Predictors of mortality, strategies to reduce readmission, and economic impact of acute decompensated heart failure: Results of the Vellore Heart Failure Registry

John, Kevin John; Turaka, Vijay Prakash; Bharathy, K Muruga; Kumar, Chetan; Jayaseelan, L; Visalakshi, J; Nadaraj, Ambily; Mathew, Anoop; Mariam, Faith; Nellimala, Nevin Joseph; Joy, Anisha; Punitha, J.V.; Koshy, Maria; Chandy, Gina Maryann; Gunasekaran, Karthik; Sudarsanam, Thambu David

doi:10.1016/j.ihj.2020.03.005

Cited by 11 publications

(6 citation statements)

References 21 publications

(5 reference statements)

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“…As the research framework for examining risk factors for unplanned readmission within 30 days of discharge, we proposed the following variables as risk factors based on the literature and patients' medical records: ‘emergency admission’ (van Walraven et al, 2012b) refers to being initially admitted owing to an emergency or not; ‘non‐surgical admission’ (Schmidt et al, 2018) is for a patient that did not have surgery during hospitalization; ‘type of residence’ (Shimatani et al, 2021) means older patients living alone or with others; ‘need for long‐term care’ (Tonkikh et al, 2016); and ‘decline in ADL owing to hospitalization’ (Greysen et al, 2015) were extracted from nurse‐assessed discharge support screening; ‘disease’ refers to malignancy (Donzé et al, 2017), heart failure (John et al, 2020), diabetes (Robbins et al, 2019) and COPD (Ridwan et al, 2019; we excluded COPD because the number of patients [ n = 3] was insufficient for analysis); ‘age’ refers to being aged >75 years or not; and ‘hospital stay’ refers to staying ≥15 days or not because, in Japan, reimbursement is evaluated differently depending on whether the hospital stay is 2 weeks or longer and the type of medical insurance is different for those aged 75 and above.…”

Section: Methodsmentioning

confidence: 99%

Multiple risk factors for unplanned readmissions within 1 month of hospital discharge in acute care hospitals in Japan

Tomita,

Murata,

Suzuki

et al. 2024

Int J of Nursing Practice

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AimThe aim of this study is to analyse the risk factors for unplanned readmissions within 1 month after hospital discharge to develop a seamless support system from discharge to home care.BackgroundWith shorter hospital stay lengths, understanding the characteristics of patients with multiple risk factors is important to prevent rehospitalization.DesignThis is a single‐centre retrospective descriptive study.MethodsLogistic regression and decision tree analyses were performed using eight items from the records of 3117 patients discharged from a university hospital between April–September 2017 as risk factors.ResultsUnplanned readmission risk was significantly associated with emergency hospitalization (odds ratio [OR]: 3.12, 95% confidence interval [CI]: 2.04–4.77), malignancy (OR: 2.16, 95% CI: 1.44–3.24), non‐surgical admission (OR: 1.76, 95% CI: 1.07–2.88), hospital stay of ≥ 15 days (OR: 1.66, 95% CI: 1.14–2.43) and decline in activities of daily living owing to hospitalization (OR: 1.68, 95% CI: 1.06–2.64). The highest risk combinations for rehospitalization were as follows: emergency hospitalization and malignancy; emergency admission, non‐malignancy and a hospital stay of ≥15 days; and scheduled hospitalization, no surgery and a hospital stay of ≥15 days.ConclusionsPatients with multiple risks for unplanned readmission should be accurately screened and provided with optimal home care.

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Section: Methodsmentioning

confidence: 99%

Multiple risk factors for unplanned readmissions within 1 month of hospital discharge in acute care hospitals in Japan

Tomita,

Murata,

Suzuki

et al. 2024

Int J of Nursing Practice

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“… 5 Even though this was lower as compared to reports from Trivandrum and Vellore HF registry 5 years earlier, the societal and economic effect in low- and middle-income country (LMIC) settings like India is unique and different from that of high-income countries. 6 , 7 …”

Section: Introductionmentioning

confidence: 99%

Assessment of Heart Failure Post-discharge Management Strategies, Needs and Acceptance of Mobile Application-based Remote Patient Management in South India

