Objective: To compare 2 models for managing patients admitted to a subacute medical intensive care unit (MICU) who required prolonged mechanical ventilation (> or = 7 d).
Methods: The subjects were 192 consecutive patients (mean +/- SD age 61.5 +/- 16.1 y, 52% male, 86% white) managed during alternating 7-month blocks of time by an attending physician in collaboration with an acute care nurse practitioner (ACNP) (n = 98 patients) or by an attending physician in collaboration with critical care/pulmonary fellows (n = 94 patients). The total observation time was 28 months (14 mo per team).
Results: At unit entry, there were no significant differences in age, sex, race, comorbidity, Acute Physiology and Chronic Health Evaluation III score, or time of tracheostomy between the patients managed by the 2 teams. Patients managed by the ACNP team were more likely to have required mechanical ventilation due to an acute pulmonary problem (p = 0.005). At subacute MICU discharge, the groups were not significantly different in regard to subacute MICU length of stay, days on mechanical ventilation, or discharge weaning status (p > 0.05). The number of readmissions to the MICU was similar for the ACNP team (n = 7) and fellows team (n = 8), as were readmissions to the subacute MICU < or = 72 h after discharge (ACNP = 2, fellows = 1). Each team had 2 deaths without treatment limitation.
Conclusion: As hypothesized, management of patients who required prolonged mechanical ventilation with tracheostomy had equivalent outcomes with the ACNP team or the fellows team.
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Front Digit Health
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Faculty of Engineering and Computing, Liwa College, Abu Dhabi, United Arab Emirates.
The evolution of artificial intelligence (AI) has revolutionised numerous aspects of our daily lives, with profound implications across various sectors, including healthcare. Although the concept of AI in healthcare was introduced in the early 1970s, the integration of this technology in healthcare is still in the evolution phase. Despite barriers, the current decade is witnessing an increased utility of AI into diverse specialities of the medical field to enhance precision medicine, predict diagnosis, therapeutic results, and prognosis; this includes respiratory medicine, critical care, and in their allied specialties.
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