Association between strained capacity and mortality among patients admitted to intensive care: A path-analysis modeling strategy

Bagshaw, Sean M.; Wang, Xioaming; Zygun, David; Zuege, Danny J.; Dodek, Peter; Garland, Allan; Scales, Damon C.; Berthiaume, Luc; Faris, Peter; Chen, Guanmin; Opgenorth, Dawn; Stelfox, Henry T.

doi:10.1016/j.jcrc.2017.08.032

Cited by 60 publications

(64 citation statements)

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“…The eCritical Alberta system provides high-quality data due to rigorous data quality assurance and auditing processes and has been used by our team previously in health services research. 4,[18][19][20][21] There were no missing data elements for our primary exposure and outcome (afterhours discharge and hospital mortality, respectively). Missing data for individual variables were <3% and were not replaced or imputed.…”

Section: Data Sourcesmentioning

confidence: 99%

“…2,3 Similarly, strained ICU capacity may synergistically modify care processes for discharges that occur afterhours, such as provoking premature discharges prior to resolution of acute organ dysfunction, and thereby increase patient risk for adverse outcomes. 4 Afterhours ICU discharge has shown variable association with patient outcomes. [5][6][7][8][9][10][11][12][13] A recent metaanalysis inferred that afterhours discharge was associated with higher risk for in-hospital mortality 14 , an effect consistent across varying de nitions of afterhours, patient populations, case-mix, and healthcare delivery models.…”

Section: Introductionmentioning

confidence: 99%

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Association between Afterhours Discharge from the Intensive Care Unit and Hospital Mortality: A Multi-Centre Retrospective Cohort Study

Hall

Wang

Zuege

et al. 2020

Preprint

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Background: There is conflicting evidence on the association between afterhours discharge from the intensive care unit (ICU) and hospital mortality. We examined the effects of afterhours discharge, including the potential effect of residual organ dysfunction, on hospital mortality in a large integrated health region. Methods: We performed a multi-centre retrospective cohort study of 10,463 adults discharged from nine mixed medical/surgical ICUs in Alberta from June 2012 to December 2014. We applied a two-stage modelling strategy to investigate the association between afterhours discharge (19:00h to 07:59h) and post-ICU hospital mortality. We applied mixed-effect multi-variable linear regression to assess the relationship between discharge organ dysfunction and afterhours discharge. We then applied mixed-effect multi-variable logistic regression to evaluate the direct, indirect and integrated associations of afterhours discharge on hospital mortality and hospitalization duration. Results: Of 10,463 patients, 23.7% (n=2,480) were discharged afterhours, of which 27.4% occurred on a holiday or weekend. This varied significantly by ICU size, type, and site. Patients discharged afterhours were more likely medical admissions, had greater multi-morbidity and illness acuity. Greater SOFA score in the 72 hours prior to discharge was not associated with afterhours discharge; however, was associated with hospital mortality (adjusted-OR 1.23; 95%CI,1.18-1.28). Afterhours discharge was associated with higher hospital mortality (adjusted-OR 1.19; 95%CI, 1.01-1.39), increased hospital stay (adjusted-OR 1.10; 95%CI,1.09-1.11) and increased post-ICU stay (adjusted-OR 1.16; 95%CI,1.14-1.17).Conclusions: Afterhours discharge is common, occurring in 1 in 4 discharges, and is widely variable across ICUs. Patients discharged afterhours have greater risk of hospital mortality and prolonged hospitalization.

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Section: Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Association between Afterhours Discharge from the Intensive Care Unit and Hospital Mortality: A Multi-Centre Retrospective Cohort Study

Hall

Wang

Zuege

et al. 2020

Preprint

Self Cite

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“…To complicate matters, ICU stays that are unnecessarily long, prevent timely admission of other patients requiring ICU treatment which may affect their outcome. This problem may be especially prominent in tertiary teaching hospitals where ICU capacity is often strained [8].…”

Section: Introductionmentioning

confidence: 99%

Developing a Machine Learning prediction model for bedside decision support by predicting readmission or death following discharge from the Intensive Care unit

Thoral

Fornasa²,

Bruin³

et al. 2020

Preprint

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Background Unexpected ICU readmission is associated with longer length of stay and an increase in mortality. Real time support systems could prevent untimely discharge from the ICU. We aim to develop a machine learning model for implementation at the bedside by predicting the risk of ICU readmission or death at time of potential discharge, showing feature importance and visualizing day-to-day changes in risk. Methods Data from adult patients, admitted to our mixed surgical-medical ICU between 2004 and 2016, were used in the analysis. Patient characteristics, clinical observations, (automated) physiological measurements, laboratory studies and treatment data were considered as model features. Different supervised learning algorithms were trained to predict ICU readmission and/or death, both within 7 days from ICU discharge, using 10-fold cross-validation. Feature importance was determined using SHapley Additive exPlanations. We constructed readmission probability-time curves to identify subgroups. Results Our dataset included 14,105 admissions. The combined readmission/mortality rate within seven days of ICU discharge was 5.3%. Using Gradient Boosting, the model achieved a Receiver Operating Characteristic AUC of 0.802 (95% CI 0.789-0.816) and a Precision-Recall AUC of 0.198 (95% CI 0.185-0.211). The most predictive features were well-known parameters, including physiological parameters, as well as less apparent features like nutritional support. Impact analysis using probability-time curves identified specific patients groups, that might lead to a change in discharge management with a relative risk reduction of 17%. Conclusions We developed a model that can accurately predict readmission and mortality after ICU discharge. Impact analysis showed that a relative risk reduction of 17% could be achievable. Given the large and increasing number of ICU admissions worldwide, this modest reduction may have significant impact for patients and society.

