ABSTRACT
Background: Already at the time of hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on following variables which can simply be measured at hospital admission: gender, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results: Cause-specific hazard regression models show that these baseline variables are associated with both hazards, the death as well as the discharge hazard. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC=0.872 [CI 95%: 0.835-0.910]). Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.
Subject(s)
COVID-19 , HypertensionABSTRACT
The COVID-19 is rapidly scattering worldwide, and the number of cases in the Eastern Mediterranean Region is rising, there is a need for immediate targeted actions. We designed a longitudinal study in a hot outbreak zone to analyze the serial findings between infected patients for detecting temporal changes from February 2020. In a hospital-based open-cohort study, patients are followed from admission until one year from their discharge (the 1st, 4th, 12th weeks, and the first year). The measurements included demographic, socio-economics, symptoms, health service diagnosis and treatment, contact history, and psychological variables. The signs improvement, death, length of stay in hospital were considered as primary, and impaired pulmonary function and psychotic disorders were considered as main secondary outcomes. Notably, In the last two follow-ups, each patient attends the hospital to complete the Patient Health Questionnaire-9 (PHQ-9) and the Depression Anxiety Stress Scales (DASS-21). Moreover, clinical symptoms and respiratory functions are being determined in such follow-ups. Among the first 600 COVID-19 cases, a total of 490 patients with complete information (39% female; the average age of 57{+/-}15 years) were analyzed. Seven percent of these patients died. The three main leading causes of admission were: fever (77%), dry cough (73%), and fatigue (69%). The most prevalent comorbidities between COVID-19 patients were hypertension (35%), diabetes (28%), and ischemic heart disease (14%). The percentage of primary composite endpoints (PCEP), defined as death, the use of mechanical ventilation, or admission to an intensive care unit was 18%. The following comorbidities were significantly different in the positive and negative PCEP groups: acute kidney disease (p=0.008) and diabetes (p=0.026). For signs and symptoms, fatigue (p=0.020) and sore throat (p=0.001) were significantly different. This long-term prospective Cohort may support healthcare professionals in the management of resources following this pandemic.