Readmission patterns in patients with chronic obstructive pulmonary disease, chronic heart failure and diabetes mellitus: an administrative dataset analysis

Patterns of morbidity and multimorbidity associated with early and late readmissions in an Australian regional health service

OBJECTIVES

To describe morbidity and multimorbidity patterns among adults readmitted to an Australian regional health service, in terms of occurrence of the same and different morbidities at index admission and readmission.

METHODS

This cohort study used hospital admissions data for patients admitted between 1 July 2014 and 30 June 2016 to estimate proportions of unplanned readmissions ('early' within 30 days and 'late' within 1-6 months) with the same and different morbidities as the index admission. Readmission rates were estimated by selected sociodemographic, admission and diagnostic characteristics.

RESULTS

The majority of early and late readmissions were in different diagnostic groups and for different primary morbidities to the index admission. Only 38.8% of readmissions were in the same major diagnostic group as the index admission and 18.4% in the same Adjacent Diagnosis-Related Group. Twenty one percent of admitted patients were readmitted within six months, with this increasing to 35.3% among multimorbid patients.

CONCLUSION

With increasing prevalence of multimorbidity, particularly among those at increased risk of readmission, it is essential to step away from a single disease focus in the design of both hospital avoidance and chronic disease management programmes. Holistic interventions and strategies that address multiple chronic conditions are required.

Predicting 30-days Readmission Risk for COPD Patients Care through a Federated Machine Learning Architecture on FAIR Data: Development and Validation Study (Preprint)

Background

Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers.

Objective

The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD).

Methods

The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies.

Results

Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases.

Conclusions

Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles.

The prevalence, characteristics, and risk factors of frequently readmitted patients to an internal medicine service

AIMS

To determine the prevalence, characteristics and risk factors associated with frequent readmissions to an internal medicine service at a tertiary public hospital.

METHOD

A retrospective observational study was conducted at an internal medicine service in a tertiary teaching hospital between 1st January 2010 and the 30th June 2016. Frequent readmission was defined as four or more readmissions within 12 months of discharge from the index admission. Demographic and clinical characteristics, and potential risk factors were evaluated.

RESULTS

50 515 patients were included, 1657 (3.3%) had frequent readmissions and were associated with nearly 2.5 times higher in 12-month mortality rates. They were older, had higher rates of Indigenous Australians (3.2%), more disadvantaged status (Index of Relative Socio-Economic Disadvantage decile of 5.3), and more comorbidities (mean Charlson comorbidity index 1.4) in comparison, to infrequent readmission group. The mean length of hospital stay during the index admission was 6 days for frequent readmission group (21.4% staying more than 7 days) with higher incidence of discharge against medical advice (2.0% higher). Intensive care unit admission rate was 6.6% for frequent readmission group compared to 3.9% for infrequent readmission group. Multivariate analysis showed mental disease and disorders, neoplastic, and alcohol/drug use and alcohol/drug induced organic mental disorders are associated with frequent readmission.

CONCLUSION

The risk factors associated with frequent readmission were older age, indigenous status, being socially disadvantaged, having higher comorbidities, and discharging against medical advice. Conditions that lead to frequent readmissions were mental disorders, alcohol/drug use and alcohol/drug induced organic mental disorders, and neoplastic disorders.

Postpartum Readmissions Among Women With Diabetes

OBJECTIVE

To estimate whether women with diabetes are at risk for 60-day postpartum readmissions and associated complications.

METHODS

The Nationwide Readmissions Database from 2010 to 2014 was analyzed to determine risk for 60-day postpartum readmissions among women with type 1 diabetes mellitus (DM), type 2 DM, gestational diabetes mellitus (GDM), and unspecified DM compared with women with no diabetes. Secondary outcomes included evaluating risk for overall severe maternal morbidity during readmissions, as well as wound complications, acute diabetic complications such as diabetic ketoacidosis, venous thromboembolism, and hypertensive diseases of pregnancy. Billing data were used to ascertain both exposures and outcomes. Adjusted log-linear regression models including demographic, hospital, medical and obstetric, and hospital factors were performed with adjusted risk ratios (aRRs) and with 95% Cis as measures of association.

