scholarly journals Epidemiology and outcomes of bloodstream infections in severe burn patients: a six-year retrospective study

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yangmin Hu ◽  
Danyang Li ◽  
Lingcheng Xu ◽  
Yuping Hu ◽  
Yiwen Sang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Cox Regression ◽  
Total Body Surface Area ◽  
Hospital Length Of Stay ◽  
Burn Patients ◽  
Severe Burn ◽  
Multivariate Logistic Regression ◽  
Cox Regression Model

Abstract Background Infection is the leading cause of morbidity and mortality among burn patients, and bloodstream infection (BSI) is the most serious. This study aimed to evaluate the epidemiology and clinical outcomes of BSI in severe burn patients. Methods Clinical variables of all patients admitted with severe burns (≥ 20% total body surface area, %TBSA) were analyzed retrospectively from January 2013 to December 2018 at a teaching hospital. The Kaplan–Meier method was utilized for plotting survival curves. Multivariate logistic regression and Cox regression model were also performed. Results A total of 495 patients were evaluated, of whom 136 (27.5%) had a BSI. The median time from the patients being burned to BSI was 8 days. For BSI onset in these patients, 47.8% (65/136) occurred in the first week. The most frequently isolated causative organism was A. baumannii (22.7%), followed by methicillin-resistant Staphylococcus aureus (18.7%) and K. pneumoniae (18.2%), in patients with BSI. Multivariate logistic regression analysis showed that %TBSA (p = 0.023), mechanical ventilation (p = 0.019), central venous catheter (CVC) (p < 0.001) and hospital length of stay (27d vs 50d, p < 0.001) were independent risk factors associated with BSI. Cox regression model showed that acute kidney injury (HR, 12.26; 95% CI 2.31–64.98; p = 0.003) and septic shock (HR, 4.36; 95% CI 1.16–16.34; p = 0.031) were identified as independent predictors of 30-day mortality of BSI in burn patients. Conclusions Multidrug resistant gram-negative bacteria were the main pathogens of BSI in severe burn patients. Accurate evaluation of risk factors for BSI and the mortality of BSI in severe burn patients may improve early appropriate management.

scholarly journals Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anping Guo ◽  
Jin Lu ◽  
Haizhu Tan ◽  
Zejian Kuang ◽  
Ying Luo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Liver Disease ◽  
Length Of Stay ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospital Length ◽  
Clinical Treatment ◽  
Hospital Length Of Stay ◽  
Female Sex

AbstractTreating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

scholarly journals Longitudinal Risk Factor for Developing Depression in Parkinson's Disease

2021 ◽  
Author(s):  
Tarek Eid Antar ◽  
Huw R Morris ◽  
Faraz Faghri ◽  
Hampton Leonard ◽  
Mike Nalls ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Depressive Symptoms ◽  
Regression Model ◽  
Cox Regression ◽  
Disease Patient ◽  
Daily Activities ◽  
Cox Regression Model ◽  
Antidepressant Use

Background Despite the established importance of identifying depression in Parkinson's disease, our understanding of the factors which place the Parkinson's disease patient at future risk of depression is limited. Methods Our sample consisted of 874 patients from two longitudinal cohorts, PPMI and PDBP, with median follow-up durations of 7 and 3 years respectively. Risk factors for depression at baseline were determined using logistic regression. A Cox regression model was then used to identify baseline factors that predisposed the non-depressed patient to develop depressive symptoms that were sustained for at least one year, while adjusting for antidepressant use and cognitive impairment. Common predictors between the two cohorts were identified with a random-effects meta-analysis. Results We found in our analyses that the majority of baseline non-depressed patients would develop sustained depressive symptoms at least once during the course of the study. Probable REM sleep disorder (pRBD), age, duration of diagnosis, impairment in daily activities, mild constipation, and antidepressant use were among the baseline risk factors for depression in either cohort. Our Cox regression model indicated that pRBD, impairment in daily activities, hyposmia, and mild constipation could serve as longitudinal predictors of sustained depressive symptoms. Conclusions We identified several potential risk factors to aid physicians in the early detection of depression in Parkinson's disease patients. Our findings also underline the importance of adjusting for multiple covariates when analyzing risk factors for depression.

scholarly journals C-Reactive Protein-to-Albumin Ratio Predicts Sepsis and Prognosis in Patients with Severe Burn Injury

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yaohua Yu ◽  
Weiwei Wu ◽  
Yanyan Dong ◽  
Jiliang Li
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Multivariate Logistic Regression Analysis ◽  
Severe Burn ◽  
C Reactive Protein ◽  
Multivariate Logistic Regression ◽  
Albumin Ratio ◽  
Reactive Protein ◽  
Severe Burns ◽  
Independent Risk Factors

