Health-related quality of life and readmission of patients with cardiovascular disease in South Korea

Kim, Hyun Su; Kim, Yoonjung; Kwon, Hyun Joo

doi:10.1177/1757913919892026

Cited by 9 publications

(9 citation statements)

References 37 publications

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“…Therefore, the prediction of readmission for acute care suggests that the attention to clinical findings would be considered in long-term care than in acute care settings. Similar to other reports on CVD-related 30-day readmission rates among Medicare beneficiaries, this study found higher 30-day readmission rates among men, patients aged < 65 years, those from lower-income households, and those with multiple comorbidities ( 12 , 18 – 20 , 26 , 27 , 30 ).…”

Section: Discussionsupporting

confidence: 89%

“…Risk prediction of 30-day readmission for patients with AMI could be analyzed through a variety of assessment tools ranging from patient interviews to screening methods, by using a different set of variables (26)(27)(28). Several studies have investigated the predictors, viz., demographic characteristics, admission and discharge predictors, major surgery, comorbidities, length of stay, medications, and special procedures that are associated with 30-day readmissions (29,30). One of the first steps in reducing 30-day readmissions is understanding and determining the key causes that lead to instances of readmission and developing a predictive model to assess the risk of readmission.…”

Section: Introductionmentioning

confidence: 99%

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LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital

Rajaguru

Kim

Han

et al. 2022

Front. Cardiovasc. Med.

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BackgroundThe LACE index (length of stay, acuity of admission, comorbidity index, and emergency room visit in the past 6 months) has been used to predict the risk of 30-day readmission after hospital discharge in both medical and surgical patients. This study aimed to utilize the LACE index to predict the risk of 30-day readmission in hospitalized patients with acute myocardial infraction (AMI).MethodsThis was a retrospective study. Data were extracted from the hospital's electronic medical records of patients admitted with AMI between 2015 and 2019. LACE index was built on admission patient demographic data, and clinical and laboratory findings during the index of admission. The multivariate logistic regression was performed to determine the association and the risk prediction ability of the LACE index, and 30-day readmission were analyzed by receiver operator characteristic curves with C-statistic.ResultsOf the 3,607 patients included in the study, 5.7% (205) were readmitted within 30 days of discharge from the hospital. The adjusted odds ratio based on logistic regression of all baseline variables showed a statistically significant association with the LACE score and revealed an increased risk of readmission within 30 days of hospital discharge. However, patients with high LACE scores (≥10) had a significantly higher rate of emergency revisits within 30 days from the index discharge than those with low LACE scores. Despite this, analysis of the receiver operating characteristic curve indicated that the LACE index had favorable discrimination ability C-statistic 0.78 (95%CI; 0.75–0.81). The Hosmer–Lemeshow goodness- of-fit test P value was p = 0.920, indicating that the model was well-calibrated to predict risk of the 30-day readmission.ConclusionThe LACE index demonstrated the good discrimination power to predict the risk of 30-day readmissions for hospitalized patients with AMI. These results can help clinicians to predict the risk of 30-day readmission at the early stage of hospitalization and pay attention during the care of high-risk patients. Future work is to be focused on additional factors to predict the risk of 30-day readmissions; they should be considered to improve the model performance of the LACE index with other acute conditions by using administrative data.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital

Rajaguru

Kim

Han

et al. 2022

Front. Cardiovasc. Med.

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“…Our study found higher 30-day readmission rates among men, those aged 65 years and older, patients from lower-income households, Medicare beneficiaries, and those who had multiple comorbidities, and similar results were reported on CVD-related 30-day readmission rates [12,[18][19][20]26,27,30]. We also found that patients who were discharged for other reasons, such as those discharged against medical advice or voluntarily discharged, were more likely to have 30-day readmissions compared to those who had a normal discharge.…”

Section: Discussionsupporting

confidence: 88%

“…Risk prediction of 30-day readmission for patients with AMI could be accomplished with a variety of assessment tools ranging from multidisciplinary patient interviews to simple screening tools using a handful of variables [ 26 , 27 , 28 ]. Several studies have investigated predictors: demographic characteristics, admission and discharge types, comorbidities, length of stay, medications and special procedures associated with 30-day readmissions [ 29 , 30 ]. However, healthcare professionals still struggle to predict those patients who are at high risk of hospital readmission.…”

