Background
The over-consumption of time for data collection in the Sequential Organ Failure Assessment (SOFA) score and the poor performance of the quickSOFA (qSOFA) score in predicting poor prognosis among patients with suspected infection in intensive care unit (ICU) may delay the treatment towards sepsis. The aim of this study is to develop a prediction model to early identify patients with suspected infection who are under a high risk of in-hospital mortality in ICU.
Methods
Patients with suspected infection were retrospectively retrieved from the Medical Information Mart for Intensive Care (MIMIC III) database. Objective variables whose results can be obtained within a short period of time were integrated into the uni- and multi-variate logistic regression to screen the independent predictors for the in-hospital mortality in ICU patients with suspected infection. Then, the prediction nomogram was constructed by these independent predictors in the training set, and undergone internal validation and sensitivity analysis.
Results
A total of 7000 patients with suspected infection were included into the final analysis. In the training set, multivariate logistic regression revealed that patient’s age, elixhauser index, the first result of lactate, red blood cell distribution width (RDW), white blood cell (WBC), lymphocyte, PH value and bicarbonate as well as the first hour’s mean value of respiratory rate, temperature and SpO2 since ICU admission were the independent predictors for the in-hospital mortality. Then, a nomogram for predicting the in-hospital mortality in ICU patients with suspected infection was developed in the training set and exhibited strong discrimination and calibration as well as great clinical usefulness in both of the training and validation sets as well as the sensitivity analysis.
Conclusions
The nomogram shows good predictive performance in ICU to identify adult patients with suspected infection who are likely to die in hospital, which may facilitate clinicians to initiate necessary treatment towards sepsis timely and finally improve the prognosis of septic patients.