scholarly journals Intelligent Management of Sepsis in the Intensive Care Unit

2012 ◽  
pp. 1-16
Author(s):  
Vicent J. Ribas ◽  
Juan Carlos Ruiz-Rodríguez ◽  
Alfredo Vellido
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Support Vector ◽  
Clinical Settings ◽  
Svm Model ◽  
Intelligent Management ◽  
Alternative Techniques ◽  
Research Challenge ◽  
Mortality Predictors

Sepsis is a transversal pathology and one of the main causes of death in the Intensive Care Unit (ICU). It has in fact become the tenth most common cause of death in western societies. Its mortality rates can reach up to 60% for Septic Shock, its most acute manifestation. For these reasons, the prediction of the mortality caused by Sepsis is an open and relevant medical research challenge. This problem requires prediction methods that are robust and accurate, but also readily interpretable. This is paramount if they are to be used in the demanding context of real-time decision making at the ICU. In this brief contribution, three different methods are presented. One is based on a variant of the well-known support vector machine (SVM) model and provides and automated ranking of relevance of the mortality predictors while the other two are based on logistic-regression and logistic regression over latent Factors. The reported results show that the methods presented outperform in terms of accuracy alternative techniques currently in use in clinical settings, while simultaneously assessing the relative impact of individual pathology indicators.

scholarly journals A novel model to label delirium in an intensive care unit from clinician actions

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Caitlin E. Coombes ◽  
Kevin R. Coombes ◽  
Naleef Fareed
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Support Vector ◽  
Data Set ◽  
Biological Validation ◽  
Public Data ◽  
Electronic Health

Abstract Background In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance. Methods EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation. Results Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes. Conclusions Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.

Hyperoxia is associated with adverse outcomes in the cardiac intensive care unit: insights from the Medical Information Mart for Intensive Care (MIMI-III) database

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A.Y Lui ◽  
L Garber ◽  
M Vincent ◽  
L Celi ◽  
J Masip ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Adverse Outcomes ◽  
Rank Test ◽  
Positive Pressure ◽  
Cardiac Intensive Care Unit ◽  
Multivariable Logistic Regression ◽  
Cardiac Intensive Care

Abstract Background Hyperoxia produces reactive oxygen species, apoptosis, and vasoconstriction, and is associated with adverse outcomes in patients with heart failure and cardiac arrest. Our aim was to evaluate the association between hyperoxia and mortality in patients (pts) receiving positive pressure ventilation (PPV) in the cardiac intensive care unit (CICU). Methods Patients admitted to our medical center CICU who received any PPV (invasive or non-invasive) from 2001 through 2012 were included. Hyperoxia was defined as time-weighted mean of PaO2 >120mmHg and non-hyperoxia as PaO2 ≤120mmHg during CICU admission. Primary outcome was in-hospital mortality. Multivariable logistic regression was used to assess the association between hyperoxia and in-hospital mortality adjusted for age, female sex, Oxford Acute Severity of Illness Score, creatinine, lactate, pH, PaO2/FiO2 ratio, PCO2, PEEP, and estimated time spent on PEEP. Results Among 1493 patients, hyperoxia (median PaO2 147mmHg) during the CICU admission was observed in 702 (47.0%) pts. In-hospital mortality was 29.7% in the non-hyperoxia group and 33.9% in the hyperoxia group ((log rank test, p=0.0282, see figure). Using multivariable logistic regression, hyperoxia was independently associated with in-hospital mortality (OR 1.507, 95% CI 1.311–2.001, p=0.00508). Post-hoc analysis with PaO2 as a continuous variable was consistent with the primary analysis (OR 1.053 per 10mmHg increase in PaO2, 95% CI 1.024–1.082, p=0.0002). Conclusions In a large CICU cohort, hyperoxia was associated with increased mortality. Trials of titration of supplemental oxygen across the full spectrum of critically ill cardiac patients are warranted. Funding Acknowledgement Type of funding source: None

Risk factor identification and predictive models for central line requirements for patients on vasopressors

2021 ◽  
pp. 0310057X2110242
Author(s):  
Adrian D Haimovich ◽  
Ruoyi Jiang ◽  
Richard A Taylor ◽  
Justin B Belsky
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Central Venous Catheter ◽  
Single Agent ◽  
Composite Endpoint ◽  
Venous Catheter ◽  
Central Venous Catheters ◽  
Central Venous

