Study design and study participants
A retrospective observational study was conducted at Zhenjiang First People's Hospital, Jiangsu Province, China, between September, 2016 and June, 2019. This is a tertiary teaching hospital of china. The study protocol was approved by the hospital ethics committee. All data were collected retrospectively from the hospital database. Due to the retrospective feature of the study design, informed consent was not obtained.
The study included patients aged 60 years or older who were admitted to respiratory or geriatrics department with a primary clinical diagnosis of AECOPD. A total of 1932 patients (1824 patients survived and 108 Patients died in the hospital) were screened in the study. We excluded patients hospitalized for secondary causes such as lung cancer, bronchiectasis, asthma, interstitial pulmonary disease, and active pulmonary tuberculosis. Each patient was enrolled in the study only once at their initial hospitalization. Patients with incomplete data—electrocardiograph, blood gas analysis,chest X-ray or CT scan, and hematological data—were excluded. We also excluded patients with automatic discharge or transfer to another hospital.
After the above exclusion, there were 426 survivors and 98 non-survivors. A priori differences in patient characteristics between survivors and non-survivors may lead to biased estimates. In order to decrease this bias, we used propensity score matching (PSM) matching techniques[21]. We used a logistic regression model to estimate the propensity of participating in the two groups based on a set of observed covariates. PSM matching (1:1 matching) covariates included: age, gender, number of smokers, number of drinkers, history of comorbid diseases (including type 2 diabetes, hypertension, myocardial infarction or stroke). Finally, 77 survivors and 77 non-survivors were included in the study. The flow chart of subject inclusion is summarized in Figure 1.
Definitions
COPD and AECOPD
The COPD diagnosis was established by a consistent airflow obstruction on spirometry (FEV1/forced vital capacity <0.70)[22]. The exacerbation of COPD (AECOPD)was defined as acute change in a patient’s respiratory symptoms that is beyond normal variability, and that is sufficient to warrant a change in therapy[22].
FI-Lab
Frailty in this study is defined as “a medical syndrome with multiple causes and contributors that is characterized by diminished strength, endurance, and reduced physiologic function” [23]. Rockwood[18]and Howlett [19] developed an FI (the FI-Lab) of up to 23 variables based on 21 routine blood tests plus measured systolic and diastolic blood pressure based on deficit accumulation(Supplementary table 1). The FI-Lab can meet several criterions about ideal biomarkers(influential to the pathogenesis, easy to measure, sensitive to changes, improved to intervention, prognostic to outcome) provided by Mcshane[24]and Gruttola[25].The FI-Lab was constructed by evaluating each variable as either 0 or 1. ‘0’ indicates that values are within the normal cut-offs but ‘1’ indicates that values are outside of the normal cut-offs as deficits. An FI-Lab score is constructed by counting the number of deficits and dividing by the total number of items tested to produce a score between 0 and 1. For example, a patient with deficits in six variables of the 23-item FI-Lab would have an FI-Lab score of 0.26 (6 divided by 23). A higher score indicates greater frailty.
An FI-Lab score was calculated only if more than 70% of the lab variables (items 16-23) were available for a given. In this study, we use FI-lab to evaluate frailty. We treated the FI-Lab score as a continuous variable as reported by previous studies and categorized the participants based on FI-Lab value into four grades:< 0.2, 0.2–0.4, 0.4–0.6, and >0.6. We also tried to find out an optimal cutoff of the FI-Lab value to predict mortality.
DECAF Score
DECAF consists of five parameters: dyspnea (D), eosinopenia (E), consolidation (C), acidemia (A), and atrial fibrillation (F). Evaluating DECAF in AECOPD patients can help to predict mortality. DECAF is a commonly used predictor of AECOPD with a score range between 1 and 6. A higher score indicates
poorer condition. A recent study reported that patients with a DECAF score of four or higher have a significant risk of mortality[6].Based on DECAF score,we categorized the participants into four grades:≤2, 3, 4, and ≥5.
Measurements
Electronic data were collected from inpatient hospital database. Patients characteristics such as gender, age, smoking, alcohol consumption, comorbid diseases (diabetes, hypertension, myocardial infarction or stroke),length of stay, rehospitalization,and mortality were recorded. We extracted first blood test results and systolic pressure, diastolic blood pressure on admission.
Blood test results includes: complete blood count (total leukocyte, neutrophil, eosinophil, lymphocyte, platelet counts, mean platelet volume, red cell distribution width, hemoglobin); blood biochemical (total protein, albumin, aspartate aminotransferase, calcium, creatinine, urea, fasting blood glucose, alkaline phosphatase, phosphorus, potassium, sodium); thyroid function (thyroid stimulating hormone, thyroxine, free thyroxine); syphilis; hematopoietic raw materials (serum folate, vitamin B12); inflammatory markers including C-reactive protein(CRP), neutrophil-to-lymphocyte ratio(NLR), platelet-to- lymphocyte ratio(PLR);blood gas analysis.
Statistical analysis
Data were analyzed using IBM SPSS for Windows, Version 23.0 (IBM Corp, Armonk, NY). We compared survivors to non-survivors. The baseline difference between the groups was matched by PSM. The median with interquartile range was employed for nonparametric continuous variables, and mean ± standard deviation was used for parametric continuous variables. Count and percentage were used when applicable. Mann–Whitney U-tests for nonparametric continuous variables or Student’s t-tests for parametric continuous variables. Chi-square tests were employed for dichotomous variables. Logistic regression analysis was used to select the associated predictors of in-hospital mortality. Receiver-operating characteristic (ROC) curves were calculated to estimate the area under the ROC curve (AUCs) for FI-Lab and DECAF in relation to mortality. The comparison of the AUCs was performed using the DeLong method [26]. We applied the Youden index method to determine the optimal cutoffs of FI-Lab or DECAF for predicting mortality. A two-tailed P-value 0.05 was accepted as statistically significant.