COPD is a common preventable and treatable disease that is characterized by persistent respiratory symptoms and airflow limitation, that is due to airway and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases. It is currently fourth leading cause of death in the world [1] but is projected to be third leading cause by 2020 [2]. Estimates suggest there are 30 million COPD patients in India at present. India contributes a significant and growing percentage of COPD mortality which is estimated to be amongst the highest in the world; i.e., more than 64.7 estimated age standardized death rate per 100,000 amongst both sexes [3].
In long-term follow-up, mortality is likely to be more influenced by the general characteristics of the patient’s COPD than the severity of the acute exacerbation of COPD that lead to hospital admission. Mortality occurring in less than 90 days after presentation to a hospital (short-term mortality), with an acute exacerbation of COPD, usually includes in-hospital mortality, and factors related to the severity of the exacerbation are likely to play more important role in the short-term rather than in the mid or long-term follow-up [4]. A robust clinical prediction tool could help reduce morbidity and mortality. No comprehensive data is available in this subject in context of Indian population. Moreover, many studies in west have focused on 90 days mortality [5,6]. Therefore, studies that assess prognostic indices in shorter duration of exacerbation are warranted in this subset of population.
The primary objective of this study was to identify easy to use clinical tools that can help as predictor of short-term mortality after acute exacerbation of COPD. The secondary objectives were, to know the relation of biochemical markers such as Interleukin-6 and C-reactive protein with short term mortality.
Materials and Methods
This prospective observational study was conducted in Sir Sunderlal Hospital, Banaras Hindu University, from June 2016 to April 2017 after approval of the university ethical committee in May 2016.
Patients were recruited prospectively from Chest ward, Department of TB and Respiratory diseases, Emergency department and Acute care unit, Department of General Medicine. Inclusion criteria were age more than 40 years and COPD patient presenting with an increase in at least two of the following three symptoms [7]: dyspnoea, cough and sputum purulence severe enough to warrant hospital admission. Exclusion Criteria included patients that presented with exacerbation of other chronic respiratory diseases like Bronchial asthma, Interstitial lung disease and bronchiectasis, co-morbidity expected to limit survival to less than 12 months (malignancy) and refusal to give consent by patient or their kin.
Total of 57 patients were screened to undergo the study. However, two patients withdrew consent for the study. One patient was diagnosed with squamous cell carcinoma and two patients were diagnosed as Asthma COPD overlap, hence were excluded. Two patients were lost to follow-up. Finally data of 50 patients was analysed in this study.
A thorough history was taken and patients were evaluated for various clinical parameters at the time of presentation. During hospital admission, all the patients were treated with standard protocol including oxygen support, oral or intravenous antibiotics and oral or intravenous steroid as recommended by treating physician. Non-invasive Ventilation and Invasive ventilation was done whenever mandated and possible. Patients were followed for 30 days through daily rounds if admitted and through telephone if discharged. They were later classified into two groups Survived or Non-survived depending on 30 day mortality.
Statistical Analysis
Univariate analysis of each variable was performed using Chi-square method for categorical data and coefficient of correlation for continuous data. Variables that were significantly associated (p<0.05) were subjected to multivariate regression analysis. The data was analysed accordingly using SPSS version 16.0.
Results
Out of 50 study subjects, 30% (15/50) did not survive. Mean age of entire cohort was 65.52±11.00 years. 36% patients were >70 years of age. A descriptive data at presentation of entire cohort is given in [Table/Fig-1]. Mean Cumulative smoking years for non-survivor group were 41.87±12.677 years with minimum being 10 years and maximum 58 years. In overall cohort one patient did not report any form of smoke exposure and 5 patients having biomass exposure.
Descriptive data of study population at admission.
