Abstract
Background
Frailty is a clinical syndrome and common phenomenon in the elderly, particularly when it coexists with chronic obstructive pulmonary disease (COPD). However, the relationship between frailty and its prognosis in COPD patients has not been clearly elucidated.
Methods
We collected electronic data of inpatients who were diagnosed with COPD in the First Affiliated Hospital with Nanjing Medical University (NJMU) from January 2018 to December 2020. In further, we divided them into different groups based on Frailty Index Common Laboratory Tests (FI-LAB). Binary logistic regression was performed to analyze the risk factors associated with COPD. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were applied to validate FI-LAB's value in prognosis. Primary clinical outcomes contained 30-day mortality and readmission. Moreover, we also compared the prognositic value of FI-LAB with Hospital Frailty Risk Score (HRS) by ROC curve, significance was set at P < 0·05.
Findings
The final study included 826 COPD patients, among of them, 30-day mortality and readmission of frailty group was 11·2%, 25·9%, the robust group was 4·3%, 16·0%, and p value was 0·001, 0·004 respectively. Multivariate analysis revealed that smoking, CCI≥3, oral drug≥5, pneumonia, abnormal lymphocyte, abnormal haemoglobin were independent risk factors with frailty. As for the prediction of FI-LAB about frailty in 30-day mortality, the AUC was 0·832, and 30-day readmission was 0·661. As for the prognositic value, FI-LAB and HRS showed no difference in predicting clinical outcomes.
Interpretation
COPD individuals have a higher rate of frailty and pre-frailty. There exists a strong correlation between frailty and 30-day mortality in COPD patients, and FI-LAB has good prognostic value in clinical outcomes of patients with COPD.
Keywords: Chronic obstructive pulmonary disease, Frailty, FI-LAB, Clinical outcomes and prognosis
Research in context
Evidence before this study
We searched Pubmed about systematic reviews and clinical researches from 2000 to 2018, search terms included “frailty”, “chronic obstructive pulmonary disease”, and “COPD”. It was astonished found that a series of frailty studies were prospective, and limited message was concerned about the relationship between COPD and frailty.
Added value of this study
Although there are several methods to evaluate frailty, they have not been effectively used in clinical practice because of their complexity and poor acceptance. We investigated frailty by electronic laboratory test data about inpatients who were diagnosed with COPD. In our study, We discovered the prevalence of frailty in COPD was 14.0%–18.4%. In addition, Frailty has a good predictive value in clinical outcomes.
Implications of all the available evidence
COPD individuals have a higher rate of frailty and pre-frailty. Clinicians can assess frailty based on the electronic system, FI-LAB can distinguish frailty status in special diseases. Even though there exists some limitations, it still deserves to recommend, because of the tool is acceptable and convenient.
1. Introduction
Chronic obstructive pulmonary disease (COPD) is high mortality and morbidity in worldwide [1,2]. 2019 Global Burden of Disease (GBD) showed that COPD has already become a significant factor in social and economic burden [3]. Although some progress has been made in treatment of COPD, the disease of pathogenesis remains unclear. With outbreak of coronavirus disease 2019 (COVID-19), it becomes more troublesome to treat this disease than ever before [4].
Frailty is a clinical syndrome, which is characterized by a decline in strength, endurance, and physiological function. It is common for COPD combined with frailty. Generally, frailty is reversible and regarded as an age-related condition. The research involved 21 community-based cohort studies and 61,500 elderly people, reported that frailty of prevalence range from 4·0% to 59·1% [5]. A systematic review contained 24 cross-sectional studies and several longitudinal studies, discovered that frailty in COPD was ranged from 9% to 28%, pre-frail was from 48% to 64% [6]. Another large population-based cohort study included 2142 subjects, which frailty defined by the Fried criteria, demonstrated frailty with COPD was 10·2% [7]. Altogether, the investigation of frailty in COPD is heterogeneous. Different frailty diagnostic criteria, inclusion population, and evaluation of occasions, which led to controversial results in numerous studies. At present, there exist more than 20 methods to assess frailty, Clinical frailty scale (CFS) and the FRAIL scale are the most familiar tools [8]. Recognizing frailty timely is full of challenges, because some questions need face to face, even some special tools to measure, like the power of grip. Perhaps it's necessary to establish a different assessment tool to recognize frailty via routinely collected data, which could lead to higher acceptability and feasibility of frailty screening in clinical practice [9,10]. FI-LAB is a new tool and developments by frailty index (FI), physicians can calculate frailty based on routine laboratory data, and this result has a good prediction in different diseases, such as lung cancer [11]. However, the little message could be searched about FI-LAB and COPD.
