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Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis

Gilles Louis, Florence Schleich, Michèle Guillaume, Delphine Kirkove, Halehsadat Nekoee Zahrei, Anne-Françoise Donneau, Monique Henket, Virginie Paulus, Françoise Guissard, Renaud Louis, Benoit Pétré
ERJ Open Research 2023 9: 00451-2022; DOI: 10.1183/23120541.00451-2022
Gilles Louis
1Department of Public Health, University of Liège, Liege, Belgium
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  • For correspondence: glouis@uliege.be
Florence Schleich
2Department of Pneumology, GIGAI3, University of Liège, Liege, Belgium
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  • ORCID record for Florence Schleich
Michèle Guillaume
1Department of Public Health, University of Liège, Liege, Belgium
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Delphine Kirkove
1Department of Public Health, University of Liège, Liege, Belgium
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Halehsadat Nekoee Zahrei
3Department of Public Health, Biostatistics Unit, University of Liège, Liege, Belgium
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Anne-Françoise Donneau
3Department of Public Health, Biostatistics Unit, University of Liège, Liege, Belgium
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Monique Henket
2Department of Pneumology, GIGAI3, University of Liège, Liege, Belgium
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Virginie Paulus
2Department of Pneumology, GIGAI3, University of Liège, Liege, Belgium
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Françoise Guissard
2Department of Pneumology, GIGAI3, University of Liège, Liege, Belgium
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Renaud Louis
2Department of Pneumology, GIGAI3, University of Liège, Liege, Belgium
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Benoit Pétré
1Department of Public Health, University of Liège, Liege, Belgium
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  • FIGURE 1
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    FIGURE 1

    Asthma symptom intensity scales between asthmatic (A: n=105) and nonasthmatic (NA: n=61) subjects in the training cohort (n=166).

  • FIGURE 2
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    FIGURE 2

    Receiver operating characteristic curves showing the performance of the predictive models (Models 1–8 in table 5) in the a) training and b) validation cohorts. AUC: area under the curve.

Tables

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  • TABLE 1

    Patient demographic, functional and inflammatory characteristics in the training and validation cohorts

    Training cohort (n=166)Validation cohort (n=137)
    Asthmatic subjects63 (105)58 (80)
    Age (years)51±1651±15
    Male43 (71)36 (50)
    BMI (kg·m−2)27±4.826±4.9
    Smoking status
     Nonsmoker52 (87)51 (70)
     Ex-smoker25 (42)31 (42)
     Current smoker23 (37)18 (25)
    Atopy47 (78)42 (58)
    FEV1 (% pred)92±1794±17
    FEV1/FVC (%)79±8.379±7.9
    FENO (ppb)21 (14–34)19 (13–29)
    Sputum eosinophils (%)#1 (0–3)1 (0.2–2.5)
    Blood eosinophils (%)2.5 (1.3–4.2)2.1 (1.2–3.1)
    Blood eosinophils (μL−1)170 (98–290)160 (81–250)
    Total serum IgE (kU·L−1)60 (22–252)78 (27–158)

    Data are presented as % (n), mean±sd or median (interquartile range). BMI: body mass index; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; FENO: exhaled nitric oxide fraction. #: n=95 in the training cohort and n=94 in the validation cohort.

    • TABLE 2

      Comparison between asthmatic and nonasthmatic subject demographic, functional and inflammatory characteristics in the training cohort (n=166)

