Disseminated Intravascular Coagulation

In addition to identifying a cause list and mapping this cause list across various revisions of the ICD, the largest impediment to comparability is the presence of a different set of GCs in each ICD revision. To more fully understand the problem of garbage codes, we created a typology of these codes that distinguishes four types of GCs. This typology has been developed taking into consideration the following: the likelihood that a condition can be an underlying cause of death; the need for codes that provide a location for unspecified or ambiguous causes of death; and the need for codes that represent causes that are not underlying but intermediate or final events in the chain leading to death. Four categories were identified:
1. Causes that cannot or should not be considered as underlying causes of death. These are codes that are included in the ICD because of its use for classifying health service encounters but that do not signify underlying cause of death. Examples of this type of GC are all the codes under chapter 18 of ICD-10 or R codes. This category also includes two special cases in the cardiovascular area: essential primary hypertension and atherosclerosis. Essential primary hypertension is included in the ICD to classify clinical encounters, but for most physicians, it should be considered a risk factor for cardiovascular disease and not the underlying cause. This distinction between what is a risk factor and what is an underlying cause is somewhat arbitrary but necessary to enhance comparability across revisions. Finally, we included in this category a number of causes that are described as the long-term sequelae of disease, such as G82, paraplegia and tetraplegia, or O94, sequelae of complication of pregnancy, childbirth, and the puerperium. In these cases, for public health purposes, it is more useful to assign these deaths to the underlying cause despite the long time lag between disease and death.
2. Intermediate causes of death such as heart failure, septicemia, peritonitis, osteomyelitis, or pulmonary embolism. These are clearly defined clinical entities, but each has an underlying cause that would have precipitated the chain of events leading to death. Physicians who have not been adequately trained in the principles of the ICD underlying cause of death often use these causes on death certificates.
3. Immediate causes of death that are the final steps in a disease pathway leading to death. Examples of this include disseminated intravascular coagulation or defibrination syndrome (D65). The pathway to death includes the final immediate cause, an intermediate cause, and the underlying cause that triggered the chain of events. Cardiac arrest (I46) and respiratory failure, not elsewhere classified (J96), are other examples.
4. Unspecified causes within a larger cause grouping. For many diseases, such as neoplasms, a code is included within the grouping for an unspecified site. This is an illustration of a GC that is not important for assessing aggregate deaths from neoplasms from all sites but is important when assessing site-specific death rates. Another important example is the injury category in which some injuries are coded to unspecified factors or intent.
Table 2 provides a listing of the number of each type of GC that we identified related to our 56-cause list. The largest category of GCs is type 1. Assessment of the number of GCs, especially in category 4, is a function of the level of detail in the final cause list that is being developed.

The events of interest in this study were AESIs that might need evaluation after covid-19 vaccination. This list of outcomes was based on the protocol published by the FDA Center for Biologics Evaluation and Research, the prioritised covid-19 vaccine AESI list by the Brighton Collaboration, and previous studies.4
17 We included 15 events: non-haemorrhagic and haemorrhagic stroke, acute myocardial infarction, deep vein thrombosis, pulmonary embolism, anaphylaxis, Bell’s palsy, myocarditis or pericarditis, narcolepsy, appendicitis, immune thrombocytopenia, disseminated intravascular coagulation, encephalomyelitis (including acute disseminated encephalomyelitis), Guillain-Barré syndrome, and transverse myelitis.4
Events were identified by records of the occurrence of conditions based on predefined phenotyping algorithms (eg, diagnosis codes from claims or diagnosis codes and problem lists from electronic health records). Definitions for encephalomyelitis, non-haemorrhagic and haemorrhagic stroke, and acute myocardial infarction also required the record to occur within an inpatient setting in any diagnosis positions, whereas the definition for Guillain-Barré syndrome required the condition to be recorded in an inpatient setting in the primary position. Appendix tables 2 and 3 present the full specifications of all phenotype definitions, including source codes (original codes used in the database) and standard concepts (normative expressions used to represent a unique clinical entity within the Observational Medical Outcomes Partnership common data model, which were mostly SNOMED (Systematized Nomenclature of Medicine) codes in this study).
We defined a “clean window” period before each index date, during which qualifying events (AESIs) could not be observed. If an AESI was observed during this period, the participant did not enter the study cohort for that event. If an individual had a qualified event during follow-up, this participant would contribute to the person time of that event cohort after the clean window continually until censored from the cohort.
Figure 1 shows the cohort entry, follow-up, and event definitions. In keeping with the FDA protocol, the clean window was 365 days for all events except anaphylaxis (30 days) and facial nerve palsy and encephalomyelitis (183 days).4
As the CPRD-GOLD (UK), IQVIA (France, Germany, and Australia), and IPCI (the Netherlands) databases only included primary care data, we did not use them for events where definition required an inpatient diagnosis.

Vascular phenomena included major arterial emboli, septic pulmonary infarcts, mycotic aneurysms, intracranial hemorrhages, conjunctival hemorrhages, and Janeway lesions. Immunological phenomena included glomerulonephritis, Osler’s nodes, Roth spots, decrease in complements in serum level, and rheumatoid factor positivity. Vascular and immunological phenomena were regarded as absent when none of these phenomena were documented. Antibiotic treatment was classified as appropriate or as inappropriate when the identified pathogens were sensitive and resistant, respectively, to the initially prescribed antibiotics. The antibiotic susceptibility was assessed with minimum inhibitory concentration testing according to the guidelines of the Clinical and Laboratory Standards Institute [12 ]. Persistent bacteremia was defined as any blood culture growing the same microorganism as the index culture ≥48 h after the start of the antimicrobial therapy [13 (link)]. Disseminated intravascular [13 (link)] coagulation (DIC) was diagnosed according to the disseminated intravascular coagulation diagnostic criteria established by the Japanese Association for Acute Medicine (JAAM DIC diagnostic criteria) [14 (link)]. These definitions were consistent with the previous study’s criteria [2 (link)].

The registry had medical data of 65,654 consecutive adult patients who were treated at the ICU with SOFA score evaluated upon admission in Samsung Medical Center, Seoul, Korea between June 2013 and May 2022. In this study, the SOFA score was reported in the medical record for all included patients. This large, single-center cohort was generated in a de-identified form using data extracted by the institutional electronic archive system. The “Clinical Data Warehouse Darwin-C” is an electronic system built for investigators to search and retrieve data from institutional electronic medical records for over 4 million patients with more than 900 million laboratory findings and 200 million prescriptions. For mortality data outside our institution, this system uses a unique personal identification number updated from the National Population Registry of the Korea National Statistical Office. Using an extracted raw medical record, independent investigators who were blinded to mortality organized relevant variables of demographic data and underlying diseases. Results of blood laboratory tests and SOFA score were automatically extracted.
For this study, we selected patients with available fibrinogen and albumin levels at admission to ICU. The decision to measure fibrinogen levels was based on the individual patient’s clinical situation. Fibrinogen was routinely checked on ICU admission in patients with sepsis, suspicious disseminated intravascular coagulation, solid and hematologic malignancies, post-operative care, suspicious bleeding tendency, etc. We initially stratified study patients into three groups according to tertile values of FAR upon ICU admission. After estimating an optimal threshold associated with 1-year mortality, we divided patients accordingly.