https://orcid.org/0000-0002-8316-2325
Figures
Abstract
Background
Readmission following hospital discharge is common and is a major financial burden on healthcare systems.
Objectives
Our objectives were to 1) identify studies describing post-discharge interventions and their efficacy with respect to reducing risk of mortality and rate of hospital readmission; and 2) identify intervention characteristics associated with efficacy.
Methods
A systematic review of the literature was performed. We searched MEDLINE, PubMed, Cochrane, EMBASE and CINAHL. Our selection criteria included randomized controlled trials comparing post-discharge interventions with usual care on rates of hospital readmission and mortality in high-risk chronic disease patient populations. We used random effects meta-analyses to estimate pooled risk ratios for all-cause and cause-specific mortality as well as all-cause and cause-specific hospitalization.
Results
We included 31 randomized controlled trials encompassing 9654 patients (24 studies in CHF, 4 in COPD, 1 in both CHF and COPD, 1 in CKD and 1 in an undifferentiated population). Meta-analysis showed post-discharge interventions reduced cause-specific (RR = 0.71, 95% CI = 0.63–0.80) and all cause (RR = 0.90, 95% CI = 0.81–0.99) hospitalization, all-cause (RR = 0.73, 95% CI = 0.65–0.83) and cause-specific mortality (RR = 0.68, 95% CI = 0.54–0.84) in CHF studies, and all-cause hospitalization (RR = 0.52, 95% CI = 0.32–0.83) in COPD studies. The inclusion of a cardiac nurse in the multidisciplinary team was associated with greater efficacy in reducing all-cause mortality among patients discharged after heart failure admission (HR = 0.64, 95% CI = 0.54–0.75 vs. HR = 0.87, 95% CI = 0.73–1.03).
Conclusions
Post-discharge interventions reduced all-cause mortality, cause-specific mortality, and cause-specific hospitalization in CHF patients and all-cause hospitalization in COPD patients. The presence of a cardiac nurse was associated with greater efficacy in included studies. Additional research is needed on the impact of post-discharge intervention strategies in COPD and CKD patients.
Citation: Bamforth RJ, Chhibba R, Ferguson TW, Sabourin J, Pieroni D, Askin N, et al. (2021) Strategies to prevent hospital readmission and death in patients with chronic heart failure, chronic obstructive pulmonary disease, and chronic kidney disease: A systematic review and meta-analysis. PLoS ONE 16(4): e0249542. https://doi.org/10.1371/journal.pone.0249542
Editor: Hans-Peter Brunner-La Rocca, Maastricht University Medical Center, NETHERLANDS
Received: August 19, 2020; Accepted: March 21, 2021; Published: April 22, 2021
Copyright: © 2021 Bamforth et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: We gratefully acknowledge the support of the University of Manitoba Max Rady College of Medicine.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Readmission following hospital discharge is a common occurrence and results in a major financial burden on health care systems [1]. In particular, patients suffering from chronic diseases, mainly chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF), have frequent readmissions and suffer from increased morbidity and mortality related to these events [2]. Overall, up to 19.6% of patients admitted with chronic health conditions in the United States are readmitted within one month and 34.0% within three months of discharge [3].
It has been estimated that nearly 60% of hospital readmissions are preventable [4]. Risk factors associated with avoidable readmissions include those related to patient, social, clinical and system factors such as patient behaviors, community services, adequacy and appropriateness of assessment and treatment, as well as accessibility and coordination within the healthcare delivery system [4]. Preventing such hospitalizations may result in up to $12 billion dollars of savings in the United States alone [5]. The absence of suitable post-discharge care is just one of many potential factors contributing to future hospitalizations [6] and continuity of care in high-risk chronic disease patients is crucial in order to mitigate the risk of readmission [7].
