scholarly journals Association Between Oxygen Partial Pressure Trajectories and Short-Term Outcomes in Patients With Hemorrhagic Brain Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Guolong Cai ◽  
Weizhe Ru ◽  
Qianghong Xu ◽  
Jiong Wu ◽  
Shijin Gong ◽  
...  
Keyword(s):  
Intensive Care ◽  
Brain Injury ◽  
Hospital Mortality ◽  
Oxygen Partial Pressure ◽  
Partial Pressure ◽  
Medical Information ◽  
Logistic Models ◽  
Neurological Outcomes ◽  
Risk Of Death ◽  
Logistic Analysis

Objectives: Arterial hyperoxia is reportedly a risk factor for poor outcomes in patients with hemorrhagic brain injury (HBI). However, most previous studies have only evaluated the effects of hyperoxia using static oxygen partial pressure (PaO2) values. This study aimed to investigate the association between overall dynamic oxygenation status and HBI outcomes, using longitudinal PaO2 data.Methods: Data were extracted from the Medical Information Mart for Intensive Care III database. Longitudinal PaO2 data obtained within 72 h of admission to an intensive care unit were analyzed, using a group-based trajectory approach. In-hospital mortality was used as the primary outcomes. Multivariable logistic models were used to explore the association between PaO2 trajectory and outcomes.Results: Data of 2,028 patients with HBI were analyzed. Three PaO2 trajectory types were identified: Traj-1 (mild hyperoxia), Traj-2 (transient severe hyperoxia), and Traj-3 (persistent severe hyperoxia). The initial and maximum PaO2 of patients with Traj-2 and Traj-3 were similar and significantly higher than those of patients with Traj-1. However, PaO2 in patients with Traj-2 decreased more rapidly than in patients with Traj-3. The crude in-hospital mortality was the lowest for patients with Traj-1 and highest for patients with Traj-3 (365/1,303, 209/640, and 43/85 for Traj-1, Traj-2, and Traj-3, respectively; p < 0.001), and the mean Glasgow Coma Scale score at discharge (GCSdis) was highest for patients with Traj-1 and lowest in patients with Traj-3 (13 [7–15], 11 [6–15], and 7 [3–14] for Traj-1, Traj-2, and Traj-3, respectively; p < 0.001). The multivariable model revealed that the risk of death was higher in patients with Traj-3 than in patients with Traj-1 (odds ratio [OR]: 3.3, 95% confidence interval [CI]: 1.9–5.8) but similar for patients with Traj-1 and Traj-2. Similarly, the logistic analysis indicated the worst neurological outcomes in patients with Traj-3 (OR: 3.6, 95% CI: 2.0–6.4, relative to Traj-1), but similar neurological outcomes for patients in Traj-1 and Traj-2.Conclusion: Persistent, but not transient severe arterial hyperoxia, was associated with poor outcome in patients with HBI.

scholarly journals Development of a Prognostic Model to Predict Mortality after Traumatic Brain Injury in Intensive Care Setting in a Developing Country

2021 ◽  
Vol 12 (02) ◽  
pp. 368-375
Author(s):  
Mini Jayan ◽  
Dhaval Shukla ◽  
Bhagavatula Indira Devi ◽  
Dhananjaya I. Bhat ◽  
Subhas K. Konar
Keyword(s):  
Traumatic Brain Injury ◽  
Intensive Care ◽  
Brain Injury ◽  
Hospital Mortality ◽  
Prognostic Model ◽  
Third Ventricle ◽  
The Third ◽  
Scan Data ◽  
Gcs Score ◽  
Basal Cisterns

Abstract Objectives We aimed to develop a prognostic model for the prediction of in-hospital mortality in patients with traumatic brain injury (TBI) admitted to the neurosurgery intensive care unit (ICU) of our institute. Materials and Methods The clinical and computed tomography scan data of consecutive patients admitted after a diagnosis TBI in ICU were reviewed. Construction of the model was done by using all the variables of Corticosteroid Randomization after Significant Head Injury and International Mission on Prognosis and Analysis of Clinical Trials in TBI models. The endpoint was in-hospital mortality. Results A total of 243 patients with TBI were admitted to ICU during the study period. The in-hospital mortality was 15.3%. On multivariate analysis, the Glasgow coma scale (GCS) at admission, hypoxia, hypotension, and obliteration of the third ventricle/basal cisterns were significantly associated with mortality. Patients with hypoxia had eight times, with hypotensions 22 times, and with obliteration of the third ventricle/basal cisterns three times more chance of death. The TBI score was developed as a sum of individual points assigned as follows: GCS score 3 to 4 (+2 points), 5 to 12 (+1), hypoxia (+1), hypotension (+1), and obliteration third ventricle/basal cistern (+1). The mortality was 0% for a score of “0” and 85% for a score of “4.” Conclusion The outcome of patients treated in ICU was based on common admission variables. A simple clinical grading score allows risk stratification of patients with TBI admitted in ICU.

scholarly journals Comorbidity and outcomes in traumatic brain injury: protocol for a systematic review on functional status and risk of death

