Catherine Owusuaa

Figure 2. forest plot of pooled hazard ratios for mortality with a fixed-effects model. CI: confidence interval; HR: hazard ratio; MRC: Medical Research Council. Figure 3. forest plot of pooled hazard ratios for mortality with a random-effects model. CI: confidence interval; COPD: chronic obstructive pulmonary disease; FEV1: forced expiratory volume.

Hospitalization for acute exacerbation of COPD in the previous 12 or 24 months (pooled from 6 studies; HR 1.97; 95% CI 1.32–2.95) and readmission within 30 days of discharge from the hospital (pooled from 4 studies; HR 5.01; 95% CI 2.16–11.63) significantly increased the risk of mortality. Other significant predictors were age, male sex, and long-term oxygen therapy. The presence of cardiovascular comorbidity was also significantly associated with mortality, but the Charlson comorbidity index score was not. Body mass index, FEV1, and partial pressure of carbon dioxide in the arterial blood (PaCO2) were also not significantly associated with mortality. Due to the limited number of low risk-of-bias studies, a meta-analysis with those studies only was not possible. The between-study heterogeneity was insignificant, i.e. I2 0%, for predictors age, male sex, FEV1, or PaCO2. There was moderate heterogeneity, i.e. I2 40–60%, for cardiovascular comorbidity, long-term oxygen therapy, or hospitalization for acute exacerbation of COPD. Predictors that showed substantial between-study heterogeneity, i.e. I2 >60%, were body mass index, Charlson comorbidity index score, or readmission within 30 days of discharge. Funnel plots showed no evidence of major publication bias (Supplementary material 4). A list of the variables that were excluded from the meta-analysis is presented in Supplementary material 5.

Eleven studies reported on 19 different multicomponent prognostic models for mortality within a period of 3–24 months, of which the ADO, BODE, BODEX, CODEX, and DOSE models were most studied (Table 3 and Supplementary material 6). These models partly included overlapping variables (e.g., FEV1, body mass index, dyspnea, previous exacerbations) in various combinations. All prognostic models had a moderate discriminative ability with an AUC or c-statistic ranging between 0.6–0.8 (Table 3). The models were studied for various follow-up periods, but showed comparable discriminative abilities. Model calibration was not reported for the prognostic models.

Table 3. performance of the multicomponent prognostic models.
24-month mortality: 0.741 (0.735–0.748), 0.80 (0.71–0.89), 0.67, 0.80 (0.71–0.89), 0.82 (0.74–0.90), 0.66 (0.55–0.77), 0.67, 0.68 (0.57–0.80), 0.78 (0.70–0.87), 0.69, 0.676 (0.668–0.683).
12-month mortality: 0.641 (0.609–0.673), 0.702, T: 0.675 (0.655–0.694), V: 0.568 (0.514–0.595), 0.737 (0.727–0.746), T: 0.794 (0.782–0.807), V: 0.671 (0.647–0.695), 0.682, 0.683, 0.68, 0.615 (0.582–0.648), 0.651, T: 0.483 (0.453–0.512), V: 0.413 (0.379–0.447), 0.679 (0.647–0.709), 0.671 (0.661–0.682).
6-month mortality: 0.701, 0.680, 0.680, 0.66, 0.651.
3-month mortality: 0.651 (0.618–0.682), 0.615 (0.582–0.647), 0.724 (0.693–).
Discriminative ability: AUC, c-statistic, AUC, c-statistic, AUC, AUC, AUC, AUC, c-statistic, AUC, c-statistic, AUC, AUC, AUC, c-statistic, c-statistic, AUC, c-statistic, AUC, AUC, c-statistic.
N: 606, 3,633, 54,990, 54,879, 409, 607, 409, 409, 54,990, 409, 3,633, 607, 409, 409, 3,633, 1,218, 606, 3,633, 54,990, 606, 54,879.
Study: Almagro, 2014; Marin, 2013; Bloom, 2019; Morales, 2018; Puhan, 2013; Horita, 2016; Puhan, 2013; Puhan, 2013; Bloom, 2019; Puhan, 2013; Marin, 2013; Horita, 2016; Puhan, 2013; Puhan, 2013; Marin, 2013; Martinez, 2008; Almagro, 2014; Marin, 2013; Bloom, 2019; Almagro, 2014; Morales, 2018.
Prognostic tools: ADO, BARC, BOD, BODE, ADO + handgrip strength, ADO + sit-to-stand test, BODE + handgrip strength, BODE + sit-to-stand test, eBODE, mBODE, BODEX, CODEX.
Variables: Age, dyspnea (MRC), airflow obstruction, Body mass index and blood results, Respiratory Variables (Airflow obstruction, Exacerbations, Smoking), Comorbidities, Body mass index, Airflow obstruction (FEV1), Dyspnea (mMRC), Exercise capacity (SMWD), BODE + previous exacerbations, Body mass index, Airflow obstruction (FEV1), Dyspnea (UCSD), exacerbations.
Performance continued: 0.672 (0.664–0.679), 0.72, 0.595 (0.562–0.628), T: 0.591 (0.568–0.614), V: 0.515 (0.485–0.546), 0.669 (0.658–0.679), 0.82 (0.81–0.82), T: 0.771 (0.767–0.775), V: 0.768 (0.764–0.772), 0.631, 0.642, 0.82, 0.632, 0.641, 0.678 (0.597–0.758), 0.601 (0.568–0.633).
Sample size/Study/Model: 607 (Horita, 2016, DOSE), 606 (Almagro, 2014, SAFE), 3,633 (Marin, 2013, COPD Prognostic Score), 54,990 (Bloom, 2019, ProPal-COPD), 54,879 (Morales, 2018, Unnamed model 1), 155 (Duenk, 2017, Unnamed model 2), 3,633 (Marin, 2013, Unnamed model 3), 4,803 (Man, 2006), Park, 2020, Zhan, 2020.
Variables continued: Age, Airflow obstruction (FEV1), Dyspnea (mMRC), Hemoglobin, Activity (Daily Activity Scale), Emergency admissions (last 24 months), Dyspnea (mMRC), Airflow obstruction (FEV1), Smoking status, Previous exacerbations, Body mass index, Airflow obstruction (FEV1), Dyspnea (mMRC), Comorbidity, Previous exacerbations, Surprise question, Clinical COPD questionnaire, Airflow obstruction.
SMWD: 6-minute walking distance; FEV1: forced expiratory volume in one second; mMRC: Modified Medical Research Council; PaO2/FiO2: ratio of arterial oxygen partial pressure to fractional inspired oxygen; SGRQ: St. George's Respiratory Questionnaire; Surprise question: ‘Would you be surprised if this patient died in the next year?’; T: test.

