Predictors of invasive mechanical ventilation in hospitalized COVID-19 patients: a retrospective study from Jordan

OBJECTIVES

To identify early indicators for invasive mechanical ventilation utilization among COVID-19 patients.

METHODS

This retrospective study evaluated COVID-19 patients who were admitted to hospital from September 20, 2020, to August 8, 2021. Patients' clinical characteristics, demographics, comorbidities, and laboratory results were evaluated. Multivariable logistic regression and machine learning (ML) methods were employed to assess variable significance.

RESULTS

Among 1,613 confirmed COVID-19 patients, 365 patients (22.6%) received invasive mechanical ventilation (IMV). Factors associated with IMV included older age >65 years (OR,1.46; 95%CI, 1.13 - 1.89), current smoking status (OR, 1.71; 95%CI, 1.22-2.41), critical disease at admission (OR, 1.97; 95%CI, 1.28-3.03), and chronic kidney disease (OR, 2.07; 95%CI, 1.37-3.13). Laboratory abnormalities that were associated with increased risk for IMV included high leukocyte count (OR, 2.19; 95%CI, 1.68 - 2.87), low albumin (OR, 1.76; 95%CI, 1.33 - 2.34) and high AST (OR, 1.71; 95%CI, 1.31 - 2.22).

CONCLUSION

Our study suggests that there are several factors associated with the increased need for IMV among COVID-19 patients including older age, current smoking status, critical disease status on admission, and chronic kidney disease. In addition, laboratory markers such as high leukocyte count, low albumin and high AST were determined. These findings will help in early identification of patients at high risk for IMV and reallocation of hospital resources towards patients who need them the most to improve their outcomes.

Risk Factors Associated with Mortality in COVID-19 Hospitalized Patients: Data from the Middle East

This study aimed to assess the risk factors for COVID-19 mortality among hospitalized patients in Jordan. All COVID-19 patients admitted to a tertiary hospital in Jordan from September 20, 2020, to August 8, 2021, were included in this study. Demographics, clinical characteristics, comorbidities, and laboratory results were extracted from the patients' electronic records. Multivariable logistic and machine learning (ML) methods were used to study variable importance. Out of 1,613 COVID-19 patients, 1,004 (62.2%) were discharged from the hospital (survived), while 609 (37.8%) died. Patients who were of elderly age (>65 years) (OR, 2.01; 95% CI, 1.28-3.16), current smokers (OR, 1.61; 95%CI, 1.17-2.23), and had severe or critical illness at admission ((OR, 1.56; 95%CI, 1.05-2.32) (OR, 2.94; 95%CI, 2.02-4.27); respectively), were at higher risk of mortality. Comorbidities including chronic kidney disease (OR, 2.90; 95% CI, 1.90-4.43), deep venous thrombosis (OR, 2.62; 95% CI, 1.08-6.35), malignancy (OR, 2.22; 95% CI, 1.46-3.38), diabetes (OR, 1.31; 95% CI, 1.04-1.65), and heart failure (OR, 1.51; 95% CI, 1.02-2.23) were significantly associated with increased risk of mortality. Laboratory abnormalities associated with mortality included hypernatremia (OR, 11.37; 95% CI, 4.33-29.81), elevated aspartate aminotransferase (OR, 1.81; 95% CI, 1.42-2.31), hypoalbuminemia (OR, 1.75; 95% CI, 1.37-2.25), and low platelets level (OR, 1.43; 95% CI, 1.05-1.95). Several demographic, clinical, and laboratory risk factors for COVID-19 mortality were identified. This study is the first to examine the risk factors associated with mortality using ML methods in the Middle East. This will contribute to a better understanding of the impact of the disease and improve the outcome of the pandemic worldwide.