Despite the prevalence and risk factors of frailty in patients with COPD are reported, however, the conclusion still exist dispute [12]. Therefore, we aimed to explore the association between frailty with its clinical outcome.
2. Methods
2.1. Study design and participants
This was a longitudinal cohort study of all hospitalized patients who diagnosed with COPD from 2018 to 2020. Data were extracted from the electronic system. Inclusion criteria: (1) The International Classification of Diseases-10th Revision (ICD-10) diagnosis codes about COPD was recognized, including primary diagnosis and secondary diagnosis; (2) be in hospital during 2018·1–2020·12; (3) blood samples included blood routine, biochemistry and coagulation function, were taken on admission. Exclusion criteria: (1) Lack of sufficient data to evaluate the FI-LAB; (2) repeated hospital admissions during this period, refer to the population who stayed at hospital continuously and the purpose was nursing care; (3) still in hospital at Dec 31, 2020. Follow-up data were collected by telephone with patients. The research was approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (NJMU), and the ethics number is 2021-SR-214.
2.2. Data collection
Epidemiological, demographic, clinical characteristics, laboratory data, and treatments were collected from electronic medical system. All data were checked by two physicians (ML and QS).
2.3. Statistical analysis
Frailty was measured by FI-LAB, cut-off scores were ≥0·35 (frail), 0·2 to 0·35 (pre-frail), and ≤0·2 (robust) [7,13]. When continuous univariate belongs to normal distribution, it was expressed as mean ± SD, comparison between groups by one-way ANOVA. When continuous univariate belongs to abnormal distribution, mean (Q1, Q3) and Kruskal Wallis test were used. Binary variables were presented as percentage, Pearson chi-square test or Fisher was used to compare the classification data. 30-day mortality and readmission regarded as the main clinical outcomes. To explore the association between risk factors and clinical outcomes, we used binary logistic regression analysis, adjusted for covariates including age、sex、smoke、FI-LAB、CCI score、HRS score、oral durg≥5、AECOPD、pneumonia、heart failure、white cell count (WBC)、lymphocyte count (LY)、red blood cell (RBC)、Haemoglobin、platelet、Fibrinogen、D-dimer、alanine transaminase (ALT)、aspartate transaminase (AST)、Total Protein (TP)、albumin、creatinine. These results were presented with forest plot, hazard ratios (HR) for risk of endpoints. Receiver operating characteristic (ROC) curve and area under curve were applied to obsereved the prognostic value. When compare the FI-LAB and HRS in predict clinical outcomes, delong test was performed. All analyses were conducted using the SPSS (22·0) and R (4·1·2), and P < 0·05 was set as statistically significant.
3. Results
3.1. Baseline clinical characteristics
4278 patients were screened in the First Affiliated Hospital of NJMU combined with COPD from Jan 1, 2018 to Dec 31, 2020. After excluding 39 patients that were still hospitalized at Dec 31, 2020, and 786 inpatients without available key information in their medical records, we finally included 826 individuals in our study (Fig. 1). Among of them, 11 patients died during hospitalization, and 70 patients died followed in one month. 692 patients (83·8%) were male and female 134 patients (16·2%) were female. The average age of frail was 76·0 years (69·0,83·5), pre-frail was 75·0 years (68·0,82·0), and robust was 72·5 years (66·0,79·0). Fig. 2A–E showed the distribution about percentage of population in age groups、gender、age、hospitalization expenses and length of hospital day. The prevalence of frailty status for each age and sex category is shown in Table 1.
Fig. 1.
