Predicting the mortality of necrotizing fasciitis with blood pressure and white blood cell count

Can The Systemic Immune-Inflammation Index (SII) and Charlson Comorbidity Index (CCI) be used to predict mortality in patients with necrotizing fasciitis?

PURPOSE

This study aimed to determine the impact of mortality and morbidity indices on the diagnosis and prognosis of patients suffering from necrotizing fasciitis.

METHODS

A retrospective analysis was performed on 41 patients (26 females, 15 males) with necrotizing fasciitis (NF). The SII (Systemic Immune-Inflammation Index) was computed using the formula SII = (P × N)/L, where P, N, and L measure the counts of peripheral platelets, neutrophils, and lymphocytes, respectively. This study evaluated the clinicopathological characteristics and follow-up information to assess the comparative effectiveness of SII, CCI (Charlson Comorbidity Index), and LRINEC (Laboratory Risk Indicator for Necrotizing Fasciitis) scores as mortality and morbidity indices for patients with NF.

RESULTS

The optimal cut-off for SII was determined to be 455. The SII value in the group with mortality was significantly higher compared to the group without mortality (p < 0.05). The CCI value in the group with mortality was significantly higher than the group without mortality (p < 0.05). The SII and CCI values were found to be effective in distinguishing between patients who suffered mortality and those who did not.

CONCLUSION

SII is a powerful tool for predicting mortality in patients with necrotizing fasciitis (NF). The SII index provides a novel, easily accessible, and inexpensive indicator for monitoring the progress and predicting the survival of patients with NF.

Using Data Analytics to Predict Hospital Mortality in Sepsis Patients

Predictive analytics can be used to anticipate the risks associated with some patients, and prediction models can be employed to alert physicians and allow timely proactive interventions. Recently, health care providers have been using different types of tools with prediction capabilities. Sepsis is one of the leading causes of in-hospital death in the United States and worldwide. In this study, the authors used a large medical dataset to develop and present a model that predicts in-hospital mortality among Sepsis patients. The predictive model was developed using a dataset of more than one million records of hospitalized patients. The independent predictors of in-hospital mortality were identified using the chi-square automatic interaction detector. The authors found that adding hospital attributes to the predictive model increased the accuracy from 82.08% to 85.3% and the area under the curve from 0.69 to 0.84, which is favorable compared to using only patients' attributes. The authors discuss the practical and research contributions of using a predictive model that incorporates both patient and hospital attributes in identifying high-risk patients.