Abstract
Rationale: There is a need to identify clinically meaningful predictors of mortality following hospitalized COPD exacerbation.
Objectives: The aim of this study was to systematically review the literature to identify clinically important factors that predict mortality after hospitalization for acute exacerbation of chronic obstructive pulmonary disease (COPD).
Methods: Eligible studies considered adults admitted to hospital with COPD exacerbation. Two authors independently abstracted data. Odds ratios were then calculated by comparing the prevalence of each predictor in survivors versus nonsurvivors. For continuous variables, mean differences were pooled by the inverse of their variance, using a random effects model.
Measurements and Main Results: There were 37 studies included (189,772 study subjects) with risk of death ranging from 3.6% for studies considering short-term mortality, 31.0% for long-term mortality (up to 2 yr after hospitalization), and 29.0% for studies that considered solely intensive care unit (ICU)–admitted study subjects. Twelve prognostic factors (age, male sex, low body mass index, cardiac failure, chronic renal failure, confusion, long-term oxygen therapy, lower limb edema, Global Initiative for Chronic Lung Disease criteria stage 4, cor pulmonale, acidemia, and elevated plasma troponin level) were significantly associated with increased short-term mortality. Nine prognostic factors (age, low body mass index, cardiac failure, diabetes mellitus, ischemic heart disease, malignancy, FEV1, long-term oxygen therapy, and PaO2 on admission) were significantly associated with long-term mortality. Three factors (age, low Glasgow Coma Scale score, and pH) were significantly associated with increased risk of mortality in ICU-admitted study subjects.
Conclusion: Different factors correlate with mortality from COPD exacerbation in the short term, long term, and after ICU admission. These parameters may be useful to develop tools for prediction of outcome in clinical practice.
Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide. Its prevalence is set to increase such that by 2020 it will be the third leading cause of mortality worldwide (1). The disease is punctuated by acute exacerbations that often require hospitalization. An acute exacerbation is defined as “an event in the natural course of the disease characterized by a change in the patient’s baseline dyspnea, cough, and/or sputum that is beyond normal day-to-day variations, is acute in onset, and may warrant a change in regular medication” (2). Mortality is known to be increased in both the short- and long-term period after an acute exacerbation (3, 4).
Knowledge about prognosis of disease and factors that predict poor outcome is important to enable physicians to advise patients on the expected natural course of an illness and on the likelihood of complications. Such knowledge is also vital for guiding crucial management decisions such as site of care, intensity of monitoring, decisions to escalate or withdraw treatment, and timing of follow-up after discharge. There is a large body of evidence in community-acquired pneumonia with a range of predictive factors that have been identified, and scoring systems based on these predictors have been devised and successfully implemented in clinical practice (5–10). Identifying predictors of outcome will also be useful in COPD exacerbations, to help direct resources and improve patient care of this increasingly common disease.
No clinical predictors or scoring systems are currently recommended for use by national guidelines in exacerbations of COPD (11, 12). A number of studies have assessed predictors of mortality after COPD exacerbation, but these studies vary in design, inclusion criteria, and parameters investigated. A few studies to date have evaluated existing severity scores derived for alternative use such as CURB-65 (13, 14) or attempted to derive new scores such as BAP-65 (15, 16), but the predictive value of these scores is modest. There is a need to identify robust predictors of mortality for use in clinical practice.
The aim of this study was to systematically review the published literature to identify clinically important factors predicting mortality after hospital admission for acute exacerbation of COPD.
The present study was a systematic review and meta-analysis conducted according to MOOSE (Meta-analysis of Observational Studies in Epidemiology) guidelines (17).
The study was based on a search of PubMed for articles on prognosis in acute exacerbation of COPD, using the following search strategy: (chronic obstructive pulmonary disease OR chronic obstructive airways disease OR COPD OR emphysema OR bronchitis OR airways obstruction OR obstructive lung disease) AND (exacerbation OR acute exacerbation) AND (hospitalization OR mortality OR outcome OR prognosis OR survival rate OR survival analysis OR survival prediction OR survival OR prognostic factor OR death OR endpoint).
