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REVIEW

Predictors of Readmission, for Patients with Chronic Obstructive Pulmonary Disease (COPD) – A Systematic Review

Ronald Chow1 University Health Network, University of Toronto, Toronto, ON, CanadaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-6195-8624
ORCID Icon,
Olivia W So1 University Health Network, University of Toronto, Toronto, ON, Canada
,
James HB Im2 The Hospital for Sick Children, Toronto, ON, Canada
,
Kenneth R Chapman1 University Health Network, University of Toronto, Toronto, ON, Canada
,
Ani Orchanian-Cheff1 University Health Network, University of Toronto, Toronto, ON, CanadaORCID Iconhttps://orcid.org/0000-0002-9943-2692
ORCID Icon,
Andrea S Gershon3 Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
&
Robert Wu1 University Health Network, University of Toronto, Toronto, ON, CanadaORCID Iconhttps://orcid.org/0000-0002-6895-1107
ORCID Icon show all
Pages 2581-2617 | Received 22 Apr 2023, Accepted 08 Aug 2023, Published online: 17 Nov 2023

Abstract

Introduction

Chronic obstructive pulmonary disease (COPD) is the third-leading cause of death globally and is responsible for over 3 million deaths annually. One of the factors contributing to the significant healthcare burden for these patients is readmission. The aim of this review is to describe significant predictors and prediction scores for all-cause and COPD-related readmission among patients with COPD.

Methods

A search was conducted in Ovid MEDLINE, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials, from database inception to June 7, 2022. Studies were included if they reported on patients at least 40 years old with COPD, readmission data within 1 year, and predictors of readmission. Study quality was assessed. Significant predictors of readmission and the degree of significance, as noted by the p-value, were extracted for each study. This review was registered on PROSPERO (CRD42022337035).

Results

In total, 242 articles reporting on 16,471,096 patients were included. There was a low risk of bias across the literature. Of these, 153 studies were observational, reporting on predictors; 57 studies were observational studies reporting on interventions; and 32 were randomized controlled trials of interventions. Sixty-four significant predictors for all-cause readmission and 23 for COPD-related readmission were reported across the literature. Significant predictors included 1) pre-admission patient characteristics, such as male sex, prior hospitalization, poor performance status, number and type of comorbidities, and use of long-term oxygen; 2) hospitalization details, such as length of stay, use of corticosteroids, and use of ventilatory support; 3) results of investigations, including anemia, lower FEV1, and higher eosinophil count; and 4) discharge characteristics, including use of home oxygen and discharge to long-term care or a skilled nursing facility.

Conclusion

The findings from this review may enable better predictive modeling and can be used by clinicians to better inform their clinical gestalt of readmission risk.

Introduction

Chronic obstructive pulmonary disease (COPD) is a common respiratory condition characterized by persistent airflow limitationCitation1 and is thought to affect over 10% of the population.Citation2 As a consequence of its chronicity, COPD is responsible for over 3 million deaths globally, making it the third most common cause of death.Citation3

Patients with COPD commonly require hospitalized care, and COPD is one of the most common causes of hospitalization, among chronic diseases.Citation4 Moreover, a notable proportion of patients with COPD will be readmitted, making readmission one of the factors contributing to the significant healthcare burden for these patients. It has been estimated that up to 50% of patients diagnosed with COPD are readmitted within 30 days of initial discharge in the USA.Citation5 In addition to the utilization of healthcare resources, readmission is associated with a worse overall prognosis.Citation6 Over the past decade, there has been an increased interest in identifying predictors and predictive models for readmission.Citation7 Several systematic reviews have attempted to summarize the literature, but they only focused on all-cause or COPD-related readmission alone, and/or did not undertake a quality assessment of the included studies.Citation8–10 In addition, given the rapidly developing literature, with many studies being reported in the past few years, these systematic reviews may not account for current findings.

The aim of this systematic review is to describe significant predictors and prediction scores for all-cause and COPD-related readmission among patients with COPD.

Methods

This review was registered a priori on PROSPERO (CRD42022337035) and reported as per the PRISMA statement.

Search Strategy

A comprehensive search strategy was developed for Ovid MEDLINE, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials from database inception to June 7, 2022, using a combination of database-specific subject headings and text words for the main concepts of COPD and hospital readmissions. An expanded search filter for clinical prediction guides was used. Results were limited to adult human studies. No other limits were applied (Appendix 1).

