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

Ronald Chow; Olivia W So; James H B Im; Kenneth R Chapman; Ani Orchanian-Cheff; Andrea S Gershon; Robert Wu

doi:10.2147/COPD.S418295

. 2023 Nov 18;18:2581–2617. doi: 10.2147/COPD.S418295

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

Ronald Chow ^1,^✉, Olivia W So ¹, James H B Im ², Kenneth R Chapman ¹, Ani Orchanian-Cheff ¹, Andrea S Gershon ³, Robert Wu ¹

PMCID: PMC10664718 PMID: 38022828

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.

Keywords: predictors, readmission, chronic obstructive pulmonary disease

Introduction

Chronic obstructive pulmonary disease (COPD) is a common respiratory condition characterized by persistent airflow limitation¹ and is thought to affect over 10% of the population.² As a consequence of its chronicity, COPD is responsible for over 3 million deaths globally, making it the third most common cause of death.³

Patients with COPD commonly require hospitalized care, and COPD is one of the most common causes of hospitalization, among chronic diseases.⁴ 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.⁵ In addition to the utilization of healthcare resources, readmission is associated with a worse overall prognosis.⁶ Over the past decade, there has been an increased interest in identifying predictors and predictive models for readmission.⁷ 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.^8–10 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.¹¹ For observational studies reporting on interventions, the ROBINS-I tool was used.¹² For observational studies reporting on predictors, the ROBINS-E tool was used.¹³

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 articles^14–255 reporting on 16,471,096 patients were included in this review (Figure 1). Across the literature, there was generally a low risk of bias (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 Table 1 and Table 2.

Table 1.