Patient Characteristic | Survived(n=35) | Died(n=15) | Total Cohort(n=50) |
---|
Age* (years) | 65.34±11.064 | 65.93±11.222 | 65.52±11.000 |
Sex (M/F %) | (54.28/45.71) | (53.3/46.7) | (54/46) |
Total duration of illness* (years) | 6.14±4.460 | 8.00±5.606 | 6.70±4.850 |
Pack year* (years) | 25.21±8.675 | 41.87±12.677 | 30.31±12.602 |
History of MV** | 5.7% | 86.7% | 30% |
Glasgow Coma Scale* | 14.91±0.507 | 13.73±0.884 | 14.56±0.837 |
Systolic Blood Pressure* (mm Hg) | 128.63±13.423 | 122.53±15.519 | 126.80±14.205 |
Diastolic Blood Pressure* (mm Hg) | 77.71±7.763 | 75.07±9.647 | 76.92±8.361 |
Respiratory Rate* (breaths/minute) | 29.74±3.381 | 35.00±2.420 | 31.32±3.941 |
Use of accessory muscle** | 45.7% | 100% | 62% |
Clubbing** | 8.6% | 20% | 12% |
Jugular Venous Pressure** | 20% | 86.7% | 40% |
Pedal oedema** | 31.4% | 93.3% | 50% |
Cyanosis** | 5.7% | 66.7% | 24% |
*= Mean±SD, **= (Present/ N) %, MV=Mechanical ventilation
Among the participants, 16% did not report any previous use of bronchodilators. Most of the patients belonged to COPD Group D (66%), followed by Group C (28%) while none belonged to Group A. Secondary infection was present in 85.7% (30/35) cases in survivor group and in 80% (12/15) cases in non- survivor group, with fever and increased purulence of sputum as predominant initial symptoms. Laboratory parameters are shown in [Table/Fig-2].
Laboratory parameters at admission.
Lab Investigation* | Survived(n=35) | Died(n=15) | Total Cohort(n=50) |
---|
Haemoglobin (mg/dl) | 11.33±2.16 | 12.97±3.75 | 12.54±2.85 |
Total Leukocyte Count (cells/cubic mm) | 12440±5124.5 | 12940±4386.0 | 12540±4876.5 |
Creatinine (mg/dl) | 1.05±0.44 | 1.44±0.71 | 1.16±0.56 |
Urea (mg/dl) | 56.03±37.48 | 77.53±39.84 | 62.48±39.08 |
Sodium (mEq/L) | 134.03±7.06 | 129.27±10.70 | 132.60±8.50 |
Potassium (mEq/L) | 4.26±0.89 | 5.18±0.99 | 4.54±1.01 |
RBC (mg/dl) | 101.2±38.9 | 110.3±45.8 | 104.0±40.8 |
pH | 7.35±0.08 | 7.19±0.08 | 7.30±0.11 |
SpO2 % | 93.05±7.58 | 82.58±11.61 | 89.91±10.09 |
pO2 (mm Hg) | 133.15±51.22 | 113.88±49.91 | 127.37±51.11 |
pCO2 (mm Hg) | 59.25±19.77 | 84.99±12.99 | 66.97±21.48 |
HCO3 (mEq/L) | 30.13±6.08 | 35.56±5.65 | 31.76±6.41 |
Anion Gap (mEq/L) | 8.56±4.11 | 10.42±5.02 | 9.12±4.43 |
FEV1% predicted | 62.40±11.57 | 27.00±5.60 | 53.93±18.47 |
Body Mass Index (Kg/m2) | 25.58±2.60 | 22.78±2.00 | 24.74±2.74 |
C-Reactive Protein (mg/dl) | 0.689±0.264 | 1.686±0.470 | 0.988±0.570 |
Interleukin-6 (pg/ml) | 247.91±108.94 | 431.53±97.25 | 294.68±88.62 |
*= Mean±SD
Mean total duration of illness in non-survivor group was 8.00±5.606 years with 24 years being maximum duration and 3 years being minimum duration. [Table/Fig-3] shows results of univariate analysis of all the variables considered in this study with p-value <0.05 considered significant.
Univariate analysis for significant association.
