"Predict readmission"

BACKGROUND: Despite increasing utilization and survival benefit over the last decade, extracorporeal membrane oxygenation (ECMO) remains resource-intensive with significant complications and rehospitalization risk. We thus utilized machine learning (ML) to develop prediction models for 90-day nonelective readmission following ECMO. METHODS: All adult patients receiving ECMO who survived index hospitalization were tabulated from the 2016-2020 Nationwide Readmissions Database...

BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explainability of machine learning algorithms in predicting unplanned readmission and death in cardiovascular patients at 30 days and 180 days from discharge. METHODS: Gradient boosting machines were trained and evaluated using data from hospital electronic medical records linked to hospital administrative and mortality data for 39,255 patients admitted to four hospitals in New South Wales, Australia between 2017 and 2021...

INTRODUCTION: Since February 2020, over 104 million people in the United States have been diagnosed with SARS-CoV-2 infection, or COVID-19, with over 8.5 million reported in the state of Texas. This study analyzed social determinants of health as predictors for readmission among COVID-19 patients in Southeast Texas, United States. METHODS: A retrospective cohort study was conducted investigating demographic and clinical risk factors for 30, 60, and 90-day readmission outcomes among adult patients with a COVID-19-associated inpatient hospitalization encounter within a regional health information exchange between February 1, 2020, to December 1, 2022...

BACKGROUND: Frailty correlates with poor clinical outcomes and is not routinely assessed in neurorehabilitation inpatient settings. METHODS: We recruited adults from two neurorehabilitation units. We administered six validated tools for assessing frailty and collected data around length of stay, discharge, readmission and change in rehabilitation outcome measures. RESULTS: Seventy-eight participants aged between 31 and 84 years were recruited with a range of neurological diagnoses...

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA...

Heart failure has become a huge public health problem, and failure to accurately predict readmission will further lead to the disease's high cost and high mortality. The construction of readmission prediction model can assist doctors in making decisions to prevent patients from deteriorating and reduce the cost burden. This paper extracts the patient discharge records from the MIMIC-III database. It divides the patients into three research categories: no readmission, readmission within 30 days, and readmission after 30 days, to predict the readmission of patients...

OBJECTIVE: To identify risk factors for readmission following methotrexate treatment for tubal pregnancy. METHODS: A retrospective study undertaken in two tertiary medical centers, including all individuals with medically treated tubal pregnancy (N = 511), between December 2009 and June 2021. Individuals with and without readmission following methotrexate treatment were compared. The primary outcome was the readmission rate. Secondary outcomes included the rate of post-discharge gynecological emergency department visits, tubal rupture rate, and the eventual need for surgical treatment...

INTRODUCTION: Unplanned readmission is often seen after excisional hemorrhoidectomy. This study aims to explore associations between patient and operative factors, and readmission rates in excisional hemorrhoidectomy. METHODS: We performed a retrospective analysis of all excisional hemorrhoidectomies performed in Capital and Coast District Health Board for an 8-year period from January 1, 2012, to December 31, 2020. The primary outcome measure was 30-day readmissions post hemorrhoidectomy...

PURPOSE: Coronary artery disease (CAD) patients frequently face readmissions due to suboptimal disease management. Prediction models are pivotal for detecting early unplanned readmissions. This review offers a unified assessment, aiming to lay the groundwork for enhancing prediction models and informing prevention strategies. METHODS: A search through five databases (PubMed, Web of Science, EBSCOhost, Embase, China National Knowledge Infrastructure) up to September 2023 identified studies on prediction models for coronary artery disease patient readmissions for this review...

BACKGROUND: Analyzing the risk factors that predict readmissions can potentially lead to more individualized patient care. The 11-factor modified frailty index is a valuable tool for predicting postoperative outcomes following surgery. The objective of this study is to determine whether the frailty index can effectively predict readmissions within 90 days after lung resection surgery in cancer patients within a single healthcare institution. METHODS: Patients who underwent elective pulmonary resection for non-small cell lung cancer (NSCLC) between January 2012 and December 2020 were selected from the hospital's database...

