Paraquat (PQ) concentration–time data have been used to predict prognosis for 3 decades. The aim of this study was to find a more accurate method to predict the probability of survival.
This study included 788 patients with PQ poisoning who were diagnosed using plasma PQ concentration between January 2005 and August 2012. We divided these patients into 2 groups (survivors vs. nonsurvivors), compared their clinical characteristics, and analyzed the predictors of survival.
The mean age of the included patients was 57 years (range, 14–95 years). When we compared clinical characteristics between survivors (
Age, ln(Cr), ln(time), and ln(PQ) were important prognostic factors in PQ poisoning, and our equation can be helpful to predict the survival in acute PQ poisoning patients.
Paraquat (PQ; 1,1'-dimethyl-4,4'-bipyridinium) dichloride is a nonselective herbicide that has been widely used in many countries since the 1960s. It has unique properties which make it important to agriculture; it is a fast-acting broad-spectrum contact weedkiller which is very rainfast and is deactivated on contact with soil. However, ingestion of the concentrated formulation is very toxic to humans with no specific antidote or conclusively effective treatment demonstrated
The prognosis of acute PQ poisoning is dependent on the plasma PQ concentrations, and PQ concentration–time data have been used to predict outcomes for 3 decades
Therefore, we investigated prognostic factors affecting survival in patients with PQ poisoning and estimated the predicted probability of survival through logistic regression analysis using plasma PQ concentration, time since ingestion, and other variables.
Eight hundred ten patients who had ingested PQ visited our hospital between January 2005 and December 2012. We excluded 22 patients who were transferred to other hospitals during treatment or otherwise lost to follow-up. Therefore, 788 patients were included in this study and were divided into 2 groups: survival (
Physicians treated the patients and recorded all the information on a standardized data collection form. Standardized medical emergency procedures were conducted according to the Presbyterian Medical Center protocol for PQ poisoning (
We developed 3 models to predict survival according to the interval after ingestion and initial creatinine. Model 1 was based on the initial plasma PQ concentration and time since ingestion. Model 2 was based on adding of prognostic factors to predict the survival of the patients with PQ poisoning in our study to Model 1. Model 3 was based on a 2-hour PQ level instead of the initial PQ level.
Blood samples for the measurement of plasma PQ concentration (PQ 0 hour) were collected as soon as patients arrived at the emergency department. Samples were centrifuged at 1,600 ×
All data are presented as mean ± standard deviation unless otherwise specified. Differences in covariates between survivors and nonsurvivors were tested with the Student
The baseline characteristics of the 788 patients are presented in
When we compared clinical characteristics between survivors (
The equation for the predicted probability of survival was exp (logit)/[1 + exp(logit)]. When time since ingestion and PQ 0-hour level were used, the equation was as follows: logit = 0.006 + [1.519 × ln(time)] + [2.444 × ln(PQ 0 hours)] (Model 1;
The survival rate was 19% in our study. When compared with nonsurvivors, survivors were younger and showed better renal function on admission. The mean plasma PQ level was lower in survivors than in nonsurvivors. Age, ln(Cr), ln(time), and ln(PQ) predicted survival in patients with PQ poisoning. We calculated the predicted probability of survival using significant prognostic factors after adjusting for sex.
PQ is a nonselective, fast-acting herbicide that is environmentally harmless because of its rapid decomposition into nontoxic compounds after soil contact
Several parameters including liver enzymes, serum creatinine, potassium, arterial blood bicarbonate, respiratory index, and plasma and urinary PQ concentrations have been proposed as prognostic indications
We calculated the predicted probability of survival with time and concentration as variables. The sensitivity and specificity of our equation were 86.1% and 96.6%, respectively, when only time and PQ concentration were included in the equation. However, after adjustment for sex, we included age, ln(Cr), ln(time), and ln(PQ). The equation was exp (logit)/[1 + exp(logit)], where logit = –1.347 + [0.212 × sex (male = 1, female = 0)] + (0.032 × age) + [1.551 × ln(Cr)] + [0.391 × ln(time)] + [1.076 × ln(PQ)]. The sensitivity and specificity of this equation were increased to 86.5% and 98.7%, respectively, which were higher than those of the equation using only time and concentration. Therefore, we believe that our equation could be helpful to predict the survival in acute PQ poisoning patients. Furthermore, accurate prediction of survival can be useful to decide the treatment strategy in patients with PQ poisoning.
Some data suggest that the plasma PQ concentration peaks within 2–4 hours of ingestion, with a distribution half-life of 5 hours
Our study has some limitations. This is a retrospective study, and the study population comprised only Asian people. Although all patients received antioxidant therapy, some of the patients (25%) did not undergo hemoperfusion therapy in our study. In addition, we used PQ 2 hours as a variable in Model 3. However, we did not collect PQ 2 hours in all patients. Therefore, prospective randomized study is needed to predict the survival in PQ poisoning.
In conclusion, reliable predictors of prognosis can guide treatment and future clinical research on antidotes and therapies. In this study, the survival rate was 19%, and age, ln(Cr), ln(time), and ln(PQ) were the important prognostic factors in PQ poisoning. We calculated the predicted probability of survival using these variables, which had better sensitivity and specificity than those of previous studies. Therefore, our equation may be helpful in predicting mortality in acute PQ poisoning.
All authors have no conflicts of interest to declare.
The authors alone were responsible for the content and writing of the paper.
