Introduction

The recent Global Burden of Disease study reported that chronic obstructive pulmonary disease (COPD) was a prevalent cause of death worldwide [1]. Acute exacerbations of COPD (AECOPD), which are characterised by a worsening of respiratory signs and symptoms and an increasing of domiciliary therapies [2], may occur during the clinical course of disease and increase the mortality. Related to this aspect, some patients need hospitalisation depending on the severity of the AECOPD [2, 3] and if it prolonged, this can increase the costs of managing the disease [4].

The length of hospital stay (LHS) for an AECOPD is related to several factors. These include age [5], disease severity [5, 6], the presence of comorbidities [7, 8], high carbon dioxide partial pressures (PCO2) [7], the need of mechanical ventilation [6] or an intensive care unit (ICU) [9], a low serum albumin level [7], the dyspnoea perception [10] and the respiratory rate [8]. Other variables, such as admissions at the weekend [8] and social factors [11], have also been considered relevant. However, three aspects need to be highlighted in these studies. First, some of them were based on audit data [6], medical records [7] or retrospective analysis [10, 11] and not from prospective observational data. Second, different thresholds have been used to define a prolonged LHS (e.g., 4 days [8], 8 days [10], 9 days [9], or 11 days [7]), despite evidences that the normal LHS required for AECOPD should be 6 or 7 days [12], based on the occurrence and timing of complications. Third, no studies have evaluated several clinical variables all together at the time and before hospital admission.

In this prospective study, we aimed to identify the clinical variables at the time and before hospital admission that increase the risk of prolonged LHS (defined as > 7 days) in patients hospitalised for AECOPD. Due to the impact of LHS on clinical, social and economic outcomes, we believe that identifying the predictors of prolonged LHS could help clinicians to develop targeted interventions for these patients.

Methods

Study cohort

We performed a prospective study at the Hospital Clinic of Barcelona, Spain, over a 7-years period from May 2009 to May 2016. We systematically enrolled all patients admitted for an AECOPD to our pneumology department. Included patients had to meet the diagnostic criteria for COPD, as set out by the Global Initiative for Chronic Obstructive Lung Disease (GOLD) [2]. For the diagnosis, we considered spirometry measurements in the stable phase at least six months before hospital admission and patients were considered positive for smoking if they had a history of 20 pack-years [2]. According to the GOLD document [2], AECOPD was defined as a recent worsening of respiratory symptoms that required a change in domiciliary therapy, with the need for hospitalisation based on the severity of AECOPD and the presence of indicators [2]. According to the literature [12] and related to the median value of the LHS in our study sample that was 7 days, patients with an AECOPD were divided into a group with normal LHS (≤7 days) and a group with prolonged LHS (> 7 days). We excluded patients with a documented history of other concomitant chronic respiratory diseases (e.g., asthma, cystic fibrosis or interstitial lung disease) or those in whom a community-acquired pneumonia or acute heart failure were identified by clinical signs, chest X-ray or computed tomography at admission.

The hospital’s ethics committee approved the study protocol (CEIC 2008/4106), and we conducted the study according to the requirements of Good Clinical Practice and the declaration of Helsinki, including later revisions. All participants gave signed informed consent.

Microbiological sample collection

On the first day of hospitalisation, we collected sputum from a spontaneous cough sample; if adequate (a count of > 25 leukocytes and < 10 epithelial cells per field) it was processed by Gram staining and culture. In patients who did not provide a spontaneous sputum sample, we obtained an induced sputum production by inhalation of a 5% hypertonic saline solution delivered via nebuliser for 5 to 10 min.

General measurements

We recorded data about demographic variables, body mass index, smoking habit (current or former, including the number of pack-years), number of comorbidities (using the Charlson index), prevalence of ischaemic heart disease and diabetes, dyspnoea grade (measured by the modified Medical Research Council [mMRC] scale), disease severity (using the COPD severity score [COPD-SS] questionnaire) and use of long-term oxygen therapy (LTOT). We also collected data about the season of occurrence of AECOPD, the characteristics and numbers of AECOPD events in the last year, and details of home care medications (short-acting β2 agonists, long-acting β2 agonists, anticholinergics and inhaled corticosteroids).

We recorded body temperature, respiratory rate, heart rate and blood pressure (systolic and diastolic) at admission. In addition, we recorded gas analysis (pH, partial arterial carbon dioxide pressure [PaCO2], the ratio of partial arterial oxygen pressure to the fraction of inspired oxygen [PaO2/FiO2], serum bicarbonate [HCO3−], and base excess [BE]), systemic response (i.e., leukocytes, haematocrit, haemoglobin, C-reactive protein [CRP], glucose and creatinine) as both at admission and at day 3. Finally, we recorded how many patients used systemic corticosteroids and antibiotics, the antibiotic classes and the duration of antibiotic treatment.

