Effect of fluid administration on scene to traffic accident patients by EMS personnel: a propensity score-matched study using population-based ambulance records and nationwide trauma registry in Japan

Abstract Purpose The aim of this study was to assess the effect of fluid administration by emergency life-saving technicians (ELST) on the prognosis of traffic accident patients by using a propensity score (PS)-matching method. Methods The study included traffic accident patients registered in the JTDB database from January 2016 to December 2017. The main outcome was hospital mortality, and the secondary outcome was cardiopulmonary arrest on hospital arrival (CPAOA). To reduce potential confounding effects in the comparisons between two groups, we estimated a propensity score (PS) by fitting a logistic regression model that was adjusted for 17 variables before the implementation of fluid administration by ELST at the scene. Results During the study period, 10,908 traffic accident patients were registered in the JTDB database, and we included 3502 patients in this study. Of these patients, 142 were administered fluid by ELST and 3360 were not administered fluid by ELST. After PS matching, 141 patients were selected from each group. In the PS-matched model, fluid administration by ELST at the scene was not associated with discharge to death (crude OR: 0.859 [95% CI, 0.500–1.475]; p = 0.582). However, the fluid group showed statistically better outcome for CPAOA than the no fluid group in the multiple logistic regression model (adjusted OR: 0.231 [95% CI, 0.055–0.967]; p = 0.045). Conclusion In this study, fluid administration to traffic accident patients by ELST was associated not with hospital mortality but with a lower proportion of CPAOA.

Download Full-text

Attributable Cost of Clostridium difficile Infection in Pediatric Patients

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2017.240 ◽

2017 ◽

Vol 38 (12) ◽

pp. 1472-1477 ◽

Cited By ~ 6

Author(s):

Preeti Mehrotra ◽

Jisun Jang ◽

Courtney Gidengil ◽

Thomas J. Sandora

Keyword(s):

Logistic Regression ◽

Clostridium Difficile ◽

Propensity Score ◽

Regression Model ◽

Generalized Linear Model ◽

Logistic Regression Model ◽

Propensity Scores ◽

Healthcare Research ◽

Design And Methods ◽

Inpatient Database

OBJECTIVESThe attributable cost of Clostridium difficile infection (CDI) in children is unknown. We sought to determine a national estimate of attributable cost and length of stay (LOS) of CDI occurring during hospitalization in children.DESIGN AND METHODSWe analyzed discharge records of patients between 2 and 18 years of age from the Agency for Healthcare Research and Quality (AHRQ) Kids’ Inpatient Database. We created a logistic regression model to predict CDI during hospitalization based on demographic and clinical characteristics. Predicted probabilities from the logistic regression model were then used as propensity scores to match 1:2 CDI to non-CDI cases. Charges were converted to costs and compared between patients with CDI and propensity-score–matched controls. In a sensitivity analysis, we adjusted for LOS as a confounder by including it in both the propensity score and a generalized linear model predicting cost.RESULTSWe identified 8,527 pediatric hospitalizations (0.53%) with a diagnosis of CDI and 1,597,513 discharges without CDI. In our matched cohorts, the attributable cost of CDI occurring during a hospitalization ranged from $1,917 to $8,317, depending on whether model was adjusted for LOS. When not adjusting for LOS, CDI-associated hospitalizations cost 1.6 times more than non-CDI associated hospitalizations. Attributable LOS of CDI was approximately 4 days.CONCLUSIONSClostridium difficile infection in hospitalized children is associated with an economic burden similar to adult estimates. This finding supports a continued focus on preventing CDI in children as a priority. Pediatric CDI cost analyses should account for LOS as an important confounder of cost.Infect Control Hosp Epidemiol 2017;38:1472–1477

Download Full-text

Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

Frontiers in Pediatrics ◽

10.3389/fped.2021.745204 ◽

2021 ◽

Vol 9 ◽

Author(s):

Huabin Wang ◽

Zhongyuan He ◽

Jiahong Li ◽

Chao Lin ◽

Huan Li ◽

...

Keyword(s):

