There is no known antidote for diquat poisoning, and high doses often cause irreversible effects [14]. The incidence of diquat poisoning has increased over the years, with suicide being the most common cause of poisoning [15]. Mortality rates have been reported to vary from 43.00% [5] to 60.00% [15], while the mortality rate was 42.05% in the present study. In this research, a comprehensive array of latent influencing factors derived from expert experience and findings from prior research were gathered to evaluate their association with the risk of mortality. Nomogram models can be used to visualize influencing factors clearly and effectively. In this study, we analyzed early clinical data from 107 patients diagnosed with diquat poisoning. Using stepwise multifactorial logistic regression analyses, we identified four prognostic factors: BP, WBC, RDW-SD, and eGFR. Then we developed a nomogram that predicted the risk of death in patients after 30 days of poisoning. The generalizability of the nomogram relies on patients receiving identical treatment. A lower eGFR, higher WBC and RDW-SD, and abnormal BP are correlated with a heightened risk of death. The AUC, confusion matrix, calibration curve, Brier score, and DCA confirmed that the model has good discriminative ability and excellent calibration ability, and internal verification was carried out by bootstrap resampling.

We also used the data to externally validate the APACHE II scoring model [8], which achieved an AUC of up to 0.95 (95% CI = 0.90–0.99), an accuracy of 0.90, a sensitivity of 0.87, a specificity of 0.92, a Youden index of 0.79, and an F1 score of 0.88.

The study results indicate that the deceased patients had significantly greater WBC and RDW-SD counts than did the surviving patients (P < 0.01). Additionally, the eGFR was significantly lower in the deceased group (P < 0.01), and there was a significantly greater number of patients with abnormal BP. A systemic inflammatory response may occur in patients with DQ [16]. Previous studies have also reported a close association between early elevation of WBC and adverse prognosis, suggesting that WBC may serve as a prognostic factor, as confirmed by the present study [5, 10, 17]. The RDW has recently been found to have a strong predictive capacity for the risk of death and adverse outcomes of other infectious and serious diseases, such as COVID-19 [18], acute respiratory obstruction syndrome [19], and sepsis [20]. Studies of ICU patients have shown that the RDW is an independent risk factor for death and is significantly and independently related to mortality [21]. A study also revealed that an increased RDW can serve as an independent predictor of 30-day mortality in patients with organophosphorus poisoning [22]. An increase in RDW may be attributed to various metabolic abnormalities, such as oxidative stress, inflammation, poor nutritional status, and high blood pressure [21]. DQ poisoning can cause oxidative stress-related damage to the body, activate the NF-κB pathway, and induce an inflammatory response in the body, affecting red blood cell stability and survival [23]. Furthermore, there is a strong, graded, and independent correlation between RDW and eGFR [21]. Clinically, ADP patients are characterized by multiorgan damage, primarily involving the kidneys and central nervous system [17], and some patients have complications such as anemia [24], systemic inflammatory reactions [16], and acute respiratory distress syndrome [5] in the later stages. Glomerular filtration rate is the rate at which plasma is filtered to produce ultrafiltrate. Therefore, measurement of the simpler eGFR is widely used in the clinical front line, as is its ability to reflect the magnitude and direction of the true GFR serving during acute kidney injury [25]. DQ in the bloodstream is mostly excreted by the kidney [26], so the occurrence of kidney damage is closely related to a decrease in the ability to remove toxins, which may have a significant impact on patient prognosis. Kidney injury is a prominent and early effect of ADP, with an incidence rate as high as 82.95% [27], and is characterized mainly by a significant decrease in the eGFR and delayed recovery of renal function [6]. A biopsy reveals acute tubular necrosis in a patient with diquat poisoning[6]. However, tubular recovery after acute kidney injury is vital for recovery of kidney function, including improvement of GFR, and likely determines which patients fully recover from acute kidney injury or progress to chronic kidney disease [28], which may also explain why some patients have a longer duration of kidney injury. Many studies have confirmed that DQ can cause nephrotoxicity [29, 30], and its early onset may indicate a poor prognosis [31]. Alterations in an individual’s blood pressure can serve as an indicator of the individual’s fundamental physiological state. Hypertensive patients experience accelerated blood flow, which can lead to faster toxin distribution to organs. The blood pressure of patients who died due to diquat poisoning decreased within 8 to 30 h after ingestion, and death occurred within 2 to 14 h after the drop [5]. Patients with secondary acute hypotension may develop organ hypoperfusion [32] and cannot promptly supply enough nutrition and oxygen to vital organs in the body, which worsens the patient’s condition and creates a vicious cycle.