A variety of post-discharge interventions have been proposed to reduce this readmission risk, ranging from minimal (i.e. follow-up telephone calls), to complex, multifaceted interventions such as “virtual wards”, which provide patients with a period of intensive multidisciplinary team management, often employing telemonitoring and nurse led case-management strategies [8]. Use of virtual wards following hospital discharge has been associated with a lower risk of readmission in certain health conditions. In a recent systematic review, we found that virtual wards were effective in improving clinical outcomes and reducing hospital readmissions in heart failure [9]. However, the costs and complexities associated with virtual wards may be a barrier to implementation. The question of whether, and to what degree, less complex and less costly interventions can be as effective as highly complex virtual wards in minimizing hospital readmissions and death in high-risk chronic disease patient populations is unknown.
To address this knowledge gap, we conducted a systematic review of randomized clinical trials examining different follow-up programs and strategies specific to high-risk chronic disease populations after hospital discharge. These interventions ranged from simple (e.g., telephone calls) to more complex and costly programs such as virtual wards. We focused primarily on chronic kidney disease (CKD), heart failure (HF), and chronic obstructive pulmonary disease (COPD). We chose these conditions because all of them are common and associated with frequent and costly exacerbations/acute decompensations necessitating admission for stabilization and associated with high risk of recurrence [2, 3, 10–12]. Our co-primary objectives were: 1) to estimate the efficacy of post-discharge interventions in each chronic disease reviewed, and 2) identify intervention components most strongly associated with efficacy.
Materials and methods
This systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Statement [13]. It was performed under a pre-written protocol that was not registered with a traditional systematic review registry.
Data sources and searches
We searched MEDLINE, PubMed, Cochrane, EMBASE and CINAHL identifying relevant studies published up to November 2020. The goal of our literature search was to identify all relevant studies in CHF, COPD and CKD that included any post-discharge intervention. In collaboration with a medical librarian, we developed search terms tailored to each database. We incorporated keywords such as “virtual ward”, “telemedicine,” “case management”, and other types of interventions to maximize the capture of potentially relevant studies from the available literature. The complete search strategy can be found in supplemental materials (S1 Table).
Study selection
We included randomized controlled trials comparing post-discharge interventions with usual care in community-dwelling adult patients (≥18 years of age) recruited to the trial directly following or within three months of hospitalization. The main outcomes of interest were rates of hospital readmission and mortality. No restrictions were placed on dates or language. Two independent reviewers (JS, DP) screened the titles and abstracts of all articles identified in the database search. Potentially eligible articles underwent independent full text review by the same two reviewers to identify the final set of articles. Included articles went on to data extraction. Reasons for exclusion were documented for the remaining articles. Disagreements about inclusion were settled by consensus, with the assistance of a third reviewer (CR, RC) as necessary.
Data extraction
Relevant data, including first author, year of publication, study location (country), sample size, study population, mean age, proportion of women, study design including components associated with risk of bias, description of usual care, as well as description of intervention(s) employed were extracted from the included studies. All-cause hospitalization, cause-specific hospitalization, all-cause mortality, and cause-specific mortality for both control and intervention groups were also extracted from studies. All data was abstracted in duplicate (JS, DP). Conflicts were resolved by consensus, or by a third reviewer if consensus was not reached (CR, RC).
Classification of intervention strategies
Based on our review, we identified five types of post-discharge intervention strategies that were variably incorporated into a given intervention. These strategies included pre-discharge disease specific patient education, post-discharge follow-up telephone calls, home visits, continuous or semi-continuous telemonitoring of vital signs, and coordinated multidisciplinary team care. For the purposes of our review, multidisciplinary team care was defined as care offered by at least three health care providers in three different areas of patient care. We also captured information on whether a given team member was described as having (or not having) any disease specific training or expertise (e.g., heart failure specialist vs. general internist; Heart failure nurse vs. general nurse). Patient education was deemed to be additional instruction or training specific to self-management of the disease in question, beyond what is generally provided to patients upon discharge. Telemonitoring was defined as continuous or semi-continuous monitoring of patient data via a dedicated or web-based platform. There was no inclusion threshold placed on number telephone calls, home visits, or telemonitoring frequency. Additionally, telephone calls, home visits, and telemonitoring could be conducted by any care provider. In most studies, these interventions were overseen or applied by nurses. For our analysis, we recorded the number and type(s) of intervention(s) used in each study.