Logistic Regression ◽

Intensive Care ◽

Hospital Mortality ◽

Regression Model ◽

Clinical Outcomes ◽

Poor Prognosis ◽

Logistic Regression Model ◽

Plasma Osmolality ◽

Pediatric Intensive Care ◽

Linear Spline

Objective: Identifying high-risk children with a poor prognosis in pediatric intensive care units (PICUs) is critical. The aim of this study was to assess the predictive value of early plasma osmolality levels in determining the clinical outcomes of children in PICUs.Methods: We retrospectively assessed critically ill children in a pediatric intensive care database. The locally weighted-regression scatter-plot smoothing (LOWESS) method was used to explore the approximate relationship between plasma osmolality and in-hospital mortality. Linear spline functions and stepwise expansion models were applied in conjunction with a multivariate logistic regression to further analyze this relationship. A subgroup analysis by age and complications was performed.Results: In total, 5,620 pediatric patients were included in this study. An approximately “U”-shaped relationship between plasma osmolality and mortality was detected using LOWESS. In the logistic regression model using a linear spline function, plasma osmolality ≥ 290 mmol/L was significantly associated with in-hospital mortality [odds ratio (OR) 1.020, 95% confidence interval (CI) 1.010–1.031], while plasma osmolality <290 mmol/L was not significantly associated with in-hospital mortality (OR 0.990, 95% CI 0.966–1.014). In the logistic regression model with plasma osmolality as a tri-categorical variable, only high osmolality was significantly associated with in-hospital mortality (OR 1.90, 95% CI 1.38–2.64), whereas low osmolality was not associated with in-hospital mortality (OR 1.28, 95% CI 0.84–1.94). The interactions between plasma osmolality and age or complications were not significant.Conclusion: High osmolality, rather than low osmolality, can predict a poor prognosis in children in PICUs.

Download Full-text

Machine-Learning vs. Expert-Opinion Driven Logistic Regression Modelling for Predicting 30-Day Unplanned Rehospitalisation in Preterm Babies: A Prospective, Population-Based Study (EPIPAGE 2)

Frontiers in Pediatrics ◽

10.3389/fped.2020.585868 ◽

2021 ◽

Vol 8 ◽

Author(s):

Robert A. Reed ◽

Andrei S. Morgan ◽

Jennifer Zeitlin ◽

Pierre-Henri Jarreau ◽

Héloïse Torchin ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Random Forest ◽

Regression Model ◽

Expert Opinion ◽

Logistic Regression Model ◽

Population Based ◽

Regression Modelling ◽

Preterm Babies ◽

Logistic Regression Modelling

Introduction: Preterm babies are a vulnerable population that experience significant short and long-term morbidity. Rehospitalisations constitute an important, potentially modifiable adverse event in this population. Improving the ability of clinicians to identify those patients at the greatest risk of rehospitalisation has the potential to improve outcomes and reduce costs. Machine-learning algorithms can provide potentially advantageous methods of prediction compared to conventional approaches like logistic regression.Objective: To compare two machine-learning methods (least absolute shrinkage and selection operator (LASSO) and random forest) to expert-opinion driven logistic regression modelling for predicting unplanned rehospitalisation within 30 days in a large French cohort of preterm babies.Design, Setting and Participants: This study used data derived exclusively from the population-based prospective cohort study of French preterm babies, EPIPAGE 2. Only those babies discharged home alive and whose parents completed the 1-year survey were eligible for inclusion in our study. All predictive models used a binary outcome, denoting a baby's status for an unplanned rehospitalisation within 30 days of discharge. Predictors included those quantifying clinical, treatment, maternal and socio-demographic factors. The predictive abilities of models constructed using LASSO and random forest algorithms were compared with a traditional logistic regression model. The logistic regression model comprised 10 predictors, selected by expert clinicians, while the LASSO and random forest included 75 predictors. Performance measures were derived using 10-fold cross-validation. Performance was quantified using area under the receiver operator characteristic curve, sensitivity, specificity, Tjur's coefficient of determination and calibration measures.Results: The rate of 30-day unplanned rehospitalisation in the eligible population used to construct the models was 9.1% (95% CI 8.2–10.1) (350/3,841). The random forest model demonstrated both an improved AUROC (0.65; 95% CI 0.59–0.7; p = 0.03) and specificity vs. logistic regression (AUROC 0.57; 95% CI 0.51–0.62, p = 0.04). The LASSO performed similarly (AUROC 0.59; 95% CI 0.53–0.65; p = 0.68) to logistic regression.Conclusions: Compared to an expert-specified logistic regression model, random forest offered improved prediction of 30-day unplanned rehospitalisation in preterm babies. However, all models offered relatively low levels of predictive ability, regardless of modelling method.

