Journal of critical care
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Journal of critical care · Feb 2020
Observational StudyMachine learning for prediction of septic shock at initial triage in emergency department.
We hypothesized utilizing machine learning (ML) algorithms for screening septic shock in ED would provide better accuracy than qSOFA or MEWS. ⋯ ML classifiers significantly outperforms clinical scores in screening septic shock at ED triage.
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Journal of critical care · Feb 2020
Delayed vasopressor initiation is associated with increased mortality in patients with septic shock.
Mortality rate for septic shock, despite advancements in knowledge and treatment, remains high. Treatment includes administration of broad-spectrum antibiotics and stabilization of the mean arterial pressure (MAP) with intravenous fluid resuscitation. Fluid-refractory shock warrants vasopressor initiation. There is a paucity of evidence regarding the timing of vasopressor initiation and its effect on patient outcomes. ⋯ Vasopressor initiation after 6 h from shock recognition is associated with a significant increase in 30-day mortality. Vasopressor administration within 6 h was associated with shorter time to achievement of MAP goals and higher vasopressor-free hours within the first 72 h.
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Journal of critical care · Feb 2020
The Japanese Intensive care PAtient Database (JIPAD): A national intensive care unit registry in Japan.
The Japanese Intensive care PAtient Database (JIPAD) was established to construct a high-quality Japanese intensive care unit (ICU) database. ⋯ The data revealed that the SMRs based on general severity scores in adults were low because of high proportions of elective and monitoring admission. The development of a new mortality prediction model for Japanese ICU patients is needed.
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Journal of critical care · Feb 2020
Multicenter StudyAssessment of the current capacity of intensive care units in Uganda; A descriptive study.
To describe the organizational characteristics of functional ICUs in Uganda. ⋯ This study shows limited accessibility to critical care services in Uganda. With a high variability in the ICU operational characteristics, there is a need for standardization of ICU care in the country.
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Journal of critical care · Feb 2020
Multicenter StudyMachine learning to predict 30-day quality-adjusted survival in critically ill patients with cancer.
To develop and compare the predictive performance of machine-learning algorithms to estimate the risk of quality-adjusted life year (QALY) lower than or equal to 30 days (30-day QALY). ⋯ Except for basic decision trees, predictive models derived from different machine-learning algorithms discriminated the QALY risk at 30 days well. Regarding calibration, artificial neural network model presented the best ability to estimate 30-day QALY in critically ill oncologic patients admitted to ICUs.