a predictive model for respiratory distress in patients with covid 19 a retrospective CORD-Papers-2021-10-25 (Version 1)

Title: A predictive model for respiratory distress in patients with COVID-19: A retrospective study
Abstract: Background: Coronavirus disease 2019 (COVID-19), associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis We retrospectively evaluated 863 hospitalized patients with COVID-19 infection, designated IWCH-COVID-19 Methods: We built a successful predictive model after investigating the risk factors to predict respiratory distress within 30 days of admission These variables were analyzed using Kaplan-Meier and Cox proportional hazards (PHs) analyses Hazard ratios (HRs) and performance of the final model were determined Results: Neutrophil count >6 3109/L, D-dimer level 1 00 mg/L, and temperature 37 3 at admission showed significant positive association with the outcome of respiratory distress in the final model Complement C3 (C3) of 0 91 8 g/L, platelet count >350109/L, and platelet count of 125350109/L showed a significant negative association with outcomes of respiratory distress in the final model The final model had a C statistic of 0 891 (0 8670 915), an Akaikes information criterion (AIC) of 567 65, and a bootstrap confidence interval (CI) of 0 866 (0 8420 89) This five-factor model could help in early allocation of medical resources Conclusions: The predictive model based on the five factors obtained at admission can be applied for calculating the risk of respiratory distress and classifying patients at an early stage Accordingly, high-risk patients can receive timely and effective treatment, and health resources can be allocated effectively
Published: 2020
Journal: Annals of Translational Medicine
Author Name: Zhang, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_x
Author Name: Wang, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_w
Author Name: Wan, C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wan_c
Author Name: Cheng, G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cheng_g
Author Name: Yin, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yin_y
Author Name: Cao, K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cao_k
Author Name: Wang, Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_z
Author Name: Miao, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/miao_s
Author Name: Yu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yu_y
Author Name: Hu, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hu_j
Author Name: Huang, R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/huang_r
Author Name: Ge, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ge_y
Author Name: Chen, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_y
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #1006756
has_full_text: FALSE
G_ID: a_predictive_model_for_respiratory_distress_in_patients_with_covid_19_a_retrospective
S2 ID: 228816565