVID-NoLab and COVID-SimpleLab Risk Scores for Prognosis in 6 US Health Systems

Mark H. Ebell, Xinyan Cai, Robert Lennon, Derjung M. Tarn, Arch G. Mainous, Aleksandra E. Zgierska, Bruce Barrett, Wen-Jan Tuan, Kevin Maloy, Munish Goyal and Alex Krist
The Journal of the American Board of Family Medicine February 2021, 34 (Supplement) S127-S135; DOI: https://doi.org/10.3122/jabfm.2021.S1.200464
Mark H. Ebell
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
MD, MS
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Xinyan Cai
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
MPH
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Robert Lennon
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
MD
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Derjung M. Tarn
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
MD, PhD
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Arch G. Mainous III
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
PhD
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Aleksandra E. Zgierska
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
MD, PhD
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Bruce Barrett
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
MD, PhD
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Wen-Jan Tuan
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
DHA, MS, MPH
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Kevin Maloy
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
MD
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Munish Goyal
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
MD
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Alex Krist
From the Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens (MHE, XC); Department of Family and Community Medicine, Penn State College of Medicine, Hershey (RL); Department of Family Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles (DMT); Department of Health Services Research, Management and Policy, University of Florida, Gainesville (AGM); Departments of Public Health Sciences, and Anesthesiology and Perioperative Medicine, Penn State College of Medicine, Hershey (AEZ); Department of Family Medicine and Community Health, University of Wisconsin, Madison (BB, WJT); Department of Emergency Medicine, MedStar Washington Hospital Center, Washington, DC (KM, MG); Department of Family Medicine, Virginia Commonwealth University, Richmond (AK).
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    Table 1.

    Characteristics of Included Patients From Each Institution

     Georgetown University (n = 582)Virginia Commonwealth University (n = 223)University of Wisconsin (n = 102)Penn State University (n = 69)University of Florida (n = 133)University of California, LA (n = 333)Total (n = 1442)
    Disposition       
    Discharge home485 (83.3%)190 (85.2%)88 (86.3%)68 (98.6%)121 (91%)301 (90.4%)1253 (86.9%)
    Deceased97 (16.7%)33 (14.8%)14 (13.7%)1 (1.4%)12 (9%)32 (9.6%)189 (13.1%)
    Demographics and vitals       
    Age: median (interquartile range)61 (22)61 (22)62 (25)63 (29)57 (39)52 (30)61 (25)
    Male: female287:295119:10455:4734:3553:80197:143745:704
    • View popup
    Table 2.

    Association Between Individual Clinical Variables and the Outcome of Mortality

    Clinical PredictorsDischarged AliveDeceasedP-value
    Categorical variable (n [%])1253189 
    Female sex615 (49.1)84 (44.4)0.267
    Chronic obstructive pulmonary disease180 (14.4)44 (23.3)0.002
    Asthma139 (11.1)23 (12.2)0.754
    Diabetes421 (33.6)91 (48.1)<0.001
    Cardiovascular disease217 (17.3)56 (29.6)<0.001
    Temperature ≥ 39°C69 (5.8)11 (6.0)1
    Temperature ≥ 38°C203 (17.0)34 (18.6)0.684
    Age (years)  <0.01
     <50396 (32.3)9 (5) 
     50 to 65415 (33.8)42 (23.2) 
     66 to 79270 (22)79 (43.7) 
     >80146 (11.9)51 (28.1) 
    Body mass index >=40 kg/m2147 (14.0)27 (15.9)0.604
    Body mass index >=30 kg/m2513 (49.0)69 (40.6)0.051
    Oxygen saturation < 93%82 (7.0)36 (19.3)<0.001
    Respiratory rate ≥ 25 breaths/minute215 (17.9)67 (35.6)<0.001
    Respiratory rate ≥ 30 breaths/minute96 (8.0)40 (21.3)<0.001
    C-reactive protein
     > 10 mg/dL228 (33.0)77 (67.5)<0.001
     > 15 mg/dL119 (17.2)46 (40.4)<0.001
     > 20 mg/dL42 (6.1)15 (13.2)0.011
     > 40 mg/dL2 (0.3)2 (1.8)0.18
    White blood cell count > 10 × 109/mL172 (14.9)60 (33.0)<0.001
    Lymphocytes < 0.8 × 109/mL271 (26.0)68 (40.5)<0.001
    Neutrophils > 8 × 109/mL145 (13.9)49 (29.2)<0.001
    Serum creatinine >= 2.0 mg/dL114 (9.9)66 (36.1)<0.001
    Continuous variables (median [interquartile range])
     Age in years59 [44.5, 71]72.5 [64.8, 80]<0.001
     Temperature °C37.2 [36.8, 37.8]37.2 [36.8, 37.8]0.831
     Oxygen saturation97% [95%, 98%]95% [93%, 97%]<0.001
     Respiratory rate (breaths/minute)20 [18, 23]20 [18, 27]<0.001
     White blood cell count (× 109/mL)6.1 [4.6, 8.1]7.5 [5.5, 11.8]<0.001
     Lymphocytes (× 109/mL)1.1 [0.8, 1.5]0.8 [0.6, 1.3]<0.001
     Neutrophils (× 109/mL)4.4 [3, 6.2]5.9 [4, 8.7]<0.001
     Body mass index (kg/m2)29.8 [25.3, 35.8]28.5 [24.6, 35.8]0.254
     Serum creatinine (mg/dL)0.9 [0.8, 1.3]1.5 [1.1, 2.7]<0.001
     C-reactive protein (mg//dL)6.2 [2.4, 12.3]13 [9, 17.3]<0.001
     Lactate dehydrogenase (U/L)313 [241, 409]441 [349, 590]<0.001
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    Table 3.