RESULTS

Of an estimated 15.7 million delivery hospitalizations, 1.1 million occurred among women with diabetes, of whom 3.2% had type 1 DM, 9.1% type 2 DM, 86.6% GDM, and 1.1% unspecified diabetes. Compared with women without diabetes (1.5% risk for readmission), risk for readmission was significantly higher for women with type 1 DM (4.4%), unspecified diabetes (4.0%), type 2 DM (3.9%), and GDM (2.0%) (P<.01). After adjusting for hospital, demographic, medical, and obstetric risk factors, type 1 DM (aRR 1.77, 95% CI 1.69-1.87), type 2 DM (aRR 1.46, 95% CI 1.42-1.51), unspecified (aRR 1.73, 95% CI 1.59-1.89) and gestational diabetes (aRR 1.16, 95% CI 1.14-1.17) retained increased risk. Among women with diabetes public insurance, lower ZIP code income quartiles, and hospitals with high safety net burdens were associated with higher risk for readmission. In both unadjusted and adjusted analyses, all diabetes diagnoses were associated with readmissions for wound complications, hypertensive diseases of pregnancy, and severe maternal morbidity.

CONCLUSION

Although overall risk for readmission is low, pregnancies complicated by pregestational diabetes in particular are at increased risk. Women in this high-risk group should receive coordinated care and be monitored closely in the postpartum period.

Unplanned readmission or death after discharge for Aboriginal and non-Aboriginal people with chronic disease in NSW Australia: a retrospective cohort study

BACKGROUND

Admitted patients with chronic disease are at high risk of an unplanned hospital readmission, however, little research has examined unplanned readmission among Aboriginal people in Australia. This study aimed to examine whether rates of unplanned 28 day hospital readmission, or death, significantly differ between Aboriginal and non-Aboriginal patients in New South Wales, Australia, over a nine-year period.

METHODS

A retrospective cohort analysis of a sample of de-identified linked hospital administrative data was conducted. Eligible patients were: 1) aged ≥18 years old, 2) admitted to an acute facility in a NSW public hospital between 30th June 2005 and 1st July 2014, and 3) admitted with either cardiovascular disease, chronic respiratory disease, diabetes or renal disease. The primary composite outcome was unplanned readmission or death within 28 days of discharge. Generalized linear models and a test for trend were used to assess rates of unplanned readmission or death over time in Aboriginal and non-Aboriginal patients with chronic disease, accounting for sociodemographic variables.

RESULTS

The final study cohort included 122,145 separations corresponding to 48,252 patients (Aboriginal = 57.2%, n = 27,601; non-Aboriginal = 42.8%, n = 20,651). 13.9% (n = 16,999) of all separations experienced an unplanned readmission or death within 28 days of discharge. Death within 28 days of discharge alone accounted for only a small number of separations (1.4%; n = 1767). Over the nine-year period, Aboriginal separations had a significantly higher relative risk of an unplanned readmission or death (Relative risk = 1.34 (1.29, 1.40); p-value < 0.0001) compared with non-Aboriginal separations once adjusted for sociodemographic, disease variables and restricted to < 75 years of age. A test for trend, including an interaction between year and Aboriginal status, showed there was no statistically significant change in proportions over the nine-year period for Aboriginal and non-Aboriginal separations (p-value for trend = 0.176).

CONCLUSION

Aboriginal people with chronic disease had a significantly higher risk of unplanned readmission or death 28 days post discharge from hospital compared with non-Aboriginal people, and there has been no significant change over the nine year period. It is critical that effective interventions to reduce unplanned readmissions for Aboriginal people are identified.

Risk factors and direct medical cost of early versus late unplanned readmissions among diabetes patients at a tertiary hospital in Singapore

OBJECTIVE

To examine the risk factors and direct medical costs associated with early (≤30 days) versus late (31-180 days) unplanned readmissions among patients with type 2 diabetes in Singapore.

METHODS

Risk factors and associated costs among diabetes patients were investigated using electronic medical records from a local tertiary care hospital from 2010 to 2012. Multivariable logistic regression was used to identify risk factors associated with early and late unplanned readmissions while a generalized linear model was used to estimate the direct medical cost. Sensitivity analysis was also performed.

RESULTS

A total of 1729 diabetes patients had unplanned readmissions within 180 days of an index discharge. Length of index stay (a marker of acute illness burden) was one of the risk factors associated with early unplanned readmission while patient behavior-related factors, like diabetes-related medication adherence, were associated with late unplanned readmission. Adjusted mean cost of index admission was higher among patients with unplanned readmission. Sensitivity analysis yielded similar results.