Flowchart of the subjects selection process.
Fig. 2.
The proportion of frailty in different baseline characteristics. (A) The percentage of population in different age groups. (B) The percentage of gender in different frailty status. (C) The average of age in different frailty status. (D) The average hospitalization expenses in different frailty status. (E) The average length of hospital days in different frailty status.
Table 1.
Demographics and clinical characteristics.
| robust (N = 350) | pre-frail (N = 360) | frail (N = 116) | Overall (N = 826) | P value | |
|---|---|---|---|---|---|
| Age | 72·5 (66, 79) | 75·0 (68·0, 82·0) | 76·0 (69·0, 83·5) | 74·0 (67·0, 81·0) | <0·001 |
| <65 | 72 (20·6%) | 55 (15·3%) | 16 (13·8%) | 143 (17·3%) | 0·004 |
| 65-70 | 77 (22·0%) | 73 (20·3%) | 15 (12·9%) | 165 (20·0%) | |
| 70-75 | 76 (21·7%) | 53 (14·7%) | 26 (22·4%) | 155 (18·8%) | |
| 75-80 | 43 (12·3%) | 60 (16·7%) | 17 (14·7%) | 120 (14·5%) | |
| 80-85 | 51 (14·6%) | 65 (18·0%) | 20 (17·2%) | 136 (16·5%) | |
| >85 | 31 (8·9%) | 54 (15·0%) | 22 (19·0%) | 107 (12·9%) | |
| Gender | |||||
| female | 53 (15·1%) | 66 (18·3%) | 15 (12·9%) | 134 (16·2%) | 0·300 |
| male | 297 (84·6%) | 294 (81·7%) | 101 (87·1%) | 692 (83·8%) | |
| Smoke history | |||||
| no | 128 (36·6%) | 144 (40·0%) | 44 (37·9%) | 316 (38·3%) | 0·836 |
| yes | 217 (62·0%) | 212 (58·9%) | 71 (61·2%) | 500 (60·5%) | |
| missing | 5 (1·4%) | 4 (1·1%) | 1 (0·9%) | 10 (1·2%) | |
| COPD is primary diagnosis | |||||
| no | 319 (91·1%) | 320 (88·9%) | 99 (85·3%) | 738 (89·3%) | 0·200 |
| yes | 31 (8·9%) | 40 (11·1%) | 17 (14·7%) | 88 (10·7%) | |
| COPD is second diagnosis | |||||
| no | 31 (8·9%) | 41 (11·4%) | 17 (14·7%) | 89 (10·7%) | 0·192 |
| yes | 319 (91·1%) | 319 (8·9%) | 99 (85·3%) | 737 (89·3%) | |
| AECOPD | |||||
| no | 309 (88·3%) | 312 (86·7%) | 96 (82·8%) | 717 (86·8%) | 0·311 |
| yes | 41 (11·7%) | 48 (13·3%) | 20 (17·2%) | 109 (13·2%) | |
| NYHA class | |||||
| I-II | 296 (84·6%) | 279 (77·5%) | 88 (75·9%) | 663 (80·3%) | 0·027 |
| III-IV | 54 (15·4%) | 81 (22·5%) | 28 (24·1%) | 163 (19·7%) | |
| inhospital cost | 20233·2 (11087·9, 44696·6) | 21905·2 (11731·7, 47900·2) | 22867·6 (12598·0, 45054·8) | 21349·3 (11578·8, 46090·1) | 0·799 |
| inhospital days | 8·5 (6·0, 14·0) | 10·0 (7·0, 15·0) | 11·0 (7·0, 18·0) | 9·0 (6·0, 15·0) | 0·003 |
| Temperature (°C) | 36·5 (36·4, 36·8) | 36·6 (36·5, 36·8) | 36·7 (36·4, 36·9) | 36·4 (36·2, 36·6) | 0·040 |
| Heart rate (b·p·m.) | 78 (70, 85) | 80 (72, 89) | 80 (75, 95) | 80 (72, 88) | 0·001 |
