scholarly journals Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK

BMJ Open ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. e025925 ◽  
Author(s):  
Christopher J McWilliams ◽  
Daniel J Lawson ◽  
Raul Santos-Rodriguez ◽  
Iain D Gilchrist ◽  
Alan Champneys ◽  
...  
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Medical Information ◽  
Learning Algorithm ◽  
Adverse Outcomes ◽  
Royal Infirmary ◽  
Automated Method ◽  
Improved Performance ◽  
Discharge Criteria ◽  
Mimic Iii

ObjectiveThe primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care.DesignWe used two datasets of routinely collected patient data to test and improve on a set of previously proposed discharge criteria.SettingBristol Royal Infirmary general intensive care unit (GICU).PatientsTwo cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from Medical Information Mart for Intensive Care (MIMIC)-III.ResultsIn both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability.ConclusionsOur findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

scholarly journals Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic health care data from MIMIC-III and Bristol, UK.

2018 ◽  
Author(s):  
Christopher McWilliams ◽  
Daniel J Lawson ◽  
Raul Santos-Rodriguez ◽  
Iain D Gilchrist ◽  
Alan Champneys ◽  
...  
Keyword(s):  
Intensive Care ◽  
Decision Support ◽  
Learning Algorithm ◽  
Primary Objective ◽  
Adverse Outcomes ◽  
Royal Infirmary ◽  
Automated Method ◽  
Improved Performance ◽  
Discharge Criteria ◽  
Mimic Iii

Objective: The primary objective is to develop an automated method for detecting patients that are ready for discharge from intensive care. Design: We used two datasets of routinely collected patient data to test and improve upon a set of previously proposed discharge criteria. Setting: Bristol Royal Infirmary general intensive care unit (GICU). Patients: Two cohorts derived from historical datasets: 1870 intensive care patients from GICU in Bristol, and 7592 from MIMIC-III (a publicly available intensive care dataset). Results: In both cohorts few successfully discharged patients met all of the discharge criteria. Both a random forest and a logistic classifier, trained using multiple-source cross-validation, demonstrated improved performance over the original criteria and generalised well between the cohorts. The classifiers showed good agreement on which features were most predictive of readiness-for-discharge, and these were generally consistent with clinical experience. By weighting the discharge criteria according to feature importance from the logistic model we showed improved performance over the original criteria, while retaining good interpretability. Conclusions: Our findings indicate the feasibility of the proposed approach to ready-for-discharge classification, which could complement other risk models of specific adverse outcomes in a future decision support system. Avenues for improvement to produce a clinically useful tool are identified.

scholarly journals Early Coagulation Disorder Is Associated With an Increased Risk of Atrial Fibrillation in Septic Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Yunxiang Long ◽  
Yingmu Tong ◽  
Runchen Miao ◽  
Rong Fan ◽  
Xiangqi Cao ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Intensive Care ◽  
Medical Information ◽  
International Normalized Ratio ◽  
Coagulation Disorder ◽  
Increased Risk ◽  
Coagulation Tests ◽  
Independent Risk Factors ◽  
The Relationship ◽  
Mimic Iii

Background: Atrial fibrillation (AF) and coagulation disorder, two common complications of sepsis, are associated with the mortality. However, the relationship between early coagulation disorder and AF in sepsis remains elusive. This study aimed to evaluate the interaction between AF and early coagulation disorder on mortality.Methods: In this retrospective study, all data were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Septic patients with coagulation tests during the first 24 h after admission to intensive care units (ICUs) meeting study criteria were included in the analysis. Early coagulation disorder is defined by abnormalities in platelet count (PLT), international normalized ratio (INR) and activated partial thromboplastin time (APTT) within the first 24 h after admission, whose score was defined with reference to sepsis-induced coagulopathy (SIC) and coagulopathy. Patients meeting study criteria were divided into AF and non-AF groups.Results: In total, 7,528 septic patients were enrolled, including 1,243 (16.51%) with AF and 5,112 (67.91%) with early coagulation disorder. Compared with patients in the non-AF group, patients in the AF group had higher levels of INR and APTT (P < 0.001). Multivariable logistic regression analyses showed that stroke, early coagulation disorder, age, gender, congestive heart failure (CHF), chronic pulmonary disease, renal failure, and chronic liver disease were independent risk factors for AF. In addition, AF was related to in-hospital mortality and 90-day mortality. In the subgroup analysis stratified by the scores of early coagulation disorder, AF was associated with an increased risk of 90-day mortality when the scores of early coagulation disorder were 1 or 2 and 3 or 4.Conclusion: In sepsis, coagulation disorder within the first 24 h after admission to the ICUs is an independent risk factor for AF. The effect of AF on 90-day mortality varies with the severity of early coagulation disorder.

