Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia: a single-center retrospective study
Zeng, L.; Li, J.; Liao, M.; Hua, R.; Huang, P.; Zhang, M.; Zhang, Y.; Shi, Q.; Xia, Z.; Ning, X.; Liu, D.; Mo, J.; Zhou, Z.; Li, Z.; Fu, Y.; Liao, Y.; Yuan, J.; Wang, L.; He, Q.; Liu, L.; Qiao, K.
Show abstract
BackgroundManagement of high mortality risk due to significant progression requires prior assessment of time-to-progression. However, few related methods are available for COVID-19 pneumonia. MethodsWe retrospectively enrolled 338 adult patients admitted to one hospital between Jan 11, 2020 to Feb 29, 2020. The final follow-up date was March 8, 2020. We compared characteristics between patients with severe and non-severe outcome, and used multivariate survival analyses to assess the risk of progression to severe conditions. ResultsA total of 76 (31.9%) patients progressed to severe conditions and 3 (0.9%) died. The mean time from hospital admission to severity onset is 3.7 days. Age, body mass index (BMI), fever symptom on admission, co-existing hypertension or diabetes are associated with severe progression. Compared to non-severe group, the severe group already demonstrated, at an early stage, abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen and coagulation function. The cohort is characterized with increasing cumulative incidences of severe progression up to 10 days after admission. Competing risks survival model incorporating CT imaging and baseline information showed an improved performance for predicting severity onset (mean time-dependent AUC = 0.880). ConclusionsMultiple predisposition factors can be utilized to assess the risk of progression to severe conditions at an early stage. Multivariate survival models can reasonably analyze the progression risk based on early-stage CT images that would otherwise be misjudged by artificial analysis.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Clinical Characteristics and Short-Term Outcomes of Severe Patients with COVID-19 in Wuhan, China 94%
- Clinical findings in critical ill patients infected with SARS-Cov-2 in Guangdong Province, China: a multi-center, retrospective, observational study 94%
- Prognostic factors for COVID-19 pneumonia progression to severe symptom based on the earlier clinical features: a retrospective analysis 94%
Similar papers in this journal
- Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study 94%
- Rapid Clinical Screening and Staging for COVID-19 Severe Outcome A Hospitalization Study in New York City 94%
- Results of an early second PCR test performed on SARS-CoV-2 positive patients may indicate risk for severe COVID-19 93%
Similar papers in this journal
- Accuracy of deep learning based computed tomography diagnostic system of COVID-19: a consecutive sampling external validation cohort study 95%
- Survival analysis of hospital length of stay of novel coronavirus (COVID-19) pneumonia patients in Sichuan, China 95%
- Novel prognostic determinants of COVID-19-related mortality: a pilot study on severely-ill patients in Russia 95%
Similar papers in this journal
Similar papers in this journal
- Impact of Antibody Cocktail Therapy Combined with Casirivimab and Imdevimab on Clinical Outcome for Covid-19 patients in A Real-Life Setting: A Single Institute Analysis 94%
- Clinical characteristics of 25 death cases infected with COVID-19 pneumonia: a retrospective review of medical records in a single medical center, Wuhan, China 93%
- Epidemiological characteristics of 1212 COVID-19 patients in Henan, China 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.