Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices

Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices Wen Wu, Yupei Zhang, Yilan Zhang, Xingguang Qu, Zhaohui Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4502096/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Acute kidney injury (AKI) following multiple wasp stings is a severe complication with potentially poor outcomes. Despite extensive research on AKI's risk factors, predictive models for wasp sting-related AKI are limited. This study aims to develop and validate a machine learning-based clinical prediction model for AKI in individuals with wasp stings. We retrospectively analyzed clinical data from 214 patients with wasp sting injuries. Among these patients, 34.6% (74/214) developed AKI. Using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, the number of stings, presence of gross hematuria, systemic inflammatory response index (SIRI), and platelet count were identified as prognostic factors. A nomogram was constructed and evaluated for its predictive accuracy, showing an area under the curve (AUC) of 0.757 (95% CI 0.711 to 0.804) and a concordance index (C-index) of 0.75. The model's performance was assessed through internal validation, leave-one-out cross-validation, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Validation confirmed the model's reliability and superior discrimination ability over existing models, as demonstrated by NRI, IDI, and DCA. This nomogram accurately predicts the risk of AKI in wasp sting patients, facilitating early identification and management of those at risk. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Risk factors Wasp sting Acute kidney injury Machine learning model Nomogram Prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Wasp stings represent a significant public health issue in tropical regions, particularly in Southern China, Southeast Asia, and Latin America 1,2 . The venom from wasps, a complex concoction of peptides, enzymes, proteins, and other chemicals, can cause cellular damage, ranging from localized allergic reactions to multiorgan dysfunction 3,4 . Life-threatening conditions have been associated with multiple wasp stings 5 , leading to a distinct syndrome characterized by intravascular hemolysis, rhabdomyolysis, hepatic dysfunction, coagulopathy, acute respiratory distress syndrome, encephalopathy, and acute kidney injury (AKI) 6 . Wasp venom-induced AKI is defined as potentially fatal complication that follows mass attacks 7 . Our previous findings 8 , aligning with studies from Gong et al. 9 and Sitprija et al. 3 indicate that between 20–50% of individuals subjected to wasp stings may experience rapid progression to kidney failure and multiple organ dysfunction syndrome (MODS). The nephrotoxic effects of wasp venom, including rhabdomyolysis, intravascular hemolysis 10 , and diminished renal blood flow, are implicated in renal ischemia and damage, ultimately leading to acute tubular necrosis (ATN) 4,9 . Moreover, it's essential to recognize that the mechanisms behind wasp venom-induced AKI include both inflammatory and toxic effects. Additionally, our studies 8 have identified that the systemic inflammatory response index (SIRI), which gauges the immune system's inflammatory reaction and overall immune status, is associated with MODS after multiple wasp stings. The factors associated with AKI have been extensively studied, but only a limited number of studies have developed prediction models. To our knowledge, few studies have developed the prediction models for wasp sting induced AKI 11,12 . These models, though comprehensive in incorporating laboratory tests for risk factors such as myoglobin, urinary monocyte chemotactic protein-1, aspartate transaminase (AST), total bilirubin (TBIL), and lactate dehydrogenase (LDH), are not practical for patients in suburban or rural areas with limited medical resources. Consequently, we aim to develop and validate a simple, cost-effective, and clinical characteristics-based risk prediction model for early detection of AKI caused by wasp stings. This model is designed for broad applicability across various healthcare settings, especially in resource-constrained environments. Materials and methods Study setting and design This was a retrospective cohort study involving patients diagnosed with wasp stings from July 2013 to April 2023 at Yichang Central People’s Hospital, the First College of Clinical Medicine Science, China Three Gorges University. We initially identified 314 consecutive admissions of patients with wasp stings to our hospital between July 1, 2013 and April 30, 2023.Cases diagnosed with wasp stings were extracted from the hospital’s electronic chart database. Patients were selected in strict accordance with predetermined exclusion criteria to ensure the relative homogeneity of the selected patients. After excluding patients who were unable to follow-up and incomplete data collection due to missing information, 214 eligible patients were included in the analyses (Fig. 1). A standard wasp sting management protocol was followed for the emergent management. Patients were treated according to the Chinese expert consensus for wasp sting 13 . The study, conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Yichang Central People’s Hospital (Ethics NO. 2023-055-01), waived the need for informed consent due to de-identification of patient data. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Outcomes and other definitions Detailed history was recorded and clinical examinations and investigations were performed to provide complete clinical characteristics. The primary outcomes were as follows: acute kidney injury (AKI). AKI was defined by Kidney Disease Improving Global Outcomes (KDIGO) definition criteria as any of the following: increase in serum creatinine by ≥ 0.3 mg/dl (≥ 26.4 µmol/L ) within 48 h; or increase in serum creatinine by ≥ 1.5 times baseline, which is known or presumed to have occurred within the prior 7 days; or Urine volume ≤ 0.5 ml/kg/h for 6 h 14 .MODS was diagnosed according to the criteria 15 , including confirmed wasp stings, and the occurrence of dysfunction or failure of 2 or more organs successively or simultaneously 24 h after the wasp stings. An organ dysfunction score ≥ 2 was defined as organ failure. Gross hematuria was defined as the presence of dark or reddish-brown or tea-colored urine in patients who have experienced severe wasp stings 12,16 .This indicates a significant amount of hemoglobin or myoglobin in the urine. Complete blood count analysis including white blood cell count, hemoglobin content, neutrophil count (NEU), lymphocyte count (LYM), monocyte count (MONO), and platelet count (PLT) were also collected. Systemic inflammatory response index (SIRI) was calculated by NEU × MONO / LYM 8 . For all admissions, a comprehensive review of all medical records was conducted to gather information on the characteristics of the patients, including gender, age, co-morbidities (such as hypertension, coronary heart disease, chronic obstructive pulmonary disease and diabetes), clinical symptoms at admission (such as gross hematuria, nausea, vomiting, chest tightness, palpitations), time of visit Emergency Department after injury, number of sting wounds, mortality, lengths of hospitalization, and acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score within 24 h of admission. Furthermore, we only included the laboratory test results from the first 24h hospitalization for patients. Biochemical