Accessing the public MIMIC-II intensive care relational database for clinical research.

BACKGROUND: The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database is a free, public resource for intensive care research. The database was officially released in 2006, and has attracted a growing number of researchers in academia and industry. We present the two major software tools that facilitate accessing the relational database: the web-based QueryBuilder and a downloadable virtual machine (VM) image. RESULTS: QueryBuilder and the MIMIC-II VM have been developed successfully and are freely available to MIMIC-II users. Simple example SQL queries and the resulting data are presented. Clinical studies pertaining to acute kidney injury and prediction of fluid requirements in the intensive care unit are shown as typical examples of research performed with MIMIC-II. In addition, MIMIC-II has also provided data for annual PhysioNet/Computing in Cardiology Challenges, including the 2012 Challenge "Predicting mortality of ICU Patients". CONCLUSIONS: QueryBuilder is a web-based tool that provides easy access to MIMIC-II. For more computationally intensive queries, one can locally install a complete copy of MIMIC-II in a VM. Both publicly available tools provide the MIMIC-II research community with convenient querying interfaces and complement the value of the MIMIC-II relational database.

Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database.

OBJECTIVE: We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. DESIGN: Data collection and retrospective analysis. SETTING: All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. PATIENTS: Adult patients admitted to intensive care units between 2001 and 2007. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1-4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). CONCLUSIONS: MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.

Using inverse electrocardiography to image myocardial infarction--reflecting on the 2007 PhysioNet/Computers in Cardiology Challenge.

The goal of the 2007 PhysioNet/Computers in Cardiology Challenge was to try to establish how well it is possible to characterize the location and extent of old myocardial infarcts using electrocardiographic evidence supplemented by anatomical imaging information. A brief overview of the challenge and how different challengers approached the competition is provided, followed by detailed response of the first author to integrate electrophysiologic and anatomical data. The first author used the provided 120-electrode body-surface potential mapping data and magnetic resonance imaging heart and torso images to calculate epicardial potentials on customized ventricular geometries. A method was developed to define the location and extent of scar tissue based on the morphology of computed epicardial electrograms. Negative Q-wave deflection followed by R-wave on the left ventricular surface seemed to correspond with the location of the scar as determined by the gadolinium-enhanced magnetic resonance imaging gold standard in the supplied data sets. The method shows promising results as a noninvasive imaging tool to quantitatively characterize chronic infarcts and warrants further investigation on a larger patient data set.

Automated de-identification of free-text medical records.

BACKGROUND: Text-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification. METHODS: We describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. The software uses lexical look-up tables, regular expressions, and simple heuristics to locate both HIPAA PHI, and an extended PHI set that includes doctors' names and years of dates. To develop the de-identification approach, we assembled a gold standard corpus of re-identified nursing notes with real PHI replaced by realistic surrogate information. This corpus consists of 2,434 nursing notes containing 334,000 words and a total of 1,779 instances of PHI taken from 163 randomly selected patient records. This gold standard corpus was used to refine the algorithm and measure its sensitivity. To test the algorithm on data not used in its development, we constructed a second test corpus of 1,836 nursing notes containing 296,400 words. The algorithm's false negative rate was evaluated using this test corpus. RESULTS: Performance evaluation of the de-identification software on the development corpus yielded an overall recall of 0.967, precision value of 0.749, and fallout value of approximately 0.002. On the test corpus, a total of 90 instances of false negatives were found, or 27 per 100,000 word count, with an estimated recall of 0.943. Only one full date and one age over 89 were missed. No patient names were missed in either corpus. CONCLUSION: We have developed a pattern-matching de-identification system based on dictionary look-ups, regular expressions, and heuristics. Evaluation based on two different sets of nursing notes collected from a U.S. hospital suggests that, in terms of recall, the software out-performs a single human de-identifier (0.81) and performs at least as well as a consensus of two human de-identifiers (0.94). The system is currently tuned to de-identify PHI in nursing notes and discharge summaries but is sufficiently generalized and can be customized to handle text files of any format. Although the accuracy of the algorithm is high, it is probably insufficient to be used to publicly disseminate medical data. The open-source de-identification software and the gold standard re-identified corpus of medical records have therefore been made available to researchers via the PhysioNet website to encourage improvements in the algorithm.

Robust detection of heart beats in multimodal data.