PURPOSE: The incidence of oral cancers, particularly HPV-related oropharyngeal cancer, is steadily increasing worldwide, presenting a significant healthcare challenge. This study investigates trends and predictors of unplanned hospitalizations for oral cavity cancer (OCC) and oropharyngeal cancer (OPC) patients in the province of Alberta, Canada. METHODS: This retrospective, population-based, cohort study used administrative data collected from all hospitals in the province...

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA...

INTRODUCTION: Patients with cocaine use disorders have very high readmission rates. Our aim was to model the relationships between attributes of patients with cocaine use disorder at the beginning of treatment, therapeutic process indicators of time in treatment or proportion of appointments attended, and treatment outcomes, including outcomes at discharge and non-readmissions posttreatment as indicators of therapeutic success. METHOD: A retrospective observational design was used with 10,298 cocaine use disorder patients...

BACKGROUND: Quality rating systems exist to grade the value of care provided by hospitals, but the extent to which these rating systems correlate with patient outcomes is unclear. The association of quality rating systems and hospital characteristics with excess readmission penalties for total hip arthroplasty (THA) and total knee arthroplasty (TKA) was studied. METHODS: The fiscal year 2022 Inpatient Prospective Payment System final rule was used to identify 2,286 hospitals subject to the Hospital Readmissions Reduction Program...

BACKGROUND: There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days. METHODS: We recruited patients with respiratory failure at the First People's Hospital of Yancheng and the People's Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score...

BACKGROUND: Acute lower gastrointestinal bleeding (ALGIB) is a common cause of hospitalization. Recent guidelines recommend the use of prognostic scales for risk stratification. However, it remains unclear whether risk scores are more accurate than some simpler prognostic variables. OBJECTIVE: To compare the predictive values of haemoglobin alone and the Oakland score for predicting outcomes in ALGIB patients. DESIGN: Single-centre, retrospective study at a University Hospital...

BACKGROUND/AIMS: Primary simultaneous bilateral total joint arthroplasty (simBTJA) can cause postoperative anemia. Clinicians might hesitate to discharge patients who have large changes in hemoglobin [Hgb], despite Hgb levels remaining above transfusion thresholds. This study was conducted to evaluate if delta Hgb or perioperative blood loss correspond with readmission in primary simBTJA patients not transfused perioperatively. METHODS: From 2015 - 2020, a retrospective chart review of primary simultaneous bilateral total hip/knee arthroplasty cases was conducted...

Transformation of patient data extracted from a database into fixed-length numerical vectors requires expertise in topical medical knowledge as well as data manipulation-thus, manual feature design is labor-intensive. In this study, we propose a machine learning-based method to for this purpose applicable to electronic medical data recorded during hospitalization, which utilizes unsupervised feature extraction based on graph embedding. Unsupervised learning is performed on a heterogeneous graph using Graph2Vec, and the inclusion of clinically useful data in the obtained embedding representation is evaluated by predicting readmission within 30 days of discharge based on it...

BACKGROUND: The Centers for Medicare & Medicaid Services currently incentivizes hospitals to reduce postdischarge adverse events such as unplanned hospital readmissions for patients who underwent total joint arthroplasty (TJA). This study aimed to predict 90-day TJA readmissions from our comprehensive electronic health record data and routinely collected patient-reported outcome measures. METHODS: We retrospectively queried all TJA-related readmissions in our tertiary care center between 2016 and 2019...

BACKGROUND: Readmissions following acute type A aortic dissections (ATAAD) are associated with potentially worse clinical outcomes and increased hospital costs. Predicting which patients are at risk for readmission may guide patient management prior to discharge. METHODS: The National Readmissions Database was utilized to identify patients treated for ATAAD between 2010 and 2018. Univariate mixed effects logistic regression was used to assess each variable. Variables were assigned risk points based off the bootstrapped (bias-corrected) odds ratio of the final variable model according to the Johnson's scoring system...