| Respiratory Rate（b·p·m.） | 18 (16·0, 18·0) | 18 (16·0, 19·0) | 18 (16·0, 19·3) | 18·0 (16·0, 18·3) | 0·076 |
| DBP(mmHg) | 75·5 (67, 83) | 74 (65, 82) | 75 (66, 83) | 75 (66, 82) | 0·143 |
| SBP(mmHg) | 130 (120, 142) | 130 (116, 140) | 128 (113, 142) | 130 (118, 140) | 0·271 |
| Symptoms | |||||
| fever | 19 (5·4%) | 37 (10·3%) | 11 (9·5%) | 67 (8·1%) | 0·051 |
| cough | 73 (20·9%) | 84 (23·3%) | 38 (32·8%) | 195 (23·6%) | 0·032 |
| expectoration | 56 (16·0%) | 58 (16·1%) | 26 (22·4%) | 140 (16·9%) | 0·239 |
| breathless | 74 (21·1%) | 78 (21·7%) | 32 (27·6%) | 184 (22·3%) | 0·329 |
| syncope | 2 (0·6%) | 0 (0·0%) | 1 (0·9%) | 3 (0·4%) | 0·282 |
| chest distress | 78 (22·3%) | 75 (20·8%) | 20 (17·2%) | 173 (20·9%) | 0·511 |
| chest pain | 15 (4·3%) | 18 (5·0%) | 6 (5·2%) | 39 (4·7%) | 0·877 |
| hypodynamia | 14 (4·0%) | 21 (5·8%) | 12 (10·3%) | 47 (5·7%) | 0·038 |
| hemoptysis | 7 (2·0%) | 3 (0·8%) | 2 (1·7%) | 12 (1·5%) | 0·415 |
| others | 166 (47·4%) | 174 (48·3%) | 39 (33·6%) | 379 (45·9%) | 0·016 |
| Comorbidities | |||||
| Hypertension | 179 (51·1%) | 196 (54·4%) | 57 (49·1%) | 432 (52·3%) | 0·518 |
| Stroke | 91 (26·0%) | 80 (22·2%) | 23 (19·8%) | 194 (23·5%) | 0·299 |
| Coronary heart disease | 93 (26·6%) | 95 (26·4%) | 29 (25·0%) | 217 (26·3%) | 0·944 |
| Pneumonia | 66 (18·9%) | 101 (28·1%) | 47 (40·5%) | 214 (25·9%) | 0·000* |
| Respiratory failure | 27 (7·7%) | 45 (12·5%) | 20 (17·2%) | 92 (11·1%) | 0·010 |
| Asthma | 19 (5·4%) | 16 (4·4%) | 7 (6·0%) | 42 (5·1%) | 0·738 |
| Cancer | 76 (21·7%) | 82 (22·8%) | 21 (18·1%) | 179 (21·7%) | 0·568 |
| Renal dysfunction | 6 (1·7%) | 17 (4·7%) | 11 (9·5%) | 34 (4·1%) | 0·001 |
| Diabetes | 68 (19·4%) | 60 (16·7%) | 21 (18·1%) | 149 (18·0%) | 0·633 |
| CCI score | |||||
| <3 | 253 (72·3%) | 262 (72·8%) | 81 (69·8%) | 596 (72·2%) | 0·825 |
| ≥3 | 97 (27·7%) | 98 (27·2%) | 35 (30·2%) | 230 (27·8%) | |
| HRS score | |||||
| <5 | 23 (6·6%) | 22 (6·1%) | 10 (8·6%) | 55 (6·7%) | 0·912 |
| 5-15 | 263 (75·1%) | 270 (75·0%) | 86 (74·1%) | 619 (74·9%) | |
| >15 | 64 (18·3%) | 68 (18·9%) | 20 (17·2%) | 152 (18·4%) | |
| Laboratory results | |||||
| WBC, × 10⁹ per L | 6·1 (5·1, 7·5) | 6·5 (4·9, 9·2) | 7·3 (5·1, 10·8) | 6·4 (5·0, 8·3) | 0·003 |
| Lymphocyte count, × 10⁹ per L | 23·4 (17·2, 29·2) | 19·4 (11·6, 27·3) | 16·2 (9·9, 23·2) | 20·7 (13·0, 27·9) | 0·000* |
| RBC, × 10^12/L | 4·5 (4·0, 4·8) | 4·0 (3·5, 4·4) | 3·8 (3·4, 4·3) | 4·2 (3·7, 4·6) | 0·000* |
