Peri-discharge complex interventions for reducing 30-day hospital readmissions among heart failure patients: overview of systematic reviews and network meta-analysis

Abstract

Aims:

An overview of systematic reviews (SRs) and network meta-analysis (NMA) was conducted to synthesize evidence of comparative effectiveness of different peri-discharge complex interventions for reducing 30-day hospital readmissions among heart failure (HF) patients.

Methods:

We searched five databases for SRs from their inception to August 2019 and conducted additional search for randomized controlled trials (RCTs) published between 2003 and 2020. We used random-effect pairwise meta-analysis with pooled risk ratios (RRs) and 95% confidence intervals (CIs) to quantify the effect of complex interventions, and NMA to evaluate comparative effectiveness among complex interventions. Primary outcome was 30-day all-cause hospital readmissions, while secondary outcomes were 30-day HF-related hospital readmissions, 30-day mortality, and 30-day emergency department visits.

Results:

From 20 SRs and additional RCT search, 21 eligible RCTs (n = 5362) assessing eight different peri-discharge complex interventions were included. Pairwise meta-analysis showed no significant difference between peri-discharge complex interventions and controls on all outcomes, except that peri-discharge complex interventions were significantly more effective than controls in reducing 30-day mortality (pooled RR = 0.68, 95% CI: 0.49–0.95, 5 RCTs). NMA indicated that for reducing 30-day all-cause hospital readmissions, supportive–educative intervention had the highest probability to be the best intervention, followed by disease management; while for reducing 30-day HF-related hospital readmissions, disease management is likely to be the best intervention.

Conclusions:

Our results suggest that disease management has the best potential to reduce 30-day all-cause and HF-related hospital readmissions. Benefits of the interventions may vary across health system contexts. Evidence-based complex interventions require local adaptation prior to implementation.

Keywords

heart failure hospital readmissions peri-discharge complex intervention overview of systematic reviews network meta-analysis

Introduction

Heart failure (HF) is a major non-communicable disease which results in a substantial burden on the healthcare system.^1,2 In 2017, the number of HF patients was approximately 64 million.³ HF is also a leading cause of 30-day hospital readmissions,⁴ which are considered a highly common, costly, and unfavorable outcome of health systems.⁵–⁸ In the US, nearly 25% of HF patients were readmitted within 30 days of hospital discharge, accounting for nearly $17 billion of Medicare expenditure in 2011.^4,9 In addition, 30-day hospital readmissions are considered to be associated with lower overall patient satisfaction,¹⁰ higher risk of mortality, and longer cumulative length of hospital stay among HF patients.¹¹

It is estimated that up to 75% of 30-day hospital readmissions among HF patients are preventable.¹² Numerous peri-discharge complex interventions have been proposed, evaluated, and implemented to reduce 30-day hospital readmissions,¹³ but existing clinical evidence is conflicting. A systematic review (SR) of four trials showed that when compared with usual care, a home-visiting program significantly reduced 30-day all-cause hospital readmissions among HF patients while neither telemonitoring nor structured telephone support would reduce 30-day all-cause hospital readmissions.¹⁴ However, another SR showed that telemonitoring with medical support during office hours or 24/7 was associated with significant reductions in 30-day all-cause hospital readmissions among HF patients.¹⁵ Inconsistent evidence summarized in different SRs makes it difficult to conclude which peri-discharge complex interventions may be used to reduce 30-day hospital readmissions among HF patients. To address this gap, we performed an overview of SRs and network meta-analysis (NMA) to assess comparative effectiveness of different peri-discharge complex interventions for reducing 30-day hospital readmissions among HF patients. This is the Patient, Intervention, Comparison and Outcome (PICO) questions we would like to answer.

Methods

This study was reported in accordance to the Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) extension statement for NMA.¹⁶

Protocol and registration

We registered the protocol for this study in PROSPERO database (Registration No. CRD42020189557).

Eligibility criteria

SR is an ‘endeavor to identify, appraise, and synthesize all the evidence that fulfills prespecified eligibility criteria to answer a specific research question’, as defined in the Cochrane Handbook version 6.¹⁷ Accordingly, to be eligible for this overview, an SR should:¹⁸ (1) clarify research questions; (2) describe sources searched, with a reproducible comprehensive search strategy; (3) report inclusion and exclusion criteria; (4) include citation screening methods; (5) critically appraise risk of bias among included studies; (6) report data analysis and synthesis methods which allow reproducibility; (7) be published in English or Chinese and satisfy the following criteria for PICO. From the eligible SRs, we extracted embedded randomized controlled trials (RCTs) which satisfied the PICO criteria below for further evaluation and data analysis.

Patient

Adult patients (⩾18 years) who were admitted from the community to a hospital inpatient ward for 24 h or more with a diagnosis of HF were included. Patients with co-existing psychiatric, behavioral health, substance use, and pediatric or obstetric hospital admissions were excluded.

Interventions and comparisons

Peri-discharge complex interventions, which were compared with any types of controls, including usual care, were considered as eligible. Aside from acute hospitals, peri-discharge complex interventions implemented in the following settings were also eligible: convalescent hospitals, nursing homes, hospices, or patients’ homes. There were no restrictions on the number of components included in packages of complex interventions.

Outcomes

Eligible SRs should report the outcomes of hospital readmissions in both intervention and control groups. To be included in pairwise meta-analysis and NMA, RCTs should report the outcomes of 30-day all-cause or HF-related hospital readmissions in both intervention and control groups.¹⁹ Primary outcome of this overview was prespecified as 30-day all-cause hospital readmissions, while secondary outcomes included 30-day HF-related hospital readmissions, 30-day mortality, and 30-day emergency department (ED) visits.

Literature search

We searched for SRs in MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, Global Health, and AMED from inception to 30 August 2019. We applied specialized filters for SRs in MEDLINE and EMBASE. No restrictions on publication status were imposed.

The earliest search end date among included SRs was 2003. Hence, we updated search for potentially eligible RCTs in MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials published between January 2003 and March 2020. Detailed search strategies for SRs and RCTs search works are shown in Supplemental Appendices 1a and 1b, respectively.

Literature selection, data extraction, assessments of methodological quality, risk of bias assessment, and quality of evidence rating

Two reviewers screened for eligible SRs and RCTs, conducted data extraction, assessed methodological quality of eligible SRs, and evaluated risk of bias of included RCTs independently. Disagreements were resolved by discussion and consensus between the two reviewers. A third reviewer was consulted to settle unsolved discrepancies.

