Early Determinants of Length of Hospital Stay: A Case Control Survival Analysis among COVID-19 Patients admitted in a Tertiary Healthcare Facility of East India

Agarwal, Neeraj; Biswas, Bijit; Singh, Chandramani; Nair, Rathish; Mounica, Gera; H, Haripriya; Jha, Amit; Das, Kumar M

doi:10.1177/21501327211054281

Cited by 9 publications

(6 citation statements)

References 34 publications

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“…Similarly, sociodemographic aspects such as advanced age (80 years or older), male sex, race/non-white color and being a public hospital user have been shown to contribute to a higher risk of death [ 5 , 6 , 7 ]. In addition, it should be noted that a more critical course of the disease may promote increased hospital admissions and hospital length of stay [ 8 ], generating demands for health systems.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Survival of Patients Hospitalized with COVID-19 in Espírito Santo, Brazil

Garbin

Leite

Lopes-Júnior

et al. 2022

IJERPH

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Objective: To analyze the survival of patients hospitalized with COVID-19 and its associated factors. Methods: Retrospective study of survival analysis in individuals notified and hospitalized with COVID-19 in the state of Espírito Santo, Brazil. As data source, the reports of hospitalized patients in the period from 1 March 2020, to 31 July 2021 were used. The Cox regression analysis plus the proportional risk assessment (assumption) were used to compare hospitalization time until the occurrence of the event (death from COVID-19) associated with possible risk factors. Results: The sample comprised 9806 notifications of cases, with the occurrence of 1885 deaths from the disease (19.22%). The mean age of the group was 58 years (SD ± 18.3) and the mean hospital length of stay was 10.5 days (SD ± 11.8). The factors that presented a higher risk of death from COVID-19, associated with a lower survival rate, were non-work-related infection (HR = 4.33; p < 0.001), age group 60–79 years (HR: 1.62; p < 0.001) and 80 years or older (HR = 2.56; p < 0.001), presence of chronic cardiovascular disease (HR = 1.18; p = 0.028), chronic kidney disease (HR = 1.5; p = 0.004), smoking (HR = 1.41; p < 0.001), obesity (HR = 2.28; p < 0.001), neoplasms (HR = 1.81; p < 0.001) and chronic neurological disease (HR = 1.68; p < 0.001). Conclusion: It was concluded that non-work-related infection, age group above or equal to 60 years, presence of chronic cardiovascular disease, chronic kidney disease, chronic neurological disease, smoking, obesity and neoplasms were associated with a higher risk of death, and, therefore, a lower survival in Brazilian patients hospitalized with COVID-19. The identification of priority groups is crucial for Health Surveillance and can guide prevention, control, monitoring, and intervention strategies against the new coronavirus.

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Section: Introductionmentioning

confidence: 99%

Analysis of Survival of Patients Hospitalized with COVID-19 in Espírito Santo, Brazil

Garbin

Leite

Lopes-Júnior

et al. 2022

IJERPH

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“…We compared patient survival experiences using the Kaplan-Meier survival function and Nelson-Aalen cumulative hazard plots with the Log-rank test. The mortality rate was estimated using the Poisson survival regression model while the Weibull regression model was used to estimate the associated predictors [21]. Model comparison was done using the Akaike Information Criteria (AIC), the lowest value indicating the best-fitting model.…”

Section: Discussionmentioning

confidence: 99%

Predictors of mortality among COVID-19 patients at Kilimanjaro Christian Medical Centre in Northern Tanzania: A hospital-based retrospective cohort study

