Data source
The Medical Information Mart for Intensive Care-III (MIMIC-III) database was established in 2003 with funding from the National Institutes of Health (NIH) at Beth Israel Deaconess Medical Center and Massachusetts General Hospital, Massachusetts Institute of Technology (MIT)15. The current (July 2018) version of the MIMIC-III database is version 1.4 and covers data obtained from 2001 to 2012 from more than 58,000 hospitalizations of patients at Beth Israel Deaconess Medical Center, including 38,645 adult patients and 7875 neonatal patients16.
This database provides a large amount of real data that can be utilized in clinical research and comprises information related to patients admitted to critical care units at large tertiary care hospitals. Data include vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. All data can be extracted in the SQL language for further analysis. The personnel involved in this research participated in a series of courses provided by the NIH and obtained authorization to access the MIMIC-III database after passing the required assessment (certificate number 38601114). The patient information in the database is anonymous, so informed consent is not required.
Study population
We used ICD-9 codes 99591, 99592, and 78552 to extract data from 7770 (patients diagnosed with sepsis, severe sepsis, and septic shock from the MIMIC-III database. According to the new definition of sepsis, septic shock has already included severe sepsis in the old definition. However, since the data collected in the 1.4 version of the database was from 2001 to 2012, the definition of sepsis may still be used in the old version, so we still used the diagnosis of sepsis, severe sepsis and septic shock when extracting the data. The inclusion criteria were as follows:(1) patients who were 18 years of age or older;(2) patients with more than a 24-hour stay in the ICU to ensure sufficient data for evaluation; and(3) patients diagnosed with sepsis according to the Third International Consensus Definitions of Sepsis and Septic Shock (Sepsis-3), including infection and organ failure (SOFA score ≥2).For patients who have been admitted to ICU twice or more, we only focus on the information of their first admission to ICU.
Data extraction
All data were translated into SQL for further analysis. The hadm_id variable for each included patient was used to extract the following information from the MIMIC-III database: sex; age; SOFA score; continuous renal replacement therapy (CRRT) use; first care unit (SICU,TSICU,MICU,CCU,CSRU); comorbidities namely congestive heart failure, cardiac arrhythmia, renal failure, liver disease, metastatic cancer (MC), diabetes, coagulopathy, fluid electrolytes, and blood loss anaemia; laboratory tests namely white blood cell count (WBC), neutrophil percentage (NET), red blood cell distribution width (RDW), haematocrit (HCT), sodium, potassium, albumin, lactate, and blood pH; and vital signs such as(namely) heart rate, respiratory rate, body temperature, and SpO2。All of the above information and data were extracted from the first 24 h of ICU stay
Statistical analysis and nomogram construction
Continuous variables are expressed as the mean and standard deviation, while categorical variables are expressed as percentages. Multivariate Cox regression was used to select variables for plotting the 30-, 60-, and 90-days survival curves of the patients. The survival-probability nomogram was constructed using Cox regression. The included patients were divided into a training cohort and a validation cohort . The training cohort data were subjected to multifactor Cox regression analysis to control for confounding factors. The analysis presumed that the effects of the predictor variables were constant over time and that there was a linear relationship between the endpoint and predictor variables. Predictor variables that had a highly skewed distribution were subjected to logarithmic transformation to reduce the effect of extreme values, in which case the value of log(variable) could be entered as a predictor variable. SPSS (version 24.0, Chicago, Illinois, USA) and R (version 4.0.2; https://www.r-project.org/) were used for data analysis. A P value <0.05 in a two-sided test was considered statistically significant.
Nomogram validation and performance evaluation
The validity of the nomogram was assessed based on its discrimination performance and by constructing both internal (with the training cohort) and external (with the validation cohort) calibration curves. A comparison can be made between the performance of the two models using receiver operating characteristic (ROC) curve analysis and the area under the ROC curve(AUC),
The predictive accuracy of the model) was determined by calculating the integrated discrimination improvement (IDI) and the net reclassification improvement (NRI)17. NRI is used to compare the diagnostic ability of two indicators at a certain threshold, whether one indicator is more accurate than the other. If the NRI is much higher than 0, it is positive improvement (improved prediction ability); if it is little more than 0, it is negative improvement (decreased prediction ability); if it is equal to 0, it is no improvement. IDI is used to reflect the overall improvement of the model at different thresholds. Similar to NRI, if IDI > 0, it is positive improvement, indicating that the prediction ability of the new model is improved compared with the old model; if IDI < 0, it is negative improvement, and the prediction ability of the new model is decreased; if IDI = 0, it is considered that the new model is not improved. “Positive outcomes are situations such as succeeding, winning, or being cured of an illness, while negative outcomes are situations such as failing, losing, or succumbing to an illness. Finally, the net clinical benefit of the predictive model developed in the present study was assessed using decision-curve analysis (DCA)18.