This editorial reviews the background issues, the design, the key achievements, and the follow-up research generated as a result of the PhysioNet/Computing in Cardiology (CinC) Challenge 2014, published in the concurrent focus issue of Physiological Measurement. Our major focus was to accelerate the development and facilitate the comparison of robust methods for locating heart beats in long-term multi-channel recordings. A public (training) database consisting of 151 032 annotated beats was compiled from records that contained ECGs as well as pulsatile signals that directly reflect cardiac activity, and other signals that may have few or no observable markers of heart beats. A separate hidden test data set (consisting of 152 478 beats) is permanently stored at PhysioNet, and a public framework has been developed to provide researchers with the ability to continue to automatically score and compare the performance of their algorithms. A scoring criteria based on the averaging of gross sensitivity, gross positive predictivity, average sensitivity, and average positive predictivity is proposed. The top three scores (as of March 2015) on the hidden test data set were 93.64%, 91.50%, and 90.70%.

A physiological time series dynamics-based approach to patient monitoring and outcome prediction.

Cardiovascular variables such as heart rate (HR) and blood pressure (BP) are regulated by an underlying control system, and therefore, the time series of these vital signs exhibit rich dynamical patterns of interaction in response to external perturbations (e.g., drug administration), as well as pathological states (e.g., onset of sepsis and hypotension). A question of interest is whether "similar" dynamical patterns can be identified across a heterogeneous patient cohort, and be used for prognosis of patients' health and progress. In this paper, we used a switching vector autoregressive framework to systematically learn and identify a collection of vital sign time series dynamics, which are possibly recurrent within the same patient and may be shared across the entire cohort. We show that these dynamical behaviors can be used to characterize the physiological "state" of a patient. We validate our technique using simulated time series of the cardiovascular system, and human recordings of HR and BP time series from an orthostatic stress study with known postural states. Using the HR and BP dynamics of an intensive care unit (ICU) cohort of over 450 patients from the MIMIC II database, we demonstrate that the discovered cardiovascular dynamics are significantly associated with hospital mortality (dynamic modes 3 and 9, p=0.001, p=0.006 from logistic regression after adjusting for the APACHE scores). Combining the dynamics of BP time series and SAPS-I or APACHE-III provided a more accurate assessment of patient survival/mortality in the hospital than using SAPS-I and APACHE-III alone (p=0.005 and p=0.045). Our results suggest that the discovered dynamics of vital sign time series may contain additional prognostic value beyond that of the baseline acuity measures, and can potentially be used as an independent predictor of outcomes in the ICU.

Protocol to assess robustness of ST analysers: a case study.

This paper proposes principles and methods for assessing the robustness of ST segment analysers and algorithms. We describe an evaluation protocol, procedures and performance measures suitable for assessing the robustness. An ST analyser is robust if its performance is not critically dependent on the variation of the noise content of input signals and on the choice of the database used for testing, and if its analysis parameters are not critically tuned to the database used for testing. The protocol to assess the robustness includes: (1) a noise stress test addressing the aspect of variation of input signals; (2) a bootstrap evaluation of algorithm performance addressing the aspect of distribution of input signals and (3) a sensitivity analysis addressing the aspect of variation of analyser's architecture parameters. An ST analyser is considered to be robust if the performance measurements obtained during these procedures remain above the predefined critical performance boundaries. We illustrate the use of the robustness protocol and robustness measures by a case study in which we assessed the robustness of our Karhunen-Loève transform based ischaemic ST episode detection and quantification algorithm using the European Society of Cardiology ST-T database.

An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave.

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases. Using the WFDB Toolbox for MATLAB/Octave, users have access to over 50 physiological databases in PhysioNet. The toolbox provides access over 4 TB of biomedical signals including ECG, EEG, EMG, and PLETH. Additionally, most signals are accompanied by metadata such as medical annotations of clinical events: arrhythmias, sleep stages, seizures, hypotensive episodes, etc. Users of this toolbox should easily be able to reproduce, validate, and compare results published based on PhysioNet's software and databases.

Non-invasive fetal ECG analysis.

Despite the important advances achieved in the field of adult electrocardiography signal processing, the analysis of the non-invasive fetal electrocardiogram (NI-FECG) remains a challenge. Currently no gold standard database exists which provides labelled FECG QRS complexes (and other morphological parameters), and publications rely either on proprietary databases or a very limited set of data recorded from few (or more often, just one) individuals.The PhysioNet/Computing in Cardiology Challenge 2013 enables to tackle some of these limitations by releasing a set of NI-FECG data publicly to the scientific community in order to evaluate signal processing techniques for NI-FECG extraction. The Challenge aim was to encourage development of accurate algorithms for locating QRS complexes and estimating the QT interval in non-invasive FECG signals. Using carefully reviewed reference QRS annotations and QT intervals as a gold standard, based on simultaneous direct FECG when possible, the Challenge was designed to measure and compare the performance of participants' algorithms objectively. Multiple challenge events were designed to test basic FHR estimation accuracy, as well as accuracy in measurement of inter-beat (RR) and QT intervals needed as a basis for derivation of other FECG features.This editorial reviews the background issues, the design of the Challenge, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement.

