Materiały źródłowe

• #1 Predicting the outcome in poisoned patients: look at the past! – PubMed

  https://pubmed.ncbi.nlm.nih.gov/38683032/

  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. […] 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. […] 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. […] A significant source of this uncertainty stems from patients’ limited awareness of the specific chemical(s) causing their symptoms, making a targeted approach challenging.

• #2 Predicting the outcome in poisoned patients: look at the past! – PubMed

  https://pubmed.ncbi.nlm.nih.gov/38683032/

  Therefore, a key objective of a predictive model is to determine the necessity for intensive care unit admission. […] 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. […] To enhance prediction models, leveraging modern methodologies like machine learning on large datasets (big data) are crucial. […] The field of clinical toxicology stands to benefit greatly from the creation and integration of large datasets to advance toxicological prognostication.

• #3 Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10845715/

  Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. […] This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis. […] The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. […] ML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. […] The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis.

• #4 Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms | BMC Medical Informatics and Decision Making | Full Text

  https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02443-0

  Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. […] This study aims to bridge the existing knowledge gap by developing a machine-learning prediction model for forecasting the prognosis of the poisoned patient undergoing hemodialysis. […] The experimental results showed that ten variables had a significant influence on prognosis outcomes including age, intubation, acidity (PH), previous medical history, bicarbonate (HCO3), Glasgow coma scale (GCS), intensive care unit (ICU) admission, acute kidney injury, and potassium. […] ML-based predictive models can predict the prognosis of poisoned patients undergoing hemodialysis with high performance. […] The developed ML models demonstrate valuable potential for providing frontline clinicians with data-driven, evidence-based tools to guide time-sensitive prognosis evaluations and care decisions for poisoned patients in need of hemodialysis.

• #5 Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10845715/

  Prediction of hemodialysis outcomes depends on many factors and is different in different populations. […] Risk prediction models are created to estimate the likelihood of an unfavorable outcome, such as mortality, based on various variables including demographic and clinical factors. […] In our study, the HGB classifier showed better performance on the test dataset. […] The effectiveness of a machine learning model, like the HGB classifier, compared to other classifiers, varies depending on the particular problem and dataset at hand. […] This research is the first to use ML models for predicting the outcome of poisoned patients who are about to undergo hemodialysis.

• #6 Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms | BMC Medical Informatics and Decision Making | Full Text

  https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02443-0

  Prediction of hemodialysis outcomes depends on many factors and is different in different populations. […] Risk prediction models are created to estimate the likelihood of an unfavorable outcome, such as mortality, based on various variables including demographic and clinical factors. […] These novel analytical methods have the potential to uncover previously unidentified variables that can enhance the accuracy of predictions. […] The results demonstrated that the HGB classifier outperforms the other classifiers in this study, as indicated by its superior Kappa metrics and lower error rate. […] Thus, given these findings, the HGB classifier is considered the optimal algorithm for building a CDSS interface to predict the prognosis of poisoned patients who may need dialysis. […] This research is the first to use ML models for predicting the outcome of poisoned patients who are about to undergo hemodialysis.

• #7 Characteristics and predictive factors of severe or fatal suicide outcome in patients hospitalized due to deliberate self-poisoning | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0276000

  Prediction of clinical course of intoxication is essential for timely initiation of appropriate medical treatment in patients hospitalized due to suicidal self-poisoning. […] Multivariable analysis identified no alcohol co-ingestion (OR 3.23; 95%CI 1.3, 8.07; p = 0.012) and self-poisoning with non-medicinal substances (OR 5.4; 95%CI 1.78, 16.34; p = 0.003) as factors predictive for severe or fatal suicide outcome. […] The conclusion for clinical practice is that male patients hospitalized due to self-poisoning, with a low initial GCS score, who did not co-ingest alcohol, attempted suicide with non-pharmaceutical substances or antidepressants are at a higher risk of severe/fatal outcome of suicide. […] Our study demonstrated that male patients hospitalized due to self-poisoning who did not co-ingest alcohol, attempted suicide with non-pharmaceutical substances or antidepressants and had a low initial GCS score are at a higher risk of severe/fatal outcome.

