Predicting Future Health Risk in COPD: Differential Impact of Disease-Specific and Multi-Morbidity-Based Risk Stratification

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

Background

Mortality due to chronic obstructive pulmonary disease (COPD) is increasing. However, dead space fractions at rest (VD/VTrest) and peak exercise (VD/VTpeak) and variables affecting survival have not been evaluated. This study aimed to investigate these issues.

Methods

This retrospective observational cohort study was conducted from 2010-2020. Patients with COPD who smoked, met the Global Initiatives for Chronic Lung Diseases (GOLD) criteria, had available demographic, complete lung function test (CLFT), medication, acute exacerbation of COPD (AECOPD), Charlson Comorbidity Index, and survival data were enrolled. VD/VTrest and VD/VTpeak were estimated (estVD/VTrest and estVD/VTpeak). Univariate and multivariable Cox regression with stepwise variable selection were performed to estimate hazard ratios of all-cause mortality.

Results

Overall, 14,910 patients with COPD were obtained from the hospital database, and 456 were analyzed after excluding those without CLFT or meeting the lung function criteria during the follow-up period (median (IQR) 597 (331-934.5) days). Of the 456 subjects, 81% had GOLD stages 2 and 3, highly elevated dead space fractions, mild air-trapping and diffusion impairment. The hospitalized AECOPD rate was 0.60 ± 2.84/person/year. Forty-eight subjects (10.5%) died, including 30 with advanced cancer. The incidence density of death was 6.03 per 100 person-years. The crude risk factors for mortality were elevated estVD/VTrest, estVD/VTpeak, ≥2 hospitalizations for AECOPD, advanced age, body mass index (BMI) <18.5 kg/m2, and cancer (hazard ratios (95% C.I.) from 1.03 [1.00-1.06] to 5.45 [3.04-9.79]). The protective factors were high peak expiratory flow%, adjusted diffusing capacity%, alveolar volume%, and BMI 24-26.9 kg/m2. In stepwise Cox regression analysis, after adjusting for all selected factors except cancer, estVD/VTrest and BMI <18.5 kg/m2 were risk factors, whereas BMI 24-26.9 kg/m2 was protective. Cancer was the main cause of all-cause mortality in this study; however, estVD/VTrest and BMI were independent prognostic factors for COPD after excluding cancer.

Conclusions

The predictive formula for dead space fraction enables the estimation of VD/VTrest, and the mortality probability formula facilitates the estimation of COPD mortality. However, the clinical implications should be approached with caution until these formulas have been validated.

Prognostication of co-morbidity clusters on hospitalisation and mortality in advanced COPD

RATIONALE

As the prevalence of multimorbidity increases, understanding the impact of isolated comorbidities in people COPD becomes increasingly challenging. A simplified model of common comorbidity patterns may improve outcome prediction and allow targeted therapy.

OBJECTIVES

To assess whether comorbidity phenotypes derived from routinely collected clinical data in people with COPD show differences in risk of hospitalisation and mortality.

METHODS

Twelve clinical measures related to common comorbidities were collected during annual reviews for people with advanced COPD and k-means cluster analysis performed. Cox proportional hazards with adjustment for covariates was used to determine hospitalisation and mortality risk between clusters.

MEASUREMENTS AND MAIN RESULTS

In 203 participants (age 66 ± 9 years, 60 % male, FEV1%predicted 31 ± 10 %) no comorbidity in isolation was predictive of worse admission or mortality risk. Four clusters were described: cluster A (cardiometabolic and anaemia), cluster B (malnourished and low mood), cluster C (obese, metabolic and mood disturbance) and cluster D (less comorbid). FEV1%predicted did not significantly differ between clusters. Mortality risk was higher in cluster A (HR 3.73 [95%CI 1.09-12.82] p = 0.036) and B (HR 3.91 [95%CI 1.17-13.14] p = 0.027) compared to cluster D. Time to admission was highest in cluster A (HR 2.01 [95%CI 1.11-3.63] p = 0.020). Cluster C was not associated with increased risk of mortality or hospitalisation.

CONCLUSIONS

Despite presence of advanced COPD, we report striking differences in prognosis for both mortality and hospital admissions for different co-morbidity phenotypes. Objectively assessing the multi-system nature of COPD could lead to improved prognostication and targeted therapy for patients.