Making big data useful for health care: a summary of the inaugural mit critical data conference.

With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines-including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology-gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

LightWAVE: Waveform and Annotation Viewing and Editing in a Web Browser.

This paper describes LightWAVE, recently-developed open-source software for viewing ECGs and other physiologic waveforms and associated annotations (event markers). It supports efficient interactive creation and modification of annotations, capabilities that are essential for building new collections of physiologic signals and time series for research. LightWAVE is constructed of components that interact in simple ways, making it straightforward to enhance or replace any of them. The back end (server) is a common gateway interface (CGI) application written in C for speed and efficiency. It retrieves data from its data repository (PhysioNet's open-access PhysioBank archives by default, or any set of files or web pages structured as in PhysioBank) and delivers them in response to requests generated by the front end. The front end (client) is a web application written in JavaScript. It runs within any modern web browser and does not require installation on the user's computer, tablet, or phone. Finally, LightWAVE's scribe is a tiny CGI application written in Perl, which records the user's edits in annotation files. LightWAVE's data repository, back end, and front end can be located on the same computer or on separate computers. The data repository may be split across multiple computers. For compatibility with the standard browser security model, the front end and the scribe must be loaded from the same domain.

Tracking progression of patient state of health in critical care using inferred shared dynamics in physiological time series.

Physiologic systems generate complex dynamics in their output signals that reflect the changing state of the underlying control systems. In this work, we used a switching vector autoregressive (switching VAR) framework to systematically learn and identify a collection of vital sign dynamics, which can possibly be recurrent within the same patient and shared across the entire cohort. We show that these dynamical behaviors can be used to characterize and elucidate the progression of patients' states of health over time. Using the mean arterial blood pressure time series of 337 ICU patients during the first 24 hours of their ICU stays, we demonstrated that the learned dynamics from as early as the first 8 hours of patients' ICU stays can achieve similar hospital mortality prediction performance as the well-known SAPS-I acuity scores, suggesting that the discovered latent dynamics structure may yield more timely insights into the progression of a patient's state of health than the traditional snapshot-based acuity scores.

Noninvasive Fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013.

The PhysioNet/CinC 2013 Challenge aimed to stimulate rapid development and improvement of software for estimating fetal heart rate (FHR), fetal interbeat intervals (FRR), and fetal QT intervals (FQT), from multichannel recordings made using electrodes placed on the mother's abdomen. For the challenge, five data collections from a variety of sources were used to compile a large standardized database, which was divided into training, open test, and hidden test subsets. Gold-standard fetal QRS and QT interval annotations were developed using a novel crowd-sourcing framework. The challenge organizers used the hidden test subset to evaluate 91 open-source software entries submitted by 53 international teams of participants in three challenge events, estimating FHR, FRR, and FQT using the hidden test subset, which was not available for study by participants. Two additional events required only user-submitted QRS annotations to evaluate FHR and FRR estimation accuracy using the open test subset available to participants. The challenge yielded a total of 91 open-source software entries. The best of these achieved average estimation errors of 187bpm2 for FHR, 20.9 ms for FRR, and 152.7 ms for FQT. The open data sets, scoring software, and open-source entries are available at PhysioNet for researchers interested on working on these problems.

Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012.

Acuity scores, such as APACHE, SAPS, MPM, and SOFA, are widely used to account for population differences in studies aiming to compare how medications, care guidelines, surgery, and other interventions impact mortality in Intensive Care Unit (ICU) patients. By contrast, the focus of the PhysioNet/CinC Challenge 2012 is to develop methods for patient-specific prediction of in-hospital mortality. The data used for the challenge consisted of 5 general descriptors and 36 time series (measurements of vital signs and laboratory results) from the first 48 hours of the first available ICU stay of 12,000 adult patients from the MIMIC II database. The challenge was organized as two events: event 1 measured performance of a binary classifier, and event 2 measured performance of a risk estimator. The score of event 1 was the lower of sensitivity and positive predictive value. The score for event 2 was a range-normalized Hosmer-Lemeshow statistic. A baseline algorithm (using SAPS-1) obtained event 1 and 2 scores of 0.3125 and 68.58 respectively. Most participants submitted entries that outperformed the baseline algorithm. The top final scores for events 1 and 2 were 0.5353 and 17.88 respectively.

