Prediction the prognosis of the poisoned patients undergoing hemodialysis using machine learning algorithms.

BACKGROUND: Hemodialysis is a life-saving treatment used to eliminate toxins and metabolites from the body during poisoning. Despite its effectiveness, there needs to be more research on this method precisely, with most studies focusing on specific 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. METHODS: Using a registry database from 2016 to 2022, this study conducted a retrospective cohort study at Loghman Hakim Hospital. First, the relief feature selection algorithm was used to identify the most important variables influencing the prognosis of poisoned patients undergoing hemodialysis. Second, four machine learning algorithms, including extreme gradient boosting (XGBoost), histgradient boosting (HGB), k-nearest neighbors (KNN), and adaptive boosting (AdaBoost), were trained to construct predictive models for predicting the prognosis of poisoned patients undergoing hemodialysis. Finally, the performance of paired feature selection and machine learning (ML) algorithm were evaluated to select the best models using five evaluation metrics including accuracy, sensitivity, specificity the area under the curve (AUC), and f1-score. RESULT: The study comprised 980 patients in total. 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. Out of the four models evaluated, the HGB classifier stood out with superior results on the test dataset. It achieved an impressive mean classification accuracy of 94.8%, a mean specificity of 93.5 a mean sensitivity of 94%, a mean F-score of 89.2%, and a mean receiver operating characteristic (ROC) of 92%. CONCLUSION: 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. Further large-scale multi-center studies are warranted to validate the efficacy of these models across diverse populations.

Main predictors for repetition of suicidal behaviour among women referred to a single public sector tertiary care hospital in Iran.

OBJECTIVE: To assess the main predictors for repetition of suicidal behaviour among women. METHODS: This cross-sectional study was conducted at Loghman Hakim Hospital, Tehran, Iran, in 2014, and comprised women patients. The patients were divided into two groups, i.e. women repeating suicide and women without repeating suicide. Data was collected through a checklist and then analysed with SPSS 20. RESULTS: Of the 300 women, 121(40.3%) repeated suicide and 179(59.7%) did not. The overall mean age was 26.9±9.1 years (range: 14-80 years). High prevalence of psychological drug usage, alcohol use, history of self-mutilation (self-harm), psychotic disturbances, sexual relationships, as well as smoking and opium addition was revealed as major factors in repeated suicidal behaviour in women when compared with other women. The result of multivariate logistic regression model showed two factors of self-mutilation (odds ratio =2.692, p=0.002) and underlying psychotic disorders (odds ratio = 2.780, p<0.001) as main predictors of suicide in women. In this regard, demographic characteristics could not predict repeating suicidal attempts (p>0.05). CONCLUSIONS: The presence of underlying psychotic disorders and self-mutilation were main predictors for repetition of suicidal behaviour.

Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence).

BACKGROUND: Treatment management for opioid poisoning is critical and, at the same time, requires specialized knowledge and skills. This study was designed to develop and evaluate machine learning algorithms for predicting the maintenance dose and duration of hospital stay in opioid poisoning, in order to facilitate appropriate clinical decision-making. METHOD AND RESULTS: This study used artificial intelligence technology to predict the maintenance dose and duration of administration by selecting clinical and paraclinical features that were selected by Pearson correlation (filter method) (Stage 1) and then the (wrapper method) Recursive Feature Elimination Cross-Validated (RFECV) (Stage2). The duration of administration was divided into two categories: A (which includes a duration of less than or equal to 24 h of infusion) and B (more than 24 h of naloxone infusion). XGBoost algorithm model with an accuracy rate of 91.04%, a prediction rate of 91.34%, and a sensitivity rate of 91.04% and area under the Curve (AUC) 0.97 was best model for classification patients. Also, the best maintenance dose of naloxone was obtained with XGBoost algorithm with R2 = 0.678. Based on the selected algorithm, the most important features for classifying patients for the duration of treatment were bicarbonate, respiration rate, physical sign, The partial pressure of carbon dioxide (PCO2), diastolic blood pressure, pulse rate, naloxone bolus dose, Blood Creatinine(Cr), Body temperature (T). The most important characteristics for determining the maintenance dose of naloxone were physical signs, bolus dose of 4.5 mg/kg, Glasgow Coma Scale (GCS), Creatine Phosphokinase (CPK) and intensive care unit (ICU) add. CONCLUSION: A predictive model can significantly enhance the decision-making and clinical care provided by emergency physicians in hospitals and medical settings. XGBoost was found to be the superior model.

