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

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.

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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.

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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

European Journal of Trauma and Emergency Surgery ◽

10.1007/s00068-020-01590-z ◽

2021 ◽

Author(s):

Yusuke Katayama ◽

Tetsuhisa Kitamura ◽

Kosuke Kiyohara ◽

Kenichiro Ishida ◽

Tomoya Hirose ◽

...

Keyword(s):

Logistic Regression ◽

Propensity Score ◽

Hospital Mortality ◽

Regression Model ◽

Traffic Accident ◽

Logistic Regression Model ◽

Cardiopulmonary Arrest ◽

Population Based ◽

Fluid Administration ◽

Secondary Outcome

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.

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Prognostic CT Features and Prediction Model of Patients With Primary Hepatocellular Carcinomas Undergoing Partial Hepatectomy

10.21203/rs.3.rs-949721/v1 ◽

2021 ◽

Author(s):

Cuiping Zhou ◽

Xiaohua Ban ◽

Huijun Hu ◽

Qiuxia Yang ◽

Rong Zhang ◽

...

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Clinical Outcomes ◽

Partial Hepatectomy ◽

Logistic Regression Model ◽

Univariate Analysis ◽

Binary Logistic Regression ◽

Roc Curve Analysis ◽

Hepatocellular Carcinomas ◽

Ct Features

Abstract Background: Hepatocellular carcinoma (HCC) is the most common primary malignant tumor in the liver. Partial hepatectomy is one of the most effective therapies for HCC but suffer from the high recurrence rate. At present, the studies of association between clinical outcomes and CT features of patients with HCCs undergoing partial hepatectomy are still limited. The purpose of this study is to determine the predictive CT features and establish a model for predicting relapse or metastasis in patients with primary hepatocellular carcinomas (HCCs) undergoing partial hepatectomy.Methods: The clinical data and CT features of 112 patients with histopathologically confirmed primary HCCs were retrospectively reviewed. The clinical outcomes were categorized into two groups according to whether relapse or metastasis occurred within 2 years after partial hepatectomy. The association between clinical outcomes and CT features including tumour size, margin, shape, vascular invasion (VI), arterial phase hyperenhancement, washout appearance, capsule appearance, satellite lesion, involvement segment, cirrhosis, peritumoral enhancement and necrosis was analyzed using univariate analysis and binary logistic regression. Then establish logistic regression model, followed by receiver operating characteristic (ROC) curve analysis.Results: CT features including tumor size, margin, shape, VI, washout appearance, satellite lesion, involvement segment, peritumoral enhancement and necrosis were associated with clinical outcomes, as determined by univariate analysis (P<0.05). Only tumor margin and VI remained independent risk factors in binary logistic regression analysis (OR=6.41 and 10.92 respectively). The logistic regression model was logit(p)=-1.55+1.86 margin +2.39 VI. ROC curve analysis showed that the area under curve of the obtained logistic regression model was 0.887(95% CI：0.827-0.947).Conclusion: Patients with ill-defined margin or VI of HCCs were independent risk predictors of poor clinical outcome after partial hepatectomy. The model as logit(p)= -1.55+1.86 margin +2.39 VI was a good predictor of the clinical outcomes.

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Perception of the Futile Care and its Relationship With Moral Distress in Nurses of Intensive Care Units

Journal of Holistic Nursing and Midwifery ◽

10.32598/jhnm.31.2.2119 ◽

2021 ◽

Vol 31 (2) ◽

pp. 85-92

Author(s):

Somayeh Moaddaby ◽

◽

Masoomeh Adib ◽

Sadra Ashrafi ◽

Ehsan Kazemnezhad Leili ◽

...

Keyword(s):

Logistic Regression ◽

Intensive Care ◽

Regression Model ◽

Intensive Care Units ◽

Work Experience ◽

Logistic Regression Model ◽

Moral Distress ◽

Cross Sectional ◽

Futile Care ◽

The Mean

Introduction: The development of science and technology has provided more opportunities for patients to live and even receiving futile medical care or treatment with no hope of recovery. This process leads to awkward experiences and moral distress in nurses who frequently deliver with such care. Objective: This study aimed to determine the perception of futile care and its relationship with moral distress in nurses working in intensive care units Materials and Methods: This is a cross-sectional study conducted on 155 nurses working in Intensive Care Units (ICUs) employed in educational-therapeutic centers and hospitals of Guilan Province, Iran. They were selected by convenience sampling method. The study data were collected using the researcher-made questionnaire and Corley moral distress questionnaire. The obtained data were analyzed using descriptive statistics and inferential statistics the Kolmogorov-Smirnov test, nonparametric Mann-Whitney U, Kruskal-Wallis, Fisher exact and Backward logistic regression model. Results: The mean±SD age of the samples was 34.71±6.68 years; their mean±SD work experience was 10.24±5.63 years, and the mean±SD work experience in the ICU was 6.76±4.64 years. The results indicated that their mean±SD perception of futile care was 63±7, and their mean±SD moral distress was 92±54. The score of moral distress showed a low but significant and positive correlation with the legal and organizational aspects of futile care (r=0. 279, P=0.001) and the total score of perception futile care (r=0.2, P=0.012). In the multivariate analysis based on the logistic regression model of futile care, only the relationship between the legal and organizational score in care had a significant relationship with moral distress. So that by increasing one unit in the legal and organizational aspect of care, the chances of scoring above the mean of moral distress increases 1.2 times (P=0.0001, 95% CI; 1.077-1.324). Conclusion: Perhaps by familiarizing nurses with the legal and organizational nature of patient’s care, the moral distress of caring can be reduced.