Outcomes

Clinical progression was evaluated based on the need for non-invasive and invasive mechanical ventilation (NIMV and IMV, respectively) and for admission to ICU. Data on prognosis (i.e., cumulative number of all-cause deaths and estimated time to death) were recorded during follow-up at 30 days, 6 months, 1 year and 3 years. The date of death was identified using centralised registries.

Statistical analysis

We reported categorical variables as numbers and percentages and we reported continuous variables as means ± standard deviations or as medians (1st quartile; 3rd quartile) for normal and non-normal distributions, respectively. Categorical variables were compared using the X [2] test or the Fisher exact test, while continuous variables with the t test or the non-parametric Mann-Whitney test, as appropriate. All statistical analyses were performed using IBM SPSS, version 25.0 (IBM Corp., Armonk, NY, USA). A p-value of < 0.05 was considered statistically significant.

Univariate and multivariate regression logistic models were performed with the stepwise method to predict the probability of a prolonged LHS (the dependent variable). The independent variables in the univariate analyses were as follows: mMRC dyspnoea score (≥ 2); GOLD 2017 stages A, B, C or D; number of previous AECOPD episodes requiring hospitalisation (≥1); use of LTOT; symptom duration before admission (≥7 days); pre-admission therapy, including antibiotic use within 3 months, salbutamol use within two weeks and ipratropium bromide use within two weeks; the COPD-SS (≥15); the presence of a comorbidity such as ischaemic or congestive heart disease, diabetes or chronic kidney failure); the presence of acute respiratory acidosis (pH < 7.35), at admission and day 3, hypercapnia (PCO2 > 45 mmHg), acute severe hypoxemia (PaO2/FiO2 ratio < 200) and renal bicarbonate retention (HCO3 > 30 mmol/L); NIMV use; a positive sputum culture for Pseudomonas aeruginosa in the previous year or during hospitalisation, a microorganism resistant to conventional treatment (MRCT) or Streptococcus pneumoniae; antibiotic use during hospitalisation. Variables that showed a significant result (p < 0.1) were included in the subsequent multivariate regression stepwise model. To detect collinearity, we calculated the r coefficient of two variables and for those highly correlated (r > | ± 0.30|) the variable with the largest variance was excluded from the multivariate analysis [13]. Odds ratios (ORs) and 95% confidence intervals (CI) were then calculated.

Time-to-event variables were analysed by Kaplan–Meier survival curves and the Gehan–Breslow–Wilcoxon test was applied to emphasise early differences [14]. Patients lost to follow-up were censored in the survival analysis. Cox proportional hazard regression models were used to report the mortality at 30 days, 6 months, 1 year and 3 years [15], and we calculated the hazard ratios and 95% CIs.

Results

General data on study cohort

Over the 7-year study period, we enrolled 449 patients admitted for an AECOPD; of these, 12 died during hospitalisation and so excluded. Among the 437 remaining patients, 213 had a normal LHS and 224 had a prolonged LHS. In comparison to the normal LHS group, the prolonged LHS group had higher rates of GOLD D stage, LTOT, associated congestive heart disease and prior hospitalisations for AECOPD. The prolonged LHS group had also worse dyspnoea perception, longer symptom duration before admission and greater requirement of salbutamol and ipratropium in the two weeks before admission. Figure 1 shows the flow diagram for the study and Table 1 shows the baseline characteristics.

Fig. 1
figure 1

Study flow chart

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Table 1 Baseline characteristics of patients by the length of hospital stay
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Clinical, laboratory and microbiological variables

Blood gas analysis at admission (Fig. 2) revealed worse values in the prolonged LHS group than in the normal LHS groups for pH (median [1st quartile; 3rd quartile] 7.37 [7.32; 7.42] versus 7.41 [7.36; 7.44]), PaCO2 (51.7 mmHg [40.3; 66] versus 44.3 mmHg [37.4; 53]), PaO2/FiO2 (251.9 [204.2; 306.6] versus 272.3 [231.6; 315.7]), HCO3− (28.9 mmol/L [24.9; 34] versus 26.7 mmol/L [24.4; 30.3]). At day 3, different levels for pH and PaCO2 were confirmed in the study groups. In the prolonged LHS group, fluoroquinolone use was lower and cephalosporin use was higher when compared with the normal LHS group. All other clinical and laboratory variables were similar between the study groups (Table 2).

