Risk factors for 28-day mortality in patients with sepsis-related myocardial injury in intensive care units

Zhang, Meng; Duan, Meili; Zhi, Deyuan; Lin, Jinxi; Liu, Pei; Wang, Yajun

doi:10.1177/03000605211004759

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…As mentioned before, PaO 2 /FiO 2 as one of items in SOFA score could reflect the severity of illness. Some studies have shown that PaO 2 /FiO 2 was an independent risk factor for 28-day death in patients with sepsis [ 9 , 10 , 20 ], which was also confirmed our study. However, there was a U-shape relationship between PaO 2 /FiO 2 and 28-day mortality in patients with sepsis through analysis using generalized additive model and smoothed curve fitting.…”

Section: Discussionsupporting

confidence: 92%

“…A retrospective study of 135 elderly patients with sepsis showed that PaO 2 /FiO 2 was a promising tool and biomarker for predicting 28-day mortality [ 9 ]. High PaO 2 /FiO 2 was an independent risk factor for 28-day mortality in patients with sepsis-related myocardial injury [ 10 ]. In addition, a novel blended machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis identified the minimum PaO 2 /FiO 2 as one of the top important predictors [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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The PaO2/FiO2 is independently associated with 28-day mortality in patients with sepsis: a retrospective analysis from MIMIC-IV database

Liu

Chen

et al. 2023

BMC Pulm Med

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Background To clarify the relationship between the PaO2/FiO2 and 28-day mortality in patients with sepsis. Methods This was a retrospective cohort study regarding MIMIC-IV database. Nineteen thousand two hundred thirty-three patients with sepsis were included in the final analysis. PaO2/FiO2 was exposure variable, 28-day mortality was outcome variable. PaO2/FiO2 was log-transformed as LnPaO2/FiO2. Binary logistic regression was used to explore the independent effects of LnPaO2/FiO2 on 28-day mortality using non-adjusted and multivariate-adjusted models. A generalized additive model (GAM) and smoothed curve fitting was used to investigate the non-linear relationship between LnPaO2/FiO2 and 28-day mortality. A two-piecewise linear model was used to calculate the OR and 95% CI on either side of the inflection point. Results The relationship between LnPaO2/FiO2 and risk of 28-day death in sepsis patients was U-shape. The inflection point of LnPaO2/FiO2 was 5.30 (95%CI: 5.21—5.39), which indicated the inflection point of PaO2/FiO2 was 200.33 mmHg (95%CI: 183.09 mmHg—219.20 mmHg). On the left of inflection point, LnPaO2/FiO2 was negatively correlated with 28-day mortality (OR: 0.37, 95%CI: 0.32—0.43, p < 0.0001). On the right of inflection point, LnPaO2/FiO2 was positively correlated with 28-day mortality in patients with sepsis (OR: 1.53, 95%CI: 1.31—1.80, p < 0.0001). Conclusions In patients with sepsis, either a high or low PaO2/FiO2 was associated with an increased risk of 28-day mortality. In the range of 183.09 mmHg to 219.20 mmHg, PaO2/FiO2 was associated with a lower risk of 28-day death in patients with sepsis.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

The PaO2/FiO2 is independently associated with 28-day mortality in patients with sepsis: a retrospective analysis from MIMIC-IV database

Liu

Chen

et al. 2023

BMC Pulm Med

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“…SpO 2 and PaO 2 were also independent predictors for predicting the mortality of septic patients with AF. Previous studies have shown that SpO 2 and PaO 2 were associated with mortality of many diseases including aortic dissection, acute respiratory distress syndrome, and sepsis-induced myocardial injury (35)(36)(37). In addition, first 24-h urine output was also a risk factor associated with the prognosis of septic patients with AF.…”

Section: Discussionmentioning

confidence: 99%

Development of a Machine Learning Model for Predicting 28-Day Mortality of Septic Patients With Atrial Fibrillation

Wang

Zhang

Chao

et al. 2023

Shock

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Introduction: Septic patients with atrial fibrillation (AF) are common in the intensive care unit accompanied by high mortality. The early prediction of prognosis of these patients is critical for clinical intervention. This study aimed to develop a model by using machine learning (ML) algorithms to predict the risk of 28-day mortality in septic patients with AF. Methods: In this retrospective cohort study, we extracted septic patients with AF from the Medical Information Mart for Intensive Care III (MIMIC-III) and IV database. Afterward, only MIMIC-IV cohort was randomly divided into training or internal validation set. External validation set was mainly extracted from MIMIC-III database. Propensity score matching was used to reduce the imbalance between the external validation and internal validation data sets. The predictive factors for 28-day mortality were determined by using multivariate logistic regression. Then, we constructed models by using ML algorithms. Multiple metrics were used for evaluation of performance of the models, including the area under the receiver operating characteristic curve, sensitivity, specificity, recall, and accuracy. Results: A total of 5,317 septic patients with AF were enrolled, with 3,845 in the training set, 960 in the internal testing set, and 512 in the external testing set, respectively. Then, we established four prediction models by using ML algorithms. AdaBoost showed moderate performance and had a higher accuracy than the other three models. Compared with other severity scores, the AdaBoost obtained more net benefit. Conclusion: We established the first ML model for predicting the 28-day mortality of septic patients with AF. Compared with conventional scoring systems, the AdaBoost model performed moderately. The model established will have the potential to improve the level of clinical practice.

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“…The inclusion criteria were as follows. I diagnosis of septic shock according to the sepsis-3 definition [ 7 ] on admission to the ICU; II cardiac troponin I (cTnI) levels >0.04 ng/mL (detected by the PATHFAST chemiluminescence immunoassay; Hangzhou Red Cross Hospital, Zhejiang, China) on admission to the ICU [ 8 ]; III age ≥18 years. …”

Section: Methodsmentioning

confidence: 99%

“…II cardiac troponin I (cTnI) levels >0.04 ng/mL (detected by the PATHFAST chemiluminescence immunoassay; Hangzhou Red Cross Hospital, Zhejiang, China) on admission to the ICU [ 8 ];…”

Section: Methodsmentioning

confidence: 99%

Fibroblast growth factor 23 during septic shock and myocardial injury in ICU patients

Yang,

Wang,

et al. 2024

Heliyon

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Risk factors for 28-day mortality in patients with sepsis-related myocardial injury in intensive care units

Cited by 4 publications

References 19 publications

The PaO2/FiO2 is independently associated with 28-day mortality in patients with sepsis: a retrospective analysis from MIMIC-IV database

The PaO2/FiO2 is independently associated with 28-day mortality in patients with sepsis: a retrospective analysis from MIMIC-IV database

Development of a Machine Learning Model for Predicting 28-Day Mortality of Septic Patients With Atrial Fibrillation

Fibroblast growth factor 23 during septic shock and myocardial injury in ICU patients

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