The concentration of toxicants in the blood is considered to be the gold standard for theoretically evaluating a patient’s condition [33]. We recorded the plasma DQ concentrations at the time of admission in 57 patients because some patients were tested only for urine DQ concentrations. The risk of death is positively correlated with the concentration of toxins in the body, which increases as the concentration increases (Fig. 5. a). The median plasma DQ concentration was 221.40[56.00,1571.12] in the surviving patients (n = 36) and 5386.53[2587.30,9860.76] in the death group (n = 21), which was significantly higher than that of the surviving group (P < 0.05). Compared to the model described in the text, the plasma DQ concentration used to predict patient prognosis had an AUC of 0.89 (95% CI: 0.81–0.98), which was lower than the AUC of the aforementioned model. This finding was associated with a decrease in accuracy, and the results of the 1000 bootstrap replicates were also not as good (ACC = 0.80, kappa = 0.56). The severity index of diquat poisoning (SIDP) is calculated by multiplying the plasma DQ concentration by the duration of poisoning [8]. Additionally, the SIDP used to predict patient prognosis had an AUC (0.88 [0.78, 0.98]) was less than the single concentration, the ACC (0.84), and the Youden index (0.69) were slightly greater, while the overall result was inferior to the concentration model. To determine the type and severity of poisoning, the blood concentration of the poison is considered the gold standard. A retrospective cohort study of 50 patients confirmed a relationship between plasma DQ concentrations and in-hospital mortality (AUC = 0.97 [0.91, 1.00], cutoff = 3516.89 ng/ml [sensitivity, 90.90%; specificity, 96.00%]) [33]. In contrast, some patients did not receive complete continuous treatment, which may have affected the study’s results. However, it can be challenging to measure toxicant concentrations in patient body fluids accurately in primary health centers and some hospitals due to the lack of access to high-precision equipment.

The study also revealed a distinct correlation between the dose taken and mortality (P < 0.01). The group of individuals who died ingested a significantly greater dosage than the group of survivors did, consistent with findings from previous research [10, 17], and some studies have included it as a prognostic influencing factor or even constructed relevant models [10, 34, 35]. However, this variable was not included in the present study after analysis. Considering toxic doses did not improve the predictive effectiveness of the model. Currently, the most precise approach for determining the dosage of orally ingested poisons is the oral water method [36]. The method of operation is to prepare a bottle of mineral water (250 ml) with a diameter similar to that of the pesticide bottle, simulate the situation of taking poison, where the patient takes the same number of mouthfuls of mineral water, and estimate the dose of poison taken by the patient through the measurement of the amount of water remaining in the bottle. Nonetheless, this method may be challenging to use for certain critically ill patients because the information provided by family members may lack accuracy, and patients may also experience recall bias, leading to a more subjective assessment. Therefore, considering the number of variables, we still chose the final model without the toxic dose.

In this study, a nomogram prediction model of mortality risk in patients with acute diquat poisoning was established, combining objective indicators and patients’ status and assigning a score to each risk factor to provide the corresponding probability of mortality risk, which can rapidly identify patients with critical diquat poisoning at an early stage and assess the risk of mortality, help clinicians choose the most optimal treatment decision. If the model predicted a low risk of death for the patient, giving the current standardized treatment would improve the prognosis. Conversely, even with standardized treatment, those who are at high risk of mortality may not always improve their prognosis. Thus, economic costs and predicted values should be appropriately taken into account, and suitable steps should be taken to lessen their suffering. However, the model’s generalization and extrapolation accuracy remain to be verified since the study was not externally validated. Additionally, the study has some limitations. First, the small sample size and single-center retrospective design of this study limit the amount of data collected, which may introduce bias that affects the universality of the research results. Furthermore, despite the inclusion of many variables in this study to cover all the influencing factors as much as possible, certain specificity indicators identified in prior research, such as body mass index [37], neutrophil gelatinase-associated lipocalin [38, 39], serum toxicant concentration [33], and lactate concentration [10], were not adequately captured for various reasons. Finally, although the prediction model demonstrated a certain degree of accuracy, the initial clinical data within 24 h after admission did not fully reflect the degree of organ damage caused by DQ. Moreover, certain patients did not manifest abnormal clinical symptoms or atypical blood test results upon admission [14]. Therefore, it is necessary to monitor and observe specific indicators during follow-up, and the construction of the final prediction model requires further exploration and analysis.