Factors associated with the duration of hospitalisation among COVID-19 patients in Vietnam: A survival analysis

Abstract Background The median duration of hospital stays due to COVID-19 has been reported in several studies on China as 10−13 days. Global studies have indicated that the length of hospitalisation depends on different factors, such as the time elapsed from exposure to symptom onset, and from symptom onset to hospital admission, as well as specificities of the country under study. The goal of this paper is to identify factors associated with the median duration of hospital stays of COVID-19 patients during the second COVID-19 wave that hit Vietnam from 5 March to 8 April 2020. Method We used retrospective data on 133 hospitalised patients with COVID-19 recorded over at least two weeks during the study period. The Cox proportional-hazards regression model was applied to determine the potential risk factors associated with length of hospital stay. Results There were 65 (48.9%) females, 98 (73.7%) patients 48 years old or younger, 15 (11.3%) persons with comorbidities, 21 (16.0%) severely ill patients and 5 (3.8%) individuals with life-threatening conditions. Eighty-two (61.7%) patients were discharged after testing negative for the SARS-CoV-2 virus, 51 were still in the hospital at the end of the study period and none died. The median duration of stay in a hospital was 21 (IQR: 16–34) days. The multivariable Cox regression model showed that age, residence and sources of contamination were significantly associated with longer duration of hospitalisation. Conclusion A close look at how long COVID-19 patients stayed in the hospital could provide an overview of their treatment process in Vietnam, and support the country's National Steering Committee on COVID-19 Prevention and Control in the efficient allocation of resources over the next stages of the COVID-19 prevention period.

Download Full-text

Prognostic factors and survival of patients with brain metastasis (BM) from breast cancer (BC) who underwent craniotomy.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.15_suppl.e22017 ◽

2013 ◽

Vol 31 (15_suppl) ◽

pp. e22017-e22017

Author(s):

Jose Pablo Leone ◽

Diana E. Cunningham ◽

Adrian Lee ◽

Rohit Bhargava ◽

Ronald L. Hamilton ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

Proportional Hazards ◽

Cox Regression ◽

Primary Diagnosis ◽

Cox Proportional Hazards ◽

Factors Associated ◽

Brain Radiotherapy ◽

Whole Brain Radiation ◽

First Recurrence

e22017 Background: BC is the second most frequent cause of BM after lung cancer, with metastases occurring in 10-16% of all patients. BM in patients with BC is a catastrophic event that results in poor prognosis. Identification of prognostic factors associated with breast cancer brain metastases (BCBM) could help to identify patients at risk. The aim of this study was to assess clinical characteristics, prognostic factors and survival of patients with BCBM who had craniotomy and resection in a series of patients treated with modern multimodality therapy. Methods: We analyzed 42 patients with BCBM who underwent resection. Patients were diagnosed with BC between April 1994 and May 2010. Cox proportional hazards regression was selected to describe factors associated with time to BM, survival from the date of first recurrence, and overall survival (OS). Results: Median age was 51 years (range 24-74). Median follow-up was 4.2 years (range 0.6-18.5). The mean time to BM from primary diagnosis was 49 months (range 0-206.22). Patients had a median of 2 BM with a median size of 3.25 cm. The proportion of the biological subtypes of BC was ER+/HER2- 25%, ER+/HER2+ 15%, ER-/HER2+ 30% and ER-/HER2- 30%. Brain radiotherapy was given to 28 patients, of which 10 had stereotactic radiosurgery, 7 whole brain radiation, and 11 both. Median OS from the date of primary diagnosis was 5.74 years. Median survival after diagnosis of BM was 1.33 years. In multivariate Cox regression analyses, stage was the only factor associated with shorter time to the development of BM (P=0.059), whereas age was the only factor associated with survival from the date of recurrence (P=0.027) and with OS (P=0.037). Controlling for age and stage, neither the biological subtype of cancer, the radiation modality nor the site of first recurrence showed any impact on survival. Conclusions: Stage at primary diagnosis correlated with shorter time to the development of BM, while age at diagnosis was associated with shorter survival in BCBM. None of the other clinical factors had influence on survival.