Background. Sepsis is a leading cause of mortality among severe burns. This study was conducted to investigate the predictive role of C-reactive protein-to-albumin ratio (CAR) for sepsis and prognosis in severe burns. Methods. Patients with severe burn injuries from 2013 to 2017 were enrolled and divided into septic and nonseptic groups based on the presence of sepsis within 30 days postburn. Independent risk factors for sepsis were performed by the univariate and multivariate logistic regression analyses. The association between CAR level at admission and postburn 30-day mortality was designed via the Kaplan–Meier method. Results. Of all the 196 enrolled patients, 83 patients developed sepsis within 30 days postburn injury, with an incidence of 42.3%. TBSA percentage (OR: 1.65, 95% CI: 1.17-2.32, P = 0.014 ) and CAR at admission (OR: 2.25, 95% CI: 1.33-3.56, P = 0.009 ) were the two independent risk factors for sepsis in severe burns by the multivariate logistic regression analysis. A higher CAR level (≥1.66) at admission was associated with a lower postburn 30-day survival rate ( P = 0.005 ). Conclusions. The CAR level at admission was an independent risk factor for sepsis and prognosis in severe burns.

Risk Factors for Wound Infection in Outpatients With Lower Extremity Burns

2020 ◽  
pp. 000313482095238
Author(s):  
Husayn A. Ladhani ◽  
Brian T. Young ◽  
Sarah E. Posillico ◽  
Charles J. Yowler ◽  
Christopher P. Brandt ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lower Extremity ◽  
Wound Infection ◽  
Logistic Regression Analysis ◽  
Total Body Surface Area ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors

Background We sought to evaluate risk factors for wound infection in patients with lower extremity (LE) burn. Methods Adults presenting with LE burn from January 2014 to July 2015 were included. Data regarding demographics, injury characteristics, and outcomes were obtained. The primary outcome was wound infection. Multivariate logistic regression analysis was performed to identify independent risk factors for wound infection. Results 317 patients were included with a mean age of 43 years and median total body surface area of .8%; 22 (7%) patients had a component of full-thickness (FT) burn; and 212 (67%) patients had below-the-knee (BTK) burn. The incidence of wound infection was 15%. The median time to infection was 5 days, and majority (61%) of the patients developed wound infection by day 5. Patients who developed wound infection were more likely to have an FT burn (22% vs. 5%, P < .001) and BTK burn (87% vs. 64%, P = .002), without a difference in other variables. Multivariate logistic regression analysis showed age (Odds ratio (OR) 1.02 and CI 1.00-1.04), presence of FT burn (OR 5.33 and CI 2.09-13.62), and BTK burn (OR 3.42 and CI 1.37-8.52) as independent risk factors for wound infection (area under the curve = .72). Conclusion Age, presence of FT burn, and BTK burn are independent risk factors for wound infection in outpatients with LE burns.

scholarly journals Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 639
Author(s):  
Rolando de la Cruz ◽  
Oslando Padilla ◽  
Mauricio A. Valle ◽  
Gonzalo A. Ruz
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cox Regression ◽  
Bayesian Regression ◽  
Rate Model ◽  
Cure Rate ◽  
Cox Regression Model ◽  
Cure Rate Model ◽  
Random Survival Forest

This study aims to analyze and explore criminal recidivism with different modeling strategies: one based on an explanation of the phenomenon and another based on a prediction task. We compared three common statistical approaches for modeling recidivism: the logistic regression model, the Cox regression model, and the cure rate model. The parameters of these models were estimated from a Bayesian point of view. Additionally, for prediction purposes, we compared the Cox proportional model, a random survival forest, and a deep neural network. To conduct this study, we used a real dataset that corresponds to a cohort of individuals which consisted of men convicted of sexual crimes against women in 1973 in England and Wales. The results show that the logistic regression model tends to give more precise estimations of the probabilities of recidivism both globally and with the subgroups considered, but at the expense of running a model for each moment of the time that is of interest. The cure rate model with a relatively simple distribution, such as Weibull, provides acceptable estimations, and these tend to be better with longer follow-up periods. The Cox regression model can provide the most biased estimations with certain subgroups. The prediction results show the deep neural network’s superiority compared to the Cox proportional model and the random survival forest.

scholarly journals 282. Risk Factors for Mortality in Severe COVID-19 Patients Admitted to the Intensive Care Unit: A Retrospective Single-Center Study in Saudi Arabia

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S246-S247
Author(s):  
Sherif Khattab ◽  
Souad AlMuthree ◽  
Mohamed Bakry ◽  
Noha Ibraheem ◽  
Omar Alghamdi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Logistic Regression ◽  
Regression Model ◽  
Sofa Score ◽  
Logistic Regression Model ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Independent Risk Factors ◽  
Cox Proportional Hazard Regression