Studies Assessing Predictors

Study	Country	n	Age (years)		% Female
Study	Country	n	Mean ± SD	Median (IQR)	% Female
Abrams 2011¹⁴	USA	28,156	69.1±10.6		3.1%
Abusaada 2017¹⁵	USA	1419	65.1±2.3		47.7%
Agarwal 2016¹⁸	USA	7257		75–84	64.2%
Aksoy 2018¹⁹	Turkey	2727	69.8±10.2		31.5%
Al Aqqad 2017²⁰	Malaysia	81		72.0 (66.4–78.0)	2.5%
Almagro 2006²¹	Spain	129	72.0±9.2		7.0%
Almagro 2012²²	Spain	606	72.6±9.9		2.0%
Almagro 2014²³	Spain	983	72.3±9.7		8.5%
Alpaydin 2021²⁴	Turkey	300	73.1±10.1		28.3%
Alqahtani 2021²⁵	UK	82	71.0±10.4		51.2%
Bade 2019²⁹	USA	48,888	68.7±10.1		3.7%
Bahadori 2009³⁰	Canada	310	74.0±12.0		46.5%
Baker 2013³¹	USA	6095		55–59	58.9%
Barba 2012³³	Spain	275,512	72.0±15.4		30.0%
Bartels 2018³⁴	Canada	511	66.5±13.3		35.2%
Belanger 2018³⁶	Canada	479	68.9±9.4		48.0%
Bernabeu-Mora 2017³⁸	Spain	103	71.0±9.1		6.8%
Bishwakarma 2017⁴⁰	USA	6066	76.9±7.2		67.3%
Boeck 2014⁴¹	Switzerland	43	Not reported		53.5%
Boixeda 2017⁴²	Spain	120	72.9±8.6		2.5%
Bollu 2013⁴³	USA	2463	68.6±10.6		57.2%
Bollu 2017⁴⁴	USA	13,675	67.1±12.4		55.6%
Breyer-Kohansal 2019⁴⁵	Austria	823	68.5±10.2		40.9%
Brownridge 2017⁴⁶	Australia	130	72.9±10.7		51.6%
Buhr 2019⁴⁹	USA	1,622,983	68.0±11.9		58.9%
Buhr 2020⁴⁸	USA	1,622,983	68.0±11.9		57.8%
Candrilli 2015⁵⁰	USA	264,526	67.6±11.2		50.9%
Carneiro 2010⁵¹	Portugal	45	68±12.4		15.6%
Chan 2011⁵²	Hong Kong	65,497	76.8±9.6		23.0%
Chang 2014⁵³	China	135		66 (60–74)	11.9%
Chawla 2014⁵⁴	USA	54	70.0±12.0		70.0%
Chen 2006⁵⁷	Taiwan	145	72.2±10.0		26.9%
Chen 2009⁵⁶	Canada	108,726	72.3±10.9		45.5%
Chen 2021⁵⁵	China	636	70.8±9.9		33.2%
Chu 2004⁵⁸	Hong Kong	110	73.7±7.6		22.7%
Chung 2010⁵⁹	Australia	100	70.6±9.5		44.0%
Coban Agca 2017⁶⁰	Turkey	1490	67.7±11.1		35.0%
Connolly 2006⁶²	New Zealand	7113		65–74	47.7%
Couillard 2017⁶⁵	Canada	167	71.4±10.3		48.5%
Coventry 2011⁶⁶	UK	79	65.3±9.9		44.3%
Crisafulli 2014⁶⁸	Spain	123	69.4±9.8		6.6%
Crisafulli 2015⁷⁰	Spain	125	69.2±9.8		6.4%
Crisafulli 2016⁶⁹	Spain	110	70.5±9.6		6.4%
de Miguel-Diez 2016⁷⁴	Spain	301,794	74.8±10.0		14.0%
Duman 2015⁷⁶	Turkey	1704	Not reported		34.5%
Ehsani 2019⁷⁹	USA	42	70.4±8.1		33.3%
Emtner 2007⁸⁰	Sweden	21	65.0±9.3		66.7%
Eriksen 2010⁸¹	Denmark	300	72.1		61.7%
Ernst 2019⁸²	Canada	203,642	Not reported		Not reported
Euceda 2018⁸³	USA	272	73.2±12.4		56.3%
Fernandez-Garcia 2020⁸⁴	Spain	253	68.99.8		22.5%
Fu 2015⁸⁵	USA	15,755	71.0±12.5		52.9%
Ganapathy 2017⁸⁶	USA	11,496	70.7±10.8		52.5%
Garcia-Aymerich 2003⁸⁷	Spain	340	69±9		Not reported
Garcia-Pachon 2021⁸⁹	Spain	106	73±10		21.7%
Garcia-Sanz 2020⁹⁰	Spain	602	73.8±10.6		14.0%
Gavish 2015⁹¹	Israel	195	66±10		17.4%
Ghanei 2007⁹⁷	Iran	98	58.3±11.0		37.0%
Giron 2009⁹⁸	Spain	78	71±10		0.0%
Glaser 2015⁹⁹	USA	617	Not reported		Not reported
Gonzalez 2008¹⁰⁰	Spain	112	69.3±7.5		Not reported
Goto 2017¹⁰²	USA	845,465	70		59.0%
Goto 2018¹⁰¹	USA	76,697		76 (71–83)	59.6%
Goto 2020¹⁰³	USA	905		76 (68–82)	54.0%
Gudmundsson 2005¹⁰⁴	Sweden	406	69.2±10.5		51.2%
Guerrero 2016¹⁰⁵	Spain	378	71.4±10.0		15.9%
Hajizadeh 2015¹⁰⁸	USA	4791	74.3±6.4		Not reported
Hakansson 2020¹⁰⁹	Denmark	4022		73.1 (63.7–81.1)	55.2%
Harries 2017¹¹⁰	UK	19,551	72.4±10.8		47.8%
Hartl 2016¹¹¹	European countries	16,016	70.8±10.8		32.2%
Hasegawa 2016¹¹²	USA	3084		70 (61–79)	50.0%
Hegewald 2020¹¹⁴	USA	2445	68.4±11.6		50.7%
Hemenway 2017¹¹⁵	USA	369	66		57.5%
Huertas 2017¹¹⁶	Spain	150		70 (65–76)	3.0%
Ingadottir 2018¹¹⁷	Iceland	121	73.7±9.0		57.0%
