The contribution of estimated dead space fraction to mortality prediction in patients with chronic obstructive pulmonary disease—a new proposal

Introduction

Chronic obstructive pulmonary disease (COPD) is a common lung disease, with a global prevalence of 11.7% (GOLD Committees, 2022). The mortality rate associated with COPD continues to increase, even though those associated with other chronic diseases have decreased (WHO, 2022). Survival in COPD has been extensively studied using various techniques, including composite indexes (Celli et al., 2004; Leivseth et al., 2013; Puhan et al., 2009), Global Initiatives for Chronic Lung Diseases (GOLD) classification (Gedebjerg et al., 2018; Lee et al., 2019; Leivseth et al., 2013) and indexes using multivariate analysis (Domingo-Salvany et al., 2002; Martinez et al., 2006; Puhan et al., 2009; Soler-Cataluna et al., 2009). However, Gosker et al. (2002) and Hurst & Wedzicha (2007) suggested that COPD survival factors can be stratified in a step-by-step hierarchical fashion according to the conceptual framework between pathogenesis and survival of COPD patients. Accordingly, the survival factors are categorized as follows: (1) primary lung function variables i.e. FEV₁% (Leivseth et al., 2013; Ou et al., 2014; Soriano et al., 2013), its related variables (Huang et al., 2018), diffusing capacity of the lungs for carbon monoxide (D_LCO) (de-Torres et al., 2021), inspiratory capacity (Phillips et al., 2022) and total lung capacity ratio (IC/TLC) (Aalstad et al., 2018); primary lung structural variable i.e. emphysema (Martinez et al., 2006); (2) secondary lung variables i.e. hypoxemia and hypercapnia (Dave et al., 2021; Soler-Cataluna et al., 2005); and (3) tertiary symptoms, exercise intolerance or health status (Nishimura et al., 2002; Oga et al., 2003), and acute exacerbations (Soler-Cataluna et al., 2005, 2009) caused by deranged lungs. Non-lung factors may also affect COPD survival, including age, sex, BMI, muscle power, cardiac function, and comorbidities (Divo et al., 2012).

However, elevated dead space fraction (V_D/V_T) is a dysfunctional primary lung variable. The Bohr-Enghoff standard equation has been clearly outlined for its calculation (Chuang, Hsieh & Lin, 2021; Wasserman et al., 2005). Typically, the V_D/V_T represents physiological V_D/V_T. It is more related to clinical characteristics and gas exchange than FEV₁ (Chuang, 2020; Chuang, Hsieh & Lin, 2021). It is also associated with tidal inspiratory flow and operational lung volume at peak exercise, and the diffusing capacity of the lungs for carbon monoxide (DLCO) at rest (Chuang, 2022). V_D/V_T has yet to be shown to a predictor of COPD mortality; however, it is related to hypoxemia and hypercapnia, which are in turn related to mortality (Calverley, 2003; Dave et al., 2021; Mathews et al., 2020; Nizet et al., 2005). Therefore, the association of V_D/V_T with COPD mortality is speculative. However, to obtain V_D/V_T invasive arterial catheterization must be established, and applying an invasive method in a large-scale study is difficult. Thus, using predictive formulae to estimate V_D/V_T values at rest (estV_D/V_Trest) and peak exercise (estV_D/V_Tpeak) may be a more suitable option. In addition as the causes of death in patients with mild COPD are predominantly cancer and cardiovascular disease (Berry & Wise, 2010) and the mortality rate of patients with cancer with metastasis is speculated to be much higher than that of COPD alone, where patients with advanced-stage malignancy of any organ have been excluded in previous studies on COPD outcomes (Huang et al., 2018). We hypothesized that adjusting for the relevant factors without cancer, may affect the survival analysis of COPD.

Hence, this study aimed to assess (1) all-cause mortality and (2) the independent contribution of V_D/V_T, excluding cancer, to predict mortality in patients with COPD.

Methods

Portions of this text were previously published as part of a preprint (https://doi.org/10.21203/rs.3.rs-2383786/v1).