Eligibility Criteria

Two review authors (RC, OWS) independently screened articles for their eligibility for inclusion. A calibration exercise of 20 articles was undertaken to ensure concordance between reviewers. Discrepancies were resolved by discussion and consensus. If consensus could not be achieved, a third review author (RW) participated in the discussion to resolve discrepancies.

Articles were eligible after level 1 title and abstract screening, if they reported on primary research articles reporting on patients with COPD and readmission. Secondary research articles, such as review articles and economic analyses, as well as editorials/commentaries, were excluded at this stage. Studies included after level 2 full-text screening eligibility criteria required studies to report on patients at least 40 years old with COPD, readmission data within 1 year of a COPD hospitalization, and predictors of readmission. Studies including patients admitted for reasons unrelated to acute exacerbations of COPD (eg pneumonia, acute hypercapnic respiratory failure, obstructive sleep apnea, lung cancer, anxiety/depression) and studies reporting on home care/telemonitoring were excluded at this stage, to limit included articles to only patients with COPD.

Data Extraction

Two of the three review authors (RC, OWS, JHBI) conducted data extraction. As with screening, discrepancies were resolved by discussion and consensus, with or without the input of a third reviewer (RW). Study characteristics of country, sample size, age of participants, and percentage of females enrolled in study were noted. Studies were classified as either assessing predictors or assessing interventions. Studies assessing interventions were further subclassified as either observational cohort studies or randomized controlled trials. Significant predictors of readmission and the degree of significance, as noted by the p-value, were extracted for each study. For studies that did not report p-values, p-values were calculated based on the provided statistics (eg odds ratio and 95% confidence intervals) where possible.

Study Quality

Study quality was assessed for each study. For randomized controlled trials, the risk of bias version 2 tool was used.Citation11 For observational studies reporting on interventions, the ROBINS-I tool was used.Citation12 For observational studies reporting on predictors, the ROBINS-E tool was used.Citation13

Synthesis

Significant predictors were reported by the time of readmission post-discharge and the degree of significance. Predictors were reported as significant predictors for the timepoint of 1-month readmission, the interval of 2–3-month readmission, and the interval of 6–12-month readmission. Predictors were further reported based on whether they were significant at a type I error of 0.05, type I error of 0.01, or no degree of significance available. Significant predictors, as reported by the authors (Supplementary Tables 1 and 2), were grouped together into similarly reported predictors across the literature (eg all mentions of hospital length of stay were grouped together).

Because of the non-uniform reporting of non-significant predictors, where some studies explicitly reported non-significant predictors in the methods/results and others only mentioned significant predictors, non-significant predictors were not presented.

Results

In total, 4035 articles were identified from the database search. After 970 duplicates were removed, 3065 records were screened. Ultimately, 242 articlesCitation14–255 reporting on 16,471,096 patients were included in this review (). Across the literature, there was generally a low risk of bias ().

Figure 1 PRISMA flow diagram.

Figure 1 PRISMA flow diagram.

Figure 2 Quality assessment: (a) studies assessing predictors (ROBINS-E); (b) cohort studies assessing interventions (ROBINS-I); (c) randomized controlled trials assessing interventions (RoB 2).

Figure 2 Quality assessment: (a) studies assessing predictors (ROBINS-E); (b) cohort studies assessing interventions (ROBINS-I); (c) randomized controlled trials assessing interventions (RoB 2).

Overall, 153 studies were observational studies reporting on predictors; 57 studies were observational studies reporting on interventions; and 32 were randomized controlled trials of interventions. The studies were published between 1997 and 2022, with over half of the studies published since 2017. Over one-third of articles (91 studies, 37.6%) originated from the USA; 31 (12.8%) studies originated from Spain, 14 (5.8%) from Canada, 13 (5.4%) from the UK, and 13 (5.4%) from China. Sample sizes ranged from 8 to 4,587,542. The mean/median age was greater than 60 years for nearly all studies. The percentage of females in a study ranged from 0.0% to 80.0%. Individual study characteristics are reported in and .