    Multivariate Models Using Limited Clinical Data to Predict Mortality in Hospitalized Patients With COVID-19

    Model using no laboratory values (COVID-NoLab)
    Predictorsß-CoefficientStd. ErrorZ ValuePr(>|z|)
    Constant−4.0100.465−8.6200.001
    Age
     50 to 65 years1.6310.4983.2700.001
     > 65 years2.6020.4785.450< 0.001
    Respiratory rate ≥ 30/min1.3520.2934.620< 0.001
    Oxygen saturation < 93%1.0360.2883.600< 0.001
    AUROCC = 0.771 in the derivation group and 0.803 in the validation group.
    Model adding simple blood tests (COVID-SimpleLab)
    Predictorsß-CoefficientStd. ErrorZ ValuePr(>|z|)
    Constant−4.8990.669−7.3300.000
    C-reactive protein
     11–20 mg/dL1.3160.3294.0100.000
     > 20 mg/dL1.0570.5102.0800.038
    Respiratory rate ≥ 30/min1.5220.4033.7700.000
    Oxygen saturation < 93%0.9110.4182.1800.029
    Age
     50 to 65 years1.3100.6611.9800.047
     > 65 years2.2330.6403.4900.000
    Asthma0.9530.4282.2300.026
    White blood cell count > 10 × 109/mL0.5790.3481.6600.097
    Serum creatinine > 2.0 mg/dL1.1520.3483.3100.001
    AUROCC = 0.835 in the derivation group and 0.833 in the validation group.
    • AUROCC, area under the receiver operating characteristic curve.

    • View popup
    Table 4.

    Calculation of the COVID-NoLab and COVID-SimpleLab Risk Scores and Their Classification Accuracy in Derivation and Validation Groups

    Derivation GroupValidation Group
    COVID-NoLab ScoreMortalityMortality
    Clinical PredictorPointsRisk Groupn/Total (%)SSLRn/Total (%)SSLR
    AgeLow (0 to 1)3/167 (1.8%)0.101/129 (0.8%)0.06
    50 to 65 years3Moderate (2 to 5)74/543 (13.6%)0.8940/350 (11.4%)0.95
    > 65 years5High (6+)44/96 (45.8%)4.7923/57 (40.4%)4.99
    Respiratory rate ≥ 303
    O2 saturation < 93%2
    Derivation GroupValidation Group
    COVID-SimpleLab ScoreMortalityMortality
    Clinical PredictorPointsRisk Groupn/Total (%)SSLRn/Total (%)SSLR
    C-reactive protein > 10 mg/dL5Low (0 to 7)0/129 (0.0%)0.01/97 (1.0%)0.07
    Respiratory rate ≥ 305Moderate (8 to 11)10/131 (8.3%)0.469/99 (9.1%)0.66
    O2 saturation < 93%4High (12+)58/185 (45.7%)2.5329/99 (29.3%)2.73
    Age
    50 to 65 years6
    > 65 years8
    Asthma4
    White blood cell count > 10 × 109/mL3
    Serum creatinine > 2.0 mg/dL4
    • SSLR, stratum specific likelihood ratio.

    • View popup
    Table 5.

    Selected Clinical Prediction Rules for COVID-19 Prognosis

    StudyPerformance in Original StudyPerformance in 5 US Sites
    Lu et al., 20209Low risk: 0%Low risk: 2/108 (1.8%)
    Moderate risk: 6%Moderate risk: 24/359 (6.7%)
    High risk: 33%High risk: 87/324 (26.9%)
    Xie et al., 202010AUROCC (derivation) = 0.893AUROCC = 0.7981
    AUROCC (test) = 0.980Hosmer–Lemeshow P = .23
    Yan et al., 202011Low risk: 3/189 (1.6%)Low risk: 10/243 (4.1%)
    High risk: 157/162 (96.9%)High risk: 85/374 (22.7%)
    • AUROCC, area under the receiver operating characteristic curve.

    • See appendix for details regarding calculation of risk scores.