Quality assessment
Risk of bias was determined via the application of the Cochrane Collaboration’s Tool (CCT) for assessing risk of bias in randomized trials [14, 15]. This tool considers 6 domains of bias (selection bias, performance bias, detection bias, attrition bias, reporting bias, and other bias). Studies were classified as being at low, unclear, or high risk of bias in each of the 6 bias domains. The evaluation was conducted by two reviewers (JS, DP), with discrepancies resolved by consensus (CR, RC) as described for study inclusion above. Further information on the risk of bias criteria is located in supplemental materials (S2 Table).
Data synthesis and analysis
We used random effects meta-analyses to estimate pooled risk ratios and 95% confidence intervals for all-cause and cause-specific mortality as well as all-cause and cause-specific hospitalization for each chronic disease category [16]. To address our pre-hoc hypothesis that number, type, or specificity of intervention strategies used might influence effectiveness, we conducted subgroup analyses to: 1) compare efficacy in studies using fewer or greater than the median number of strategies; 2) compare studies according to type of intervention strategy used; 3) compare studies with disease specific teams. These latter analyses were only performed for the heart failure studies, as the number of COPD and CKD studies was too small. In a post hoc, secondary analysis, we created a simple scoring system to rank intervention strategies in terms of complexity: pre-discharge patient education = 1; post-discharge telephone calls = 2; home visits = 3; continuous tele-monitoring = 4; and multidisciplinary team care = 5. We defined a complexity score for a study as the simple sum of the complexity ranks of all strategies employed in that study. We then used meta-regression to examine whether complexity as measured using this score was associated with efficacy of the intervention. Additional sensitivity analyses were performed by excluding studies at high risk of reporting, attrition, selection or concealment bias to assess uncertainty. Finally, to examine whether there is evidence of an era effect, we conducted a subgroup analysis including only recently published heart-failure studies (since January 2009).
Results
Study selection
A flow diagram summarizing our literature search and selection of relevant articles is shown below in Fig 1. A total of 8690 articles were identified based on our search strategy.
The titles and abstracts of these articles were screened, and 214 were selected for full text review. Of these 214 articles, 31 met all the inclusion criteria. A summary of the full-text review is located in supplemental materials (S3 Table).
Characteristics of selected studies
We included 31 randomized controlled trials encompassing 9654 patients in our systematic review [17–47]. The mean age of the various study populations ranged from 56 to 80 years. The study populations consisted of patients admitted to hospital within the past three months due to HF, CKD, or COPD, and discharged home. Most studies had a larger proportion of males than females. Of the included studies, 24 were specifically on congestive heart failure [17–37, 44, 46, 47], and 1 study included both congestive heart failure and chronic obstructive pulmonary disease [38]. There were 4 studies specifically on chronic obstructive pulmonary disease [39–41, 45], 1 on chronic kidney disease [42], and 1 study which focused on undifferentiated, high risk chronic disease patient populations [43]. The last study could not be included because separate outcome data for each chronic disease of interest was not reported [43].
In all studies, the control group consisted of patients receiving “usual care” following hospital discharge. The definition of usual care varied across studies, however. Typically, usual care was comprised of an outpatient follow-up appointment scheduled with the patient’s primary care physician. However, some studies described more intensive usual care practices, such as follow-up with a disease focused team.
The interventions described also varied between studies. However, all included one or more of the following elements: patient education, telephone calls, home visits, telemonitoring, and multidisciplinary care. The intervention period varied greatly between studies, as did the follow-up period (from six months to two years). Additional details on all included studies can be found in Tables 1 and 2.