The search included articles published between 1966 and September 2012. No language criteria were applied. Full articles of potentially appropriate abstracts were reviewed. Only peer-reviewed data were included. Conference abstracts were excluded. The search was repeated in Embase and Web of Science to obtain any articles missed by the initial search. The search strategy was supplemented by reviewing of the reference lists, bibliographies, and the investigators’ files.
Two investigators independently reviewed the titles of all articles (A.S. and J.D.C.). Nonrelevant studies were excluded on the basis of title and abstract review alone. Potentially relevant studies were reviewed by at least two researchers who performed data extraction and quality assessment in a blinded fashion. Any disagreement between investigators was resolved independently by a third investigator (S.S.). Where possible, study authors were contacted to clarify inconsistencies or to obtain missing data.
All studies were considered eligible if they fulfilled the following criteria: original publications; inclusion of consecutive adults hospitalized with COPD exacerbation; spirometric, ICD-9 code, or clinical diagnosis of COPD; standardized definition of COPD exacerbation. Studies assessing COPD exacerbation with complicating radiographic consolidation and/or nonpneumonic COPD exacerbation were included.
A priori, short-term mortality was defined as mortality occurring not more than 90 days after first presentation to hospital with exacerbation (including any studies that considered “in-hospital mortality” as an end point). Long-term mortality was defined as mortality occurring between 90 days and 2 years after first presentation with exacerbation.
Studies were excluded if they combined COPD exacerbations with other acute diagnoses, for example, asthma exacerbations, or if they included alternative reasons for admission in a study subject with preexisting COPD, for example, acute cardiac failure or myocardial infarction. Also excluded were studies that considered a mixture of hospitalized and outpatient-based exacerbations, unless data were presented separately for hospitalized exacerbations of COPD or such data could be obtained through correspondence with the authors. Also excluded were case reports and small case series (<25 study subjects), as well as review articles, editorials, and letters without original data.
There are no widely accepted quality criteria for observational studies. To assess quality, modified criteria based on the criteria of Hayden and colleagues (18) were used. Two reviewers independently assessed quality and the agreement between the two reviewers was measured using the kappa statistic.
As predictors were reported both as categorical and continuous variables, these were analyzed separately. To determine the association between categorical predictors and mortality, odds ratios were calculated comparing the prevalence of each predictor in survivors against nonsurvivors. Crude odds ratios were then pooled using a Mantel-Haenszel random effects model. For continuous variables, the mean and standard deviation between survivors and nonsurvivors were compared. Mean differences were pooled by the inverse of their variance, using a random effects model. A random effects model was used for all analyses because of expected heterogeneity between studies. Statistical heterogeneity was assessed using the Cochran Q (χ2) test and the Higgins I2 tests. For the Cochran Q test, P < 0.1 was considered to represent significant heterogeneity. For the Higgins test, I2 < 25% indicates low heterogeneity, 25–50% moderate, and > 50% severe heterogeneity. Analyses were conducted with Review Manager 5 (Cochrane Collaboration, Oxford, UK).
A total of 2,495 abstracts were reviewed and 156 papers were potentially eligible and reviewed in depth. The process of literature review is shown in Figure 1. A total of 37 papers were included in the final meta-analysis, incorporating 189,772 study subjects. The characteristics of the study cohorts included in the meta-analysis are shown in Table 1. The mean cohort size was 5,129 study subjects (range, 41 to 88,074). Details of individual studies are shown in Table E1 in the online supplement.
Figure 1. Process of literature review. COPD = chronic obstructive pulmonary disease.
[More] [Minimize]| Number of Studies | |
|---|---|
| Sample size | |
| <100 | 5 |
| 100–299 | 17 |
| 300–499 | 2 |
| 500–699 | 1 |
| 700–899 | 4 |
| >900 | 8 |
| Design | |
| Prospective | 22 |
| Retrospective | 15 |
| Focus | |
| Intensive care study subjects | 12 |
| Elderly | 2 |
| Short-term mortality | 17 |
| Long-term mortality (>90 d) | 8 |
| Quality assessments* | |
| High quality | 5 |
| Moderate quality | 18 |
| Low quality | 14 |
There were 17 studies that assessed short-term mortality as the end point (total, 184,696 study subjects) (13, 15, 19–33). The overall short-term cumulative incidence of death was 3.6% (6,580 deaths). Short-term cumulative incidence of death varied from 1.8 to 20.4%. Thirteen studies assessed in-hospital mortality, 1 study assessed 30-day mortality, and 3 studies assessed 90-day mortality. Table 2 shows association of individual factors with short-term mortality.