CONCLUSIONS

Existing routinely captured data can be used to develop prediction models that flag high risk patients during their index admission, potentially helping to support clinical decisions and prevent such readmissions.

Hospital readmissions after elective lower extremity vascular procedures

Background This study evaluated risk factors associated with 30-day readmission after open and endovascular lower extremity revascularization. Methods Patients admitted with peripheral artery disease and lower extremity procedures were selected from national electronic medical record data, Cerner Health Facts® (2008-2014). Thirty-day readmission was determined. Logistic regression models identified characteristics independently associated with readmission. Results There were 2781 open and 2611 endovascular procedures. Readmission was 10.9% (9.6% open versus 12.3% endovascular, p<.0001). Greater disease severity was associated with readmission for both groups. Readmission factors for lower extremity bypass: blood transfusions (OR 2.25, 95% CI 1.62-3.13), hyponatremia (OR 1.72, 95% CI 1.15-2.57), heart failure (OR 1.57, 95% CI 1.07-2.29), bronchodilators (OR 1.50, 95% CI 1.13-2.00), black race (OR 1.43, 95% CI 1.03-1.99), and hypokalemia (OR 0.43, 95% CI 0.20-0.95). Readmission factors for endovascular procedures: vasodilators (OR 1.63, 95% CI 1.22-2.16), end-stage renal disease (OR 1.43, 95% CI 1.02-2.01), fluid and electrolyte disorders (OR 1.44, 95% CI 1.00-2.06), hypertension (OR 1.33, 95% CI 0.99-1.76), coronary artery disease (OR 1.31, 95% CI 1.02-1.67), and diuretics (OR 1.30, 95% CI 1.01-1.70). Conclusions Readmission after lower extremity revascularization is associated with disease severity for both procedures. Factors associated with readmission following lower extremity bypass included heart failure, transfusions, hyponatremia, black race, and bronchodilator use. Risk factors for endovascular readmissions were often chronic conditions including coronary artery disease, kidney disease, hypertension, and hypertensive medications. Awareness of risk factors may help providers identify high-risk patients who may benefit from increased surveillance and programs to lower readmission.

Risk factors for readmission to hospital in patients with tuberculosis in Tehran, Iran: three-year surveillance

Tuberculosis (TB) is still a major health problem and TB hospital readmission could increase health system costs. In a retrospective study in a tertiary referral hospital for TB in Tehran, Iran, TB patients with readmission were evaluated. These TB patients in the index year who were then readmitted were compared with TB patients in the same year who were not readmitted during the follow-up period. One hundred and forty-six patients had hospital readmission within three-year follow-up with mean age of 51.6 years old of whom 78 patients (53.5%) were men. Univariate analysis revealed married status, smoking, opium smoking, and medical comorbidities (chronic obstructive pulmonary disease [COPD], hypertension, and human immunodeficiency virus [HIV] infection) as risk factors. Final logistic regression model revealed married status and smoking values of (0.478 odds ratio [OR], 0.310-0.737; 95% confidence interval [CI], P = 0.001) and (1.932 OR, 1.269-2.941; 95% CI, P = 0.002), respectively. Readmission predicted probability was 37% for married patients and 31% for active smokers. The most common medical comorbidities in the first readmission were COPD and HIV infection. Dyspnea and anti-TB drug-induced hepatitis were a common cause of early readmission, while failure and default of treatment were more frequent causes of late readmission. Admission and discharge guidelines, outpatient follow-up, and smoking cessation intervention were proposed as important factors in decreasing the readmission rate.

Effectiveness of interventions utilising telephone follow up in reducing hospital readmission within 30 days for individuals with chronic disease: a systematic review

BACKGROUND

Rates of readmission to hospital within 30 days are highest amongst those with chronic diseases. Effective interventions to reduce unplanned readmissions are needed. Providing support to patients with chronic disease via telephone may help prevent unnecessary readmission. This systematic review aimed to determine the methodological quality and effectiveness of interventions utilising telephone follow up (TFU) alone or in combination with other components in reducing readmission within 30 days amongst patients with cardiovascular disease, chronic respiratory disease and diabetes.