Islam 2015¹²⁰	USA	350	Not reported		54.9%
Iyer 2016¹²¹	USA	422	64.8±11.7		49.9%
Jacobs 2018¹²²	USA	1,055,830		68 (59–77)	58.5%
Janson 2020¹²³	Sweden	51,247	74.6±10.1		54.8%
Jing 2016¹²⁷	China	8	Not reported		Not reported
Jo 2020¹²⁸	South Korea	15,101	73.4±9.7		25.4%
Johannesdottir 2013¹²⁹	Denmark	3176		72.1 (65.2–77.7)	55.2%
Jones 2020¹³⁰	UK	1029	74.4±9.9		49.0%
Kasirye 2013¹³²	USA	209	Not reported		Not reported
Kerkhof 2020¹³⁵	UK	16,661	75.1±9.9		50.3%
Keshishian 2019¹³⁶	USA	7892	78.1±7.6		57.7%
Kim 2010¹³⁹	South Korea	77	69.2±9.4		16.9%
Kim 2021¹⁴⁰	South Korea	4867		75–79	30.9%
Kishor 2020¹⁴³	India	100	64.0±8.5		16.0%
Ko 2020¹⁴⁴	Hong Kong	346	74.9±7.8		3.8%
Lau 2017¹⁵¹	USA	597,502	Not reported		55.3%
Law 2016¹⁵²	Australia	90	70.7±9.3		50.0%
Li 2020¹⁵⁶	China	108	70.6±9.3		21.3%
Lindenauer 2014¹⁵⁸	USA	25,628		69 (61–77)	56.6%
Lindenauer 2018¹⁵⁷	USA	2340	76.3±7.5		56.1%
Liu 2007¹⁵⁹	Taiwan	100	73.8±10.6		15.0%
Loh 2017¹⁶⁰	USA	123	64.9±11.3		47.2%
Marcos 2017¹⁶¹	USA	143	72.3±10.0		7.0%
Martinez-Gestoso 2021¹⁶²	Spain	615	73.9±10.6		13.8%
Myers 2021¹⁶⁸	USA	7825	Not reported		55.1%
Myers 2021¹⁶⁷	USA	333,429		70 (61–80)	57.1%
Nantsupawat 2012¹⁷⁰	USA	81	73.9		53.1%
Narewski 2015¹⁷¹	USA	160	63.9±10.8		58.8%
Nastars 2019¹⁷²	USA	298,706	77.7±7.7		59.6%
Ng 2007¹⁷³	China	376	72.2±8.4		14.9%
Nguyen 2015¹⁷⁶	USA	2910	72±11		57.1%
Niu 2021¹⁷⁷	China	378	75.2±8.9		15.9%
Njoku 2022¹⁷⁸	Australia	2448		72 (64–80)	50.1%
Osman 1997¹⁸⁰	UK	266	68.0±9.1		47.0%
Ozyilmaz 2013¹⁸²	Turkey	107	66.3±8.6		15.0%
Park 2016¹⁸⁵	South Korea	339,379	71.5±11.6		29.8%
Peng 2021¹⁸⁷	China	123	71.1±9.6		26.8%
Pienaar 2015¹⁸⁹	South Africa	178	63±12		42.1%
Ponce Gonzalez 2017¹⁹¹	Spain	361	75.0±11.5		21.1%
Portoles-Callejon 2020¹⁹²	Spain	108	71.5±11.7		18.5%
Pouw 2000¹⁹⁴	Netherlands	28	70.0±7.2		42.9%
Pozo-Rodriguez 2015¹⁹⁵	Spain	5174	Not reported		Not reported
Price 2006¹⁹⁶	UK	7529	Not reported		Not reported
Quintana 2022¹⁹⁸	Spain	876	73.7±9.4		20.5%
Rahimi-Rad 2015¹⁹⁹	Iran	100	70.8±10.3		31.0%
Rinne 2015²⁰³	USA	25,301	68.9±10.5		3.2%
Rinne 2017²⁰²	USA	33,558	68.7±10.4		3.4%
Rinne 2017²⁰¹	USA	33,558	68.7±10.4		3.4%
Roberts 2002²⁰⁴	UK	1373		72 (66–78)	Not reported
Roberts 2011²⁰⁵	UK	9716		65–74	50.0%
Roberts 2015²⁰⁸	USA	306	70.3±12.3		56.2%
Roberts 2016²⁰⁶	USA	3612	66.6±12.1		67.2%
Roberts 2020²⁰⁷	USA	10,405	72.6±10.3		62.3%
Rodrigo-Troyano 2018²⁰⁹	Spain	106	71±8		17.9%
Ruby 2020²¹⁰	Egypt	190	63.1±10.1		0.0%
Shah 2015²¹⁴	USA	947,084	Not reported		Not reported
Shani 2022²¹⁵	Israel	1203	70.6±11.0		37.3%
Sharif 2014²¹⁶	USA	8263	56.5±5.7		58.8%
Shay 2020²¹⁸	USA	111	67.1±11.7		62.2%
Simmering 2016²²¹	USA	286,313	Not reported		Not reported
Singer 2020²²³	USA	28,240	72.7±8.7		51.9%
Singh 2016²²⁴	USA	135,498		75–84	60.2%
Snider 2015²²⁵	USA	378,419	76.2±6.9		56.8%
Stefan 2017²²⁸	USA	13,893		69	57.6%
Stuart 2010²³²	USA	6322	74.7±0.4		48.9%
Tran 2016²³⁴	USA	375	59.3±7.4		64.0%
Turner 2014²³⁵	England	1942	Not reported		Not reported
Ushida 2022²³⁶	Japan	3396	75.0±11.2		20.4%
Wang 2013²⁴²	Norway	481	72.8±10.5		53.4%
Wong 2008²⁴⁴	Canada	109	63.0±14.5		38.5%
Wu 2020²⁴⁵	Taiwan	625	76.3±10.6		12.0%
Wu 2021²⁴⁷	Taiwan	625	76.3±10.6		12.0%
Wu 2021²⁴⁶	USA	91	60±11		63.7%
Yilmaz 2021²⁴⁹	Turkey	110	67.8±9.3		18.2%
Yu 2015²⁵⁰	USA	18,282	56.6±5.8		62.4%
Zapatero 2013²⁵³	Spain	313,233	72.7±15.7		30.3%
Zhou 2021²⁵⁴	China	417	75±12		20.4%
Zhu 2021²⁵⁵	China	239		72	16.7%