parameters including albumin (ALB), blood urea nitrogen (BUN), creatinine (CRE), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), creatine kinase (CK), creatine kinase isoenzyme (CK-MB), hydroxybutyrate dehydrogenase (HBDH), cardiac troponin I (cTnI), myoglobin (MYO), prothrombin time (PT), activated partial thromboplastin time (APTT), C-reactive protein (CRP). Statistical analysis Continuous variables with normal distribution were expressed as mean with standard deviation (SD), and differences between groups were compared by an independent sample t -test. Continuous variables that were not normally distributed were represented by median and range, and differences between groups were compared using the Mann-Whitney U-test. Categorical variables were presented as frequencies or percentages, and the Chi-square test was used to compare differences between groups. Several steps were performed to develop and validate nomograms for predicting wasp stings associated AKI. First, three methods of best subsets regression (BSR), the forward stepwise regression (FSR), and least absolute shrinkage and selection operator (LASSO) regression was conducted to screen the potential prognostic factors. The least absolute contraction and selection operator (LASSO) regression model was used to deal with the collinearity problem of candidate variables to select the optimal predictor variables. Second, multivariable logistic regression analysis was performed to identify the significant prognostic factors associated with AKI. A logistic regression analysis was used to estimate univariate and multivariate odds ratios and 95% confidence intervals. Third, all potential prognostic factors were used to build the model, and a nomogram was used to visualize the model. Receiver operating characteristic (ROC), concordance index (C-index), and calibration curve analyses were used to evaluate the discrimination and calibration of the model. Furthermore, the corrected ROC, C-index, and calibration curve analyses were calculated using 1000 bootstrap sampling. The C-index indicates the discriminative ability of the model; 0.5–0.7 suggests low discriminative ability, 0.7–0.9 indicates moderate discriminative ability, and > 0.9 indicates high diagnostic value. Previous AKI associated with wasp stings prediction model developed by Tang et al. was calculated based on the number of stings, time from stings to admission (> 12h), and two biochemistry parameters—aspartate aminotransferase (> 147 U/L) and lactate dehydrogenase (> 477 U/L) 11 . The predictive performance of the nomogram and Tang’s model were evaluated by NRI and IDI, the comparison of the Harrell’s C index and AUC values. Finally, the decision curve analysis (DCA) was used to evaluate the clinical utility and net benefits of the model for both our center and Tang et al.’s cohorts 11 . Missing data were processed by multiple imputations. Imputation for missing variables was considered if missing values were less than 10%. All analyzes were performed with the statistical software package R version 3.3.2 ( https://www.r-project.org , The R Foundation) and the Free Statistics software package version 1.9. Two-tailed tests were performed, with p-values < 0.05 being considered statistically significant. Results Characteristics of patients and incidence of AKI 214 cases were included in the analysis, with 140 (65.4%) cases of non-AKI outcomes and 74 (34.6%) cases of AKI outcomes. The median age of the included patients was 49.6 ± 18.4 years, and 60.3% (129/214) were male. In total, 79 (36.9%) were admission to ICU and 14 were dead. Table 1 shows the characteristics of the non-AKI and AKI groups. Details of the missing data are listed in Table S1 . There was no significant difference in gender