We screened title and abstract of retrieved SRs and assessed full text for eligibility. A list of included RCTs was generated from eligible SRs as well as from additional RCT search. For duplicates or overlapping RCTs, the single most updated and comprehensive version was selected for inclusion. To be included in NMA, RCTs should share a common comparator which serves as a bridge for indirect comparison of various peri-discharge complex interventions.

We used a predesigned data extraction form to collect the following information from each included RCT: year of publication, country, follow-up period of the study, details of interventions and comparators, number of patients analyzed and randomized, patient age range, and results of all prespecified outcomes.

Peri-discharge complex interventions for reducing hospital readmissions evaluated included RCTs composed of different components. To facilitate analysis, we coded components of interventions delivered in both groups based on a published classification framework of complex interventions for preventing avoidable hospital readmissions (see Supplemental Appendix 2). Two reviewers performed the coding process independently after co-piloting. A third reviewer would make the decision if disagreement persisted.

We appraised methodological quality of included SRs using the validated AMSTAR 2 instrument.²⁰ Overall methodological quality of each SR was rated as high, moderate, low, or critically low. For each included RCT, we assessed risk of bias using the Cochrane Risk of Bias Tool 2.0.²⁰ The overall risk of bias of each trial was judged as low risk of bias, some concerns, or high risk of bias.²¹

We assessed the overall quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.²² The GRADE approach allows the quality of evidence for each outcome to be graded as high, moderate, low, and very low.

Data analysis

Pairwise meta-analyses

We conducted pairwise meta-analyses first, followed by NMA, of which the sequence is regarded as a standard methodology in the field.²³ For pairwise meta-analyses, we applied a random-effect model to synthesize effectiveness data comparing peri-discharge complex interventions with controls.²⁴ We used pooled risk ratios (RRs) with 95% confidence intervals (CIs) to express dichotomous data, including the primary outcome of 30-day all-cause hospital readmissions as well as the secondary outcomes of 30-day HF-related hospital readmissions, 30-day mortality, and 30-day ED visits. I² values quantified the level of heterogeneity, with <25% regarded as low level, 25%–50% as moderate level, and >50% as high level.²⁵

We conducted subgroup analysis on the primary outcome by stratifying RCTs based on different types of control interventions. We also conducted a sensitivity analysis by focusing on RCTs with an overall low risk of bias on the primary outcome of 30-day all-cause hospital readmissions. The pairwise meta-analyses were conducted using Revman version 5.3. Sensitivity analyses were performed in which one study was removed at a time and the rest analyzed to estimate whether the pooled RR could have been influenced markedly by the single study.²⁶ The analyses were performed using metainf command in STATA 14.

NMA

Detailed instruction of NMA could be found in Supplemental Appendix 3. A network plot was generated to display all types of interventions included in the NMA.¹⁶ Comparative effectiveness results of all possible pairs of comparisons were summarized with an odds ratio (OR) and 95% CI.²⁷ Surface under the cumulative ranking curve (SUCRA) was used to provide an effectiveness hierarchy ranking.²⁸ Probability that an intervention being, relatively speaking, the ‘most’ effective option, the second-best option, and so on was deduced accordingly. The larger the SUCRA, the higher effectiveness ranking the intervention would have.

Consistency of direct evidence and indirect evidence on the same comparison is a key assumption of NMA.²⁹–³¹ The amount of inconsistency was measured by inconsistency factor, which refers to the absolute mean difference between direct and indirect estimates in the loop.³⁰ We used the separating indirect from direct evidence (SIDE) approach to calculate inconsistency factors and associated 95% CIs.³⁰ NMA was conducted using STATA version 14.0.³²

Publication bias

Publication bias on the primary outcome was assessed via a contour-enhanced funnel plot produced by R version 3.6.1.

Results

Results on literature search and selection

Through the literature search, a total of 20 SRs were identified and considered as eligible for this overview (Supplemental Appendix 5a). These 20 SRs synthesized a total of 564 studies, of which 554 were excluded due to the following reasons: duplicate (n = 315), not evaluating interventions for HF patients (n = 10); no 30-day readmissions outcome reported (n = 205); not RCT (n = 17); and language other than English/Chinese (n = 7). Additional literature search for potential RCTs from January 2003 to March 2020 identified 11 RCTs that were considered eligible (Supplemental Appendix 4). Therefore, a total of 21 RCTs were included (Supplemental Appendix 5b). Details of the literature search and selection of SRs were presented in Figure 1.

Figure 1.

Flowchart of literature search and selection for systematic reviews and randomized controlled trials

Characteristics of included RCTs

Participants

Characteristics of the 21 included RCTs are summarized in Table 1. They included a total of 5362 HF patients, with sample sizes varying from 16 to 1437. The mean age range of participants was from 57 to 83 years.

Table 1.

Main characteristics of included randomized controlled trials (RCTs) (n = 21).