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Note: Blacked out cells indicate that data are not available/applicable.

Table 2.

Studies Assessing Interventions

Study	Country	n	Age (years)		% Female
Study	Country	n	Mean ± SD	Median (IQR)	% Female
Cohort Studies
Adamson 2016¹⁶	Canada	462	70.6±13.2		40.9%
Agarwal 2018¹⁷	USA	1248	Not reported		Not reported
Alshehri 2021²⁶	Saudi Arabia	80	67.0±10.3		41.3%
Ankjaergaard 2017²⁷	Denmark	201	71.5±10.8		56.7%
Ban 2012³²	Malaysia	193	68.5±8.8		13.0%
Bashir 2016³⁵	USA	461	71.7±13.3		32.5%
Bhatt 2017³⁹	USA	187	70.4±11.2		61.0%
Collinsworth 2018⁶¹	USA	308	70.5±12.2		58.4%
Cope 2015⁶⁴	UK	464	Not reported		Not reported
Dalal 2012⁷¹	USA	1936	63.9±9.9		55.4%
De Batlle 2012⁷²	Spain	274	68±8		6.9%
Gay 2020⁹²	USA	157	70.6±11.2		56.1%
Gentene 2021⁹³	USA	Not reported	Not reported		Not reported
George 2016⁹⁴	Singapore	340	72.6±9.1		11.8%
Gerber 2018⁹⁵	Australia	381	71±12		39.9%
Gerrits 2003⁹⁶	Netherlands	1219		65–74	40.4%
Gulati 2018¹⁰⁶	USA	250	69±11		42.0%
Ingadottir 2018¹¹⁸	Iceland	99		73.0 (71.0–77.0)	54.5%
Ingadottir 2018¹¹⁷	Iceland	121	73.7±9.0		57.0%
Jeffs 2005¹²⁴	Australia	216	67.5		63.9%
Joyner 2022¹³¹	USA	253	73.6±7.1		65.2%
Kawasumi 2013¹³³	Canada	3723	72.8		50.8%
Kim 2020¹³⁸	USA	65	62.5±9.0		58.5%
Kiri 2005¹⁴¹	UK	2557	71.1±9.0		49.6%
Kiser 2019¹⁴²	USA	28,700		60–69	53.9%
Ko 2014¹⁴⁷	Hong Kong	185	76.9±7.37		10.3%
Ko 2021¹⁴⁸	Hong Kong	185	76.9±7.37		10.3%
Lalmolda 2017¹⁴⁹	Spain	48	72.5±7.2		6.2%
LaRoche 2016¹⁵⁰	USA	3024	Not reported		Not reported
Lee 2016¹⁵³	USA	995	67.3±10.5		52.6%
Matsui 2017¹⁶³	Japan	12,572	78.4±9.5		18.7%
McGurran 2019¹⁶⁴	USA	2885	70.5±11.5		46.9%
Moullec 2012¹⁶⁶	Canada	189	72.1±10.4		50.3%
Myers 2020¹⁶⁹	USA	805,764	Not reported		56.3%
Nguyen 2014¹⁷⁵	USA	4596	72.3±10.8		Not reported
Nguyen 2021¹⁷⁴	USA	128	64.6±9.2		56.3%
Ohar 2018¹⁷⁹	USA	1274	Not reported		56.3%
Pant 2020¹⁸³	Nepal	86	70.6±11.0		47.7%
Parikh 2016¹⁸⁴	USA	44	66		40.9%
Pendharkar 2018¹⁸⁶	Canada	1435	70±12		48.5%
Petite 2020¹⁸⁸	USA	358	67.1±11.6		60.9%
Pitta 2006¹⁹⁰	Belgium	17		69 (60−78)	5.9%
Puebla Neira 2021¹⁹⁷	USA	4,587,542	Not reported		42.2%
Revitt 2013²⁰⁰	UK	160	70.4±8.6		45.6%
Rueda-Camino 2017²¹¹	Spain	87	70.4±9.3		11.5%
Russo 2017²¹²	USA	160	65.9±10.0		52.5%
Seys 2018²¹³	European countries	257	69.8±10.3		33.9%
Sharma 2010²¹⁷	USA	62,746		75–84	58.6%
Shi 2018²¹⁹	China	6333	67.5±9.5		Not reported
Shin 2019²²⁰	South Korea	308	72.3±9.5		23.7%
Sin 2001²²²	Canada	22,620	75.1±6.7		43.5%
Sonstein 2014²²⁶	USA	420	66.5±11.2		49.5%
Stefan 2013²²⁷	USA	53,900		70 (61–78)	58.0%
Stefan 2021²²⁹	USA	197,376	76.9±7.6		58.6%
Suh 2015²³³	England	120	70±9		51.7%
van Eeden 2017²³⁸	Netherlands	10	62.9±9.6		80.0%
Werre 2015²⁴³	USA	244	Not reported		Not reported
Zafar 2019²⁵²	USA	Not reported	Not reported		Not reported
Zafar 2020²⁵¹	USA	133	60.0±9.8		36.1%
Randomized Controlled Trials
Atwood 2022²⁸	Canada	3710	71.7±12.4		49.7%
Benzo 2016³⁷	USA	215	68.0±9.5		54.9%
Bucknall 2012⁴⁷	UK	464	69.1±9.3		63.4%
Conti 2002⁶³	Italy	49	71.8±7.8		Not reported
Criner 2018⁶⁷	USA	64	61.7±7.9		60.9%
De Jong 2007⁷³	Netherlands	210	70.7±8.4		25.3%
Deutz 2021⁷⁵	USA	214	74.8±7.3		52.8%
Eaton 2006⁷⁷	New Zealand	78	77.3±7.1		53.8%
Eaton 2009⁷⁸	New Zealand	97	69.9±9.8		56.7%
Garcia-Aymerich 2007⁸⁸	Spain	113	73±8		14.2%
Gunen 2007¹⁰⁷	Turkey	159	64.1±8.9		12.0%
Hegelund 2020¹¹³	Denmark	100		73 (45–89)	58.0%
Ip 2004¹¹⁹	Hong Kong	130	80.5±6.6		0.0%
Jennings 2015¹²⁵	USA	172	64.7±10.6		55.2%
Jimenez 2021¹²⁶	Spain	737	70.4±9.9		26.5%
Kebede 2022¹³⁴	Norway	40	73.8±8.2		62.5%
Khosravi 2020¹³⁷	Iran	60	71.0±8.9		28.3%
Ko 2011¹⁴⁶	Hong Kong	60	73.6±7.0		1.7%
Ko 2017¹⁴⁵	Hong Kong	180	74.8±8.2		4.4%
Ko 2021¹⁴⁸	Hong Kong	136	75.0±7.6		2.9%
Lellouche 2016¹⁵⁴	Canada	50	72±8		46.0%
Li 2020¹⁵⁵	China	378	66.3±8.1		15.9%
Monreal 2016¹⁶⁵	Spain	120		71 (61–78)	33.3%
Ozturk 2020¹⁸¹	Turkey	61	62.5±8.6		11.5%
Pourrashid 2018¹⁹³	Iran	62	63.4±8.5		16.1%
Stolz 2007²³⁰	Switzerland	226	69.5		50.4%
Struik 2014²³¹	Netherlands	201	63.7±8.3		58.7%
Utens 2012²³⁷	Netherlands	139	68.0±10.8		38.1%
Vanhaecht 2016²³⁹	European countries	342	69.9±10.3		32.2%
Vermeersch 2019²⁴⁰	Belgium	301	65.5±9.5		43.9%
Wang 2016²⁴¹	China	191	72.9±9.6		28.3%
Xia 2022²⁴⁸	China	337		70.0 (65.0–75.0)	16.9%

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Note: Blacked out cells indicate that data are not available/applicable.

