A nomogram proposal for early prediction of intensive care unit admission in patients with acute antipsychotic poisoning

Abstract

Background Early identification of antipsychotic poisoned patients who may have a potential risk for intensive care unit (ICU) admission is crucial especially when resources are limited. Nomograms were previously used as a practical tool to predict prognosis and planning the treatment of some diseases including some poisoning conditions. However, they were not previously investigated in antipsychotic poisoning. Aim The current study aimed to construct a nomogram to predict the need for ICU admission in acute antipsychotic poisoning. Patients and methods: This 2-year study included 140 patients acutely intoxicated with antipsychotics and admitted at Tanta University Poison Control Centre throughout July 2019 to June 2021. Personal and toxicological data, findings of clinical examination and electrocardiography, as well as, results of laboratory investigations at time of admission were recorded. According to the outcome, patients were divided into ICU-admitted and ICU-not admitted groups. Results The results of this study provided a proposed nomogram that included five significant independent predictors for ICU admission in acute antipsychotic intoxications; the presence of seizures (OR: 31132.26[108.97-Inf]), corrected QT interval (OR: 1.04[1.01–1.09]), mean arterial blood pressure (OR: 0.83[0.70–0.92]), oxygen saturation (OR: 0.62[0.40 to 0.83)], and Glasgow Coma Scale (OR: 0.25 [0.06–0.56]). Conclusion It could be concluded that the developed nomogram is a promising tool for easy and rapid decision making to predict the need for ICU admission in acute antipsychotic poisoning.

A predictive model of nosocomial infection in patients with upper urinary tract stones after flexible ureterorenoscopy with laser lithotripsy: A retrospective study

“…Nomograms are widely used in different clinical settings to predict the probability of an event based on the risk variables. [11][12][13] Therefore, the aim of this study was to construct a nomogram prediction model of nosocomial infection in patients with UUT stones after FURSLL to optimize patient management. Observation indicators: Age, gender, and body mass index (BMI) of each patient were recorded.…”