Author, year	Country	Follow-up period of the study	Interventions/comparators	No. of patients in the group (A/R)	Age range/mean ± SD (years)	Primary outcome: 30-day readmissions for all-cause: no. of events (%)	Secondary outcome: 30-day readmissions for HF: no. of events (%)	Secondary outcome: 30-day mortality: no. of events (%)	Secondary outcome: 30-day ED visits: no. of events (%)
Bowles, 2011	USA	Mar 2006–Nov 2009	Home care	101/101	71.3 ± 10.2	16 (15.8)	8 (7.9)	NR	10 (9.9)
			Usual care	116/116	73.5 ± 9.6	22 (19)	10 (8.6)	NR	12 (10.3)
Boyde, 2018	Australia	Jul 2012–Mar 2015	Supportive–educative intervention	97/100	64 ± 12.4	13 (13)	7 (7)	NR	NR
			Discharge education	98/100	64 ± 12.9	14 (14)	4 (4)	NR	NR
Breathett, 2018	USA	Feb 2015–Mar 2016	Supportive–educative intervention	53/60	61.1 ± 12.8	7 (11.7)	NR	NR	NR
			Discharge education	60/66	60.2 ± 13.6	16 (24.2)	NR	NR	NR
Chen, 2019	China	Dec 2011–Sep 2015	Structured telephone support	255/255	62 ± 14	30 (11.8)	NR	8 (3.1)	NR
			Supportive–educative intervention	252/252	60 ± 15	27 (10.7)	NR	7 (2.8)	NR
			Medication counseling	260/260	61 ± 15	34 (13.1)	NR	10 (3.8)	NR
Davis, 2014	USA	Jul 2010–Jun 2011	Disease management	63/63	60 ± 13	14 (22.2)	NR	NR	NR
			Medication counseling	62/62	57 ± 13	12 (19.4)	NR	NR	NR
Gallagher, 2017	USA	Dec 2014–Aug 2015	Remote monitoring	20/20	68 (49–79)	6 (30)	5 (25)	NR	NR
			Usual care	20/20	62 (52–75)	4 (20)	3 (15)	NR	NR
Hummel, 2018	USA	NR	Home care	33/33	71 ± 8	4 (12.1)	3 (9.1)	NR	NR
			Discharge education	33/33	70 ± 8	9 (27.3)	9 (27.3)	NR	NR
Huynh, 2019	Australia	NR	Disease management	215/215	74.7 ± 12.0	44 (20.5)	NR	12 (5.6)	NR
			Discharge education	197/197	73.9 ± 11.8	64 (32.5)	NR	18 (9.1)	NR
Jaarsma, 1999	USA	May 1994–Mar 1997	Supportive–educative intervention	84/84	73 ± 9	11 (13.1)	8 (9.5)	NR	3 (11.9)
			Medication counseling	95/95	73 ± 9	14 (14.7)	11 (11.6)	NR	3 (10.5)
McDonald, 2001	Ireland	Nov 1998–Aug 1999	Care transition	35/35	69.9 ± 11.3	NR	0 (0)	0 (0)	NR
			Medication counseling	35/35	67.9 ± 12.0	NR	0 (0)	0 (0)	NR
McWilliams, 2018	USA	Feb 2016–Jan 2017	Care transition	41/225	NR	9 (4)	NR	NR	NR
			Discharge education	48/218	NR	9 (4.1)	NR	NR	NR
Ong, 2016	USA	Mar 2014–Oct 2015	Remote monitoring	715/715	73 (62–84)	162 (22.7)	NR	24 (3.4)	NR
			Discharge education	722/722	74 (63–82)	156 (21.6)	NR	39 (5.4)	NR
Oscalices, 2019	Brazil	Jan 2016–Dec 2016	Supportive–educative intervention	100/100	62.6 ± 15.2	2 (2)	NR	0 (0)	NR
			Discharge education	101/101		5 (5)	NR	0 (0)	NR
Pekmezaris, 2012	USA	Jul 2007–Mar 2009	Remote monitoring	83/83	81 ± 7	25 (30.1)	NR	NR	NR
			Home care	85/85	83 ± 7	25 (29.4)	NR	NR	NR
Riegel, 2004	USA	1999–2001	Social support	45/45	72.64 ± 13.0	8 (17.8)	4 (8.9)	NR	NR
			Discharge education	43/43	73.28 ± 13.09	5 (11.6)	2 (4.7)	NR	NR
Riegel, 2006	USA	2002–2004	Disease management	69/70	71.6 ± 10.8	11 (15.7)	6 (8.6)	NR	NR
			Discharge education	65/65	72.7 ± 11.2	13 (20)	9 (13.8)	NR	NR
Ritchie, 2016	USA	Feb 2010–Nov 2011	Remote monitoring	168/168	62.7 ± 12.5	27 (16.1)	NR	4 (2.4)	NR
			Discharge education	178/178	63.8 ± 13.5	26 (14.6)	NR	5 (2.8)	NR
Sales, 2013	USA	Jun 2010–Dec 2010	Social support	70/70	72.5 ± 14.8	NR	5 (7.1)	5 (7.1)	NR
			Supportive–educative intervention	67/67	72.6 ± 13.4	NR	13 (19.4)	5 (7.5)	NR
Vinluan, 2015	USA	Dec 2012–May 2013	Medication counseling	7/7	74 ± 5.9	0 (0)	NR	0 (0)	NR
			Discharge education	9/9	71 ± 6.9	1 (11.1)	NR	0 (0)	NR
Wong, 2016	China	May 2013–Dec 2014	Care transition	43/43	78.3 ± 16.8	9 (20.9)	NR	NR	NR
			Usual care	41/41	78.4 ± 10.0	12 (29.3)	NR	NR	NR
Young, 2016	USA	Sep 2013–Oct 2015	Home care	51/54	68.7 ± 11.8	10 (18.5)	NR	NR	6 (18.5)
			Discharge education	49/51	71.8 ± 12.6	3 (5.9)	NR	NR	1 (2.0)

A: number of patients analyzed; R: number of patients randomized; SD: standard deviation; NR: not reported. HF: heart failure. ED visits: emergency departments visits.

Usual care is defined as routine care provided by the hospital.

Interventions

There were eight peri-discharge complex interventions evaluated in the intervention group: care transition (n = 3), disease management (n = 3), home care (n = 3), medication counseling (n = 1), social support (n = 2), structured telephone support (n = 1), supportive–educative intervention (n = 5), and remote monitoring (n = 4). Components of each peri-discharge complex interventions are presented in Table 2.

Table 2.

Components of peri-discharge complex interventions evaluated in included randomized controlled trials (RCTs) (n = 21)

