Development and validation of a prediction index for recent mortality in advanced COPD patients.

The primary barrier to initiating palliative care for advanced COPD patients is the unpredictable course of the disease. We enroll 752 COPD patients into the study and validate the prediction tools for 1-year mortality using the current guidelines for palliative care. We also develop a composite prediction index for 1-year mortality and validate it in another cohort of 342 patients. Using the current prognostic models for recent mortality in palliative care, the best area under the curve (AUC) for predicting mortality is 0.68. Using the Modified Medical Research Council dyspnea score and oxygen saturation to define the combined dyspnea and oxygenation (DO) index, we find that the AUC of the DO index is 0.84 for predicting mortality in the validated cohort. Predictions of 1-year mortality based on the current palliative care guideline for COPD patients are poor. The DO index exhibits better predictive ability than other models in the study.

Introduction

The prevalence of and mortality associated with chronic obstructive pulmonary disease (COPD) have been increasing annually[1]. However, current treatments have been disappointing in terms of controlling airflow obstructions and reducing mortality[2-4]. Although palliative care is shown to be effective in patients with COPD, these patients have fewer opportunities to receive palliative care than patients with cancer[5,6]. Jabbarian et al. found that the failure to implement advance care planning (ACP) in chronic diseases is mainly due to the complexity and unpredictability of the disease[7], and the uncertainty of disease trajectory is even greater in COPD than in cancer[8-12]. In addition, COPD patients typically want to know more about their prognosis in the early stages[13,14]. Therefore, enormous effort has been made to find indicators to predict a poor prognosis accurately.

Researchers have found many indicators related to various adverse outcomes for COPD, including patient age, body mass index (BMI), dyspnea, smoking status, exercise capacity, acute exacerbation, symptoms, and biological indicators[15-17]. Unfortunately, as was the case with the first proposed indicator, FEV1, there was no optimal way to predict mortality based on the indicator[17,18]. After the multisystem involvement characteristic of COPD became known, the focus was moved to composite indicators to achieve better predictive outcomes[15,19]. The earliest developed and most widely investigated multicomponent indicators included the Body-Mass Index, Airflow Obstruction, Dyspnea, and Exercise Capacity (BODE) Index[20], which was also recommended for predicting outcomes by the Global Initiative for Chronic Obstructive Lung Disease (GOLD)[21]. Later, numerous different indices were developed, including the Dyspnea and Airflow Obstruction (ADO) Index, the Dyspnea, Obstruction, Smoking, Exacerbation (DOSE) Index, and various modifications of the BODE index[22]. However, most of these indices were developed to predict long-term survival. They all lacked accuracy when applied to short-term events of <12 months[20-23]. Marin et al.[23] validated a number of existing prognostic indices in a large individual pooled data set (n = 3633) from multiple cohort studies with different stages of COPD. These prognostic indices included the original BODE, the modified BODE (replacing the 6-min walk distance (6MWD) with peak oxygen uptake V’O2 as % predicted), the BODEx (replacing the 6MWD with exacerbations), the eBODE (BODE plus exacerbations), the SAFE (SGRQ score, air-flow limitation, and exercise tolerance), the ADO, and the DOSE. All-cause mortality prediction at 12 months was assessed for these indices, where the indices determined to be optimal for prediction was the ADO (C statistic = 0.70). Boeck et al.[24] developed the B-AE-D indices (BMI, acute exacerbations, dyspnea) for 2-year mortality in the PROMISE study, and external validation of the B-AE-D was performed in COCOMICS and the COMIC study for 1-year all-cause mortality (C statistic = 0.68 and 0.74, respectively). Therefore, none of these indices had the strong predictive ability for 1-year mortality. In addition, none of these models were developed with the specific aim of predicting all-cause mortality in stable COPD patients within 12 months.

To the best of our knowledge, Bloom et al.[25] was the only research group to develop indicators (the BARC index) for predicting 1-year mortality with the aim of palliative care in advanced COPD (C statistic = 0.78 and 0.70 for the development and validation cohorts, respectively). The variables in the BARC only required routinely collected non-specialist information, which, therefore, helped identify patients seen in primary care institutions, but a total of 18 variables were required. Because no existing indices had strong enough predictive ability for 1-year mortality in clinical practice, and very few indices were developed with the specific aim of predicting 1-year mortality for palliative care in stable COPD. In this study, we aimed to validate the currently recommended prediction indices for palliative care, we also developed a new predictive index for 1-year mortality in hospitalized ambulatory COPD patients.