Results

A total of 14,910 subjects with COPD were obtained from the hospital’s database (Fig. 1), of whom 1,280 had complete lung function tests. Of these patients, 456 were retained for analysis after excluding 719 subjects whose spirometry data did not meet the inclusion criteria or met the exclusion criteria, and 105 subjects who had duplicate data or the lung volume measures using the dilution method between January 01, 2010 and December 31, 2012. Between 2013 and 2020 of the study period, the median (IQR) follow-up period was 597 (331–934.5) days. Overall, 81% of the 456 subjects had GOLD stages 2 and 3 (Table 1), and 17.3% and 1.8% had GOLD stages 1 and 4, respectively. The average estV_D/V_Trest and estV_D/V_Tpeak values were highly elevated (60.8 ± 5.4% and 44.0 ± 6.4%, respectively), the average RV/TLC value was mildly elevated (137.0 ± 23.6%), and the average D_LCO value was mildly reduced (69.5 ± 24.1%). The hospitalized AECOPD rate was 0.60 ± 2.84 PPPY; 13.2% had one hospitalized AECOPD, 10.7% had ≥2 hospitalized AECOPDs, and 76.1% were not hospitalized for AECOPDs (Table 2). Forty-eight subjects (10.5%) died during the 7-year study period, including 30 with cancer (median 1.64 years, range 0.1–3.89 years); the 1, 2, 3, and 4-year mortality rates were 5.2%, 11.2%, 17.5%, and 20.1%, respectively. The incidence density of death was 6.03 (95% CI [4.54–8.00]) per 100 person-years. The causes of non-cancer death (n = 18) included acute respiratory failure (n = 9), cardiovascular disease (n = 3), diabetes mellitus-related complications (n = 2), connective tissue disease (n = 2), meningitis (n = 1), and others (n = 1).

Univariate and multivariable analyses. In Table 3, Model 1 used univariate analysis; Model 2 used multivariable analysis including all variables in Model 1; Model 3 used multivariable analysis including all variables in Model 2 without cancer; Model 4 used multivariable model with stepwise selection. The risk factors for mortality with significant crude HRs were elevated estV_D/V_Trest and estV_D/V_Tpeak, ≥2 hospitalized AECOPDs, advanced age, BMI < 18.5 kg/m², and cancer; whereas the protective factors were high PEF%, D_LCO/V_A%, V_A%, and BMI 24–26.9 kg/m² (Table 3). In multivariable analysis, only cancer remained significant (Table 3). As the mortality rate of patients with cancer with metastasis is speculated to be much higher than that of COPD alone and the study was aimed to investigate the impact of lung physiology on survival, advanced-stage malignancy of any organ was not adjusted in the subsequent analysis. In the Model 3 analysis, paradoxically, FEV₁% was a risk factor for mortality, whereas a BMI of 24–26.9 kg/m² was protective. After stepwise variable selection, estV_D/V_Trest and BMI < 18.5 kg/m² were risk factors for mortality, whereas a BMI of 24–26.9 kg/m² remained a protective factor (Table 3 and Fig. 2, log-rank, p < 0.001). The estV_D/V_Trest was subsequently separated into two categories: <61% and ≥61% based on its median value. The categories showed a significant difference in the survival rate (Fig. 2, log-rank, p = 0.015).

The mortality probability formula was estimated as follows.

p = 1/(1+e^{6.404–7.3×estVD/VT}) for those BMI < 18.5 kg/m²

p = 1/(1+e^{6.404–7.3×estVD/VT+1.037×2}) for those BMI 24–26.9 kg/m²

p = 1/(1+e^{6.404–7.3×estVD/VT+0.889×3}) for those BMI ≥ 27 kg/m²

where estV_D/V_Trest using the value was counted to the 2^nd decimal.