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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.

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Two-stage prediction model for in-hospital mortality of patients with influenza infection

BMC Infectious Diseases ◽

10.1186/s12879-021-06169-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Chan-Wa Cheong ◽

Chien-Lin Chen ◽

Chih-Huang Li ◽

Chen-June Seak ◽

Hsiao-Jung Tseng ◽

...

Keyword(s):

Logistic Regression ◽

Critical Care ◽

Critical Illness ◽

Hospital Mortality ◽

Prediction Model ◽

Regression Model ◽

Logistic Regression Model ◽

Stage I ◽

Multiple Logistic Regression ◽

Two Stage

Abstract Background Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality. Methods This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model. Results In the present study, 1680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model’s S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively. Conclusions The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.

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The impact of early neuraminidase inhibitor therapy on clinical outcomes in patients hospitalised with influenza A-related pneumonia: a multicenter, retrospective study

10.21203/rs.2.20901/v3 ◽

2020 ◽

Author(s):

Liang Chen ◽

Xiudi Han ◽

YanLi Li ◽

Chunxiao Zhang ◽

Xiqian Xing

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Clinical Outcomes ◽

Influenza A ◽

Logistic Regression Model ◽

Neuraminidase Inhibitor ◽

Multivariate Logistic Regression Model ◽

Multivariate Logistic Regression ◽

Severe Influenza ◽

The Impact

Abstract Background Guidelines emphasize prompt antiviral treatment in severe influenza patients. Although nearly a 50% of severe influenza present with pneumonia, the effect of early (≤ 2 days after illness onset) neuraminidase inhibitor (NAI) use on the clinical outcomes of influenza A-related pneumonia (FluA-p) has rarely been assessed. Furthermore, data about the administration of NAIs in the real-world management of Flu-p in China are limited.Methods: Data of patients hospitalised with FluA-p from five teaching hospitals in China from 1 January 2013 to 31 December 2018 were reviewed retrospectively. The impact of early NAI therapy on the outcomes in FluA-p patients, and the indications of early NAI administration by clinicians were evaluated by logistic regression analysis.Results: In total, 693 FluA-p patients were included. Of these patients, 33.5% (232/693) were treated early. After adjusting for weighted propensity scores for treatment, systemic corticosteroid and antibiotic use, a multivariate logistic regression model showed that early NAI therapy was associated with decreased risk for invasive ventilation [odds ratio (OR) 0.511, 95% confidence interval (CI) 0.312–0.835, p = 0.007) and 30-day mortality (OR 0.533, 95% CI 0.210–0.807, p < 0.001) in FluA-p patients. A multivariate logistic regression model confirmed early NAI use (OR 0.415, 95% CI 0.195–0.858, p = 0.001) was a predictor for 30-day mortality in FluA-p patients and a positive rapid influenza diagnostic test was the only indication (OR 3.586, 95% CI 1.259–10.219, p < 0.001) related to the prescription of early NAI by clinicians.Conclusions: Early NAI therapy is associated with better outcomes in FluA-p patients. Improved education and training of clinicians on the guidelines of influenza are needed.

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Two-stage Prediction Model for in-hospital Mortality of Patients With Influenza Infection

10.21203/rs.3.rs-146599/v1 ◽

2021 ◽

Author(s):

Chan-Wa Cheong ◽

Chien-Lin Chen ◽

Chih-Huang Li ◽

Chen-June Seak ◽

Hsiao-Jung Tseng ◽

...

Keyword(s):

