Literature DB >> 27632681

Readmissions to Intensive Care: A Prospective Multicenter Study in Australia and New Zealand.

John D Santamaria1, Graeme J Duke, David V Pilcher, D James Cooper, John Moran, Rinaldo Bellomo.   

Abstract

OBJECTIVES: To determine factors independently associated with readmission to ICU and the independent association of readmission with subsequent mortality.
DESIGN: Prospective multicenter observational study.
SETTING: Forty ICUs in Australia and New Zealand. PATIENTS: Consecutive adult patients discharged alive from ICU to hospital wards between September 2009 and February 2010.
INTERVENTIONS: Measurement of hospital mortality.
MEASUREMENTS AND MAIN RESULTS: We studied 10,210 patients and 674 readmissions. The median age was 63 years (interquartile range, 49-74), and 6,224 (61%) were male. The majority of readmissions were unplanned (84.1%) but only deemed preventable in a minority (8.9%) of cases. Time to first readmission was shorter for unplanned than planned readmission (3.2 vs 6.9 d; p < 0.001). Primary diagnosis changed between admission and readmission in the majority of patients (60.2%) irrespective of planned (58.2%) or unplanned (60.6%) status. Using recurrent event analysis incorporating patient frailty, we found no association between readmissions and hospital survival (hazard ratios: first readmission 0.88, second readmission 0.90, third readmission 0.44; p > 0.05). In contrast, age (hazard ratio, 1.03), a medical diagnosis (hazard ratio, 1.43), inotrope use (hazard ratio, 3.47), and treatment limitation order (hazard ratio, 17.8) were all independently associated with outcome.
CONCLUSIONS: In this large prospective study, readmission to ICU was not an independent risk factor for mortality.

Entities:  

Mesh:

Year:  2017        PMID: 27632681     DOI: 10.1097/CCM.0000000000002066

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  6 in total

1.  Preventable readmission to intensive care unit in critically ill cancer patients.

Authors:  Hai-Jun Wang; Yong Gao; Shi-Ning Qu; Chu-Lin Huang; Hao Zhang; Hao Wang; Quan-Hui Yang; Xue-Zhong Xing
Journal:  World J Emerg Med       Date:  2018

2.  Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk.

Authors:  Sebastiano Barbieri; James Kemp; Oscar Perez-Concha; Sradha Kotwal; Martin Gallagher; Angus Ritchie; Louisa Jorm
Journal:  Sci Rep       Date:  2020-01-24       Impact factor: 4.379

3.  Modified Early Warning Score as a predictor of intensive care unit readmission within 48 hours: a retrospective observational study.

Authors:  Ahmed Naji Balshi; Basim Mohammed Huwait; Alfateh Sayed Nasr Noor; Abdulrahman Mishaal Alharthy; Ahmed Fouad Madi; Omar Elsayed Ramadan; Abdullah Balahmar; Huda A Mhawish; Bobby Rose Marasigan; Alva Minette Alcazar; Muhammad Asim Rana; Waleed Tharwat Aletreby
Journal:  Rev Bras Ter Intensiva       Date:  2020-07-13

4.  Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists.

Authors:  Patrick J Thoral; Mattia Fornasa; Daan P de Bruin; Michele Tonutti; Hidde Hovenkamp; Ronald H Driessen; Armand R J Girbes; Mark Hoogendoorn; Paul W G Elbers
Journal:  Crit Care Explor       Date:  2021-09-10

5.  Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines.

Authors:  Stefan Hegselmann; Christian Ertmer; Thomas Volkert; Antje Gottschalk; Martin Dugas; Julian Varghese
Journal:  Front Med (Lausanne)       Date:  2022-08-23

6.  Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach.

Authors:  Thomas Desautels; Ritankar Das; Jacob Calvert; Monica Trivedi; Charlotte Summers; David J Wales; Ari Ercole
Journal:  BMJ Open       Date:  2017-09-15       Impact factor: 2.692

  6 in total

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