Impact of Acid-Base Status on Mortality in Patients with Acute Pesticide Poisoning

Strong ion gap and anion gap corrected for albumin and lactate in patients with sepsis in the intensive care unit

INTRODUCTION

Metabolic acidosis is very common amongst critically ill sepsis patients partly due to the presence of unmeasured ions in serum. These ions can be detected by anion gap (AG) or strong ion gap (SIG) concentration values. The purpose of this study is to assess the correlation and potential agreement of the two methods in critically ill patients with sepsis.

MATERIALS AND METHODS

The present is a retrospective study including septic patients admitted to the Intensive Care Unit from December 2014 to July 2016. The [SIG] and the [AG] corrected for albumin and lactate ([AGcl]) were calculated on admission and on sepsis remission or deterioration. The correlation of the two parameters was assessed in all patient groups using the Pearson correlation coefficient and linear regression analysis and the agreement with Bland-Altman plots. ROC survival curves were also generated for the patients in relation to the values of [AGcl], [SIG] and inorganic [SIG] ([SIGi]) on admission.

RESULTS

There was a strong correlation linking [AGcl] and [SIG] values (r>0.9, P<0.05) in all patient groups. The results from all three linear regression equations were statistically significant as the models predicted the [AGcl] value from the [SIG] value with high accuracy. The mean difference of the two methods (i.e. [AGcl] - [SIG] in every patient separately) in septic patients on admission was 11.75 mEq/l with 95% limits of agreement [9.7-13.8]; in patients with sepsis deterioration, it was 11.8 mEq/l with 95% limits of agreement [9.8-13.7] and in patients with sepsis remission, it was 11.5 mEq/l with 95% limits of agreement [10.4-12.7]. ROC survival curves demonstrated a small area under the curve (AUC): [SIG] AUC: 0.479, 95% CI [0.351, 0.606], [SIGi] AUC: 0.581, 95% CI [0.457, 0.705], [AGcl] AUC: 0.529, 95% CI [0.401, 0.656].

CONCLUSION

[AGcl] and [SIG] demonstrate excellent correlation in septic patients, with a mean difference of about 12 mEq/l. Both parameters failed to demonstrate any predictive ability regarding patient mortality.

Acetamiprid and pyridaben poisoning: A case report

Background

The agricultural industry has experienced beneficial outcomes by implementing contemporary synthetic pesticides, specifically, the mixture of acetamiprid and pyridaben. However, concerns regarding public health have arisen due to the increased number of suicides caused by insecticide poisoning. Nevertheless, limited reports of human exposure to these pesticides have reported various adverse clinical effects. In this study, we present the case of an individual who consumed the acetamiprid and pyridaben mixture for suicidal purposes, and subsequently developed central nervous system depression, hyperlactacidemia, and metabolic acid poisoning, which thus required clinical management.

Case report

A 74-year-old woman was transported to our hospital after ingesting a combination of 30 mL of acetamiprid 5 % and pyridaben 5 %. The patient displayed nausea and vomiting symptoms, followed by confusion. An arterial blood gas analysis revealed metabolic acidosis and hyperlactacidemia. The patient was carefully monitored for vital signs and treated with gastric lavage, purgation, and proton pump inhibitors to reduce gastric acid, blood volume, and electrolyte resuscitation. In addition, the patient received 24 h of hemoperfusion (HP) and continuous renal replacement therapy (CRRT). As a result of these interventions, the patient had a speedy recovery and was discharged 10 days later.

Conclusion

This case report provided the details of a rare instance of acute poisoning in humans resulting from exposure to newer synthetic pesticides, specifically acetamiprid and pyridaben. The report described the clinical manifestations and effective supportive therapy management. Future clinicians may find the results of this report valuable for identifying clinical symptoms and treating acute poisoning caused by newer synthetic pesticides.