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

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. So, our study aims to assess Explainable Artificial Intelligence (XAI) for predicting intubation necessity in methanol-poisoned patients, comparing deep learning and machine learning models. This study analyzed a dataset of 897 patient records from Loghman Hakim Hospital in Tehran, Iran, encompassing cases of methanol poisoning, including those requiring intubation (202 cases) and those not requiring it (695 cases). Eight established ML (SVM, XGB, DT, RF) and DL (DNN, FNN, LSTM, CNN) models were used. Techniques such as tenfold cross-validation and hyperparameter tuning were applied to prevent overfitting. The study also focused on interpretability through SHAP and LIME methods. Model performance was evaluated based on accuracy, specificity, sensitivity, F1-score, and ROC curve metrics. Among DL models, LSTM showed superior performance in accuracy (94.0%), sensitivity (99.0%), specificity (94.0%), and F1-score (97.0%). CNN led in ROC with 78.0%. For ML models, RF excelled in accuracy (97.0%) and specificity (100%), followed by XGB with sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%). Overall, RF and XGB outperformed other models, with accuracy (97.0%) and specificity (100%) for RF, and sensitivity (99.37%), F1-score (98.27%), and ROC (96.08%) for XGB. ML models surpassed DL models across all metrics, with accuracies from 93.0% to 97.0% for DL and 93.0% to 99.0% for ML. Sensitivities ranged from 98.0% to 99.37% for DL and 93.0% to 99.0% for ML. DL models achieved specificities from 78.0% to 94.0%, while ML models ranged from 93.0% to 100%. F1-scores for DL were between 93.0% and 97.0%, and for ML between 96.0% and 98.27%. DL models scored ROC between 68.0% and 78.0%, while ML models ranged from 84.0% to 96.08%. Key features for predicting intubation necessity include GCS at admission, ICU admission, age, longer folic acid therapy duration, elevated BUN and AST levels, VBG_HCO3 at initial record, and hemodialysis presence. This study as the 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.

Prediction of acute methanol poisoning prognosis using machine learning techniques.

Methanol poisoning is a global public health concern, especially prevalent in developing nations. This study focuses on predicting the severity of methanol intoxication using machine learning techniques, aiming to improve early identification and prognosis assessment. The study, conducted at Loghman Hakim Hospital in Tehran, Iran. The data pertaining to individuals afflicted with methanol poisoning was retrieved retrospectively and divided into training and test groups at a ratio of 70:30. The selected features were then inputted into various machine learning methods. The models were implemented using the Scikit-learn library in the Python programming language. Ultimately, the efficacy of the developed models was assessed through ten-fold cross-validation techniques and specific evaluation criteria, with a confidence level of 95%. A total number of 897 patients were included and divided in three groups including without sequel (n = 573), with sequel (n = 234), and patients who died (n = 90). The two-step feature selection was yielded 43 features in first step and 23 features in second step. In best model (Gradient Boosting Classifier) test dataset metric by 32 features younger age, higher methanol ingestion, respiratory symptoms, lower GCS scores, type of visual symptom, duration of therapeutic intervention, ICU admission, and elevated CPK levels were among the most important features predicting the prognosis of methanol poisoning. The Gradient Boosting Classifier demonstrated the highest predictive capability, achieving AUC values of 0.947 and 0.943 in the test dataset with 43 and 23 features, respectively. This research introduces a machine learning-driven prognostic model for methanol poisoning, demonstrating superior predictive capabilities compared to traditional statistical methods. The identified features provide valuable insights for early intervention and personalized treatment strategies.

Investigation of the effects of Chlorella vulgaris supplementation on the modulation of oxidative stress in apparently healthy smokers.

BACKGROUND: Smoking is among the established yet modifiable risk factors for cancers, cardiovascular diseases, and pulmonary disorders. Oxidative stress has been proposed as a key mechanism mediating the deleterious consequences of smoking. The present study evaluated the effect of supplementation with Chlorella vulgaris, a nutrient and bioactive green microalgae with proven antioxidant capacity, on the burden of oxidative stress in Iranian smokers. METHODS: Thirty-eight smokers (mean age: 37.11 +/- 1.69 years; females: 18.4%) were administered C. vulgaris extract (3600 mg/day) for a period of 6 weeks. Fasted serum samples collected at baseline and after the completion of study were analyzed for the concentrations of vitamin C, vitamin E, glutathione, and malonedialdehyde (MDA) as well as activities of superoxide dismutase, glutathione peroxidase, and catalase. Total antioxidant capacity of serum was also determined by the ability of serum to inhibit the formation of ferryl myoglobin radical species. RESULTS: Six-week supplementation with C. vulgaris extract in smokers was associated with marked elevation of all assessed serum antioxidant measures (p < 0.001) and significant reduction of MDA levels (p = 0.002). After gender segregation, a similar pattern of changes was observed for both male and female subjects apart from lack of significant change in serum vitamin E status in females. Although the magnitude of change in serum vitamin E was significantly greater in males compared to females (p = 0.014), there was no significant change in the magnitude of changes for other assessed parameters between the genders. CONCLUSIONS: Supplementation with C. vulgaris extract significantly improves antioxidant status and attenuates lipid peroxidation in chronic cigarette smokers. Hence, C. vulgaris might prevent the disease burden and mortality rate associated with smoking.