| Factor | Study Cohorts Reporting Data | Subjects in Study Cohorts Reporting Data | OR (95% CI) (Nonsurvivors:Survivors) or Mean Difference (Nonsurvivors – Survivors) of Factor Indicated* | P Value | I2† |
|---|---|---|---|---|---|
| Demographic | |||||
| Age | 9 | 15,158 | 4.87 (2.45–7.29)* | <0.001 | 93% |
| Age > 75 yr | 5 | 81,007 | 1.96 (1.58–2.44) | <0.001 | 75% |
| Male sex | 11 | 173,653 | 1.17 (1.07–1.27) | <0.001 | 9% |
| Comorbidities | |||||
| BMI | 6 | 2,828 | −1.67 (–2.48 to –0.87)* | <0.001 | 24% |
| Cardiac failure | 4 | 12,792 | 1.20 (1.08–1.34) | 0.001 | 0% |
| Chronic renal failure | 3 | 100,206 | 1.97 (1.31–2.94) | 0.001 | 86% |
| Current smoking | 4 | 1,802 | 0.83 (0.47–1.50) | 0.54 | 40% |
| Diabetes | 2 | 11,588 | 0.87 (0.76–0.98) | 0.02 | 0% |
| Ischemic heart disease | 3 | 13,333 | 1.38 (0.82–2.32) | 0.22 | 81% |
| Malignancy | 3 | 99,570 | 1.75 (0.98–3.12) | 0.06 | 93% |
| History and physical examination | |||||
| Confusion | 5 | 90,111 | 4.22 (3.79–4.69) | <0.001 | 0% |
| Long-term oxygen | 7 | 3,625 | 2.93 (1.99–4.30) | <0.001 | 43% |
| Lower limb edema | 4 | 3,262 | 1.84 (1.46–2.31) | <0.001 | 0% |
| Chronic steroid use | 6 | 14,156 | 1.39 (1.00–1.93) | 0.05 | 49% |
| Disease-specific severity features | |||||
| FEV1 | 5 | 2,034 | −3.81 (–8.77 to 1.15) | 0.13 | 82% |
| GOLD stage 4 | 2 | 1,086 | 8.24 (4.37–15.53) | <0.001 | 0% |
| Cor pulmonale | 4 | 90,250 | 1.83 (1.59–2.10) | <0.001 | 0% |
| Blood parameters | |||||
| Acidemia (pH < 7.35) | 3 | 89,548 | 4.49 (2.68–7.52) | <0.001 | 78% |
| pH (continuous) | 5 | 2,192 | −0.02 (–0.04 to 0.00)* | 0.08 | 72% |
| Elevated plasma troponin level | 3 | 88,318 | 3.16 (1.66–6.01) | <0.001 | 29% |
| PCO2 on admission | 6 | 2,477 | 0.05 (–1.71 to 1.80)* | 0.96 | 35% |
| PO2 on admission | 6 | 2,477 | −0.89 (–2.07 to 0.29)* | 0.14 | 14% |
There were 8 studies that assessed long-term mortality end points (total, 2,300 study subjects) (4, 34–40). The overall long-term cumulative incidence of death was 31.0% (712 deaths). Long-term cumulative incidence of death varied from 18.8 to 45.4%. The following mortality end points were assessed: 6 months (one study), 1 year (three studies), and 2 years (four studies). Table 3 shows association of individual factors with long-term mortality.