• #8 Prediction of Patient Survival in Cases of Acute Paraquat Poisoning | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111674

  Paraquat concentration-time data have been used to predict the clinical outcome following ingestion. […] The purpose of this study was to develop a simple and reliable model to predict survival according to the time interval post-ingestion in patients with acute paraquat poisoning. […] The external validation study showed that our model was highly accurate for the prediction of survival (C statics 0.964; 95% CI [0.9520.975]). […] The purpose of our study was to develop a reliable model to predict survival after PQ poisoning. […] Our study has some limitations. This is a retrospective study and the study population comprised only Asian people. […] In conclusion, we consider that our equation and nomogram are reliable and accurate for the purpose of predicting survival in patients with acute PQ poisoning.

• #9 Prediction of Patient Survival in Cases of Acute Paraquat Poisoning

  https://ideas.repec.org/a/plo/pone00/0111674.html

  Paraquat concentration-time data have been used to predict the clinical outcome following ingestion. […] The purpose of this study was to develop a simple and reliable model to predict survival according to the time interval post-ingestion in patients with acute paraquat poisoning. […] The overall survival rate was 44% (939/2136). […] The probability of survival for any specified time and concentration could be predicted as (exp(logit))/(1+exp(logit)), where logit = 1.3544+[3.4688log10(plasma paraquat g/M)]+[2.3169log10(hours since ingestion)]. […] The external validation study showed that our model was highly accurate for the prediction of survival (C statics 0.964; 95% CI [0.9520.975]). […] We have developed a model that is effective for predicting survival after paraquat intoxication.

• #10 The Accuracy Comparison of Scoring Systems in the Outcome Prediction of Acute Organophosphate Poisoning

  https://ajfm.journals.ekb.eg/article_30444.html

  Organophosphates (OP) with high morbidity and mortality are one of the most common causes of poisoning especially in developing countries. […] The objective of this study is to compare the accuracy of scoring systems (the sequential organ failure assessment (SOFA) score, acute physiology and chronic health evaluation II (APACHE II) score and glasgow coma scale (GCS)) in the prediction of mortality of acute organophosphate poisoning. […] The study results revealed 180 (90%) patients recovered and 20 (10%) patients died. […] It could be concluded that SOFA score, APACHE II score and GCS systems can be used to predict mortality of acute organophosphate poisoning with preference to SOFA score which had the highest accuracy and predictive ability. […] From the previous results, our study recommends that application of SOFA score, APACHE II score and GCS are good predictors of high mortality in OP intoxicated patients which helps in proper allocation of resources particularly for intensive care unit patients.

• #11 Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models | Scientific Reports

  https://www.nature.com/articles/s41598-024-66481-4

  The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately predicting intubation needs for methanol-poisoned patients. […] This study showcases XAI’s effectiveness in predicting intubation necessity in methanol-poisoned patients. ML models, particularly RF and XGB, outperform DL counterparts, underscoring their potential for clinical decision-making. […] The requirement for intubation in acutely ill patients is often urgent and unpredictable for physicians. […] Therefore, accurately predicting the necessity for intubation at an early stage can offer additional time for preparation, thereby enhancing safety margins and preventing the potential risks associated with delayed intubation.

• #12 Prediction of acute methanol poisoning prognosis using machine learning… | Shahin Shadnia

  https://www.linkedin.com/posts/shahin-shadnia-05204729_prediction-of-acute-methanol-poisoning-prognosis-activity-7195702009751126016-GLsP

  Prediction of acute methanol poisoning prognosis using machine learning techniques […] https://doi.org/10.1016/j.tox.2024.153770

• #13 Trend analysis and prediction of disease burden of occupational carbon monoxide poisoning in China based on the past two decades | Scientific Reports

  https://www.nature.com/articles/s41598-025-99887-9

  This study aims to analyse and predict the disease burden of occupational carbon monoxide (CO) poisoning in China and provide recommendations for its prevention and treatment. […] The results show a decreasing trend in age-standardised mortality rate (ASMR) and age-standardised disability-adjusted life years (ASDR) from 2000 to 2021, with male rates consistently higher than female rates. […] ARIMA modelling projections indicate a further decline in ASMR and ASDR from 2022 to 2031. […] The decline in disease burden is likely to be due to advances in medical technology and improved efforts to prevent occupational diseases, but it remains higher than the global average and in several SDI regions. […] The ASMR for CO poisoning attributable to occupational factors in China’s total population declined from 0.273/105 in 2000 to 0.113/105 in 2021.