scholarly journals Central venous pressure measurement is associated with improved outcomes in septic patients: an analysis of the MIMIC-III database

2020 ◽  
Author(s):  
Hui Chen ◽  
Zhu Zhu ◽  
Chenyan Zhao ◽  
Yanxia Guo ◽  
Dongyu Chen ◽  
...  
Keyword(s):  
Intensive Care ◽  
Central Venous Pressure ◽  
Medical Information ◽  
Venous Pressure ◽  
Serum Lactate ◽  
Mediating Effect ◽  
Mediation Effect ◽  
Clinical Tool ◽  
Central Venous ◽  
Mimic Iii

Abstract Purpose: Measurement of central venous pressure (CVP) can be a useful clinical tool. However, the formal utility of CVP measurement in preventing mortality in septic patients has never been proven.Methods: The Medical Information Mart for Intensive Care III (MIMIC-III) database was searched to identify septic patients with and without CVP measurements. The primary outcome was 28-day mortality. Multivariate regression was used to elucidate the relationship between CVP measurement and 28-day mortality, and propensity score matching (PSM) and an inverse probability of treatment weighing (IPTW) were employed to validate our findings. Results: A total of 10275 patients were included in our study, of which 4516 patients (44%) underwent CVP measurement within 24 h of intensive care unit (ICU) admission. The risk of 28-day mortality was reduced in the CVP group (OR 0.60 (95% CI 0.51-0.70; p<0.001)). Patients in the CVP group received more fluid on day 1, and had a shorter duration of mechanical ventilation and vasopressor use, and the reduction in serum lactate was greater than that in the no CVP group. The mediating effect of serum lactate reduction was significant for the whole cohort (p=0.04 for the average causal mediation effect (ACME)) and patients in the CVP group with an initial CVP level below 8 mmHg (p=0.04 for the ACME).Conclusion: CVP measurement was associated with decreased risk-adjusted 28-day mortality among patients with sepsis and was proportionally mediated through serum lactate reduction.

scholarly journals Comparing the impact on the prognosis of acute myocardial infarction critical patients of using midazolam, propofol, and dexmedetomidine for sedation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaowei Jiang ◽  
Min Yan
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Intensive Care ◽  
Medical Information ◽  
Unmeasured Confounding ◽  
Value Analysis ◽  
Critical Patients ◽  
Mechanical Ventilation Time ◽  
The Impact ◽  
Mimic Iii

Abstract Background There are less studies focusing on the sedative therapy of acute myocardial infarction (AMI) critical patients. This study aim to compare the impact on the prognosis of AMI critical patients of using midazolam, propofol and dexmedetomidine. Methods We collected clinical data from the Medical Information Mart for Intensive Care III (MIMIC III) database. Data on 427 AMI patients with sedatives using were recruited from in Coronary Heart Disease Intensive Care unit (CCU). Results There were 143 patients in midazolam using, 272 in propofol using and 28 in dexmedetomidine using. The rate of 28-days mortality was 23.9% in overall patients. Through logistic regression analysis, only midazolam using was significant association with increased 28-days mortality when compared with propofol or dexmedetomidine using. In the subgroup analysis of age, gender, body mass index (BMI), white blood cell (WBC), beta-block, and revascularization, the association between midazolam using and increased 28-days mortality remained significantly. Through propensity score matching, 140 patients using midazolam and 192 using non-midazolam were successfully matched, the midazolam using presented with higher rate of CCU mortality, hospital mortality and 28-days mortality, longer of mechanical ventilation time and CCU duration. E-value analysis suggested robustness to unmeasured confounding. Conclusion Propofol or dexmedetomidine are preferred to be used in AMI critical patients for sedative therapy.

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.