Table 1 Studies Assessing Predictors

Table 2 Studies Assessing Interventions

A total of 64 significant predictors for all-cause readmission were reported across the literature. Summarizing across all readmission time frames, male sex, prior hospitalization, poorer performance status/activities of daily living, and older age were the most frequently reported patient characteristics that were predictors of readmission. Other significant predictors were COPD severity, alcohol/drug abuse, malnutrition, and history of community-acquired pneumonia (). Heart failure, mental health comorbidity, higher Charlson comorbidity index, diabetes, higher number of comorbidities, chronic kidney disease, and cancer were among the most commonly reported comorbidity predictors of readmission (). Among medications used prior to admission that were significant of readmission, long-term oxygen therapy was the most commonly reported predictor (). Hospital length of stay, non-invasive ventilation, intubation, and admission to the intensive care unit were the most common hospital care predictors of readmission (). Among laboratory values, lower FEV1 and anemia were the most common predictors (). Use of systemic corticosteroids during hospital admission was the most frequently reported predictor of readmission, among medications used during admission (). Discharge to long-term care or a skilled nursing facility was the most commonly reported predictor of readmission, of assessed predictors after admission (). Degrees of significance, and the specific studies reporting on each significant predictor, are reported in .

Figure 3 Continued.

Figure 3 Continued.

Figure 3 Significant predictors of all-cause readmission: (a) patient characteristics; (b) comorbidities; (c) medications prior to admission; (d) hospital care; (e) investigations; (f) medications during hospitalization; (g) medications on discharge; (h) disposition.

Figure 3 Significant predictors of all-cause readmission: (a) patient characteristics; (b) comorbidities; (c) medications prior to admission; (d) hospital care; (e) investigations; (f) medications during hospitalization; (g) medications on discharge; (h) disposition.

Table 3 Significant Predictors for All-Cause Readmission

For COPD-related readmission, 23 significant predictors were found. The most common patient characteristics that were predictors were older age and prior hospitalization (). Mental health comorbidity, diabetes, high Charlson/Elixhauser comorbidity index, and cancer were the most frequently reported comorbidity predictors of readmission (). Longer hospital length of stay, higher eosinophil count, and home oxygen after discharge were also frequently reported predictors of readmission ( and ). Degrees of significance, and the specific studies reporting on each significant predictor, are reported in .

Figure 4 Significant predictors of COPD-related readmission: (a) patient characteristics; (b) comorbidities; (c) hospital care; (d) investigations; (e) medications on discharge.

Figure 4 Significant predictors of COPD-related readmission: (a) patient characteristics; (b) comorbidities; (c) hospital care; (d) investigations; (e) medications on discharge.

Table 4 Significant Predictors of COPD-Related Readmission

Six prediction scores – the BODEX index,Citation23 CODEX index,Citation23 CORE score,Citation247 DOSE index,Citation23 PEARL score,Citation143 and RACE scaleCitation151 – were reported to be predictive of all-cause readmission. The included components of each prediction score are reported in . CORE, PEARL, and RACE were reported to have good predictive value for readmission as a time-to-event outcome variable. The BODEX index, CODEX index, and DOSE index were reported to have good predictive ability for 2–3-month readmission. The CODEX index was reported to have good predictive ability for 6–12-month readmission ().

Table 5 Characteristics of Prediction Scores

Some studies reported significant interventions that can reduce all-cause and COPD-related readmission, most notably use of a COPD-specific care package (Supplementary Tables 3 and 4). Most studies reporting on interventions reported that their intervention was not associated with a significant reduction in readmission rates.

Discussion

This is the largest systematic review to date, reporting on predictors for readmission of patients with COPD, with 242 articles reporting on 16,471,096 patients included in this review. We comprehensively report on predictors for both all-cause and COPD-related readmissions, for readmission at 1 month, 2–3 months, and 6–12 months. The included studies originated from around the world, and there was generally a low risk of bias. There were 64 predictors for all-cause readmission and 23 predictors for COPD-specific readmission. Significant predictors for all-cause readmissions included 1) pre-admission patient characteristics, such as male sex, prior hospitalization, poor performance status, number and type of comorbidities, and use of long-term oxygen; 2) hospitalization details, such as length of stay, use of corticosteroids, and use of ventilatory support; 3) results of investigations, including anemia, lower FEV1, and higher eosinophil count; and 4) discharge characteristics, including the use of home oxygen and discharge to long-term care or a skilled nursing facility.

