A clinical description of the IMPROVE Data Library

Using "off-the-shelf" tools for terabyte-scale waveform recording in intensive care: computer system design, database description and lessons learned

Until now, the creation of massive (long-term and multichannel) waveform databases in intensive care required an interdisciplinary team of clinicians, engineers and informaticians and, in most cases, also design-specific software and hardware development. Recently, several commercial software tools for waveform acquisition became available. Although commercial products and even turnkey systems are now being marketed as simple and effective, the performance of those solutions is not known. The additional expense upfront may be worthwhile if commercial software can eliminate the need for custom software and hardware systems and the associated investment in teams and development. We report the development of a computer system for long-term large-scale recording and storage of multichannel physiologic signals that was built using commercial solutions (software and hardware) and existing hospital IT infrastructure. Both numeric (1 Hz) and waveform (62.5-500 Hz) data were captured from 24 SICU bedside monitors simultaneously and stored in a file-based vital sign data bank (VSDB) during one-year period (total DB size is 4.21TB). In total, vital signs were recorded from 1,175 critically ill patients. Up to six ECG leads, all other monitored waveforms, and all monitored numeric data were recorded in most of the cases. We describe the details of building blocks of our system, provide description of three datasets exported from our VSDB and compare the contents of our VSDB with other available waveform databases. Finally, we summarize lessons learned during recording, storage, and pre-processing of physiologic signals.

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.

Toward optimal display of physiologic status in critical care: I. Recreating bedside displays from archived physiologic data

BACKGROUND

Physiologic data display is essential to decision making in critical care. Current displays echo first-generation hemodynamic monitors dating to the 1970s and have not kept pace with new insights into physiology or the needs of clinicians who must make progressively more complex decisions about their patients. The effectiveness of any redesign must be tested before deployment. Tools that compare current displays with novel presentations of processed physiologic data are required. Regenerating conventional physiologic displays from archived physiologic data is an essential first step.

OBJECTIVES

The purposes of the study were to (1) describe the SSSI (single sensor single indicator) paradigm that is currently used for physiologic signal displays, (2) identify and discuss possible extensions and enhancements of the SSSI paradigm, and (3) develop a general approach and a software prototype to construct such "extended SSSI displays" from raw data.

RESULTS

We present Multi Wave Animator (MWA) framework--a set of open source MATLAB (MathWorks, Inc., Natick, MA, USA) scripts aimed to create dynamic visualizations (eg, video files in AVI format) of patient vital signs recorded from bedside (intensive care unit or operating room) monitors. Multi Wave Animator creates animations in which vital signs are displayed to mimic their appearance on current bedside monitors. The source code of MWA is freely available online together with a detailed tutorial and sample data sets.

Integrating data, models, and reasoning in critical care

Modern intensive care units (ICUs) employ an impressive array of technologically sophisticated instrumentation to provide detailed measurements of the pathophysiological state of each patient. Providing life support in the ICU is becoming an increasingly complex task, however, because of the growing volume of relevant data from clinical observations, bedside monitors, mechanical ventilators, and a wide variety of laboratory tests and imaging studies. The enormous amount of ICU data and its poor organization makes its integration and interpretation time-consuming and inefficient. There is a critical need to integrate the disparate clinical information into a single, rational framework and to provide the clinician with hypothesis-driven displays that succinctly summarize a patient's trajectory over time. In this paper, we present our recent efforts towards the development of such an advanced patient monitoring system that aims to improve the efficiency, accuracy, and timeliness of clinical decision making in intensive care.

Human factors error and patient monitoring

A wide range of studies have shown that human factors errors are the major cause of critical incidents that threaten patient safety in the medical environments where patient monitoring takes place, contributing to approximately 87% of all such incidents. Studies have also shown that good cognitively ergonomic design of monitoring equipment for use in these environments should reduce the human factors errors associated with the information they provide. The purpose of this review is to consider the current state of knowledge concerning human factors engineering in its application to patient monitoring. It considers the prevalence of human factors error, principles of good human factors design, the effect of specific design features and the problem of the measurement of the effectiveness of designs in reducing human factors error. The conclusion of the review is that whilst the focus of human factors studies has, in recent years, moved from instrument design to organizational issues, patient monitor designers still have an important contribution to make to improving the safety of the monitored patient. Further, whilst better psychological understanding of the causes of human factors errors will in future guide better human factors engineering, in this area there are still many practical avenues of research that need exploring from the current base of understanding.

Context related artefact detection in prolonged EEG recordings

The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. Although different EEG analysis systems have been described, only few general applicable artefact recognition techniques have emerged. This paper tackles the problem of artefact detection in seven 24 h EEG recordings in the intensive care unit. ICU recordings have received less attention than, e.g. epilepsy monitoring, although recordings in this environment present an interesting application area. The EEG data used here was recorded during the difficult circumstances of an explorative ICU study. The data set includes a diverse set of EEG patterns, as well as EEG artefacts. The study investigates objective artefact detection methods based on statistical differences between signal parameters, using time-varying autoregressive modelling (AR) and Slope detection. In addition to matching the performance of artefact detection against two human observers, the study focuses on the optimal settings for context incorporation by testing the algorithms for different time windows and epoch lengths. Results indicate that a relatively short period (20-40 s) provides sufficient context information for the methods used. The combined AR and Slope detection parameters yielded good performance, detecting approximately 90% of the artefacts as indicated by the consensus score of the human observers.

Computerized physiologic monitoring

Computers can offer significant enhancement to the monitoring of the critically ill. Their value is derived from improved vigilance, better charting, and an opportunity to assess practitioner compliance with unit protocols. However, their true value can only be attained when they are integrated into a total information system.