Predicting the outcome in poisoned patients: look at the past!

INTRODUCTION: When predicting future events, we often rely on analyzing past occurrences and projecting them forward. This methodology is crucial in various fields, including toxicology, in which predicting outcomes in poisoned patients plays a vital role in guiding treatment decisions and improving patient care. IMPORTANCE OF PREDICTING OUTCOMES IN POISONED PATIENTS: In cases of poisoning, understanding a patient's medical history, current physiological status, and the toxicokinetics of the ingested substance is essential for predicting potential outcomes and determining appropriate interventions. WHAT TO PREDICT?: Predicting whether an intoxicated patient needs (further) treatment or even admission to the hospital is one of the most difficult decisions a clinician needs to make. The prediction of the course of an intoxication often lacks crucial information, leaving physicians with a sense of uncertainty in treating and advising patients. A significant source of this uncertainty stems from patients' limited awareness of the specific chemical(s) causing their symptoms, making a targeted approach challenging. Adding to the complexity, both patients and physicians frequently lack knowledge of the exposure dose, onset time, and potential interactions, further complicating the prediction of symptom progression. Patients are commonly placed in observation wards until the pharmacodynamic effects have diminished, leading to extended observation periods and unnecessary healthcare utilization and costs. Therefore, a key objective of a predictive model is to determine the necessity for intensive care unit admission. PREDICTING THE REQUIREMENT FOR ADMISSION TO AN INTENSIVE CARE UNIT: Factors such as age, Glasgow Coma Scale, and specific comorbidities like dysrhythmias and chronic respiratory insufficiency significantly influence the likelihood of intensive care unit admission. By examining a patient's trajectory based on past medical history and organ function deterioration, clinicians can better anticipate the need for critical care support. ENHANCING PREDICTION MODELS FOR IMPROVED PATIENT CARE: To enhance prediction models, leveraging modern methodologies like machine learning on large datasets (big data) are crucial. These advanced techniques can uncover previously unknown patient groups with similar outcomes or treatment responses, leading to more personalized and effective interventions. Regular updates to clustering, discrimination, and calibration processes ensure that predictive models remain accurate and relevant as new data emerges. CONCLUSIONS: The field of clinical toxicology stands to benefit greatly from the creation and integration of large datasets to advance toxicological prognostication. By embracing innovative approaches and incorporating diverse data sources, clinicians can enhance their ability to predict outcomes in poisoned patients and improve overall patient management strategies.

Management of intoxicated patients - a descriptive outcome analysis of 4,267 ICU patients.

INTRODUCTION: Intoxications are common in intensive care units (ICUs). The number of causative substances is large, mortality usually low. This retrospective cohort study aims to characterize differences of intoxicated compared to general ICU patients, point out variations according to causative agents, as well as to highlight differences between survivors and non-survivors among intoxicated individuals in a large-scale multi-center analysis. METHODS: A total of 105,998 general ICU patients and 4,267 individuals with the admission diagnoses "overdose" and "drug toxicity" from the years 2014 and 2015 where included from the eICU Collaborative Research Database. In addition to comparing these groups with respect to baseline characteristics, intensive care measures and outcome parameters, differences between survivors and non-survivors from the intoxication group, as well as the individual groups of causative substances were investigated. RESULTS: Intoxicated patients were younger (median 41 vs. 66 years; p<0.001), more often female (55 vs. 45%; p<0.001), and normal weighted (36% vs. 30%; p<0.001), whereas more obese individuals where observed in the other group (37 vs. 31%; p<0.001). Intoxicated individuals had a significantly lower mortality compared to general ICU patients (1% vs. 10%; aOR 0.07 95%CI 0.05-0.11; p<0.001), a finding which persisted after multivariable adjustment (aOR 0.17 95%CI 0.12-0.24; p<0.001) and persisted in all subgroups. Markers of disease severity (SOFA-score: 3 (1-5) vs. 4 (2-6) pts.; p<0.001) and frequency of vasopressor use (5 vs. 15%; p<0.001) where lower, whereas rates of mechanical ventilation where higher (24 vs. 26%; p<0.001) in intoxicated individuals. There were no differences with regard to renal replacement therapy in the first three days (3 vs. 4%; p=0.26). In sensitivity analysis (interactions for age, sex, ethnicity, hospital category, maximum initial lactate, mechanical ventilation, and vasopressor use), a trend towards lower mortality in intoxicated patients persisted in all subgroups. CONCLUSION: This large-scale retrospective analysis indicates a significantly lower mortality of intoxicated individuals compared to general ICU patients.

External Validation of Model-Based Dosing Guidelines for Vancomycin, Gentamicin, and Tobramycin in Critically Ill Neonates and Children: A Pragmatic Two-Center Study.

BACKGROUND: The Dutch Pediatric Formulary (DPF) increasingly bases its guidelines on model-based dosing simulations from pharmacokinetic studies. This resulted in nationwide dose changes for vancomycin, gentamicin, and tobramycin in 2015. OBJECTIVE: We aimed to evaluate target attainment of these altered, model-based doses in critically ill neonates and children. METHODS: This was a retrospective cohort study in neonatal intensive care unit (NICU) and pediatric ICU (PICU) patients receiving vancomycin, gentamicin, or tobramycin between January 2015 and March 2017 in two university hospitals. The first therapeutic drug monitoring concentration for each patient was collected, as was clinical and dosing information. Vancomycin and tobramycin target trough concentrations were 10-15 and ≤ 1 mg/L, respectively. Target gentamicin trough and peak concentrations were < 1 and 8-12 mg/L, respectively. RESULTS: In total, 482 patients were included (vancomycin [PICU] n = 62, [NICU] n = 102; gentamicin [NICU] n = 97; tobramycin [NICU] n = 221). Overall, median trough concentrations were within the target range for all cohorts but showed large interindividual variability, causing nontarget attainment. Trough concentrations were outside the target range in 66.1%, 60.8%, 14.7%, and 23.1% of patients in these four cohorts, respectively. Gentamicin peak concentrations were outside the range in 69% of NICU patients (term neonates 87.1%, preterm infants 57.1%). Higher creatinine concentrations were associated with higher vancomycin and tobramycin trough concentrations. CONCLUSION: This study illustrates the need to validate model-based dosing advice in the real-world setting as both sub- and supratherapeutic concentrations of vancomycin, gentamicin, and tobramycin were very prevalent. Our data underline the necessity for further individualization by addressing the high interindividual variability to improve target attainment.