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 (Figure 3a). 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 (Figure 3b). Among medications used prior to admission that were significant of readmission, long-term oxygen therapy was the most commonly reported predictor (Figure 3c). Hospital length of stay, non-invasive ventilation, intubation, and admission to the intensive care unit were the most common hospital care predictors of readmission (Figure 3d). Among laboratory values, lower FEV₁ and anemia were the most common predictors (Figure 3e). Use of systemic corticosteroids during hospital admission was the most frequently reported predictor of readmission, among medications used during admission (Figure 3f). Discharge to long-term care or a skilled nursing facility was the most commonly reported predictor of readmission, of assessed predictors after admission (Figure 3f–h). Degrees of significance, and the specific studies reporting on each significant predictor, are reported in Table 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

Variable Type	Type I Error	1 Month	2–3 Months	6–12 Months
Patient Characteristics
Age	<0.05	Correlated²⁹^,⁵³^,¹⁰⁵^,¹⁷² Inversely correlated¹⁹¹	Correlated³⁸ Inversely correlated¹⁸³	Correlated⁵³^,⁶⁶^,¹²⁷
	<0.01	Correlated³³^,⁴⁹^,⁵⁰^,¹²³^,¹⁸⁵^,²³³^,²⁵³ Inversely correlated³⁴^,¹²²^,¹⁵¹^,²¹⁴^,²²¹	Correlated¹¹¹^,¹²³	Correlated¹²³ Inversely correlated⁷⁶
	Not reported	Correlated²⁹
Alcohol/drug abuse	<0.05	Correlated²⁹
	<0.01	Correlated⁴⁹^,²²⁴	Correlated³⁴^,²⁰⁵
	Not reported
Community-acquired pneumonia	<0.05	Correlated⁸³^,²¹⁰ Inversely correlated²⁰⁸	Correlated²⁵⁴
	<0.01	Correlated⁵⁰	Correlated⁵⁰
	Not reported
COPD severity, assessed by scales	<0.05
	<0.01	Correlated²⁵^,⁴⁹^,⁵⁰^,¹⁰⁵^,²⁴⁷^,²⁵⁰	Correlated⁵⁰	Correlated⁵³^,⁵⁵^,¹²⁷^,²⁴⁴
	Not reported
Dyspnea	<0.05	Correlated⁵⁴	Correlated³⁸	Correlated⁸⁴
	<0.01	Correlated¹⁰⁵
	Not reported
Income	<0.05	Correlated²¹⁵
	<0.01	Inversely correlated¹²²^,²²⁴		Correlated⁹⁷
	Not reported
Insurance: public insurance/Medicare/Medicaid vs private insurance	<0.05
	<0.01	Correlated⁴⁹^,⁵³^,¹²²^,¹²⁸^,¹⁵¹
	Not reported
Male	<0.05	Correlated¹⁶^,³⁵^,¹⁵¹^,¹⁷²^,¹⁷⁸^,²¹⁵^,²¹⁶ Inversely correlated¹²³	Inversely correlated¹²³	Correlated⁵⁹ Inversely correlated¹²³
	<0.01	Correlated⁴⁹^,⁵³^,⁷⁴^,¹²²^,¹²⁸^,¹⁴⁰^,¹⁸⁵^,²¹⁴^,²²⁴^,²³² Inversely correlated²⁵³	Correlated¹⁹⁵	Correlated⁴¹^,⁷⁶
	Not reported	Correlated²⁰¹
Malnutrition	<0.05	Correlated⁷⁴		Correlated¹³⁹
	<0.01	Correlated³³^,⁷⁴^,²⁵³
	Not reported
Poorer performance status/activities of daily living	<0.05	Correlated²⁵^,⁸⁴^,²⁴⁷	Correlated²⁵^,¹⁹⁵^,¹⁹⁸	Correlated³⁰^,⁸⁴^,¹⁰⁴
	<0.01		Correlated⁶²
	Not reported
Prior hospitalization	<0.05	Correlated⁵⁴^,⁸⁴^,¹⁷⁸^,²¹²^,²³³	Correlated²⁰^,³⁸^,¹⁷⁸^,¹⁸³	Correlated¹⁷⁸
	<0.01	Correlated²⁵^,³¹^,⁴⁹^,¹⁰⁵^,¹²⁵^,¹⁷²^,¹⁷⁵^,¹⁷⁶^,²⁰⁸^,²¹⁵^,²¹⁶	Correlated²²^,²⁵^,³¹^,⁴⁵^,⁸⁴^,⁸⁹^,⁹¹^,¹¹¹^,¹²⁵^,¹⁹⁵^,¹⁹⁸^,²⁰⁹^,²¹²^,²¹⁵	Correlated⁴¹^,⁵⁵^,⁸¹^,⁸⁷^,¹⁰⁴
	Not reported		Correlated⁶²
