A new machine-learning method to prognosticate paraquat poisoned patients by combining coagulation, liver, and kidney indices

Liver injury in paraquat poisoning: A retrospective cohort study

BACKGROUND AND AIMS

Liver injury is one of the common complications of paraquat (PQ) poisoning, but whether the degree of liver injury is related to patient prognosis is still controversial. This study aimed to investigate whether liver injury was a risk factor for death in PQ-poisoned patients.

METHODS

We conducted a retrospective cohort study of PQ-poisoned patients from the past 10 years (2011-2020) from a large tertiary academic medical centre in China. PQ-poisoned patients were divided into a normal liver function group (n = 580) and a liver injury group (n = 60). Propensity score matching (PSM) analysis was then performed.

RESULTS

A total of 640 patients with PQ poisoning were included in this study. To reduce the impact of bias, dose of PQ, urinary PQ concentration and time from poisoning to hospital admission were matched between the two groups. A 3:1 PSM analysis was performed, ultimately including 240 patients. Compared with the normal liver function group, patients in the liver injury group were older, had a higher R value ([ALT/ULN]/[ALP/ULN]) (p < .001) and had a higher mortality rate. Cox regression analysis showed that there was no significant association between alanine aminotransferase, alkaline phosphatase, total bilirubin levels and hazard of death, but age, PQ dose, creatine kinase isoenzyme, creatine kinase, white blood cell count, neutrophil percentage and lymphocyte percentage were associated with mortality in patients with PQ poisoning.

CONCLUSIONS

The occurrence of liver injury within 48 h after PQ poisoning was a risk factor for mortality, and such liver injury was likely of a hepatocellular nature. Age, PQ dose, creatine kinase isoenzyme and white blood cell count were positively correlated with mortality, while creatine kinase, percentage of neutrophils and lymphocytes were inversely correlated.

Development and validation of a prognostic nomogram for predicting in-hospital mortality of patients with acute paraquat poisoning

This study aimed to develop and validate a predictive model to determine the risk of in-hospital mortality in patients with acute paraquat poisoning. This retrospective observational cohort study included 724 patients with acute paraquat poisoning whose clinical data were collected within 24 h of admission. The primary outcome was in-hospital mortality. Patients were randomly divided into training and validation cohorts (7/3 ratio). In the training cohort, the least absolute shrinkage and selection operator regression models were used for data dimension reduction and feature selection. Multivariate logistic regression was used to generate a predictive nomogram for in-hospital mortality. The prediction model was assessed for both the training and validation cohorts. In the training cohort, decreased level of consciousness (Glasgow Coma Scale score < 15), neutrophil-to-lymphocyte ratio, alanine aminotransferase, creatinine, carbon dioxide combining power, and paraquat plasma concentrations at admission were identified as independent predictors of in-hospital mortality in patients with acute paraquat poisoning. The calibration curves, decision curve analysis, and clinical impact curves indicated that the model had a good predictive performance. It can be used on admission to the emergency department to predict mortality and facilitate early risk stratification and actionable measures in clinical practice after further external validation.

Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection

Many works have employed Machine Learning (ML) techniques in the detection of Diabetic Retinopathy (DR), a disease that affects the human eye. However, the accuracy of most DR detection methods still need improvement. Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM) is one of the most popular ML algorithms, and can be considered as an accurate algorithm in the process of classification, but has not been used in solving DR detection. Therefore, this work aims to apply the GWO-ELM classifier and employ one of the most popular features extractions, Histogram of Oriented Gradients-Principal Component Analysis (HOG-PCA), to increase the accuracy of DR detection system. Although the HOG-PCA has been tested in many image processing domains including medical domains, it has not yet been tested in DR. The GWO-ELM can prevent overfitting, solve multi and binary classifications problems, and it performs like a kernel-based Support Vector Machine with a Neural Network structure, whilst the HOG-PCA has the ability to extract the most relevant features with low dimensionality. Therefore, the combination of the GWO-ELM classifier and HOG-PCA features might produce an effective technique for DR classification and features extraction. The proposed GWO-ELM is evaluated based on two different datasets, namely APTOS-2019 and Indian Diabetic Retinopathy Image Dataset (IDRiD), in both binary and multi-class classification. The experiment results have shown an excellent performance of the proposed GWO-ELM model where it achieved an accuracy of 96.21% for multi-class and 99.47% for binary using APTOS-2019 dataset as well as 96.15% for multi-class and 99.04% for binary using IDRiD dataset. This demonstrates that the combination of the GWO-ELM and HOG-PCA is an effective classifier for detecting DR and might be applicable in solving other image data types.