Several prior systematic reviews have also reported on predictors. Alqahtani et al reviewed 14 studies, stating that comorbidities, previous exacerbations/hospitalizations, and increased length of initial hospital stay were major risk factors for 30- and 90-day all-cause readmission.Citation8 Heart failure, renal failure, depression, and alcohol use were also associated with increased 30-day all-cause readmission, with being female described as a protective factor for readmission. Bahadori and Fitzgerald examined 17 studies, and found that previous hospital admission, dyspnea, and oral corticosteroids were significant risk factors for readmission.Citation9 Njoku et al reviewed 57 studies, and found that hospitalization in the year prior to index admission, comorbidities (such as asthma), living in a deprived area, and living in/or discharge to a nursing home were key predictors of COPD-related readmission.Citation10

This review identifies some notable predictors worth highlighting that are not contained in previous studies, which were parsimonious. While prior studies reported heart failure and neuromuscular disease, we identified other significant preadmission comorbidities, including alcohol use, diabetes, and mental health. Similarly, poor performance status and malnutrition were both identified as important predictors of readmission. In-hospital use of critical care, including non-invasive ventilation, invasive ventilation, and ICU stay, was also identified as predictors. Use of steroids was also predictive of readmission; this was probably related to the severity of disease. Eosinophil count was both correlated and inversely correlated in different studies. While all studies excluded corticosteroid use prior to measurement of the eosinophil count, the studies used various cut-offs to define eosinopenia.Citation114,Citation135,Citation199,Citation247 Further research to determine the utility of eosinophil count is needed.

With COPD patients having a high all-cause readmission rate of 50%Citation5 and being the largest single group of chronic disease patients reported in the literature, identifying those at greatest risk of readmission is a priority as more resources can be directed to this group. This comprehensive systematic review identifies many predictors across multiple domains, including prior to admission, during hospitalization, and post-hospitalization. Current prediction rules for readmissions have areas under the receiver operating characteristics curve in the range 0.70–0.72, and may be limited by lacking variables in all domains (). The findings from this systematic review can be used to develop other prediction scores with higher predictive power. The findings can also be used in clinical practice to help identify individual patients who may benefit from more resources to reduce their risk of readmission. While most prediction scores for COPD readmission are parsimonious, having five or fewer variables for ease of use, a more complicated model with more predictors may be more accurate. More complex models may be enabled through the increase in electronic patient records, which enable more discrete data elements as well as computer decision support.Citation256

This review was not without limitations. There was heterogeneous reporting on some predictor variables; many studies used different cut-off points for predictor variables. We therefore reported on the general directionality of a predictor variable as it relates to readmission. We have reported the predictors as reported by the studies, using their original cut-off points and without any synthesis, in Supplementary Tables 1 and 2. In addition, we were unable to report non-significant predictors owing to non-uniform reporting and therefore the total number of studies investigating each predictor. It is therefore unclear how many studies investigated specific predictors, and what proportion of them reported significant correlation with readmission. For certain predictors that may not be as well studied (eg malnutrition), there could be underestimation of importance.

It is also important to note that some published literature suggests that not all patients discharged with a diagnosis of “COPD” have spirometrically confirmed COPD, and therefore patients discharged with “COPD” may in fact have other comorbidities, such as congestive heart failure.Citation257 Therefore, caution is needed in the interpretation of some of the included studies, given that they simply included patients with a diagnosis of COPD which may not necessarily be confirmed on spirometry. Future studies could look to assess only patients who have spirometrically confirmed COPD.

There may also be some concerns over the generalizability of individual studies to the larger population of patients with COPD admitted to hospitals. There were three studiesCitation127,Citation190,Citation238 with sample sizes of less than 20, and another three studiesCitation80,Citation134,Citation194 with sample sizes of 20–40 patients. Moreover, there were three studiesCitation98,Citation119,Citation210 with no females included in the sample, and another 52 studiesCitation20–23,Citation29,Citation32,Citation38,Citation42,Citation51,Citation53,Citation68–70,Citation72,Citation74,Citation88,Citation90,Citation91,Citation94,Citation116,Citation139,Citation143–149,Citation155,Citation159,Citation161–163,Citation173,Citation177,Citation181,Citation182,Citation190,Citation192,Citation193,Citation201–203,Citation209,Citation211,Citation245,Citation247,Citation249,Citation255 where less than 20% of the sample comprised of females. Reassuringly, the significant predictors reported by these studies agree with larger and more representative studies. In addition, a large proportion of the studies originated from the USA, which may make the results of this review more generalizable to the US population and slightly less generalizable to other countries, especially given the lack of a universal healthcare system in the USA and therefore the potential confounding effect on readmissions.

In conclusion, we found that predictors of readmissions after an admission for COPD exacerbation included patient characteristics prior to and at admission, hospitalization management, results from admission investigations, and discharge characteristics. Findings from this review may enable better model generation if predictors from all these domains are included. These findings may also be used to identify new predictors in the different domains and can be used by clinicians to help generate their gestalt of readmission.

Disclosure

The authors report no conflicts of interest in this work.

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