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“…A escassez de leitos e elevada carga de trabalho dos profissionais atuantes são fatores que contribuem para essa tensão. Nesse sentido, a tensão sustentada por diversos fatores contribui para a utilização ineficiente e desigual de recursos finitos da UTI (REWA et al, 2018;BAGSHAW et al, 2018).…”

Section: Critérios De Admissão E Alta Da Utiunclassified

“…Estudo de coorte retrospectivo multicêntrico desenvolvido em um complexo de nove UTI médico-cirúrgicas revelou que uma em cada cinco admissões foi realizada sob circunstâncias de capacidade forçada quando da baixa capacidade de leitos pela elevada taxa de ocupação, notadamente em UTI de hospital universitário. As taxas de mortalidade foram maiores nos períodos de maior ocupação de leitos (BAGSHAW et al, 2018 prognóstico da doença aguda, número de leitos e salas cirúrgicas ocupadas; enquanto que os profissionais de UTI privadas, pela pressão do médico solicitante e temor por processos por má prática .…”

Section: Critérios De Admissão E Alta Da Utiunclassified

Avaliação da demanda por leitos de Unidade de Terapia Intensiva e classificação do paciente segundo critério de prioridades: diagnóstico situacional

Nassiff¹

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Aos meus pais, Nassiff José (in memorian) e Débora, e as minhas irmãs, Natália e Natali. Vocês são o meu alicerce e a principal motivação para que eu chegasse até aqui. AGRADECIMENTOS A Deus e a Nossa Senhora Aparecida, que sempre iluminaram meus caminhos e decisões, me proporcionando sabedoria, força, conforto e perseverança, principalmente nos momentos difíceis dessa caminhada. A meu melhor amigo e noivo Murilo, que com seu amor e paciência sempre me incentivou a seguir em frente e compreendeu meus momentos de ausência durante essa longa jornada. Obrigada por nunca ter desistido de nós! À minha orientadora, Prof.ª Dr.ª Ana Maria Laus, minha referência de docente e ser humano, que desde 2015, com a iniciação científica, me acolheu com carinho e paciência e conduziu com excelência para a concretização desse trabalho. Gratidão eterna a você! A minha amiga e Prof.ª Dr.ª Mayra Menegueti, por ter contribuído não apenas com seu conhecimento para concepção desse mestrado, mas também com suas palavras de carinho e motivação. À Profª. Drª. Lucieli Dias Pedreschi Chaves e a Profª. Drª. Maria Auxiliadora Martins, pelas valiosas contribuições no exame de qualificação. A minha grande amiga e companheira de graduação, Jeniffer, por toda ajuda, incentivo e carinho desde o início. As minhas amigas e companheiras do grupo de pesquisa, Thamiris, Maísa, Amanda e Francine por compartilharem comigo as alegrias e angústias inerentes à essa caminhada. As minhas queridas Drª. Maria Fernanda Diniz e Daniele Juliani que com muito profissionalismo, apoio e dedicação, me ajudaram a acreditar que seria capaz de tornar mais esse sonho possível. Ao Mateus Okubo, colaborador do Centro de Informações e Análises por toda ajuda e paciência no fornecimento dos relatórios de dados, sem os quais não seria possível a realização do presente trabalho, A enfermeira e estatística Miyeko Hayashida por toda dedicação e ajuda na organização e análise dos dados. A todos os docentes e colaboradores da Escola de Enfermagem de Ribeirão Preto da Universidade de São Paulo que desde 2012, me acolhem com carinho e fazem deste lugar minha segunda casa. Ao Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), pelo apoio financeiro por meio da bolsa de mestrado, sem a qual não seria possível realiza-lo.

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Association between strained capacity and mortality among patients admitted to intensive care: A path-analysis modeling strategy

Cited by 60 publications

References 34 publications

Association between Afterhours Discharge from the Intensive Care Unit and Hospital Mortality: A Multi-Centre Retrospective Cohort Study

Association between Afterhours Discharge from the Intensive Care Unit and Hospital Mortality: A Multi-Centre Retrospective Cohort Study

Developing a Machine Learning prediction model for bedside decision support by predicting readmission or death following discharge from the Intensive Care unit

Avaliação da demanda por leitos de Unidade de Terapia Intensiva e classificação do paciente segundo critério de prioridades: diagnóstico situacional

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