Peri-discharge complex interventions	RCTs (Author, year)	Components	CA	CM	CS	DP	FS	HV	MI	PC	PE	PH	PI	SM	TE	TM
Care transition	McDonald, 2001	CA + CM + PC	1	1	0	0	0	0	0	1	0	0	0	0	0	0
	McWilliams, 2018	CA + CM + PC	1	1	0	0	0	0	0	1	0	0	0	0	0	0
	Wong, 2016	CA + CM + PC	1	1	0	0	0	0	0	1	0	0	0	0	0	0
Disease management	Davis, 2014	CA + DP + MI + PE + SM + TE	1	0	0	1	0	0	1	0	1	0	0	1	1	0
	Huynh, 2019	CA + DP + MI + PE + SM + TE	1	0	0	1	0	0	1	0	1	0	0	1	1	0
	Riegel, 2006	CA + DP + MI + PE + SM + TE	1	0	0	1	0	0	1	0	1	0	0	1	1	0
Discharge education^a	Boyde, 2018	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Breathett, 2018	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Hummel, 2018	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Huynh, 2019	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	McWilliams, 2018	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Ong, 2016	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Oscalices, 2019	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Riegel, 2004	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Riegel, 2006	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Ritchie, 2016	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Vinluan, 2015	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
	Young, 2016	PE + PI	0	0	0	0	0	0	0	0	1	0	1	0	0	0
Home care	Bowles, 2011	HV + SM	0	0	0	0	0	1	0	0	0	0	0	1	0	0
	Hummel, 2018	HV + SM	0	0	0	0	0	1	0	0	0	0	0	1	0	0
	Pekmezaris, 2012^b	HV + SM	0	0	0	0	0	1	0	0	0	0	0	1	0	0
	Young, 2016	HV + SM	0	0	0	0	0	1	0	0	0	0	0	1	0	0
Medication counseling	Chen, 2019^c	MI + PE	0	0	0	0	0	0	1	0	1	0	0	0	0	0
	Davis, 2014^c	MI + PE	0	0	0	0	0	0	1	0	1	0	0	0	0	0
	Jaarsma, 1999^c	MI + PE	0	0	0	0	0	0	1	0	1	0	0	0	0	0
	McDonald, 2001^c	MI + PE	0	0	0	0	0	0	1	0	1	0	0	0	0	0
	Vinluan, 2015	MI + PE	0	0	0	0	0	0	1	0	1	0	0	0	0	0
Social support	Riegel, 2004	CS + FS + MI + PE + SM + TE	0	0	1	0	1	0	1	0	1	0	0	1	1	0
	Sales, 2013	CS + FS + MI + PE + SM + TE	0	0	1	0	1	0	1	0	1	0	0	1	1	0
Structured telephone support	Chen, 2019	TE + PH	0	0	0	0	0	0	0	0	0	1	0	0	1	0
Supportive–educative intervention	Boyde, 2018	PE + MI + SM	0	0	0	0	0	0	1	0	1	0	0	1	0	0
	Breathett, 2018	PE + MI + SM	0	0	0	0	0	0	1	0	1	0	0	1	0	0
	Chen, 2019	PE + MI + SM	0	0	0	0	0	0	1	0	1	0	0	1	0	0
	Jaarsma, 1999	PE + MI + SM	0	0	0	0	0	0	1	0	1	0	0	1	0	0
	Oscalices, 2019	PE + MI + SM	0	0	0	0	0	0	1	0	1	0	0	1	0	0
	Sales, 2013^d	PE + MI + SM	0	0	0	0	0	0	1	0	1	0	0	1	0	0
Remote monitoring	Gallagher, 2017	TM + FS + MI + TE	0	0	0	0	1	0	1	0	0	0	0	0	1	1
	Ong, 2016	TM + FS + MI + TE	0	0	0	0	1	0	1	0	0	0	0	0	1	1
	Pekmezaris, 2012	TM + FS + MI + TE	0	0	0	0	1	0	1	0	0	0	0	0	1	1
	Ritchie, 2016	TM + FS + MI + TE	0	0	0	0	1	0	1	0	0	0	0	0	1	1

CA: case management; CM: timely primary care provider communication; CS: community service; DP: discharge planning; FS: follow-up scheduled; HV: home visits; MI: medication intervention; PC: provider continuity; PE: patient education; PH: patient hotline; PI: patient-centered discharge instructions; SM: self-management; TE: telephone follow-up; TM: telemonitoring.

Value of ‘0’ means that the component (column) was not presented in the intervention package; value of ‘1’ means that the component (column) was presented in the intervention package.

Discharge education is the control intervention in the following studies: Boyde (2018), Breathett (2018), Hummel (2018), Huynh (2019), McWilliams (2018), Ong (2016), Oscalices (2019), Riegel (2004), Riegel (2006), Ritchie (2016), Vinluan (2015), and Young (2016).

Home care is the control intervention of Pekmezaris (2012).

Medication counseling is the control intervention in the following studies: Chen (2019), Davis (2014), Jaarsma (1999), and McDonald (2001).

Supportive–educative intervention is the control intervention in Sales (2013).

Controls

Among control groups, the following peri-discharge complex interventions were evaluated: discharge education (n = 12), medication counseling (n = 4), home care (n = 1), and supportive–educative intervention (n = 1). Three studies reported usual care as control, which was defined as routine care provided by the hospital.

Methodological quality of included SRs and risk of bias among included RCTs

Among the 20 included SRs, we rated the methodological quality of one (5%) SR as high, six (30%) as low, and 13 (65%) as critically low. A total of 19 SRs (95%) used a satisfactory technique for assessing risk of bias in individual included studies. However, only four (20%) SRs provided a list of excluded studies with justifications. Details of the methodological quality of included SRs are shown in Supplemental Appendix 6a. For the 21 included RCTs, we judged the overall risk of bias for four (29%) RCTs as low, six (19%) as high, and the remaining 11 (52%) as having some concerns (Supplemental Appendix 6b). Over 90% of RCTs had low risk of bias in the two domains: (1) bias due to missing outcome data and (2) bias in measurement of the outcome. Nevertheless, approximately 50% of RCTs had some concerns or high risk of bias arising from the randomization process. Details of risk of bias assessment on each domain are presented in Supplemental Appendix 6c.

Results of pairwise meta-analyses

For 30-day all-cause hospital readmissions, results from pairwise meta-analyses showed that there was no significant difference between peri-discharge complex interventions and controls (pooled RR = 0.91, 95% CI: 0.79–1.04, I² = 8%, 19 RCTs) (Figure 2). The overall quality of evidence was high (Table 3).

Figure 2.

Pairwise meta-analyses of 19 RCTs: peri-discharge complex interventions versus controls. Outcome: 30-day all-cause hospital readmissions

Table 3.