Does organophosphate poisoning cause cardiac injury?

Organophosphates are insecticides which are widely used as a suicidal agent in Iran. They are associated with different types of cardiac complications including cardiac arrest and arrhythmia, however their role in cardiac injury is not known yet. The aim of this study was to investigate the presence of myocardial damage in patients with cholinesterase poisoning.It was a prospective study conducted from January 2008 to March 2010. Cohorts of patients with cholinesterase poisoning due to suicidal attempt who have been referred to Loghman hospital were selected. Patients who have taken more than one poison or were used concomitant drugs were excluded. Physical examination was performed on admission to discover warning sign. Peripheral arterial blood gases, creatine kinase, creatine kinase-myocardial band, troponin-T measurements were performed in all cases. There were 24 patients, 7 of them women, with the mean age of 41.2±15.05 who were included in this study. Non-survivors had significantly higher levels of systolic blood pressure, partial pressure of oxygen in arterial blood, partial pressure of carbon dioxide, bicarbonate Glasgow Coma Scale scoring and longer duration of mechanical ventilation. Our findings showed that cardiac injury is an important cause of death in organophosphate poisoning. It could be hypothesized that cardiac injury is a strong predictor of death in patients with organophosphate poisoning.

Mercury exposure of gold mining workers in the northwest of Iran.

Mercury exposure is a health concern in the occupational settings like gold mining and chloralkali industries and blood and urine levels of mercury are used as exposure indicators. In this study, blood and urine concentrations of mercury were determined using hydride generation atomic absorption spectrophotometery (HGAAS) in sixteen gold miners with neuropsychiatric symptoms. The patients treated with two chelating agents, dimercaprol and D-penicillamine. The mean serum mercury levels before and after chelation therapy were 208.14 µg/L(-1) and 10.50 µg/L(-1), respectively. The mean urinary mercury levels before and after chelation therapy were 134.70 µg/L(-1) and 17.23 µg/L(-1), respectively. The results of this study showed that there are significant differences between concentration of blood and urine mercury before and after intervention (p<0.005). There were no significant differences between in the biochemistry parameters of patients before and after treatment. This study indicated that the gold miners in the northwest of Iran had been exposed to high levels of mercury vapors [Hg((0))].

A rare case report of lead encephalopathy due to high blood lead level.

Here we report a case of lead poisoning having a serum lead level of 412 mcg dL-1 who presented with decreasing level of consciousness and recurrent seizures. He responded well to treatment with chelation therapy.

Montelukast Is Effective in Treating Rhabdomyolysis Due to Intoxication: A Randomized Clinical Trial.

INTRODUCTION: Rhabdomyolysis is a clinical syndrome accompanied with biochemical changes that is diagnosed in some patients with acute chemical or drug poisoning. In this regard, the present study aimed to evaluate the effects of Montelukast in the treatment of intoxication-induced rhabdomyolysis. METHODS: This single-blind randomized clinical trial study was conducted in Loghman Hakim Hospital from March 2021 to March 2022. The study participants were 60 individuals evenly distributed into experimental and control groups. The experimental group received Montelukast plus routine treatment and the control group Creatine phosphokinase (CPK), urea, creatinine, aspartate aminotransferase (AST) and alanine transaminase (ALT) levels were monitored daily in both groups for seven days. The variables of age, gender and history of diabetes mellitus and kidney diseases were recorded. RESULTS: The mean age was 39.9 ± 16.87 and 38.2 ± 16.3 years in the control and intervention groups, respectively. Montelukast significantly (P < .05) reduced CPK levels on days five and seven, urea on days three, four, five and seven, and creatinine on days two to seven. The AST and ALT levels, unlike the control group which has a decreasing trend, increased first in the Montelukast group and then decreased on the sixth and seventh days. CONCLUSION: The results showed that Montelukast effectively reduced CPK, urea and creatinine levels, as well as the recovery time in patients with poison-induced rhabdomyolysis. In other words, Montelukast is effective in the treatment of rhabdomyolysis.  DOI: 10.52547/ijkd.7222.

Visual impairment following aluminum phosphide poisoning: A rare case.