Discussion

In this study, we found that estV_D/V_Trest and BMI < 18.5 kg/m² were risk factors for mortality, whereas a BMI of 24–26.9 kg/m² was a protective factor. BMI is reported that >21 kg/m² is a protective factor to survival, whereas ≤21 kg/m² is detrimental (Celli et al., 2004). To the best of our knowledge, this is the first report to link estV_D/V_T with the risk of death in patients with COPD without adjusting cancer. A mortality probability formula was generated; however, its utility warrants further studies. The mechanism underlying the predictive ability of estV_D/V_Trest for COPD death is not clear; however, it may be due to its broad associations with lung and non-lung factors (Chuang, Hsieh & Lin, 2021) and is related to hypoxemia and hypercapnia, and thus related to mortality (Calverley, 2003; Dave et al., 2021; Mathews et al., 2020; Nizet et al., 2005). Furthermore, measured V_D/V_Tpeak is a primary pulmonary factor related to exertional dyspnea and then to exercise intolerance (Chuang, 2022). The value of estV_D/V_Trest was extended to COPD mortality prediction in the current study. The discovery of estV_D/V_Trest in predicting survival among patients with COPD is groundbreaking, suggesting that the application of estV_D/V_Trest could significantly influence survival predictions.

Lung function variables. Although FEV₁ (Leivseth et al., 2013; Ou et al., 2014; Soriano et al., 2013), D_LCO (de-Torres et al., 2021), IC% (Phillips et al., 2022) and IC/TLC (Aalstad et al., 2018) are the primary pulmonary factors and have been reported to contribute to mortality in patients with COPD, they were not selected in this study. Huang et al. (2018) investigated the performance of seven staging methods of FEV₁, i.e. GOLD, quartiles of FEV₁%, z-score of FEV₁, quartiles and Miller’s cut-off points (FEV₁·height⁻²_Miller, range: 0.3, 0.4, 0.5) of the ratio of FEV₁ over height squared, quartiles of the ratio of FEV₁·height⁻³ and FEV₁ quotient (FEV₁Q i.e. FEV₁ in liters/0.5 L for males; 0.4 L for females) in predicting outcomes of patients with COPD (Huang et al., 2018), and found that staging based on quartile of FEV₁Q was the best predictor, followed by FEV₁·height⁻²_Miller. We tested these two variables in univariate survival analysis and found that the fourth quartile of FEV₁Q (i.e. ≥3.82) had a protective effect (0.41 [0.17–0.98], p = 0.046), whereas FEV₁·height⁻²_Miller did not. Although other authors did not find that FEV₁% was a protective factor (Martinez et al., 2006), FEV₁% was a risk factor for mortality in the Model 3 analysis without adjusting for cancer in this study. This type of paradoxical phenomenon occasionally happens when multiple regression and adjustments are performed. Furthermore, in the present study, FEV₁% showed no association with estV_D/V_Trest, consistent with the previous reports (Chuang, Hsieh & Lin, 2021). For details on estV_D/V_Trest, refer to the information provided below.

In contrast, estV_D/V_Trest, estV_D/V_Tpeak, PEF%, D_LCO/V_A%, and V_A% were significantly contributing primary lung factors to all-cause mortality in the patients with COPD before adjustment in this study (Table 3). V_D/V_Trest is a sophisticated marker of resting gas exchange and is more related to cigarette smoking, carboxyhemoglobin level, pulmonary hypertension and P_aCO₂ than FEV₁% (Chuang, Hsieh & Lin, 2021). Moreover, elevated P_aCO₂ is related to mortality (Calverley, 2003; Dave et al., 2021; Mathews et al., 2020; Nizet et al., 2005). V_D/V_Trest can be estimated by cigarette consumption, minute ventilation/CO₂ output, arterial oxyhemoglobin saturation, and tidal volume × inspiratory duty cycle (Chuang, Hsieh & Lin, 2021). All the reasons mentioned above are likely contributors to the association between estV_D/V_Trest and COPD mortality. In this study, we re-derived the estV_D/V_Trest prediction equation using lung function variables and demographic data alone. This simplification may allow for the more general use of the prediction equation, even though the predictive power was lower than the previous equation, calculating estV_D/V_Trest requires many variables, and the method has yet to be validated. After multivariable Cox regression analysis, estV_D/V_Trest remained a significant risk factor (Table 3), however estV_D/V_Tpeak did not. This might be because estV_D/V_Tpeak and estV_D/V_Trest were co-linear (r = 0.36, p = 0.02).