Parameters | OR | 95% CI | p-value |
---|
Age | 0.17 | 0.11-0.43 | 0.864 |
Total Duration of illness | 1.24 | 0.87-1.57 | 0.218 |
Pack year | 5.35 | 2.65-18.86 | <0.001 |
History of MV | 1.35 | 0.57-4.87 | <0.001 |
GCS | 5.97 | 3.40-11.78 | <0.001 |
Systolic Blood Pressure | 1.40 | 1.10-3.23 | 0.167 |
Diastolic Blood Pressure | 1.02 | 0.22-7.83 | 0.310 |
Respiratory Rate | 5.44 | 1.89-22.14 | <0.001 |
Use of accessory muscle | 1.34 | 1.12-3.54 | <0.001 |
Clubbing | 2.46 | 1.55-4.49 | 0.432 |
Pedal oedema | 3.70 | 2.14-5.92 | 0.003 |
JVP | 3.97 | 1.83-6.28 | <0.001 |
Cyanosis | 1.78 | 0.88-3.42 | 0.021 |
Haemoglobin | 4.72 | 1.97-11.36 | 0.523 |
TLC | 0.35 | 0.12-2.53 | 0.724 |
Platelet | 4.05 | 3.77-38.20 | 0.125 |
Creatinine | 2.33 | 1.17-9.56 | 0.024 |
Urea | 1.82 | 1.38-7.62 | 0.074 |
Sodium | 1.86 | 1.25-5.37 | 0.069 |
Potassium | 3.23 | 1.98-21.77 | 0.002 |
RBS | 0.71 | 0.29-1.99 | 0.479 |
pH | 6.37 | 4.67-36.76 | <0.001 |
SpO2 % | 3.79 | 1.56-7.49 | <0.001 |
pO2 | 1.22 | 1.20-15.78 | 0.225 |
pCO2 | 4.61 | 3.12-16.73 | <0.001 |
HCO3 | 2.95 | 1.07-5.75 | 0.005 |
Anion Gap | 1.36 | 1.01-13.43 | 0.177 |
FEV1 % predicted | 9.73 | 2.46-78.45 | <0.001 |
BMI | 3.71 | 2.81-5.70 | 0.001 |
CRP | 9.55 | 6.78-43.67 | <0.001 |
IL-6 | 2.23 | 1.76-3.52 | <0.001 |
Parameters such as number of pack years, history of mechanical ventilation in past one year, low GCS, respiratory rate, use of accessory muscle, pedal oedema, raised JVP and cyanosis were found significant in history and examination. Among lab parameters raised serum creatinine, serum potassium, pCO2, serum bicarbonate and serum CRP level, serum IL-6 level along with low pH, oxygen saturation, body mass index and FEV1% predicted were significant. Multivariate logistic regression was done for all these significant variables. [Table/Fig-4] shows results of regression analysis.
Parameters | OR | 95% CI | p-value |
---|
Pack year | 3.87 | 0.78-15.35 | 0.435 |
History of MV | 6.78 | 3.54-29.66 | 0.007 |
GCS | 1.59 | 1.24-3.50 | 0.002 |
Respiratory rate | 1.94 | 1.47-4.26 | 0.769 |
Use of accessory muscle | 9.46 | 2.89-48.34 | 0.09 |
Raised JVP | 2.32 | 2.18-3.90 | <0.001 |
Pedal oedema | 0.68 | 0.24-7.89 | 0.09 |
Cyanosis | 6.44 | 2.38-36.46 | 0.528 |
Creatinine | 0.37 | 0.13-2.53 | 0.193 |
Potassium | 5.73 | 4.18-17.37 | 0.264 |
pH | 1.20 | 0.83-1.98 | <0.001 |
SpO2 | 1.67 | 1.26-2.88 | 0.732 |
PCO2 | 4.49 | 3.88-6.42 | 0.623 |
Bicarbonate | 0.77 | 0.42-1.56 | 0.272 |
FEV1 % predicted | 10.23 | 2.64-68.35 | 0.069 |
BMI | 2.49 | 1.34-9.43 | 0.168 |
CRP | 1.89 | 1.12-4.49 | 0.337 |
IL-6 | 0.59 | 0.23-3.38 | 0.006 |
History of mechanical ventilation in past one year, low GCS, raised JVP, low pH and raised IL-6 levels were significant in multivariate analysis (p-value <0.05).
Discussion
In our study, age was not a predictive factor for short-term mortality. This stands in contrast with findings of a 2003 study [8]. They however studied 1 year mortality rate following exacerbation. Some retrospective studies [9,10] have also shown age as a predictive factor for mortality. However, later few studies [11,12] had findings similar to the present study. Mean age in this study was 65.52±11.00 years and similar age has been reported by another Indian study [13]. Cumulative smoking history (pack year) has been established as predictor of mortality in stable COPD by previous studies. It was found to be significant in acute exacerbation in our study on univariate analysis and supported by recent study in India [14], they looked at mortality following exacerbation, for 18 months. However on multivariate analysis, we did not find it to be affecting mortality in acute exacerbation.