Race: black	<0.05	Inversely correlated¹⁷²
	<0.01	Correlated¹⁰²^,¹⁵¹^,²¹⁴
	Not reported
Tobacco smoker	<0.05	Correlated²⁴⁷	Correlated⁴⁵
	<0.01	Inversely correlated²⁵³	Inversely correlated³⁸
	Not reported
Vaccination: influenza	<0.05	Correlated¹⁷⁰
	<0.01	Inversely correlated²³²
	Not reported
Comorbidities
Number of comorbidities	<0.05	Correlated²⁹^,³⁴^,³⁵^,¹²³^,¹⁷²	Correlated³⁸^,¹²³	Correlated¹²³
	<0.01	Correlated¹²²	Correlated³⁴^,²⁰⁵	Correlated²⁴⁴
	Not reported
Arrhythmias	<0.05	Correlated²⁴		Correlated⁵⁶
	<0.01
	Not reported
Cancer	<0.05		Correlated³¹
	<0.01	Correlated³¹^,³³^,⁴⁹^,⁵⁰^,²⁵³	Correlated⁵⁰
	Not reported
Cardiovascular comorbidities	<0.05	Correlated³⁴^,¹⁰³	Correlated³⁸^,¹⁹⁸
	<0.01	Correlated⁴⁹	Correlated³⁴
	Not reported
Cerebrovascular disease	<0.05
	<0.01	Correlated⁴⁹		Correlated³¹
	Not reported
Charlson comorbidity index	<0.05	Correlated⁷⁴^,²¹⁵^,²⁵⁵	Correlated¹⁹⁵^,²¹⁵	Inversely correlated¹⁷⁸
	<0.01	Correlated³³^,⁵⁰^,¹²⁸^,¹⁷⁵^,¹⁷⁶^,²¹⁴^,²⁵³	Correlated⁵⁰^,¹¹¹
	Not reported
Chronic kidney disease	<0.05	Correlated⁵⁴	Correlated³¹	Inversely correlated⁷⁶
	<0.01	Correlated³³^,⁴⁹^,⁵⁰^,¹²²^,²⁵⁰	Correlated⁵⁰	Correlated³¹
	Not reported
Connective tissue/rheumatologic disease	<0.05			Correlated³¹
	<0.01		Correlated²⁰⁸
	Not reported
Coronary heart disease/myocardial infarction	<0.05		Correlated³¹
	<0.01	Correlated⁴⁹ Inversely correlated²⁵⁵		Correlated³¹
	Not reported
Diabetes	<0.05	Correlated²¹²^,²³²	Correlated³¹^,²⁰⁵ Inversely correlated¹¹¹	Correlated¹⁰⁸
	<0.01	Correlated³¹^,³³^,⁴⁹^,¹²²^,¹⁵¹^,¹⁷⁶^,²³²		Correlated³⁰
	Not reported
Gastrointestinal comorbidities	<0.05	Correlated²¹²	Inversely correlated²⁰⁸
	<0.01	Correlated⁴⁹
	Not reported
Heart failure	<0.05	Correlated²⁴^,²⁹^,²¹⁶^,²³²		Correlated⁵⁶^,¹⁰⁸
	<0.01	Correlated³¹^,³³^,⁴⁹^,¹²²^,¹⁷⁶^,²⁵⁰^,²⁵³	Correlated³¹	Correlated³¹
	Not reported
Right-sided heart failure/cor pulmonale	<0.05		Inversely correlated²⁰⁸
	<0.01		Correlated²⁰⁵
	Not reported
Hematologic malignancy	<0.05
	<0.01	Inversely correlated²⁰⁸	Correlated²⁰⁸
	Not reported
Hypertension	<0.05	Correlated²¹²^,²³²
	<0.01	Inversely correlated⁴⁹
	Not reported
Ischemic heart disease	<0.05	Correlated²³²	Correlated²⁰
	<0.01		Correlated⁵⁰^,²⁰⁵
	Not reported
Liver disease	<0.05			Correlated³¹
	<0.01	Correlated³¹^,⁴⁹	Correlated³¹
	Not reported
Lung cancer	<0.05
	<0.01	Correlated⁵⁰^,²¹⁶	Correlated⁵⁰^,²⁰⁵
	Not reported
Mental health comorbidity	<0.05	Correlated²⁵^,²⁹^,¹⁷⁶		Correlated⁶⁶^,⁹⁷^,¹⁰⁸
	<0.01	Correlated¹⁴^,⁴⁹^,⁸³^,¹⁵¹^,²¹⁶^,²²⁴^,²³²^,²⁵⁰
	Not reported
Musculoskeletal comorbidity	<0.05
	<0.01	Correlated²¹⁶^,²³²^,²⁴⁷
	Not reported
Obesity	<0.05			Correlated⁸⁴
	<0.01	Inversely correlated⁷⁴^,¹²²^,²⁵³
	Not reported
Peripheral vascular disease	<0.05
	<0.01	Correlated⁴⁹		Correlated³¹
	Not reported
Pulmonary hypertension	<0.05
	<0.01	Correlated¹⁷⁶^,²⁵⁰
	Not reported
Pulmonary vascular disease	<0.05		Inversely correlated²⁰⁸
	<0.01	Correlated²⁵⁰		Correlated⁵⁶
	Not reported
Medications Prior to Admission
Long-term oxygen therapy	<0.05	Correlated²⁴	Correlated¹⁸³	Correlated¹⁰⁰