BMI. Reduced BMI is an independent risk factor for COPD and mortality (Celli, 2010; Harik-Khan, Fleg & Wise, 2002). Even in matched-FEV₁, BMI has still been reported to be a marker of COPD phenotypes (Chuang & Lin, 2014). COPD patients with a reduced BMI may have a higher rate of impaired peripheral oxygenation (anemia, circulation impairment and deconditioning), where they are taller and more malnourished, anemic and have more hyper-inflation, air-trapping, and diffusion impairment (Chuang & Lin, 2014). According to the 10-point BODE index (B: body mass index (BMI), O: obstruction of airflow, D: dyspnea score, E: exercise capacity delineated by 6-min walking distance), BMI > 21 kg/m² is protective for survival, whereas BMI ≤ 21 kg/m² is detrimental (Celli et al., 2004). Moreover, FEV₁% and mid-thigh muscle cross sectional area obtained by computed tomography are related to survival (Nici et al., 2006), suggesting that fat-free muscle mass is important; however, BMI is easily measured. In this study, the under-weight COPD patients (BMI < 18.5 kg/m²) had a high risk of death (HR of 2.68), the overweight patients (BMI, 24–26.9 kg/m²) had a low risk of death (HR of 0.34), and the obese patients (BMI ≥ 27 kg/m²) had neither effect (Fig. 2). However, obesity was a risk factor for poor COPD-related outcomes (quality of life, dyspnea, 6-min walking distance, and severe AECOPD) and was dose-dependent (Lambert et al., 2017). Nevertheless, BMI is not universally selected in previous studies with multivariable regression analysis (Soler-Cataluna et al., 2005).

AECOPD and co-morbidity. Some studies have not encompassed AECOPDs or co-morbidities when performing survival analysis. For example, Martinez et al. (2006) included many risk variables in a Cox regression model; other studies constructed composite indexes: BODE (Celli et al., 2004); DO (D: dyspnea score, O: obstruction of airflow i.e. pre-bronchodilator FEV₁% or GOLD grade and exertional dyspnea) (Leivseth et al., 2014); and ADO (A: age, D: dyspnea score, O: obstruction of airflow) (Puhan et al., 2009). However, Soler-Cataluna et al. (2005) reported that severe AECOPDs ≥3 episodes were an independent negative impact on prognosis. The authors further integrated exacerbations into the BODE index, where the “E” of BODE was replaced (Soler-Cataluna et al., 2009). This omitted the cumbersome 6-min walking test and did not lose power of survival prediction (Soler-Cataluna et al., 2009). Although acute respiratory failure accounted for half of the causes of non-cancer death in the current study, acute respiratory failure death only accounted for nine of 109 AECOPD, suggesting that most of the AECOPD were not respiratory failure and the management was effective. The co-morbidity test (COTE) index has been used to assess the risk of mortality in patients with COPD (Divo et al., 2012). Recently, individual diseases such as heart failure and ischemic heart disease rather than CCI score have been shown to have a large effect size on mortality prediction (Shah et al., 2022). However, CCI score was used in the current study and stratified as ≤2 or >2. Nevertheless, co-morbidities was also excluded from the COPD all-cause mortality prediction in the previous reports (Phillips et al., 2022). Thus, AECOPD and co-morbidities did not contribute to the risk of mortality in multivariable analysis. Even though there are several prediction models, none of them is perfect, and the current study may be helpful to refine future models.

A similar study on hospitalizations and all-cause mortality used logistic regression with stepwise risk stratification (Groves et al., 2021), in which demographic and COPD-specific data, and multi-morbidities were used. However, these variables were not sorted clearly, i.e. hemoglobin as a COPD-specific variable and anemia as a co-morbidity variable; BMI as a COPD-specific variable. They also included cancer and lung cancer as co-morbidities; however, mortality was best predicted by disease severity (area under the curve (AUC) 0.816; 95% CI [0.805–0.827]), and the predictive ability was only marginally enhanced by adding multi-morbidity indices (AUC 0.829; 95% CI [0.818–0.839]). In contrast, we and other investigators (Berry & Wise, 2010; Huang et al., 2018) found that cancer was the risk factor highly associated with mortality. In the Model 3 analysis, without adjusting for cancer, BMI and estV_D/V_Trest were found to be significantly associated with COPD mortality. The discrepancies across these studies may be due to the use of different variables such as CCI or individual diseases (Shah et al., 2022) to score co-morbidities, and differences in the stratification of risk factors (Groves et al., 2021). Lastly, composite indexes are generally thought to be better than the primary lung variables for survival prediction. However, some reports do not support this notion, partly because survival prediction is not their initial purpose for example, GOLD guidelines (Gedebjerg et al., 2018; Lee et al., 2019; Leivseth et al., 2013; Ou et al., 2014; Soriano et al., 2013).