Effect estimates and quality of evidence ratings for comparisons of interventions in pairwise meta-analysis, sensitivity, and subgroup analysis

Outcomes	Study design /participants	Risk of bias	Inconsistency	Indirectness	Imprecision	Publication bias	Pooled RR (95% CI)	Quality
30-day all-cause hospital readmissions	Nineteen RCTs/5155 participants	No serious^a	No serious inconsistency	No serious indirectness	No serious imprecision	No publication bias	0.91 (0.79,1.04)	⨁⨁⨁⨁High
30-day HF-related hospital readmissions	Eight RCTs/1062 participants	No serious	No serious inconsistency	No serious indirectness	No serious imprecision	NA	0.79 (0.52,1.21)	⨁⨁⨁⨁High
30-day mortality	Five RCTs/3099 participants	No serious	No serious inconsistency	No serious indirectness	No serious imprecision	NA	0.68 (0.49,0.95)	⨁⨁⨁⨁High
30-day ED visits	Three RCTs/501 participants	No serious	No serious inconsistency	No serious indirectness	Serious imprecision^b	NA	1.30 (0.55,3.03)	⨁⨁⨁◯Moderate
30-day all-cause hospital readmissions (sensitivity analysis focused on RCTs with low risk of bias)	Six RCTs/476 participants	No serious	No serious inconsistency	No serious indirectness	No serious imprecision	NA	1.01 (0.87,1.18)	⨁⨁⨁⨁High
30-day all-cause hospital readmissions (in subgroup 1: discharge education as control interventions)	Twelve RCTs/3575 participants	No serious^a	No serious inconsistency^c	No serious indirectness	No serious imprecision	NA	0.87 (0.68,1.11)	⨁⨁⨁⨁High
30-day all-cause hospital readmissions (in subgroup 2: medication counseling as control interventions)	Three RCTs/1071 participants	No serious	No serious inconsistency^c	No serious indirectness	No serious imprecision	NA	0.92 (0.67,1.25)	⨁⨁⨁⨁High
30-day all-cause hospital readmissions (in subgroup 3: usual care as control interventions)	Three RCTs/341 participants	No serious	No serious inconsistency^c	No serious indirectness	No serious imprecision	NA	0.87 (0.57–1.33)	⨁⨁⨁⨁High

CI: confidence interval; ED visits: emergency departments visits; NA: not applicable; HF: heart failure; RCT: randomized control trial; RR: risk ratio.

GRADE Working Group grades of evidence.

High quality: we are very confident that the true effect lies close to that of the estimate of the effect.

Moderate quality: we are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.

Low quality: our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect.

Very low quality: we have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect.

Most information is from studies at low risk of bias or some concerns. Plausible bias is unlikely to seriously alter the results.

The certainty of evidence is downgraded one level for serious imprecision because the 95% CI overlaps the RR of 1.0, but includes important benefit or important harm (RR estimates below 0.5 and above 2.0 are considered clinically important).

The certainty of evidence for subgroup analysis is not downgraded for inconsistency as there is little variability in results between studies and no suggestion of a subgroup effect.

For 30-day HF-related hospital readmissions, results from pairwise meta-analyses showed that there was no significant difference between peri-discharge complex interventions and controls (pooled RR = 0.79, 95% CI: 0.52–1.21, I² = 20%, eight RCTs) (Supplemental Appendix 7). Peri-discharge complex interventions were significantly more effective than controls in reducing 30-day mortality (pooled RR = 0.68, 95% CI: 0.49–0.95, I² = 0%, five RCTs) (Supplemental Appendix 8). Quality of evidence for both 30-day HF-related hospital readmissions and 30-day mortality were high (Table 3). For 30-day ED visits, no significant difference was found between peri-discharge complex interventions and controls (pooled RR = 1.30, 95% CI: 0.55–3.03, I² = 21%, three RCTs) (Supplemental Appendix 9), as supported by moderate quality evidence (Table 3).

Subgroup and sensitivity analysis results

Results of subgroup analysis based on different control groups were presented in Supplemental Appendix 10. There was no significant difference in the following three subgroups for reducing 30-day all-cause hospital readmissions: (1) peri-discharge complex interventions versus discharge education (pooled RR = 0.87, 95% CI: 0.68–1.11, I² = 38%, 12 RCTs); (2) peri-discharge complex interventions versus medication counseling (pooled RR = 0.92, 95% CI: 0.67–1.25, I² = 0%, 3 RCTs); and (3) peri-discharge complex interventions versus usual care (pooled RR = 0.87, 95% CI: 0.57–1.33, I² = 0%, 3 RCTs). The overall quality of evidence for the three subgroup analyses was high (Table 3).

By only including RCTs with overall low risk of bias, the sensitivity analysis results showed no significant difference between peri-discharge complex interventions and controls for 30-day all-cause hospital readmissions (pooled RR = 1.01, 95% CI: 0.87–1.18, I² = 0%, six RCTs) (Supplemental Appendix 11), as supported by high quality of evidence (Table 3).

A sensitivity analysis which one study was removed at a time was conducted to evaluate the stability of results. For reducing 30-day all-cause hospital readmissions, the summary RR ranged from 0.85 (95% CI: 0.74–0.98) (when excluding the study by Ong et al.³³) to 0.96 (95% CI: 0.85–1.09) (when excluding the study by Huynh et al.³⁴), indicating stability of our results as the 95% CIs of the two summary RRs are overlapping (Supplemental Appendix 12a). For reducing 30-day HF-related hospital readmissions, the summary RR ranged from 0.72 (95% CI: 0.47–1.10) (when excluding the study by Boyde et al.³⁵) to 0.89 (95% CI: 0.59–1.35) (when excluding the study by Sales et al.³⁶), indicating stability of our results as the 95% CIs of the two summary RRs are also overlapping (Supplemental Appendix 12b).

Results of NMA

For reducing 30-day all-cause hospital readmissions, the network included one three-arm trial and 18 two-arm trials (Figure 3). The NMA effect estimates on 30-day all-cause hospital readmissions among these nine different peri-discharge complex interventions and usual care were shown in Figure 4. Results showed that supportive–educative intervention was significantly more effective than remote monitoring (OR = 1.96, 95% CI: 1.12–3.45) and discharge education (OR = 1.81, 95% CI: 1.09–2.99), as supported by moderate quality of evidence.

Figure 3.

Network plot of comparisons among nine different peri-discharge complex interventions and usual care in the NMA for reducing 30-day all-cause hospital readmissions

Figure 4.

Comparative effectiveness of nine different peri-discharge complex interventions and usual care for reducing 30-day all-cause hospital readmissions

When compared to disease management, both remote monitoring (OR = 0.55, 95% CI: 0.36–0.86) and discharge education (OR = 0.60, 95% CI: 0.41–0.87) were significantly less effective in reducing 30-day all-cause hospital readmissions, both with moderate quality of evidence.

