Background/Objective: Systemic inflammation is common in chronic obstructive pulmonary disease (COPD), and evidence suggests that inflammatory biomarkers can predict acute exacerbations (AECOPDs). The aim of this study was to analyse whether C-reactive protein (CRP), fibrinogen, white blood cell count (WBC), or the blood cell indices PLR (platelet-to-lymphocyte ratio), SII (systemic immune inflammation index), SIRI (systemic inflammation response index), and AISI (aggregate index of systemic inflammation) can predict future AECOPDs. Methods: In the Tools Identifying Exacerbations (TIE) cohort study, participants with spirometry-confirmed COPD were recruited from primary and secondary care in three Swedish regions and assessed during a stable phase of COPD. AECOPD frequency during the three-year follow-up was reviewed in medical records. Associations were analysed via ordinal logistic regressions. Results: Of the 571 participants, 46% had ≥1 AECOPD during follow-up, and the mean ± SD AECOPD frequency was 0.63 ± 1.2/year. In unadjusted analyses, high levels of CRP (odds ratio 1.86, 95% CI 1.29–2.67), fibrinogen (2.09, 1.38–3.16), WBCs (2.18, 1.52–3.13), SII (1.52, 1.05–2.19), SIRI (1.76, 1.23–2.52), and AISI (1.99, 1.38–2.87) were associated with a higher AECOPD frequency. After adjustment for AECOPD history, age, sex, smoking, body mass index, COPD Assessment Test score, lung function, and inhaled corticosteroid use, associations remained for high levels of CRP (adjusted odds ratio of 1.64; 95% CI of 1.08–2.49), fibrinogen (1.55; 1.07–2.24), and WBC (1.65; 1.10–2.47). Conclusions: CRP, fibrinogen, and WBC, assessed during stable-phase COPD, enhanced AECOPD prediction, whereas PLR, SII, SIRI, and AISI did not.

Background: Blood neutrophil-to-lymphocyte ratio (NLR) and blood eosinophils (B-Eos) are emerging biomarkers in COPD. This study examined whether they could predict acute exacerbations of COPD (AECOPDs), and determined their longitudinal stability.Methods: In this closed cohort study, Swedish subjects with spirometry-verified COPD attended three yearly visits in a stable phase of the disease. Blood cell counts, spirometry and questionnaire-assessed AECOPD-history (worsening of COPD leading to an unscheduled visit and/or use of antibiotics and/or oral corticosteroids) were collected at each visit.Results: Of 466 included subjects 57% were female. Baseline mean±sdforced expiratory volume in 1 s was 58±17% predicted. High NLR (≥3.0) was more common in subjects with previous AECOPDs than in those without (33.5%versus20.4%, p=0.002). In two-level mixed-effects logistic regression models adjusted for confounders, NLR as a continuous variable (OR 1.20, 95% CI 1.04–1.38) and B-Eos ≥300 cells·µL⁻¹(OR 1.54, 95% CI 1.06–2.24) were associated with future AECOPDs. In 386 subjects with blood cell data available at all three visits, the intraclass correlation coefficient for NLR was 0.61 (95% CI 0.56–0.66) and for B-Eos 0.69 (95% CI 0.64–0.73). NLR was persistently ≥3.0 in 10.6% and B-Eos was persistently ≥300 cells·µL⁻¹in 15.3%.Conclusions: Stable phase NLR and B-Eos were associated with future AECOPDs. NLR on its own is probably not useful to predict AECOPDs but might be included in a risk scoring index. A minority of subjects with COPD had persistently elevated stable-phase NLR or B-Eos, and the biomarkers showed fair longitudinal reliability.