Methods

Study design

We conducted this cohort study in the National Cheng Kung University Hospital (NCKUH) from August 2006 to December 2015. The patients included in the present study were part of another previous study[26]. The patients were eligible for inclusion if they had received regular management for COPD at our hospital for >1 year prior to their recruitment. All patients were diagnosed with COPD by pulmonologists according to the GOLD guidelines for diagnostic criteria[1]. The criteria were as follows: age >40 years, typical symptoms, such as cough, dyspnea, wheezing, or chest tightness in combination with evidence of chronic airflow obstruction, as defined by a postbronchodilator ratio of forced expiratory volume in 1 s (FEV1) to a forced vital capacity (FVC) of <70%. Pulmonary function tests were performed following the standard protocols of the American Thoracic Society[27]. All patients were enrolled under clinically stable conditions. We excluded patients who were unwilling to participate and those who had advanced lung cancer and pulmonary fibrosis because of anticipated death in the near future. Patients with missing data and those lost to follow-up in the first year were also excluded from the analysis. In total, 752 patients with COPD were analyzed (Supplementary Fig. 1). The Institutional Review Board of NCKUH approved this study before commencement (IRB number: B-ER-105-386 and B-ER-98-289). Written informed consent was obtained for all participants while enrollment.

Prognostic variables and outcome

A total of 752 consecutive COPD patients were recruited. All patients were monitored through December 2016 or until death. We acquired age, smoking history, BMI, the severity of dyspnea assessed by grade on the modified Medical Research Council (mMRC) dyspnea scale[28], the degree of comorbidity as evaluated using the Charlson index[29], oxygen saturation levels as detected by pulse oximetry in room air (SpO2), and status of long-term home oxygen usage from every patient at the time of inclusion as determined by research assistants in the study. Comorbidity was evaluated using the Charlson index and included congestive heart failure, coronary artery disease, systemic hypertension, peptic ulcer, and diabetes mellitus as identified from the patient files and detailed interviews. A severe acute exacerbation of COPD was defined as an acute event characterized by a worsening of the patient’s respiratory symptoms that were beyond day-to-day variations that also required hospitalization. The number of severe exacerbations in the preceding year was recorded by research assistants according to the patient’s chart as the primary means of data collection; self-reported data was used to supplement this data.

All-cause mortality was defined as the endpoint of the study. The survival status of all patients was evaluated using a prospective observation, as reported in a previous study[26]. All patients were contacted during regular clinic visits or by telephone interviews (if they missed an appointment). Most patients who died during the study period had been regularly followed and had visited the hospital for treatment before their death. Their dates of death were recorded and verified using hospital records. Research assistants obtained the date of death of patients who died outside the hospital by telephone contact with partners or family members. Survival status was also verified through linkage with the Taiwan National Mortality Registry.

Predictive variables for palliative care

In the first part of the study, we evaluated the predictive ability of the currently recommended variables for estimating 1-year mortality in the palliative care guideline for COPD. We selected several variables for building the predictive model based on a review of the currently recommended prediction variables[30-32]. The variables included (1) mMRC score = 4, (2) frequent, severe AE (two or more AEs requiring hospitalization in the preceding year), (3) hypoxemia (SpO2 < 90% in ambient air), (4) BMI < 21, and (5) predicted FEV1 < 30%. We used several combined indices to test the accuracy of the prediction for 1-year mortality. The patients were subdivided into four groups: Group 1 was defined as patients with frequent, severe AE in combination with severe dyspnea (mMRC = 4). Group 2 was defined as patients with frequent, severe AE in combination with SpO2 < 90% in ambient air. Group 3 was defined as patients with frequent, severe AE combined with predicted FEV1 < 30%. Group 4 was defined as patients with frequent, severe AE in combination with BMI < 21.

Modeling the predictive scores

Because of the generally unsatisfactory predictive power found in previous studies and with validating our results, we wanted to derive a new predictive model for 1-year mortality from the patient variables, including age, sex, BMI, disease severity, such as mMRC dyspnea score, FEV1, SpO2, and comorbidities. The variables were evaluated using multivariate Cox regression models with a forward entering approach and a 5% significance level for the selection criteria. Significant regression coefficients were converted to exponential expressions for the weighting of the variables used for the predictive indices.

Validation of the predicting index

To validate the predictive performance of our model, we selected a second cohort. All patients in the development group were recruited from pulmonary outpatient departments. Considering that if the validation group and the developmental group exhibited high homogeneity, it was expected that the proposed model would obtain very similar results for the two groups of patients. Patients in the validation group were recruited by screening individuals who had been diagnosed with COPD, not only in the pulmonary outpatient department but also in the Center for Hospice Palliative Shared Care at NCKUH from July 2012 to August 2019. All patients were aged ≥40 years; COPD was defined according to the GOLD diagnostic guidelines and criteria as the developmental group; patients with advanced lung cancer or pulmonary fibrosis were excluded. The date of recruitment of some patients from the Center for Hospice Palliative Shared Care overlapped with the time periods during which the development group was recruited. These patients were not excluded from this study since the source of patients was different from that for the development group (Center for Hospice Palliative Shared Care versus the pulmonary outpatient department). All patients had complete follow-up for 1 year or until death.

