Patient characteristics in sepsis-related deaths: prevalence of advanced frailty, comorbidity, and age in a Norwegian hospital trust

Torvik, Marianne Ask; Nymo, Stig Haugset; Nymo, Ståle H.; Bjørnsen, Lars Petter; Kvarenes, Hanne Winge; Ofstad, Eirik Hugaas

doi:10.1007/s15010-023-02013-y

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…The CCI, developed in 1987, is considered the gold standard for assessing comorbidities in clinical studies [ 32 ], as a tool used to predict long-term mortality in patients [ 33 ]. Previous studies have also shown an increase in patient mortality with increasing CCI [ 34 , 35 ], consistent with our results. We usually use the GCS which is a scale used to assess a patient’s level of consciousness [ 36 ].…”

Section: Discussionsupporting

confidence: 93%

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury

Wen,

Zhang,

et al. 2024

PLoS ONE

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Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medical Information Mart for Intensive Care (MIMIC-IV, v2.2, MIMIC-III, v1.4) were used in this study. The study cohort from MIMIC-IV was randomized to the training set (0.7) and the internal validation set (0.3), with MIMIC-III (2001 to 2008) as external validation. The features with more than 20% missing values were deleted and the remaining features were multiple interpolated. Lasso-CV that lasso linear model with iterative fitting along a regularization path in which the best model is selected by cross-validation was used to select important features for model development. Eight machine learning models including Random Forest (RF), Logistic Regression, Decision Tree, Extreme Gradient Boost (XGBoost), K Nearest Neighbor, Support Vector Machine, Generalized Linear Models in which the best model is selected by cross-validation (CV_glmnet), and Linear Discriminant Analysis (LDA) were developed. Shapley additive interpretation (SHAP) was used to improve the interpretability of the optimal model. At last, a total of 1043 patients were included, of whom 710 were from MIMIC-IV and 333 from MIMIC-III. Twenty-four clinically relevant parameters were selected for model construction. For the prediction of 28-day mortality of SALI in the internal validation set, the area under the curve (AUC (95% CI)) of RF was 0.79 (95% CI: 0.73–0.86), and which performed the best. Compared with the traditional disease severity scores including Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction Score (LODS), Systemic Inflammatory Response Syndrome (SIRS), and Acute Physiology Score III (APS III), RF also had the best performance. SHAP analysis found that Urine output, Charlson Comorbidity Index (CCI), minimal Glasgow Coma Scale (GCS_min), blood urea nitrogen (BUN) and admission_age were the five most important features affecting RF model. Therefore, RF has good predictive ability for 28-day mortality prediction in SALI. Urine output, CCI, GCS_min, BUN and age at admission(admission_age) within 24 h after intensive care unit(ICU) admission contribute significantly to model prediction.

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Section: Discussionsupporting

confidence: 93%

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury

Wen,

Zhang,

et al. 2024

PLoS ONE

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“…We accept that there may be an age-related selection bias for admissions to a specialist cardiology ward. Older patients who present with cardiac arrest usually have an underlying coronary artery disease, whereas younger patients tend to have structural heart disease and/or arrhythmogenic disorders [ 22 , 23 , 24 , 25 ]. Out of these young patients, there was one cardiac arrest event that resulted in death in whom arrhythmogenic right ventricular cardiomyopathy was identified.…”

Section: Discussionmentioning

confidence: 99%

What Are the Causes of Death among Patients Admitted to a Contemporary Tertiary-Level Cardiology Department? An Analysis of 10 Years of Morbidity and Mortality Meetings

Kwok,

Tafuro,

Wong

et al. 2023

Pathophysiology

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Despite the efforts to deliver the best evidence-based care, in-hospital death is an inevitable event among some patients hospitalized in cardiology departments. We conducted a retrospective evaluation of mortality events from inpatient admissions to the cardiology department between 2010 and 2019. Data were collected from morbidity and mortality meeting presentations that evaluated comorbidities, medical history, treatments, and causes of death for the overall cohort and according to age group and sex. There were 1182 registered deaths. The most common causes of death among patients were acute myocardial infarction (AMI, 53.0%), heart failure (HF, 11.7%), cardiac arrest (CA, 6.6%), HF with complication/defined cardiomyopathy (6.3%), and sepsis (4.4%). We observed a decline in deaths from AMI from 61.9% in 2010 to 46.7% in 2019, while there was a clear increase in deaths from HF (11.1% in 2010 to 25.9% in 2019). Compared to patients ≥65 years, younger patients were more likely to have died from CA (15.7% vs. 4.3%, p < 0.001) and other cardiac reasons (3.0% vs. 0.4%, p < 0.001). The majority of deaths were due to AMI, HF, and CA. We observed a significant declining trend in the proportion of deaths due to AMI in recent years, with an increase in deaths due to HF.

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“…Some studies have focused on precisely characterizing the specific preexisting chronic conditions responsible for enhancing the mortality risk of sepsis. [4,8,14] One study found that the second most common cause of mortality in patients with sepsis is due to chronic heart disease (15.3%), but the exact type of chronic heart disease was not specified. [4] Coronary artery disease (CAD), an atherosclerotic disease that involves the buildup of plaque in the vessel walls and narrowing of the coronary arteries that supply the heart muscle, is the most common form of chronic heart disease.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the effects of coronary artery disease as a preexisting comorbidity on mortality in hospitalized septic patients: a retrospective observation study

Multani,

Stahl,

Johnson

et al. 2024

Emerg Crit Care Med

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Background Sepsis has high prevalence and mortality rate, and it is imperative to identify populations at risk of poor sepsis outcomes. Septic patients with preexisting chronic comorbidities are shown to have worse sepsis outcomes. By identifying comorbidities with greater influence on sepsis progression, we can direct limited resources to septic patients with comorbidities and reduce health care costs. Chronic comorbidities can impact the risk of developing sepsis and having worse outcomes. Coronary artery disease (CAD) is a common comorbidity, especially in the elderly, and a leading cause of death globally. We wished to investigate the influence of CAD as a comorbidity on sepsis and hypothesized that preexisting CAD would increase mortality in hospitalized septic patients. Methods We conducted retrospective observational study using patient data from Freeman Health System in Joplin, MO. We analyzed patient records from Freeman Health System database from January 1, 2019, to June 30, 2020. Septic patients were identified using the International Classification of Diseases, Tenth Revision sepsis codes. To identify septic patients with preexisting CAD, we used International Classification of Diseases, Tenth Revision codes for CAD. We compared mortality rates for septic patients with and without CAD. Results Two-sample proportion test was conducted to test the difference in mortality between septic patients with and without preexisting CAD. The difference in mortality for the total population was −0.016 (P = 0.553). In the male and female subgroups, the differences in mortality were 0.0122 (P = 0.739) and −0.0511 (P = 0.208), respectively. The differences in mortality in patients aged 40 to 64 years and 65 years and older were −0.0077 (P = 0.870) and 0.0007 (P = 0.983), respectively. The statistical tests failed to find significant differences when comparing septic patients with and without preexisting CAD. There was no significant difference in the age and sex subgroups. Conclusions Our study showed that CAD alone was not associated with higher mortality due to sepsis in our population.

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Patient characteristics in sepsis-related deaths: prevalence of advanced frailty, comorbidity, and age in a Norwegian hospital trust

Cited by 11 publications

References 19 publications

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury

What Are the Causes of Death among Patients Admitted to a Contemporary Tertiary-Level Cardiology Department? An Analysis of 10 Years of Morbidity and Mortality Meetings

Exploring the effects of coronary artery disease as a preexisting comorbidity on mortality in hospitalized septic patients: a retrospective observation study

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