In our study, number of stings, HB < 110 g/dl, TBI > 34 mg/dl, ALT > 40 U/L and APTT > 47 s were identified as independent risk factors for AKI following wasp stings according to univariable and multivariable logistic regression analysis in the training set. And the predictive formula and the individual nomogram that included those independent risk factors was developed and validated to predict the probability of the AKI following wasp stings. Those were demonstrated having the sufficient accuracy and good predictive capability based on the internal validation and external validation in the training set and validation set respectively. In addition, the predictive formula and individual nomogram both have clinical significance to assess the probability of the AKI following wasp stings and make a decision for therapy by those easy, convenient and effective methods.
At present, wasp stings are reported frequently, especially in rural areas where patients may have low incomes[22–24], which is a common challenge to society. In the present study, the incidence rate of AKI is 30.5% (155/508) in patients with wasp stings, which is nearly equal to results in the previous studies that reported the incidence rate of AKI is 20–25%[11, 25, 26]. In addition, the mortality in patients with wasp stings is 5.7% (29/508), however the mortality (17.4% [27/155]) in the AKI group is apparently higher than that (0.6% [2/353]) in the non-AKI group (P < 0.001 as shown Figure A4 in Additional file 7). Those indicated that it is significant to understand, risk predict and early diagnose the AKI following wasp stings. Of note, 55.6% (15/27) patients with AKI following wasp stings died within 72 hours after admission, that is similar to the result of previous study[14]. At present, AKI following wasp stings with relatively high mortality rate and rapid onset were called the “Silent Killer” because it threated to human public health. Therefore, early detection and diagnosis should be performed promptly to help clinicians make therapeutic decisions for obtaining a good prognosis.
However, the prediction model of AKI following wasp stings is rarely reported in previous studies. We established the predictive formula and the individual nomogram based on the independent risk factor, which predicted the AKI of patients undergoing wasp stings with a good validation. As reported in previous studies, nomogram is extensively used to predict the probability of a disease or a clinical outcome based on multiple variables[27–30]. In the present study, the visual nomogram could calculated the specific probability of AKI following wasp stings based on the sum of the scores of each risk factor, that is the most user-friendly tool to judge the specific situation of each patient[31]. The nomogram is intuitive and easy-to-understand not only clinicians but for patients as well, which might make it easy for communication between clinicians and patients. Of note, nomograms have never ever been reported for AKI following wasp stings to our knowledge, we conduct the first nomogram to predict the AKI following wasp stings.
Besides, the predictive formula also is conducted to assess whether occur AKI or not in patients with wasp stings, that is judged according to whether the calculated probability > the cut off of the model (0.338) or not. One of the two methods could be selected according to the habits and preferences of clinicians, or both to mutually detect and support the results. Those are composed of common clinical parameters, that are easy to obtain from laboratorial blood tests. In addition, the sufficient accuracy and good predictive capability of the model are verified by AUC of ROC (0.912 in the training set and 0.936 in the validation set), and the net benefit is verified by DCA. Therefore, the model might provide a clinical assistance in early recognition, detection, diagnosis and intervention of AKI following wasp stings.
According to previous studies, AKI is induced in wasp stings based on direct toxicity of the venom components, hypotension, intravascular hemolysis and rhabdomyolysis[32]. The venom components, such as PLA2 that is mainly included in wasp venom and melittin that is the mainly included in the bee venom, both have strong hemolytic toxicity and direct toxic effect for inducing the apoptosis of renal tubule epithelial cells[16, 33]. Hypotension might lead to ischemic renal lesion, which is induced by main components of bee venom such as hyaluronidase, apamin and substances induced by those venom themselves such as histamine, serotonin, bradykinin.
Besides, rhabdomyolysis and hemolysis induced AKI by renal vasoconstriction, formation of intratubular deposits of myoglobin and direct cytotoxicity of myoglobin and HB that are release from muscle and red blood cells. However, there is no full understanding in the mechanism through which renal damage occurs. Nonetheless, it is certain that rhabdomyolysis and hemolysis are thought play important roles in AKI following wasp stings.
In the present study, compared with non-AKI group, we actually find the levels of CK, ALT, AST elevate in the AKI group, which might be associated with rhabdomyolysis. We also find LDH increase and anemia (HB in the red blood cells decrease) in the AKI group, which might be is concerned with hemolysis. They also are the risk factors of AKI following wasp sting according to univariable logistic regression analysis. Continuous variables are transformed into categorical variables according to their reference range, which is beneficial to full use the results through constructing the model.
According to multivariable logistic regression analysis, number of stings, HB < 110 g/dl, TBI > 34 mg/dl, ALT > 40 U/L and APTT > 47 s are considered as independence risk factors and could help to predict AKI in patients with wasp stings. Rhabdomyolysis and hemolysis could induce the level of indirect bilirubin to elevate, which could be one explanation of the increasing of TBI in AKI following wasp stings. According to the study reported, disseminated intravascular coagulation (DIC) might be a possible factor that contributes to AKI, described in rhabdomyolysis. While DIC occurred, thromboplastin is released and micro thrombi is formed in the glomeruli, which causing the consequent glomerular filtration rate reduction[34]. DIC might induce APTT prolonged by increasing the consumption of coagulation factors. We think those might explain why prolonged APTT is an independence risk factors in AKI following wasp stings. In addition, we also find that wasp compared with other bee species, sting at head and face compared with other locations, the greater number of stings and large area of sting might be associated with AKI following wasp stings.
However, a recent study with a retrospective cohort study involving 112 patients conducted by Hai Yuan et al, which showed that elevated leukocytes, high myoglobin, high urinary monocyte chemotactic protein-1 (MCP-1) are the independence risk factors of AKI induced by multiple wasp stings[25]. In fact, our results also find that, while compared with non-AKI, the level of leukocytes is higher in the patients with AKI following wasp stings. However, we do not think elevated leukocytes is an independence risk factor of AKI following wasp stings based on the multivariable logistic regression analysis. There exists a difference between the two studies. It's worth noting that the results are obtained based on the single center as well as the small sample size in the Hai Yuan’s study. In addition, 12 variables included in the multivariable logistic regression analysis to find out the independence risk factors based on only 54 patients who occurred AKI. We think those might restrict external validity and cause the different results in the Hai Yuan’s study. Besides, Hai Yuan’s study only reported some independence risk factors, which did not construct a model to predict AKI for fully utilization the result. We construct the first model based on a large data from the multicenter prospective cohort study and validate it.
There exist some limitations in the present study. First, there are some variables missing too much so that we have to exclude them such as cystatin -C and urine protein, although we already try our best to collect the complete data of each patient. Second, although data were collected from multicenter, the enrolled patients all were of the same ethnicity, which might limit the scalability of the model. Third, we select the variable by forward stepwise in multivariable logistic regression analysis, that might induce the final model that contain terms of little values. Besides, the validation was performed by bootstrapping technology, however there also need further external validation in further.