Statistical analysis

Continuous variables are presented as the median and interquartile range because the number of deaths was not large and therefore may not follow a normal distribution. Therefore, comparisons between survivors and nonsurvivors were performed using Mann–Whitney U-test. Comparisons between categorical variables were performed using chi-square tests or Fisher’s exact tests.

Kaplan–Meier survival curves and log-rank tests were used for comparing different predictive variables. The ability to predict mortality within 1 year was analyzed using logistic regression models and the receiver operating characteristic (ROC) curve to calculate the area under the curve (AUC). Data processing and analyses were performed using the SPSS for Windows version 17.0 statistical software (IBM, Armonk, NY, USA).

Table 1

Demographic and patient characteristics of survivors and nonsurvivors.

Characteristica	Survivors (n = 692)	Nonsurvivors (n = 60)	p Value
Age, median (IQR)	71.2 (64.6, 78.7)	78.4 (72.5, 81.6)	<0.01
Male n (%)	640 (92.5)	57 (95.0)	0.61
Current smoker, n (%)	189 (27.3)	13 (21.7)	0.30
Smoking quantity (pack-years)	45 (23, 70)	50 (20, 62)	0.92
FEV1%	64 (48, 82)	50 (34, 64)	<0.01
BMI	23.3 (20.5, 25.8)	20.5 (17.0, 24.5)	<0.01
SpO2%	97.0 (95.0, 98.0)	95.5 (92.0, 97.0)	<0.01
CI score	2.0 (1.0, 3.0)	3.0 (1.0, 5.0)	<0.01
Severe AE ≥ 2, n (%)	111 (16.0)	22 (36.7)	<0.01
6MWT (meter)	344.0 (248.0, 400.0)	278.0 (206.0, 313.0)	0.11
SGRQ score	33.22 (18.2, 51.0)	59.13 (46.4, 65.3)	<0.01
mMRC = 4	26 (3.7)	17 (28.3)	<0.01
LTOT, n (%)	71 (10.3)	15 (25.0)	<0.01

aDiscrete data are presented as number (percentage), and continuous variables are presented as median (IRQ).

FEV forced expiratory volume in 1 s, BMI body mass index, SpO oxygen saturation (%) detected with pulse oximeter when breathing in room air, CI Charlson index, severe AE ≥ 2 history more than one acute exacerbation that required hospitalization in the preceding year, 6MWT 6 min walking test, SGRQ St. George’s Respiratory Questionnaire, mMRC modified Medical Research Council Dyspnea Scale, LTOT long-term oxygen therapy.

Table 2

Predictive accuracy of different recommended palliative care indices for 1-year mortality.

Prognostic index	Sensitivity	Specificity	PPV	NPV	Accuracy	AUC
mMRC = 4	28.3%	96.2%	39.5%	93.9%	90.8%	0.623
Severe AE ≥ 2	36.7%	84.0%	16.5%	93.9%	80.2%	0.603
Group 1	13.3%	98.1%	38.1%	92.9%	91.4%	0.684
Group 2	3.3%	99.1%	25.0%	92.2%	91.5%	0.657
Group 3	3.3%	98.0%	12.5%	92.1%	90.4%	0.634
Group 4	18.3%	94.4%	22.0%	93.0%	88.9%	0.679

mMRC modified Medical Research Council Dyspnea Scale in stable condition, Severe AE ≥ 2 more than one acute exacerbation that required hospitalization in the preceding year, Group 1 mMRC = 4 + severe AE ≥ 2, Group 2 severe AE ≥ 2 + SpO2 < 90%, Group 3 severe AE ≥ 2 + FEV1 < 30%, Group 4 severe AE ≥ 2 + BMI < 21, PPV positive predictive value, NPV negative predictive value, AUC area under the curve, SpO oxygen saturation (%) detected with a pulse oximeter when breathing room air, FEV forced expiratory volume in 1 s, BMI body mass index.

Table 3

Weighting of variables in DO index.

Variable	β	Adjusted HR	Score
SpO2 (%)
95–100	0	1	1
90–94	0.55	1.7	2
85–89	1.05	2.9	3
<85	2.00	7.3	7
mMRC score
0–2	0	1	1
3	0.64	1.9	2
4	2.21	9.1	9

Coding according to the regression coefficient for DO index construction.

DO dyspnea and oxygenation.

Table 4

Survival analysis of 1-year mortality for different DO index scores of patients with severe and very severe COPD (n = 180).

Score	Survived (n)	Died (n)	Survival rate (%)	Statistics (chi-square)	p Valuea
DO				58.61	<0.001
2	56	2	96.55
3	58	2	96.67
4	30	3	90.91
5	2	0	100.00
8	1	0	100.00
9	0	1	0
10	10	2	83.3
11	4	4	50
12	1	2	33.3
16	0	2	0

aWilcoxon test.

bLog-rank test.