      Asthmatic subjects (n=105)Nonasthmatic subjects (n=61)
      Age (years)52±1648±17
      Male40 (42)47 (29)
      BMI (kg·m−2)27±4.327±5.5
      Smoking status
       Nonsmoker48 (51)59 (36)
       Ex-smoker28 (29)21 (13)
       Current smoker24 (25)20 (12)
      Atopy48 (50)44 (28)
      FEV1 (% pred)86±1898±16****
       FEV1 <80% pred27 (28)11 (7)
      FEV1/FVC (%)76±982±6.7****
       FEV1/FVC <75%43 (46)16 (10)
      FENO (ppb)22 (15–37)19 (13–27.5)*
       FENO >25 ppb40 (42)36 (22)
      Sputum eosinophils (%)#1 (0–5)0.8 (0.05–2.65)
      Blood eosinophils (%)2.7 (1.45–4.5)2.1 (1.3–3.2)
      Blood eosinophils (μL−1)190 (110–320)140 (98–260)
       Blood eosinophils >300 μL−126 (27)18 (11)
      Total serum IgE (kU·L−1)80 (27.5–272)42 (17–148)
      Wheezing intensity score1.48±1.10.84±1.1***
      Dyspnoea intensity score2.09±1.11.87±1.1
      Cough intensity score1.63±11.52±1.1
      Airway secretion intensity score1.28±11.08±1.1
      Chest tightness intensity score1.57±1.21.52±1

      Data are presented as % (n), mean±sd or median (interquartile range). BMI: body mass index; FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; FENO: exhaled nitric oxide fraction. #: n=57 asthmatic subjects and n=38 nonasthmatic subjects. *: p<0.05; ***: p<0.001; ****: p<0.0001.

      • TABLE 3

        Performance of each symptom intensity scale, spirometric indices and exhaled nitric oxide fraction (FENO) to diagnose asthma in the training cohort (n=166)

        ThresholdAUC (95% CI)Sensitivity (%)Specificity (%)p-value AUC
        Wheezing intensity score0.50.67 (0.59–0.76)78 (69–86)54 (41–67)0.0002
        Dyspnoea intensity score2.50.56 (0.46–0.64)38 (29–48)69 (56–80)0.2485
        Cough intensity score0.50.53 (0.44–0.62)88 (80–93)20 (11–32)0.5410
        Airway secretion intensity score0.50.56 (0.47–0.65)75 (66–83)34 (23–48)0.1847
        Chest tightness intensity score2.50.51 (0.42–0.60)28 (19–37)77 (65–87)0.8762
        FEV1 (% pred)960.68 (0.60–0.77)71 (62–80)59 (46–71)<0.0001
        FEV1/FVC (%)780.69 (0.61–0.77)54 (44–64)79 (66–88)<0.0001
        FENO (ppb)330.56 (0.47–0.66)32 (23–43)83 (71–92)0.1839

        FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity. Bold indicates statistical significance.

        • TABLE 4

          Univariate logistic regression on the training cohort (n=166)

          OR (95% CI)
          Wheezing intensity score1.72 (1.26–2.40)***
          Dyspnoea intensity score1.20 (0.89–1.62)
          Cough intensity score1.10 (0.81–1.49)
          Airway secretion intensity score1.21 (0.89–1.66)
          Chest tightness intensity score1.03 (0.79–1.35)
          FEV1 (% pred)0.95 (0.93–0.97)****
          FEV1/FVC (%)0.90 (0.86–0.95)****
          FENO (ppb)1.02 (1.002–1.04)*

          FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; FENO: exhaled nitric oxide fraction. *: p<0.05; ***: p<0.001; ****: p<0.0001.

          • TABLE 5

            Multivariate logistic regression on the training cohort (n=166)

            OR (95% CI)
            Model 1Wheezing1.72 (1.26–2.40)**
            Model 2Wheezing1.59 (1.16–2.22)**
            FEV1 (%)0.96 (0.93–0.98)**
            Model 3Wheezing1.61 (1.17–2.27)**
            FEV1/FVC (%)0.91 (0.86–0.96)**
            Model 4Wheezing1.62 (1.19–2.25)**
            FENO1.02 (0.99–1.04)
            Model 5Wheezing1.57 (1.14–2.19)**
            FEV1 (% pred)0.97 (0.94–0.99)*
            FEV1/FVC (%)0.94 (0.88–0.99)*
            Model 6Wheezing1.53 (1.11–2.18)*
            FEV1 (% pred)0.95 (0.93–0.98)***
            FENO1.02 (1.00–1.05)*
            Model 7Wheezing1.50 (1.08–2.11)*
            FEV1/FVC (%)0.91 (0.86–0.96)***
            FENO1.01 (0.99–1.03)
            Model 8Wheezing1.48 (1.07–2.11)*
            FEV1 (% pred)0.96 (0.94–0.99)*
            FEV1/FVC (%)0.94 (0.88–1.01)
            FENO1.02 (0.99–1.04)

            FEV1: forced expiratory volume in 1 s; FVC: forced vital capacity; FENO: exhaled nitric oxide fraction. *: p<0.05; **: p<0.01; ***: p<0.001.