| Factor | Number of Studies Reporting | Number of Study Subjects | OR (95% CI) (Nonsurvivors:Survivors) or Mean Difference (Nonsurvivors – Survivors) of Factor Indicated* | P Value | I2† |
|---|---|---|---|---|---|
| Demographic | |||||
| Age | 6 | 1,275 | 3.31 (2.26–4.36)* | <0.001 | 0% |
| Male sex | 4 | 1,672 | 0.46 (0.15–1.40) | 0.17 | 94% |
| Comorbidities | |||||
| BMI | 4 | 718 | −2.01 (–2.95 to –1.06)* | <0.001 | 0% |
| Cardiac failure | 2 | 993 | 3.77 (2.73–5.19) | <0.001 | 0% |
| Current smoking | 2 | 1,113 | 2.92 (0.23–36.37) | 0.41 | 98% |
| Diabetes mellitus | 4 | 1,670 | 2.64 (1.23–5.68) | 0.01 | 79% |
| Ischemic heart disease | 4 | 1,670 | 2.44 (1.04–5.73) | 0.04 | 86% |
| Malignancy | 2 | 109 | 4.48 (2.88–6.98) | <0.001 | 0% |
| Stroke | 2 | 993 | 1.56 (0.98–2.48) | 0.06 | 0% |
| Disease-specific features | |||||
| FEV1 | 6 | 1,988 | −5.29 (–7.44 to –3.14)* | <0.001 | 33% |
| Long-term oxygen therapy | 2 | 993 | 2.19 (1.41–3.39) | <0.001 | 0% |
| Blood parameters | |||||
| Serum albumin | 2 | 357 | −0.21 (–0.45 to 0.03)* | 0.09 | 78% |
| PaO2 on admission | 2 | 340 | −7.18 (–8.61 to –5.75)* | <0.001 | 0% |
| PaCO2 on admission | 2 | 340 | 0.52 (–5.24 to 6.29)* | 0.86 | 91% |
There were 12 studies that assessed solely ICU-admitted study subjects (total, 2,776 study subjects) (40–51). The overall cumulative incidence of death for ICU-admitted study subjects was 29.0% (805 deaths). The cumulative incidence of death in ICU-admitted study subjects varied from 17.6 to 48.8%. Table 4 shows association of individual factors with mortality in ICU-admitted study subjects.
| Factor | Study Cohorts Reporting Data | Subjects in Study Cohorts Reporting Data | OR (95% CI) (Nonsurvivors:Survivors) or Mean Difference (Nonsurvivors – Survivors) of Factor Indicated* | P Value | I2† |
|---|---|---|---|---|---|
| Demographic | |||||
| Age | 9 | 2,491 | 2.54 (3.79 to 1.28)* | <0.001 | 36% |
| Age > 75 yr | 3 | 8,251 | 3.52 (1.41–8.77) | <0.001 | 58% |
| Male sex | 6 | 1,570 | 0.82 (0.64–1.04) | 0.1 | 0% |
| Comorbidities | |||||
| Cardiac failure | 3 | 338 | 0.73 (0.40–1.31) | 0.3 | 0% |
| Chronic renal failure | 2 | 248 | 0.36 (0.01–5.74) | 0.4 | 71% |
| Diabetes | 2 | 217 | 1.50 (0.47–4.77) | 0.5 | 39% |
| Ischemic heart disease | 3 | 229 | 0.59 (0.15–2.36) | 0.5 | 66% |
| History and physical examination | |||||
| Glasgow Coma Scale | 3 | 304 | −2.41 (–1.64 to –3.19)* | <0.001 | 4% |
| Long-term oxygen | 3 | 319 | 1.15 (0.61–2.17) | 0.7 | 0% |
| Chronic steroid use | 3 | 258 | 0.75 (0.22–2.59) | 0.7 | 68% |
| Disease-specific severity features | |||||
| FEV1 | 5 | 564 | 1.12 (–5.78 to 8.01)* | 0.8 | 67% |
| Blood parameters | |||||
| pH | 5 | 612 | −0.02 (–0.04 to 0.00)* | 0.02 | 0% |
| Albumin | 5 | 604 | 0.36 (0–0.71)* | 0.05 | 85% |
| Pco2 | 5 | 653 | 2.56 (–2.8 to 7.92)* | 0.4 | 62% |
| Po2 | 4 | 429 | 8.93 (–1.8 to 19.66)* | 0.1 | 82% |
| C-reactive protein | 2 | 267 | −2.60 (–11.42 to 6.22)* | 0.6 | 94% |
There were 16 studies that reported prospectively collected data (9 studies considering short-term mortality and 7 studies considering long-term mortality). Tables E2 and E3 show association of individual factors with short- and long-term mortality, respectively, in prospective studies only (see the online supplement).