Waria,

Muro,

Jonas

et al. 2024

Preprint

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Background COVID-19 disease is a global public health disaster causing a range of social, economic, and healthcare difficulties, border restrictions, high human loss, lockdown, and transportation challenges. Despite it being a global pandemic, there are few studies conducted in Tanzania to examine the predictors of mortality. This disease has caused a significant number of mortalities worldwide but literature shows low mortality and better survival in Africa than in other WHO regions. Therefore, this study aimed to determine the predictors of mortality among COVID-19 patients at KCMC Hospital in Northern Tanzania. Methodology This was the hospital-based retrospective cohort study, conducted at KCMC Hospital in Northern Tanzania among all admitted patients with confirmed COVID-19, from 10th March 2020 to 26th January 2022. The main study event was COVID-19 mortality. The predictors of mortality were determined by using the Weibull survival regression model and the statistically significant results were declared at a p-value of <0.05. Results A total of 547 confirmed COVID-19 patient records were included in the study. Their median age was 63 (IQR; 53-83), about 60% were aged 60 years and above, and 56.7% were males. The most common clinical features were; fever (60.8%), a severe form of the disease (44.4%), difficulty in breathing (73.3%), chest pain (46.1%), and generalized body weakness (71.3%). Of all participants, over one-third (34.6%) died (95%CI; 0.31-0.39). The median survival time was 7 days (IQR; 3-12). The overall mortality rate was 32.33 per 1000 person-days while the independent predictors of higher mortality risk were age ≥60 years (AHR=2.01; 95%CI 1.41-2.87; P<0.001), disease severity (AHR=4.44; 95%CI 2.56-7.73; P<0.001) and male sex (AHR=1.28; 95%CI; 0.93-1.73; P=0.128). Conclusion Mortality was higher in elderly male patients, with a severe form of the disease and those with any comorbidities. Therefore, more attention should be provided to older patients including uptake of the current vaccine and ensuring standard and supportive care at primary health facilities is available.

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“…According to reports, shorter hospital stays are linked to lower mortality rates, fewer nosocomial infections, lower patient financial burden, and higher hospital bed turnover rates. 9,12 Predicting LOS-related factors and mortality rate in COVID-19 patients can help better prioritize patients, make emergent decisions, provide healthcare services, and seek useful and necessary solutions to reduce mortality and hospital LOS.…”

Section: Introductionmentioning

confidence: 99%

“…Hospital LOS is a crucial predictor for healthcare planning. According to reports, shorter hospital stays are linked to lower mortality rates, fewer nosocomial infections, lower patient financial burden, and higher hospital bed turnover rates 9,12 …”

Section: Introductionmentioning

confidence: 99%

Predictors of hospital length of stay and mortality among COVID‐19 inpatients during 2020–2021 in Hormozgan Province of Iran: A retrospective cohort study

Mastaneh

Mouseli

Mohseni

et al. 2023

Health Science Reports

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Background and Aims: About one-fifth of patients with COVID-19 need to be hospitalized. Predicting factors affecting the hospital length of stay (LOS) can be effective in prioritizing patients, planning for services, and preventing the increase in LOS and death of patients. The present study aimed to identify the factors that predict LOS and mortality in COVID-19 patients in a retrospective cohort study. Methods: A total of 27,859 patients were admitted to 22 hospitals from February 20, 2020 to June 21, 2021. The data collected from 12,454 patients were screened according to the inclusion and exclusion criteria. The data were captured from the MCMC (Medical Care Monitoring Center) database. The study tracked patients until their hospital discharge or death. Hospital LOS and mortality were assessed as the study outcomes.Results: As the results revealed, 50.8% of patients were male and 49.2% were female. The mean hospital LOS of the discharged patients was 4.94. Yet, 9.1% of the patients (n = 1133) died. Among the predictors of mortality and long hospital LOS were the age above 60, admission to the ICU, coughs, respiratory distress, intubation, oxygen level less than 93%, cigarette and drug abuse, and a history of chronic diseases. Masculinity, gastrointestinal symptoms, and cancer were the effective variables in mortality, and positive CT was a factor significantly affecting the hospital LOS. Conclusion:Paying special attention to high-risk patients and modifiable risk factors such as heart disease, liver disease, and other chronic diseases can diminish the complications and mortality rate of COVID-19. Providing training, especially for those who care for patients experiencing respiratory distress such as nurses and operating room personnel can improve the qualifications and skills of medical staff. Also, ensuring the availability of sufficient supply of medical equipment is strongly recommended.

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Early Determinants of Length of Hospital Stay: A Case Control Survival Analysis among COVID-19 Patients admitted in a Tertiary Healthcare Facility of East India

Cited by 9 publications

References 34 publications

Analysis of Survival of Patients Hospitalized with COVID-19 in Espírito Santo, Brazil

Analysis of Survival of Patients Hospitalized with COVID-19 in Espírito Santo, Brazil

Predictors of mortality among COVID-19 patients at Kilimanjaro Christian Medical Centre in Northern Tanzania: A hospital-based retrospective cohort study

Predictors of hospital length of stay and mortality among COVID‐19 inpatients during 2020–2021 in Hormozgan Province of Iran: A retrospective cohort study

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