Prognostic indicators in critically ill poisoned patients: development of a risk-prediction nomogram

Abstract

Abstract Objectives The prognosis of acutely poisoned patients is a significant concern for clinical toxicologists. In this study, we sought to determine the clinical and laboratory findings that can contribute to predicting the medical outcomes of poisoned patients admitted to intensive care units (ICUs). Methods This retrospective study was performed from January 2009 to January 2016 in the ICU of Vali-e-Asr Hospital in Birjand, Iran. We included all patients with the diagnosis of acute poisoning admitted to the ICU. Demographic data, laboratory results, the Sequential Organ Failure Assessment (SOFA), and acute physiology score + age points + chronic health points (APACHE) II, and the Simplified Acute Physiology Score (SAPS) II, and outcome were collected. Univariate analysis (Mann–Whitney or t-test), multiple logistic regression, receiver operating characteristics (ROC) curve analysis, and Pearson’s correlation test were performed using SPSS, STATA/SE 13.0, and Nomolog software programs. Results The multiple logistic regression analysis revealed that five factors were significant for predicting mortality including age (OR 95% CI: 1.1[1.05–1.12], p<0.001), Glasgow Coma Score (GCS) (OR 95% CI: 0.71[0.6–0.84], p<0.001), white blood cell (WBC) count (OR 95% CI: 1.1[1.01–1.12], p=0.04), serum sodium (Na) (OR 95% CI: 1.08[1.01–1.15], p=0.02), and creatinine levels (Cr) (OR 95% CI: 1.86 [1.23–2.81], p=0.003). We generated a five-variable risk-prediction nomogram which could both predict mortality risk and identify high-risk patients. Conclusions Age, GCS, WBC, serum creatinine, and sodium levels are the best prognostic factors for mortality in poisoned patients admitted to the ICU. The APACHE II score can discriminate between non-survivors and survivors. The nomogram developed in the current study can provide a more precise, quick, and simple analysis of risks, thereby enabling the users to predict mortality and identify high-risk patients.