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A Predictive Model for 30-Day Mortality of Fungemia in ICUs
Peng Xie1,2, Wenqiang Wang3, Maolong Dong1,4
1Department of Emergency Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
Background:
Few predictive models have been established to predict the risk of 30-day mortality from fungemia. This study aims to create a nomogram to predict the 30-day mortality of fungemia in ICUs.
Methods:
Data of ICU patients with fungemia from both the Medical Information Mart for Intensive Care (MIMIC-III) database and the Grade-III Class-A hospital in China were collected. The data extracted from the MIMIC-III database functioned as the training dataset, which was used to construct a predictive model for 30-day mortality risk in ICU patients with fungemia; the data from the hospital functioned as the validation dataset, which was used to validate the model. A predictive model for 30-day mortality risk in ICU patients with fungemia was then built based on R software. Such indicators as C-index and calibration curve were utilized to evaluate the prediction ability of the model. Data of ICU patients with fungemia from the hospital were used as a validation dataset to validate the model.
Results:
Predictive models were constructed by age, international normalized ratio (INR), renal failure, liver disease, respiratory rate (RR), glucocorticoid therapy, antifungal therapy, and platelets. The C-index value of the models was 0.838 (95% CI: 0.79096-0.88504). Attested by external validation results, the model has satisfactory predictive ability.
Conclusion:
The 30-day mortality risk predictive model for ICU patients with fungemia constructed in this study has good predictive ability and may hopefully provide a 30-day mortality risk screening tool for ICU patients with fungemia.
Insights
This study developed a nomogram to predict 30-day mortality in intensive care unit (ICU) patients with fungemia. The model shows good predictive ability, offering a potential screening tool for this high-risk population.
Area of Science:
- Critical Care Medicine
- Infectious Diseases
- Biostatistics
Background:
- Fungemia poses a significant risk of mortality in intensive care units (ICUs).
- Predictive models for 30-day mortality in fungemia patients are limited.
- Accurate risk stratification is crucial for timely intervention.
Purpose of the Study:
- To develop and validate a nomogram for predicting 30-day mortality in ICU patients diagnosed with fungemia.
- To identify key clinical factors associated with fungemia-related mortality.
- To provide a practical tool for risk assessment in clinical settings.
Main Methods:
- Retrospective data collection from the MIMIC-III database (training) and a Chinese Grade-III Class-A hospital (validation).
- Development of a predictive model using R software, incorporating variables like age, INR, renal failure, liver disease, RR, glucocorticoid and antifungal therapy, and platelets.
- Model performance evaluated using C-index and calibration curves.
Main Results:
- The predictive model incorporated age, INR, renal failure, liver disease, respiratory rate, glucocorticoid therapy, antifungal therapy, and platelets.
- The model achieved a C-index of 0.838 (95% CI: 0.79096-0.88504).
- External validation confirmed the model's satisfactory predictive ability.
Conclusions:
- A nomogram was successfully developed to predict 30-day mortality in ICU patients with fungemia.
- The model demonstrates good predictive performance and clinical utility.
- This tool can aid in the early identification and management of high-risk fungemia patients in ICUs.
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