METHODS

A systematic search of MEDLINE, the Cochrane Library and EMBASE were conducted for articles published from database inception to 19(th) May 2015. Interventions which included TFU alone, or in combination with other components, amongst patients with chronic disease, reported 30 day readmission outcomes and met Effective Practice and Organisation of Care design criteria were included. The titles and abstracts of all identified articles were initially assessed for relevance and rejected on initial screening by one author. Full text articles were assessed against inclusion criteria by two authors with discrepancies resolved through discussion.

RESULTS

Ten studies were identified, of which five were effective in reducing readmissions within 30 days. Overall, the methodological quality of included studies was poor. All identified studies combined TFU with other intervention components. Interventions that were effective included three studies which provided TFU in addition to pre-discharge support; and two studies which provided TFU with both pre- and post-discharge support which included education, discharge planning, physical therapy and dietary consults, medication assessment, home visits and a resident curriculum. There was no evidence that TFU and telemedicine or TFU and post-discharge interventions was effective, however, only one to two studies examined each of these types of interventions.

CONCLUSIONS

Evidence is inconclusive for the effectiveness of interventions utilising TFU alone or in combination with other components in reducing readmissions within 30 days in patients with chronic disease. High methodological quality studies examining the effectiveness of TFU in a standardised way are needed. There is also potential importance in focusing interventions on enhancing provider skills in patient education, transitional care and conducting TFU.

Identifying risk factors and patterns for unplanned readmission to a general medical service

OBJECTIVE

To identify factors and patterns associated with 7- and 28-day readmission for general medicine patients at a tertiary public hospital.

METHODS

A retrospective observational study was conducted using an administrative database at a general medicine service in a tertiary public hospital between 1 January 2007 and 31 December 2011. Demographic and clinical factors, as well as readmission patterns, were evaluated for the association with 7- and 28-day readmission.

RESULTS

The study cohort included 13 802 patients and the 28-day readmission rate was 10.9%. In multivariate analysis, longer hospital stay of the index admission (adjusted relative risk (ARR) 1.34), Charlson index ≥ 3 (ARR 1.28), discharge against medical advice (ARR 1.87), active malignancy (ARR 1.83), cardiac failure (ARR 1.48) and incomplete discharge summaries (ARR 1.61) were independently associated with increased risk of 28-day readmission. Patients with diseases of the respiratory system, neurological or genitourinary disease, injury and unclassifiable conditions were likely to be readmitted within 7 days. Patients with circulatory and respiratory disease were likely to be readmitted with the same system diagnosis.

CONCLUSION

Readmission of general medicine patients within 28 days is relatively common and is associated with clinical factors and patterns. Identification of these risk factors and patterns will enable the interventions to reduce potentially preventable readmissions.

Hospitalization for ambulatory care sensitive conditions at health insurance organization hospitals in Alexandria, Egypt

This study aimed at estimating the percentage of hospital discharges and days of care accounted for by Ambulatory Care Sensitive Conditions (ACSCs) at Health Insurance Organization (HIO) hospitals in Alexandria, calculating hospitalization rates for ACSCs among HIO population and identifying determinants of hospitalization for those conditions. A sample of 8300 medical records of patients discharged from three hospitals affiliated to HIO at Alexandria was reviewed. The rate of monthly discharges for ACSCs was estimated on the basis of counting number of combined ACSCs detected in the three hospitals and the hospitals' average monthly discharges. ACSCs accounted for about one-fifth of hospitalizations and days of care at HIO hospitals (21.8% and 20.8%, respectively). Annual hospitalization rates for ACSCs were 152.5 per 10,000 insured population. The highest rates were attributed to cellulitis/abscess (47.3 per 10,000 population), followed by diabetes complications and asthma (42.8 and 20.8 per 10,00 population). Logistic regression indicated that age, number of previous admissions, and admission department are significant predictors for hospitalization for an ACSC.

Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study

BACKGROUND

Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission.

METHODS

This is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis.The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts.

RESULTS

3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64).

CONCLUSIONS

The readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged.

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study

Background

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time.

Methods

Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak.

Results

The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%.

Conclusion

The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

Recurrent readmissions in medical patients: a prospective study

BACKGROUND

Hospital readmissions are common and costly. A recent previous hospitalization preceding the index admission is a marker of increased risk of future readmission.

OBJECTIVES

To identify factors associated with an increased risk of recurrent readmission in medical patients with 2 or more hospitalizations in the past 6 months.

DESIGN

Prospective cohort study.

