- Pulmonary
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An algorithm to predict the need for invasive mechanical ventilation in hospitalized COVID-19 patients: the experience in Sao Paulo
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Eduardo Atsushi Osawa, Alexandre Toledo Maciel
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Acute Crit Care. 2022;37(4):580-591. Published online September 8, 2022
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DOI: https://doi.org/10.4266/acc.2022.00283
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- Background
We aimed to characterize patients hospitalized for coronavirus disease 2019 (COVID-19) and identify predictors of invasive mechanical ventilation (IMV). Methods: We performed a retrospective cohort study in patients with COVID-19 admitted to a private network in Sao Paulo, Brazil from March to October 2020. Patients were compared in three subgroups: non-intensive care unit (ICU) admission (group A), ICU admission without receiving IMV (group B) and IMV requirement (group C). We developed logistic regression algorithm to identify predictors of IMV. Results: We analyzed 1,650 patients, the median age was 53 years (42–65) and 986 patients (59.8%) were male. The median duration from symptom onset to hospital admission was 7 days (5–9) and the main comorbidities were hypertension (42.4%), diabetes (24.2%) and obesity (15.8%). We found differences among subgroups in laboratory values obtained at hospital admission. The predictors of IMV (odds ratio and 95% confidence interval [CI]) were male (1.81 [1.11– 2.94], P=0.018), age (1.03 [1.02–1.05], P<0.001), obesity (2.56 [1.57–4.15], P<0.001), duration from symptom onset to admission (0.91 [0.85–0.98], P=0.011), arterial oxygen saturation (0.95 [0.92– 0.99], P=0.012), C-reactive protein (1.005 [1.002–1.008], P<0.001), neutrophil-to-lymphocyte ratio (1.046 [1.005–1.089], P=0.029) and lactate dehydrogenase (1.005 [1.003–1.007], P<0.001). The area under the curve values were 0.860 (95% CI, 0.829–0.892) in the development cohort and 0.801 (95% CI, 0.733–0.870) in the validation cohort. Conclusions: Patients had distinct clinical and laboratory parameters early in hospital admission. Our prediction model may enable focused care in patients at high risk of IMV.
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Citations
Citations to this article as recorded by  - Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases
Guang Zhang, Qingyan Xie, Chengyi Wang, Jiameng Xu, Guanjun Liu, Chen Su
Medical & Biological Engineering & Computing.2024;[Epub] CrossRef
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