A comprehensive evaluation for the prediction of mortality in intensive care units with LSTM networks: patients with cardiovascular disease

2020 ◽  
Vol 65 (4) ◽  
pp. 435-446
Author(s):  
Saumil Maheshwari ◽  
Aman Agarwal ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Cardiovascular Disease ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Medical Information ◽  
Mortality Prediction ◽  
Clinical Variable ◽  
Support Vector ◽  
Clinical Variables ◽  
Prediction Of Mortality ◽  
Mimic Iii

AbstractIntensive care units (ICUs) are responsible for generating a wealth of useful data in the form of electronic health records. We aimed to build a mortality prediction model on a Medical Information Mart for Intensive Care (MIMIC-III) database and to assess whether the use of deep learning techniques like long short-term memory (LSTM) can effectively utilize the temporal relations among clinical variables. The models were built on clinical variable dynamics of the first 48 h of ICU admission of 12,550 records from the MIMIC-III database. A total of 36 variables including 33 time series variables and three static variables were used for the prediction. We present the application of LSTM and LSTM attention (LSTM-AT) model for mortality prediction with such a large number of clinical variables dataset. For training and validation purpose, we have used International Classification of Diseases, 9th edition (ICD-9) codes for extracting the patients with cardiovascular disease, and infections and parasitic disease, respectively. The effectiveness of the LSTM model is achieved over non-recurrent baseline models like naïve Bayes, logistic regression (LR), support vector machine and multilayer perceptron (MLP) by generating state of the art results (area under the curve [AUC], 0.852). Next, by providing attention at each time stamp, we developed a model, LSTM-AT, which exhibits even better performance (AUC, 0.876).

scholarly journals Data-driven curation process for describing the blood glucose management in the intensive care unit

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Aldo Robles Arévalo ◽  
Jason H. Maley ◽  
Lawrence Baker ◽  
Susana M. da Silva Vieira ◽  
João M. da Costa Sousa ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Blood Glucose ◽  
Real World ◽  
Medical Information ◽  
Data Extraction ◽  
Clinical Care ◽  
Blood Glucose Reading ◽  
Glucose Management ◽  
Mimic Iii

AbstractAnalysis of real-world glucose and insulin clinical data recorded in electronic medical records can provide insights into tailored approaches to clinical care, yet presents many analytic challenges. This work makes publicly available a dataset that contains the curated entries of blood glucose readings and administered insulin on a per-patient basis during ICU admissions in the Medical Information Mart for Intensive Care (MIMIC-III) database version 1.4. Also, the present study details the data curation process used to extract and match glucose values to insulin therapy. The curation process includes the creation of glucose-insulin pairing rules according to clinical expert-defined physiologic and pharmacologic parameters. Through this approach, it was possible to align nearly 76% of insulin events to a preceding blood glucose reading for nearly 9,600 critically ill patients. This work has the potential to reveal trends in real-world practice for the management of blood glucose. This data extraction and processing serve as a framework for future studies of glucose and insulin in the intensive care unit.

scholarly journals Total SOFA and SOFA Kidney are Associated with Short Term and Long term Outcoming of Sepsis-Associated Liver Injury

2021 ◽  
Author(s):  
He Miao ◽  
Hong Liang ◽  
Yuteng Ma ◽  
Cong Tian ◽  
Yanan Ma ◽  
...  
Keyword(s):  
Intensive Care ◽  
Liver Injury ◽  
Sofa Score ◽  
Medical Information ◽  
Close Association ◽  
Confounding Factors ◽  
Icu Mortality ◽  
Short Term ◽  
Mimic Iii

