Acute and Critical Care Formulas and Laboratory Values

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Would you like us to take another look at this review? No, cancel Yes, report it Thanks! You've successfully reported this review. We appreciate your feedback. OK, close. Highly structured text notes which are selected from drop down menus are retained within the database and present in the note table. Each record in the table is stored with the time of documentation and the time at which the assessment is relevant. Patient care information is documented in the nurseCare table for the following categories: nutrition, activity, hygiene, wound care, line care, drain status, patient safety, alarms, isolation precautions, equipment, restraints, and other nursing care data.

A custom hierarchy is used to group and store data. The majority of bedside documentation is entered into a ''flowsheet'', a tabular style interface with time in columns usually hourly and observations in rows. The nurseCharting table contains this information using a entity-attribute-value model, where the entity is a patient identifier, the attribute is the type of data recorded e. Each charted item is stored with a ''chart time'' nursingChartOffset , which specifies when the measurement was relevant, and a ''validation time'' nursingChartEntryOffset , which indicates when the measurement was verified by staff.

Vital signs available include: heart rate, heart rhythm, blood pressure, respiratory rate, peripheral oxygen saturation, temperature, location of temperature measurement, central venous pressure, oxygen flow in liters, oxygen device used for oxygen flow, and end tidal CO2. Other data elements available in nurseCharting include assessments made, commonly tabulated scores neurological function scales, sedation scales, pain scales , and other physiologic measurements or device settings.

Information related a patient's relevant past medical history is stored in the pastHistory table. Providing a detailed past history is not common, but items such as AIDS, cirrhosis of the liver, hepatic failure, chronic renal failure, transplant, pre-existing cancers, and immunosuppression are more reliably documented due to their importance in severity of illness scoring. Elements of past medical history are documented using a custom hierarchical coding system and stored with the charted time pastHistoryOffset and with the entry time pastHistoryEntryOffset.

Results of physical exams performed are stored in the physicalExam table. Data for physical exams are entered directly into eCareManager. Free text sections are not included in the database. There is a large variety of drop-down menus for the physical exams recorded, with specific text entry boxes allowing for the creation of a structured physical exam.

This table contains information related to respiratory care. Unlike other tables, the respiratoryCare table does not use an entity-value-attribute model, but instead has many columns for each setting, most of which are empty for a given time of data recording. Charted data which relate to a patient's ventilation status, including the configuration of the bedside mechanical ventilator, are stored in the respiratoryCharting table. Each setting is stored with an entry time respChartEntryOffset and an observation time respChartOffset. Examples of settings include the percentage of oxygen inspired, tidal volumes, pressure settings, and other ventilator parameters.

A custom hierarchical coding system is used to record active treatments, and there are are 2, unique treatments documented in eICU-CRD. The most frequent treatments explicitly documented in the table across patients were mechanical ventilation Large quantities of data are continuously recorded on ICU patients and displayed via bedside monitors.

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The vitalPeriodic and vitalAperiodic tables contain data derived directly from these bedside monitors. Unlike other data elements in the database, the data collected in these tables are not entered or validated by providers of care: the periodic and aperiodic vital sign data have been automatically derived and archived with no human verification. Continuously measured vital signs are recorded in the vitalPeriodic table and include heart rate, respiratory rate, oxygen saturation, temperature, invasive arterial blood pressure, pulmonary artery pressure, ST levels, and intracranial pressure ICP.

Table 7 summarizes data completion for periodic vital signs. Conversely, while the average number of heart rate measurements among patients with at least one recording of heart rate is Thus, while monitoring of ICP is infrequent across all patients, when it is performed it results in a large number of observations. In order to maintain data fidelity, very little post-processing has been performed. Table 8 presents data completion across tables, showing the number of hospitals with low, medium, and high data completion. Thus, care must be taken when using the data, as inconsistencies which are inconsequential for clinical care may impact analyses performed.

A public issue tracker is used as a forum for reporting technical issues and describing solutions The correction of technical errors will be made with updated data releases. Data can be accessed via a PhysioNet repository Details of the data access process are available online Use of the data requires proof of completion of a course on human subjects research e.

INTRODUCTION

Data access also requires a data use agreement that stipulates, among other items, that the user will not share the data, will not attempt to re-identify any patients or institutions, and will release code associated with any publication using the data. Updates which change the schema for currently available data, and as such break code syntactically, will result in a major version change. Release of new tables, correction of issues found in currently released data, and insertion of additional data into currently available tables will result in an increment in the minor version.

Due to the complexity of the deidentification process and the high sensitivity required, not all data could be made available in the current version of eICU-CRD. Updates to the current dataset will be made as data are certified safe for release. Future updates will be made to ensure data remain contemporary.

A core aim in publicly releasing the eICU-CRD is to foster collaboration in secondary analysis of electronic health records, so we have created an openly available repository for sharing code We believe that publicly accessible code to extract reliable and consistent definitions for key clinical concepts is of utmost importance, both to accelerate research in the field and to ensure reproducibility of future studies 19 , Detailed documentation is available online 17 and includes information regarding data access, table contents, and a schematic of the relationships between tables in the data.

The documentation is source controlled within the code repository allowing for collaborative development Discussion around data usage, highlighting of issues, and best practices can be made via the issues panel of the GitHub repository.

KEY POINTS

We have provided publicly accessible Jupyter Notebooks 21 , 22 to demonstrate usage of the data These notebooks supplement online documentation and include a detailed review of each table, with commentary on best practices when working with the data. More general notebooks are available in the code repository referenced earlier, and include notebooks for cohort extraction, summary of demographic characteristics, and visualization of time-series data. Figure 2 visualizes of a subset of variables available during a single patient stay and can be generated using a notebook provided online Data shown are a subset of all data available, and include: high granularity vital signs dashed lines, sourced from vitalPeriodic and vitalAperiodic , nurse validated vital signs solid markers, sourced from nurseCharting , blood product administration green cross, sourced from intakeOutput , and laboratory measurements sourced from lab.

How to cite this article : Pollard, T. Data doi: Kelly, F. Intensive care medicine is 60 years old: the history and future of the intensive care unit. Clinical medicine 14 , — Adhikari, N. Critical care and the global burden of critical illness in adults. The Lancet , — Celi, L. Johnson, A.


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  • Machine learning and decision support in critical care. Proceedings of the IEEE , — Lilly, C. A multicenter study of icu telemedicine reengineering of adult critical care. McShea, M. The eICU research institute-a collaboration between industry, health-care providers, and academia.

    Saeed, M. Critical care medicine 39 , — Scientific Data 3 United States. Health Insurance Portability and Accountability Act of Neamatullah, I. Automated de-identification of free-text medical records. BMC medical informatics and decision making 8 , 1—32 Finney, J. An efficient record linkage scheme using graphical analysis for identifier error detection. Pollard, T. Zimmerman, J. Critical Care Medicine 34 , — Goldberger, A.

    Circulation , e—e Braunschweiger, P. The CITI program: an international online resource for education in human subjects protection and the responsible conduct of research. Academic Medicine 82 , — Endocrinology and Metabolism Formulas. Environmental Facts and Formulas.

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