| Haemoglobin, g/dL | 137·0 (121·3, 148·0) | 121·0 (107·0, 133·3) | 118·0 (102·3, 128·8) | 126·0 (113·0, 140·0) | 0·000* |
| PLT, × 10⁹ per L | 185·0 (147·0, 237·0) | 184·0 (129·5, 240·5) | 181·5 (138·3, 261·0) | 183·0 (139·0, 240·0) | 0·300 |
| Fibrinogen,g/L | 2·4 (2·1, 2·8) | 2·6 (2·0, 3·4) | 2·7 (2·0, 3·8) | 3·1 (2·4, 4·1) | 0·000* |
| D-dimer, μg/L | 1·0 (1·0, 1·1) | 1·0 (1·0, 1·1) | 1·1 (1·1, 1·2) | 0·7 (0·3, 2·0) | 0·000* |
| ALT,U/L | 16·0 (11·4, 22·6) | 16·6 (11·3, 27·0) | 30·9 (12·8, 75·6) | 16·6 (11·5, 26·8) | 0·000* |
| AST,U/L | 20·6 (16·9, 25·5) | 21·9 (17·6, 29·5) | 33·1 (20·5, 70·4) | 21·7 (17·6, 29·1) | 0·000* |
| Total protein,g/L | 62·7 (58·5, 67·3) | 61·8 (56·3, 66·6) | 59·7 (55.0, 64.2) | 61·4 (57·2, 66·8) | 0·000* |
| Albumin, g/L | 36·6 (34·0, 39·2) | 33·9 (30·2, 37·5) | 30·5 (27·4, 34·7) | 35·2 (31·4, 38·1) | 0·000* |
| Creatinine,μmol/L | 72·7 (63·2, 82·5) | 73·0 (59·4, 93·4) | 76·0 (56·9, 137·7) | 72·9 (61·8, 88·8) | 0·303 |
| Outcomes | |||||
| 30-day mortality | 15 (4·3%) | 42 (11·7%) | 13 (11·2%) | 70 (8·5%) | 0·001 |
| 30-day readmission | 56 (16·0%) | 60 (16·7%) | 30 (25·9%) | 146 (17·7%) | 0·044 |
| 3-month readmission | 75 (21·4%) | 80 (22·2%) | 38 (32·8%) | 193 (23·4%) | 0·035 |
| 6-month readmission | 83 (23·7%) | 85 (23·6%) | 39 (33·6%) | 207 (25·1%) | 0·072 |
Abbreviations: NYHA, New York Heart Association; CCI, Charlson Comorbidity Index; HRS, Hospital Frailty Risk Score; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure.
The median time from illness to discharge was 9·0 (6·0,15.0) days, whereas the frailty group was 11·0 (7·0,18·0) days. More than 3 diseases are accounting for 27·8%, 351 patients (42·5%) received more than 5 drugs, the detailed drug condition are shown in Table 2. Frailty patients presented with higher score in CCI, and this ratio up to 30·2%. With average 6 months follow-up, frailty group have higher rate in 30-day mortality, 30-day readmission, and 3-month readmission. However, there is no difference in 6-month readmission between groups (Fig. 3A–D). Fig. 4A showed baseline characteristics for different frailty population based on binary logistic regression, we concluded that CCI score (2.216 [1.222,4.021], p = 0.009), durg≥5 (0.133 [0.062,0.284],p < 0.001) could regarded as independent risk factor in patients' 30-day mortality. We used the same method to analysis different time about clinical outcomes, found there didn't exist statistically significant in 30-day readmission, 3-month readmission and 6-month readmission (Fig. 4B–D).
Table 2.
