Design and setting
This retrospective observational study used a part of a large-scale commercial database covering approximately 23% of acute-care hospitals in Japan that contained about 30 million patients until October 2019 (Medical Data Vision Co., Tokyo, Japan). The database includes data on age, sex, primary diagnoses, concomitant diagnoses, complication diagnoses, procedures, prescriptions, discharge status, and laboratory tests. In this database, the diagnoses are recorded using International Classification of Diseases Tenth Revision (ICD-10) codes. Among the overall patient data registered in the database, this study included patient data from 42 acute-care hospitals having laboratory data.
This study followed the principles of the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board for Clinical Research of Osaka General Medical center (IRB No. S201916015). The board waived the requirement for informed consent because of the anonymous nature of the data and because no information on individual patients, hospitals, or treating physicians was obtained.
Participants
We included all patients requiring unplanned hospital admission and were diagnosed as having sepsis from February 1, 2006 to December 31, 2019. In this study, sepsis was defined as having a proven/suspected infection and the development of organ dysfunction (total SOFA score of ≥ 2 points at the time of admission), according to the Sepsis-3 criteria.1 A proven/suspected infection was defined as having any of the infection-related ICD-10 codes previously proposed by The Institute for Health Metrics and Evaluation (IHME)11 in the primary diagnosis or the diagnosis that triggered hospitalization. The detailed set of the ICD-10 codes for the presence of infection is listed in Table S1. We excluded patients who were admitted to hospital with a diagnosis of sepsis more than once during the study period (i.e., had a second or subsequent record of admission).
Data collection
We collected the following data on baseline patient characteristics: age, sex, height, weight, Charlson Comorbidity Index,12 intensive care unit admission, anatomical site of infection, organ dysfunction, use of catecholamines, and laboratory tests including platelet count, white blood cell count, bilirubin, creatinine, prothrombin time, C-reactive protein, blood glucose, lactate dehydrogenase, total protein, albumin, uric acid, blood urea nitrogen, and creatine kinase.
We used previously published ICD-10 coding algorithms for defining Charlson comorbidities.13 Organ dysfunction was evaluated by ICD-10 coding algorithms previously published by Angus et al.14 and Martin et al.15 The presence and severity of organ dysfunction at the time of hospital admission were also evaluated according to the SOFA score.
The renal, hepatic, and coagulation subscores of SOFA were calculated based on the laboratory tests (i.e., creatinine, bilirubin, and platelet count). The cardiovascular, respiratory, and neurological subscores were calculated by a previously published modified method16 (Table S2) because sufficient data for the calculation of these subscores by standard methods were not recorded in this study registry. The Japan Coma Scale, used for calculating the neurological subscore instead of the Glasgow Coma Scale, has four main grades (grade 0: alert; grade 1: possible verbal response without any stimulation, not lucid; grade 2: possible eye-opening, verbal and motor response upon stimulation; and grade 3: no eye-opening and coma upon stimulation). The primary outcome measure was all-cause in-hospital mortality.
Statistical analysis
Descriptive statistics were calculated as group medians with the first and third quartiles for continuous variables, and frequencies with percentages for categorical variables. The differences in patient characteristics, general laboratory test results, and severity of illness scores between survivors and non-survivors were evaluated by the Mann-Whitney U test or chi-square test.
To evaluate the nonlinear association between mortality and the biomarkers, we constructed restricted cubic spline curves using logistic regression models. The knot values were determined based on Harrell’s recommended percentiles, with the knots placed at equally spaced percentiles of the original variable’s marginal distribution.17 The number of knots in each analysis was determined by the Wald test in such way that the explanatory variables at all sections divided by the knots were significant.18
To assess the synergistic increases (interactions) in mortality according to the simultaneous increase in every two SOFA subscores, we evaluated whether the odds ratio of each subscore positive (2 point or more increase) for mortality was significantly increased when another score was also increased above 2 points by logistic regression analyses including two-way interaction terms between the subscores.
Because there were moderate proportions of missing data, we performed sensitivity analyses using a multiple imputation technique to calculate missing values for biomarkers because the probability of missing data for these markers could be assumed not to depend on the unobserved data themselves (missing at random). We created 10 imputations for each missing value using the other available variables and then fit the desired models separately on each of the 10 imputed datasets and combined the results based on the concepts developed by Rubin.19
All hypotheses were two-sided, and a p value of < 0.05 indicated statistical significance. Because of the underpowered nature of the analyses investigating potential heterogeneities of treatment effects, we used a two-sided significance level of 20% with statistical inferences for the analyses of effect modification.20 All statistical analyses were conducted using STATA Data Analysis and Statistical Software version 15.0 (StataCorp LLC, College Station, TX).