# Development and validation of a Nomogram for the prediction of patients with sepsis-induced multiple organ dysfunction syndrome

## TL;DR

This study creates a prediction model to help doctors assess the risk of multiple organ failure in sepsis patients.

## Contribution

A novel nomogram is developed and validated for predicting sepsis-induced multiple organ dysfunction syndrome.

## Key findings

- The model identified seven key variables for predicting SI-MODS.
- The model showed strong performance with an area under the curve of 0.903.
- The nomogram provides a net benefit for clinical decision-making across a wide threshold probability range.

## Abstract

To develop and validate a model capable of predicting the risk of Sepsis-induced multiple organ dysfunction syndrome (SI-MODS) in hospitalized sepsis patients.

A retrospective cohort study was performed to analyze the clinical data of 415 patients admitted to Department of Medical Laboratory, The Affiliated Huai’an No.1 People’s Hospital of Nanjing between January 2019 and January 2022. The least absolute shrinkage and selection operator (LASSO) regression analysis was employed to pinpoint potential variables. A nomogram was developed through multivariate logistic regression. For internal validation, the bootstrapping method was utilized. The nomogram’s performance was assessed through calibration, discrimination, and clinical utility analyses.

Among the 415 patients, SI-MODS was identified in 46 individuals (11.1%). This model identified seven key variables. The model’s internal validation yielded an area under the curve of 0.903 (95% CI: 0.863-0.943). The model’s calibration was strong, and results from a decision curve analysis showed that the created nomogram provided a net benefit across a threshold probability range of 1–66% for predicting SI-MODS.

Our study develops a nomogram incorporating based on PaO2, LAC, multidrug resistant bacteria, septic shock, coagulation disorder, mechanical ventilation, and kidney failure can predict the risk of MODS in sepsis patients, which helps clinicians make risk based decisions and treatment strategies.