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

How to cite: Savieri, P.; Nyamandi, C.; Sigwadhi, L.N.; Tamuzi, J.; Allwood, B.; Koegelenberg, C.F.; Irusen, E.M.; Lalla, U.; Ngah, V.D.; Zemlin, A.E.; Chapanduka, Z.C.; Erasmus, R.T.; Matsha, T.E.; Daramola, J.O.; Zumla, A.; Nyasulu, P.S. 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. Preprints 2024, 2024030973. https://doi.org/10.20944/preprints202403.0973.v1 Savieri, P.; Nyamandi, C.; Sigwadhi, L.N.; Tamuzi, J.; Allwood, B.; Koegelenberg, C.F.; Irusen, E.M.; Lalla, U.; Ngah, V.D.; Zemlin, A.E.; Chapanduka, Z.C.; Erasmus, R.T.; Matsha, T.E.; Daramola, J.O.; Zumla, A.; Nyasulu, P.S. 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. Preprints 2024, 2024030973. https://doi.org/10.20944/preprints202403.0973.v1

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MDPI and ACS Style

Savieri, P.; Nyamandi, C.; Sigwadhi, L.N.; Tamuzi, J.; Allwood, B.; Koegelenberg, C.F.; Irusen, E.M.; Lalla, U.; Ngah, V.D.; Zemlin, A.E.; Chapanduka, Z.C.; Erasmus, R.T.; Matsha, T.E.; Daramola, J.O.; Zumla, A.; Nyasulu, P.S. 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. Preprints 2024, 2024030973. https://doi.org/10.20944/preprints202403.0973.v1

APA Style

Savieri, P., Nyamandi, C., Sigwadhi, L.N., Tamuzi, J., Allwood, B., Koegelenberg, C.F., Irusen, E.M., Lalla, U., Ngah, V.D., Zemlin, A.E., Chapanduka, Z.C., Erasmus, R.T., Matsha, T.E., Daramola, J.O., Zumla, A., & Nyasulu, P.S. (2024). 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. Preprints. https://doi.org/10.20944/preprints202403.0973.v1

Chicago/Turabian Style

Savieri, P., Alimuddin Zumla and Peter Suwirakwenda Nyasulu. 2024 "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" Preprints. https://doi.org/10.20944/preprints202403.0973.v1

Abstract

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.

Keywords

length of stay, COVID-19, ICU, generalized linear model, count regression, Hurdle model

Subject

Medicine and Pharmacology, Epidemiology and Infectious Diseases

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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