There were 10 studies that evaluated severity prediction tools specifically for outcome in COPD exacerbations (3, 13–16, 28, 33, 53–55) (see Table 5). The area under the curve for these scores ranged from 0.68 for CRB-65 (52) to 0.83 for a derived score by Roche and colleagues (28). All scores were level 1–2 evidence, as determined by the criteria of Reilly and Evans (56), indicating scores with limited or no external validation. Only CURB-65 and BAP-65 had been assessed in more than one study and no studies assessed any potential impact of severity assessment tools in clinical practice.
| Study | Severity Assessment Tool Assessed | Components | Outcome | n | AUC | Grade of Evidence* |
|---|---|---|---|---|---|---|
| Chang et al. (13) | CURB-65 | Confusion, urea, RR, BP, age ≥ 65 yr | 30-d mortality | 249 | 0.73 | 2 |
| Steer et al. (14) | CURB-65 | As above | 30-d mortality | 920 | 0.72 | 2 |
| Edwards et al. (53) | CRB-65 | Confusion, RR, BP, age ≥ 65 yr | In-hospital and 30-d mortality | 133 | 0.68 | 1 |
| Roche et al. (28) | Not named | Age, dyspnea severity, clinical severity | In-hospital mortality | 794 | Derivation 0.79 Validation 0.83 | 2 |
| Tabak et al. (15) | BAP-65 | Urea, confusion, HR, age ≥ 65 yr | In-hospital mortality | 88,074 | Derivation 0.72Validation 0.71 | 2 |
| Shorr et al. (16) | BAP-65 | Urea, confusion, HR, age ≥ 65 yr | Hospital mortality or requirement for mechanical ventilation | 34,699 | 0.79 | 2 |
| Ruiz Gonzalez et al. (54) | Not named | Confusion, CRP ≥ 50 mg/L, ≥2 comorbidities, current smoker | 15-d mortality, need for ICU or development of acute cardiac failure | 147 | 0.80 | 1 |
| Connors et al. (3) | Not named | APACHE III, PaO2/FiO2 ratio, BMI, comorbidity, albumin, CHF, cor pulmonale, functional status | 6-mo mortality | 1,016 | 0.73 | 1 |
| Wildman et al. (55) | CAOS COPD/asthma prognostic score | Multiple variables | In-hospital mortality in ICU-admitted study subjects | 832 | 0.72 | 1 |
| Steer et al. (14) | Extended MRC dyspnea score (eMRCD) | Extended version of MRC dyspnea score | In-hospital mortality | 920 | 0.79 | 1 |
| Steer et al. (33) | DECAF score | eMRCD scoreEosinopenia, consolidation, acidemia, AF | 30-d mortality | 920 | 0.82 | 1 |
Several parameters were identified during the literature search that were associated with mortality but for which primary data could not be extracted for meta-analysis. For short-term mortality, these include hypotension (13), tachycardia (15), tachypnea (15), hyperglycemia (15), anemia (15), urea (13, 15), and biomarkers such as procalcitonin (46). For long-term mortality, the literature identified poor functional status, poor health-related quality of life, and depression as predictors of poor outcome (36, 39). However, these could not be pooled because of different methods being used to assess these factors. A number of parameters have been reported to be associated with poor outcome in ICU-admitted study subjects including length of hospital stay (55), dysrhythmia (48), and low bicarbonate (48).
On the basis of the quality assessments, only five studies were classified as high quality with a low risk of bias. Eighteen were moderate quality and 14 were low quality with a high risk of bias. There was significant agreement between reviewers in quality assessment (kappa statistic, 0.6). Funnel plots were analyzed for each variable and no evidence of publication bias was evident.