The treatments of inclusion patients.
| robust (N = 350) | pre-frail (N = 360) | frail (N = 116) | Overall (N = 826) | P value | |
|---|---|---|---|---|---|
| ≥5 drugs | 151 (41·9%) | 151 (41·9%) | 49 (42·2%) | 351（42·5%） | 0·948 |
| Drugs of COPD | |||||
| Expectorant | 340 (97·1%) | 347 (96·4%) | 111 (95·7%) | 798 (96·6%) | 0·720 |
| LABA | 16 (4·6%) | 13 (3·6%) | 9 (7·8%) | 38 (4·6%) | 0·179 |
| SABA | 0 (0%) | 0 (0%) | 1 (0·9%) | 1 (0·1%) | 0·047 |
| LAMA | 36 (10·3%) | 41 (11·4%) | 8 (6·9%) | 85 (10·3%) | 0·383 |
| SAMA | 0 (0%) | 0 (0%) | 1 (0·9%) | 1 (0·1%) | 0·047 |
| LAMA + LABA | 1 (0·3%) | 0 (0·0%) | 0 (0·0%) | 1 (0·1%) | 0·506 |
| ICS + LABA | 47 (13·4%) | 46 (12·8%) | 15 (12·9%) | 108 (13·1%) | 0·966 |
| Cardiovascular Disease | |||||
| Corticosteroids | 18 (5·1%) | 30 (8·3%) | 7 (6·0%) | 55（6·7%） | 0·224 |
| Antibiotics | 63 (18·0%) | 99 (27·5%) | 48 (41·4%) | 210 (25·5%) | 0·000* |
| Diuretics | 44 (12·6%) | 44 (12·2%) | 14 (12·1%) | 102 (12·3%) | 0·985 |
| ACEI | 26 (7·4%) | 19 (5·3%) | 7 (6·0%) | 52 (6·3%) | 0·495 |
| ARB | 24 (6·9%) | 22 (6·1%) | 3 (2·6%) | 49 (5·9%) | 0·236 |
| CCB | 24 (6·9%) | 36 (10·0%) | 6 (5·2%) | 66 (8·0%) | 0·146 |
| MRA | 27 (7·7%) | 27 (7·5%) | 7 (6·0%) | 61 (7·4%) | 0·830 |
| Statins | 67 (19·1%) | 54 (15·0%) | 4 (3·4%) | 125 (15·1%) | 0·001* |
| Aspirin | 58 (16·6%) | 43 (11·9%) | 8 (6·9%) | 109 (13·2%) | 0·018 |
| Beta-blocker | 50 (14·3%) | 37 (10·3%) | 12 (10·3%) | 99 (12·0%) | 0·218 |
| Digoxin | 8 (2·3%) | 9 (2·5%) | 2 (1·7%) | 19 (2·3%) | 0·889 |
| Amiodarone | 13 (3·7%) | 4 (1·1%) | 3 (2·6%) | 20 (2·4%) | 0·078 |
| warfarin | 20（5·7%) | 13 (3·6%) | 5 (4·3%) | 38 (4·6%) | 0·404 |
| Rivaroxaban | 12 (3·4%) | 8 (2·2%) | 3 (2·6%) | 23 (2·8%) | 0·615 |
| Dabigatran | 9 (2·6%) | 4 (1·1%) | 2 (1·7%) | 15 (1·8%) | 0·345 |
Abbreviations: ICS, inhalation corticosteroids; LABA, long acting beta agonist; LAMA, long acting muscarinic antagonist; SABA, short acting beta agonist; SAMA, short acting muscarinic antagonist; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; CCB, calcium channel blockers; MRA, mineralocorticoid receptor antagonist.
Fig. 3.
The proportion of primary clinical outcomes in different frailty status. (A) 30-day mortailty. (B) 30-day readmission. (C) 3-month readmission. (D) 6-month readmission.
Fig. 4.
Forest plot represents the HRs from the multivariable analysis for association between risk factors with COPD. (A) 30-day mortality. (B) 30-day readmission. (C) 3-month readmission. (D) 6-month readmission. NYHA, New York Heart Association. CCI, Charlson Comorbidity Index. HR, hazard ratio, P ≤ 0·05 was considered statistically significant.