Logistic Regression ◽

Critical Care ◽

Critical Illness ◽

Hospital Mortality ◽

Prediction Model ◽

Regression Model ◽

Logistic Regression Model ◽

Stage I ◽

Multiple Logistic Regression ◽

Two Stage

Abstract Background: Infleunza is a challenging issue in public health. The mortality and morbidity associated with epidemic and pandemic influenza puts a heavy burden on health care system. Most patients with influenza can be treated on an outpatient basis but some required critical care. It is crucial for frontline physicians to stratify influenza patients by level of risk. Therefore, this study aimed to create a prediction model for critical care and in-hospital mortality.Methods: This retrospective cohort study extracted data from the Chang Gung Research Database. This study included the patients who were diagnosed with influenza between 2010 and 2016. The primary outcome of this study was critical illness. The secondary analysis was to predict in-hospital mortality. A two-stage-modeling method was developed to predict hospital mortality. We constructed a multiple logistic regression model to predict the outcome of critical illness in the first stage, then S1 score were calculated. In the second stage, we used the S1 score and other data to construct a backward multiple logistic regression model. The area under the receiver operating curve was used to assess the predictive value of the model.Results: In the present study, 1,680 patients met the inclusion criteria. The overall ICU admission and in-hospital mortality was 10.36% (174 patients) and 4.29% (72 patients), respectively. In stage I analysis, hypothermia (OR = 1.92), tachypnea (OR = 4.94), lower systolic blood pressure (OR = 2.35), diabetes mellitus (OR = 1.87), leukocytosis (OR = 2.22), leukopenia (OR = 2.70), and a high percentage of segmented neutrophils (OR = 2.10) were associated with ICU admission. Bandemia had the highest odds ratio in the Stage I model (OR = 5.43). In stage II analysis, C-reactive protein (OR = 1.01), blood urea nitrogen (OR = 1.02) and stage I model’s S1 score were assocaited with in-hospital mortality. The area under the curve for the stage I and II model was 0.889 and 0.766, respectively.Conclusions: The two-stage model is a efficient risk-stratification tool for predicting critical illness and mortailty. The model may be an optional tool other than qSOFA and SIRS criteria.

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Association Between Red Cell Distribution Width and Hospital Mortality in Patients with Sepsis

Journal of International Medical Research ◽

10.1177/03000605211004221 ◽

2021 ◽

Vol 49 (4) ◽

pp. 030006052110042

Author(s):

Yide Li ◽

Yingfang She ◽

Le Fu ◽

Ruitong Zhou ◽

Wendi Xiang ◽

...

Keyword(s):

Logistic Regression ◽

Hospital Mortality ◽

Regression Model ◽

Roc Curve ◽

Prognostic Value ◽

Logistic Regression Model ◽

Cell Distribution ◽

Multiple Logistic Regression ◽

Distribution Width ◽

Apache Iv

Objective Sepsis is the leading cause of death in patients admitted to adult intensive care units (ICUs). We aimed to determine the predictive value of red blood cell distribution width (RDW) in patients with sepsis in a large cohort. Methods This retrospective observational study used data from the eICU Collaborative Research Database. The prognostic value of RDW was investigated using the receiver operating characteristic (ROC) curve, multiple logistic regression model, integrated discriminatory index (IDI), and net reclassification index (NRI). Results In total, 9743 patients were included. The area under the ROC curve of the RDW for predicting hospital mortality was 0.631 (95% confidence interval [CI]: 0.616–0.645). Based on the multiple logistic regression model, an RDW of ≥14.5% was correlated with hospital mortality, regardless of Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation IV (APACHE IV) scores (odds ratio [OR]: 1.838, 95% CI: 1.598–2.119). Using SOFA and APACHE IV scores as reference, the IDI and continuous NRI of RDW for hospital mortality was about 0.3 and 0.014, respectively. Conclusions The RDW may be useful in predicting hospital mortality in patients with sepsis, offering extra prognostic value beyond SOFA and APACHE IV scores.

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The impact of early neuraminidase inhibitor therapy on clinical outcomes in patients hospitalised with influenza A-related pneumonia: a multicenter, retrospective study

10.21203/rs.2.20901/v1 ◽

2020 ◽

Author(s):

Liang Chen ◽

Xiudi Han ◽

YanLi Li ◽

Chunxiao Zhang ◽

Xiqian Xing

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Clinical Outcomes ◽

Influenza A ◽

Logistic Regression Model ◽

Neuraminidase Inhibitor ◽

Multivariate Logistic Regression Model ◽

Multivariate Logistic Regression ◽

Severe Influenza ◽

The Impact

Abstract Background Guidelines emphasize prompt antiviral treatment in severe influenza patients. Although nearly a 50% of severe influenza present with pneumonia, the effect of early (≤ 2 days after illness onset) neuraminidase inhibitor (NAI) use on the clinical outcomes of influenza A-related pneumonia (FluA-p) has rarely been assessed. And there is limited data about the administration of NAIs in the real-world management of Flu-p in China.Methods Data of patients hospitalised with FluA-p from five teaching hospitals in China from 1 January 2013 to 31 December 2018 were reviewed retrospectively. The impact of early NAI therapy on the outcomes in FluA-p patients, and the indications of early NAI administration by clinicians were evaluated by logistic regression analysis.Results Totally, 693 FluA-p patients were included. Of these patients, 33.5% (232/693) were treated early. After adjusting for weighted propensity scores for treatment, systemic corticosteroid and antibiotic use, a multivariate logistic regression model showed that early NAI therapy was associated with decreased risk for invasive ventilation [ odds ratio ( OR ) 0.511, 95% confidence interval (CI) 0.312–0.835, p = 0.007) and 30-day mortality ( OR 0.533, 95% CI 0.210–0.807, p < 0.001) in FluA-p patients. A multivariate logistic regression model confirmed early NAI use ( OR 0.415, 95% CI 0.195–0.858, p = 0.001) was a predictor for 30-day mortality in FluA-p patients and a positive rapid influenza diagnostic test was the only indication ( OR 3.586, 95% CI 1.259–10.219, p < 0.001) related to the prescription of early NAI by clinicians.Conclusions Early NAI therapy is associated with better outcomes in FluA-p patients. Improved education and training of clinicians on the guidelines of influenza are needed.

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