This is a retrospective cohort study, and it was approved by the Biomedical Ethics
Committee. This study was registered at the Chinese Clinical Trial Register (CCTR
number: ChiCTR-ORC-16010138, registered 12 December 2016). URL:
http://www.chictr.org.cn/listbycreater.aspx. Written informed consent was not obtained from the patients or their relatives due
to the retrospective study design of using the electronic health records and no additional
interventions were given to the subjects.
We conducted the study in a 48-bed general intensive care unit (GICU) of Sichuan University
West China Hospital, (Chengdu, Sichuan Province, China). The periods of recruitment
in the study included one whole year from 1 January to 31 December 2015. We identified those with suspected infection for the purpose of criteria comparison
among 1243 electronic health records. The dealline for follow-up time was defined
as the end of in-hospital for every patient according to the electronic health records.
The included criteria of participants:
1. The participants with suspected infection need to simutanuously meet the following
three requirements (
17
</a>). Firstly, the initial episode of suspected infection was defined as a combination
of body fluid cultures and antibiotics. Secondly, it is necessary that the combination
of antibiotics and culture sampling occurred within a specific time limits. If the antibiotic was administered first, the culture sampling must have been obtained
within 24 hours. If the culture sampling occurred first, antibiotic administration must have been instigated
within 72hours. Finally, the “onset” of infection included the time when the first
of the 2 events took place.</p>
3. Length of stay in GICU ≥24 hours.
In the study, we followed up all patients after hospital discharge using their medical
records and all-cause in-hospital mortality was the primary outcome. The secondary
outcome was the risks of an ICU stay of 3 or more days. The exposures of risk factors
for in-hospital death included scores of SIRS, qSOFA and SOFA.
Effect Modifiers and Potential Confounders
The major effect modifiers were age and acute physiology and chronic health evaluation
(APACHE) II in the study due to the hospital mortality gradually increased with the
increasing age and APACHE II; sex was the potential confounders due to the difference between male and female was evident
in hospital mortality.
Data Sources and Bias Control
Each component of SIRS, qSOFA and SOFA derived from indicators of every medical record. For the time window from 48 hours before to 24 hours after the onset of infection,
we calculated the maximum score of SIRS, qSOFA, and SOFA (
17
</a>). In sepsis occurring before, near the moment of, or after infection, organ dysfunction
is recognized by clinicians. Before to up to 24 hours after the onset of infection,
we calculated a change of 2 points or more in the SOFA score from up to 48 hours.</p>
The researchers in this study collected general information from medical records of
patients admitted to the ICU: medical identification, demographic characteristics,
vital signs, and results of laboratory tests. We calculated the qSOFA, SIRS and SOFA
scores for each patient using those data. Acute Physiology and APACHE II collected
to assess the illness severity of members of the enrolled participant.
These study designers did not participate in the data collection, and those who participated
in data collection of the study were blind to the study design.
Comparability of Assessment Methods
The comparable cohorts including Sepsis 1.0 and Sepsis 3.0 were generated from the
database of critically ill patients with infection. We compared with the baseline
characteristics and in-hospital death of both cohorts.
Descriptive variables with a normal distribution were expressed as means ± standard
deviations and were analysed using an independent sample t test. We expressed variables with a skewed distribution as medians and quartiles
and analysed them using the Mann-Whitney U test. We employed the χ2 test for comparison of frequencies. To assess the baseline risk of outcomes, we analysed
the demographic variables that significantly differed among patients with opposite
outcomes by means of univariate and multivariate logistic regression analysis; we
determined the independent predictors. We constructed and compared the area under
the curve of receiver operating characteristic (AUROC) was determined to assess predictive
values.
We performed all the statistical analyses using MedCalc® (version 15.8) statistical
software (
18
</a>), and Empower Stats software. All the statistical tests were two-tailed, and <em>P</em><0.05 was considered significant. We considered the area under the receiver operating
characteristic curve (AUROC) to be poor at 0.6 − 0.7, adequate at 0.7 − 0.8, good
at 0.8 − 0.9, and excellent at 0.9 or higher (<a href="#_ENREF_19">
19
</a>).</p>