            • TABLE 6

              Diagnostic performance of the models derived from the training cohort and applied to the training cohort (n=166)

              AUC (95% CI)ThresholdSensitivity (95% CI)Specificity (95% CI)NPVPPV
              Model 10.67 (0.59–0.76)0.200.78 (0.62–0.86)0.54 (0.35–0.67)0.590.74
              Model 20.73 (0.65–0.81)0.220.80 (0.58–0.89)0.59 (0.41–0.71)0.630.77
              Model 30.74 (0.65–0.82)0.330.75 (0.57–0.87)0.62 (0.44–0.74)0.590.77
              Model 40.67 (0.58–0.76)0.240.74 (0.53–0.85)0.58 (0.36–0.71)0.590.73
              Model 50.74 (0.67–0.83)0.120.85 (0.62–0.93)0.57 (0.41–0.69)0.690.77
              Model 60.75 (0.67–0.83)0.140.83 (0.64–0.91)0.59 (0.34–0.71)0.690.76
              Model 70.74 (0.65–0.82)0.340.73 (0.53–0.83)0.64 (0.42–0.76)0.600.76
              Model 8#0.76 (0.68–0.84)0.250.77 (0.54–0.85)0.69 (0.44–0.81)0.660.80

              AUC: area under the curve; NPV: negative predictive value; PPV: positive predictive value. #: best performing model.

              • TABLE 7

                Diagnostic performance of the models derived from the training cohort and applied to the validation cohort (n=137)

                AUC (95% CI)ThresholdSensitivity (95% CI)Specificity (95% CI)NPVPPV
                Model 10.61 (0.52–0.71)0.550.69 (0.50–0.80)0.54 (0.36–0.68)0.550.67
                Model 20.70 (0.61–0.77)0.460.86 (0.57–0.95)0.46 (0.28–0.58)0.700.69
                Model 30.67 (0.58–0.76)0.710.45 (0.22–0.59)0.86 (0.68–0.93)0.530.82
                Model 40.61 (0.51–0.71)0.570.64 (0.35–0.76)0.61 (0.37–0.72)0.570.68
                Model 50.70 (0.62–0.79)0.710.42 (0.22–0.54)0.89 (0.70–0.98)0.520.85
                Model 60.71 (0.61–0.80)0.370.88 (0.65–0.97)0.42 (0.24–0.54)0.740.66
                Model 70.70 (0.61–0.79)0.640.55 (0.33–0.68)0.83 (0.54–0.92)0.590.81
                Model 8#0.73 (0.65–0.82)0.700.52 (0.21–0.64)0.91 (0.63–0.96)0.600.88

                AUC: area under the curve; NPV: negative predictive value; PPV: positive predictive value. #: best performing model.

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                Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis
                Gilles Louis, Florence Schleich, Michèle Guillaume, Delphine Kirkove, Halehsadat Nekoee Zahrei, Anne-Françoise Donneau, Monique Henket, Virginie Paulus, Françoise Guissard, Renaud Louis, Benoit Pétré
                ERJ Open Research Jan 2023, 9 (1) 00451-2022; DOI: 10.1183/23120541.00451-2022

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                Development and validation of a predictive model combining patient-reported outcome measures, spirometry and exhaled nitric oxide fraction for asthma diagnosis
                Gilles Louis, Florence Schleich, Michèle Guillaume, Delphine Kirkove, Halehsadat Nekoee Zahrei, Anne-Françoise Donneau, Monique Henket, Virginie Paulus, Françoise Guissard, Renaud Louis, Benoit Pétré
                ERJ Open Research Jan 2023, 9 (1) 00451-2022; DOI: 10.1183/23120541.00451-2022
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