PhysioNet: physiologic signals, time series and related open source software for basic, clinical, and applied research.

PhysioNet provides free web access to over 50 collections of recorded physiologic signals and time series, and related open-source software, in support of basic, clinical, and applied research in medicine, physiology, public health, biomedical engineering and computing, and medical instrument design and evaluation. Its three components (PhysioBank, the archive of signals; PhysioToolkit, the software library; and PhysioNetWorks, the virtual laboratory for collaborative development of future PhysioBank data collections and PhysioToolkit software components) connect researchers and students who need physiologic signals and relevant software with researchers who have data and software to share. PhysioNet's annual open engineering challenges stimulate rapid progress on unsolved or poorly solved questions of basic or clinical interest, by focusing attention on achievable solutions that can be evaluated and compared objectively using freely available reference data.

The PhysioNet/Computing in Cardiology Challenge 2010: Mind the Gap.

Participants in the 11th annual PhysioNet/CinC Challenge were asked to reconstruct, using any combination of available prior and concurrent information, 30-second segments of ECG, continuous blood pressure waveforms, respiration, and other signals that had been removed from recordings of patients in intensive care units.Fifteen of the 53 participants provided reconstructions for the entire test set of 100 ten-minute recordings. The mean correlation between the segments that had been removed (the "target signals") and the reconstructions produced using the two most successful methods is 0.9, and the sum of the squared residual errors in these reconstructions is less than 20% of the energy of the target signals.Sources for the most successful methods developed for this challenge have been made available by their authors to support research on robust estimation of parameters derived from unreliable signals, detection of changes in patient state, and recognition of signal corruption.

Hypotension as a Risk Factor for Acute Kidney Injury in ICU Patients.

In the context of critical illness, hypotension may be associated with acute kidney injury (AKI). Using the MIMIC II database, we studied the risk of AKI in ICU patients as a function of both the severity and duration of hypotension. Multivariate logistical regression was performed to find correlations between hypotension and AKI. Minimum mean arterial blood pressure (MAP) and the amount of time MAP was below a range of hypotension thresholds in a target 48-hour window (prior to AKI onset) were used as primary predictive variables in the multivariate model. Our results indicate that the risk of AKI was related to the severity of hypotension with an odds ratio (OR) of 1.03, 95% CI 1.02-1.04 (p < 0.0001) per 1 mmHg decrease in minimum MAP ≥ 80 mmHg. For each additional hour MAP was less than 70, 60, 50 mmHg, the risk of AKI increased by 2% (OR 1.02, 95% CI 1.00-1.03, p = 0.0034), 5% (OR 1.05, 95% CI 1.02-1.08, p = 0.0028), and 22% (OR 1.22, 95% CI 1.04-1.43, p = 0.0122) respectively.

CVSim: An Open-Source Cardiovascular Simulator for Teaching and Research.

CVSim is a lumped-parameter model of the human cardiovascular system that has been developed and used for research and for teaching quantitative physiology courses at MIT and Harvard Medical School since 1984. We present a brief historical background of lumped-parameter cardiovascular system models, followed by an overview of the development of the major versions of CVSim over a 25-year period in our laboratory. We describe the features and differences of four versions of CVSim that are freely available in open-source form via PhysioNet (http://physionet.org). These include a six-compartment cardiovascular model with an arterial baroreflex system, implemented in C for efficiency, with an X-based graphical user interface; a six-compartment model with a more extensive short-term regulatory system and incorporating resting physiologic perturbations, available as a stand-alone MATLAB application; and a pair of elaborated versions consisting of 6- and 21-compartment computational models implemented in C, with a separate and enhanced Java graphical user interface. We conclude with a discussion of the educational and research applications for which we have used CVSim.

PhysioNet: an NIH research resource for complex signals.

The Research Resource for Complex Physiologic Signals, supported by the National Institutes of Health (NIH), is intended to promote and facilitate investigations in the study of cardiovascular and other complex biomedical signals. The resource website (www.physionet.org) has 3 interdependent components: 1) PhysioBank is an archive of well-characterized digital recordings of physiologic signals and related data, including databases of electrocardiogram and heart rate time series from patients with heart failure, coronary disease, sleep apnea syndromes, and cardiac arrhythmias; 2) PhysioToolkit is a library of open-source software for physiologic signal processing and analysis; and 3) PhysioNet, for which the resource is named, is an on-line forum for dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. PhysioNet, in cooperation with the annual Computers in Cardiology conference, hosts a series of challenges inviting participants to tackle clinically interesting problems that are either unsolved or not well solved. PhysioNet invites contributions of databases and software from the biomedical community.