Risk factors and optimal predictive scoring system of mortality for children with acute paraquat poisoning

BACKGROUND

There is no suitable scoring system that can be used to predict mortality in children with acute paraquat intoxication (APP).

AIM

To optimize a predictive scoring system for mortality in children with APP.

METHODS

A total of 113 children with APP from January 1, 2010 to January 1, 2020 were enrolled in this study. These patients were divided into survivors and non-survivors. We compared the clinical characteristics between the two groups and analyzed the independent prognostic risk factors. The survival rates of patients with different values of the pediatric critical illness score (PCIS) were assessed using kaplan-meier survival analysis. The best scoring system was established by using the area under the receiver operating characteristic curve analysis.

RESULTS

The overall mortality rate was 23.4%. All non-survivors died within 20 days; 48.1% (13/27) died within 3 days, and 70.3% (19/27) died within 7 days. Compared to survivors, the non-survivors were older, had higher white blood cell count, alanine aminotransferase (ALT), aspartate aminotransferase, serum creatinine, blood urea nitrogen, glucose, and pediatric early warning score, and had lower platelet count, albumin, Serum sodium (Na⁺) and PCIS. ALT and PCIS were the independent prognostic risk factors for children with APP. The survival rate of children classified as extremely critical patients (100%) was lower than that of children classified as critical (60%) or noncritical (6.7%) patients. The specificity of ALT was high (96.51%), but the sensitivity was low (59.26%). The sensitivity and specificity of ALT combined with PCIS were high, 92.59% and 87.21%, respectively. The difference in mortality was significantly higher for ALT combined with PCIS (area under the receiver operating characteristic: 0.937; 95%CI: 0.875-0.974; P < 0.05).

CONCLUSION

In our study, ALT and PCIS were independent prognostic risk factors for children with APP. ALT combined with PCIS is an optimal predictive mortality scoring system for children with APP.

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.

Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design

The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA.

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.

Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm

Kernel extreme learning machine (KELM) has been widely used in the fields of classification and identification since it was proposed. As the parameters in the KELM model have a crucial impact on performance, they must be optimized before the model can be applied in practical areas. In this study, to improve optimization performance, a new parameter optimization strategy is proposed, based on a disperse foraging sine cosine algorithm (DFSCA), which is utilized to force some portions of search agents to explore other potential regions. Meanwhile, DFSCA is integrated into KELM to establish a new machine learning model named DFSCA-KELM. Firstly, using the CEC2017 benchmark suite, the exploration and exploitation capabilities of DFSCA were demonstrated. Secondly, evaluation of the model DFSCA-KELM on six medical datasets extracted from the UCI machine learning repository for medical diagnosis proved the effectiveness of the proposed model. At last, the model DFSCA-KELM was applied to solve two real medical cases, and the results indicate that DFSCA-KELM can also deal with practical medical problems effectively. Taken together, these results show that the proposed technique can be regarded as a promising tool for medical diagnosis.

Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation

Breast cancer is one of the most dangerous diseases for women's health, and it is imperative to provide the necessary diagnostic assistance for it. The medical image processing technology is one of the most critical of all complementary diagnostic technologies. Image segmentation is the core step of image processing, where multilevel image segmentation is considered one of the most efficient and straightforward methods. Many multilevel image segmentation methods based on evolutionary and population-based methods have been proposed in recent years, but many have the fatal weakness of poor convergence accuracy and the tendency to fall into local optimum. Therefore, to overcome these weaknesses, this paper proposes a modified differential evolution (MDE) algorithm with a vision based on the slime mould foraging behavior, where the recently proposed slime mould algorithm (SMA) inspires it. Besides, to obtain high-quality breast cancer image segmentation results, this paper also develops an excellent MDE-based multilevel image segmentation model, the core of which is based on non-local means 2D histogram and 2D Kapur's entropy. To effectively validate the performance of the proposed method, a comparison experiment between MDE and its similar algorithms was first carried out on IEEE CEC 2014. Then, an initial validation of the MDE-based multilevel image segmentation model was performed by utilizing a reference image set. Finally, the MDE-based multilevel image segmentation model was compared with peers using breast invasive ductal carcinoma images. A series of experimental results have proved that MDE is an evolutionary algorithm with high convergence accuracy and the ability to jump out of the local optimum, as well as effectively demonstrated that the developed model is a high-quality segmentation method that can provide practical support for further research of breast invasive ductal carcinoma pathological image processing.