Section: Introductionmentioning

confidence: 99%

Ability of the LACE Index to Predict 30-Day Readmissions in Patients with Acute Myocardial Infarction

Rajaguru

Kim

Shin

et al. 2022

JPM

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Aims: This study aimed to utilize the existing LACE index (length of stay, acuity of admission, comorbidity index and emergency room visit in the past six months) to predict the risk of 30-day readmission and to find the associated factors in patients with AMI. Methods: This was a retrospective study and LACE index scores were calculated for patients admitted with AMI between 2015 and 2019. Data were utilized from the hospital’s electronic medical record. Multivariate logistic regression was performed to find the association between covariates and 30-day readmission. The risk prediction ability of the LACE index for 30-day readmission was analyzed by receiver operating characteristic curves with the C statistic. Results: A total of 205 (5.7%) patients were readmitted within 30 days. The odds ratio of older age group (OR = 1.78, 95% CI: 1.54–2.05), admission via emergency ward (OR = 1.45; 95% CI: 1.42–1.54) and LACE score ≥10 (OR = 2.71; 95% CI: 1.03–4.37) were highly associated with 30-day readmissions and statistically significant. The receiver operating characteristic curve C statistic of the LACE index for AMI patients was 0.78 (95% CI: 0.75–0.80) and showed favorable discrimination in the prediction of 30-day readmission. Conclusion: The LACE index showed a good discrimination to predict the risk of 30-day readmission for hospitalized patients with AMI. Further study would be recommended to focus on additional factors that can be used to predict the risk of 30-day readmission; this should be considered to improve the model performance of the LACE index for other acute conditions by using the national-based administrative data.

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“…The results of this study contrast with a previous study, where a low QOL predicted readmission. Indeed, QOL is recognised as a significant predictor of readmission for CAD patients (Dhital et al, 2018;Heo et al, 2009;O'Loughlin et al, 2010), where CAD patients who experience physical and emotional symptoms that result in a poor QOL are linked to a higher frequency of readmission (Kim et al, 2019;Sethares and Elliott, 2004;O'Loughlin et al, 2010). Likewise, CAD was found to be a chronic condition with an unstable course and unpredictable exacerbations and progression of symptoms, which resulted in a poor QOL and was linked to an increased frequency of readmission (Kim et al, 2019;Sethares and Elliott, 2004;O'Loughlin et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

A cross-sectional study of factors predicting readmission in Thais with coronary artery disease

Polsook

Aungsuroch

2020

Journal of Research in Nursing

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Background Coronary artery disease is a major cause of morbidity and mortality with high readmission rates. Hospital readmissions for coronary artery disease contribute to rising healthcare costs and are a marker of quality of care. Despite this, prior studies have found that readmission rates vary widely. Aims This study aims to determine the impact of social support, depression, comorbidities, symptom severity, quality of life and readmission among coronary artery disease patients in Thailand. Methods A total of 321 coronary artery disease patients from tertiary care hospitals across all regions of Thailand were recruited for this study. Data were analysed using multiple regression analysis. Results The coefficient for social support (beta = −0.22) was found to be significant ( p < 0.05), whereas comorbidity, symptom severity, depression and quality of life were not significant. Thus, social support was found to be the most significant predictive factor for readmission. Conclusions Accordingly, when designing effective nursing interventions, nurses should promote social support interventions for coronary artery disease patients to improve the quality of care, decrease readmission rates and improve patients' quality of life.

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Health-related quality of life and readmission of patients with cardiovascular disease in South Korea

Cited by 9 publications

References 37 publications

LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital

LACE Index to Predict the High Risk of 30-Day Readmission in Patients With Acute Myocardial Infarction at a University Affiliated Hospital

Ability of the LACE Index to Predict 30-Day Readmissions in Patients with Acute Myocardial Infarction

A cross-sectional study of factors predicting readmission in Thais with coronary artery disease

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