scholarly journals Novel Method of Atrial Fibrillation Case Identification and Burden Estimation Using the MIMIC-III Electronic Health Data Set

2019 ◽  
Vol 34 (10) ◽  
pp. 851-857 ◽  
Author(s):  
Eric Y. Ding ◽  
Daniella Albuquerque ◽  
Michael Winter ◽  
Sophia Binici ◽  
Jaclyn Piche ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Gold Standard ◽  
Onset Time ◽  
Data Set ◽  
Case Identification ◽  
Novel Method ◽  
Electronic Health ◽  
Mimic Iii

Background: Atrial fibrillation (AF) portends poor prognoses in intensive care unit patients with sepsis. However, AF research is challenging: Previous studies demonstrate that International Classification of Disease ( ICD) codes may underestimate the incidence of AF, but chart review is expensive and often not feasible. We aim to examine the accuracy of nurse-charted AF and its temporal precision in critical care patients with sepsis. Methods: Patients with sepsis with continuous electrocardiogram (ECG) waveforms were identified from the Medical Information Mart for Intensive Care (MIMIC-III) database, a de-identified, single-center intensive care unit electronic health record (EHR) source. We selected a random sample of ECGs of 6 to 50 hours’ duration for manual review. Nurse-charted AF occurrence and onset time and ICD-9-coded AF were compared to gold-standard ECG adjudication by a board-certified cardiac electrophysiologist blinded to AF status. Descriptive statistics were calculated for all variables in patients diagnosed with AF by nurse charting, ICD-9 code, or both. Results: From 142 ECG waveforms (58 AF and 84 sinus rhythm), nurse charting identified AF events with 93% sensitivity (95% confidence interval [CI]: 87%-100%) and 87% specificity (95% CI: 80%-94%) compared to the gold standard manual ECG review. Furthermore, nurse-charted AF onset time was within 1 hour of expert reader onset time for 85% of the reviewed tracings. The ICD-9 codes were 97% sensitive (95% CI: 88-100%) and 82% specific (95% CI: 74-90%) for incident AF during admission but unable to identify AF time of onset. Conclusion: Nurse documentation of AF in EHR is accurate and has high precision for determining AF onset to within 1 hour. Our study suggests that nurse-charted AF in the EHR represents a potentially novel method for AF case identification, timing, and burden estimation.

scholarly journals Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Dongkai Li ◽  
Jie He ◽  
Fanglan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Set ◽  
Machine Learning Methods ◽  
Heparin Treatment ◽  
Neural Network Algorithm ◽  
Mimic Iii ◽  
Machine Learning Models

BACKGROUND Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

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.

scholarly journals An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database

JAMIA Open ◽  
2021 ◽  
Vol 4 (3) ◽  
Author(s):  
Ali S Afshar ◽  
Yijun Li ◽  
Zixu Chen ◽  
Yuxuan Chen ◽  
Jae Hun Lee ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Intensive Care Unit ◽  
Heart Rate ◽  
Time Series ◽  
Intensive Care ◽  
Data Quality ◽  
Vital Signs ◽  
Data Set ◽  
Mimic Iii

Abstract Physiological data, such as heart rate and blood pressure, are critical to clinical decision-making in the intensive care unit (ICU). Vital signs data, which are available from electronic health records, can be used to diagnose and predict important clinical outcomes; While there have been some reports on the data quality of nurse-verified vital sign data, little has been reported on the data quality of higher frequency time-series vital signs acquired in ICUs, that would enable such predictive modeling. In this study, we assessed the data quality issues, defined as the completeness, accuracy, and timeliness, of minute-by-minute time series vital signs data within the MIMIC-III data set, captured from 16009 patient-ICU stays and corresponding to 9410 unique adult patients. We measured data quality of four time-series vital signs data streams in the MIMIC-III data set: heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), and arterial blood pressure (ABP). Approximately, 30% of patient-ICU stays did not have at least 1 min of data during the time-frame of the ICU stay for HR, RR, and SpO2. The percentage of patient-ICU stays that did not have at least 1 min of ABP data was ∼56%. We observed ∼80% coverage of the total duration of the ICU stay for HR, RR, and SpO2. Finally, only 12.5%%, 9.9%, 7.5%, and 4.4% of ICU lengths of stay had ≥ 99% data available for HR, RR, SpO2, and ABP, respectively, that would meet the three data quality requirements we looked into in this study. Our findings on data completeness, accuracy, and timeliness have important implications for data scientists and informatics researchers who use time series vital signs data to develop predictive models of ICU outcomes.