and the time from wasp sting to hospitalization between the two groups (P > 0.05), but patients who suffered from AKI tended to be older, have more comorbidities, a higher number of sting wounds, higher SOFA and APACHE II scores, longer hospital stays, and higher rates of MODS, myocardial damage, hemopurification, mechanical ventilation, complications, and death. They also had elevated levels of WBC, Hb, PLT, NEU, MONO, AST, ALT, CRP, CK, CK-MB, LDH, HBDH, PT, APTT, BUN, CRE, SIRI, and lower levels of LYM (P < 0.05) (Table 1 ). Variables Total (n = 214) Non-AKI (n = 140) AKI (n = 74) P -value Sex (%) 0.444 Male 129 (60.3) 87 (62.1) 42 (56.8) Female 85 (39.7) 53 (37.9) 32 (43.2) Age (years) 49.6 ± 18.4 44.4 ± 18.9 59.2 ± 12.8 < 0.001 ICU admission (%) 79 (36.9) 27 (19.3) 52 (70.3) < 0.001 Post injury time (hours) 13.0 (6.0, 30.8) 13.0 (5.0, 30.0) 14.0 (7.0, 43.8) 0.151 Comorbidity (%) < 0.001 No comorbidity 176 (82.2) 126 (90) 50 (67.6) Hypertension 29 (13.6) 8 (5.7) 21 (28.4) CHD 7 (3.3) 4 (2.9) 3 (4.1) T2DM 2 (0.9) 2 (1.4) 0 (0) Number of sting wounds 10.0 (1.2, 25.0) 2.5 (1.0, 10.0) 30.0 (20.0, 40.0) < 0.001 Number of sting wounds (%) < 0.001 <10 100 (46.7) 99 (70.7) 1 (1.4) 11–29 67 (31.3) 33 (23.6) 34 (45.9) ≥ 30 47 (22.0) 8 (5.7) 39 (52.7) Chest tightness and Palpitation (%) 44 (20.6) 14 ( 10 ) 30 (40.5) < 0.001 Rash (%) 171 (79.9) 102 (72.9) 69 (93.2) < 0.001 Gross Hematuria, n (%) 58 (27.1) 10 (7.1) 48 (64.9) < 0.001 Mechanical Ventilation (%) 29 (13.6) 4 (2.9) 25 (33.8) < 0.001 MODS (%) 105 (49.1) 33 (23.6) 72 (97.3) < 0.001 Myocardial Damage (%) 79 (36.9) 34 (24.3) 45 (60.8) < 0.001 Received Hemopurification (%) 80 (37.4) 17 (12.1) 63 (85.1) < 0.001 Complication (%) < 0.001 No complication 164 (76.6) 131 (93.6) 33 (44.6) Gastrointestinal hemorrhage 11 (5.1) 0 (0) 11 (14.9) Cerebral hemorrhage 6 (2.8) 2 (1.4) 4 (5.4) Acute ischemic stroke 2 (0.9) 0 (0) 2 (2.7) Pneumonia 31 (14.5) 7 ( 5 ) 24 (32.4) SOFA 2.0 (0.0, 6.0) 0.0 (0.0, 2.0) 7.0 (5.0, 13.0) < 0.001 APACHEII 4.0 (2.0, 10.0) 3.0 (0.0, 4.0) 11.0 (8.0, 20.5) < 0.001 ICU LOS (days) 0.0 (0.0, 2.0) 0.0 (0.0, 0.0) 2.0 (0.0, 6.0) < 0.001 Hospital LOS (days) 5.0 (3.0, 11.0) 4.0 (3.0, 8.0) 10.0 (3.0, 25.2) < 0.001 Death (%) 14 (6.5) 2 (1.4) 12 (16.2) < 0.001 SBP (mmHg) 131.4 ± 26.9 123.2 ± 22.6 147.0 ± 27.6 < 0.001 HR (bpm) 91.6 ± 20.8 96.5 ± 20.1 82.5 ± 19.2 < 0.001 WBC (×10 9 /L) 13.7 (9.3, 20.2) 11.0 (8.7, 16.0) 19.9 (15.1, 26.6) < 0.001 Hb (g/L) 121.5 (107.2, 129.8) 123.0 (114.8, 131.0) 112.5 (90.5, 129.0) < 0.001 PLT (×10 9 /L) 232.5 (161.0, 294.8) 252.0 (200.2, 316.8) 169.0 (92.0, 238.0) < 0.001 NEU (×10 9 /L) 10.5 (5.8, 18.4) 8.1 (4.8, 13.1) 18.6 (13.4, 24.1) < 0.001 LYM (×10 9 /L) 1.5 (0.7, 2.6) 1.9 (1.0, 3.3) 0.8 (0.5, 1.5) < 0.001 MONO (×10 9 /L) 0.6 (0.4, 0.8) 0.6 (0.4, 0.8) 0.7 (0.5, 1.1) 0.012 AST (U/L) 62.0 (30.0, 441.0) 43.0 (26.0, 102.0) 975.0(236.5, 3462.2) < 0.001 ALT (U/L) 36.0 (17.8, 135.0) 23.0 (15.0, 50.5) 245.0 (68.2, 822.5) < 0.001 ALB (g/L) 39.5 (34.9, 43.3) 41.9 (38.4, 44.5) 35.5 (31.6, 37.8) < 0.001 CRP (mg/L) 5.6 (1.5, 13.9) 2.2 (0.9, 7.8) 13.5 (5.8, 38.1) < 0.001 CK (IU/L) 403.5 (149.0, 4685.5) 179.5 (122.2, 853.8) 4223.5 (994.8, 14500.5) < 0.001 CK-MB (U/L) 34.0 (18.0, 140.5) 25.5 (16.8, 43.8) 173.0 (59.3, 352.0) < 0.001 LDH (IU/L) 447.5 (289.2, 1848.8) 322.5 (258.2, 459.0) 2462.5 (1363.0, 5219.5) < 0.001 HBDH (IU/L) 324.0 (218.0, 1085.0) 243.0 (188.0, 343.0) 1388.0 (780.0, 3007.5) < 0.001 BUN (mmol/L) 6.4 (4.5, 10.6) 5.2 (4.2, 6.7) 12.4 (9.3, 15.9) < 0.001 CRE (µmol/L) 74.0 (61.0, 163.0) 64.0 (57.0, 74.0) 253.0 (160.5, 419.5) < 0.001 PT (s) 13.5 (12.6, 15.0) 13.3 (12.3, 14.1) 14.7 (12.9, 16.1) < 0.001 APTT (s) 42.9 (35.2, 94.9) 39.7 (33.8, 45.3) 111.6 (51.7, 155.8) < 0.001 SIRI 3.9 (1.3, 13.1) 2.3 (0.9, 5.0) 13.1 (5.4, 29.5) < 