Download Full-text

Incidence and maternal-fetal risk factors of stillbirth: A population-based historical cohort and a nested case-control study

10.21203/rs.2.18523/v1 ◽

2019 ◽

Author(s):

Zahra Maleki ◽

Haleh Ghaem ◽

Mozhgan Seif ◽

Sedigheh Foruhari

Keyword(s):

Risk Factors ◽

Regression Model ◽

Proportional Hazards ◽

Cox Regression ◽

Gestational Hypertension ◽

Population Based ◽

Cox Proportional Hazards ◽

Historical Cohort ◽

Significance Level ◽

Fetal Risk

Abstract Background: For parents, stillbirth is a disappointing phenomenon; thus, identifying the associated risk factors can be beneficial in order to prevent this event. This study aimed to investigate the incidence and risk factors associated with stillbirth.Methods: In this historical cohort study, a total of 18129 birth records were investigated. For each case of stillbirth, three live birth infants on the same day and same hospital were selected as the controls, which were matched for gestational age. The data was collected using a researcher-made checklist. Finally, data were analyzed using STATA, 13.0 with Cox proportional hazards regression model at the significance level of 0.05.Results: The cumulative incidence of still birth was 9.48 per 1000 live births. Based on multivariate Cox regression model, five risk factors for stillbirth were identified, including male gender, fetal diseases, gestational hypertension, gestational diabetes, and hypothyroidism, (all hazard ratios > 1 and p<0.05).Conclusion: For the first time, maternal hypothyroidism, oligohydramnios and polyhydramnios were shown as risk factors for stillbirth, which were not evaluated in any previous study. The findings of this study suggest that some maternal and fetal risk factors can be recognized as predictors of stillbirth, which might help to prevent and detect high-risk parents at early stages in order to avoid adverse health consequences in the mother and her neonate.

Download Full-text

Incorporating sampling weights into robust estimation of Cox proportional hazards regression model, with illustration in the Multi-Ethnic Study of Atherosclerosis

BMC Medical Research Methodology ◽

10.1186/s12874-020-00945-9 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Colleen M. Sitlani ◽

Thomas Lumley ◽

Barbara McKnight ◽

Kenneth M. Rice ◽

Nels C. Olson ◽

...

Keyword(s):

Cohort Study ◽

Regression Model ◽

Proportional Hazards ◽

Cox Regression ◽

Cox Proportional Hazards ◽

Robust Methods ◽

Time To Event ◽

Sampling Weights ◽

Cox Proportional Hazards Regression ◽

Proportional Hazards Regression

Abstract Background Cox proportional hazards regression models are used to evaluate associations between exposures of interest and time-to-event outcomes in observational data. When exposures are measured on only a sample of participants, as they are in a case-cohort design, the sampling weights must be incorporated into the regression model to obtain unbiased estimating equations. Methods Robust Cox methods have been developed to better estimate associations when there are influential outliers in the exposure of interest, but these robust methods do not incorporate sampling weights. In this paper, we extend these robust methods, which already incorporate influence weights, so that they also accommodate sampling weights. Results Simulations illustrate that in the presence of influential outliers, the association estimate from the weighted robust method is closer to the true value than the estimate from traditional weighted Cox regression. As expected, in the absence of outliers, the use of robust methods yields a small loss of efficiency. Using data from a case-cohort study that is nested within the Multi-Ethnic Study of Atherosclerosis (MESA) longitudinal cohort study, we illustrate differences between traditional and robust weighted Cox association estimates for the relationships between immune cell traits and risk of stroke. Conclusions Robust weighted Cox regression methods are a new tool to analyze time-to-event data with sampling, e.g. case-cohort data, when exposures of interest contain outliers.

