Abstract
This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients hospitalized in the general floor will need escalated care early on using neural networks (NNs). Analysis was performed on hospitalized COVID-19 patients between 7 February 2020 and 4 May 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs and blood gases were collected. We compared those data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: (i) COVID-19 patients admitted to the general floor (N = 1203) vs. those directly admitted to ICU (N = 104), and (ii) patients not upgraded to ICU (N = 979) vs. those upgraded to the ICU (N = 224) from the general floor. A NN algorithm was used to predict ICU admission, with 80% training and 20% testing. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis (ROC). We found that C-reactive protein, lactate dehydrogenase, creatinine, white-blood cell count, D-dimer and lymphocyte count showed temporal divergence between COVID-19 patients hospitalized in the general floor that were upgraded to ICU compared to those that were not. The NN predictive model essentially ranked the same laboratory variables to be important predictors of needing ICU care. The AUC for predicting ICU admission was 0.782 ± 0.013 for the test dataset. Adding vital sign and blood-gas data improved AUC (0.822 ± 0.018). This work could help frontline physicians to anticipate downstream ICU need to more effectively allocate healthcare resources.
Highlights
Since it was first reported in Wuhan, China in December 2019 (Huang et al, 2020; Li et al, 2020b; Zhu et al, 2020b), the coronavirus disease 2019 (COVID-19) has infected over 27 million people and killed more than 880,000 people worldwide (6 September 2020) (Johns Hopkin University Coronavirus Resource Center, 2021)
We argue that it is more relevant to study hospitalized COVID-19 patients in the general floor who were subsequently upgraded to intensive care unit (ICU) to identify the clinical variables that predict escalated care
This study investigated the clinical variables associated with direct ICU admission and upgrade to ICU from the general floor
Summary
Since it was first reported in Wuhan, China in December 2019 (Huang et al, 2020; Li et al, 2020b; Zhu et al, 2020b), the coronavirus disease 2019 (COVID-19) has infected over 27 million people and killed more than 880,000 people worldwide (6 September 2020) (Johns Hopkin University Coronavirus Resource Center, 2021). Some earlier studies found that: (i) age and CRP thresholds are good predictor of mortality (Lu et al, 2020), (ii) age, lymphocyte count, lactate dehydrogenase (LDH) and SpO2 are independent predictors of mortality (Xie et al, 2020), (iii) comorbidity, older age, lower lymphocyte and higher LDH at presentation to be independent high-risk factors for COVID-19 progression (Ji et al, 2020), (iv) mildly elevated alanine aminotransferase (ALT), myalgias and hemoglobin at presentation to be predictive of severe acute respiratory distress syndrome of COVID-19 with 70% to 80% accuracy (Jiang et al, 2020) and (v) LDH, procalcitonin (procal), SpO2, smoking history and lymphocyte count were predictive of ICU admission, and heart failure, procal, LDH, chronic obstructive pulmonary disease (COPD), SpO2, HR and age were predictive of mortality (Zhao et al, 2020).
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