2,443 research outputs found

    Using Shock Index as a Predictor of ICU Readmission: A Quality Iimprovement Project

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    Background: Adverse events will occur in one-third of patients discharged from the intensivecare unit (ICU) and evidence shows that ICU readmissions increase a patientā€™s length of stay,mortality, hospital costs, and nosocomial infections, as well as decrease long-term survival.Specific predictive factors that will accurately predict which patients are at risk of adverseevents requiring readmission are needed.Aim: The specific aim of this project was to identify if shock index (SI) values higher than 0.7at the time of transfer from the ICU are a useful predictor of ICU readmission.Methods: Using the Plan, Do, Study, Act (PDSA) framework, a retrospective chart review wasperformed using a matched cohort of 34 patients readmitted with 72 hours of discharge from theICU and 34 controls to obtain SI values at admission, transfer from and readmission to the ICU.A second PDSA cycle looked for SI trends within 24 hours prior to discharge from the ICU.Results: An odds ratio calculating the risk of readmission of patients with an elevated SI was2.96 (Confidence Interval (CI) 1.1 to 7.94, p-value=0.03). The odds ratio for an 80% SIelevation over 24 hours prior to discharge was 1.56 (CI 0.36 to 6.76, p-value=0.55).Conclusion and Implications for CNL Practice: Patients with elevated SIs at the time oftransfer are three times more likely to be readmitted to the ICU. Patients with elevations in atleast 80% of the 24 hour pre-discharge SIs showed no significant differences between thecontrol and readmitted cohorts. Implications of these results for the clinical nurse leader will bediscussed

    Outcome-Oriented Predictive Process Monitoring to Predict Unplanned ICU Readmission in MIMIC-IV Database

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    Unplanned readmission entails unnecessary risks for patients and avoidable waste of medical resources, especially intensive care unit (ICU) readmissions, which increases likelihood of length of stay and more severely mortality. Identifying patients who are likely to suffer unplanned ICU readmission can benefit both patients and hospitals. Readmission is typically predicted by statistical features extracted from completed ICU stays. The development of predictive process monitoring (PPM) technique aims to learn from complete traces and predict the outcome of ongoing ones. In this paper, we adopt PPM to view ICU stay from electronic health record (EHR) as a continuous process trace to enable us to discover clinical and also process features to predict likelihood of readmission. Using events logs extracted from MIMIC-IV database, the results show that our approach can achieve up to 65% accuracy during the early stage of each ICU stay and demonstrate the feasibility of applying PPM to unplanned ICU readmission prediction

    Design and implementation of a deep recurrent model for prediction of readmission in urgent care using electronic health records

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    There has been a steady growth in machine learning research in healthcare, however, progress is difficult to measure because of the use of different cohorts, task definitions and input variables. To take the advantage of the availability and value of digital health data, we aim to predict unplanned readmissions to the intensive care unit (ICU) from a publicly available Critical Care dataset called Medical Information Mart for Intensive Care (MIMIC-III). In this research, we formulate a heterogeneous LSTM and CNN architecture specifically to create a model of readmission risk. Our proposed predictive framework outperformed all the benchmark classifiers such as support vector machine, random forest and logistic regression models on all performance measures (AUC, accuracy and precision) except on recall where random forest performed slightly better. Predictions from these models will help in resource planning and decrease mortality or length of stay in clinical care settings

    Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

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    [Abstract] The advances achieved in recent decades regarding cardiac surgery have led to a new risk that goes beyond surgeonsā€™ dexterity; postoperative hours are crucial for cardiac surgery patients and are usually spent in intensive care units (ICUs), where the patients need to be continuously monitored to adjust their treatment. Clinical decision support systems (CDSSs) have been developed to take this real-time information and provide clinical suggestions to physicians in order to reduce medical errors and to improve patient recovery. In this review, an initial total of 499 papers were considered after identification using PubMed, Web of Science, and CINAHL. Twenty-two studies were included after filtering, which included the deletion of duplications and the exclusion of titles or abstracts that were not of real interest. A review of these papers concluded the applicability and advances that CDSSs offer for both doctors and patients. Better prognosis and recovery rates are achieved by using this technology, which has also received high acceptance among most physicians. However, despite the evidence that well-designed CDSSs are effective, they still need to be refined to offer the best assistance possible, which may still take time, despite the promising models that have already been applied in real ICUs.Xunta de Galicia; ED431C 2018/4

    Readmission Prediction with Knowledge Graph Attention and RNN-Based Ordinary Differential Equations

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    Predicting the readmission risk within 30 days on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in healthcare domain. Deep-learning-based models are recently utilized to address this task since those models can relatively improve prediction performance and work as decision aids, which helps reduce unnecessary readmission and recurrence risk. However, existing prediction models, limited by fuzzy relevance of patient data, are unable to get higher prediction accuracy due to data noise generated by patients with different disease types. To solve this problem, we propose an end-to-end model called GROM, which integrates knowledge graph to alleviate the interference of data noise generated in the processing of irregularity dynamic clinical data with neural ordinary differential equation (ODE). The experimental results show that our model achieved the highest average precision and proved that the graph attention mechanism is suitable to improve performance of predicting the risk of readmission within 30 days

    Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

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    Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks

    Rapid Response Teams versus Critical Care Outreach Teams: Unplanned Escalations in Care and Associated Outcomes

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    The incidence of unplanned escalations during hospitalization is undocumented, but estimates may be as high as 1.2 million occurrences per year in the United States. Rapid Response Teams (RRT) were developed for the early recognition and treatment of deteriorating patients to deliver time-sensitive interventions, but evidence related to optimal activation criteria and structure is limited. The purpose of this study is to determine if an Early Warning Score-based Critical Care Outreach (CCO) model is related to the frequency of unplanned intra-hospital escalations in care compared to a RRT system based on staff nurse identification of vital sign derangements and physical assessments. The RRT model, in which staff nurses identified vital sign derangements to active the system, was compared with the addition of a CCO model, in which rapid response nurses activated the system based on Early Warning Score line graphs of patient condition over time. Logistic regressions were used to examine retrospective data from administrative datasets at a 237-bed community non-teaching hospital during two periods: 1) baseline period, RRT model (n=5,875) (Phase 1: October 1, 2010 ā€“ March 31, 2011), and; 2) intervention period, RRT/CCO model (n=6,273). (Phase 2: October 1, 2011 ā€“ March 31, 2012). The strongest predictor of unplanned escalations to the Intensive Care Unit was the type of rapid response system model. Unplanned ICU transfers were 1.4 times more likely to occur during the Phase 1 RRT period. In contrast, the type of rapid response model was not a significant predictor when all unplanned escalations (any type) were grouped together (medical-surgical-to-intermediate, medical-surgical-to-ICU and intermediate-to-ICU). This is the first study to report a relationship between unplanned escalations and different rapid response models. Based on the findings of fewer unplanned ICU transfers in the setting of a CCO model, health services researchers and clinicians should consider using automated Early Warning score graphs for hospital-wide surveillance of patient condition as a safety strategy
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