Download Full-text

Gene selection in Cox regression model based on a new adaptive penalized method

International Journal of Advanced Statistics and Probability ◽

10.14419/ijasp.v8i1.30566 ◽

2020 ◽

Vol 8 (1) ◽

pp. 16

Author(s):

Oday Isam Alskal ◽

Zakariya Yahya Algamal

Keyword(s):

Regression Model ◽

Survival Data ◽

Proportional Hazards ◽

Gene Selection ◽

Cox Regression ◽

Area Under The Curve ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Regression ◽

Proportional Hazards Regression ◽

Selection Operator

The common issues of high dimensional gene expression data for survival analysis are that many of genes may not be relevant to their diseases. Gene selection has been proved to be an effective way to improve the result of many methods. The Cox proportional hazards regression model is the most popular model in regression analysis for censored survival data. In this paper, an adaptive penalized Cox proportional hazards regression model is proposed, with the aim of identification relevant genes and provides high classification accuracy, by combining the Cox proportional hazards regression model with the weighted least absolute shrinkage and selection operator (LASSO) method. Experimental results show that the proposed method significantly outperforms two competitor methods in terms of the area under the curve and the number of the selected genes.

Download Full-text

Predictors and Complications of Post Kidney Transplant Leukopenia

Progress in Transplantation ◽

10.1177/15269248211024614 ◽

2021 ◽

pp. 152692482110246

Author(s):

Amanda Vinson ◽

Alyne Teixeira ◽

Bryce Kiberd ◽

Karthik Tennankore

Keyword(s):

Kidney Transplantation ◽

Kidney Transplant ◽

Proportional Hazards ◽

Cox Regression ◽

Selection Process ◽

Cox Proportional Hazards ◽

Post Transplant ◽

Factors Associated ◽

Infectious Risk ◽

Cox Proportional Hazards Models

Background: Leukopenia occurs frequently following kidney transplantation and is associated with adverse clinical outcomes including increased infectious risk. In this study we sought to characterize the causes and complications of leukopenia following kidney transplantation. Methods: In a cohort of adult patients (≥18 years) who underwent kidney transplant from Jan 2006-Dec 2017, we used univariable Cox proportional Hazards models to identify predictors of post-transplant leukopenia (WBC < 3500 mm3). Factors associated with post-transplant leukopenia were then included in a multivariable backwards stepwise selection process to create a prediction model for the outcome of interest. Cox regression analyses were subsequently used to determine if post-transplant leukopenia was associated with complications. Results: Of 388 recipients, 152 (39%) developed posttransplant leukopenia. Factors associated with leukopenia included antithymocyte globulin as induction therapy (HR 3.32, 95% CI 2.25-4.91), valganciclovir (HR 1.84, 95% CI 1.25-2.70), tacrolimus (HR 3.05, 95% CI 1.08-8.55), prior blood transfusion (HR 1.17 per unit, 95% CI 1.09- 1.25), and donor age (HR 1.02 per year, 95% CI 1.00-1.03). Cytomegalovirus infection occurred in 26 patients with leukopenia (17.1%). Other than cytomegalovirus, leukopenia was not associated with posttransplant complications. Conclusion: Leukopenia commonly occurred posttransplant and was associated with modifiable and non-modifiable pretransplant factors.

Download Full-text

Unplanned Reoperation After Trapeziometacarpal Arthroplasty: Rate, Reasons, and Risk Factors

Hand ◽

10.1177/1558944716679605 ◽

2016 ◽

Vol 12 (5) ◽

pp. 446-452 ◽

Cited By ~ 5

Author(s):

Suzanne C. Wilkens ◽

Zichao Xue ◽

Jos J. Mellema ◽

David Ring ◽

Neal Chen

Keyword(s):

Proportional Hazards ◽

Proportional Hazards Model ◽

Cox Regression ◽

Persistent Pain ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Cox Regression Analysis ◽

