Length of Stay Analysis of COVID-19 Intensive Care Unit Admissions Using Count Regression and Hurdle Regression Models: A Study in a Tertiary Hospital, Cape Town, South Africa (preprint)

Objective: To evaluate the variables influencing the length of stay (LoS) for COVID-19 ICU patients at Tygerberg Hospital (Cape Town) and to identify the covariates that significantly influenced it and any potential risk factors associated with LoS. Methods and Results: Poisson, negative binomial (NB), Hurdle–Poisson, and Hurdle–NB regression models were used to model the LoS in this prospective cohort study. The fitted models were compared using the Akaike information criterion (AIC), Vuong’s test criteria, and Rootograms. Based on the chosen performance criteria, the NB model provided the best fit outperforming other candidate models. The baseline LoS count was 8 days. On average, antibiotics reduced LoS by 0.74-fold (95% CI 0.62-0.89) compared to not taking antibiotics. The second wave had a significant effect on the average LoS, which decreased by 0.36-fold (95% CI 0.14-0.93) compared to the first wave. Average LoS increased by 1.01-fold (95% CI 1.01-1.02) for every one-year increase in the age of the patient and by 1.02-fold (95% CI 1.01-1.03) for every 1 unit increase in neutrophils. A 1 ng/L increase in log (TropT) levels decreased the average LoS by 0.87-fold (95% CI 0.81-0.93) similarly, a unit increase in the PF ratio decreased the average LoS by 0.998-fold (95% CI 0.997-0.999) respectively. Conclusion: The study identified common clinical characteristics associated with length of stay in ICU for COVID-19 patients, including age at admission, PF ratio, neutrophils, TropT, Wave, and antibiotic use. These results can aid in identifying risk factors for increased length of stay, assist in healthcare systems planning, and aid in evaluating different models for analysing this type of data.

Laboratory biomarkers associated with mortality in COVID-19 patients in Addis Ababa, Ethiopia (preprint)

Background: Laboratory biomarkers are amongst the best imperative predictors of disease outcomes in hospital-admitted COVID-19 patients. Although data is available in this regard at a global level, there is a paucity of information in Ethiopia. Thus, this study aimed to assess the laboratory biomarkers association with death among COVID-19 patients in Ethiopia. Methods: A health facility-based longitudinal study was conducted from 2020 to 2022 among RT-PCR-confirmed COVID-19 patients admitted and on treatment follow-up at COVID-19 treatment hospitals in Addis Ababa. A robust Poisson regression model was fitted to assess the association between demographic, clinical, and laboratory factors and death. Significance was determined at p<0.05, and variables with p < 0.15 in bivariate analyses were included in the final multivariable models. Incidence rate ratio (IRR) with a 95% confidence interval (CI) was used to describe associations. Results: Of the 2357 COVID-19 patients, 248 (10.5%) died. The median age of participants was 59 (IQR= 45- 70) years, and the majority (64.9%) of them were male. Lower median RBC was observed among those who died at 4.58 (4.06-5.07) as compared to those who survived at 4.69 (4.23-5.12) whereas high median (IQR) WBC was a predictor of mortality with 11.2 (7.7-15.9). After adjusting for confounders, death was associated with age >74 years having adjusted incidence rate ratio [aIRR (95%CI): 2.46 (1.40-4.34)], and critical clinical situations [aIRR (95% CI): 4.04 (2.18-7.52)]. Conclusion: Our results demonstrate that abnormal liver function tests, abnormal white blood cells, age of the patients, and clinical status of the patients during admission are associated with unfavorable outcomes of COVID-19. Hence, timely monitoring of these laboratory results at the earliest phase of the disease was highly commendable.