SARS-COV-2 comorbidity network and outcome in hospitalized patients in Crema, Italy

Abstract

We report onset, course, correlations with comorbidities, and diagnostic accuracy of nasopharyngeal swab in 539 individuals suspected to carry SARS-COV-2 admitted to the hospital of Crema, Italy. All individuals underwent clinical and laboratory exams, SARS-COV-2 reverse transcriptase-polymerase chain reaction on nasopharyngeal swab, and chest X-ray and/or computed tomography (CT). Data on onset, course, comorbidities, number of drugs including angiotensin converting enzyme (ACE) inhibitors and angiotensin-II-receptor antagonists (sartans), follow-up swab, pharmacological treatments, non-invasive respiratory support, ICU admission, and deaths were recorded. Among 411 SARS-COV-2 patients (67.7% males) median age was 70.8 years (range 5–99). Chest CT was performed in 317 (77.2%) and showed interstitial pneumonia in 304 (96%). Fatality rate was 17.5% (74% males), with 6.6% in 60–69 years old, 21.1% in 70–79 years old, 38.8% in 80–89 years old, and 83.3% above 90 years. No death occurred below 60 years. Non-invasive respiratory support rate was 27.2% and ICU admission 6.8%. Charlson comorbidity index and high C-reactive protein at admission were significantly associated with death. Use of ACE inhibitors or sartans was not associated with outcomes. Among 128 swab negative patients at admission (63.3% males) median age was 67.7 years (range 1–98). Chest CT was performed in 87 (68%) and showed interstitial pneumonia in 76 (87.3%). Follow-up swab turned positive in 13 of 32 patients. Using chest CT at admission as gold standard on the entire study population of 539 patients, nasopharyngeal swab had 80% accuracy. Comorbidity network analysis revealed a more homogenous distribution 60–40 aged SARS-COV-2 patients across diseases and a crucial different interplay of diseases in the networks of deceased and survived patients. SARS-CoV-2 caused high mortality among patients older than 60 years and correlated with pre-existing multiorgan impairment.

COVID-19 mortality surveillance in Lebanon

“…Similar findings were reported by the US Centers for Disease Control and Prevention (CDC) which revealed that 94% of ICU deaths occurred among patients with one or more underlying health conditions 20 . Our findings are also in line with prior Italian 21 , 22 and Chinese 23 , 24 studies, implying that interventions to safeguard individuals with pre-existing medical disorders should be implemented to reduce COVID-19-related deaths.…”

Covid-19 Infection in India: A Comparative Analysis of the Second Wave with the First Wave

“…Patient age is considered as one of the important factors for hospitalization and mortality [ 18 ]. Irrespective of gender and ethnicity, older patients appear to be at higher risk of mortality than younger patients.…”

Machine learning and comorbidity network analysis for hospitalized patients with COVID-19 in a city in Southern Brazil

“…Along with machine learning techniques to predict the profile of hospitalized patients and the outcome, several works attempted to evaluate the contribution of comorbidities through a network approach ( Espinosa et al, 2020 ; Gili et al, 2021 ; Khedr et al, 2020 ; Mason et al, 2021 ). Comorbidity network analysis is a graph-theoretic approach to study associations from disease co-occurrence data.…”