Lalthanthuami,

Ramamoorthy,

Satheesh

et al. 2024

Journal of Patient Experience

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The demand for digital platforms in managing heart failure (HF) is expected to increase with promising effects on readmission and health expenditure. The study aims to explore current post-discharge management strategies and identify the need and acceptance of digital platforms, to ensure the development of a user-friendly mobile application for HF patients. Using a cross-sectional analytical research design, 90 consecutive patients diagnosed with HF who were discharged from a Tertiary Care Center were enrolled. Tele-interview was conducted using a self-developed and validated tool. The mean age of participants was 55.54 ± 10.33 years. The participants’ adherence to HF management strategies was low in terms of physical exercise and weight monitoring. More than one-third were willing to self-record their measurements and use a mobile application. The common mobile application features requested were medication information/reminder (88.6%), health education (84.3%), chat with nurses (84.3%), physical activity (81.4%), symptoms (78.6%), diet (78.6%) and weight management (72.9%). The findings from this initial phase of mobile development are expected to help leverage better development of digital interventions for HF patients.

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“…As the largest developing country and the second-largest economy in the world, China is no exception ( 3 ). It is estimated that there are over 4.5 million heart failure patients requiring heart transplantation surgery annually in China, costing the economy 30.7 billion dollars per year ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients

Wang

Yan

et al. 2022

Front. Cardiovasc. Med.

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BackgroundPost-operative heart transplantation patients often require admission to an intensive care unit (ICU). Early prediction of the ICU length of stay (ICU-LOS) of these patients is of great significance and can guide treatment while reducing the mortality rate among patients. However, conventional linear models have tended to perform worse than non-linear models.Materials and MethodsWe collected the clinical data of 365 patients from Wuhan Union Hospital who underwent heart transplantation surgery between April 2017 and August 2020. The patients were randomly divided into training data (N = 256) and test data (N = 109) groups. 84 clinical features were collected for each patient. Features were validated using the Least Absolute Shrinkage and Selection Operator (LASSO) regression’s fivefold cross-validation method. We obtained Shapley Additive explanations (SHAP) values by executing package “shap” to interpret model predictions. Four machine learning models and logistic regression algorithms were developed. The area under the receiver operating characteristic curve (AUC-ROC) was used to compare the prediction performance of different models. Finally, for the convenience of clinicians, an online web-server was established and can be freely accessed via the website https://wuhanunion.shinyapps.io/PredictICUStay/.ResultsIn this study, 365 consecutive patients undergoing heart transplantation surgery for moderate (NYHA grade 3) or severe (NYHA grade 4) heart failure were collected in Wuhan Union Hospital from 2017 to 2020. The median age of the recipient patients was 47.2 years, while the median age of the donors was 35.58 years. 330 (90.4%) of the donor patients were men, and the average surgery duration was 260.06 min. Among this cohort, 47 (12.9%) had renal complications, 25 (6.8%) had hepatic complications, 11 (3%) had undergone chest re-exploration and 19 (5.2%) had undergone extracorporeal membrane oxygenation (ECMO). The following six important clinical features were selected using LASSO regression, and according to the result of SHAP, the rank of importance was (1) the use of extracorporeal membrane oxygenation (ECMO); (2) donor age; (3) the use of an intra-aortic balloon pump (IABP); (4) length of surgery; (5) high creatinine (Cr); and (6) the use of continuous renal replacement therapy (CRRT). The eXtreme Gradient Boosting (XGBoost) algorithm presented significantly better predictive performance (AUC-ROC = 0.88) than other models [Accuracy: 0.87; sensitivity: 0.98; specificity: 0.51; positive predictive value (PPV): 0.86; negative predictive value (NPV): 0.93].ConclusionUsing the XGBoost classifier with heart transplantation patients can provide an accurate prediction of ICU-LOS, which will not only improve the accuracy of clinical decision-making but also contribute to the allocation and management of medical resources; it is also a real-world example of precision medicine in hospitals.

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Predictors of mortality, strategies to reduce readmission, and economic impact of acute decompensated heart failure: Results of the Vellore Heart Failure Registry

Cited by 11 publications

References 21 publications

Multiple risk factors for unplanned readmissions within 1 month of hospital discharge in acute care hospitals in Japan

Multiple risk factors for unplanned readmissions within 1 month of hospital discharge in acute care hospitals in Japan

Assessment of Heart Failure Post-discharge Management Strategies, Needs and Acceptance of Mobile Application-based Remote Patient Management in South India

Comparison of Four Machine Learning Techniques for Prediction of Intensive Care Unit Length of Stay in Heart Transplantation Patients

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