The acute paraquat poisoning mortality (APPM) score to predict the risk of death in paraquat-poisoned patients

CONTEXT

Mortality prediction in paraquat poisoning is a major issue since most prediction rules are inapplicable if the exact ingestion time cannot be determined and/or the serum paraquat concentration is not readily available, as in most countries. Therefore, we aimed to develop and validate a new prediction rule not requiring these two parameters.

METHODS

We designed a 10-year observational cohort study including all consecutive paraquat-poisoned patients managed in two Taiwanese hospitals. We built one cohort to define and one cohort to validate this prediction rule. Parameters independently related to mortality determined using a multivariate analysis were used to formulate the Acute Paraquat Poisoning Mortality (APPM) score.

RESULTS

Overall, 321 paraquat-poisoned patients were included, 156 in the derivation and 165 in the validation cohort. Mortality rates in the derivation and validation cohorts were 73% and 81%, respectively (p = 0.20). The three parameters chosen of 28-day mortality at presentation were urine paraquat level >10 ppm (using a colorimetric sodium dithionite-based test; odds ratio (OR), 12.70; 95% confidence interval (CI), 2.64-61.24), white blood cells >13.0 G/L (OR, 5.50; CI, 1.41-21.48) and blood glucose >140 mg/dL [7.8 mmol/L] (OR, 7.45; CI, 1.70-32.86). In the derivation cohort, the area under the ROC curve (AUC-ROC) of the APPM score did not significantly differ from AUC-ROCs of serum paraquat (0.95, p = 0.25) and the Severity Index of Paraquat Poisoning (0.95, p = 0.33). AUC-ROCs of the APPM score in the derivation and validation cohorts were 0.91 and 0.94, respectively.

CONCLUSION

We built and validated a reliable score to predict 28-day mortality in paraquat-poisoned patients at presentation, independently from the ingestion time and serum paraquat measurement.

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.

Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine

Coronavirus Disease 2019 (COVID-19) was distributed globally at the end of December 2019 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Early diagnosis and successful COVID-19 assessment are missing, clinical care is ineffective, and deaths are high. In this study, we investigate whether the level of biochemical indicators helps to discriminate and classify the severity of the COVID-19 using the machine learning method. This research creates an efficient intelligence method for the diagnosis of COVID-19 from the perspective of biochemical indexes. The framework is proposed by integrating an enhanced new stochastic called the colony predation algorithm (CPA) with a kernel extreme learning machine (KELM), abbreviated as ECPA-KELM. The core feature of the approach is the ECPA algorithm which incorporates the two main operators that have been abstained from the grey wolf optimizer and moth-flame optimizer to improve and restore the CPA research functions and are simultaneously used to optimize the parameters and to select features for KELM. The ECPA output is checked thoroughly using IEEE CEC2017 benchmark to verify the capacity of the proposed methodology. Finally, in the diagnosis of COVID-19 using biochemical indexes, the designed ECPA-KELM model and other competing KELM models based on other optimization are used. Checking statistical results will display improved predictive properties for all metrics and higher stability. ECPA-KELM can also be used to discriminate and classify the severity of the COVID-19 as a possible computer-aided method and provide effective early warning for the treatment and diagnosis of COVID-19.

SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection

The wide spread of fake news has caused huge losses to both governments and the public. Many existing works on fake news detection utilized spreading information like propagators profiles and the propagation structure. However, such methods face the difficulty of data collection and cannot detect fake news at the early stage. An alternative approach is to detect fake news solely based on its content. Early content-based methods rely on manually designed linguistic features. Such shallow features are domain-dependent, and cannot easily be generalized to cross-domain data. Recently, many natural language processing tasks resort to deep learning methods to learn word, sentence, and document representations. In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations. In SemSeq4FD, we model the global pair-wise semantic relations between sentences as a complete graph, and learn the global sentence representations via a graph convolutional network with self-attention mechanism. Considering the importance of local context in conveying the sentence meaning, we employ a 1D convolutional network to learn the local sentence representations. The two representations are combined to form the enhanced sentence representations. Then a LSTM-based network is used to model the sequence of enhanced sentence representations, yielding the final document representation for fake news detection. Experiments conducted on four real-world datasets in English and Chinese, including cross-source and cross-domain datasets, demonstrate that our model can outperform the state-of-the-art methods.