Study limitations. As this was a retrospective study, some variables could not be assessed, such as emphysema (Martinez et al., 2006), secondary factors i.e. hypoxemia, hypercapnia (Dave et al., 2021) and other gas exchange variables, other tertiary factors i.e. dyspnea, peak oxygen uptake, frailty (Lee et al., 2022; Nishimura et al., 2002), and health-related quality of life (Nishimura et al., 2002). In this context, composite indexes (Celli et al., 2004; Gedebjerg et al., 2018; Lee et al., 2019; Ou et al., 2014; Puhan et al., 2009; Soler-Cataluna et al., 2009) cannot be calibrated with this dataset. Although chest computed tomography pulmonary angiography (CTPA) may offer detailed diagnostic information in pulmonary arterial hypertension (PAH) (Condliffe et al., 2023), chest X-ray (CXR) is more widely used due to its availability (Mirsadraee et al., 2013) and lower cost. However, the hila-thoracic ratio (HTR) was not mentioned in the ESC/ERS guidelines for pulmonary hypertension (Humbert et al., 2022), despite being first reported by Chetty, Brown & Light, 1982 and subsequently confirmed by Mirsadraee et al. (2013). As this study is retrospective, CTPA data are not available. Furthermore, the performance of the simplified predictive equations for estV_D/V_Trest and estV_D/V_Tpeak was deemed insufficient, raising concerns about the accuracy of these predictions. To be noted, all estV_D/V_Trest and estV_D/V_Tpeak data were derived from the predictive equations rather than direct measurements. Even with direct measurements, there is still a potential for bias, particularly if the subjects exhibit arrhythmias (such as atrial fibrillation) or have NYHA IV heart failure with oscillatory ventilation (Wasserman et al., 2005). In addition, COPD-asthma overlap, a phenotype with different outcomes (Jones et al., 2009), cannot be fully excluded, even though all of the subjects met the definition of COPD and the diagnostic criteria of the GOLD guidelines, and none of the subjects had a bronchodilator effect in spirometry and were former or current cigarette smokers. Nevertheless, one may still question whether the diagnosis of COPD also included subjects with asthma, as approximately 50% of the enrolled subjects were treated with LABA+ICS and only 1.5% were treated with LABA + LAMA. However, LABA + ICS was advocated for patients with COPD in the GOLD guidelines between 2013 and 2020, and this period overlapped with that of the current study. Being a current smoker is a strong risk factor for mortality in patients with COPD (Jones et al., 2009; Shah et al., 2022); however, the status of cigarette smoking in some patients was not clear. In addition, as estV_D/V_Trest and estV_D/V_Tpeak were not measured and their predictive formulae have not been externally validated and only a few patients were included in the study, further studies are needed to verify our results. Moreover, different COPD phenotypes including classical, historical, and new phenotypes may respond to different treatments (Siafakas, Corlateanu & Fouka, 2017), potentially introducing bias to the results. Lastly, there may be collection bias concerning the recorded causes of death, as this is a retrospective study.

Conclusions

Cancer was the main cause of all-cause mortality in this study; however, estV_D/V_Trest and BMI were independent prognostic factors for COPD-related mortality without adjusting for cancer. V_D/V_T may be a new prognostic factor for COPD and may help to elucidate the relationship between pathogenesis and survival of COPD. Although V_D/V_Trest can be estimated using a formula derived from comprehensive pulmonary physiology variables, its clinical implications for survival prediction should be interpreted with caution until the formula has been validated. Formulas for estimating the probability of mortality were proposed; however, further studies are needed to verify their utility.

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