Overall, the quality of evidence for 23 comparisons (51.1%) produced by NMA was moderate, and the remaining 22 (48.9%) was of low quality (Supplemental Appendix 13). In terms of cumulative probabilities, supportive–educative intervention had the highest probability of being the best option for reducing 30-day all-cause hospital readmissions, followed by disease management, and structured telephone support, as suggested by the SUCRA results (Figure 5).

Figure 5.

Comparative effectiveness of nine different peri-discharge complex interventions and usual care: surface under the cumulative ranking curves (SUCRA) for reducing 30-day all-cause hospital readmissions

For secondary outcome of HF-related hospital readmissions, the network included 8 two-arm trials (Supplemental Appendix 14). Results from NMA showed that home care was significantly less effective than disease management (OR = 0.16, 95% CI: 0.03–0.94, moderate quality of evidence) (Supplemental Appendix 15). SUCRA results showed that disease management had the highest probability to be the best intervention for reducing 30-day HF-related hospital readmissions (Supplemental Appendix 16). Quality of evidence for 12 comparisons in this network was low, and for the remaining 16 was moderate (Supplemental Appendix 17).

Results for evaluating inconsistency using the SIDE approach in the networks of reducing 30 day all-cause and HF-related hospital readmissions were shown in Supplemental Appendices 18 and 19, respectively. As the difference between direct and indirect estimates for each pairwise comparison was not statistically significant, there was no significant inconsistency in the two NMA.

Publication bias assessment

Judging from visual inspection of contour-enhanced funnel plot of 19 included RCTs (Supplemental Appendix 20), there was no evidence of funnel plot asymmetry, indicating absence of publication bias.

Discussion

Summary of findings

In this overview of SRs, pairwise meta-analyses showed that there was no significant difference between peri-discharge complex interventions and controls on reducing 30-day all-cause hospital readmissions, 30-day HF-related hospital readmissions, and 30-day ED visits among HF patients. Peri-discharge complex interventions were significantly more effective than controls in reducing 30-day mortality. As shown in the NMA results, supportive–educative intervention had the highest probability of being the best option for reducing 30-day all-cause hospital readmissions among HF patients, followed by disease management. Disease management also had the highest probability to be the best interventions for reducing 30-day HF-related hospital readmissions.

Implications for practice

Components of disease management complex intervention consisted of all parts of supportive–educative intervention. The former included case management, discharge planning, telephone follow-up, and all components of supportive–educative intervention (patient education, medication intervention, and self-management). This six-component combination had the second-highest effectiveness ranking in reducing 30-day all-cause hospital readmission and the highest for HF-related hospital readmissions. Hence, disease management is likely to serve dual goals of reducing both types of readmission.

However, quality of evidence supporting this NMA-based conclusion varied from moderate to low. The decision to implement disease management in the local context requires careful deliberation. Criteria listed in the GRADE Evidence to Decision (EtD) framework²² may help guide the decision-making process.

Benefits of multicomponent disease management

Disease management consists of different interacting components. From patients’ perspective, case management helps to tailor health service to patients’ and familys’ health needs through communication and coordinating available resources.³⁷ An SR indicated that case management could increase continuity of care and promote positive patient experience³⁸ by providing HF patients with high quality and accessibility care. The process can create more time for patients to ask questions, to develop trusting relationships with health providers, and therefore enhance the provision of patient-centered care.^38,39 Discharge planning can help prepare and assist HF patients and their families to move to the next level of care.⁴⁰ In the peri-discharge process, quality discharge planning can increase trust and subsequently enhance patients’ adherence to their discharge plans, as well as patient satisfaction.^40,41 Postdischarge telephone calls can provide HF patients with medical advice for managing symptoms and reassurance, as well as an opportunity to identify complications early for outpatient management.⁴² It can also improve patient satisfaction and compliance to HF management.⁴³ Patient education, medication intervention, and self-management can provide essential information and support for HF patients, thus reducing chance of medication non-adherence. These also enhance patients’ ability to carry out self-care.⁴⁴–⁴⁸ Combination of benefits from all these components are possible explanations for the observed benefit of disease management in reducing readmission.

Acceptability of disease management among providers

From the provider perspective, disease management for HF has been endorsed by American Heart Association and European Society of Cardiology guidelines.^49,50 Individual components in disease management programs are also well accepted. For instance, case management is recognized for fostering strong provider-patient relationship,³⁸ while discharge planning is considered as a key peri-discharge intervention from a wide spectrum of providers ranging from policymakers to frontline nurses.⁵¹ Telephone follow-up from the hospital is considered to be the most accessible tool for arranging further assessment or eliciting patient feedback among both providers and patients.⁵² Finally, it is acknowledged that HF patients need education about their condition, risk factors, medications, dietary requirements, and appropriate activity level in order to acquire skills for managing their own condition.^53,54 Consensus guidelines from Canada, America, and Europe already stated self-management as a key recommendation.⁵⁵–⁵⁷

Feasibility of implementing disease management

Despite its benefits and acceptability, the implementation of disease management problems could be challenging. Potential difficulties may include poor communication and coordination between providers and patients, and the lack of policy support at the health system level.⁵⁸ Current experiences suggest that implementation strategies^59,60 may start with addressing system-level issues of service provision structure, organizational coordination, and securing sufficient resources for the program. Then, at provider level, training on communication and team efficiency is essential. Last but not least, the program must consider patients as partners in implementation. The program should ensure intervention elements that would empower patients’ ability to engage in the intervention are in place.

Implications for research

The GRADE-ADOLOPMENT approach is a well-designed method that provides an explicit framework for guiding a localized decision-making process.²² Future research may invite key stakeholders to conduct a GRADE-ADOLOPMENT-based Delphi survey for generating consensus on whether disease management should be implemented in a particular health system context.²² In this process, stakeholders can consider their recommendations taking into account problem priority, benefits, harms, equity, acceptability, and feasibility. Finally, we observed an interesting trend that peri-discharge complex interventions were significantly more effective than controls in reducing 30-day mortality among HF patients. A more in-depth evaluation of this observation is warranted.