Download Full-text

Association between Maternal Pre-pregnancy Body Mass Index and Breastfeeding Duration in Taiwan: A Population-Based Cohort Study

Nutrients ◽

10.3390/nu12082361 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2361

Author(s):

Chi-Nien Chen ◽

Hung-Chen Yu ◽

An-Kuo Chou

Keyword(s):

Body Mass Index ◽

Logistic Regression ◽

Regression Model ◽

Body Mass ◽

Logistic Regression Model ◽

Maternal Obesity ◽

Population Based ◽

Normal Weight ◽

Breastfeeding Duration ◽

Breastfeeding Cessation

An association between high pre-pregnancy body mass index (BMI) and early breastfeeding cessation has been previously observed, but studies examining the effect of underweight are still scant and remain inconclusive. This study analyzed data from a nationally representative cohort of 18,312 women (mean age 28.3 years; underweight 20.1%; overweight 8.2%; obesity 1.9%) who delivered singleton live births in 2005 in Taiwan. Comprehensive face-to-face interviews and surveys were completed at 6 and 18 months postpartum. BMI status and breastfeeding duration were calculated from the self-reported data in the questionnaires. In the adjusted ordinal logistic regression model, maternal obesity and underweight had a higher odds of shorter breastfeeding duration compared with normal-weight women. The risk of breastfeeding cessation was significantly higher in underweight women than in normal-weight women after adjustments in the logistic regression model (2 m: aOR = 1.11, 95% CI = 1.03–1.2; 4 m: aOR = 1.32, 95% CI = 1.21–1.43; 6 m: aOR = 1.3, 95% CI = 1.18–1.42). Our findings indicated that maternal underweight and obesity are associated with earlier breastfeeding cessation in Taiwan. Optimizing maternal BMI during the pre-conception period is essential, and future interventions to promote and support breastfeeding in underweight mothers are necessary to improve maternal and child health.

Download Full-text

Association of time-to-treatment with outcomes of Pneumocystis pneumonia with respiratory failure in HIV-negative patients

Respiratory Research ◽

10.1186/s12931-019-1188-6 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 2

Author(s):

Ryoung-Eun Ko ◽

Soo Jin Na ◽

Kyungmin Huh ◽

Gee Young Suh ◽

Kyeongman Jeon

Keyword(s):

Logistic Regression ◽

Respiratory Failure ◽

Hospital Mortality ◽

Regression Model ◽

Clinical Outcomes ◽

Logistic Regression Model ◽

Pneumocystis Pneumonia ◽

Definitive Treatment ◽

Hypoxemic Respiratory Failure ◽

Hiv Negative

Abstract Background The prevalence of pneumocystis pneumonia (PCP) and associated hypoxic respiratory failure is increasing in human immunodeficiency virus (HIV)-negative patients. However, no prior studies have evaluated the effect of early anti-PCP treatment on clinical outcomes in HIV-negative patient with severe PCP. Therefore, this study investigated the association between the time to anti-PCP treatment and the clinical outcomes in HIV-negative patients with PCP who presented with hypoxemic respiratory failure. Methods A retrospective observational study was performed involving 51 HIV-negative patients with PCP who presented in respiratory failure and were admitted to the intensive care unit between October 2005 and July 2018. A logistic regression model was used to adjust for potential confounding factors in the association between the time to anti-PCP treatment and in-hospital mortality. Results All patients were treated with appropriate anti-PCP treatment, primarily involving trimethoprim/sulfamethoxazole. The median time to anti-PCP treatment was 58.0 (28.0–97.8) hours. Thirty-one (60.8%) patients were treated empirically prior to confirmation of the microbiological diagnosis. However, the hospital mortality rates were not associated with increasing quartiles of time until anti-PCP treatment (P = 0.818, test for trend). In addition, hospital mortality of patients received early empiric treatment was not better than those of patients received definitive treatment after microbiologic diagnosis (48.4% vs. 40.0%, P = 0.765). In a multiple logistic regression model, the time to anti-PCP treatment was not associated with increased mortality. However, age (adjusted OR 1.07, 95% CI 1.01–1.14) and failure to initial treatment (adjusted OR 13.03, 95% CI 2.34–72.65) were independently associated with increased mortality. Conclusions There was no association between the time to anti-PCP treatment and treatment outcomes in HIV-negative patients with PCP who presented in hypoxemic respiratory failure.

Download Full-text

Glaucoma may be a non-memory manifestation of Alzheimer's disease in older people

International Psychogeriatrics ◽

10.1017/s1041610217000801 ◽

2017 ◽

Vol 29 (9) ◽

pp. 1535-1541 ◽

Cited By ~ 12

Author(s):

Shih-Wei Lai ◽

Cheng-Li Lin ◽

Kuan-Fu Liao

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Logistic Regression ◽

Older People ◽

Regression Model ◽

Logistic Regression Model ◽

Population Based ◽

Multivariable Logistic Regression Model ◽

Retrospective Case ◽

Taiwan National Health Insurance

ABSTRACTBackground:The purpose of this paper was to examine whether glaucoma could be a non-memory manifestation of Alzheimer's disease in older people.Methods:We conducted a population-based, retrospective, case-control study to analyze the database of the Taiwan National Health Insurance Program. There were 1,351 subjects ≥65 years old with newly diagnosed Alzheimer's disease as the cases, and 5,329 subjects without any type of dementias as the controls during 2000–2011. The odds ratio (OR) and 95% confidence interval (CI) for the risk of Alzheimer's disease associated with glaucoma was estimated by the multivariable unconditional logistic regression model.Results:After controlling for confounders, the multivariable logistic regression model demonstrated that the adjusted OR of Alzheimer's disease was 1.50 in subjects with glaucoma (95% CI 1.19, 1.89), compared to subjects without glaucoma.Conclusions:Older people with glaucoma are associated with 1.5-fold increased odds of Alzheimer's disease in Taiwan. Glaucoma may be a non-memory manifestation of Alzheimer's disease in older people. Further research is needed to confirm this issue.