Vasopressors are ubiquitous in intensive care units. While central venous catheters are the preferred route of infusion, recent evidence suggests peripheral administration may be safe for short, single-agent courses. Here, we identify risk factors and develop a predictive model for patient central venous catheter requirement using the Medical Information Mart for Intensive Care, a single-centre dataset of patients admitted to an intensive care unit between 2008 and 2019. Using prior literature, a composite endpoint of prolonged single-agent courses (>24 hours) or multi-agent courses of any duration was used to identify likely central venous catheter requirement. From a cohort of 69,619 intensive care unit stays, there were 17,053 vasopressor courses involving one or more vasopressors that met study inclusion criteria. In total, 3807 (22.3%) vasopressor courses involved a single vasopressor for less than six hours, 7952 (46.6%) courses for less than 24 hours and 5757 (33.8%) involved multiple vasopressors of any duration. Of these, 3047 (80.0%) less than six-hour and 6423 (80.8%) less than 24-hour single vasopressor courses used a central venous catheter. Logistic regression models identified associations between the composite endpoint and intubation (odds ratio (OR) 2.36, 95% confidence intervals (CI) 2.16 to 2.58), cardiac diagnosis (OR 0.72, CI 0.65 to 0.80), renal impairment (OR 1.61, CI 1.50 to 1.74), older age (OR 1.002, Cl 1.000 to 1.005) and vital signs in the hour before initiation (heart rate, OR 1.006, CI 1.003 to 1.009; oxygen saturation, OR 0.996, CI 0.993 to 0.999). A logistic regression model predicting the composite endpoint had an area under the receiver operating characteristic curve (standard deviation) of 0.747 (0.013) and an accuracy of 0.691 (0.012). This retrospective study reveals a high prevalence of short vasopressor courses in intensive care unit settings, a majority of which were administered using central venous catheters. We identify several important risk factors that may help guide clinicians deciding between peripheral and central venous catheter administration, and present a predictive model that may inform future prospective trials.

scholarly journals Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Comparative Machine Learning Study (Preprint)

2020 ◽  
Author(s):  
Sujeong Hur ◽  
Ji Young Min ◽  
Junsang Yoo ◽  
Kyunga Kim ◽  
Chi Ryang Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Patient Safety ◽  
Intensive Care ◽  
Predictive Value ◽  
Prediction Models ◽  
Support Vector ◽  
Unplanned Extubation ◽  
Electronic Health Record Data ◽  
Icu Patients

BACKGROUND Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered as the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. OBJECTIVE This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. METHODS This study was conducted an academic tertiary hospital in Seoul. The hospital had approximately 2,000 inpatient beds and 120 intensive care unit (ICU) beds. The number of patients, on daily basis, was approximately 9,000 for the out-patient. The number of annual ICU admission was approximately 10,000. We conducted a retrospective study between January 1, 2010 and December 31, 2018. A total of 6,914 extubation cases were included. We developed an unplanned extubation prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used area under the receiver operator characteristic curve (AUROC). Sensitivity, specificity, positive predictive value negative predictive value, and F1-score were also determined for each model. For performance evaluation, we also used calibration curve, the Brier score, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS Among the 6,914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was more likely to occur during the night shift compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality was higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.762, and for SVM was 0.740. CONCLUSIONS We successfully developed and validated machine learning-based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787, which was obtained using RF. CLINICALTRIAL N/A

scholarly journals Effect of Sleep Disorder on Delirium in Post-Cardiac Surgery Patients

2020 ◽  
Vol 47 (5) ◽  
pp. 627-633 ◽  
Author(s):  
Hongbai Wang ◽  
Liang Zhang ◽  
Qipeng Luo ◽  
Yinan Li ◽  
Fuxia Yan
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cardiac Surgery ◽  
Intensive Care ◽  
Sleep Quality ◽  
Sleep Disorder ◽  
Logistic Regression Analysis ◽  
Univariate Analysis ◽  
Post Cardiac Surgery