Using hypergraphs to quantify importance of sets of diseases by healthcare resource utilisation: A retrospective cohort study

Rates of Multimorbidity (also called Multiple Long Term Conditions, MLTC) are increasing in many developed nations. People with multimorbidity experience poorer outcomes and require more healthcare intervention. Grouping of conditions by health service utilisation is poorly researched. The study population consisted of a cohort of people living in Wales, UK aged 20 years or older in 2000 who were followed up until the end of 2017. Multimorbidity clusters by prevalence and healthcare resource use (HRU) were modelled using hypergraphs, mathematical objects relating diseases via links which can connect any number of diseases, thus capturing information about sets of diseases of any size. The cohort included 2,178,938 people. The most prevalent diseases were hypertension (13.3%), diabetes (6.9%), depression (6.7%) and chronic obstructive pulmonary disease (5.9%). The most important sets of diseases when considering prevalence generally contained a small number of diseases, while the most important sets of diseases when considering HRU were sets containing many diseases. The most important set of diseases taking prevalence and HRU into account was diabetes & hypertension and this combined measure of importance featured hypertension most often in the most important sets of diseases. We have used a single approach to find the most important sets of diseases based on co-occurrence and HRU measures, demonstrating the flexibility of the hypergraph approach. Hypertension, the most important single disease, is silent, underdiagnosed and increases the risk of life threatening co-morbidities. Co-occurrence of endocrine and cardiovascular diseases was common in the most important sets. Combining measures of prevalence with HRU provides insights which would be helpful for those planning and delivering services.

Elevated BUN Upon Admission as a Predictor of in-Hospital Mortality Among Patients with Acute Exacerbation of COPD: A Secondary Analysis of Multicenter Cohort Study

Background

High blood urea nitrogen (BUN) is observed in a subset of patients with acute exacerbation of COPD (AECOPD) and may be linked to clinical outcome, but findings from previous studies have been inconsistent.

Methods

We performed a retrospective analysis of patients prospectively enrolled in the MAGNET AECOPD Registry study (ChiCTR2100044625). Receiver operating characteristic (ROC) was used to determine the level of BUN that discriminated survivors and non-survivors. Univariate and multivariate Cox proportional hazards regression analyses were performed to assess the impact of BUN on adverse outcomes.

Results

Overall, 13,431 consecutive inpatients with AECOPD were included in this study, of whom 173 died, with the mortality of 1.29%. The non-survivors had higher levels of BUN compared with the survivors [9.5 (6.8-15.3) vs 5.6 (4.3-7.5) mmol/L, P < 0.001]. ROC curve analysis showed that the optimal cutoff of BUN level was 7.30 mmol/L for in-hospital mortality (AUC: 0.782; 95% CI: 0.748-0.816; P < 0.001). After multivariate analysis, BUN level ≥7.3 mmol/L was an independent risk factor for in-hospital mortality (HR = 2.099; 95% CI: 1.378-3.197, P = 0.001), also for invasive mechanical ventilation (HR = 1.540; 95% CI: 1.199-1.977, P = 0.001) and intensive care unit admission (HR = 1.344; 95% CI: 1.117-1.617, P = 0.002). Other independent prognostic factors for in-hospital mortality including age, renal dysfunction, heart failure, diastolic blood pressure, pulse rate, PaCO2 and D-dimer.

Conclusion

BUN is an independent risk factor for in-hospital mortality in inpatients with AECOPD and may be used to identify serious (or severe) patients and guide the management of AECOPD.

Clinical Trial Registration

MAGNET AECOPD; Chinese Clinical Trail Registry NO.: ChiCTR2100044625; Registered March 2021, URL: http://www.chictr.org.cn/showproj.aspx?proj=121626.

Comparison of Predictive Properties between Tools of Patient-Reported Outcomes: Risk Prediction for Three Future Events in Subjects with COPD

BACKGROUND

Patient-reported outcome (PRO) measures must be evaluated for their discriminatory, evaluative, and predictive properties. However, the predictive capability remains unclear. We aimed to examine the predictive properties of several PRO measures of all-cause mortality, acute exacerbation of chronic obstructive pulmonary disease (COPD), and associated hospitalization.

METHODS

A total of 122 outpatients with stable COPD were prospectively recruited and completed six self-administered paper questionnaires: the COPD Assessment Test (CAT), St. George's Respiratory Questionnaire (SGRQ), Baseline Dyspnea Index (BDI), Dyspnoea-12, Evaluating Respiratory Symptoms in COPD and Hyland Scale at baseline. Cox proportional hazards analyses were conducted to examine the relationships with future outcomes.

RESULTS

A total of 66 patients experienced exacerbation, 41 were hospitalized, and 18 died. BDI, SGRQ Total and Activity, and CAT and Hyland Scale scores were significantly related to mortality (hazard ratio = 0.777, 1.027, 1.027, 1.077, and 0.951, respectively). The Hyland Scale score had the best predictive ability for PRO measures, but the C index did not reach the level of the most commonly used FEV₁. Almost all clinical, physiological, and PRO measurements obtained at baseline were significant predictors of the first exacerbation and the first hospitalization due to it, with a few exceptions.

CONCLUSIONS

Measurement of health status and the global scale of quality of life as well as some tools to assess breathlessness, were significant predictors of all-cause mortality, but their predictive capacity did not reach that of FEV₁. In contrast, almost all baseline measurements were unexpectedly related to exacerbation and associated hospitalization.