"Predict readmission"

BACKGROUND: Unplanned readmission to the surgical intensive care unit has been demonstrated to worsen patient outcomes. Our objective was to identify risk factors and outcomes associated with unplanned surgical intensive care unit readmission and to develop a predictive scoring model to identify patients at high risk of readmission. METHODS: We retrospectively analyzed patients admitted to the surgical intensive care unit (2020-2021) and categorized them as either with or without unplanned readmission...

BACKGROUND: The purpose of the study was to use Machine Learning (ML) to construct a risk calculator for patients who undergo Total Joint Arthroplasty (TJA) on the basis of New York State Statewide Planning and Research Cooperative System (SPARCS) data and externally validate the calculator on a single TJA center. METHODS: Seven ML algorithms, i.e., logistic regression, adaptive boosting, gradient boosting (Xg Boost), random forest (RF) classifier, support vector machine, and single and a five-layered neural network were trained on the derivation cohort...

The Rothman Index (RI) is a real-time health indicator score that has been used to quantify readmission risk in several fields but has never been studied in gastrointestinal surgery. In this retrospective single-institution study, the association between RI scores and readmissions after unplanned colectomy or proctectomy was evaluated in 427 inpatients. Patient demographics and perioperative measures, including last RI, lowest RI, and increasing/decreasing RI score, were collected. In the selected cohort, 12...

BACKGROUND: The Pancreatic Stone Protein (PSP) is an acute-phase protein that is mainly secreted by pancreatic cells in response to stress. The current literature supports its use as a predictor of sepsis. Its prognostic role has recently been evaluated in a point-of-care setting, mostly in high-risk patients. We conducted a prospective observational cohort study to evaluate its utility in the prognosis of patients admitted to the hospital with a diagnosis of intra-abdominal infection...

BACKGROUND: Sensitive and interpretable machine learning (ML) models can provide valuable assistance to clinicians in managing patients with heart failure (HF) at discharge by identifying individual factors associated with a high risk of readmission. In this cohort study, we delve into the factors driving the potential utility of classification models as decision support tools for predicting readmissions in patients with HF. OBJECTIVE: The primary objective of this study is to assess the trade-off between using deep learning (DL) and traditional ML models to identify the risk of 100-day readmissions in patients with HF...

BACKGROUND: Dynamic respiratory maneuvers induce heterogenous changes to flow-pulsatility in continuous-flow left ventricular assist device patients. We evaluated the association of these pulsatility responses with patient hemodynamics and outcomes. METHODS: Responses obtained from HVAD (Medtronic) outpatients during successive weekly clinics were categorized into three ordinal groups according to the percentage reduction in flow-waveform pulsatility (peak-trough flow) upon inspiratory-breath-hold, (%∆P): (1) minimal change (%∆P ≤ 50), (2) reduced pulsatility (%∆P > 50 but <100), (3) flatline (%∆P = 100)...

BACKGROUND: The National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator from the American College of Surgeons is a widely available tool for peri-operative risk prediction. This study investigates its predictive performance in an Australian setting. METHODS: A single-centre retrospective external validation study was conducted at a tertiary referral centre in New South Wales, Australia. Data from a general surgical cohort in a 2-year period from 2020 to 2021 was collected from the NSQIP database and entered into the NSQIP calculator...

INTRODUCTION: Readmission is a poor outcome for both patients and healthcare systems. The association of certain sociocultural and demographic characteristics with likelihood of readmission is uncertain in general surgical patients. METHOD: A multi-centre retrospective cohort study of consecutive unique individuals who survived to discharge during general surgical admissions was conducted. Sociocultural and demographic variables were evaluated alongside clinical parameters (considered both as raw values and their proportion of change in the 1-2 days prior to admission) for their association with 7 and 30 days readmission using logistic regression...

OBJECTIVES: Machine learning algorithms are being increasingly used for predicting hospital readmissions. This meta-analysis evaluated the performance of logistic regression (LR) and machine learning (ML) models for the prediction of 30-day hospital readmission among patients in the US. METHODS: Electronic databases (i.e., Medline, PubMed, and Embase) were searched from January 2015 to December 2019. Only studies in the English language were included. Two reviewers performed studies screening, quality appraisal, and data collection...

