A Study on Factors Impacting Length of Hospital Stay of COVID-19 Inpatients

Abstract

Knowing the typical length of hospital stay of COVID-19 patientsand which factors affecting the stay time are important for hospital management.A factor can impact the stay time in conflicting ways: a condition may,by the same mechanism, increase the stay time for a severe patient while decrease the time for an even more severe and deceased patient.Material and Method: 3184 COVID-19 patients from the Tokat State Hospital were examined on arrival to determine if they are required further treatment in hospital or not. Among them,404 are inpatients whose hospital stay time is available. By a simple and conditional linear regressions, we examine which factors statistically impact the hospital stay time,both overall and after taking into account the patient's age or eventual survival status.We also use causespecific Cox proportional-hazard regression for competing risk to examine contribution from a factor to the rate of event of mortality, and to the event of discharge.Results: Surviving ICU patients have longer hospital stay time than nonsurviving ICU patients, which in turn longer than non-ICU patients. Older age within acertain range is always correlated with a longer hospital stay. The factors with strongest signal in our dataset are C-reactive protein (CRP), hemoglobin (HGB)and calcium level: increased CRP level, decreased HGB level and calcium level are associated with longer hospital stay, independent from the contribution from surviving status. Other potentially staytime-impacting factors include d-dimer, urea, glucose, white blood cell (WBC) count, neutrophil, but these signals may not be robust against logtransformation of the stay time, or confounding with age. We also observed that glucose is more important than HbA1C or diabetes status in its influence on hospital stay time. Conclusion:Almost all factors we collected contribute to a faster/slower mortality or discharge rate, in particular C-reactive protein and hemoglobin. Measurement of the associated factors in COVID-19 patients could be used for a better hospital bed management.

A Composite Ranking of Risk Factors for COVID-19 Time-To-Event Data from a Turkish Cohort

“…The short-term glucose has been shown to be a much stronger predictor for COVID-19 severity than the history of diabetes status (Singh and Singh, 2020; Coppelli et al, 2020; Ling et al, 2021; Cetin et al, 2021d; Guarisco et al, 2022). In a recent study, it was shown that higher glucose level ( > 180) had increased the odds of death by 4-fold for non-diabetes, whereas only 1.8-fold increase for diabetes COVID-19 patients (Skwiersky et al, 2021).…”

A composite ranking of risk factors for COVID-19 time-to-event data from a Turkish cohort

“… Elevated ferritin serum levels have been found to significantly correlate with COVID-19 severity, as discussed in these papers ( Lin et al, 2020 , Carubbi et al, 2021 , Ahmed et al, 2021 , Kaushal et al, 2022 , among others. The short-term glucose has been shown to be a much stronger predictor for COVID-19 severity than the history of diabetes status ( Singh and Singh, 2020 , Coppelli et al, 2020 , Ling et al, 2020 , Cetin et al, 2021d , Guarisco et al, 2022 . In a recent study, it was shown that higher glucose level (> 180) had increased the odds of death by 4-fold for non-diabetes, whereas only 1.8-fold increase for diabetes COVID-19 patients ( Skwiersky et al, 2021) .…”

“…The short-term glucose has been shown to be a much stronger predictor for COVID-19 severity than the history of diabetes status ( Singh and Singh, 2020 , Coppelli et al, 2020 , Ling et al, 2020 , Cetin et al, 2021d , Guarisco et al, 2022 . In a recent study, it was shown that higher glucose level (> 180) had increased the odds of death by 4-fold for non-diabetes, whereas only 1.8-fold increase for diabetes COVID-19 patients ( Skwiersky et al, 2021) .…”

A study of Factors Affecting the length of Hospital Stay of COVID-19 Patients By Cox-Regression Analysis

“…Yaşam analizinde, yaşam süresini etkileyecek değişkenleri belirlemek için Cox orantılı hazard modelinin kullanılabilmesi için değişkenlerin orantılı hazard varsayımını sağlaması gerekmektedir. Orantılı hazard varsayımının test edilmesinde; log(-log) yaşam eğrileri, gözlenen ve beklenen yaşam eğrileri, Schoenfeld artıkları ile yaşam süresinin rankı arasındaki korelasyon testi yöntemi ve modele zamana bağlı açıklayıcı değişkenlerin eklenmesi yöntemleridir [8,15,16].…”

“…Çetin vd. 'leri Tokat devlet hastanesinde yatılı olarak tedavi gören 404 hastanın; yaş, cinsiyet, yoğun bakımda tedavi olma durumu ile bazı klinik faktörlerin yatılı olarak kalma süresi üzerindeki etkisini doğrusal regresyon ve Cox-regresyon modelleri ile ortaya koymuşlardır [8].…”