Abstract Background The first case of COVID-19 in the Kingdom of Saudi Arabia (KSA) was reported in March 2020. This study aims to describe the overall mortality in the ICU during the COVID-19 pandemic and to determine independent risk factors for overall survival & 29 days mortality. Methods This is a retrospective single-center study; data for adult patients admitted to the ICU with COVID-19 between 1st March 2020 to 31st December 2020 were extracted and reviewed. Overall survival was described using Kaplan-Meier curves with reporting of median overall survival and 29 days survival estimates. Multivariate analysis was performed using Cox proportional hazards model and multivariate logistic regression model. Figure 1. Study flow chart Table 1. Demographic characteristics categorized by Gender Results Eligible subjects were 209 (Figure 1) and subjects demographics are summarized in (Table1). Observed death events were 82 (39.2% of the total cohort), 61% of deaths reached at 2 weeks of ICU stay (n.= 50), median overall survival (OS) was reached at day 23, as shown in (Figure 2). The multivariate Cox proportional hazard regression analysis (Figure 3) showed elevated SOFA score [aHR= 1.10, P &lt; 0.001] and Vasopressors [aHR= 3.23, P= 0.002] as independent risk factors for overall ICU mortality. Independent protective factors were: Systemic corticosteroids use (P= 0.019), Insulin use (P= 0.026) and Liposomal Amphotericin B (LAMB) use (P= 0.019). For mortality at day 29, the multivariate logistic regression model (Figure. 4) showed elevated SOFA score (P= 0.005), any need for ventilation escalation after ICU admission (P= 0.014), Ribavirin use (P=0.016) and Vasopressors use ( P&lt; 0.001) as independent risk factors. Angiotensin-Converting Enzyme inhibitors (ACEi) use was a protective factor (P=0.025). Figure 2. Overall Survival (OS) for patients admitted to the ICU due to COVID-19 - Kaplan Meier (KM) Figure 3. Multivariate Cox proportional hazard regression model for factors associated with overall mortality in patients admitted to the ICU due to COVID-19 Figure 4. Multivariate logistic regression model for factors associated with 29 days mortality in patients admitted to the ICU due to COVID-19 Conclusion SOFA score and vasopressors are independent predictors for overall survival and 29-day mortality in the ICU. The need for ventilation escalation after ICU admission appeared to lead to poor prognosis in regard to 29-day mortality only. Systemic corticosteroids are lifesaving, further studies are required to confirm the observed clinical benefits with insulin, LAMB and ACEi use in the ICU and to investigate any hazardous impact of ribavirin on COVID-19 outcomes. Study limitations Residual confounding of other measured and/or unobserved factors cannot be ruled out. Disclosures Sherif Khattab, BPharm, Gilead Sciences (Employee, Shareholder) Mohamed Bakry, MBBCh, Gilead Sciences (Employee)Roche Pharma (Employee)

scholarly journals Risk Factors of Mortality of Hospitalised Adult Burn Patients a Malawian Tertiary Hospital Burns Unit

2018 ◽  
Author(s):  
Stephen Kasenda ◽  
Donnie Mategula ◽  
Geoffrey Elihu Manda ◽  
Tilinde Keith Chokotho
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Surface Area ◽  
Logistic Regression Model ◽  
Burn Injury ◽  
Univariate Analysis ◽  
Burn Patients ◽  
Risk Of Death ◽  
Burns Unit

ABSTRACTIntroductionMalawi has the highest rates of mortality directly or indirectly associated with burn injuries in Southern Africa. There is however no published literature on risk factors of mortality among adult patients.MethodsWe conducted a retrospective cross sectional audit records of patients admitted at the burns unit of Queen Elizabeth Central Hospital (QECH) between the years 2007 and 2017. Death due to burns was our outcome of interest. We collected patient data including demographic information, details of the burn injury and its management and determined how these factors were associated with the risk of death using Person Chi square tests in a univariate analysis and likelihood ratio tests in a multivariate logistic regression model. We also determined the odds ratios of death within the categories of the risk factors after adjusting for important variables using a logistic regression model.ResultsAn analysis of 500 burns patient records showed that 132(26.4%) died during the 10-year period. The lethal area for 50% of burns (LA50) was 28.75% and mortality reached 100% at 40% total burn surface area. The following variables were found to be significantly associated with mortality after controlling for confounders: scalds (OR 0.13; 95% CI 0.05-0.33; <0.0001), increasing total burn surface area (p<0.0001), time lapse to hospital presentation between 48 hours and one week(OR 0.27; 95%CI 0.11-0.68; <0.0001), inhalation burns (OR 5.2; 95% CI 2.0-13.3 p 0.0004) and length of hospital stay greater than two months (OR 0.04 95, CI 0.01-0.15; P<0.0001).ConclusionsRisk factors for mortality are connected by their association with post-burn hypermetabolism. Further studies to are needed to identify the best and cost-effective ways of preventing death in burn patients.

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