ABSTRACT:Background:Post-cardiac surgery patients exhibit a higher incidence of postoperative delirium (PD) compared to non-cardiac surgery patients. Patients with various cardiac diseases suffer from preoperative sleep disorder (SPD) induced by anxiety, depression, breathing disorder, or other factors.Objective:To examine the effect of sleep disorder on delirium in post-cardiac surgery patients.Methods:We prospectively selected 186 patients undergoing selective cardiac valve surgery. Preoperative sleep quality and cognitive function of all eligible participants were assessed through the Pittsburgh Sleep Quality Index (PSQI) and the Montreal Cognitive Assessment, respectively. The Confusion Assessment Method for Intensive Care Unit was used to assess PD from the first to seventh day postoperatively. Patients were divided into two groups according to the PD diagnosis: (1) No PD group and (2) the PD group.Results:Of 186 eligible patients, 29 (15.6%) were diagnosed with PD. A univariate analysis showed that gender (p = 0.040), age (p = 0.009), SPD (p = 0.008), intraoperative infusion volume (p = 0.034), postoperative intubation time (p = 0.001), and intensive care unit stay time (p = 0.009) were associated with PD. A multivariate logistic regression analysis demonstrated that age (odds ratio (OR): 1.106; p = 0.001) and SPD (OR: 3.223; p = 0.047) were independently associated with PD. A receiver operating characteristic curve demonstrated that preoperative PSQI was predictive of PD (area under curve: 0.706; 95% confidence interval: 0.595–0.816). A binomial logistic regression analysis showed that there was a significant association between preoperative 6 and 21 PSQI scores and PD incidence (p = 0.009).Conclusions:Preoperative SPD was significantly associated with PD and a main predictor of PD.

scholarly journals Data Science for Extubation Prediction and Value of Information in Surgical Intensive Care Unit

2019 ◽  
Vol 8 (10) ◽  
pp. 1709 ◽  
Author(s):  
Tsung-Lun Tsai ◽  
Min-Hsin Huang ◽  
Chia-Yen Lee ◽  
Wu-Wei Lai
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Prediction Model ◽  
Value Of Information ◽  
Data Science ◽  
Apache Ii ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Science Framework

Besides the traditional indices such as biochemistry, arterial blood gas, rapid shallow breathing index (RSBI), acute physiology and chronic health evaluation (APACHE) II score, this study suggests a data science framework for extubation prediction in the surgical intensive care unit (SICU) and investigates the value of the information our prediction model provides. A data science framework including variable selection (e.g., multivariate adaptive regression splines, stepwise logistic regression and random forest), prediction models (e.g., support vector machine, boosting logistic regression and backpropagation neural network (BPN)) and decision analysis (e.g., Bayesian method) is proposed to identify the important variables and support the extubation decision. An empirical study of a leading hospital in Taiwan in 2015–2016 is conducted to validate the proposed framework. The results show that APACHE II and white blood cells (WBC) are the two most critical variables, and then the priority sequence is eye opening, heart rate, glucose, sodium and hematocrit. BPN with selected variables shows better prediction performance (sensitivity: 0.830; specificity: 0.890; accuracy 0.860) than that with APACHE II or RSBI. The value of information is further investigated and shows that the expected value of experimentation (EVE), 0.652 days (patient staying in the ICU), is saved when comparing with current clinical experience. Furthermore, the maximal value of information occurs in a failure rate around 7.1% and it reveals the “best applicable condition” of the proposed prediction model. The results validate the decision quality and useful information provided by our predicted model.

Multicenter outcome study of cancer patients admitted to the intensive care unit: a probability of mortality model.

1998 ◽  
Vol 16 (2) ◽  
pp. 761-770 ◽  
Author(s):  
J S Groeger ◽  
S Lemeshow ◽  
K Price ◽  
D M Nierman ◽  
P White ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Cancer Patients ◽  
The United States ◽  
Categorical Variables ◽  
Allogeneic Bone ◽  
Icu Admission ◽  
Hospital Survival

PURPOSE To develop prospectively and validate a model for probability of hospital survival at admission to the intensive care unit (ICU) of patients with malignancy. PATIENTS AND METHODS This was an inception cohort study in the setting of four ICUs of academic medical centers in the United States. Defined continuous and categorical variables were collected on consecutive patients with cancer admitted to the ICU. A preliminary model was developed from 1,483 patients and then validated on an additional 230 patients. Multiple logistic regression modeling was used to develop the models and subsequently evaluated by goodness-of-fit and receiver operating characteristic (ROC) analysis. The main outcome measure was hospital survival after ICU admission. RESULTS The observed hospital mortality rate was 42%. Continuous variables used in the ICU admission model are PaO2/FiO2 ratio, platelet count, respiratory rate, systolic blood pressure, and days of hospitalization pre-ICU. Categorical entries include presence of intracranial mass effect, allogeneic bone marrow transplantation, recurrent or progressive cancer, albumin less than 2.5 g/dL, bilirubin > or = 2 mg/dL, Glasgow Coma Score less than 6, prothrombin time greater than 15 seconds, blood urea nitrogen (BUN) greater than 50 mg/dL, intubation, performance status before hospitalization, and cardiopulmonary resuscitation (CPR). The P values for the fit of the preliminary and validation models are .939 and .314, respectively, and the areas under the ROC curves are .812 and .802. CONCLUSION We report a disease-specific multivariable logistic regression model to estimate the probability of hospital mortality in a cohort of critically ill cancer patients admitted to the ICU. The model consists of 16 unambiguous and readily available variables. This model should move the discussion regarding appropriate use of ICU resources forward. Additional validation in a community hospital setting is warranted.