Fig. 2
figure 2

Blood gas analysis on admission and day 3. White and grey represent the AECOPD patients with normal LHS and prolonged LHS, respectively. Abbreviations: PaCO2, partial pressure of arterial carbon dioxide; PaO2/FiO2, ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen; HCO3−, serum bicarbonate; BE, base excess

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Table 2 Clinical and laboratory variables
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Table 3 summarises the results of microbiological testing. In the year before hospitalisation, the prevalence of patients with positive cultures, ≥1 pathogens or positive samples for P. aeruginosa or MRCT was higher in the prolonged LHS group compared with the normal LHS group. In the samples collected during hospitalisation, P. aeruginosa and MRCT occurred more frequently in the prolonged LHS group, while S. pneumoniae occurred less frequently. Finally, the prevalence rates of P. aeruginosa and MRCT colonisation were higher in the prolonged LHS group (100 and 90% of cases, respectively).

Table 3 Microbiological variables
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Study outcomes

All the outcomes (i.e., NIMV use, IMV use, ICU admission, and mortality) were worse in the prolonged LHS group compared with the normal LHS group (Table 4). Similarly, the survival time and the Kaplan–Meier curves in the three follow-up periods (i.e., 6 months, 1 year and 3 years) were significantly different between groups, with worse prognosis for patients with prolonged LHS (Fig. 3).

Table 4 Study outcomes
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Fig. 3
figure 3

Kaplan-Meier survival curves at 6 months, 1 year and 3 years by length of hospitalisation

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Prediction analyses

Several clinical variables predicted LHS in the univariate analyses (Table 5). The following were significant predictors of an increased risk of a prolonged LHS: mMRC dyspnoea score ≥ 2, GOLD stage B and D, ≥1 previous AECOPD requiring hospitalisation, LTOT use, symptom duration ≥7 days before admission, drug use before admission (antibiotics three months before and salbutamol and ipratropium two weeks before), COPD-SS questionnaire ≥15, the presence of a congestive heart disease, acute respiratory acidosis or severe hypoxemia at admission, hypercapnia at admission and day 3, renal bicarbonate retention at admission, the need for NIMV, the presence of P. aeruginosa or of MRCT colonisation and the use of cephalosporins during hospitalisation. The presence of S. pneumonia and the use of fluoroquinolones were associated with a reduced risk of a prolonged LHS.

Table 5 Univariate and multivariate regression analyses predicting the probability to have a prolonged hospitalisation
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In the multivariate and multivariate adjusted analyses, an mMRC dyspnoea score ≥ 2 and the presence of acute respiratory acidosis at admission were independently confirmed as predictors of a prolonged LHS.

Readmissions and LHS

In comparison with the normal LHS group, the prolonged LHS group had higher probability of readmission, especially for one readmission and for the period ≤30 days after discharge (Fig. 4). The time to first readmission was lower in the prolonged LHS group compared with the normal LHS group (47 days [19; 167] versus 95 days [38; 221].

Fig. 4
figure 4

The readmission rates were evaluated for the periods ≤30 days and > 30 days from discharge. White and grey represent the AECOPD patients with normal LHS and prolonged LHS, respectively

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Supplementary analysis not shown

We repeated all predictive and prognosis analyses after excluding patients admitted to ICU during hospitalisation (n = 52) or who required IMV (n = 17) [16] or who required facilities at discharge (n = 25) and the results were equal to those reported for the full cohort. Therefore, we have excluded this detail from the main report.

Discussion

This prospective observational study of hospitalised patients for AECOPD allows us to make three important conclusions. First, we showed that a LHS of ≥7 days identified a typology of severe AECOPD with common chronic baseline characteristics, including worse disease staging, worse symptom perception, LTOT use and colonisation by P. aeruginosa or MRCT. These patients were also at high risk of early readmission to hospital. Second, patients with a prolonged LHS had a worse prognosis until at least 3 years after discharge. Third, at hospital admission, a severe perception of dyspnoea (mMRC scale ≥2) and the presence of acute respiratory acidosis significantly increased the risk of a prolonged LHS. These three aspects of a presentation could help us focusing which patients are at risk of prolonged LHS and ultimately, providing tailored interventions [17,18,19].