Quality of reporting and risk of bias
Risk of bias for the included studies is summarized in the supplemental material (S4 Table). Most studies were judged to be at low risk of reporting, detection, and selection biases. Blinding of participants was not attempted in any of the studies. While this is understandable given the nature of the intervention, lack of participant blinding does increase the risk of performance bias in all studies. Moreover, a description of steps taken to ensure blinding of outcome assessors was not included in many studies, leading to a high overall risk of measurement bias in the included studies. Overall, three of the thirty-one studies were deemed at high risk of bias [26, 33, 38]. In sensitivity analyses, the calculated risk ratios were found to be of similar direction and magnitude when studies at high risk of bias were excluded (S1–S3 Figs).
Qualitative analysis
Two of the thirty-one included studies were excluded from the meta-analysis. The first study was excluded as it was the sole study in CKD patients that met inclusion criteria and we were unable to meta-analyze it on its own [42] The second study was excluded due to the inability to extract outcome data for each separate chronic disease of interest [42, 43]. Li et al. examined the effectiveness of telephone support in patients undergoing peritoneal dialysis in China [42]. Overall, 135 patients were recruited (66 randomized to control group, 69 to intervention group) with results showing a statistically significant difference in quality of life measures (symptom/problem, work status, staff encouragement, patient satisfaction and energy/fatigue as measured by the KDQOL-SF) favoring the intervention group, with no differences in blood chemistry and complication control as well as health-care utilization (readmissions) between the control and intervention groups [42]. Dhalla et al. evaluated the effectiveness of virtual wards on readmissions and death in high-risk patients (based upon length of stay, acuity of the admission, comorbidities, and emergency department visits in the previous 6 months) [43]. Patients randomized to the intervention group were admitted to the virtual ward once discharged, providing them telephone access to a multidisciplinary team consisting of a case coordinator, part-time pharmacist, part-time nurse, or nurse-practitioner, full-time physician, and a clerical assistant, in addition to usual care. The authors found that virtual wards had no statistically significant effect on readmissions and death at 30 and 60 days, 6 months and 1 year post discharge in comparison to usual care [43].
Studies of heart failure patients
Twenty-five studies [17–38, 44, 46, 47] on heart failure were included in the meta-analysis. Post-discharge interventions reduced the risk of all-cause mortality (RR = 0.73, 95% CI = 0.65–0.83; I2 = 0%; Fig 2A), all-cause hospitalization (RR = 0.90, 95% CI = 0.81–0.99; I2 = 61%; Fig 2B), cause-specific mortality (RR = 0.68, 95% CI = 0.54–0.84; I2 = 0%; Fig 2C), and cause-specific hospitalization (RR = 0.71, 95% CI = 0.63–0.80; I2 = 4%; Fig 2D), in heart failure patients.
A) all-cause mortality, B) all-cause hospitalization, C) cause-specific mortality, D) cause-specific hospitalization in heart failure patients; E) all-cause mortality, F) all-cause hospitalization in COPD patients.
Studies of COPD patients
All 5 of the studies examining chronic obstructive pulmonary disease were included in a meta-analysis [38–41, 45]; however, only two outcomes, all-cause mortality and all-cause hospitalization could be analyzed based on the outcomes reported in the studies. Post-discharge interventions reduced the risk of all-cause hospitalization in COPD patients (RR 0.52, 95% CI = 0.32–0.83; I2 = 61%; Fig 2F) yet did not appear to reduce all-cause mortality (RR 0.96, 95% CI = 0.62–1.48; I2 = 0%; Fig 2E).