0.001 PLT (%) < 0.001 <100 24 (11.2) 3 (2.1) 21 (28.4) ≥ 100 190 (88.8) 137 (97.9) 53 (71.6) Table 1 . Clinical characteristics in the AKI and non-AKI groups. Mean and interquartile range for continuous variables: P value was calculated by weighted linear regression model. % for categorical variables: P value was calculated by weighted chi-square test, P < 0.05 was considered statistically significant. AKI acute kidney injury, ICU intensive care unit, MODS multiple organ dysfunction syndrome, SOFA sequential organ failure assessment, APACHE II acute physiology and chronic health evaluation II, LOS length of stay, SBP systolic blood pressure, HR heart rate, WBC white blood cell count, Hb hemoglobin level, PLT platelet count, NEU neutrophil count in peripheral blood, LYM lymphocyte count in peripheral blood, MONO monocyte count in peripheral blood, AST aspartate aminotransferase, ALT alanine aminotransferase, ALB albumin, CRP C-reactive protein, CK creatine kinase, CK-MB creatine kinase isoenzyme, LDH lactate dehydrogenase, HBDH alpha-hydroxybutyrate dehydrogenase, BUN blood urea nitrogen, CRE serum creatinine, PT prothrombin time, APTT activated partial thromboplastin time, SIRI systemic inflammatory response index. Model derivation First, we adopted LASSO regression penalty to streamline the dimension and select the most meaningful prognostic indicators. Subsequently, a tenfold cross-validation of the lasso model was performed for tuning parameter selection via the minimum criteria (Fig. 2 A). The trajectory of each prognostic indicator coefficient was observed in the LASSO coefficient profiles with the changing of the log-transformed lambda in LASSO algorithm (Fig. 2 B). When the lambda optimal value was 0.05 (1 standard error of the minimum criteria), four non-zero coefficient variables were selected as potential prognosis-related indicators, including wasp sting numbers, gross hematuria, SIRI, and PLT. Furthermore, we also used Best Subsets Regression (BSR) to filter the variables of the minimum Bayesian Information Criterion (BIC) to be retained (Fig. 2 C and 2 D), and the results were consistent with the LASSO regression. Significant variables (P value < 0.01) of the univariate analysis were entered into a multivariate logistic regression model, and showed that wasp sting numbers, gross hematuria, SIRI, and PLT affected AKI significantly (all P < 0.05) (Table 2 ). According to multivariate logistic regression analyses, four prognostic factors were used to establish the nomogram (Fig. 3 ). Table 2 Univariate and multivariable logistic analyses of the risk factors for AKI. SBP systolic blood pressure, HR heart rate, SIRI systemic inflammatory response index, Hb hemoglobin level, PLT platelet count, OR odds ratio, CI confidence interval. Variables Univariate analysis OR (95% CI) P-value Multivariate analysis OR (95% CI) P -value Sex 1.251 (0.705 ~ 2.218) 0.4441 Age 1.062 (1.038 ~ 1.086) < 0.001 1.002 (0.965 ~ 1.041) 0.901 Post injury time 1.003 (1 ~ 1.006) 0.0704 Comorbidity 4.32 (2.069 ~ 9.019) < 0.001 1.508 (0.401 ~ 5.675) 0.544 SBP 1.038 (1.024 ~ 1.051) < 0.001 1.013 (0.991 ~ 1.035) 0.238 HR 0.964 (0.949 ~ 0.98) < 0.001 0.994 (0.967 ~ 1.021) 0.645 Wasp sting numbers 1.133 (1.096 ~ 1.171) < 0.001 1.068 (1.024 ~ 1.114) 0.002 Gross hematuria 24 (10.774 ~ 53.461) < 0.001 6.09 (1.939 ~ 19.124) 0.002 Myocardial Damage 4.838 (2.639 ~ 8.867) < 0.001 1.38 (0.467 ~ 4.081) 0.560 Chest tightness Palpitation 6.136 (2.983 ~ 12.623) < 0.001 1.89 (0.58 ~ 6.16) 0.291 Rash 5.141 (1.927 ~ 13.715) 0.0011 1.085 (0.267 ~ 4.411) 0.909 SIRI 1.215 (1.063 ~ 1.439) < 0.001 1.102 (1.036 ~ 1.362) 0.009 Hb 0.969 (0.955 ~ 0.984) < 0.001 0.996 (0.969 ~ 1.024) 0.764 PLT 0.988 (0.984 ~ 0.992) < 0.001 0.991 (0.985 ~ 0.997) 0.003 Model validation