This is the first study to systematically review the literature on factors predictive of mortality in hospitalized acute exacerbation of COPD. We identified 12 factors with a consistent and statistically significant association with short-term mortality. Of these, five are related to patient demographics or comorbidities (age, male sex, low body mass index [BMI], cardiac failure, and chronic renal failure), four are related to underlying COPD severity (long-term oxygen therapy, lower limb edema, Global Initiative for Chronic Lung Disease [GOLD] Stage 4, and cor pulmonale), and three are related to acute physiological derangements (acidemia, confusion, and elevated plasma troponin level). Only acidemia could be considered as potentially “modifiable” in the acute setting, with all other factors being nonmodifiable acutely but some being potentially modifiable longer term (low BMI and cardiac failure). Information about 11 of these factors is available to clinicians on patients’ initial presentation. One factor (elevated plasma troponin level) can be measured within a few hours of admission in hospitals where the test is available. The presence or absence of these factors could be useful to help guide important management decisions such as initial site of care (higher level vs. general ward care or home therapy), intensity of therapy, and decisions to escalate or withdraw treatment. Knowledge of factors that are shown to be consistently associated with mortality across multiple studies will be helpful to those wishing to develop future scoring systems for predicting mortality in COPD exacerbation.
Our study identified nine factors with a statistically significant association with long-term mortality. Of these, six factors are related to patient demographics or comorbidities (age, low BMI, cardiac failure, diabetes mellitus, ischemic heart disease, and malignancy) and two are related to underlying COPD disease (FEV1 and long-term oxygen therapy). One factor is related to both underlying COPD disease and acute physiological derangement (PaO2). Of these factors, low BMI and cardiac failure are potentially modifiable in the long term and PaO2 may be partially correctable with oxygen therapy in the acute setting. Knowledge of these factors may allow optimal targeting of specific patients for more frequent monitoring postdischarge as well as identifying subgroups that may benefit from future novel therapies.
There were three factors significantly associated with increased risk of mortality in studies that considered solely ICU-admitted study subjects (age, low Glasgow Coma Scale score, acidemia). Low Glasgow Coma Scale score and acidemia reflect physiological derangement and are frequently included in ICU-specific severity scores (57, 58). The decision to admit a patient with decompensated advanced COPD to the ICU can often be difficult to make; our data offer some guidance regarding factors on presentation most frequently associated with adverse outcome.
Comorbidities were found to be associated with increased mortality in the short- and long-term periods after an exacerbation. Our finding that low BMI is associated with increased mortality further supports growing interest in cachexia and reduced muscle mass as systemic manifestations of COPD, believed to be related to spreading of inflammation from the lungs into the systemic circulation (59). Our data highlight the importance of this comorbidity in influencing outcome after acute exacerbation. To date, results from clinical trials assessing nutritional supplementation have been disappointing, although one study has shown improved survival in individuals who gain weight (60).
Our study found a history of cardiac failure and elevated plasma troponin level to be significantly associated with increased short-term mortality and a history of ischemic heart disease to be significantly associated with increased long-term mortality. Cardiac events have been shown to be common after COPD exacerbations (61). Our data confirm that concomitant cardiac disease may predispose to worse outcome after acute COPD exacerbation. There are a number of potential pathophysiological mechanisms that could explain the interaction between COPD and cardiovascular disease. These include spillover of pulmonary inflammation directly leading to development of atheromatous plaque formation and arterial remodeling, development of pulmonary hypertension in COPD leading to right ventricular systolic failure, smoking as a shared risk factor, and lung hyperinflation reducing intrathoracic blood volume and left ventricular performance (62). Given that cardiac disease is an independent cause of mortality worldwide, it is unclear whether the observed association with mortality is directly related to the presence of COPD or simply reflects a common cause of death in this population. Future therapies to target cardiac manifestations may be beneficial in improving outcomes after exacerbation. Our data also add weight to the importance of cardiac risk factor management in patients with COPD and use of medications such as statins and β-blockers, which have been suggested to have beneficial effects in COPD (63, 64).
Disease-specific severity factors were strongly associated with both short-term mortality (GOLD stage 4 and long-term oxygen therapy) and long-term mortality (long-term oxygen and FEV1). This association is logical as study subjects with more severe airway obstruction have reduced respiratory reserve and would be expected to be more likely to decompensate on development of acute exacerbation. Interestingly, FEV1 was a significant predictor of long-term but not short-term mortality. This may be due to consideration of baseline airflow obstruction alone as a continuous variable reflecting a range of disease severities. Conversely, GOLD stage 4 and long-term oxygen therapy are evaluated as categorical variables. Their presence is associated with definite severe disease and thus significantly increased short-term mortality.