Abstract Background: Liver injury is considered as a common complication of sepsis. However, there are still few studies on short-term and long-term prognostic factors of sepsis-associated liver injury (SALI). The objective of our study is to conduct a large sample data cohort study to explore the risk factors for short-term and long-term prognosis of SALI.Methods: Data from a public, US-based, critical-care database (Medical Information Mart for Intensive Care-III [MIMIC-III]) was used. Septic patients who met the definition of acute liver injury were enrolled. Variables extracted from MIMIC-III were used to evaluate patient demographics, clinical characteristics on Day 1 of intensive care unit admission, and clinical outcomes. The Logistic regression models were used to calculate risk ratio (RR) and 95% confidence intervals (CIs) after adjusting for potential factors. Results: Among the 14687 participants in our study, there were 3140 (21.38%) with SALI. SALI was significantly positively associated with ICU mortality (RR, 1.54; 95% CI, 1.32, 1.79), 28-day mortality (RR, 1.27; 95% CI, 1.11, 1.45) and 1-year mortality (RR, 1.19; 95% CI, 1.06, 1.34) after adjusting confounding factors. Stratified by SOFA, there was a positive association between SALI and ICU mortality (RR, 2.15; 95% CI, 1.64, 2.80), 28-day mortality (RR, 1.60; 95% CI, 1.28, 1.99), 1-year mortality (RR, 1.24; 95% CI, 1.04, 1.48) after adjusting confounding factors among people with sofa score ≤ 5. Similar results were also obtained between SALI and ICU mortality (RR,1.40; 95% CI, 1.17, 1.67), 28-day mortality (RR, 1.17; 95% CI, 0.99, 1.38), 1-year mortality (RR, 1.19; 95% CI, 1.02, 1.38) after adjusting confounding factors among people with sofa score> 5. Compared with SOFA renal> 1, SALI had a stronger positive correlation with ICU mortality (RR, 1.36; 95% CI, 1.01, 1.84), 28-day mortality (RR, 1.19; 95% CI, 0.91, 1.56), 1-year mortality (RR, 1.11; 95% CI, 0.88, 1.41) after adjusted confounding factors among people with SOFA renal ≤ 1.Conclusions: SALI was an independent risk factor for ICU mortality, 28-day mortality and 1-year mortality. And there is a close association between liver and kidney in sepsis, but the mechanism is still unclear and requires further study.

scholarly journals Development and validation of an automated algorithm to detect atrial fibrillation within stored intensive care unit continuous electrocardiographic data (Preprint)

2020 ◽  
Author(s):  
Allan J Walkey ◽  
Syed K Bashar ◽  
Billal Hossain ◽  
Eric Ding ◽  
Daniella Albuquerque ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Medical Information ◽  
Heart Rhythm ◽  
Adverse Outcomes ◽  
Healthcare Data ◽  
Automated Algorithm ◽  
Large Databases

BACKGROUND Atrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes. OBJECTIVE To develop and validate an automated algorithm to accurately identify AF within electronic healthcare data among critically ill patients with sepsis. METHODS Retrospective cohort study of patients hospitalized with sepsis identified from Medical Information Mart for Intensive Care (MIMIC III) electronic health data with linked electrocardiographic (ECG) telemetry waveforms. Within three separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregularly irregular rhythm and premature beats in order to identify AF. We compared the automated algorithm to current methods of AF identification in large databases, including ICD-9 codes and hourly nurse annotation of heart rhythm. Methods of AF identification were tested against gold standard manual ECG review. RESULTS AF detection algorithms that did not differentiate AF from premature atrial and ventricular beats performed modestly, with 76% (95% CI, 61-87%) accuracy. Performance improved (p=0.02) with the addition of premature beat detection (validation set accuracy: 94% [95% CI, 83-99%]). Median time between automated and manual detection of AF onset was 30 minutes (25th-75%ile 0-208 minutes). The accuracy of ICD-9 codes (68%, p=0.0002 vs. automated algorithm) and nurse charting (80%, p=0.02 vs. algorithm) was lower than the automated algorithm. CONCLUSIONS An automated algorithm using telemetry ECG data can feasibly and accurately detect AF among critically ill patients with sepsis, and represents an improvement in AF detection within large databases. CLINICALTRIAL na

scholarly journals Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding

2021 ◽  
Vol 28 (1) ◽  
pp. e100245
Author(s):  
Riccardo Levi ◽  
Francesco Carli ◽  
Aldo Robles Arévalo ◽  
Yuksel Altinel ◽  
Daniel J Stein ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Clinical Decision Making ◽  
Medical Information ◽  
Learning Algorithm ◽  
Observation Time ◽  
Research Database ◽  
Gi Bleeding ◽  
Healthcare Applications

ObjectiveGastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU.MethodsA machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates.ResultsThe optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all.ConclusionsThe potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.

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