key: cord-1055340-bfomwy0c
authors: You, Yeon Seok; Kim, Jong Seung
title: Influenza and Anosmia: important prediction factors for severity and death of COVID-19
date: 2021-08-19
journal: J Infect
DOI: 10.1016/j.jinf.2021.08.024
sha: 02fa08c2290b4185934b96f347ff94ff5bf8a690
doc_id: 1055340
cord_uid: bfomwy0c

Objectives: To investigate the factors related to the severity and mo rtality of COVID-19 using big data-machine learning techniques. Methods: This study included 8070 patients in South Korea diagnosed with COVID-19 between January and July 2020, and whose data were available from the National-Health-Insurance-Service. Results: Machine-learning algorithms were performed to evaluate the effects of comorbidities on severity and mortality of COVID-19. The most common comorbidities of COVID-19 were pulmonary inflammation followed by hypertension. The model that best predicted severity was a neural network (AUC: 85.06%). The most important variable for predicting severity in the neural network model was a history of influenza (relative importance: 0.083). The model that best predicted mortality was the logistic regression elastic net (EN) model (AUC: 93.86%). The most important variables for mortality in the EN model were age (coefficient: 2.136) and anosmia (coefficient: –1.438). Conclusions: In COVID-19 patients, influenza was found to be a major adverse factor in addition to old age and male. In addition, anosmia was found to be a major factor associated with lower severity and mortality. Therefore, in the current situation where there is no adequate COVID-19 treatment at present, examining the patient's history of influenza vaccination and anosmia in addition to age and sex will be an important indicator for predicting the severity and mortality of COVID-19 patients.

family as the MERS virus that circulated in 2015, but it is much more infectious, and the world is currently experiencing a pandemic. 2 However, the factors affecting disease severity and mortality have not yet been clearly identified. The machine learning (ML) algorithm is a model suitable for the medical field because it has a fairly accurate prediction capability for large-scale new, never-seen-before inputs such as COVID-19 pandemic. 3 In this paper, we have analyzed the factors affecting the severity and mortality of 8070 COVID-19 patients registered in the National Health Insurance Service (NHIS) of South Korea using ML algorithms.

The severity of COVID-19 was defined as the end result with one of following conditions. 1) Intensive care unit (ICU) care; 2) Extracorporeal membrane oxygenation (ECMO) treatment;

3) Mechanical ventilator care; 4) Oxygen supply. The mortality of COVID-19 was also checked because the NHIS data was connected to the Korea Disease Control and Prevention Agency and Statistics Korea, which has the mortality data. Figure 1D ). The most important variable for predicting severity in the neural network model was a history of influenza (relative importance: 0.083). ( Figure 1E , Table 1 ).

The model with the best prediction of death was the logistic regression EN model with an AUC value of 93.86%, followed by the logistic regression lasso model (93.74%), the neural network model (93.73%) ( Figure 1F ). The most important variables for mortality in the EN model were age (coefficient: 2.136) and anosmia (coefficient: -1.438) ( Figure 1G , Table 1 ).

We analyzed 24 factors affecting severity and mortality in 8070 patients using a novel ML algorithm that has recently emerged. Foremost, influenza history was a very important variable in terms of COVID-19 severity (neural network 1st, ridge 6th) and mortality (EN 4th, lasso 2nd, ridge 5th). (Figure 1E The best predictive models for mortality were the EN and lasso models, and the second most important variable in both these models was anosmia. This means that the mortality rate was low in patients with olfactory loss after the COVID-19 diagnosis. There are papers which indicate that recent olfactory loss in mild to moderate COVID-19 patients is an important factor that differentiates COVID-19 from other infectious disease, and in most cases, the sense of smell recovers well. 5, 6 Another paper reports that anosmia is associated with lower in-hospital mortality in COVID-19, which is in line with our research results. 7 The novel finding in our study is that anosmia will continue to be an indicator that should be carefully examined in COVID-19 infection. 8 Influenza was found to be a major adverse factor in COVID-19 in addition to the factors of old age and male sex, and which are already known to be related to disease severity and mortality. In addition, anosmia was found to be a major factor associated with lower severity and mortality rates. Therefore, in the current situation where there is no adequate COVID-19 treatment at present, examining the history of influenza vaccination and anosmia in addition to age and sex will be important indicators for predicting the severity and mortality of COVID-19 patients.

None Figure 1A . 

Rigidity of the outer shell predicted by a protein intrinsic disorder model sheds light on the COVID-19 (Wuhan-2019-nCoV) infectivity

Addressing influenza vaccination disparities during the COVID-19 pandemic

Prevalence and duration of acute loss of smell or taste in COVID-19 patients

Smell and taste disorders in Spanish patients with mild COVID-19

Anosmia is associated with lower in-hospital mortality in COVID

Main symptoms in patients presenting in the COVID-19 period

This paper was supported by a fund of the Biomedical Research Institute at Jeonbuk National University Hospital.