BMJ Open ◽  
2017 ◽  
Vol 7 (10) ◽  
pp. e018626 ◽  
Author(s):  
Tatyana Mollayeva ◽  
Chen Xiong ◽  
Sara Hanafy ◽  
Vincy Chan ◽  
Zheng Jing Hu ◽  
...  
Keyword(s):  
Systematic Review ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Status ◽  
Medical Information ◽  
Longitudinal Design ◽  
Adverse Outcomes ◽  
Current Evidence ◽  
Risk Of Death ◽  
The Impact

IntroductionReports on the association between comorbidity and functional status and risk of death in patients with traumatic brain injury (TBI) have been inconsistent; it is currently unknown which additional clinical entities (comorbidities) have an adverse influence on the evolution of outcomes across the lifespan of men and women with TBI. The current protocol outlines a strategy for a systematic review of the current evidence examining the impact of comorbidity on functional status and early-term and late-term mortality, taking into account known risk factors of these adverse outcomes (ie, demographic (age and sex) and injury-related characteristics).Methods and analysisA comprehensive search strategy for TBI prognosis, functional (cognitive and physical) status and mortality studies has been developed in collaboration with a medical information specialist of the large rehabilitation teaching hospital. All peer-reviewed English language studies with longitudinal design in adults with TBI of any severity, published from May 1997 to April 2017, found through Medline, Central, Embase, Scopus, PsycINFO and bibliographies of identified articles, will be considered eligible. Study quality will be assessed using published guidelines.Ethics and disseminationThe authors will publish findings from this review in a peer-reviewed scientific journal(s) and present the results at national and international conferences. This work aims to understand how comorbidity may contribute to adverse outcomes in TBI, to inform risk stratification of patients and guide the management of brain injury acutely and at the chronic stages postinjury on a population level.PROSPERO registration numberCRD42017070033.

scholarly journals Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach

2021 ◽  
Vol 8 (1) ◽  
pp. e000761
Author(s):  
Hao Du ◽  
Kewin Tien Ho Siah ◽  
Valencia Zhang Ru-Yan ◽  
Readon Teh ◽  
Christopher Yu En Tan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Receiver Operating Curve ◽  
Study Cohort ◽  
Risk Of Death ◽  
Severity Scores ◽  
Mimic Iii ◽  
Care Database

Research objectivesClostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database.MethodologyThe demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model.Summary of resultsFrom 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models.ConclusionOur machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.

scholarly journals The Association between Nutritional Status and In-Hospital Mortality of COVID-19 in Critically-Ill Patients in the ICU

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3302
Author(s):  
Michał Czapla ◽  
Raúl Juárez-Vela ◽  
Vicente Gea-Caballero ◽  
Stanisław Zieliński ◽  
Marzena Zielińska
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Nutritional Status ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Invasive Mechanical Ventilation ◽  
Nutritional Risk ◽  
Risk Of Death ◽  
The Impact

Background: Coronavirus disease 2019 (COVID-19) has become one of the leading causes of death worldwide. The impact of poor nutritional status on increased mortality and prolonged ICU (intensive care unit) stay in critically ill patients is well-documented. This study aims to assess how nutritional status and BMI (body mass index) affected in-hospital mortality in critically ill COVID-19 patients Methods: We conducted a retrospective study and analysed medical records of 286 COVID-19 patients admitted to the intensive care unit of the University Clinical Hospital in Wroclaw (Poland). Results: A total of 286 patients were analysed. In the sample group, 8% of patients who died had a BMI within the normal range, 46% were overweight, and 46% were obese. There was a statistically significantly higher death rate in men (73%) and those with BMIs between 25.0–29.9 (p = 0.011). Nonsurvivors had a statistically significantly higher HF (Heart Failure) rate (p = 0.037) and HT (hypertension) rate (p < 0.001). Furthermore, nonsurvivors were statistically significantly older (p < 0.001). The risk of death was higher in overweight patients (HR = 2.13; p = 0.038). Mortality was influenced by higher scores in parameters such as age (HR = 1.03; p = 0.001), NRS2002 (nutritional risk score, HR = 1.18; p = 0.019), PCT (procalcitonin, HR = 1.10; p < 0.001) and potassium level (HR = 1.40; p = 0.023). Conclusions: Being overweight in critically ill COVID-19 patients requiring invasive mechanical ventilation increases their risk of death significantly. Additional factors indicating a higher risk of death include the patient’s age, high PCT, potassium levels, and NRS ≥ 3 measured at the time of admission to the ICU.

scholarly journals Disparities in Intensive Care Unit Admission and Mortality Among Patients With Schizophrenia and COVID-19: A National Cohort Study

2020 ◽  
Author(s):  
Guillaume Fond ◽  
Vanessa Pauly ◽  
Marc Leone ◽  
Pierre-Michel Llorca ◽  
Veronica Orleans ◽  
...  
Keyword(s):  
Health Care ◽  
Intensive Care Unit ◽  
Mental Illness ◽  
Cohort Study ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Severe Mental Illness ◽  
Vulnerable Population ◽  
Icu Admission ◽  
Risk Of Death