• #14 Trend analysis and prediction of disease burden of occupational carbon monoxide poisoning in China based on the past two decades | Scientific Reports

  https://www.nature.com/articles/s41598-025-99887-9

  The ASDR value for the total population is 14.174/105 in 2000 and 6.099/105 in 2021. […] The ASMR and ASDR for occupationally attributable CO poisoning in China, globally and in different SDI regions showed a decreasing trend over time from 2000 to 2021. […] The prediction results showed that the ASMR of CO poisoning attributable to occupational factors in China from 2022 to 2031 showed a decreasing trend from 0.109/105 in 2022 to 0.073/105 in 2031. […] The ASDR of CO poisoning attributable to occupational factors in China from 2022 to 2031 decreased from 0.109/105 to 0.073/105. […] The results of this study show that the overall trend of ASMR and ASDR in China is decreasing during 2000-2021, indicating that China has made some achievements in occupational safety and health and in the treatment and prognosis of occupational diseases in the 21st century. […] The predicted results showed that the ASMR and ASDR were decreasing in the next 10 years.

• #15

  https://journals.lww.com/md-journal/fulltext/2017/03240/development_and_validation_of_a_risk_prediction.41.aspx

  The objective is to create and validate a risk-prediction nomogram for use in the ED to predict the risk of in-hospital mortality in adults from acute poisoning with drugs and nonpharmaceutical agents. […] The resulting nomogram showed excellent survival/mortality discrimination (area under the curve [AUC] 0.976, 95% confidence interval [CI] 0.9540.998, P 0.0001 for the derivation cohort; AUC 0.957, 95% CI 0.8921, P 0.0001 for the validation cohort). […] This nomogram provides more precise, rapid, and simple risk-analysis information for individual patients acutely exposed to drugs and nonpharmaceutical agents, and accurately estimates the probability of in-hospital death, exclusively using the results of objective tests available in the ED. […] Our aim was to construct and validate a simple, accurate, and widely applicable nomogram offering an early estimate of the risk of in-hospital mortality, using objective data, immediately available upon presentation to the ED, derived from a population of subjects following acute poisoning with drugs and nonpharmaceutical agents, irrespective of the dose, route of exposure, or mechanism of toxicity.

• #16

  https://journals.lww.com/md-journal/fulltext/2017/03240/development_and_validation_of_a_risk_prediction.41.aspx

  The significant independent covariates for the mortality risk in the present study were age, DT as a measure of impaired LV diastolic function, initial lactate level, CKMB as a marker of myocardial injury, the QTc interval, and the potassium level. […] This risk-prediction nomogram may have an advantage over traditional tools, such as GCS, PSS, or other clinical scores, because the association between predictors (age, sex, QTc interval, DT, CKMB, lactate and K+) and the predicted variable (death) is visible at a glance. […] We developed a 7-variable risk-prediction nomogram based on demographic, routine laboratory tests, and ECG and echocardiography parameters, which accurately predicts the probability of in-hospital mortality for nondiabetic subjects acutely exposed to drugs and nonpharmaceutical agents, exclusively from the objective tests available in the ED.

• #17 Risk stratified monitoring for methotrexate toxicity in immune mediated inflammatory diseases: prognostic model development and validation using primary care data from the UK | The BMJ

  https://www.bmj.com/content/381/bmj-2022-074678

  Objective To develop and validate a prognostic model to inform risk stratified decisions on frequency of monitoring blood tests during long term methotrexate treatment. […] A prognostic model was developed and validated that uses information collected during routine clinical care and may be used to risk stratify the frequency of monitoring blood test during long term methotrexate treatment. […] We developed and externally validated a prognostic model for predicting the likelihood of methotrexate discontinuation owing to abnormal blood test results that utilises disease and demographic factors ascertained during routine clinic visits in either primary or secondary care. […] Our prognostic model performed well in predicting outcomes by five years and did so in clinically relevant subgroups defined by age group, type of immune mediated inflammatory disease, methotrexate dose, and route of administration.

• #18 Risk stratified monitoring for methotrexate toxicity in immune mediated inflammatory diseases: prognostic model development and validation using primary care data from the UK | The BMJ

  https://www.bmj.com/content/381/bmj-2022-074678

  The risk score output from the prognostic model may be used to decide individual monitoring strategies after the first six months of a prescription for methotrexate in primary carethat is, patients at low risk of toxicity could be advised to undergo less frequent monitoring blood tests, whereas those at high risk of toxicity undergo more frequent testing.

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