	<0.01			Correlated¹⁰⁴
	Not reported
Steroid	<0.05		Correlated²¹⁵	Correlated²⁴⁴
	<0.01
	Not reported
Hospital Care
Admission to ICU	<0.05	Correlated¹⁷²^,²²⁴^,²⁵⁰	Correlated¹⁸³
	<0.01	Correlated²¹⁴
	Not reported
Hospital length of stay	<0.05	Correlated²⁵^,³³^,¹⁷²^,²¹⁶^,²²⁴^,²⁵⁰	Correlated³⁸ Inversely correlated¹⁸³	Correlated⁷⁶^,¹⁰⁴
	<0.01	Correlated⁴⁹^,⁵⁰^,¹⁰⁵^,¹²²^,¹²⁸^,¹⁴⁰^,¹⁸⁵^,²⁰²^,²¹⁴^,²⁵³	Correlated⁵⁰
	Not reported
Intubation	<0.05		Inversely correlated²⁰⁸
	<0.01	Correlated⁴⁹	Correlated¹¹¹
	Not reported
Non-invasive ventilation	<0.05	Correlated⁷⁴^,²¹²
	<0.01	Correlated³³^,⁴⁹^,²⁴⁷	Correlated³⁸
	Not reported
Steroid treatment success	<0.05	Inversely correlated⁶⁹		Inversely correlated⁶⁹
	<0.01		Inversely correlated⁶⁹
	Not reported
Investigations
Anemia	<0.05			Correlated¹⁰⁸ Inversely correlated⁷⁶
	<0.01	Correlated³³^,⁴⁹^,¹⁷⁵^,¹⁷⁶^,²⁵⁰
	Not reported
C-reactive protein	<0.05	Correlated⁶⁰
	<0.01			Correlated⁵³^,¹²⁷ Inversely correlated⁷⁶
	Not reported
Eosinophil count	<0.05	Inversely correlated¹³⁵^,¹⁹⁹	Correlated¹¹⁴	Correlated⁶⁵ Inversely correlated⁷⁶^,¹⁵⁶
	<0.01	Correlated²⁴⁷		Correlated¹¹⁴
	Not reported
FEV₁	<0.05	Inversely correlated²⁰⁴^,²³⁵^,²⁴⁹	Inversely correlated⁸⁹^,¹⁸²	Inversely correlated⁶⁶
	<0.01	Inversely correlated¹⁰⁵^,²¹²^,²⁴⁷		Inversely correlated⁵³^,⁸⁷^,¹⁰⁴
	Not reported
Leukocyte count	<0.05
	<0.01	Correlated⁶⁰		Correlated⁷⁶
	Not reported
Neutrophil-to-lymphocyte ratio	<0.05
	<0.01	Correlated⁶⁰		Correlated⁷⁶
	Not reported
PaCO₂	<0.05			Correlated¹⁰⁰^,¹³⁹
	<0.01	Correlated¹⁰⁵
	Not reported
pH	<0.05	Inversely correlated¹⁰⁵^,²⁵⁵
	<0.01
	Not reported
Medications During Hospitalization
Anticholinergics	<0.05			Correlated⁸⁷
	<0.01			Inversely correlated¹⁰⁴
	Not reported
Inhaled corticosteroids	<0.05
	<0.01	Inversely correlated²⁰⁸		Inversely correlated⁵⁵
	Not reported
Oxygen therapy	<0.05
	<0.01	Correlated¹⁷⁵		Correlated³¹
	Not reported
Long-acting beta-agonist	<0.05	Correlated¹⁷⁶
	<0.01			Correlated⁵⁵
	Not reported
Systemic corticosteroids	<0.05	Correlated¹⁷⁵
	<0.01	Correlated¹⁷⁶	Correlated³¹	Correlated³¹^,⁷⁶
	Not reported
Medications on Discharge
Oral corticosteroids	<0.05	Inversely correlated²¹⁶	Correlated¹⁹⁵
	<0.01
	Not reported
Maintenance medication	<0.05	Inversely correlated²³²
	<0.01			Inversely correlated²³⁴
	Not reported
Short-acting muscarinic antagonist	<0.05
	<0.01		Correlated²⁰⁸ Inversely correlated²⁰⁸
	Not reported
Disposition
Discharged with home care	<0.05			Correlated⁸¹
	<0.01	Correlated²¹⁴
	Not reported
Discharged to long-term care/skilled nursing facility	<0.05	Correlated⁷⁴^,²³² Inversely correlated²²⁴		Correlated¹⁰⁸
	<0.01	Correlated³⁵^,⁴⁹^,¹²²^,¹²⁸
	Not reported
Follow-up within 30 days of discharge	<0.05		Correlated¹²⁵ Inversely correlated⁹¹
	<0.01	Inversely correlated²¹⁶	Correlated¹⁸²^,¹⁹⁸
	Not reported
Prediction Scores
BODEX index²³			p=0.008
CODEX index²³			p<0.0001	p<0.0001
CORE score²⁴⁷	p<0.001
DOSE index²³			p<0.01
PEARL score¹⁴³	p<0.0001
RACE scale¹⁵¹	R²=0.923