scholarly journals Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study

10.2196/17648 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17648
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Dongkai Li ◽  
Jie He ◽  
Fanglan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Set ◽  
Machine Learning Methods ◽  
Heparin Treatment ◽  
Neural Network Algorithm ◽  
Mimic Iii ◽  
Machine Learning Models

Background Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. Objective The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. Methods Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. Results Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. Conclusions The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

scholarly journals Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 269
Author(s):  
Syed Khairul Bashar ◽  
Eric Y. Ding ◽  
Allan J. Walkey ◽  
David D. McManus ◽  
Ki H. Chon
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Intensive Care ◽  
Critically Ill ◽  
Predictive Value ◽  
Support Vector ◽  
Feature Subset ◽  
Data Set ◽  
Time Frequency ◽  
Mimic Iii

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time–frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients’ AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.

scholarly journals Diuretic strategies in patients with resistance to loop-diuretics in the intensive care unit: A retrospective study from the MIMIC-III database

2021 ◽  
Vol 65 ◽  
pp. 282-291
Author(s):  
Jean-Maxime Côté ◽  
Josée Bouchard ◽  
Patrick T. Murray ◽  
William Beaubien-Souligny
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Retrospective Study ◽  
Loop Diuretics ◽  
Mimic Iii

scholarly journals Assessment of atrial fibrillation in emergency department

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
S Manzo-Silberman ◽  
T Chouihed ◽  
L Fraticelli ◽  
A Peiretti ◽  
C Claustre ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Vitamin K ◽  
De Novo ◽  
Bleeding Event ◽  
Vitamin K Antagonist ◽  
Funding Source ◽  
Private Company

Abstract Introduction Atrial Fibrillation (AF) is the most common arrythmia, especially in older adults. AF represents 1% of emergency department (ED) visits a third of which are de novo or recurrent. While the diagnosis is given quickly by reading the electrocardiogram (ECG), its management both remains complex. European guidelines have been published in 2016. Purpose Our study aimed to investigate guidelines implementation in French ED. Methods Prospective national multicenter study (clinical trials NCT 03836339) and core interpretation of ECG. Consecutive patients admitted in 32 French ED for AF confirmed by ECG were prospectively included. Clinical characteristics at admission were recorded by the physician. The 3-months telephone follow-up was ensured by one operator. Results From 1/10/2018 to 30/11/2018, 1369 patients with AF were included, of whom 295 (21.55%) had a de novo AF. Patients were 80 [65; 87] years old, 51.17% of men, 71.53% self-ruling, 91.53% living at home, 65.42% transported by firemen or by ambulances and 4,07% by a mobile intensive care unit. Twenty-six (8.84%) patients had a history of stroke or transient ischemic stroke and none of them on anticoagulants. CHA2DS2-VASC score was performed in 66.78% of patients and was 0 in 14 (7.11%) patients. HAS-BLED score = 2 [1; 3]. At admission 50.17% of patients received anticoagulants, of whom 49.32% a non-vitamin K antagonist oral anticoagulant, 0.68% Vitamin K antagonists, 50.68% UFH or LMWH. Beta-blockers were administered in 102 (24.01%) patients and amiodarone in 38 (12.89%). Cardiac echography has been performed in 20.34% of patients. Atrial fibrillation was the primary diagnosis in 42.71% of patients. It has been associated to a pneumopathy in 25.17% of patients, a pulmonary embolism in 4.76% and acute alcoholism in 1.36% of them. Precipitating factor was often undetermined. The discharge to the home concerned 18.64% of patients, 26.78% of patients were hospitalized in ED hospitalization unit, 23.05% in cardiology or intensive care unit. At 3 months, 49% of patients were on anticoagulants, of whom 90% on non-vitamin K antagonist oral anticoagulants, 95% of them didn't report any bleeding event and 41.77% of them were able to have a cardiology consultation within three months. Three-months mortality was about 22.09%, and rehospitalization rate about 22.89%. Conclusion It seems to be a reticence to initiate anticoagulation of patients admitted to ED with a de novo AF. It could be explained by both the advanced age of the patients and the lack of an organized access to a systematic cardiology consultation at discharge. Patients with chronic AF are subject to high mortality at 3 months and a significant risk of readmission. The application of the guidelines could be optimized by a better training program and the implementation of a dedicated pathway of care. Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Bayer

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