Factors Associated ◽

Hazards Model ◽

Kaplan Meier

Background: Trapeziometacarpal (TMC) arthritis is an expected part of ageing to which most patients adapt well. Patients who do not adapt to TMC arthritis may be offered operative treatment. The factors associated with reoperation after TMC arthroplasty are incompletely understood. The purpose of this study was to determine the rate of, the underlying reasons for, and the factors associated with unplanned reoperation after TMC arthroplasty. Methods: In this retrospective study, we included all adult patients who had TMC arthroplasty for TMC arthritis at 1 of 3 large urban area hospitals between January 2000 and December 2009. Variables were inserted into a multivariable Cox proportional hazards model to determine factors associated with unplanned reoperation, and the Kaplan-Meier curve was used to estimate and describe the probability of unplanned reoperation over time. Results: Among 458 TMC arthroplasties, 19 (4%) had an unplanned reoperation; 16 of 19 (84%) for persistent pain and two-thirds within the first year. The multivariate Cox regression analysis showed that unplanned reoperation was independently associated with younger age, surgeon inexperience, and index procedure type. Conclusions: Surgeons should be aware as well as patients should be informed that as many as 4% are offered or request a second surgery, usually for persistent pain and often within the 1-year window when additional improvement is anticipated.

Download Full-text

Prognostic factors and survival of patients with brain metastasis (BM) from breast cancer (BC) who underwent craniotomy.

Journal of Clinical Oncology ◽

10.1200/jco.2013.31.26_suppl.143 ◽

2013 ◽

Vol 31 (26_suppl) ◽

pp. 143-143

Author(s):

Jose Pablo Leone ◽

Diana E. Cunningham ◽

Adrian Lee ◽

Rohit Bhargava ◽

Ronald L. Hamilton ◽

...

Keyword(s):

Breast Cancer ◽

Prognostic Factors ◽

Proportional Hazards ◽

Cox Regression ◽

Primary Diagnosis ◽

Cox Proportional Hazards ◽

Factors Associated ◽

Brain Radiotherapy ◽

Whole Brain Radiation ◽

First Recurrence

143 Background: BC is the second most frequent cause of BM after lung cancer, with metastases occurring in 10% - 16% of all patients. BM in patients with BC is a catastrophic event that results in poor prognosis. Identification of prognostic factors associated with breast cancer brain metastases (BCBM) could help to identify patients at risk. The aim of this study was to assess clinical characteristics, prognostic factors and survival of patients with BCBM who had craniotomy and resection in a series of patients treated with modern multimodality therapy. Methods: We analyzed 42 patients with BCBM who underwent resection. Patients were diagnosed with BC between April 1994 and May 2010. Cox proportional hazards regression was selected to describe factors associated with time to BM, survival from the date of first recurrence, and overall survival (OS). Results: Median age was 51 years (range 24-74). Median follow-up was 4.2 years (range 0.6-18.5). The mean time to BM from primary diagnosis was 49 months (range 0-206.22). Patients had a median of 2 BM with a median size of 3.25 cm. The proportion of the biological subtypes of BC was ER+/HER2- 25%, ER+/HER2+ 15%, ER-/HER2+ 30% and ER-/HER2- 30%. Brain radiotherapy was given to 28 patients, of which 10 had stereotactic radiosurgery, 7 whole brain radiation, and 11 both. Median OS from the date of primary diagnosis was 5.74 years. Median survival after diagnosis of BM was 1.33 years. In multivariate Cox regression analyses, stage was the only factor associated with shorter time to the development of BM (P=0.059), whereas age was the only factor associated with survival from the date of recurrence (P=0.027) and with OS (P=0.037). Controlling for age and stage, neither the biological subtype of cancer, the radiation modality nor the site of first recurrence showed any impact on survival. Conclusions: Stage at primary diagnosis correlated with shorter time to the development of BM, while age at diagnosis was associated with shorter survival in BCBM. None of the other clinical factors had influence on survival.

