Risk factors for severe COVID-19 in middle-aged patients without comorbidities: a multicentre retrospective study

Abstract

Background Information regarding characteristics and risk factors of COVID-19 amongst middle-aged (40–59 years) patients without comorbidities is scarce. Methods We therefore conducted this multicentre retrospective study and collected data of middle-aged COVID-19 patients without comorbidities at admission from three designated hospitals in China. Results Among 119 middle-aged patients without comorbidities, 18 (15.1%) developed into severe illness and 5 (3.9%) died in hospital. ARDS (26, 21.8%) and elevated D-dimer (36, 31.3%) were the most common complications, while other organ complications were relatively rare. Multivariable regression showed increasing odds of severe illness associated with neutrophil to lymphocyte ratio (NLR, OR, 11.238; 95% CI 1.110–1.382; p < 0.001) and D-dimer greater than 1 µg/ml (OR, 16.079; 95% CI 3.162–81.775; p = 0.001) on admission. The AUCs for the NLR, D-dimer greater than 1 µg/ml and combined NLR and D-dimer index were 0.862 (95% CI, 0.751–0.973), 0.800 (95% CI 0.684–0.915) and 0.916 (95% CI, 0.855–0.977), respectively. SOFA yielded an AUC of 0.750 (95% CI 0.602–0.987). There was significant difference in the AUC between SOFA and combined index (z = 2.574, p = 0.010). Conclusions More attention should be paid to the monitoring and early treatment of respiratory and coagulation abnormalities in middle-aged COVID-19 patients without comorbidities. In addition, the combined NLR and D-dimer higher than 1 μg/ml index might be a potential and reliable predictor for the incidence of severe illness in this specific patient with COVID-19, which could guide clinicians on early classification and management of patients, thereby relieving the shortage of medical resource. However, it is warranted to validate the reliability of the predictor in larger sample COVID-19 patients.

Impacts of Demographic and Clinical Characteristics on Disease Severity and Mortality in Patients with Confirmed COVID-19

“… 19 In another study, increased D-dimer (> 1 μg/mL) levels and hypoalbuminemia were identified as risk factors for disease severity in patients with COVID-19. 20 In our study, the median neutrophil counts on admission were significantly higher among patients with critical disease than among patients with mild or severe disease, and the median lymphocyte counts on admission were significantly lower in patients with critical disease. Additionally, the median CRP, procalcitonin, LDH, creatinine, D-dimer, and troponin levels were significantly higher among patients with critical disease.…”

Clinical and radiological characteristics of COVID‑19 patients without comorbidities

“…In the literature, few articles have been published commenting on risk factors for severe COVID-19 patients without comorbidities, unfortunately impact of the detailed imaging findings on disease severity was not assessed in these studies. A multicenter study reported that higher neutrophil to lymphocyte ratio with higher D-dimer levels (> 1 µg/ml) on admission might be a potential and reliable predictor for severe progression [19].…”

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

“…The study indicated that patients with serve illness were more likely to have respiratory diseases (OR = 3.42 (1.89 to 6.11)), hypertension (OR = 2.66 (1.46 to 3.82)), and cardiovascular disease (OR = 3.44 (1.44 to 3.82)). Another study [ 144 ] analyzed the risk factors of death among COVID-19 patients. The study reported negative markers between COVID-19 infections and other chronic diseases, such as diabetes (33.31%), hypertension (35.16%), chronic kidney disease (17.87%), and diseases of the circulatory system (22.53%).…”

“…Therefore, to provide a precise and accurate prediction model, a patient’s history of other ailments (diabetes, liver, kidney, heart disease, etc.) must be taken into consideration in both the COVID-19 prediction and detection process [ 144 , 145 , 146 ]. High computational resources are required to build complex DL models, processing, and interpreting big data, compared to working with IoT devices.…”