Clinical characteristics and outcome of Covid-19 illness and predictors of in-hospital mortality in Saudi Arabia

Abstract

Background Patients’ race and ethnicity may play a role in mortality from Covid-19. Studies in China, the US, and Europe have been conducted on the predictors of Covid-19 mortality, yet in the EMR countries, such studies are scarce. Therefore, we aimed to describe the hospitalization rate, ICU-admission, and in-hospital mortality of Covid-19 and predictors of in-hospital mortality in Saudi Arabia. Methods E-medical records were examined for all Covid-19 patients diagnosed in five tertiary hospitals affiliated with the Saudi-National Guard-Health Affairs during March 21, 2020, and September 12, 2021, based on a positive SARS-CoV-2 RT-PCR test, (n = 35,284). Data were collected on patients’ characteristics, comorbidities, laboratory findings, hospitalization, ICU admission, and in-hospital and overall mortality. Logestic regressions were used to identify the independent predictors of in-hospital mortality. The best laboratory parameters cut-off values to predict in-hospital mortality were identified using the area under the receiver operating characteristic curve (AUC). Significance was considered at p < 0.05. Results Of all 35,284 Covid-19 patients, 81.8% were adults and 21.7% were hospitalized. Compared to non-hospitalized patients, hospitalized patients were more of female gender (52.1% versus 47.3%, p < 0.001) and had higher mean age (p < 0.001), higher mean BMI (p < 0.001), and higher rates of: diabetes (p < 0.001), hypertension (p < 0.001), ischemic heart disease (p < 0.001), cancer (p < 0.001), COPD (p < 0.001) and asthma (p = 0.011). The study showed 3.1% overall case-fatality, 20.3% ICU admission rate, and 9.7% in-hospital mortality. Predictors of in-hospital mortality among adult patients were; patients’ age ≥ 70 years (OR = 6.93, 95% CI 1.94–24.79), ischemic heart disease (OR = 1.80, 95% CI 1.05–3.09), ICU admission (OR = 24.38, 95% CI 15.64–38.01), abnormal C-reactive protein “CRP” (OR = 1.85, 95% CI 1.08–3.16), abnormal D-dimer (OR = 1.96, 95% CI 1.15–3.36), lymphopenia (OR = 2.76, 95% CI 2.03–3.3.76), high neutrophil count (OR = 2.10, 95% CI 1.54–2.87), and abnormal procalcitonin (OR = 3.33, 95% CI 1.88–5.90). The best laboratory parameters cut-off values to predict in-hospital mortality were CRP > 72.25 mg/L (AUC = 0.64), d-dimer > 1125 µg/L (AUC = 0.75), neutrophils count > 5,745 × 10^9/L (AUC = 0.70), lymphocytic count < 1.10 × 10^9/L (AUC = 0.72), and procalcitonin > 0.18 ng/mL (AUC = 0.76). Conclusions Rates of hospitalization, ICU-admission, in-hospital mortality and overall case fatality were nearly comparable to the rates in western countries. Early interventions are necessary for high-risk Covid-19 patients, especially elderly patients and those with cardiac diseases.

Medical Comorbidities and Risk Of COVID-19 Severity

“…In another study in Saudi Arabia, out of over l 35,284 COVID-19 patients, 81.8% were adults, and 21.7% were hospitalized. Compared to non-hospitalized patients, hospitalized patients had more women (52.1% vs. 47.3%, and an elevated incidence of diabetes mellitus, arterial hypertension, coronary artery disease, cancer, COPD, and asthma (Abolfotouh et al, 2022).…”

Clinical characteristics and factors for serious outcomes among outpatients infected with the Omicron subvariant BF.7

“…Consequently, the inclusion of CT tests and laboratory values in our study was contingent upon whether the doctors ordered these tests. Therefore, we were unable to incorporate CT tests and laboratory values in our study, even though they have been found to be associated with disease severity in the hospitalized population 48,49 . However, based on our current knowledge, these factors have not been included in previous studies conducted specifically on outpatients.…”

“…Therefore, we were unable to incorporate CT tests and laboratory values in our study, even though they have been found to be associated with disease severity in the hospitalized population. 48,49 However, based on our current knowledge, these factors have not been included in previous studies conducted specifically on outpatients. This could also be related to the incomplete availability of CT tests and laboratory values data for outpatient patients.…”

Detection and risk assessment of COVID-19 through machine learning

“…However, they only limit themselves to comparing textual information. Abolfotouh et al (2022) used records of patient characteristics such as comorbidities, laboratory findings, hospitalization, admission to Intensive Care Units (ICU), and in-hospital and overall mortality, with the aim of describing hospitalization rates, ICU admission, and identifying predictors of in-hospital mortality for COVID-19, that is, they identify causes of in-hospital mortality but do not make predictions of either COVID-19 detection or severe cases. For their part, the work proposed by Perez et al (2021) performed a multivariate logistic regression analysis to determine the effects of age, sex, previous medical condition, and COVID-19 symptoms to determine the probability of positive cases and hospitalizations during the first wave of the pandemic, March-April 2020, but do not make predictions of severe cases, intubated cases, and cases requiring ICU admission.…”