SETTING

Australian teaching hospital acute medical wards, February 2006-February 2007.

PARTICIPANTS

142 inpatients aged ≥ 50 years with a previous hospitalization ≤ 6 months preceding the index admission. Patients from residential care, with terminal illness, or with serious cognitive or language difficulties were excluded.

VARIABLES OF INTEREST

Demographics, previous hospitalizations, diagnosis, comorbidities and nutritional status were recorded in hospital. Participants were assessed at home within 2 weeks of hospital discharge using validated questionnaires for cognition, literacy, activities of daily living (ADL)/instrumental activities of daily living (IADL) function, depression, anxiety, alcohol use, medication adherence, social support, and financial status.

MAIN OUTCOME MEASURE

Unplanned readmission to the study hospital within 6 months.

RESULTS

A total of 55 participants (38.7%) had a further unplanned hospital admission within 6 months. In multivariate analysis, chronic disease (adjusted odds ratio [OR] 3.4; 95% confidence interval [CI], 1.3-9.3, P = 0.002), depressive symptoms (adjusted OR, 3.0; 95% CI, 1.3-6.8, P = 0.01), and underweight (adjusted OR, 12.7; 95% CI, 2.3-70.7, P = 0.004) were significant predictors of readmission after adjusting for age, length of stay and functional status.

CONCLUSIONS

In this high-risk patient group, multiple chronic conditions are common and predict increased risk of readmission. Post-hospital interventions should consider targeting nutritional and mood status in this population.

Evaluating causes for unplanned hospital readmissions of palliative care patients

This study evaluated reasons why palliative care patients were readmitted within 30 days of discharge. A secondary purpose was to determine whether length of stay (LOS) was different between readmission reasons. From July 2006 to June 2007, 156 palliative care readmissions were identified. Codes were assigned to each readmission and included compliance issues, discharge planning, disease process, new diagnosis, premature discharge, surgical complications, and other. Results demonstrated that disease progression (63%) and development of new co-morbidities (17%) were the primary readmission causes. No significant differences among readmission causes for LOS were identified. As the primary reason for readmission was the disease process, a closer look at the most common disease processes and the specific complications that resulted in a readmission would be helpful in planning patient care.

Respiratory rehabilitation after acute exacerbation of COPD may reduce risk for readmission and mortality -- a systematic review

Background

Acute exacerbations of chronic obstructive pulmonary disease (COPD) represent a major burden for patients and health care systems. Respiratory rehabilitation may improve prognosis in these patients by addressing relevant risk factors for exacerbations such as low exercise capacity. To study whether respiratory rehabilitation after acute exacerbation improves prognosis and health status compared to usual care, we quantified its effects using meta-analyses.

Methods

Systematic review of randomized controlled trials identified by searches in six electronic databases, contacts with experts, hand-searches of bibliographies of included studies and conference proceedings. We included randomized trials comparing the effect of respiratory rehabilitation and usual care on hospital admissions, health-related quality of life (HRQL), exercise capacity and mortality in COPD patients after acute exacerbation. Two reviewers independently selected relevant studies, extracted the data and evaluated the study quality. We pooled the results using fixed effects models where statistically significant heterogeneity (p ≤ 0.1) was absent.

Results

We identified six trials including 230 patients. Respiratory rehabilitation reduced the risk for hospital admissions (pooled relative risk 0.26 [0.12–0.54]) and mortality (0.45 [0.22–0.91]). Weighted mean differences on the Chronic Respiratory Questionnaire were 1.37 (95% CI 1.13–1.61) for the fatigue domain, 1.36 (0.94–1.77) for emotional function and 1.88 (1.67–2.09) for mastery. Weighted mean differences for the St. Georges Respiratory Questionnaire total score, impacts and activities domains were -11.1 (95% CI -17.1 to -5.2), -17.1 (95% CI -23.6 to -10.7) and -9.9 (95% CI -18.0 to -1.7). In all trials, rehabilitation improved exercise capacity (64–215 meters in six-minute walk tests and weighted mean difference for shuttle walk test 81 meter, 95% CI 48–115).

Conclusion

Evidence from six trials suggests that respiratory rehabilitation is effective in COPD patients after acute exacerbation. Larger trials, however, are needed to further investigate the role of respiratory rehabilitation after acute exacerbation and its potential to reduce costs caused by COPD.