Coagulation measures after cardiac arrest (CMACA)

Kim, Hyojoon; Kurz, Michael; Wee, Jung Hee; Oh, Joo Suk; Kim, Won Young; Cho, In Soo; Lee, Mi Jin; Lee, Dong Hun; Kim, Yong Hwan; Youn, Chun Song

doi:10.1371/journal.pone.0279653

Cited by 6 publications

(1 citation statement)

References 28 publications

(37 reference statements)

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“…Previous studies have demonstrated the relationship between coagulation changes after CA and prognosis, as well as attempts to use various indicators and models to predict the clinical outcomes, 19 – 22 but there is still a lack of indicators and models that are accepted by most clinicians. Regardless of the coagulation indices used to assess the prognosis of CA patients, however, we cannot avoid the relevant studies of sepsis because sepsis and PCAS have very similar pathophysiological characteristics.…”

Section: Discussionmentioning

confidence: 99%

Cardiac Arrest-Associated Coagulopathy Could Predict 30-day Mortality: A Retrospective Study from Medical Information Mart for Intensive Care IV Database

Duan,

Ge,

Fan

et al. 2024

Clin Appl Thromb Hemost

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Background Cardiac arrest (CA) can activate the coagulation system. Some coagulation-related indicators are associated with clinical outcomes. Early evaluation of patients with cardiac arrest-associated coagulopathy (CAAC) not only predicts clinical outcomes, but also allows for timely clinical intervention to prevent disseminated intravascular coagulation. Objective To assess whether CAAC predicts 30-day cumulative mortality. Methods From the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, we conducted a retrospective cohort study from 2008 to 2019. Based on international normalized ratio (INR) value and platelet count, we diagnosed CAAC cases and made the following stratification of severity: mild CAAC was defined as 1.4 > INR≧1.2 and 100,000/µL < platelet count≦150,000/µL; moderate CAAC was defined with either 1.6 > INR≧1.4 or 80,000/µL < platelet count≦100,000/µL; severe CAAC was defined as an INR≧1.6 and platelet count≦80,000/µL. Results A total of 1485 patients were included. Crude survival analysis showed that patients with CAAC had higher mortality risk than those without CAAC (33.0% vs 52.0%, P < 0.001). Unadjusted survival analysis showed an incremental increase in the risk of mortality as the severity of CAAC increased. After adjusting confounders (prehospital characteristics and hospitalization characteristics), CAAC was independently associated with 30-day mortality (hazard rate [HR] 1.77, 95% confidence interval [CI] 1.41–2.25; P < 0.001); moderate CAAC (HR 1.48, 95% CI 1.09–2.10; P = 0.027) and severe CAAC (HR 2.22, 95% CI 1.64–2.97; P < 0.001) were independently associated with 30-day mortality. Conclusion The presence of CAAC identifies a group of CA at higher risk for mortality, and there is an incremental increase in risk of mortality as the severity of CAAC increases. However, the results of this study should be further verified by multicenter study.

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Section: Discussionmentioning

confidence: 99%

Cardiac Arrest-Associated Coagulopathy Could Predict 30-day Mortality: A Retrospective Study from Medical Information Mart for Intensive Care IV Database

Duan,

Ge,

Fan

et al. 2024

Clin Appl Thromb Hemost

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Prediction model of in‐hospital cardiac arrest using machine learning in the early phase of hospitalization

Wu,

Kor,

Chou

et al. 2024

The Kaohsiung J of Med Scie

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In hospitals, the deterioration of a patient's condition leading to death is often preceded by physiological abnormalities in the hours to days beforehand. Several risk‐scoring systems have been developed to identify patients at risk of major adverse events; however, such systems often exhibit low sensitivity and specificity. To identify the risk factors associated with in‐hospital cardiac arrest (IHCA), we conducted a retrospective cohort study at a tertiary medical center in Taiwan. Four machine learning algorithms were employed to identify the factors most predictive of IHCA. The support vector machine model was discovered to be the most effective at predicting IHCA. The ten most critical physiological parameters at 8 h prior to the event were pulse rate, age, white blood cell count, lymphocyte count, body temperature, body mass index, systolic and diastolic blood pressure, platelet count, and use of central nervous system‐active medication. Using these parameters, we can enhance early warning and rapid response systems in our hospital, potentially reducing the incidence of IHCA in clinical practice.

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Association of coagulation function with the risk of in-hospital mortality in patients with severe acute respiratory distress syndrome

Li,

Pan

2024

The American Journal of the Medical Sciences

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Coagulation measures after cardiac arrest (CMACA)

Cited by 6 publications

References 28 publications

Cardiac Arrest-Associated Coagulopathy Could Predict 30-day Mortality: A Retrospective Study from Medical Information Mart for Intensive Care IV Database

Cardiac Arrest-Associated Coagulopathy Could Predict 30-day Mortality: A Retrospective Study from Medical Information Mart for Intensive Care IV Database

Prediction model of in‐hospital cardiac arrest using machine learning in the early phase of hospitalization

Association of coagulation function with the risk of in-hospital mortality in patients with severe acute respiratory distress syndrome

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