Download Full-text

The survival rate of tuberculosis patients in HIV-treated cohort of 2008-2018 in Almaty, Kazakhstan

The Journal of Infection in Developing Countries ◽

10.3855/jidc.11955 ◽

2020 ◽

Vol 14 (11.1) ◽

pp. 116S-121S

Author(s):

Ainur Zhandybayeva ◽

Nune Truzyan ◽

Elina Shahumyan ◽

Aizat Kulzhabaeva ◽

Zhamilya Nugmanova ◽

...

Keyword(s):

Regression Model ◽

Treatment Outcomes ◽

Proportional Hazards ◽

Cox Regression ◽

Survival Function ◽

Cox Proportional Hazards ◽

Cd4 Count ◽

Medical Decision ◽

Life Duration ◽

Tb Treatment

Introduction: HIV/TB comorbidity is responsible for 1.6 million deaths worldwide. HIV/TB control and patients’ survival are still among priorities of the national HIV and TB programs. We aimed to evaluate the HIV/TB survival in connection with TB treatment outcomes and factors influencing life duration of the cohort 2008-2018 in Almaty, Kazakhstan. Methodology: This retrospective cohort study extracted data for all HIV and pulmonary TB adults coinfected during 2008-2018 in Almaty from national registries to apply descriptive, Kaplan-Meier estimation, and Cox proportional hazards regression model. Survival function for the TB treatment outcomes and factors predicting the probability of survival were tested and described. Results: The cohort population (n = 521) mean age was 37.4 years with 405 (77.7%) males and 210 (40.3%) marrieds. More than one TB treatment had 181 (34.7%) patients, 291 (55.9%) were smear-positive (SS+), and 423 (81.2%) were on antiretroviral therapy with mean CD4 count 254.22cells/µL. Probability to live longer was higher (128 versus 37 months, p = 0.003; 95% confidence interval (CI) 71.65, 184.35) for those who succeeded in TB treatment compared to “lost to follow-up” and “failed” treatment outcomes. Adjusted Cox regression model death hazard showed association with missing ART treatment (HR: 1.699, 95%CI 1.164, 2.481, p = 0.006) and having CD4 count < 499 (HR 2.398, 95%CI 1.191, 4.830, p < 0.014). Conclusion: TB treatment outcomes, ART treatment, and the CD4 count of HIV/TB coinfected population substantially influence their life duration. The medical decision- and policy-makers should take this into consideration when implementing targeted improvements in the national HIV and TB programs.

Download Full-text

Characteristics, outcomes and risk factors for mortality of 522 167 patients hospitalised with COVID-19 in Brazil: a retrospective cohort study

BMJ Open ◽

10.1136/bmjopen-2021-049089 ◽

2021 ◽

Vol 11 (5) ◽

pp. e049089

Author(s):

Marcia C Castro ◽

Susie Gurzenda ◽

Eduardo Marques Macário ◽

Giovanny Vinícius A França

Keyword(s):