Download Full-text

COVID-19 associated hospitalization in 571 patients with fibromyalgia—A population-based study

PLoS ONE ◽

10.1371/journal.pone.0261772 ◽

2021 ◽

Vol 16 (12) ◽

pp. e0261772

Author(s):

Mor Amital ◽

Niv Ben-Shabat ◽

Howard Amital ◽

Dan Buskila ◽

Arnon D. Cohen ◽

...

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Multivariable Analysis ◽

Population Based ◽

Female Dominance ◽

Categorical Variables ◽

Continuous Variables ◽

Multivariable Logistic Regression Model ◽

Population Based Study

Objective To identify predicators of patients with fibromyalgia (FM) that are associated with a severe COVID-19 disease course. Methods We utilized the data base of the Clalit Health Services (CHS); the largest public organization in Israel, and extracted data concerning patients with FM. We matched two subjects without FM to each subject with FM by sex and age and geographic location. Baseline characteristics were evaluated by t-test for continuous variables and chi-square for categorical variables. Predictors of COVID-19 associated hospitalization were identified using univariable logistic regression model, significant variables were selected and analyzed by a multivariable logistic regression model. Results The initial cohort comprised 18,598 patients with FM and 36,985 matched controls. The mean age was 57.5± 14.5(SD), with a female dominance of 91%. Out of this cohort we extracted the study population, which included all patients contracted with COVID-19, and consisted of 571 patients with FM and 1008 controls. By multivariable analysis, the following variables were found to predict COVID-19 associated hospitalization in patients with FM: older age (OR, 1.25; CI, 1.13–1.39; p<0.001), male sex (OR, 2.63; CI, 1.18–5.88; p<0.05) and hypertension (OR, 1.75; CI, 1.04–2.95; p<0.05). Conclusion The current population-based study revealed that FM per se was not directly associated with COVID-19 hospitalization or related mortality. Yet classical risk factors endangering the general population were also relevant among patients with FM.

Download Full-text

A machine learning-based prediction of hospital mortality in patients with postoperative sepsis

10.21203/rs.2.24188/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ren-qi Yao ◽

Xin Jin ◽

Guo-wei Wang ◽

Yue Yu ◽

Guo-sheng Wu ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Hospital Mortality ◽

Regression Model ◽

Logistic Regression Model ◽

Gradient Boosting ◽

Stepwise Logistic Regression ◽

Postoperative Sepsis ◽

Extreme Gradient Boosting ◽

Stepwise Logistic Regression Model

Abstract Background: The incidence of postoperative sepsis is continually increased, while few studies have specifically focused on the risk factors and clinical outcomes associated with the development of sepsis after surgical procedures. The present study aimed to develop a mathematical model for predicting the in-hospital mortality among patients with postoperative sepsis.Methods: Surgical patients in Medical Information Mart for Intensive Care (MIMIC-III) database who simultaneously fulfilled Sepsis 3.0 as well as Agency for Healthcare Research and Quality (AHRQ) criteria during ICU admission were incorporated. We employed both extreme gradient boosting (XGBoost) and stepwise logistic regression model to predict in-hospital mortality among included patients with postoperative sepsis. Consequently, model performance was assessed from the angles of discrimination and calibration.Results: We included 3713 patients who fulfilled our inclusion criteria, in which 397 (10.7%) patients died during hospitalization, while 3316 (89.3%) of them survived through discharge. Fluid-electrolyte disturbance, coagulopathy, renal replacement therapy (RRT), urine output, and cardiovascular surgery were important features related to the in-hospital mortality. The XGBoost model had a better performance in both discriminatory ability (c-statistics, 0.835 [95% CI, 0.786 to 0.877] vs. c-statistics, 0.737 [95% CI, 0.688 to 0.786]) and goodness of fit (visualized by calibration curve) compared to the stepwise logistic regression model. Conclusion: XGBoost model appears to be a better performance in predicting hospital mortality among postoperative septic patients compared to the conventional stepwise logistic regression model. Machine learning-based algorithm might have significant application in the development of early warning system for septic patients following major operations.

Download Full-text