Prediction of acute organophosphate poisoning severity using machine learning techniques

Poisoning with organophosphate compounds is a significant public health risk, especially in developing countries. Considering the importance of early and accurate prediction of organophosphate poisoning prognosis, the aim of this study was to develop a machine learning-based prediction model to predict the severity of organophosphate poisoning. The data of patients with organophosphate poisoning were retrospectively extracted and split into training and test sets in a ratio of 70:30. The feature selection was done by least absolute shrinkage and selection operator method. Selected features were fed into five machine learning techniques, including Histogram Boosting Gradient, eXtreme Gradient Boosting, K-Nearest Neighborhood, Support Vector Machine (SVM) (kernel = linear), and Random Forest. The Scikit-learn library in Python programming language was used to implement the models. Finally, the performance of developed models was measured using ten-fold cross-validation methods and some evaluation criteria with 95 % confidence intervals. A total of 1237 patients were used to train and test the machine learning models. According to the criteria determining severe organophosphate poisoning, 732 patients were assigned to group 1 (patients with mild to moderate poisoning) and 505 patients were assigned to group 2 (patients with severe poisoning). With an AUC value of 0.907 (95 % CI 0.89-0.92), the model developed using XGBoost outperformed other models. Feature importance evaluation found that venous blood gas-pH, white blood cells, and plasma cholinesterase activity were the top three variables that contribute the most to the prediction performance of the prognosis in patients with organophosphate poisoning. XGBoost model yield an accuracy of 90.1 % (95 % CI 0.891-0.918), specificity of 91.4 % (95 % CI 0.90-0.92), a sensitivity of 89.5 % (95 % CI 0.87-0.91), F-measure of 91.2 % (95 % CI 0.90-0.921), and Kappa statistic of 91.2 % (95 % CI 0.90-0.92). The machine learning-based prediction models can accurately predict the severity of organophosphate poisoning. Based on feature selection techniques, the most important predictors of organophosphate poisoning were VBG-pH, white blood cell count, plasma cholinesterase activity, VBG-BE, and age. The best algorithm with the highest predictive performance was the XGBoost classifier.

The Anion Gap and Mortality in Critically Ill Patients with Hip Fractures

Objectives

Epidemiological evidence suggests that anion gap (AG) has been reported to serve as an independent predictor for mortality in different diseases. We studied the effect of AG on both short and long-term mortalities in critically ill patients with hip fracture.

Methods

A large clinical database was utilized to perform retrospective cohort analysis. AG was subdivided into three groups. The Cox proportional hazards regression model was employed to approximate the hazard ratio (HR) with a confidence interval (CI) of 95% for the link between AG and mortality. 30-day mortality is the primary outcome, while 90-day and 1-year mortalities represented our secondary outcomes for this study.

Results

The participants in this study were that who provided essential data on AG and the number of patients with hip fractures was 395, and they were all aged ≥16 years. The participants comprised 199 (50.4%) females as well as 196 (49.6%) males with an average age of 71.9 ± 19.4 years, and a mean AG of 12.4 ± 3.3 gmEq/L. According to an unadjusted model for 30-day all-cause mortality, the HR (95% CI) of AG ≥ 12.5 gmEq/L was 1.82 (1.11, 2.99), correspondingly, compared to the reference group (AG < 12.5 gmEq/L). This correlation was still remarkable after adjustment for r age, sex, race, SBP, DBP, WBC, heart failure, and serum chloride (HR = 1.70, 95% CI: 1.02–2.02; 2.82). For 90-day all-cause mortality, a similar correlation was observed.

Conclusions

We noted that AG was an independent indicator of both short and long-term mortalities among hip fractures individuals in this retrospective single-center cohort study. AG is a simple, readily available, and inexpensive laboratory variable that can serve as a possible risk stratification tool for hip fracture.

Prediction Model of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning by Intentional Ingestion: Prediction of Respiratory Failure in Pesticide Intoxication (PREP) Scores in Cohort Study

Acute respiratory failure is the primary cause of mortality in patients with acute pesticide poisoning. The aim of the present study was to develop a new and efficient score system for predicting acute respiratory failure in patients with acute pesticide poisoning. This study was a retrospective observational cohort study comprised of 679 patients with acute pesticide poisoning by intentional poisoning. We divided this population into a ratio of 3:1; training set (n = 509) and test set (n = 170) for model development and validation. Multivariable logistic regression models were used in developing a score-based prediction model. The Prediction of Respiratory failure in Pesticide intoxication (PREP) scoring system included a summation of the integer scores of the following five variables; age, pesticide category, amount of ingestion, Glasgow Coma Scale, and arterial pH. The PREP scoring system developed accurately predicted respiratory failure (AUC 0.911 [0.849−0.974], positive predictive value 0.773, accuracy 0.873 in test set). We came up with four risk categories (A, B, C and D) using PREP scores 20, 40 and 60 as the cut-off for mechanical ventilation requirement risk. The PREP scoring system developed in the present study could predict respiratory failure in patients with pesticide poisoning, which can be easily implemented in clinical situations. Further prospective studies are needed to validate the PREP scoring system.