3.2. The prevalence of different frailty statuses with COPD
According to FI-LAB, we found the prevalence of frailty with COPD is 14·0%. HRS is a recommended method to recognize frailty, especially for hospital patients [14]. Based on HRS, we concluded that the prevalence of frailty with COPD is 18·4% (Table 1).
3.3. Associations of frailty with clinical outcomes
We noticed that the group of frailty has higher risk in adverse clinical outcomes, such as 30-day mortality, 30-day readmission, and 3-month readmission, and the rate is 11·2%, 25·9% and 32·8%, the robust group was 4·3%, 16·0%, and 21·4%, and p value was 0·001, 0·004 and 0·035, respectively (Table 1). In our study, the pre-frail or frail group were significantly increased two doubles risk in mortality than robust group, this conclusion was consistent with Shuen [15]. To our knowledge, there exists few readmission prognosic information about frailty combined with COPD. Binary logistic regression was performed to assess the associations between frailty and risk in short-term adverse outcomes. We proved that smoke, CCI≥3, oral drug≥5, pneumonia, abnormal LY, abnormal haemoglobin were belong to independent risk factors in 30-day mortality (Fig. 4A).
3.4. Associations of frailty with comorbidity and polypharmacy
Among 826 patients, hypertension (52·3%) is the most common disease, following by stroke (23·5%) and coronary heart disease (26·3%) (Table 1). 351 (42·5%) patients received more than 5 drugs, statins is the most common drugs, followed by diuretics, aspirin, beta-blocker (Table 2). 210 (25·5%) patients received antibiotics, 102 (12·3%) received third/fourth generation cephalosporin and 54 (6·5%) received antiviral treatment.
3.5. Predictive model for the COPD outcomes with frailty
Based on Binary logistic regression analyses, some independent factors were recognized. Then, We utilized them for predicting the frailty of COPD patients outcomes (Fig. 4). We choosed FI-LAB combined with smoke history, CCI≥ 3, oral drug≥ 5, pneumonia, lymphocyte count, and hemoglobin count, to assess the value in 30-day mortality and 30-day readmission, the AUC is 0·832 and 0·661, respectively (Fig. 5A–B). In addition, we compared FI-LAB and HRS, which one was better in predicting 30-day mortality and readmission by delong test, the p value was 0·43, 0·962, and the results indicated that the difference is no significant (supplement 1A-B).
Fig. 5.
The receiver operating characteristic (ROC) curve was applied to validate FI-LAB's value in prognosis. (A) 30-day mortality; (B) 30-day readmission.
4. Discussion
Even though the concept of frailty was firstly put forward in thirty years ago, until recent years, people are beginning to be aware of the importance of frailty. As for the management of patients, frailty is always to be ignored. Co-occurrence of frailty and COPD often take up more medical resources than those who is without it, such as length of hospital stay, hospitalization expenses. As the population aging, frailty has gradually become a hotspot. Upon increase the degree of frailty, individuals will more easily suffer falls, fractures, infection, readmission, cognitive dysfunction, disability, and even mortality [16]. Thus, it's crucial to recognize and take step to prevent frailty at an early stage. Recently, more and more evidence showed that frailty has a good value in predicting cardiovascular disease events, diabetes, tumors, and surgery [17,18]. Whereas, little attention focus on the relationship between frailty with COPD.
In our study, we discovered that the ratio of frail group in 30-day mortality, 30-day readmission, 3-month readmission and 6-month readmission was higher than robust group. In view of frailty is a dynamic process, the laboratory data only reflects one state. Therefore, we highlighted the influence about frailty in short-term. Our multivariate analysis showed that frailty has a good predictive value in 30-day mortality and 30-day readmission. These conclusions are consistent with previous research [19,20].