Subgroup analyses and meta-regression
In the univariate subgroup analyses, we were unable to discern a statistically significant association between number or type of intervention strategy (multidisciplinary care, pre-discharge patient education, home visits, post-discharge telephone calls and continuous tele-monitoring) and efficacy for any of the outcomes (all-cause and cause-specific hospitalization and all-cause and cause-specific mortality) (Table 3). We did find an association between the reported presence of a trained cardiac nurse and a lower risk for all-cause mortality in heart failure studies [(HR 0.64, 95% CI = 0.54–0.75; I2 = 0%; Fig 3A)] vs. (HR 0.87, 95% CI = 0.73–1.03; I2 = 0%; Fig 3B)]. In a secondary analysis, we used a meta-regression strategy together with a simple scoring system to rank intervention strategies in terms of complexity (pre-discharge patient education = 1, post-discharge telephone calls = 2, home visits = 3, continuous tele-monitoring = 4, and multidisciplinary team care = 5). We defined a complexity score for a study as the simple sum of the complexity ranks of all strategies employed in that study. We were unable to show a clear relationship between complexity score and efficacy for any outcome in the heart-failure studies using meta-regression (Table 4). Additionally, we conducted subgroup analysis including only recently published heart-failure studies (since January 2009). Subgroup and main analysis results were found to be consistent as calculated risk ratios were of similar direction and magnitude in all cases. (S4–S7 Figs).
Discussion
In this systematic review and meta-analysis of RCT’s examining post-discharge interventions in heart failure, COPD and CKD, we found that the interventions studied consistently reduced all-cause mortality, cause-specific mortality, all-cause hospitalization, and cause-specific hospitalization in heart failure patient populations. We were unable to discern an association between complexity (number or type) of intervention strategy employed using the data available. Calculated risk ratios were similar to main analysis when removing older studies in CHF (published prior to 2009), showing no clear difference in effectiveness over time. In a post hoc analysis, inclusion of cardiac specific nurses in the intervention team was associated with greater efficacy in heart failure trials.
Previous systematic reviews have found that interventions including patient education, home visits, self-management support, and telemonitoring have been effective in reducing hospital readmissions [48–52]. As such, these findings support those reported in our previous work [8] and are broadly consistent with recent systematic reviews in heart failure [49–52]. Thus, there appears to be strong evidence for efficacy of post-discharge interventions in heart failure patients. This research adds to previous systematic reviews by including more recently published studies as well as by analyzing the presence of an era effect in heart failure studies.
The data in COPD and CKD are sparse, and at present only support the efficacy of post-discharge interventions related to all-cause hospitalization in COPD patients. More research is required in these settings.
Other systematic reviews have found that interventions with increased complexity were more effective in reducing hospital readmissions compared to less complex interventions [48]. We could not detect a clear relationship between intervention complexity and efficacy across heart failure studies, nor were we able to identify components of the interventions most responsible for variations in efficacy across studies. This was true both in our primary analysis comparing sub-groups, or in a more sophisticated supplemental analysis using meta-regression and a simple complexity score. One possible interpretation is that there is no true association, and that the effect of a post-discharge intervention on outcomes is driven primarily by increased surveillance and is less dependent on the precise nature of that surveillance. We cannot exclude, however, the possibility that such an association exists, but was obscured by the limitations of our approach. In this regard it is important to note that no standard, validated measure of intervention complexity exists. Even our meta-regression strategy using a simple weighting system based on the reported components of an intervention, while reasonable a priori, may have been too crude to capture an association that may well be complex and nonlinear. Given sufficient data points (i.e., individual studies), it might be possible to accurately model the association between intervention complexity and efficacy; however, such techniques are not possible in the context of a meta-analysis of only 23 data points (studies). Indeed, it is unlikely that sufficient data points (i.e., studies) would ever be attainable in the setting of a systematic review and meta-analysis on this topic.
We did observe that studies including a trained, cardiac-specific nurse observed greater risk reduction for mortality and hospitalizations than those without a cardiac specific nurse. Although this finding in isolation must be interpreted cautiously, it is congruent with previous observations that the disease specificity of an intervention is important [8]. Taken together, these findings suggest that a specific-disease focus and expertise is needed in designing and implementing successful post-discharge interventions.