The nomogram was assessed using the area under the curve (AUC), concordance index (C-index), and calibration curve. As shown in Fig. 4 A, the AUC for predicting AKI was 0.941 (95% CI 0.909 to 0.974); when the Youden index was 1.810, it had a sensitivity of 0.946 and specificity of 0.864. The concordance index (C-index) of the model was 0.939.The decision curve analysis showed that patients could benefit from the model when the threshold probabilities were 0.02–0.89 in Fig. 4 B.As shown in Fig. 4 B, the decision curve analysis indicated that when the threshold probabilities ranged between 2% and 89% in the cohort, the use of the nomogram to predict AKI provided greater net benefit than the “treat all” or “treat none” strategies, which indicates the clinical usefulness of the nomogram. Bootstrap sampling validation was performed, and the corrected C-index was 0.934. As shown in Fig. 4 C, to assess the accuracy of the model, a calibration curve was drawn, and the slope of the calibrate was 0.968, close to 1.0. Tang et al. 11 developed a prediction model by recruited 508 wasp sting Chinese patients from 18 hospitals in Sichuan Province from 2015 to 2019. The nomogram exhibited a better prognostic performance (C index = 0.939) compared with the Tang et al.’s model (C index = 0.917), but no statistical difference between previous Tang et al.’s model and model in nomogram (P = 0.173) (Fig. 5 A). Calibration curve also indicated that the nomogram in the present cohort showed a better prognostic performance for AKI than the previous Tang et al.’s model (Fig. 5 B). Moreover, the nomogram also improved the ability of predicting AKI (0.08, 0.958 and 0.113, IDI, NRI- Continuous and NRI- Categorical respectively, all P < 0.05) compared to Tang et al.’s model (Table 3 ).Furthermore, DCA was also conducted and indicated that the net benefit of the nomogram model was higher than that of Tang et al.’s AKI models over a wide range of threshold probabilities, suggesting that the superior clinical utility of the nomogram in the present study was generalizable (Fig. 5 C). Table 3 Prediction improvement with nomogram compared to Tang et al.’s model. IDl, integrated discrimination improvement; NRI, net reclassification improvement. IDI (95% CI) P-value NRI (Continuous) (95% CI) P -value NRI (Categorical) (95% CI) P -value AKI 0.08 (0.017–0.143) 0.013 0.958 (0.708–1.207) < 0.001 0.113 (0.005–0.221) 0.041 Discussion Wasp sting-induced AKI complicates the management of multiple wasp stings by potentially delaying renal recovery, impacting patient outcomes. This study investigated the clinical characteristics and prognostic indicators of wasp sting-induced AKI using real-world data, examining susceptibility factors including patient demographics, inflammation biomarkers, and rhabdomyolysis signs. We identified four critical prognostic factors to construct a user-friendly prediction model, represented as a nomogram for straightforward visualization and application. The model underwent comprehensive evaluation and validation through ROC and calibration curves, and decision curve analysis (DCA). Additionally, its predictive accuracy was compared with a previous model, confirming its effectiveness in forecasting wasp sting-induced AKI 17 . In our study, 33.6% of wasp sting victims developed AKI, a figure slightly higher than the 21% reported in a Chinese multi-center survey 18 but consistent with the 30–50% incidence range found in other studies 19,20 . Despite advancements in managing wasp sting-induced AKI, particularly with renal replacement therapy 21,22 , the mortality rate remains high at 5.1–8.6% 1,9,11,18 . Our in-hospital mortality rate was 6.5%, similar to rates reported in the same western China region, reflecting regional consistency