Impact of Intensive Care Unit Discharge Delays on Patient Outcomes: A Retrospective Cohort Study

Bose, Somnath; Johnson, Alistair E. W.; Moskowitz, Ari; Celi, Leo Anthony; Raffa, Jesse D.

doi:10.1177/0885066618800276

Cited by 16 publications

(17 citation statements)

References 21 publications

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“…Readmission affects 4.1% ( 51 ) to 9.2% ( 46 ) of the patients in any period of hospitalization, 2.9% ( 14 ) in 48 hours after the transfer and 2.7% ( 32 ) to 4.2% ( 13 ) within 72 hours. Other clinical outcomes analyzed are length of hospital stay ( 31 - 32 , 35 , 39 - 40 , 49 ) , care provided by a Rapid Response Team (RRT) ( 26 , 43 ) , cardiac arrest ( 47 ) , and medications-related problems ( 49 ) . Outcomes such as anxiety, stress, and satisfaction of patients and families also appear in the studies ( 24 , 28 , 30 , 44 , 48 ) .…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, some studies that tried to evidence the efficiency of strategies (such as the adoption of ICU discharge criteria, anticipated discharge planning, availability of intermediate care units, medication reconciliation, oral and written communication about the transfer, optimization of patient monitoring post-ICU and instructions to IU nurses) did not achieve significant results in reducing bad outcomes such as readmission and mortality ( 13 - 14 , 39 - 40 ) .…”

Section: Resultsmentioning

confidence: 99%

“…The readmission ( 13 - 14 , 20 , 26 , 31 - 32 , 34 - 35 , 43 , 46 - 47 , 49 - 52 ) and death ( 13 - 14 , 19 - 20 , 31 - 35 , 39 , 43 , 46 , 49 , 51 - 52 ) outcomes were more analyzed in the studies than other outcomes, with mortality rates showing a wide variation (3-30%) ( 19 , 46 ) . Few studies were devoted to assessing other adverse outcomes; however, it is important to note that not all patients undergoing an inadequate transition process evolve to death or readmission but, even so, they may be subjected to unwanted repercussions with serious consequences, such as the need to change or increase the length of treatment, increased length of hospital stay, disabilities, increased hospital costs, and dissatisfaction ( 9 ) .…”

Section: Discussionmentioning

confidence: 99%

“…Readmission risk alert (43,50) Positive The outcomes and adverse events analyzed are mostly focused on readmission (13)(14)20,26,(31)(32)(34)(35)43,(46)(47)(49)(50)(51)(52) and on mortality after discharge from the ICU (13)(14)(19)(20)(31)(32)(33)(34)(35)39,43,46,49,(51)(52) . The mortality rate after discharge from the ICU varies from 3.0% (46) to 30% (19) according to the studies.…”

Section: Factors Related To the Intensive Care Unit And The Inpatient Unitmentioning

confidence: 99%

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Transition of care at discharge from the Intensive Care Unit: a scoping review

Hervé

Zucatti

Lima

2020

Rev. Latino-Am. Enfermagem

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Objective: to map the available evidence on the components of the transition of care, practices, strategies, and tools used in the discharge from the Intensive Care Unit (ICU) to the Inpatient Unit (IU) and its impact on the outcomes of adult patients. Method: a scoping review using search strategies in six relevant health databases. Results: 37 articles were included, in which 30 practices, strategies or tools were identified for organizing and executing the transfer process, with positive or negative impacts, related to factors intrinsic to the Intensive Care Unit and the Inpatient Unit and cross-sectional factors regarding the staff. The analysis of hospital readmission and mortality outcomes was prevalent in the included studies, in which trends and potential protective actions for a successful care transition are found; however, they still lack more robust evidence and consensus in the literature. Conclusion: transition of care components and practices were identified, in addition to factors intrinsic to the patient, associated with worse outcomes after discharge from the Intensive Care Unit. Discharges at night or on weekends were associated with increased rates of readmission and mortality; however, the association of other practices with the patient’s outcome is still inconclusive.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Factors Related To the Intensive Care Unit And The Inpatient Unitmentioning

confidence: 99%

See 2 more Smart Citations

Transition of care at discharge from the Intensive Care Unit: a scoping review

Hervé

Zucatti

Lima

2020

Rev. Latino-Am. Enfermagem

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“…Many of the projects pitched and initiated at datathon events are eventually published in the scientific literature. 29–32 In addition, as mentioned in the previous section, these international collaborations have demonstrated the value of secondary EHR analysis to countless decision makers at health care institutions across the world and have led to the development of similar critical care databases. Despite the aforementioned barriers, this trend is laying the groundwork for a network of EHR data sharing that will ultimately allow for multi-national and multi-institution analyses.…”

Section: Collaborative Data Sciencementioning

confidence: 90%

Critical Care, Critical Data

Celi

Stone

2019

Biomed Eng Comput Biol

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As big data, machine learning , and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.

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Medical Time-Series Data Generation Using Generative Adversarial Networks

Dash

Yale

Guyon

et al. 2020

Lecture Notes in Computer Science

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We propose a novel bootstrap procedure for dependent data based on Generative Adversarial networks (GANs). We show that the dynamics of common stationary time series processes can be learned by GANs and demonstrate that GANs trained on a single sample path can be used to generate additional samples from the process. We find that temporal convolutional neural networks provide a suitable design for the generator and discriminator, and that convincing samples can be generated on the basis of a vector of iid normal noise. We demonstrate the finite sample properties of GAN sampling and the suggested bootstrap using simulations where we compare the performance to circular block bootstrapping in the case of resampling an AR(1) time series processes. We find that resampling using the GAN can outperform circular block bootstrapping in terms of empirical coverage. * Acknowledgements: The authors gratefully acknowledge support from the Google Tensorflow Research Cloud (TFRC). PyTorch code for this paper is available on request. We also thank Giovanni Mellace and Peter Sandholt Jensen for useful comments.

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Impact of Intensive Care Unit Discharge Delays on Patient Outcomes: A Retrospective Cohort Study

Cited by 16 publications

References 21 publications

Transition of care at discharge from the Intensive Care Unit: a scoping review

Transition of care at discharge from the Intensive Care Unit: a scoping review

Critical Care, Critical Data

Medical Time-Series Data Generation Using Generative Adversarial Networks

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