Our study has significant strengths. We adhered to PRISMA recommendations in the conduct of the review [13]. Our search strategy was broad, capturing many randomized clinical trials on diverse types of post-discharge interventions in recently discharged patients with congestive heart failure, chronic kidney disease, or chronic obstructive pulmonary disease. Our analysis included a wide spectrum of interventions that have been implemented in these patient populations in the past two decades. Our review included only randomized clinical trials reporting hard outcomes, because such studies provide the highest level of evidence for the efficacy of interventions.
Our study also has some important limitations. Few studies meeting our inclusion criteria were found for COPD and CKD, limiting conclusions about post-discharge interventions in these conditions. We were unable to show any association between complexity of the intervention and outcome. As discussed above, it may be that our study lacked sufficient power to detect a relationship between these variables if one existed, but it is also probable that this question cannot be answerable by a systematic review of any reasonable size.
Despite these limitations, we believe our findings provide a valid summary of the evidence to date, and as such have implications for research and clinical care. Our results confirm a compelling and consistent benefit for a wide array of post-discharge interventions in heart failure patients. Even though our study was unable to identify a minimum efficacious set of interventions, defining such a subset remains important, as complexity and cost are major barriers to the real-world implementation and scaling of these strategies. Our results suggest that meta-analytic techniques may not be able to answer this question and that randomized clinical trials comparing the efficacy and cost effectiveness of different types of post-discharge intervention should be conducted. In the absence of such data, programs planning on implementing post-discharge interventions for heart failure are justified in choosing specific interventions based on which of the published strategies appear most feasible in the local context. Our data further suggest that inclusion of a cardiac nurse in that strategy may be critical. Finally, evidence on post-discharge interventions in COPD and CKD are lacking, and further RCT data is urgently needed, particularly for COPD, which is one of the leading chronic diseases requiring readmission [2].
Conclusions
In conclusion, post-discharge interventions appeared effective in preventing readmission in heart failure populations. Inclusion of a trained cardiac nurse may be an important feature. Additional research is urgently needed on the impact of post-discharge intervention strategies in CKD and COPD.
Supporting information
S1 Fig. Meta-analysis of relative risks of all-cause mortality in heart failure patients, excluding those studies deemed at high risk of bias.
https://doi.org/10.1371/journal.pone.0249542.s002
(DOCX)
S2 Fig. Meta-analysis of relative risks of all-cause hospitalization in heart failure patients, excluding those studies deemed at high risk of bias.
https://doi.org/10.1371/journal.pone.0249542.s003
(DOCX)
S3 Fig. Meta-analysis of relative risks of cause-specific hospitalization in heart failure patients, excluding those studies deemed at high risk of bias.
https://doi.org/10.1371/journal.pone.0249542.s004
(DOCX)
S4 Fig. Meta-analysis of relative risks of all-cause hospitalization in heart failure patients, excluding studies published prior to 2009.
https://doi.org/10.1371/journal.pone.0249542.s005
(DOCX)
S5 Fig. Meta-analysis of relative risks of cause-specific hospitalization in heart failure patients, excluding studies published prior to 2009.
https://doi.org/10.1371/journal.pone.0249542.s006
(DOCX)
S6 Fig. Meta-analysis of relative risks of all-cause mortality in heart failure patients, excluding studies published prior to 2009.
https://doi.org/10.1371/journal.pone.0249542.s007
(DOCX)
S7 Fig. Meta-analysis of relative risks of cause-specific mortality in heart failure patients, excluding studies published prior to 2009.
https://doi.org/10.1371/journal.pone.0249542.s008
(DOCX)
S2 Table. The Cochrane Collaboration’s Tool for randomized studies risk of bias criteria.
https://doi.org/10.1371/journal.pone.0249542.s010
(XLSX)
S4 Table. Individual risk of bias assessment for all included studies using the Cochrane Collaboration’s Tool for randomized studies.
https://doi.org/10.1371/journal.pone.0249542.s012
(XLSX)
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