in outcomes 18,23 . Furthermore, in our study, 37.4% of patients underwent blood hemopurification, aligning with the 23.19–39.9% range reported by Zhang et al. and Tang et al. in similar regions of China 11,23 , and significantly exceeding the 12.4% reported by Wang et al 16 . Additionally, 13.6% of our patients required intubation and mechanical ventilation, higher than the 7.25% observed by Zhang et al. 23 , suggesting a more critical condition in our patient cohort. Several prediction models exist to identify patients at elevated risk of acute kidney injury (AKI) following wasp stings. A study of 112 patients pinpointed leukocytes, myoglobin, and urinary monocyte chemotactic protein-1 as independent risk factors for post-wasp sting AKI 24 . Tang et al. developed a model incorporating sting count, AST, LDH, APTT, and time to hospital admission as predictors for wasp sting-induced AKI 11 . The Wasp Sting Severity Score (WSS), assessing factors such as tea-colored urine, sting count, serum LDH, and TBIL, facilitates early identification of patients needing blood purification 12 . However, the reliance on complex laboratory tests may delay patient assessment and intensive care unit (ICU) admission. The accuracy of recalling the wasp sting attack time further complicates the application of these models. The complexity and reliance on extensive laboratory testing limit the practicality of these models in emergency situations, especially in resource-limited settings typical of developing countries 23,25 . In response, our study presents an accessible, cost-effective risk prediction model based on simple biomarkers, including Complete Blood Count and clinical features. This model facilitates early detection of individuals at risk of AKI, enabling prompt initiation of organ support therapy to improve renal recovery. Moreover, our nomogram outperforms Tang et al.'s model in discrimination capability, as demonstrated by superior concordance index (C index), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI) metrics. Decision Curve Analysis (DCA) further confirms its enhanced predictive benefits for AKI, underscoring its value for application across various healthcare settings, particularly where resources are constrained. The kidney's susceptibility to wasp venom, due to its high vascularity and excretory function 3 . The severity of AKI and associated mortality rates escalate with the number of stings, particularly beyond ten stings, underlining the venom's dose-dependent toxicity 4,18 . The Wasp Sting Severity Score (WSS), which includes sting count and biochemical markers, categorizing sting severity and its impact on patient outcomes 12 . Our research confirms the critical role of sting count in determining the risk and severity of AKI, emphasizing the importance of sting number in patient prognosis. Wasp venom-induced AKI is primarily attributed to acute tubular necrosis (ATN) resulting from hemolysis and rhabdomyolysis, with venom components disrupting skeletal muscle and red blood cell membranes 10 . This leads to the release of myoglobin and hemoglobin, which, upon reaching the renal parenchyma, cause intratubular obstruction and direct toxicity leading to renal failure 10,19,26 . Gross hematuria, identified by brown to tea-colored urine, indicates significant hemoglobin or myoglobin presence 16,18,22 . Xie et al. reported that 79.5% of patients with rhabdomyolysis following multiple wasp stings developed AKI, while in our cohort, 64.9% of patients exhibited gross hematuria 18 . Wasp venom initiates allergic and toxic reactions, activating immune and inflammatory pathways that increase inflammatory cytokines (IL-6, IL-8) and coagulation factors 27–29 ,leading to hemoglobin breakdown and enhancing inflammation