Development and Validation of a Radiomics Nomogram for Prognosis Prediction of Patients with Acute Paraquat Poisoning: A Retrospective Cohort Study

Objective

To evaluate the efficiency of a radiomics model in predicting the prognosis of patients with acute paraquat poisoning (APP).

Materials and Methods

Chest computed tomography images and clinical data of 80 patients with APP were obtained from November 2014 to October 2017, which were randomly assigned to a primary group and a validation group by a ratio of 7 : 3, and then the radiomics features were extracted from the whole lung. Principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were used to select the features and establish the radiomics signature (Rad-score). Multivariate logistic regression analysis was used to establish a radiomics prediction model incorporating the Rad-score and clinical risk factors; the model was represented by nomogram. The performance of the nomogram was confirmed by its discrimination and calibration.

Result

The area under the ROC curve of operation was 0.942 and 0.865, respectively, in the primary and validation datasets. The sensitivity and specificity were 0.864 and 0.914 and 0.778 and 0.929, and the prediction accuracy rates were 89.5% and 87%, respectively. Predictors included in the individualized predictive nomograms include the Rad-score, blood paraquat concentration, creatine kinase, and serum creatinine. The AUC of the nomogram was 0.973 and 0.944 in the primary and validation datasets, and the sensitivity and specificity were 0.943 and 0.955, respectively, in the primary dataset and 0.889 and 0.929 in the validation dataset, and the prediction accuracy was 94.7% and 91.3%, respectively.

Conclusion

The radiomics nomogram incorporates the radiomics signature and hematological laboratory data, which can be conveniently used to facilitate the individualized prediction of the prognosis of APP patients.

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.

A retrospective analysis reveals a predictor of survival for the patient with paraquat intoxication

Feasible and accurate predictors are urgently needed to evaluate the survival for patients with paraquat poisoning since the high mortality of paraquat poisoning always resulted in the loss of both life and money. Multiple predictors have been developed to predict prognosis of the patients with PQ poisoning, which however heavily depend on the time of admission to hospitals. Here we reported a feasible and accurate prognosis predictor for patients with paraquat poisoning that is independent of the time of admission to hospitals. Patients with paraquat poisoning were enrolled in this study according to the inclusion and exclusion criteria, which were grouped into survivors and non-survivors based on the 90-days follow-up investigation. The concentration of paraquat in serum and urine, and the baseline clinical parameters associated with the injuries of the liver, kidney, and lung were evaluated to predict the survival of these patients by using receiver operating characteristic curve (ROC) analysis, univariate and multivariate cox regression analyses. A total of 114 patients was included in this study with a survival rate of 54.4%. The median survival days of non-survivors were 6.0 (95%Cl: 4.0-7.8). A new predictor, namely paraquat concentration-associated multiorgan injury index (PCAMII), was established by integrating serum and urine paraquat concentration, serum creatinine, alanine aminotransferase, aspartate transaminase, total and direct bilirubin, at different weighting coefficients, with the accuracy of about 90%. The model to predict the survival probability by PCAMII was established with good fitness (R² = 0.9325), providing the simulated survival rates comparable to the clinical data. PCAMII, which is independent of hospital admission time, is a feasible and accurate marker to predict the survival rate of patients with PQ poisoning.

Serum anion gap at admission as a predictor of the survival of patients with paraquat poisoning: A retrospective analysis

Paraquat (PQ) poisoning is associated with high mortality rate. Therefore, an accurate method for predicting the survival of patients with PQ poisoning is required. This study evaluated the value of serum anion gap (AG) at admission in predicting the survival of such patients.Cases of patients with PQ poisoning admitted to Cangzhou Central Hospital between May 2012 and March 2019 were retrospectively analyzed. The patients were classified into survival and nonsurvival groups on the basis of their 90-day prognosis. Correlation analysis, Cox regression analysis, and receiver operating characteristic and Kaplan-Meier curve analyses were performed to assess the value of AG in predicting the 90-day survival of patients with PQ poisoning.Only 44 of the 108 patients with PQ poisoning survived; thus, the 90-day survival was 40.74%. AG levels at admission were significantly higher in nonsurvivors (26.53 ± 4.93 mmol/L) than in survivors (20.88 ± 2.74 mmol/L) (P < .001) and negatively correlated with 90-day survival (r = -0.557; P < .001). Cox regression analysis revealed that AG at admission is an independent prognostic marker of the 90-day survival of patients with PQ poisoning. AG level at admission had an area under the receiver operating characteristic curve of 0.836 (95% confidence interval: 0.763-0.909) and an optimal cut-off value of 25.5 mmol/L (59.4% sensitivity and 95.5% specificity).AG level at admission may serve as a candidate marker for predicting the survival of patients with PQ poisoning.

