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Critical care database comprising patients with infection at Zigong Fourth People's Hospital

Ping Xu Lin Chen Zhongheng Zhang

Published: Sept. 9, 2021. Version: 1.0


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Xu, P., Chen, L., & Zhang, Z. (2021). Critical care database comprising patients with infection at Zigong Fourth People's Hospital (version 1.0). PhysioNet. https://doi.org/10.13026/gz5h-e561.

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Abstract

Patients treated in the intensive care unit (ICU) are closely monitored and receive intensive treatment. Such aggressive monitoring and treatment will generate high-granularity data from both electronic healthcare records and nursing chart. These data not only provide infrastructure for daily clinical practice, but also can help to inform clinical researches. It is technically challenging to integrate and cleanse medical data from a variety of sources. Although there are several open access critical care databases from western countries, there is a lack of this kind of database for Chinese adult patients. We established a critical care database involving patients with infection. A large proportion of these patients have sepsis and/or septic shock. High-granularity data comprising laboratory findings, baseline characteristics, medications, ICD code, nursing charts and follow-up results were integrated to generate a comprehensive database. The database can be utilized for a variety of clinical studies.


Background

Infection is common in the intensive care unit (ICU) [1,2]. There are two categories of infections for ICU patients due to the place where the infection was acquired. One type of infection is the infection present on ICU admission, and most of such patients are transferred to ICU due to the development of sepsis and/or septic shock [3,4]. The other type of infection is the infection acquired after ICU admission, which is also termed the nosocomial infection [1]. Critically ill patients are at increased risk of infection because of compromised immunity, use of intravascular catheters and endotracheal intubation [5,6]. Irrespective of the places where the infection is acquired, infection can cause systematic inflammatory response (SIRS), sepsis and septic shock. These complications are associated with significantly increased risk of mortality [7,8] . Although sepsis has been widely investigated in the literature [4,9,10], the raw data are typically not publicly available due to confidential or legal issues. The restricted data usage created a barrier to reproduce and verify the results.

Although several open access critical care databases from western countries have been created to promote data sharing and reuse for the scientific community [11-15], there is a lack of such database comprising Chinese adult patients. Since the Chinese population is the largest in the world, exploring infection/sepsis in Chinese population is the key to achieve the goal proposed by the surviving sepsis campaign [16]. Furthermore, a dataset, especially those generated from electronic healthcare records are large in volume. Secondary analysis of such dataset can generate novel insights into to diseases of interest [13,17,18]. Thus, creating a critical care database relating to patients with infection can help to promote collaborative research across the globe to reveal more insights into the infections in the critically ill patients.


Methods

The study was conducted in Zigong Fourth People’s Hospital, Sichuan, China from January 2019 to December 2020, and was approved by the ethics committee of Zigong Fourth People’s Hospital (Approval Number: 2020-065). Patients with age > 18 and who had been transferred to the ICU in the index hospital were included in the dataset. Informed consent was waived due to retrospective design of the study. The study complies with the Declaration of Helsinki.

Electronic healthcare records of consecutive ICU patients with the diagnosis of infection - irrespective of the place where the infection was acquired - were included in the database. Infection was defined according to the diagnosis description that contained keywords like “infection”, “pneumonia” and “-itis”. As the original diagnosis description was recorded in simplified Chinese, the actual search was on equivalents such as “Ganran” and “Yan”. Some autoimmune or connective tissue diseases were excluded manually.

Deidentification

The data were deidentified before incorporating into the critical care database. All HIPAA (Health Insurance Portability and Accountability Act) protected health information identifiers including patient name, cell phone/telephone numbers, address, and any other variables that could uniquely identify the individual in structured data sources. The key variables PATIENT_ID and INP_NO were randomly assigned a unique number and the original patient ID and hospital ID were removed.

Event time points were replaced with an offset value measured in hours from the hospital admission time (i.e. hospital admission time was the zero point). The original time points were removed from the dataset. Patients older than 89 years were assigned a random number from 90 to 120 for the age variable.


Data Description

The critical care database was populated with data that have been acquired during routine clinical practice, and thus the establishment of the database did not interfere with the clinical practice and was not associated with increased burden on healthcare. Data were exported from several information systems including electronic healthcare records (EHR), Hospital Information System (HIS), Laboratory Information System (LIS) and critical care nursing chart system.

The critical care database is provided as a collection of comma separated value (CSV) file. Such files can be easily processed with popular languages scripts such as PostreSQL, MySQL, R and MonetDB. The data were arranged into six data tables in “.csv” format (Table 1). These data tables are linked to each other by unique patient keys (i.e. INP_NO or PATIENT_ID).