An intriguing finding of our study was that diabetes mellitus was associated with reduced short-term mortality in COPD exacerbations. This was based on only two studies, but both showed the same effect with no heterogeneity. This apparent protective effect is mirrored in a study by McGhan and colleagues, who showed that diabetes was associated with reduced readmissions after COPD exacerbations (65). The mechanism for this effect is uncertain, although there has been speculation that it may be due to study subjects with less severe or uncomplicated hospital admissions being preferentially coded with chronic conditions (65). Our study reports unadjusted odds ratios and it is not feasible to exclude the possibility that the association between diabetes mellitus and mortality is confounded by higher BMI.
Selective analysis of prospective studies alone identified similar factors correlating with short-term mortality (malignancy identified as an additional risk factor with cardiac failure, chronic renal failure, and elevated plasma troponin level no longer significantly associated). Analysis of prospective studies considering long-term mortality led to a reduction in the number of significantly associated factors from eight to five, although this subanalysis was based on only seven studies.
A severity assessment tool capable of predicting mortality in patients with COPD exacerbation could be useful to guide management. We therefore reviewed existing severity assessment tools for prediction of mortality after COPD exacerbation. CURB-65 and BAP-65 were the most frequently studied scores (two and three cohorts, respectively [13–16]), but the majority had been validated in only one study with no independent validation. On the basis of established criteria for usefulness of prognostic scores, all of these scores require additional validation before they can be used with confidence in clinical practice. Interestingly, many of the key predictors of short-term mortality identified in our study are not contained in the recently proposed severity scores. The predictive value of existing scores has been modest (area under the curve, 0.7–0.8), suggesting that more accurate prediction may be possible using future scores based on the robust predictors of mortality identified in this study.
This meta-analysis has limitations. The study was not designed to test the effect of a prespecified exposure on outcome but rather to systematically evaluate reported factors commonly measured on admission in observational studies of COPD exacerbations. A hypothesis was tested with each exposure variable and, as such, multiple comparisons were required. There was significant heterogeneity among some of the studies included. Although the use of a random effects model accounts for this to some degree, the estimated effects where there was substantial heterogeneity should be treated with caution. Studies assessed subjects with a range of different COPD phenotypes and severity and also different outcome measures. The aim of our decision to perform separate subanalyses for studies that consider short-term and long-term mortality and those that consider solely ICU-admitted study subjects was to pool similar studies together. However, studies assessing long-term mortality often did not give explicit data on when deaths occurred and a proportion of “long-term” deaths may have occurred within 90 days. This may explain some of the overlap in factors identified between short- and long-term mortality. Furthermore, short- and long-term mortality studies often did not give data on rates of ICU-admitted study subjects and/or did not present data in a form to allow specific exclusion of these subjects.
In addition, some studies included COPD exacerbations complicated with radiographic consolidation along with nonpneumonic exacerbations. Given that some of the studies in our meta-analysis used ICD coding to define exacerbation, we did not have accurate information on the presence of consolidation to allow specific exclusion of these studies. Data suggest that patients with COPD with radiographic consolidation have a worse outcome than patients with exacerbation without consolidation (14). Some parameters have been identified as independent predictors of mortality in one or more studies, but primary data were not reported and could not be extracted or obtained from the authors. These parameters could not be included in the meta-analysis but are listed at the end of Results. The majority of studies included in the meta-analysis were not high quality and further high-quality prospective studies are needed.
Hospitalization for acute COPD exacerbation is becoming more frequent, and it places an enormous burden on patients and health care systems. In conclusion, the current study has identified a number of variables associated with long- and short-term mortality that may be used to develop tools to accurately predict in-hospital death and outcomes after discharge (including readmission, symptom control, and quality of life).
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Author Contributions: Conception and design: A.S., J.D.C.; data collection: all authors; analysis and interpretation: all authors; drafting and editing manuscript: all authors.
This article has an online supplement, which is available from this issue’s table of contents at www.atsjournals.org
Author disclosures are available with the text of this article at www.atsjournals.org.