Performance of machine-learning algorithms to pattern recognition and classification of hearing impairment in Brazilian farmers exposed to pesticide and/or cigarette smoke

The use of pesticides has been increasing in agriculture, leading to a public health problem. The aim of this study was to evaluate ototoxic effects in farmers who were exposed to cigarette smoke and/or pesticides and to identify possible classification patterns in the exposure groups. The sample included 127 participants of both sexes aged between 18 and 39, who were divided into the following four groups: control group (CG), smoking group (SG), pesticide group (PG), and smoking + pesticide group (SPG). Meatoscopy, pure tone audiometry, logoaudiometry, high-frequency thresholds, and immittance testing were performed. Data were evaluated by artificial neural network (ANN), K-nearest neighbors (K-NN), and support vector machine (SVM). There was symmetry between the right and left ears, an increase in the incidence of hearing loss at high frequency and of downward sloping audiometric curve configuration, and alteration of stapedial reflex in the three exposed groups. The machine-learning classifiers achieved good classification performance (control and exposed). The best classification results occur in high type (I and II) datasets (about 90% accuracy) in k-NN test. It is concluded that both xenobiotic substances have ototoxic potential; however, their combined use does not present additive or potentiating effects recognizable by the algorithms.

Sequential organ failure assessment in predicting mortality after paraquat poisoning: A meta-analysis

Sequential organ failure assessment (SOFA) score is commonly used to determine disease severity and predict prognosis in critically ill patients. However, the prognostic value of SOFA after acute paraquat (PQ) poisoning remains unclear. This meta-analysis aimed to study the capability of SOFA to predict mortality in patients with PQ poisoning. Databases that included PubMed, Embase, Web of Science, ScienceDirect, Embase, and Cochrane Library were searched through May 2018. Six studies involving 946 patients were included in the meta-analysis. Study-specific odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, and then ORs with 95% CIs were pooled for the estimation of the prognostic role of SOFA in patients with PQ poisoning. Results showed that higher SOFA in patients with PQ poisoning was related to severe mortality (OR = 8.14, 95%CI 4.26–15.58, p<0.001). The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic OR, and area under the curve were 72% (95%CI 0.65–0.79), 75% (95%CI 0.65–0.83), 2.9 (95%CI 2.0–4.1), 0.37 (95%CI 0.28–0.41), 8 (95%CI 4–14), and 0.79 (95%CI 0.76–0.83), respectively. No evidence of publication bias was detected by funnel plot analysis and formal statistical tests. Sensitivity analyses showed no important differences in the estimates of effects. The high SOFA score (8.1-fold) was associated with severe mortality in patients with PQ poisoning.

Point-of-care testing in the early diagnosis of acute pesticide intoxication: The example of paraquat

Acute pesticide intoxication is a common method of suicide globally. This article reviews current diagnostic methods and makes suggestions for future development. In the case of paraquat intoxication, it is characterized by multi-organ failure, causing substantial mortality and morbidity. Early diagnosis may save the life of a paraquat intoxication patient. Conventional paraquat intoxication diagnostic methods, such as symptom review and urine sodium dithionite assay, are time-consuming and impractical in resource-scarce areas where most intoxication cases occur. Several experimental and clinical studies have shown the potential of portable Surface Enhanced Raman Scattering (SERS), paper-based devices, and machine learning for paraquat intoxication diagnosis. Portable SERS and new SERS substrates maintain the sensitivity of SERS while being less costly and more convenient than conventional SERS. Paper-based devices provide the advantages of price and portability. Machine learning algorithms can be implemented as a mobile phone application and facilitate diagnosis in resource-limited areas. Although these methods have not yet met all features of an ideal diagnostic method, the combination and development of these methods offer much promise.