Background: Life expectancy is significantly shorter for patients with chronic obstructive pulmonary disease (COPD) than the general population. Concurrent diseases are known to infer an increased mortality risk in those with COPD, but the effects of pharmacological treatments on survival are less established. This study aimed to examine any associations between commonly used drugs, comorbidities and mortality in Swedish real-world primary care COPD patients. Methods: Patients with physician-diagnosed COPD from a large primary care population were observed retrospectively, utilizing primary care records and mandatory Swedish national registers. The time to all-cause death was assessed in a stepwise multiple Cox proportional hazards regression model including demography, socioeconomic factors, exacerbations, comorbidities and medication. Results: During the observation period (1999– 2009) 5776 (32.5%) of 17,745 included COPD patients died. Heart failure (hazard ratio [HR]: 1.88, 95% confidence interval [CI]: 1.74– 2.04), stroke (HR: 1.52, 95% CI: 1.40– 1.64) and myocardial infarction (HR: 1.40, 95% CI: 1.24– 1.58) were associated with an increased risk of death. Use of inhaled corticosteroids (ICS; HR: 0.79, 95% CI: 0.66– 0.94), beta-blockers (HR: 0.86, 95% CI: 0.76– 0.97) and acetylsalicylic acid (ASA; HR: 0.87, 95% CI: 0.77– 0.98) was dose-dependently associated with a decreased risk of death, whereas use of long-acting muscarinic antagonists (LAMA; HR: 1.33, 95% CI: 1.14– 1.55) and N-acetylcysteine (NAC; HR: 1.26, 95% CI: 1.08– 1.48) were dose-dependently associated with an increased risk of death in COPD patients. Conclusion: This large, retrospective, observational study of Swedish real-world primary care COPD patients indicates that coexisting heart failure, stroke and myocardial infarction were the strongest predictors of death, underscoring the importance of timely recognition and treatment of comorbidities. A decreased risk of death associated with the use of ICS, beta-blockers and ASA, and an increased risk associated with the use of LAMA and NAC, was also found.

In 2017, Burgel and colleagues developed an algorithm to identify clinical phenotypes that predict mortality in COPD. Our study aimed to 1) investigate whether the phenotypes can predict acute exacerbations of COPD (AECOPD) and 2) validate their ability to predict mortality. The Tools Identifying Exacerbations (TIE) cohort study recruited participants with spirometry-verified COPD from primary and secondary care in three Swedish regions. Participants were allocated to phenotypes 1-5 using the previously developed algorithm containing comorbidities (heart failure, coronary artery disease, hypertension, and/or diabetes), dyspnoea, age, forced expiratory volume in 1 s (FEV₁), and body mass index (BMI). Data on AECOPDs and deaths during the three-year follow-up were collected from medical records and analysed with Cox proportional hazards regressions. Harrel's C-index was used to assess the models' discriminative ability. Among the 566 participants, 59 % were female, and the mean ± SD FEV₁ was 57 ± 18 % of predicted. The hazard ratios (HRs) [95 % CI] for time to AECOPD were 3.04 [1.93-4.79], 2.38 [1.54-3.66], and 3.52 [1.73-7.15] in phenotypes 1, 2, and 4 compared with 5 (C-index = 0.61). When AECOPD history was used to predict future AECOPD the C-index was 0.65. The HRs [95 % CI] for mortality were 8.24 [1.93-35.3], 6.26 [1.40-28.0], and 16.7 [3.25-86.3] in phenotypes 1, 3, and 4 compared to 5 (C-index = 0.68). For AECOPD history, the C-index was 0.55. Clinical COPD phenotypes based on comorbidities, dyspnoea, age, FEV₁, and BMI predict AECOPDs but do not perform better than AECOPD history. However, they perform better than AECOPD history in predicting mortality.

Systemic inflammation has been suggested to increase the risk of future acute exacerbations of COPD (AE). Subjects with COPD were recruited in a stable phase in three Swedish regions. Baseline data included anthropometry, spirometry, venous blood sampling, COPD Assessment Test (CAT), and self-reported data on medication, comorbidities and smoking. Medical records were used to identify AEs during 1 year prior to baseline and during the following 3 years. The outcome was annualised AEs during follow-up (aAE). Of 572 subjects (59 % female; mean ± SD age 69 ± 8 years, predicted FEV₁ 57 ± 17 %), 167 (29 %) had ≥1 AE prior to baseline. After categorising aAEs into three groups (0, >0 - <1, and ≥1 yearly AEs), a multivariate ordinal logistic regression model was fitted, including previously described predictors of AEs. After adjustment for medication, prior AE yes/no [odds ratio 3.94; 95 % confidence interval 2.65 – 5.84], FEV₁ [0.97; 0.96 – 0.98], CAT score [1.04; 1.02 – 1.07], underweight versus normal [2.40; 1.01 – 5.69], and heart failure (HF) [2.32; 1.06 – 5.08] were associated with aAEs, whereas asthma [1.15; 0.78 – 1.70] and current smoking [1.22; 0.82 – 1.81] were not. Blood and plasma inflammatory biomarkers, including eosinophils [1.97; 0.68 – 5.72], neutrophil-to-lymphocyte ratio [0.99; 0.87 – 1.14], systemic inflammatory index (platelets x neutrophils / lymphocytes) [1.00; 1.00 – 1.00], C-reactive protein [0.99; 0.96 – 1.01], and fibrinogen [1.13; 0.86 – 1.49] were added to the model, one by one, but none was associated with aAEs or changed the estimates of the basic model. In conclusion, stable-phase biomarkers of systemic inflammation did not associate with the risk of future AEs.