Abstract Patients with schizophrenia (SCZ) represent a vulnerable population who have been understudied in COVID-19 research. We aimed to establish whether health outcomes and care differed between patients with SCZ and patients without a diagnosis of severe mental illness. We conducted a population-based cohort study of all patients with identified COVID-19 and respiratory symptoms who were hospitalized in France between February and June 2020. Cases were patients who had a diagnosis of SCZ. Controls were patients who did not have a diagnosis of severe mental illness. The outcomes were in-hospital mortality and intensive care unit (ICU) admission. A total of 50 750 patients were included, of whom 823 were SCZ patients (1.6%). The SCZ patients had an increased in-hospital mortality (25.6% vs 21.7%; adjusted OR 1.30 [95% CI, 1.08–1.56], P = .0093) and a decreased ICU admission rate (23.7% vs 28.4%; adjusted OR, 0.75 [95% CI, 0.62–0.91], P = .0062) compared with controls. Significant interactions between SCZ and age for mortality and ICU admission were observed (P = .0006 and P &lt; .0001). SCZ patients between 65 and 80 years had a significantly higher risk of death than controls of the same age (+7.89%). SCZ patients younger than 55 years had more ICU admissions (+13.93%) and SCZ patients between 65 and 80 years and older than 80 years had less ICU admissions than controls of the same age (−15.44% and −5.93%, respectively). Our findings report the existence of disparities in health and health care between SCZ patients and patients without a diagnosis of severe mental illness. These disparities differed according to the age and clinical profile of SCZ patients, suggesting the importance of personalized COVID-19 clinical management and health care strategies before, during, and after hospitalization for reducing health disparities in this vulnerable population.

scholarly journals Assessing the Predictive Value of TRIPS in Newborns

2021 ◽  
Vol 18 (4) ◽  
pp. 73-79
Author(s):  
R. F. Mukhametshin ◽  
N. S. Davydova ◽  
S. V. Kinzhalova
Keyword(s):  
Cohort Study ◽  
Intensive Care ◽  
Correlation Analysis ◽  
Hospital Mortality ◽  
Predictive Value ◽  
Quantitative Data ◽  
Hospital Treatment ◽  
Binary Outcomes ◽  
Risk Of Death ◽  
Occlusive Hydrocephalus

The objective: to determine the predictive value of TRIPS at the stage of pre-transport preparation in relation to treatment outcomes of newborns.Subjects: The cohort study included data from 604 visits of the team of the intensive case and consultation center. The TRIPS score was assessed, and the outcomes of the hospital phase of treatment were studied. The AUC ROC curve of the TRIPS score was calculated in relation to the binary outcomes of hospital treatment. The correlation analysis of the quantitative data was performed by Spearman's criterion.Results. AUC was greater than 0.8 only for the risk of death (AUC 0.827 (0.764-0.891)), the formation of severe IVH (AUC 0.831 (0.786-0.877)) and the development of occlusive hydrocephalus (AUC 0.839 (0.764-0.915)). For other binary outcomes, the score shows AUC below 0.8. A weak but significant correlation was found between the TRIPS score and the duration of intensive care (r = 0.478,p < 0.0001).Conclusion: TRIPS demonstrated an acceptable level of accuracy (AUC>0.8) in predicting hospital mortality, severe IVH, and the formation of occlusive hydrocephalus. A weak but significant correlation was found with the quantitative outcomes.

scholarly journals A nomogram to predict in-hospital mortality of neonates admitted to the intensive care unit

2021 ◽  
Author(s):  
Xihua Huang ◽  
Zhenyu Liang ◽  
Tang Li ◽  
Yu Lingna ◽  
Wei Zhu ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Predictive Model ◽  
Sofa Score ◽  
Medical Information ◽  
Hospital Death ◽  
Operating Characteristics ◽  
Distribution Width ◽  
Mimic Iii

Abstract Background To explore the influencing factors for in-hospital mortality in the neonatal intensive care unit (NICU) and to establish a predictive nomogram. Methods Neonatal data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Both univariate and multivariate logit binomial general linear models were used to analyse the factors influencing neonatal death. The area under the receiver operating characteristics (ROC) curve was used to assess the predictive model, which was visualized by a nomogram. Results A total of 1258 neonates from the NICU in the MIMIC-III database were eligible for the study, including 1194 surviving patients and 64 deaths. Multivariate analysis showed that red cell distribution width (RDW) (odds ratio [OR] 0.813, p=0.003) and total bilirubin (TBIL; OR 0.644, p&lt;0.001) had protective effects on neonatal in-hospital death, while lymphocytes (OR 1.205, p=0.025), arterial partial pressure of carbon dioxide (PaCO2; OR 1.294, p=0.016) and sequential organ failure assessment (SOFA) score (OR 1.483, p&lt;0.001) were its independent risk factors. Based on this, the area under the curve of this predictive model was up to 0.865 (95% confidence interval 0.813 to 0.917), which was also confirmed by a nomogram. Conclusions The nomogram constructed suggests that RDW, TBIL, lymphocytes, PaCO2 and SOFA score are all significant predictors for in-hospital mortality in the NICU.

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