Simona Tabea Huebner1,2, Simona Henny3, Stéphanie Giezendanner3, Thomas Brack4, Martin Brutsche5, Prashant Chhajed3, Christian Clarenbach6, Thomas Dieterle7, Adrian Egli8,9, Martin Frey10, Ingmar Heijnen11, Sarosh Irani12, Noriane Andrina Sievi6, Robert Thurnheer13, Marten Trendelenburg7,14, Malcolm Kohler6, Anne Barbara Leuppi-Taegtmeyer7,15, Joerg Daniel Leuppi3,7. 1. University Clinic of Medicine, Cantonal Hospital Baselland, Liestal, Switzerland, simona.huebner@gmail.com. 2. Faculty of Medicine, University of Basel, Basel, Switzerland, simona.huebner@gmail.com. 3. University Clinic of Medicine, Cantonal Hospital Baselland, Liestal, Switzerland. 4. Department of Internal Medicine, Cantonal Hospital Glarus, Glarus, Switzerland. 5. Division of Respiratory Medicine, Cantonal Hospital St. Gallen, St. Gallen, Switzerland. 6. Division of Respiratory Medicine, University Hospital Zurich, Zurich, Switzerland. 7. Faculty of Medicine, University of Basel, Basel, Switzerland. 8. Division of Clinical Microbiology, University Hospital Basel, University of Basel, Basel, Switzerland. 9. Applied Microbiology Research, Department of Biomedicine, University of Basel, Basel, Switzerland. 10. Division of Respiratory Medicine, Hospital Barmelweid, Barmelweid, Switzerland. 11. Division of Medical Immunology, Department of Laboratory Medicine, University Hospital Basel, Basel, Switzerland. 12. Division of Respiratory Medicine, Cantonal Hospital Aarau, Aarau, Switzerland. 13. Division of Respiratory Medicine, Cantonal Hospital Münsterlingen, Münsterlingen, Switzerland. 14. Division of Internal Medicine, University Hospital Basel, and Clinical Immunology, Department of Biomedicine, University of Basel, Basel, Switzerland. 15. Department of Clinical Pharmacology, University Hospital Basel, Basel, Switzerland.
Abstract
BACKGROUND AND OBJECTIVE: Whether immunological biomarkers combined with clinical characteristics measured during an exacerbation-free period are predictive of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) frequency and severity is unknown. METHOD: We measured immunological biomarkers and clinical characteristics in 271 stable chronic obstructive pulmonary disease (COPD) patients (67% male, mean age 63 years) from "The Obstructive Pulmonary Disease Outcomes Cohort of Switzerland" cohort on a single occasion. One-year follow-up data were available for 178 patients. Variables independently associated with AECOPD frequency and severity were identified by multivariable regression analyses. Receiver operating characteristic analysis was used to obtain optimal cutoff levels and measure the area under the curve (AUC) in order to assess if baseline data can be used to predict future AECOPD. RESULTS: Higher number of COPD medications (adjusted incident rate ratio [aIRR] 1.17) and platelet count (aIRR 1.03), and lower FEV1% predicted (aIRR 0.84) and IgG2 (aIRR 0.84) were independently associated with AECOPD frequency in the year before baseline. Optimal cutoff levels for experiencing frequent (>1) AECOPD were ≥3 COPD medications (AUC = 0.72), FEV1 ≤40% predicted (AUC = 0.72), and IgG2 ≤2.6 g/L (AUC = 0.64). The performance of a model using clinical and biomarker parameters to predict future, frequent AECOPD events in the same patients was fair (AUC = 0.78) but not superior to a model using only clinical parameters (AUC = 0.79). The IFN-lambda rs8099917GG-genotype was more prevalent in patients who had severe AECOPD. CONCLUSIONS: Clinical and biomarker parameters assessed at a single point in time correlated with the frequency of AECOPD events during the year before and the year after assessment. However, only clinical parameters had fair discriminatory power in identifying patients likely to experience frequent AECOPD.
BACKGROUND AND OBJECTIVE: Whether immunological biomarkers combined with clinical characteristics measured during an exacerbation-free period are predictive of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) frequency and severity is unknown. METHOD: We measured immunological biomarkers and clinical characteristics in 271 stable chronic obstructive pulmonary disease (COPD) patients (67% male, mean age 63 years) from "The Obstructive Pulmonary Disease Outcomes Cohort of Switzerland" cohort on a single occasion. One-year follow-up data were available for 178 patients. Variables independently associated with AECOPD frequency and severity were identified by multivariable regression analyses. Receiver operating characteristic analysis was used to obtain optimal cutoff levels and measure the area under the curve (AUC) in order to assess if baseline data can be used to predict future AECOPD. RESULTS: Higher number of COPD medications (adjusted incident rate ratio [aIRR] 1.17) and platelet count (aIRR 1.03), and lower FEV1% predicted (aIRR 0.84) and IgG2 (aIRR 0.84) were independently associated with AECOPD frequency in the year before baseline. Optimal cutoff levels for experiencing frequent (>1) AECOPD were ≥3 COPD medications (AUC = 0.72), FEV1 ≤40% predicted (AUC = 0.72), and IgG2 ≤2.6 g/L (AUC = 0.64). The performance of a model using clinical and biomarker parameters to predict future, frequent AECOPD events in the same patients was fair (AUC = 0.78) but not superior to a model using only clinical parameters (AUC = 0.79). The IFN-lambda rs8099917GG-genotype was more prevalent in patients who had severe AECOPD. CONCLUSIONS: Clinical and biomarker parameters assessed at a single point in time correlated with the frequency of AECOPD events during the year before and the year after assessment. However, only clinical parameters had fair discriminatory power in identifying patients likely to experience frequent AECOPD.
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