Strengths and limitations

The use of NMA in this overview allowed the evaluation of comparative effectiveness of multiple peri-discharge complex interventions simultaneously. The NMA results would offer policy makers insight on designing relevant complex interventions to reduce preventable 30-day all-cause hospital readmissions among HF patients in different national health systems.¹²

Our findings also have several limitations. First, quality of evidence of NMA varied from moderate to low across comparisons within the network, with downgrading mainly due to serious imprecision. Second, implementation fidelity of each intervention was not reported, and such variations could bias our current results positively or negatively. Third, while components of peri-discharge complex interventions were coded based on a published classification framework of complex interventions for reducing hospital readmissions, we found that some components may be interrelated. Clear-cut coding of different intervention components is difficult. Finally, different trials might have enrolled different patients with varying risk of avoidable, or unavoidable 30-day hospital readmissions. However, such risk is known to be hard to predict⁶¹ and hence its impact on our conclusion may be limited.

Conclusion

Results from this overview suggest that, relatively speaking, disease management program comprising the components of case management, discharge planning, telephone follow-up, patient education, medication intervention, and self-management has the best potential for reducing both 30-day all-cause and HF-related hospital readmissions. Benefits of disease management may vary across health system context. Therefore, policymakers should consider carefully problem priority, benefits, harms, equity, acceptability, and feasibility before implementing relevant programs.

Supplemental Material

sj-docx-1-rsh-10.1177_1757913920985258 – Supplemental material for Peri-discharge complex interventions for reducing 30-day hospital readmissions among heart failure patients: overview of systematic reviews and network meta-analysis

Supplemental material, sj-docx-1-rsh-10.1177_1757913920985258 for Peri-discharge complex interventions for reducing 30-day hospital readmissions among heart failure patients: overview of systematic reviews and network meta-analysis by CCW Zhong, CHL Wong, WKW Cheung, E-K Yeoh, CT Hung, BHK Yip, ELY Wong, SYS Wong and VCH Chung in Perspectives in Public Health

Footnotes

Conflict of Interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Health and Medical Research Fund, Food and Health Bureau, Hong Kong (No. 16171031).

Ethical Approval

This study is an overview of systematic reviews that involves no human or animal subjects. Therefore, no ethical approval was pursued.

ORCID iDs

Claire CW Zhong

Eliza LY Wong

Supplemental material

Supplemental material for this article is available online.

References

Ponikowski

Anker

AlHabib

, et al Heart failure: preventing disease and death worldwide. ESC Heart Fail 2014;1(1):4–25.

Ambrosy

Fonarow

Butler

, et al The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. J Am Coll Cardiol 2014;63:1123–33.

Lippi

Sanchis-Gomar

. Global epidemiology and future trends of heart failure. AME Medical Journal 2020;5:21037.

Pittsburgh Regional Health Initiative. PRHI Readmission Briefs. Brief 1: Overview of Six Target Chronic Diseases, 2010. https://www.prhi.org/resources/resources-article/archives/readmission-briefs/100-readmissions-overview-of-six-target-chronic-conditions/file (Last accessed on 29 Dec 2020).

Anderson

Steinberg

. Hospital readmissions in the medicare population. New Engl J Med 1984;311:1349–53.

Vest

Gamm

Oxford

, et al Determinants of preventable readmissions in the United States: a systematic review. Implement Sci 2010;5:88.

Jencks

Williams

Coleman

. Rehospitalizations among patients in the Medicare fee-for-service program. New Engl J Med 2009;360:1418–28.

World Health Organization. Do current discharge arrangements from inpatient hospital care for the elderly reduce readmission rates, the length of inpatient stay or mortality, or improve health status? 2005. https://www.euro.who.int/en/health-topics/Life-stages/healthy-ageing/publications/pre-2009/do-current-discharge-arrangements-from-inpatient-hospital-care-for-the-elderly-reduce-readmission-rates,-the-length-of-inpatient-stay-or-mortality,-or-improve-health-status (Last accessed on 29 Dec 2020).

Desai

Stevenson

. Rehospitalization for heart failure: predict or prevent? Circulation 2012;126:501–6.

10.

Boulding

Glickman

Manary

, et al Relationship between patient satisfaction with inpatient care and hospital readmission within 30 days. Am J Manag Care 2011;17(1):41–8.

11.

Arundel

Lam

Khosla

, et al Association of 30-day all-cause readmission with long-term outcomes in hospitalized older medicare beneficiaries with heart failure. Am J Med 2016;129(11):1178–84.

12.

Van Walraven

Bennett

Jennings

, et al Proportion of hospital readmissions deemed avoidable: a systematic review. Cmaj 2011;183: E391–402.

13.

Stone

Hoffman

. Medicare hospital readmissions: issues, policy options and PPACA. Washington, DC: Congressional Research Service;2010.

14.

Feltner

Jones

Cene

, et al Transitional care interventions to prevent readmissions for persons with heart failure: a systematic review and meta-analysis. Ann Intern Med 2014;160:774–84.

15.

Pandor

Gomersall

Stevens

, et al Remote monitoring after recent hospital discharge in patients with heart failure: a systematic review and network meta-analysis. Heart 2013;99(23):1717–26.

16.

Hutton

Salanti

Caldwell

, et al The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med 2015;162:777–84.

17.

Higgins

Thomas

Chandler

, et al Cochrane handbook for systematic reviews of interventions version 6.0 (updated July 2019). Cochrane 2019. Available online at: https://methods.cochrane.org/equity/resources-review-authors

18.

Martinic

Pieper

Glatt

, et al Definition of a systematic review used in overviews of systematic reviews, meta-epidemiological studies and textbooks. BMC Med Res Methodol 2019;19:203.

19.

Benbassat

Taragin

. Hospital readmissions as a measure of quality of health care – Advantages and limitations. Archiv Intern Med 2000;160:1074–81.

20.

Shea

Reeves

Wells

, et al AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ 2017;358: j4008.

21.

Sterne

JAC

Savovic

Page

, et al RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ 2019;366: l4898.

22.

Schünemann

Wiercioch

Brozek

, et al GRADE Evidence to Decision (EtD) frameworks for adoption, adaptation, and de novo development of trustworthy recommendations: GRADE-ADOLOPMENT. J Clin Epidemiol 2017;81:101–10.

23.

Salanti

. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods 2012;3(2):80–97.

24.

Borenstein

Hedges

Higgins

, et al A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 2010;1(2):97–111.