History of mechanical ventilation in past one year was present in 86% of patients in non-survivor group whereas only 5.7% of patients in survivor group were put on assisted ventilation. This represents progressive severity of disease. This was also found to be an independent predictor of mortality in our study. Another study for in-hospital mortality supports this finding [15]. Worsening of clinical signs that represent respiratory distress (increased respiratory rate, use of accessory muscle, cyanosis) and cor-pulmonale (pedal oedema, raised jugular venous pressure) were found to be significant on univariate analysis. Raised JVP was found to be independent predictor of mortality. This is supported by a 2008 study [16], however they looked at patients presenting only to Emergency Department and those requiring immediate ventilation were excluded. Low GCS was an independent predictor of mortality. Similar findings were reported by other studies [16,17]. These results do suggest that worsening symptoms if overlooked, results in increased short term mortality and hence point towards need for more approachable treatment centers and patient awareness methods, so that early escalation of care if and when required can be easily administered.
Serum creatinine and potassium were not independent predictors of mortality. However, creatinine, sodium, potassium and total leucocyte count are a part of APACHE II score which has shown promise in predicting mortality in COPD patients admitted in ICU [11,15]. In acute exacerbation patient often presents with altered blood gases as a result of poor ventilation. In this study, changes in ABG (pH, arterial oxygen saturation, pCO2, bicarbonate ion) were significantly associated with mortality. On multivariate analysis we found pH was the only independent predictor of short-term mortality. Acidaemia has been shown to be related with short-term mortality by other studies [9,17,18].
FEV1 has long been used in COPD as the principal variable determining the presence of disease, its severity and response to different modalities of treatment. In Intermittent Positive Pressure Breathing (IPPB) trial, FEV1 was most accurate predictor of mortality in COPD patients who were followed for 3 years [19]. In our study FEV1 was not an independent predictor of mortality, while FEV1 was not available for 4 individuals in non-survived group. Use of inhaled bronchodilators in form of LABA/LAMA or LABA/ICS combination was not significant in predicting mortality. What we found alarming was approximately 16% of the patients had never used inhalers partly due to never seeking appropriate medical attention due to socio-economic constraints or due to sheer absence of health literacy among general populace.
C-reactive protein is a marker of inflammation and correlated with degree of pulmonary inflammation in COPD. A study in 2007 [20] showed that CRP is a strong long-term predictor of COPD outcome. In our study, CRP was significantly raised in univariate analysis in non-survivor group, representing overall inflammatory process. Another study in same year did not find CRP to be a predictor of mortality over 6 month’s period [21]. However, CRP levels were raised in exacerbation. Interleukin-6 plays a central role in host defence due to its wide range of immune and haemotopoietic activities and ability to induce the acute phase response. In this study, Interleukin-6 levels were raised in non-survivor group and were significant on multivariate analysis. Similar results were obtained by recent studies [22,23]. Both the studies concluded that Interleukin-6 is a better prognostic marker.
Limitation
Some of the limitations of this study are, it was a single centre study with small cohort size. We did not look at associated co-morbidities and their effect on short term mortality. A few studies [24,25] have found co-morbidities as significant predictor of 30 day mortality. Similarly use of oral glucocorticoid was not a parameter in our study but has been found significant in predicting one year mortality after exacerbation in a 2003 study [8]. Our study had higher in-hospital mortality than other studies [15], which reported 24.5 % in-hospital mortality probably due to high burden of disease and very few resources to manage respiratory emergencies. Also, we did not exclude the patients that were comatose or required immediate Non-Invasive or Invasive ventilatory support.
Conclusion
We were able to identify factors that predicted short-term mortality in acute exacerbation of COPD. Severity of disease as well as severity of present exacerbation was related with short-term mortality. This study provides clinical parameters that can be easily used in emergency ward to decide early escalation of care and informing patients and next of kin about probable short-term risk. Interleukin-6 in itself is an independent predictor of mortality. We would also like to point out that parameters like past history of mechanical ventilation and raised JVP point towards the need of overall better management of COPD patients, not only during exacerbations. There is a need for health education as well as better infrastructure, so that this disease and its exacerbation could be managed effectively.
*= Mean±SD, **= (Present/ N) %, MV=Mechanical ventilation*= Mean±SD