Summary of treatment guidelines for acute paraquat intoxication
1. Gastric lavage |
2. Dithionite urine test |
3. Fuller's earth, 100 g in 200-mL mannitol |
4. A. Antioxidant (intravenous administration) |
Vitamin B and E |
B. For renal preservation |
Furosemide |
15% mannitol |
5. Emergency hemoperfusion |
6. Key laboratory parameters |
Blood chemistry: blood urea nitrogen, creatinine, amylase, lipase |
Electrolyte: Na, K, Cl |
Arterial blood gas analysis |
Plasma paraquat level |
Clinical and laboratory findings of the 788 patients with PQ poisoning
Characteristics | |
---|---|
Age (y) | 57 ± 16 |
Male | 507 (64) |
Time since ingestion (h) | 6.6 ± 15.0 |
Hemoperfusion therapy | 594 (75) |
Serum creatinine (mg/dL) | 1.7 ± 1.3 |
Serum alanine aminotransferase (IU/L) | 36 ± 50 |
Serum lipase (IU/L) | 103 ± 184 |
P | 25.0 ± 9.1 |
HCO3 (mmol/L) | 14.8 ± 6.8 |
Amount of PQ ingested (mL) | 151 ± 124 |
Plasma PQ 0-h level (μg/mL) | 65 ± 115 |
Plasma PQ 2-h level (μg/mL) | 41 ± 80 |
Urine PQ test | |
Negative | 30 (3.8) |
Weakly positive | 84 (10.6) |
Positive | 44 (5.6) |
Strong positive | 632 (80) |
Data are presented as mean ± SD or number (%).
PQ, paraquat.
The data are available in 379 patients.
Comparison of clinical characteristics between survivors and nonsurvivors
Survivor ( | Nonsurvivor ( | ||
---|---|---|---|
Age (y) | 47.0 ± 14.0 | 59.0 ± 16.0 | <0.012 |
Male | 83 (56) | 422 (67) | 0.233 |
Time since ingestion (h) | 8.7 ± 17.2 | 6.1 ± 14.4 | 0.094 |
Hemoperfusion therapy | 141 (95) | 453 (71) | <0.015 |
Serum creatinine (mg/dL) | 1.0 ± 0.9 | 1.9 ± 1.3 | <0.012 |
Serum alanine aminotransferase (IU/L) | 32.0 ± 34.0 | 37.0 ± 53.0 | 0.230 |
Serum lipase (IU/L) | 46.0 ± 38.0 | 115.0 ± 200.0 | <0.010 |
P | 30.0 ± 7.0 | 23.0 ± 9.0 | <0.011 |
HCO3 (mmol/L) | 19.0 ± 14.0 | 13.0 ± 7.0 | <0.012 |
Amount of PQ ingested (mL) | 34.0 ± 22.0 | 178.0 ± 122.0 | <0.014 |
Plasma PQ 0-h level (μg/mL) | 0.4 ± 0.7 | 80.3 ± 123.1 | <0.010 |
Plasma PQ 2-h level (μg/mL) | 0.2 ± 0.3 | 58.9 ± 102.1 | <0.013 |
Urine PQ test | <0.010 | ||
Negative | 26 (17) | 4 (1) | |
Weakly positive | 69 (46) | 15 (2) | |
Positive | 30 (20) | 14 (2) | |
Strong positive | 24 (16) | 606 (95) |
Data are presented as mean ± SD or number (%).
NS, not significant; PQ, paraquat.
The data are available in 82 patients.
The data are available in 297 patients.
Univariate logistic regression analysis
Variables | Relative risk | 95% Confidence interval | ||
---|---|---|---|---|
Age (y) | 1.046 | 1.033 | 1.058 | <0.011 |
Male | 1.546 | 1.076 | 2.222 | 0.018 |
ln(time) | 0.820 | 0.515 | 1.307 | 0.405 |
HP | 0.139 | 0.067 | 0.290 | <0.012 |
ln(Cr) | 20.132 | 11.374 | 35.639 | <0.010 |
Serum ALT (IU/L) | 1.004 | 0.998 | 1.010 | 0.204 |
Serum lipase (IU/L) | 1.010 | 1.009 | 1.023 | <0.011 |
P | 0.912 | 0.898 | 0.939 | <0.012 |
HCO3 (mmol/L) | 0.821 | 0.804 | 0.866 | <0.013 |
ln(PQ) | 2.648 | 2.271 | 3.087 | <0.011 |
ALT, alanine aminotransferase; Cr, creatinine; HP, hemoperfusion; PQ, paraquat.
Multivariate logistic regression analysis
Variable | B | Relative risk | 95% Confidence interval | |
---|---|---|---|---|
Age (y) | 0.032 | 1.271 | 1.012–1.053 | 0.010 |
ln(Cr) | 1.551 | 4.721 | 2.553–8.715 | <0.001 |
ln(time) | 0.391 | 1.478 | 1.048–2.085 | 0.032 |
ln(PQ) | 1.076 | 2.932 | 2.406–3.573 | <0.001 |
Cr, creatinine; PQ, paraquat.
Analysis of ROC curve
Sensitivity (95% CI) | Specificity (95% CI) | PPV | NPV | AUC (95% CI) | ||
---|---|---|---|---|---|---|
Model 1 | 0.861 (0.831–0.887) | 0.966 (0.923–0.989) | 0.991 | 0.618 | 0.957 (0.941–0.670) | Models 2, 3 > Model 1 |
Model 2 | 0.865 (0.836–0.891) | 0.987 (0.952–0.998) | 0.996 | 0.631 | 0.972 (0.958–0.982) | |
Model 3 | 0.887 (0.860–0.911) | 0.980 (0.942–0.996) | 0.995 | 0.670 | 0.974 (0.960–0.984) |
AUC, area under the curve; CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value; ROC, receiver operating characteristic.