Risk Factors ◽

Cohort Study ◽

Retrospective Cohort Study ◽

Retrospective Cohort ◽

Cox Proportional Hazards ◽

Secondary Outcome ◽

Hospital Death ◽

Factors Associated ◽

Icu Admission ◽

Hospitalised Patients

ObjectiveTo provide a comprehensive description of demographic, clinical and radiographic characteristics; treatment and case outcomes; and risk factors associated with in-hospital death of patients hospitalised with COVID-19 in Brazil.DesignRetrospective cohort study of hospitalised patients diagnosed with COVID-19.SettingData from all hospitals across Brazil.Participants522 167 hospitalised patients in Brazil by 14 December 2020 with severe acute respiratory illness, and a confirmed diagnosis for COVID-19.Primary and secondary outcome measuresPrevalence of symptoms and comorbidities was compared by clinical outcomes and intensive care unit (ICU) admission status. Survival was assessed using Kaplan Meier survival estimates. Risk factors associated with in-hospital death were evaluated with multivariable Cox proportional hazards regression.ResultsOf the 522 167 patients included in this study, 56.7% were discharged, 0.002% died of other causes, 30.7% died of causes associated with COVID-19 and 10.2% remained hospitalised. The median age of patients was 61 years (IQR, 47–73), and of non-survivors 71 years (IQR, 60–80); 292 570 patients (56.0%) were men. At least one comorbidity was present in 64.5% of patients and in 76.8% of non-survivors. From illness onset, the median times to hospital and ICU admission were 6 days (IQR, 3–9) and 7 days (IQR, 3–10), respectively; 15 days (IQR, 9–24) to death and 15 days (IQR, 11–20) to hospital discharge. Risk factors for in-hospital death included old age, Black/Brown ethnoracial self-classification, ICU admission, being male, living in the North and Northeast regions and various comorbidities. Age had the highest HRs of 5.51 (95% CI: 4.91 to 6.18) for patients≥80, compared with those ≤20.ConclusionsCharacteristics of patients and risk factors for in-hospital mortality highlight inequities of COVID-19 outcomes in Brazil. As the pandemic continues to unfold, targeted policies that address those inequities are needed to mitigate the unequal burden of COVID-19.

Download Full-text

Testing the proportional hazards assumption in cox regression and dealing with possible non-proportionality in total joint arthroplasty research: methodological perspectives and review

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-04379-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ilari Kuitunen ◽

Ville T. Ponkilainen ◽

Mikko M. Uimonen ◽

Antti Eskelinen ◽

Aleksi Reito

Keyword(s):

Survival Analysis ◽

Regression Model ◽

Total Joint Arthroplasty ◽

Full Text ◽

Proportional Hazards ◽

Cox Regression ◽

Joint Arthroplasty ◽

Central Research ◽

Cox Regression Model ◽

Pubmed Database

Abstract Background Survival analysis and effect of covariates on survival time is a central research interest. Cox proportional hazards regression remains as a gold standard in the survival analysis. The Cox model relies on the assumption of proportional hazards (PH) across different covariates. PH assumptions should be assessed and handled if violated. Our aim was to investigate the reporting of the Cox regression model details and testing of the PH assumption in survival analysis in total joint arthroplasty (TJA) studies. Methods We conducted a review in the PubMed database on 28th August 2019. A total of 1154 studies were identified. The abstracts of these studies were screened for words “cox and “hazard*” and if either was found the abstract was read. The abstract had to fulfill the following criteria to be included in the full-text phase: topic was knee or hip TJA surgery; survival analysis was used, and hazard ratio reported. If all the presented criteria were met, the full-text version of the article was then read. The full-text was included if Cox method was used to analyze TJA survival. After accessing the full-texts 318 articles were included in final analysis. Results The PH assumption was mentioned in 114 of the included studies (36%). KM analysis was used in 281 (88%) studies and the KM curves were presented graphically in 243 of these (87%). In 110 (45%) studies, the KM survival curves crossed in at least one of the presented figures. The most common way to test the PH assumption was to inspect the log-minus-log plots (n = 59). The time-axis division method was the most used corrected model (n = 30) in cox analysis. Of the 318 included studies only 63 (20%) met the following criteria: PH assumption mentioned, PH assumption tested, testing method of the PH assumption named, the result of the testing mentioned, and the Cox regression model corrected, if required. Conclusions Reporting and testing of the PH assumption and dealing with non-proportionality in hip and knee TJA studies was limited. More awareness and education regarding the assumptions behind the used statistical models among researchers, reviewers and editors are needed to improve the quality of TJA research. This could be achieved by better collaboration with methodologists and statisticians and introducing more specific reporting guidelines for TJA studies. Neglecting obvious non-proportionality undermines the overall research efforts since causes of non-proportionality, such as possible underlying pathomechanisms, are not considered and discussed.

Download Full-text