DISCUSSION
This systematic review and meta-analysis aimed to identify predictors of mortality within 3–24 months in patients with COPD. We found eight predictors that were significantly associated with mortality. Four of these predictors are related to patient demographics or history (age, sex, diabetes, and cardiovascular comorbidity), three to the underlying pulmonary disease (long-term oxygen therapy, previous hospitalization for acute exacerbation of COPD, and readmission within 30 days), and one to laboratory tests (hemoglobin). Overall, all significant predictors in our study seem to be readily obtainable, by taking the patient’s medical history and by performing simple blood tests. Similar to our findings, a review by Singanayagam et al. (2013) found that age, diabetes, and cardiovascular comorbidity were associated with mortality within 3–24 months. However, they found sex and long-term oxygen therapy to be associated with 3-month mortality and not with 3–24 month mortality as it is the case in our study. Further, low body mass was associated with mortality during longer follow-ups of 4-year and 17-year in studies by, respectively, Schols et al. (1998) and Landbo et al. (1999). Additionally, dyspnea severity, which was not found to be a significant predictor in our study, was associated with 5-year mortality in a study by Nishimura et al. (2002).

We also reviewed existing prognostic models for prediction of mortality in patients with COPD. The studies that reported on prognostic models did not provide the relevant effect sizes and associated standard errors of the individual variables. Therefore, the variables from those studies could not be included in our meta-analysis. Interestingly, some of the predictors included in prognostic models were not significant in our meta-analysis, such as dyspnea, body mass index, Charlson comorbidity index, 6-minute walking distance, and FEV1. Of the predictors that were significantly associated with mortality in our meta-analysis, only age, previous hospitalization for acute exacerbation of COPD, and hemoglobin level were also included in one or more prognostic models, namely the ADO, BODEX, CODEX, COPD Prognostic Score, or ProPal-COPD models. Overall, the majority of the existing prognostic models had a moderate discriminative ability (AUC 0.6–0.8). Additionally, in a meta-analysis of the 6-minute walking distance in 14,497 patients with COPD, Celli et al. (2016) found an AUC to predict mortality of 0.71 and 0.70 at 6 and 12 months, respectively. No study reported on the calibration of the models, which is needed to judge their applicability in a specific clinical setting. Studies validating prognostic models for predicting mortality in COPD and their methodological quality should therefore be improved.

The surprise question (‘Would you be surprised if this patient died in the next year?’), which is usually recommended to be used to identify patients who are likely to die within a period of 12 months, has not been thoroughly studied for COPD yet. The surprise question was only included in the ProPal-COPD model. This model was one of the two models with a good discriminative ability (AUC >0.8). Although this finding should be interpreted with some caution due to the small sample size of the study, it suggests that the physician’s clinical judgement might be important in the prediction of mortality.

This systematic review had several limitations. Firstly, some variables were pooled from only two studies, which is not ideal for a meta-analysis. We could not pool some studies in the meta-analysis because of incomplete reporting of data. Additionally, some variables could not be included in the meta-analysis because the studies did not use uniform methods for categorization of the outcomes. Furthermore, due to the low number of studies that had a low risk-of-bias based on our customized appraisal tool, we could not perform a meta-analysis with only low risk-of-bias studies. In addition, the quality of the studies was limited by the lack of data about the loss to follow-up and handling of missing values. Studies on prognostic factors could decrease study bias by reporting the number of missing values, how those values were analyzed, and the number of patients lost to follow-up. Secondly, there was substantial heterogeneity across studies for the predictors body mass index, Charlson comorbidity index, and readmission, which may be caused by the different follow-up periods, ranging between 6 and 24 months, and different study populations regarding measured FEV1 levels. Additionally, for the Charlson comorbidity index, the heterogeneity could be especially explained by the results from the small study of Navarro et al., which were discrepant to the results of the larger ones, possibly indicating selection bias. Although the I2, which is an indicator for statistical heterogeneity, was insignificant or moderate for most predictors, the pooled overall prediction effect should be interpreted with caution. Lastly, we only included published studies, especially studies from 2000 onward, whereby we might have missed some predictors of mortality.

CONCLUSION
This systematic review and meta-analysis provide an overview of predictors and multicomponent prognostic models for mortality within 3–24 months for patients with COPD. We conclude that mortality within 3–24 months is to a certain extent predictable. The existing models showed overall moderate discriminative ability, but no information on model calibration was available. We therefore suggest that there is a need for improvement in the validation of prognostic models. A more accurate prediction of mortality might give physicians more certainty in timely initiating ACP in patients with COPD. Further prognostic research should include physician’s clinical prediction of mortality based on the ‘surprise question’.