Key Clinical Message: Aluminum phosphide poisoning may cause rare visual impairment. In a case, a 31-year-old female, visual loss was linked to shock-induced hypoperfusion, causing oxygen lack and cerebral atrophy, emphasizing the need for identifying atypical symptoms. Abstract: This case report describes the multidisciplinary evaluation of a 31-year-old female patient who suffered from visual impairment as a result of aluminum phosphide (AlP) poisoning. Phosphine, which is formed in the body when AlP reacts with water, cannot cross the blood-brain barrier; therefore, visual impairment seems unlikely to be the direct result of phosphine. To our knowledge, it is the first documented report of such impairment due to AlP.

Comparing Two Naloxone Tapering Methods in Management of Methadone Intoxication; a Quasi-experimental Study.

Introduction: Even though naloxone is the main treatment for methadone poisoning treatment there are controversies about the proper method of its tapering. This study aimed to compare two methods in this regard. Method: This study was a prospective, single-blind pilot quasi-experimental study on non-addicted adult patients poisoned with methadone. Patients were randomly divided into 2 groups. In one group, after stabilization of respiratory conditions and consciousness, naloxone was tapered using the half-life of methadone and in the other group, naloxone was tapered using the half-life of naloxone. Recurrence of symptoms and changes in venous blood gas parameters were compared between groups as outcome. Results: 52 patients were included (51.92% female). 31 cases entered Group A (tapering based on methadone's half-life) and 21 cases entered Group B (tapering based on naloxone's half-life). The two groups were similar regarding mean age (p = 0.575), gender distribution (p = 0.535), the cause of methadone use (p = 0.599), previous medical history (p = 0.529), previous methadone use (p = 0.654), drug use history (p = 0.444), and vital signs on arrival to emergency department (p = 0.054). The cases of re-decreasing consciousness during tapering (52.38% vs. 25.81%; p = 0.049) and after discontinuation of naloxone (72.73% vs. 37.50%; p = 0.050) were higher in the tapering based on naloxone half-life group. The relative risk reduction (RRR) for naloxone half-life group was -1.03 and for methadone half-life group was 0.51. The absolute risk reduction (ARR) was 0.27 (95% confidence interval (CI) = 0.01-0.53) and the number needed to treat (NNT) was 3.7 (95% CI= 1.87- 150.53). There was not any statistically significant difference between groups regarding pH, HCO3, and PCO2 changes during tapering and after naloxone discontinuation (p > 0.05). However, repeated measures analysis of variance (ANOVA), showed that in the tapering based on methadone's half-life group, the number of changes and stability in the normal range were better (p < 0.001). Conclusion: It seems that, by tapering naloxone based on methadone's half-life, not only blood acid-base disorders are treated, but they also remain stable after discontinuation and the possibility of symptom recurrence is reduced.

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.

Tricyclic antidepressant poisoning treated by magnesium sulfate: a randomized, clinical trial.

Tricyclic antidepressant (TCA) poisoning is a major problem in medicine, with a high morbidity and mortality rate. The main cause of fatality is cardiac arrhythmias resulting from intoxication. Sodium bicarbonate is the drug of choice, but severe poisoning necessitates further interventions. Magnesium sulfate seems to be effective in this condition. In a randomized, clinical trial, we evaluated all patients with a history of TCA intoxication referred to the Loghman Hakim Hospital Poison Center. The patients were randomly divided into two groups: one treated by bicarbonate infusion (control group) and the other (case group) by the infusion of magnesium sulfate in addition to the treatment in the first group. Seventy-two patients were recruited into the study (36 cases and 36 controls). Mean duration of intensive care unit stay in the cases and controls were 25.63 ± 9.33 and 82.67 ± 21.66 hours, respectively (P < 0.001). Mortality rate in the case group was 13.9% and 33.3% in the other group (P = 0.052). Magnesium sulfate can be an effective drug in the treatment of TCA poisoning; however, several randomized, clinical trials are still necessary to confirm this.

Colchicine poisoning treated with hemoperfusion and hemodialysis: A case report.

This case report described an improved case of colchicine poisoning using hemoperfusion and hemodialysis.

Sumatriptan poisoning and its clinical presentation in humans: A case report.

This case report displays some of the possible complications of sumatriptan poisoning, including nephritic syndrome.

Conservative management of massive rivaroxaban overdose: A case report and literature review.

In the cases of acute rivaroxaban overdose, conservative management without prothrombin complex concentrate or other coagulation factors may be sufficient if renal function is normal and there is no bleeding.

Post-mortem Histopathologic Findings of Vital Organs in Critically Ill Patients with COVID-19.