Regarding the threshold for defining a prolonged LHS, we have already noted the existing variations [7,8,9,10]. As outlined, we used the threshold of 7 days in line with the time when complications typically develop in an AECOPD [12]. Overall, the baseline characteristics of our AECOPD cohort were comparable to those of other studies [6, 7, 20], but we also considered variables not previously reported in this field. These included the symptom duration before admission and the use of antibiotics and the received therapy in both the short- and the long-term before hospitalisation. These allow us to state that prolonged LHS was related to increased treatment requirements in the period just prior to admission; for example, this group had higher rates of antibiotic use in the 3 months before the admission and higher rates of as-needed bronchodilator use in the 2 months before the admission (Table 1).

Concerning to the microbiological results, our data support those showing a close relationship between bacterial infection and the LHS [21]. Although coinfection with bacteria and viruses may also prolong hospitalisation [21], we did not find a statistically significant association. By contrast, we showed that P. aeruginosa [22] and MRCT [23] are clinical factors that influence outcomes and prolong hospitalisation. Consistent with these microbiological findings and data about the impact of treatment failure [24], patients treated with fluoroquinolones during hospitalisation had a lower risk of a prolonged hospitalisation (Table 5). Finally, we believe that it was noteworthy that colonisation with P. aeruginosa or MRCT (Tables 3 and 5) played a role in prolonging the LHS.

We showed, for the first time, that patients with a prolonged LHS have a higher risk of mortality until at least 3 years after discharge. This is in line with research that looked at the need for intubation and IMV with regards the LHS [16]. Given this close relationship between ICU admission and prolonged LHS, we repeated all our analyses after excluding patients transferred to ICU, but obtained similar results. The high prevalence of early readmissions (≤ 30 days the discharge) in the prolonged LHS group confirmed the results of previous research [25]. That said, it should be noted that the previous research considered the threshold for a prolonged LHS to be > 4 days, which is probably too short to cure a severe AECOPD. Nevertheless, we contend that a prolonged LHS reflects a severe and chronic underlying disease, resulting in a subgroup with a worse prognosis [26, 27].

Predictors of long hospital stay

Univariate analysis indicated that several variables predicted a prolonged LHS and this could be useful to develop integrated interventions. However, the multivariate analysis, adjusted for anthropometric characteristics, showed that only dyspnoea perception and acute respiratory acidosis significantly increase the risk of a prolonged LHS. These clinical conditions have both been associated with prolonged LHS [6, 7, 9, 20], though the respective researches used different measures of respiratory acidosis, such as dyspnoea [9, 20], hypercapnia [7] or the need for NIMV [6]. Moreover, these data were not prospectively obtained [6, 7] and they used different study designs [6, 7, 10], methodologies [6, 7, 10] and settings [8, 9].

Only the study of Tsimogianni et al. [10] considered the mMRC and body mass index as independent predictors of a prolonged LHS, but they used retrospective analysis and a threshold of > 8 days to indicate a prolonged LHS [10]. In the same cohort, our results integrate a subjective patient-reported variable (dyspnoea by mMRC) with an objectively measured variable (pH by blood gas analysis), which may provide an opportunity to develop tailored interventions. It is true that the presence of acute respiratory acidosis correlates to the need for NIMV and independently of the need for ICU admission or IMV it will require more time for cure (e.g., adapting to mechanical ventilation and recovery from acute respiratory failure). However, we believe that there is also a need to focus on the patient’s self-report of dyspnoea given its accuracy as a predictor of the LHS. In patients with COPD, the mMRC questionnaire is a simple measure of dyspnoea perception [28, 29] that is adequate for symptom assessment, relating to both health status [30] and exercise tolerance [31]. Moreover, the mMRC has been shown to predict mortality [32]. Our findings confirm the role of mMRC in the prediction of patients with a prolonged LHS.

Strength and limitation

The main strengths of this observational study were the choice of a definition of prolonged LHS based on clinical experience, the inclusion of many clinical variables, and the long follow-up period to 3 years. However, there were some important limitations. First, although we collected data for more than 400 patients, these derived from a single centre in Spain; therefore, a multicentre study is needed to confirm our results. Second, the research was based on clinical experience by pneumologists and limited to hospital care, so integrations with social variables or economic resources may have been lacking [11]. Finally, related to the potentially effect of in-hospital therapy on LHS, our findings are produced by an observational study and then with difficult comparable. A randomized trial in which all patients receive the same therapy may be useful.

Conclusions

In conclusion, an mMRC score of ≥2 and the presence of respiratory acidosis at admission predict hospitalisation for > 7 days in patients with AECOPD. Moreover, hospitalisation for > 7 days is a marker of severity that affects major prognostic outcomes. These findings could be clinically relevant by helping to identify at-risk patients when they present to hospital. If these predictors can be shown to be modifiable, which they potentially are, we may be able to offer tailored interventions both before and during admission.