To analyze the role of neutrophil-to-lymphocyte ratio (NLR) in predicting the development of postoperative complications and readmission after appendectomy in children. A retrospective single-centered case-control study was conducted on children who underwent appendectomy between 2017 and 2020. Demographics, time since symptoms onset, laboratory tests at admission, postoperative complications, and readmissions in the first 30 days after surgery were recorded. Sensitivity and specificity analysis of the parameters evaluated were determined by the area under the curve (AUC) represented on the receiver operating characteristic (ROC) curves...

BACKGROUND: Short-term unplanned readmission is always neglected, especially for elderly patients with coronary heart disease (CHD). However, tools to predict unplanned readmission are lacking. This study aimed to establish the most effective predictive model for the unplanned 7-day readmission in elderly CHD patients using machine learning (ML) algorithms. METHODS: The detailed clinical data of elderly CHD patients were collected retrospectively. Five ML algorithms, including extreme gradient boosting (XGB), random forest, multilayer perceptron, categorical boosting, and logistic regression, were used to establish predictive models...

BACKGROUND: Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context. OBJECTIVE: This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure...

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BACKGROUND: Pathophysiologically, an elevated left ventricular (LV) filling pressure is the major reason for heart failure (HF) readmission. The 2016 American Society of Echocardiography (ASE)/European Association of Cardiovascular Imaging (EACVI) guidelines provide a simplified algorithm for the echocardiographic assessment of LV filling pressure; however, this algorithm is yet to be sufficiently validated. METHODS: We retrospectively studied 139 consecutive patients with acute decompensated HF...

BACKGROUND: Readmission shortly after discharge is indicative of an increased disease severity for patients with ulcerative colitis (UC) and ineffectiveness to medical therapy, which may contribute to a dismal prognosis. OBJECTIVES: This study aimed to explore prognostic variables with a nomogram to predict unplanned UC-related readmission within 1 year after discharge. DESIGN: A retrospective cohort study. METHODS: Electronic medical records of all UC patients treated at our center between 1 January 2014 and 31 June 2021 were reviewed...

An electronic health record (EHR) is a vital high-dimensional part of medical concepts. Discovering implicit correlations in the information of this data set and the research and informative aspects can improve the treatment and management process. The challenge of concern is the data sources' limitations in finding a stable model to relate medical concepts and use these existing connections. This paper presents Patient Forest, a novel end-to-end approach for learning patient representations from tree-structured data for readmission and mortality prediction tasks...

BACKGROUND: The initial research study of the High Acuity Readmission Risk Pediatric Screen (HARRPS) Tool © focused on using retrospective data to apply weighted values to the questions within the tool, identify overall risk score, and attribute risk categories (low, moderate, high risk) to the overall risk score. This study focused on validating the data from the initial study, as well as cross examining the need to include admission diagnosis within the tool. METHOD: Study was a single-centered, retrospective chart review study using a different subset of patients from the initial study...

BACKGROUND: While bariatric surgery is an effective method for achieving long-term weight loss, postoperative readmissions are associated with negative clinical outcomes and significant costs. OBJECTIVES: We aimed to use machine learning (ML) algorithms to predict readmissions and compare results to logistic regression. SETTING: Hospitals participating in the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program, United States...

OBJECTIVE:  This study aimed to evaluate the relationship between peripartum mean arterial pressure (MAP) and postpartum readmission for preeclampsia with severe features. STUDY DESIGN:  This is a retrospective case-control study comparing adult parturients readmitted for preeclampsia with severe features to matched nonreadmitted controls. Our primary objective was to evaluate the association between MAP at three time points during the index hospitalization (admission, 24-hour postpartum, and discharge) and readmission risk...

BACKGROUND: Emergency general surgery (EGS) patients have an increased risk of mortality and morbidity compared to other surgical patients. Limited risk assessment tools exist for use in both operative and non-operative EGS patients. We assessed the accuracy of a modified Emergency Surgical Acuity Score (mESAS) in EGS patients at our institution. METHODS: A retrospective cohort study from an acute surgical unit at a tertiary referral hospital was performed. Primary endpoints assessed included death before discharge, length of stay (LOS) >5 days and unplanned readmission within 28 days...