BACKGROUND: The scientific evidence concerning pathogenesis and immunopathology of the coronavirus disease 2019 (COVID-19) is rapidly evolving in the literature. To evaluate the different tissues obtained by biopsy and autopsy from five patients who expired from severe COVID-19 in our medical center. METHODS: This retrospective study reviewed five patients with severe COVID-19, confirmed by reverse transcription-polymerase chain reaction (RT-PCR) and imaging, to determine the potential correlations between histologic findings with patient outcome. RESULTS: Diffuse alveolar damage (DAD) and micro-thrombosis were the most common histologic finding in the lung tissues (4 of 5 cases), and immunohistochemical (IHC) findings (3 of 4 cases) suggested perivascular aggregation and diffuse infiltration of alveolar walls by CD4+ and CD8+ T lymphocytes. Two of five cases had mild predominantly perivascular lymphocytic infiltration, single cell myocardial necrosis and variable interstitial edema in myocardial samples. Hypertrophic cardiac myocytes, representing hypertensive cardiomyopathy was seen in one patient and CD4+ and CD8+ T lymphocytes were detected on IHC in two cases. In renal samples, acute tubular necrosis was observed in 3 of 5 cases, while chronic tubulointerstitial nephritis, crescent formation and small vessel fibrin thrombi were observed in 1 of 5 samples. Sinusoidal dilation, mild to moderate chronic portal inflammation and mild mixed macro- and micro-vesicular steatosis were detected in all liver samples. CONCLUSION: Our observations suggest that clinical pathology findings on autopsy tissue samples could shed more light on the pathogenesis, and consequently the management, of patients with severe COVID-19.

Epidemiologic profile and clinical characteristics of ulcerative colitis in northwest of Iran: a 10-year review.

BACKGROUND AND AIM: There are few reports from Iran about the epidemiology and clinical features of inflammatory bowel disease (IBD). In this study, we aimed to determine the epidemiologic profile and clinical features of ulcerative colitis (UC) in Northwest of Iran. METHODS: This retrospective study covered the time period from 1998 to 2008 and included all patients of a private gasteroenterology clinic in the northwest of Iran, who had diagnosis of UC at the time of presentation or those whose diagnosis had been made later. In addition to description of epidemiology and clinical feature of disease, an attempt was made to identify factors associated with severity of disease. RESULTS: A total of 105 patients including 61 females (58.1%) were evaluated. Mean age of the patients was 33.5 +/- 13.1 years. The median time interval from initiation of symptoms to diagnosis was 9 months. The commonest presentation was proctosigmoiditis. (48.6%). Among extra-intestinal manifestations, sclerosing cholangitis had the highest frequency and was found in 2 (1.9%) patients. Among all evaluated variables, only family income (the higher the income the more severe the disease) and cigarette smoking (inverse association) were find to have significant association with severity of disease. CONCLUSION: A case profile of patients with ulcerative colitis from Northwest Iran suggests that the disease is seen most commonly in the third decade of life with a female preponderance. Family income and smoking influenced the course of ulcerative colitis in Iranian patients.

Comparison of the efficacy of an infusion pump or standard IV push injection to deliver naloxone in treatment of opioid toxicity.

BACKGROUND: The optimal goal of naloxone infusion in intensive care units is to ameliorate opioid-induced side effects in therapy or eliminate the symptoms of opioid toxicity in overdoses. Accurately monitoring and regulating the doses is critical to prevent adverse effects related to naloxone administration. The present study aimed to compare treatment outcomes when using two methods of intravenous naloxone infusion: an infusion pump or the standard method. METHODS: This study involved 80 patients with signs and symptoms of opioid overdose. The patients were randomly assigned into two groups with respect to intravenous infusion of naloxone by either an infusion pump or the standard method. RESULTS: Comparison of study parameters between the two groups at 12 and 24 hours after intervention showed significantly more compensatory acid-base imbalance in the naloxone infusion pump group. In the group that received naloxone by pump, only one patient experienced withdrawal symptoms, but withdrawal symptoms appeared in 12 patients (30.0%) in the standard intravenous infusion group within 12 hours and in seven additional patients (17.5%) within 24 hours of intervention. In the group receiving pump-based naloxone infusion therapy, no another complications were reported; however in the standard infusion group, the 12-hour and 24-hour complication rates were 55.0% and 32.5%, respectively. The length of hospital stay was 2.85±1.05 and 4.22±0.92 days for the pump and standard infusion groups, respectively (P<0.001). CONCLUSIONS: Naloxone infusion using an infusion pump may be safer with regard to hemodynamic stability, resulting in shorter hospitalization periods, and fewer posttreatment complications.