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For COPD-related readmission, 23 significant predictors were found. The most common patient characteristics that were predictors were older age and prior hospitalization (Figure 4a). Mental health comorbidity, diabetes, high Charlson/Elixhauser comorbidity index, and cancer were the most frequently reported comorbidity predictors of readmission (Figure 4b). Longer hospital length of stay, higher eosinophil count, and home oxygen after discharge were also frequently reported predictors of readmission (Figure 4c and e). Degrees of significance, and the specific studies reporting on each significant predictor, are reported in Table 4.

Table 4.

Significant Predictors of COPD-Related Readmission

	Type I Error	1 Month	2–3 Months	6–12 Months
Patient Characteristics
Age	<0.05	Correlated²¹⁹^,²⁴⁵		Correlated³¹^,¹²⁹^,²³⁴
	<0.01	Correlated⁵⁰^,¹¹⁰ Inversely correlated³⁴^,⁴⁸^,²⁰⁶	Correlated⁵⁰^,¹¹⁰ Inversely correlated³⁴
	Not reported
COPD severity, assessed by scales	<0.05			Correlated²¹^,¹⁸⁷
	<0.01	Correlated⁵⁰	Correlated⁵⁰
	Not reported
Dyspnea	<0.05	Correlated¹¹⁶		Correlated²¹
	<0.01	Correlated²⁰⁶		Correlated²⁰⁶
	Not reported
Male	<0.05			Correlated¹²⁹
	<0.01	Correlated⁴⁸
	Not reported	Correlated²⁰¹
Prior hospitalization	<0.05			Correlated¹²⁹
	<0.01	Correlated³¹^,⁷⁰	Correlated²²^,³¹	Correlated²¹^,³¹
	Not reported
Tobacco smoking	<0.05
	<0.01		Correlated³⁴	Correlated²⁰⁶
	Not reported
Comorbidities
Number of comorbidities	<0.05
	<0.01	Correlated²¹⁹	Correlated³⁴
	Not reported
Asthma	<0.05			Correlated¹²⁹
	<0.01	Correlated²⁰⁶	Correlated⁵⁰	Correlated²⁰⁶
	Not reported
Cancer	<0.05	Correlated³¹^,⁵⁰	Correlated³¹^,⁵⁰
	<0.01			Correlated³¹
	Not reported
Cardiovascular disease	<0.05	Correlated³⁴	Correlated³⁴
	<0.01		Correlated¹³⁵
	Not reported
Charlson/Elixhauser comorbidity index	<0.05		Correlated²²
	<0.01	Correlated⁴⁸^,⁵⁰	Correlated⁵⁰
	Not reported
Chronic kidney disease	<0.05
	<0.01	Correlated⁵⁰	Correlated⁵⁰	Correlated³¹
	Not reported
Diabetes	<0.05	Correlated²⁰⁶
	<0.01	Correlated⁷⁰		Correlated³¹^,²⁰⁶
	Not reported
Heart failure	<0.05
	<0.01			Correlated³¹^,²⁰⁶
	Not reported
Right-sided heart failure/cor pulmonale	<0.05			Correlated²¹
	<0.01		Correlated²²
	Not reported
Mental health comorbidity	<0.05			Correlated²¹^,¹²⁹
	<0.01	Correlated¹²¹^,²⁰⁶	Correlated¹²¹	Correlated¹²¹^,²⁰⁶
	Not reported
Musculoskeletal comorbidity	<0.05			Correlated¹²⁹
	<0.01			Correlated²⁰⁶
	Not reported
Myocardial infarction	<0.05
	<0.01			Correlated³¹^,¹²⁹
	Not reported	Correlated⁹⁹
Hospital Care
Hospital length of stay	<0.05	Inversely correlated¹¹⁰		Correlated¹⁸⁷
	<0.01	Correlated⁵⁰	Correlated⁵⁰^,¹¹⁰
	Not reported
Investigations
Eosinophil count	<0.05	Correlated¹¹⁴		Correlated¹¹² Inversely correlated¹⁵⁶
	<0.01	Correlated¹¹⁴^,²⁴⁵	Correlated¹¹⁴ Inversely correlated¹³⁵	Correlated³⁶^,⁶⁵^,¹¹⁴^,¹⁸⁷
	Not reported
FEV₁	<0.05
	<0.01	Inversely correlated³⁴	Inversely correlated³⁴^,¹³⁵	Inversely correlated²⁴⁵
	Not reported
Medications During Admission
Oxygen therapy	<0.05
	<0.01	Inversely correlated¹⁸⁵		Correlated³¹
	Not reported
Medications on Discharge
Home oxygen	<0.05			Correlated²¹
	<0.01		Correlated²²
	Not reported

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Six prediction scores – the BODEX index,²³ CODEX index,²³ CORE score,²⁴⁷ DOSE index,²³ PEARL score,¹⁴³ and RACE scale¹⁵¹ – were reported to be predictive of all-cause readmission. The included components of each prediction score are reported in Table 5. 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 3).

Table 5.

Characteristics of Prediction Scores

	CODEX	BODEX	PEARL	CORE	RACE
Patient characteristics	Comorbidity Number of severe exacerbations (ED or admission) mMRC scale	BMI Number of severe exacerbations (ED or admission) mMRC scale	Age Previous admissions Left heart failure/right heart failure eMRC scale Right heart failure	Lung function Neuromuscular disease exacerbations Triple inhaler management	Age Gender Income Race Payer Comorbidities
Hospitalization management	–	–	–	–
In-hospital investigations	FEV₁%	FEV₁%		Eosinophil count
Discharge characteristics	–	–	–	–

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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.⁸ 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.⁹ 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.¹⁰

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.¹¹⁴^,¹³⁵^,¹⁹⁹^,²⁴⁷ Further research to determine the utility of eosinophil count is needed.

With COPD patients having a high all-cause readmission rate of 50%⁵ 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 (Table 5). 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.²⁵⁶

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.²⁵⁷ 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 studies¹²⁷^,¹⁹⁰^,²³⁸ with sample sizes of less than 20, and another three studies⁸⁰^,¹³⁴^,¹⁹⁴ with sample sizes of 20–40 patients. Moreover, there were three studies⁹⁸^,¹¹⁹^,²¹⁰ with no females included in the sample, and another 52 studies^20–23^,²⁹^,³²^,³⁸^,⁴²^,⁵¹^,⁵³^,^68–70^,⁷²^,⁷⁴^,⁸⁸^,⁹⁰^,⁹¹^,⁹⁴^,¹¹⁶^,¹³⁹^,^143–149^,¹⁵⁵^,¹⁵⁹^,^161–163^,¹⁷³^,¹⁷⁷^,¹⁸¹^,¹⁸²^,¹⁹⁰^,¹⁹²^,¹⁹³^,^201–203^,²⁰⁹^,²¹¹^,²⁴⁵^,²⁴⁷^,²⁴⁹^,²⁵⁵ 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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Predictors of Readmission, for Patients with Chronic Obstructive Pulmonary Disease (COPD) – A Systematic Review

Ronald Chow

Olivia W So

James H B Im

Kenneth R Chapman

Ani Orchanian-Cheff

Andrea S Gershon

Robert Wu

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Methods

Search Strategy

Eligibility Criteria

Data Extraction

Study Quality

Synthesis

Results

Figure 1.

Figure 2.

Table 1.

Table 2.

Figure 3.

Figure 3.

Table 3.

Figure 4.

Table 4.

Table 5.

Discussion

Disclosure

References

ACTIONS

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RESOURCES

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PERMALINK

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

Ronald Chow

Olivia W So

James H B Im

Kenneth R Chapman

Ani Orchanian-Cheff

Andrea S Gershon

Robert Wu

Abstract

Introduction

Methods

Results

Conclusion

Introduction

Methods

Search Strategy

Eligibility Criteria

Data Extraction

Study Quality

Synthesis

Results

Figure 1.

Figure 2.

Table 1.

Table 2.

Figure 3.

Figure 3.

Table 3.

Figure 4.

Table 4.

Table 5.

Discussion

Disclosure

References

ACTIONS

PERMALINK

RESOURCES

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