Filename

Description

dtBaseline.csv

This data table contains data on baseline characteristics of individual patients. One line represents one patient entry.

dtDrugs.csv

This data table contains data from the HIS and it is medical order prescribed by physicians. The datatime represents the time of prescription and is not necessarily the time of drug administration.

dtICD.csv

This data table contains ICD code and diagnosis descriptions. The description was translated from Chinese words. The Status_Discharge column describes the status of each individual diagnosis. If a patient died on hospital discharge, Status_Discharge will be coded as “dead” for all diagnosis. This table can be used to compute hospital mortality.

dtLab.csv

Laboratory variables, as well as the reference range for each item are listed.

dtTransfer.csv

The data table contains information of transferring between different departments, i.e. from gastroenterology department to ICU.

dtNursingChart.csv

Nursing chart contains all kinds of recordings by bedside nurses. The progress notes were written in Chinese, which can be used for natural language processing.

dtOutcome.csv

The outcomes of included patients. Especially, it contains SF-36 questionnaire, which was obtained by follow-up after discharged home.

Baseline characteristics of included patients

The overall mortality rate at hospital discharge was 5.8% (161/2790). The proportion of male was higher in non-survivors than that in survivors (70% vs. 59%; p = 0.014). Patients with pneumonia were more likely to die than other site of infection. However, non-survivors showed shorter length of stay in both hospital and ICU, which was attributable to the fact that many severely ill patients chose to withdraw life-support interventions and died shortly after a few days of treatment.


Usage Notes

The dataset has not yet been utilized for clinical studies. The database can be utilized for risk factor analysis, validation for the early warning scores developed in other institutions and predictive analytics. The long-term functional outcome has not been established in previous critical care database, which is highlighted in the current study.

The database is limited by its retrospective nature and there are missing values in many variables. Furthermore, the ventilator parameters are not recorded in a high granularity and no waveform is provided.


Conflicts of Interest

The authors have no conflicts of interest to declare.


References

  1. Dawit, T. C., Mengesha, R. E., Ebrahim, M. M., Tequare, M. H. & Abraha, H. E. Nosocomial sepsis and drug susceptibility pattern among patients admitted to adult intensive care unit of Ayder Comprehensive Specialized Hospital, Northern Ethiopia. BMC Infect Dis 21, 824 (2021).
  2. Sang, L. et al. Secondary infection in severe and critical COVID-19 patients in China: a multicenter retrospective study. Ann Palliat Med apm-21-833 (2021) doi:10.21037/apm-21-833.
  3. Abe, T. et al. Epidemiology of sepsis and septic shock in intensive care units between sepsis-2 and sepsis-3 populations: sepsis prognostication in intensive care unit and emergency room (SPICE-ICU). J Intensive Care 8, 44 (2020).
  4. Zhang, Z., Bokhari, F., Guo, Y. & Goyal, H. Prolonged length of stay in the emergency department and increased risk of hospital mortality in patients with sepsis requiring ICU admission. Emerg Med J 36, 82–87 (2019).
  5. Nasa, P., Juneja, D., Singh, O., Dang, R. & Singh, A. An observational study on bloodstream extended-spectrum beta-lactamase infection in critical care unit: incidence, risk factors and its impact on outcome. Eur J Intern Med 23, 192–195 (2012).
  6. Patterson, L., McMullan, R. & Harrison, D. A. Individual risk factors and critical care unit effects on Invasive Candida Infection occurring in critical care units in the UK: A multilevel model. Mycoses 62, 790–795 (2019).
  7. Markwart, R. et al. Epidemiology and burden of sepsis acquired in hospitals and intensive care units: a systematic review and meta-analysis. Intensive Care Med 46, 1536–1551 (2020).
  8. Singer, M. et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315, 801–810 (2016).
  9. Sakr, Y. et al. Sepsis in Intensive Care Unit Patients: Worldwide Data From the Intensive Care over Nations Audit. Open Forum Infect Dis 5, ofy313 (2018).
  10. Walkey, A. J., Lagu, T. & Lindenauer, P. K. Trends in sepsis and infection sources in the United States. A population-based study. Ann Am Thorac Soc 12, 216–220 (2015).
  11. Johnson, A. E. W. et al. MIMIC-III, a freely accessible critical care database. Sci Data 3, 160035 (2016).
  12. Pollard, T. J. et al. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Sci Data 5, 180178 (2018).
  13. Schenck, E. J. et al. Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data. Journal of Biomedical Informatics 118, 103789 (2021).
  14. Thoral, P. J. et al. Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example. Crit Care Med 49, e563–e577 (2021).
  15. Fleuren, L. M. et al. The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients. Critical Care 25, 304 (2021).
  16. Nunnally, M. E. et al. The Surviving Sepsis Campaign: research priorities for the administration, epidemiology, scoring and identification of sepsis. Intensive Care Med Exp 9, 34 (2021).
  17. Zhang, Z., Mo, L., Ho, K. M. & Hong, Y. Association Between the Use of Sodium Bicarbonate and Mortality in Acute Kidney Injury Using Marginal Structural Cox Model. Crit Care Med 47, 1402–1408 (2019).
  18. Zhang, Z., Zhu, C., Mo, L. & Hong, Y. Effectiveness of sodium bicarbonate infusion on mortality in septic patients with metabolic acidosis. Intensive Care Med 44, 1888–1895 (2018).

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