Determinants of Troponin T During Exacerbation of COPD
Determinants of Troponin T During Exacerbation of COPD
During 23 months in 2005 and 2006 we prospectively included 99 unselected patients admitted with AECOPD. Among these, 41 patients had data recorded on readmission during the inclusion period, and in total we gathered data on 219 admissions. On each admission we recorded heart rate (HR), blood pressure (BP), body temperature, respiratory rate, arterial blood gas (pH, PaCO2, PaO2), arterial oxygen saturation (SaO2), use of accessory respiratory muscles, wheezing, and chest pain. Mean arterial pressure (MAP) was estimated by the formula MAP = 1/3*systolic BP + 2/3*diastolic BP.
Serum and plasma from blood drawn on admission were stored at −80 °C for subsequent analysis of creatinine and hs-cTnT (cobas e 411 immunoanalyser, Roche diagnostics). According to the manufacturer of the hs-cTnT assay, the lower limit of detection is 3.0 ng/L, and the 99 percentile in healthy volunteers was 14 ng/L. The lowest hs-cTnT level with 10% coefficient of variation was 13 ng/L. Glomerular filtration rate (GFR) was estimated by MDRD and Cockcroft-Gault formulae. From the hospital records we recorded hemoglobin (Hb), leucocytes with neutrophil count, platelets, electrolytes, and C-reactive protein (CRP). Chest radiographs were examined by two physicians blinded for clinical data. Presence or absence of cephalisation, pneumonic infiltrates and pleural effusion in addition to the size of the heart and thoracic cavity in the frontal plane were recorded. ECGs recorded on admission were scored using CIIS. A score ≥20 has been shown to be a good indicator of prior MI, and to be associated with increased mortality in AECOPD patients. Two physicians independently scored each ECG. When they disagreed on whether the CIIS was above or below 20, the score of a third physician was used. All three investigators were blinded to other data. ECGs were also analysed for the presence of arrhythmia, bundle branch block, left ventricular hypertrophy (LVH, assessed by Sokolow-Lyon criteria), signs of prior MI, or acute ischemia. We considered pathological Q-waves, loss of R-waves, T-wave inversion, and left bundle branch block to be signs of prior MI. ST-segment elevation or depression were recorded as signs of acute ischemia unless it was considered to be secondary to LVH.
Spirometry during stable phase was recorded when available. When several measurements were done, post bronchodilatation measurements prior to inclusion were preferred. Body mass index (BMI) was calculated from weight and height as recorded on the spirometry report or from the hospital records when spirometry was missing. Medical history was obtained by patient interview and hospital records. Patients were categorised as current, former (>1 year abstinence) or never smokers. Further details regarding patient inclusion and data gathering are described in a previous paper.
The study was approved by the Data Inspectorate and reviewed by the Regional Committee for Research Ethics. All included patients provided written informed consent to the participation in the study.
Due to the skewed distribution of hs-cTnT, the natural logarithm of hs-cTnT (lnTnT) was used as the dependent variable in the analyses. Samples with hs-cTnT below the limit of detection (i.e. 3.0 ng/L), were assigned a value of 3.0. Outliers were identified by visual inspection of the data points. Individual assessment of outliers determined whether they were to be excluded from further analyses. The analyses were performed in four steps: First, we analysed cross-sectional associations between lnTnT and variables recorded on the index admission using Student t-test. The continuous variables were dichotomised at predifined cut-offs: Age at the mean, FEV1/FVC at the lower quartile, BMI at the lower limit of normal (i.e. 20 kg/m), HR at 100/min, MAP at 90 mmHg, creatinine and neutrophil count at the upper quartile, pH at 7.30, PaCO2 at 6.3 kPa, PaO2 at 7.0 kPa, Hb at 12 g/dL in women and 13 g/dL in men, CRP at 50 mg/L, and CIIS at 20 points. In addition, the association between lnTnT and the following categorical variables were analysed: Gender, smoking status, history of coronary artery disease, heart failure, arterial hypertension, atrial fibrillation or diabetes, use of beta blockers, diuretics, ACE-inhibitors (ACEI) or angiotensin-II receptor blockers (ARB), statins, acetylic salicylic acid, or Warfarin, presence of atrial fibrillation, LVH, MI, or ischemia on ECG, peripheral edema, chest pain, and infiltrate or cephalisation on chest radiograph. Associations between continuous covariables at baseline and lnTnT were also analysed in a univariable linear regression model.
Second, in patients with repeat admissions, we investigated the level of lnTnT over time; first graphically and then by using time and time squared as independent variables in a linear mixed model (LMM).
In the third step, we investigated intra-individual univariable associations between lnTnT and each of the continuous covariables. We identified the minimum and maximum values of continuous time-dependent variables along with the corresponding values of hs-cTnT. We then analysed the univariable associations between lnTnT and each of the continuous covariables using LMM with random intercept. From these analyses, the antilogarithm exp(β) of the coefficient (β) between lnTnT and each covariate can be interpreted as the relative change in lnTnT for a given change in the covariate.
Fourth, the variables that were associated with lnTnT with a p-value <0.2 in the cross-sectional or longitudinal analysis were included in the initial multivariable LMM. In this model we investigated candidate covariance structures and a model with random intercept. The models were compared using the Akaike Information Criteria (AIC). Using the model with the lowest AIC, we then manually reduced the model by backward elimination of variables with p-values <0.05 unless their removal increased the AIC statistic. Finally, we investigated the changes made by adding survival status and neutrophil count to the final model. Gender was kept in the model by convention.
All analyses were performed in SAS 9.2 (SAS Institute Inc., Cary, NC, USA), using PROC MIXED for the LMM.