  • Clinical VariableNormal RangeUnits
    Demographics
    Health system
    Hospital
    Age in yearsyears
    Sex
    Race
    Comorbidities
    COPD
    Asthma
    Cardiovascular disease
    Hypertension
    Diabetes mellitus
    Vitals
    Respiratory rate12 to 20breaths/minute
    Temperature36.5 to 37.5degrees Celsius
    Heart rate60 to 100beats/minute
    Systolic blood pressure90 to 139mm Hg
    Diastolic blood pressure50 to 89mm Hg
    BMI20 to 24.9kg/m2
    O2 saturation room air95% to 100%%
    Laboratory tests
    White blood cell count4.5 to 101000 cells/microliter
    Lymphocyte count1000 to 4800cells/microliter
    Neutrophil count2500 to 7500cells/microliter
    Platelets150,000 to 450,000platelets/microliter
    Serum creatinine0.5 to 1.2mg/dL
    Blood urea nitrogen7 to 20mg/dL
    Lactate dehydrogenase (LDH)140 to 280units/L
    Aspartate aminotransferase (AST)10 to 40units/L
    Alanine aminotransferase (ALT)7 to 56units/L
    Ferritin12 to 300ng/mL
    Troponin T or I0 to 0.4ng/mL
    C-reactive protein (CRP)< 10mg/dL
    D-dimer< 0.5mg/L
    Interleukin-6 (IL6)0 to 16pg/mL
    Outcome variables
    Vasopressor needed in first 24 hours
    Discharge disposition (discharged home, still hospitalized, deceased)
    ICU admit during hospitalization (Y/N)
    Mechanical ventilation (Y/N)
    Number of days hospitalized (including observation status)
    Number of days in the ICU
    Number of days on ventilator
    • COPD, Chronic obstructive pulmonary disease; BMI, body mass index.

  • Coef.Std. Err.zP > zPoints
    White blood cell count (WBC) > 100.5860.2792.1000.0361
    Resp rate ≥ 301.4040.3444.0800.0002
    O2 sat < 93%0.9860.3372.9300.0032
    Age
    50 to 653.1011.0313.0100.0035
    > 654.1791.0204.1000.0007
    _cons−5.6991.018−5.6000.000
  • Proposed point score and its accuracy for prediction of mortality in development and validation data sets

    Development
    Risk Group (Points)DeathsSurvivorsTotalMortalityLR
    Low: 0 to 201631630.0%0.00
    Mod: 3 to 62019221210.4%0.65
    High: 7+7725433130.3%1.90
    97609706
    Validation
    Risk Group (Points)DeathsSurvivorsTotalMortalityLR
    Low: 0 to 251081134.4%0.27
    Mod: 3 to 661131195.0%0.31
    High: 7+5818023824.4%1.87
    69401470
  • Model using clinical predictors + c-reactive protein (CRP) only

    Coef.Std. Err.zP > zPoints
    CRP
    > 10 to 20 mg/dL1.5220.3094.9200.0003
    > 20 mg/dL0.9740.5391.8100.0712
    Respiratory rate ≥ 301.2460.4202.9700.0033
    Age
    50 to 651.7980.7862.2900.0224
    > 653.2790.7654.2900.0007
    Asthma0.9770.4172.3400.0192
    _cons−5.2360.782−6.6900.000
  • Training
    Risk GroupDeathsSurvivorsTotalPrevLR
    0 to 431761791.7%0.10
    5 to 82115717811.8%0.76
    9+477111839.8%3.77
    71404475
    Testing
    Risk GroupDeathsSurvivorsTotalPrevLR
    0 to 451081134.4%0.30
    5 to 8111151268.7%0.62
    9+26517733.8%3.33
    42274316
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The Journal of the American Board of Family  Medicine: 34 (Supplement)
The Journal of the American Board of Family Medicine
Vol. 34, Issue Supplement
February 2021
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Development and Validation of the COVID-NoLab and COVID-SimpleLab Risk Scores for Prognosis in 6 US Health Systems
Mark H. Ebell, Xinyan Cai, Robert Lennon, Derjung M. Tarn, Arch G. Mainous, Aleksandra E. Zgierska, Bruce Barrett, Wen-Jan Tuan, Kevin Maloy, Munish Goyal, Alex Krist
The Journal of the American Board of Family Medicine Feb 2021, 34 (Supplement) S127-S135; DOI: 10.3122/jabfm.2021.S1.200464

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Development and Validation of the COVID-NoLab and COVID-SimpleLab Risk Scores for Prognosis in 6 US Health Systems
Mark H. Ebell, Xinyan Cai, Robert Lennon, Derjung M. Tarn, Arch G. Mainous, Aleksandra E. Zgierska, Bruce Barrett, Wen-Jan Tuan, Kevin Maloy, Munish Goyal, Alex Krist
The Journal of the American Board of Family Medicine Feb 2021, 34 (Supplement) S127-S135; DOI: 10.3122/jabfm.2021.S1.200464
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    • Appendix 1. Full List of Requested Clinical Variables; Predictor Variables only Included if Ordered Within 24 Hours of Admission
    • Appendix 2. This Summarizes the Receiver Operating Characteristic (ROC) Curves and Calibration Plots for Each Model.
    • Appendix 3. Additional Model Using Clinical Variables and Complete Blood Count Only.
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  • COVID-19
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