Patient Flow Dynamics in Hospital Systems During Times of COVID-19: Cox Proportional Hazard Regression Analysis

Bhandari, Sudhir; Tak, Amit; Singhal, Sanjay; Shukla, Jyotsna; Shaktawat, Ajit Singh; Gupta, Jitendra; Patel, Bhoopendra; Kakkar, Shivankan; Dube, Amitabh; Dia, Sunita; Dia, Mahendra; Wehner, Todd C.

doi:10.3389/fpubh.2020.585850

Cited by 19 publications

(17 citation statements)

References 17 publications

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“…Our study found that the mortality rate from COVID-19 disease in hospitalized patients was 11.1 (95% CI 8.1–14.2). The overall COVID-19 death rate we found in this study is comparable with findings from India, 8.1% [ 18 ], New York, USA, 13.1% [ 19 ] and Mexico, 9.37% [ 20 ], while it is less than those from Belgium, 29.9% [ 21 ], and Italy, 25.2% [ 22 ]. This discrepancy between countries might be due to difficulties in conducting diagnosis tests for all suspected individuals, insufficient admission hospital capacity for less severe patients at the early stage of the pandemic and variation in the methodology for cases confirmation and death registery [ 20 , 23 , 24 ].…”

Section: Discussionsupporting

confidence: 81%

Survival analysis of COVID-19 patients in Ethiopia: A hospital-based study

Kaso

Agero²,

Hurissa³

et al. 2022

PLoS ONE

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Background COVID-19 is a global public health problem causing high mortality worldwide. This study aimed to assess time to death and predictors of mortality among patients hospitalized for COVID-19 in the Arsi zone treatment center. Method We performed a retrospective observational cohort study using medical records of laboratory-confirmed COVID-19 cases hospitalized at Bokoji Hospital COVID-19 treatment center from 1st July 2020 to 5th March 2021. We extracted data on the patients’ sociodemographic and clinical characteristics from medical records of hospitalized patients retrospectively. We carried out Kaplan Meier and Cox regression analysis to estimate survival probability and investigate predictors of COVID-19 death 5% level of significance. The Adjusted Hazard Ratio (aHR) with 95% Confidence Interval (CI) was estimated and interpreted for predictors of time to death in the final cox model. Result A total of 422 COVID-19 patients treated were analyzed, of these more than one tenth (11.14%) deaths, with a mortality rate of 6.35 cases per 1000 person-days. The majority (87.2%) of deaths occurred within the first 14 days of admission, with a median time-to-death of nine (IQR: 8–12) days. We found patients that age between 31 and 45 years (aHR = 2.55; 95% CI: (1.03, 6.34), older than 46 years (aHR = 2.59 (1.27, 5.30), chronic obstructive pulmonary disease (aHR = 4.60, 95%CI: (2.37, 8.91), Chronic kidney disease (aHR = 5.58, 95%CI: (1.70, 18.37), HIV/AIDS (aHR = 3.66, 95%CI: (1.20, 11.10), admission to the Intensive care unit(aHR = 7.44, 95%CI: (1.82, 30.42), and being on intranasal oxygen care (aHR = 6.27, 95%CI: (2.75, 4.30) were independent risk factors increasing risk of death from COVID-19 disease than their counterparts. Conclusion The risk of dying due to COVID-19 disease was higher among patients with HIV/AIDS, chronic obstructive pulmonary disease, and chronic kidney diseases. We also found that older people, those admitted to ICU, and patients who received intranasal oxygen care had a higher risk of dying due to COVID-19 disease. Therefore, close monitoring hospitalized patients that are old aged and those with comorbidities after hospitalization is crucial within the first ten days of admission.

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Section: Discussionsupporting

confidence: 81%

Survival analysis of COVID-19 patients in Ethiopia: A hospital-based study

Kaso

Agero²,

Hurissa³

et al. 2022

PLoS ONE

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“…With respect to the simplistic modelling approach, we note that for most of the existing studies the set of predictive variables is limited to comorbidities or specific medications. For instance, [ 3 ] describes the progression of a moderate cohort of patients to death or discharge using survival methods but only age and gender as predictors. [ 5 ] and [ 6 ] consider large cohorts and combine data from different sources but they do not consider the ICU hospitalisation as an intermediate state.…”

Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain)

et al. 2021

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This paper analyses COVID-19 patients’ dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed.

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“…Meanwhile, mathematical models have been used to predict the course of disease and mortality. [4][5][6][7] Such models have potential in disease management as well as preventive protocols that are cost-effective that can help optimum allocation of resources to manage the disease. [8][9][10][11][12][13][14] An econometric model has been proposed in the present study to predict and extrapolate the transmission of COVID-19.…”

Section: Data Acquisitionmentioning

confidence: 99%

Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

Tak¹,

Dia²,

Dia³

et al. 2021

Scripta Medica

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Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

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Patient Flow Dynamics in Hospital Systems During Times of COVID-19: Cox Proportional Hazard Regression Analysis

Cited by 19 publications

References 17 publications

Survival analysis of COVID-19 patients in Ethiopia: A hospital-based study

Survival analysis of COVID-19 patients in Ethiopia: A hospital-based study

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain)

Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

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