COPD and frailty share common factors, such as age, smoke, malnutrition, comorbidity, polypharmacy, and sarcopenia [21]. COPD is more easily combined with frailty. However, the mechanism is unclear. Inflammation aging is regarded as an important theory in the development of disease, some inflammatory cytokines released, such as interleukin-6, tumor necrosis factor-alpha. Generally, it will make individuals are staying at chronic inflammation. The state of chronic inflammation makes people more easily suffer cancer, and suppress hemoglobin production. It was regret that we didn't collect the inflammatory markers. In our sample, we found that the average hemoglobin of frailty is 118·0 g/L, pre-frail group was 121·0 g/L, robust group was 137·0 g/L, the difference was significant.
One of the main discovery is that the proportion of frailty and pre-frailty in COPD are both at a high ratio. We identified that frailty can occur at all ages, no matter whether the people are younger than 65 years old. A UK-based on prospective cohort study about outpatient showed that the overall prevalence of frailty was 25·6%, the definition of frailty by the Fried phenotype model [22]. As for the relationship between age and frailty, this conclusion is largely consistent with the previous study. An Amsterdam study revealed that frailty was rising with age, especially in those more than 70 years old [23]. In our study, there is no significant difference in gender. Actually, frailty tended to be more frequent phenomenon among women than men (29·7% vs 22·8%) [15]. The explanation may largely related to the difference of included population.
Notably, we conducted frailty assessment mainly based on laboratory data. HRS and FI-LAB are accepted measurements by the retrospective medical record. In our study, two different ways were applied. Subsequently, we compared two ways’ effect on its prognosis. At the end of study, we used ROC to confirm the prognosis value about FI-LAB, the AUC is 0·832. The result indicates FI-LAB is meaningful. As for the sample is small, we need to enlarge samples and confirm the finding in the future.
The strengths of this study are as follows: First of all, we collected information containing all ages, not only specific age groups. Moreover, the patient's medical information is objective and complete. However, there still exist some limitations. First, frailty is a dynamic process, however, the assessment of frailty was only at one time in our study just like most other studies. Second, this was a single-center retrospective study, we couldn't collect extra data about pulmonary function, Short Physical Performance Battery (SPPB), 6 min walking distance (6MWD), activities of daily living (ADL). What's more, we didn't record other adverse events, such as falls, disability. In the future, more prospective researches and research centers will be carried out to compensate these shortages.
5. Conclusion
COPD individuals have a higher rate of frailty and pre-frailty. In addition, there exists a strong correlation between frailty and 30-day mortality, and FI-LAB has good prognosis value in patients with COPD in a short time.
Declaration of competing interests
We declare that we have no conflicts of interest.
Contributors
ML and QS designed the study and statistical analysis, interpreted the data, and drafted the manuscript. ML, QS and JT collected the epidemiological and clinical data, interpreted the data, and drafted the manuscript. SS, HZ, YW, KW, WL, WZ and PH contributed to the data analysis and interpretation and revised the manuscript. BC and JW designed the study, interpreted the data, and revised the manuscript. All authors approved the final version of the manuscript.
Data sharing statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
Data availability statement
Data will be made available on request.
Declaration of interest’s statement
The authors declare no conflict of interest.
Funding
This study was supported by grants from the National Key R&D Program of China (No. 2018YFC2002100, 2018YFC2002102), the National Natural Science Foundation of China (No. 81871115), the Natural Science Foundation of Jiangsu Province (No. BK20211377), the Cadre Health Care Research Project of Jiangsu Province (No. BJ20018), the Natural science research project of colleges and universities in Jiangsu Province (No. 20KJB320002), the Outstanding Young and Middle-aged Talents Support Program of the First Affiliated Hospital with Nanjing Medical University, the Six Talent Peaks Project in Jiangsu Province (No. 2018-WSN-003).
Acknowledgments
The authors would like to thank the external reviewers for their work on the literature.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e15764.
Contributor Information
Bo Chen, Email: bchen@njmu.edu.cn.
Jianqing Wu, Email: jwuny@njmu.edu.cn.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
The comparsion between FI-LAB and HRS were performed in predicting clinical outcomes. (A) 30-day mortality; (B) 30-day readmission.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
The comparsion between FI-LAB and HRS were performed in predicting clinical outcomes. (A) 30-day mortality; (B) 30-day readmission.
Data Availability Statement
Data will be made available on request.