Development of a risk prediction nomogram for disposition of acute toxic exposure patients to intensive care unit.
Fatma M ElgazzarAhmed M AfifiMohamed Abd Elhady ShamaAhmad El AskaryGhada N El-SarnagawyPublished in: Basic & clinical pharmacology & toxicology (2021)
Early risk stratification of acutely poisoned patients is essential to identify patients at high risk of intensive care unit (ICU) admission. We aimed to develop a prognostic model and risk-stratification nomogram based on the readily accessible clinical and laboratory predictors on admission for the probability of ICU admission in acutely poisoned patients. This retrospective cohort study included adult patients with acute toxic exposure to a drug or a chemical substance. Patients' demographic, toxicologic, clinical and laboratory data were collected. Among the 1260 eligible patients, 180 (14.3%) were admitted to the ICU. We developed a generalized prognostic model for predicting ICU admission in patients with acute poisoning. The predictors included the Glasgow coma scale, oxygen saturation, diastolic blood pressure, respiratory rate and blood bicarbonate concentration. The model displayed excellent discrimination and calibration (optimistic-adjusted area under the curve = 0.924 and optimistic-adjusted Hosmer and Lemeshow test = 0.922, respectively) when internally validated. Additionally, we developed prognostic models that determine ICU admission in patients with specific poisonings. Furthermore, we constructed risk-stratification nomograms that rank the probability of ICU admission in these patients. The developed risk-stratification nomograms help decision-making regarding ICU admission in acute poisonings. Future external validation in independent cohorts is necessary before clinical application.
Keyphrases
- intensive care unit
- end stage renal disease
- blood pressure
- ejection fraction
- emergency department
- peritoneal dialysis
- newly diagnosed
- chronic kidney disease
- prognostic factors
- type diabetes
- decision making
- patient reported outcomes
- liver failure
- heart failure
- atrial fibrillation
- deep learning
- weight loss
- big data
- artificial intelligence
- respiratory failure
- drug induced