25.

Higgins

Thompson

Deeks

, et al Measuring inconsistency in meta-analyses. BMJ 2003;327:557–60.

26.

Anzures-Cabrera

Higgins

. Graphical displays for meta-analysis: an overview with suggestions for practice. Res Synth Methods 2010;1(1):66–80.

27.

White

. Multivariate random-effects meta-regression: updates to mvmeta. Stata J 2011;11:255–70.

28.

Salanti

Ades

Ioannidis

. Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J Clin Epidemiol 2011;64(2):163–71.

29.

White

. Multivariate random-effects meta-regression: updates to mvmeta. Stata Journal 2011;11:255–70.

30.

Chaimani

Caldwell

, et al Chapter 11: undertaking network meta-analyses. Cochrane Handbook Syst Rev 2019;2019:285–320.

31.

Dias

Welton

Caldwell

, et al Checking consistency in mixed treatment comparison meta-analysis. Stat Med 2010;29:932–44.

32.

Chaimani

Higgins

Mavridis

, et al Graphical tools for network meta-analysis in STATA. PLoS ONE 2013;8(10):e76654.

33.

Ong

Romano

Edgington

, et al Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure: the better effectiveness after transition-heart failure (BEAT-HF) randomized clinical trial. JAMA Intern Med 2016;176:310–18.

34.

Huynh

Whitmore

Negishi

, et al Influence of risk on reduction of readmission and death by disease management programs in heart failure. J Card Fail 2019;25(5):330–39.

35.

Boyde

Peters

New

, et al Self-care educational intervention to reduce hospitalisations in heart failure: a randomised controlled trial. Eur J Cardiovasc Nurs 2018;17(2):178–85.

36.

Sales

Ashraf

Lella

, et al Utilization of trained volunteers decreases 30-day readmissions for heart failure. J Cardiac Failure 2013;19(12):842–50.

37.

Ross

Curry

Goodwin

. Case management. What Is It and How It Can Best Be Implemented. London: The Kings Fund; 2015.

38.

King

AJL

Johnson

Cramer

, et al Community case management and unplanned hospital admissions in patients with heart failure: a systematic review and qualitative evidence synthesis. J Adv Nurs 2018;74(7):1463–73.

39.

Annema

Luttik

Jaarsma

. Do patients with heart failure need a case manager. J Cardiovasc Nurs 2009;24(2):127–31.

40.

Rose

Haugen

. Discharge planning: your last chance to make a good impression. Medsurg Nurs 2010;19(1):47–50;53.

41.

Tomura

Yamamoto-Mitani

Nagata

, et al Creating an agreed discharge: discharge planning for clients with high care needs. J Clin Nurs 2011;20(3–4):444–53.

42.

Mistiaen

Poot

. Telephone follow-up, initiated by a hospital-based health professional, for postdischarge problems in patients discharged from hospital to home. Cochrane Database Syst Rev 2006;46: 128–30.

43.

Braun

Baidusi

Alroy

, et al Telephone follow-up improves patients satisfaction following hospital discharge. Eur J Intern Med 2009;20(2):221–25.

44.

Gardetto

. Self-management in heart failure: where have we been and where should we go? J Multidiscip Healthcare 2011;4:39–51.

45.

Strömberg

. Educating nurses and patients to manage heart failure. Eur J Cardiovasc Nurs 2002;1(1):33–40.

46.

Albert

. Parallel paths to improve heart failure outcomes: evidence matters. Am J Crit Care 2013;22(4):289–96.

47.

Strömberg

. The crucial role of patient education in heart failure. Europ J Heart Failure 2005;7:363–69.

48.

Casimir

Williams

Liang

, et al Effectiveness of patient-centered self-care education for adults with heart failure on knowledge, self-care behaviors, quality of life, and readmissions: a systematic review. JBI Database Syst Rev 2013;11:107–28.

49.

Yancy

Jessup

Bozkurt

, et al 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 2013;62: e147–239.

50.

McMurray

JJV

Adamopoulos

Anker

, et al ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: the task force for the diagnosis and treatment of acute and chronic heart failure 2012 of the European Society of Cardiology. Developed in Collaboration with the Heart Failure Association (HFA) of the ESC. Europ J Heart Failure 2012;14:803–69.

51.

Gholizadeh

Janati

Delgoshaei

, et al Implementation requirements for patient discharge planning in health system: a qualitative study in Iran. Ethiop J Health Sci 2018;28(2):157–68.

52.

Nelson

. The importance of postdischarge telephone follow-up for hospitalists: a view from the trenches. Am J Med 2001;111:43–4.

53.

Hagenhoff

Feutz

Conn

, et al Patient education needs as reported by congestive heart failure patients and their nurses. J Adv Nurs 1994;19(4):685–90.

54.

Raines

Dickey

. An exploration of learning needs: identifying knowledge deficits among hospitalized adults with heart failure. AIMS Public Health 2019;6:248–67.

55.

McKelvie

Moe

Ezekowitz

, et al The 2012 Canadian Cardiovascular Society heart failure management guidelines update: focus on acute and chronic heart failure. Can J Cardiol 2013;29(2):168–81.

56.

Members

ATF

McMurray

Adamopoulos

, et al ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: the task force for the diagnosis and treatment of acute and chronic heart failure 2012 of the European Society of Cardiology. Developed in Collaboration with the Heart Failure Association (HFA) of the ESC. Europ J Heart Failure 2012;14:803–69.

57.

Adams

. An exploratory study of complementary and alternative medicine in hospital midwifery: models of care and professional struggle. Complement Ther Clin Pract 2006;12(1):40–7.

58.

Wong

ELY

Yam

CHK

Cheung

AWL

, et al Barriers to effective discharge planning: a qualitative study investigating the perspectives of frontline healthcare professionals. BMC Health Services Research 2011;11:242.

59.

McEntee

Cuomo

Dennison

. Patient-, provider-, and system-level barriers to heart failure care. J Cardiovasc Nurs 2009;24(4):290–98.

60.

Lambrinou

Protopapas

Kalogirou

. Educational challenges to the health care professional in heart failure care. Curr Heart Fail Rep 2014;11(3):299–306.

61.

Allaudeen

Schnipper

Orav

, et al Inability of providers to predict unplanned readmissions. J Gen Intern Med 2011;26(7):771–6.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

1.53 MB