Methods
During 23 months in 2005 and 2006 we prospectively included 99 unselected patients admitted with AECOPD. Among these, 41 patients had data recorded on readmission during the inclusion period, and in total we gathered data on 219 admissions. On each admission we recorded heart rate (HR), blood pressure (BP), body temperature, respiratory rate, arterial blood gas (pH, PaCO2, PaO2), arterial oxygen saturation (SaO2), use of accessory respiratory muscles, wheezing, and chest pain. Mean arterial pressure (MAP) was estimated by the formula MAP = 1/3*systolic BP + 2/3*diastolic BP.
Serum and plasma from blood drawn on admission were stored at −80 °C for subsequent analysis of creatinine and hs-cTnT (cobas e 411 immunoanalyser, Roche diagnostics). According to the manufacturer of the hs-cTnT assay, the lower limit of detection is 3.0 ng/L, and the 99 percentile in healthy volunteers was 14 ng/L. The lowest hs-cTnT level with 10% coefficient of variation was 13 ng/L. Glomerular filtration rate (GFR) was estimated by MDRD and Cockcroft-Gault formulae. From the hospital records we recorded hemoglobin (Hb), leucocytes with neutrophil count, platelets, electrolytes, and C-reactive protein (CRP). Chest radiographs were examined by two physicians blinded for clinical data. Presence or absence of cephalisation, pneumonic infiltrates and pleural effusion in addition to the size of the heart and thoracic cavity in the frontal plane were recorded. ECGs recorded on admission were scored using CIIS. A score ≥20 has been shown to be a good indicator of prior MI, and to be associated with increased mortality in AECOPD patients. Two physicians independently scored each ECG. When they disagreed on whether the CIIS was above or below 20, the score of a third physician was used. All three investigators were blinded to other data. ECGs were also analysed for the presence of arrhythmia, bundle branch block, left ventricular hypertrophy (LVH, assessed by Sokolow-Lyon criteria), signs of prior MI, or acute ischemia. We considered pathological Q-waves, loss of R-waves, T-wave inversion, and left bundle branch block to be signs of prior MI. ST-segment elevation or depression were recorded as signs of acute ischemia unless it was considered to be secondary to LVH.
Spirometry during stable phase was recorded when available. When several measurements were done, post bronchodilatation measurements prior to inclusion were preferred. Body mass index (BMI) was calculated from weight and height as recorded on the spirometry report or from the hospital records when spirometry was missing. Medical history was obtained by patient interview and hospital records. Patients were categorised as current, former (>1 year abstinence) or never smokers. Further details regarding patient inclusion and data gathering are described in a previous paper.
The study was approved by the Data Inspectorate and reviewed by the Regional Committee for Research Ethics. All included patients provided written informed consent to the participation in the study.
Statistical Analysis
Due to the skewed distribution of hs-cTnT, the natural logarithm of hs-cTnT (lnTnT) was used as the dependent variable in the analyses. Samples with hs-cTnT below the limit of detection (i.e. 3.0 ng/L), were assigned a value of 3.0. Outliers were identified by visual inspection of the data points. Individual assessment of outliers determined whether they were to be excluded from further analyses. The analyses were performed in four steps: First, we analysed cross-sectional associations between lnTnT and variables recorded on the index admission using Student t-test. The continuous variables were dichotomised at predifined cut-offs: Age at the mean, FEV1/FVC at the lower quartile, BMI at the lower limit of normal (i.e. 20 kg/m), HR at 100/min, MAP at 90 mmHg, creatinine and neutrophil count at the upper quartile, pH at 7.30, PaCO2 at 6.3 kPa, PaO2 at 7.0 kPa, Hb at 12 g/dL in women and 13 g/dL in men, CRP at 50 mg/L, and CIIS at 20 points. In addition, the association between lnTnT and the following categorical variables were analysed: Gender, smoking status, history of coronary artery disease, heart failure, arterial hypertension, atrial fibrillation or diabetes, use of beta blockers, diuretics, ACE-inhibitors (ACEI) or angiotensin-II receptor blockers (ARB), statins, acetylic salicylic acid, or Warfarin, presence of atrial fibrillation, LVH, MI, or ischemia on ECG, peripheral edema, chest pain, and infiltrate or cephalisation on chest radiograph. Associations between continuous covariables at baseline and lnTnT were also analysed in a univariable linear regression model.
Second, in patients with repeat admissions, we investigated the level of lnTnT over time; first graphically and then by using time and time squared as independent variables in a linear mixed model (LMM).
In the third step, we investigated intra-individual univariable associations between lnTnT and each of the continuous covariables. We identified the minimum and maximum values of continuous time-dependent variables along with the corresponding values of hs-cTnT. We then analysed the univariable associations between lnTnT and each of the continuous covariables using LMM with random intercept. From these analyses, the antilogarithm exp(β) of the coefficient (β) between lnTnT and each covariate can be interpreted as the relative change in lnTnT for a given change in the covariate.
Fourth, the variables that were associated with lnTnT with a p-value <0.2 in the cross-sectional or longitudinal analysis were included in the initial multivariable LMM. In this model we investigated candidate covariance structures and a model with random intercept. The models were compared using the Akaike Information Criteria (AIC). Using the model with the lowest AIC, we then manually reduced the model by backward elimination of variables with p-values <0.05 unless their removal increased the AIC statistic. Finally, we investigated the changes made by adding survival status and neutrophil count to the final model. Gender was kept in the model by convention.
All analyses were performed in SAS 9.2 (SAS Institute Inc., Cary, NC, USA), using PROC MIXED for the LMM.
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