scholarly journals ROUNDS Studies: Relation of OUtcomes with Nutrition Despite Severity—Round One: Ultrasound Muscle Measurements in Critically Ill Adult Patients

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Carlos Alfredo Galindo Martín ◽  
Reyna del Carmen Ubeda Zelaya ◽  
Enrique Monares Zepeda ◽  
Octavio Augusto Lescas Méndez
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Muscle Mass ◽  
Sofa Score ◽  
Protective Factor ◽  
Severity Of Illness ◽  
Muscle Thickness ◽  
Rectus Femoris ◽  
Adult Patients

Malnutrition (undernutrition) encompasses low intake or uptake, loss of fat mass, and muscle wasting and is associated with worse outcomes. Ultrasound has been introduced in the intensive care unit as a tool to assess muscle mass. The aim of the present study is to explore the relation between initial muscle mass and mortality in adult patients admitted to the intensive care unit. Methods. Rectus femoris and vastus intermedius thicknesses were measured by B-mode ultrasound in adult patients at admission, along with demographic characteristics, illness severity, comorbidities, biochemical variables, treatments, and in-hospital mortality as main outcomes. Analysis was made comparing survivors versus nonsurvivors and finally using binary logistic regression with mortality as dependent variable. Results. 59 patients were included in the analysis, severity measured by sequential organ failure assessment (SOFA) score was greater in nonsurvivors (17 (7) versus 24 (10) and 3 (1–5) versus 7 (3–10), resp.). Also, muscle thickness was lower in the latter group (1.44 (0.59) cm versus 0.98 (0.3) cm). Logistic regression showed severity by SOFA score as a risk factor and muscle thickness as a protective factor for mortality. Conclusion. Muscle mass showed to be a protective factor despite severity of illness; there is much more work to do regarding interventions and monitoring in order to prevent or overcome low muscle mass at admission to the intensive care unit.

scholarly journals Outcome of Patients With Necrotizing Vasculitis Admitted to the Intensive Care Unit (ICU) for Sepsis: Results of a Single-Centre Retrospective Analysis

2020 ◽  
pp. 088506662095376
Author(s):  
Marco Krasselt ◽  
Christoph Baerwald ◽  
Sirak Petros ◽  
Olga Seifert
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Tertiary Hospital ◽  
Simplified Acute Physiology Score ◽  
Apache Ii ◽  
Necrotizing Vasculitis ◽  
Multivariate Logistic Regression ◽  
Sepsis Mortality ◽  
Saps Ii

Introduction/Background: Vasculitis patients have a high risk for infections that may require intensive care unit (ICU) treatment in case of resulting sepsis. Since data on sepsis mortality in this patient group is limited, the present study investigated the clinical characteristics and outcomes of vasculitis patients admitted to the ICU for sepsis. Methods: The medical records of all necrotizing vasculitis patients admitted to the ICU of a tertiary hospital for sepsis in a 13-year period have been reviewed. Mortality was calculated and multivariate logistic regression was used to determine independent risk factors for sepsis mortality. Moreover, the predictive power of common ICU scores was further evaluated. Results: The study included 34 patients with necrotizing vasculitis (mean age 69 ± 9.9 years, 35.3% females). 47.1% (n = 16) were treated with immunosuppressives (mostly cyclophosphamide, n = 35.3%) and 76.5% (n = 26) received glucocorticoids. Rituximab was used in 4 patients (11.8%).The in-hospital mortality of septic vasculitis patients was 41.2%. The Sequential Organ Failure Assessment (SOFA) score (p = 0.003) was independently associated with mortality in multivariate logistic regression. Acute Physiology And Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score II (SAPS II) and SOFA scores were good predictors of sepsis mortality in the investigated vasculitis patients (APACHE II AUC 0.73, p = 0.02; SAPS II AUC 0.81, p < 0.01; SOFA AUC 0.898, p < 0.0001). Conclusions: Sepsis mortality was high in vasculitis patients. SOFA was independently associated with mortality in a logistic regression model. SOFA and other well-established ICU scores were good mortality predictors.

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