through hemolysis products (heme, ferrous heme, oxygen free radicals) 30 .This process mediated by the STING-TBK1-p65/IRF3 signaling pathway and involves dysregulated lipid metabolism (HDL-C, Apo-A1) 31,32 . Our study demonstrates that elevated levels of the Systemic Inflammatory Response Index (SIRI), which reflects systemic inflammation and immune status correlate with MODS and AKI in patients stung by wasps 8 .SIRI, easily assessed in emergency settings, offers a timely evaluation of the inflammatory response, proving more immediate than C-Reactive Protein (CRP) and Procalcitonin (PCT) in identifying high-risk patients. Moreover, in individuals affected by wasp stings, decreased platelet counts can lead to coagulation problems, exacerbate hemolysis, intensify bleeding-induced hypovolemia, and diminish perfusion, collectively facilitating AKI onset. Although our study is based on real-world data and provides a comprehensive overview of patient information, there are still some limitations. Firstly, the retrospective nature of our study, being confined to a single institution, introduces the possibility of selection bias. As a result, additional prospective and longitudinal studies are essential to further validate the reliability of the nomogram. Secondly, the number of patients included in the analysis in this study is still limited, so further validation with a multiple center cohort study is necessary. Thirdly, in critical situations, SIRI may be influenced by comorbidities, significant hemolysis, or simultaneous trauma, potentially not reflecting accurately the severity of wasp sting reactions. Conclusions In this study, we developed and validated a predictive nomogram consisting of four independent risk factors: the number of wasp stings, Systemic Inflammatory Response Index (SIRI), gross hematuria, and platelet counts. This nomogram may empower clinicians with earlier and more accurate information regarding the risk of wasp stings induced AKI and aid in clinical decision-making. Declarations Competing interests The authors declare no competing interests. Funding The authors received no financial support for the research, authorship, and publication of this article. Author Contribution Wen Wu and Rong Zhang conceived and designed the study, while Yupei Zhang, Yilan Zhang and Xingguang Qu acquired the data, which was analyzed by Wen Wu. Wen Wu interpreted the data and results and drafted the manuscript. Zhaohui Zhang and Rong Zhang critically revised the manuscript for intellectual content. All authors contributed to revising the article and approved the final version. Acknowledgement We gratefully thank Dr. Jie Liu of Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital for his contribution to the statistical support and comments regarding the manuscript. Data Availability Data is provided within the manuscript or supplementary information files. References Vikrant, S. & Parashar, A. Acute kidney injury due to multiple Hymenoptera stings—a clinicopathological study. Clinical Kidney Journal 10 , 532–538 (2017). Yang, L. Acute Kidney Injury in Asia. Kidney Dis 2 , 95–102 (2016). Sitprija, V. Animal toxins and the kidney. Nat Rev Nephrol 4 , 616–627 (2008). Thiruventhiran, T. et al. Nephrology Dialysis Transplantation. Nephrol Dial Transplant 14 , 214–217. Vetter, R. S., Visscher, P. K. & Camazine, S. Mass Envenomations by Honey Bees and Wasps. West J Med 170 , 223–227 (1999). Xuan, B. H. N. et al. Swarming hornet attacks: shock and acute kidney injury--a large case series from Vietnam. Nephrology Dialysis Transplantation 25 , 1146–1150 (2010). Vikrant, S. & Parashar, A. Wasp venom–induced acute kidney injury: a serious health hazard. Kidney International 92 , 1288 (2017). Zhang, Y., Wu, W. & Zhang, Z. The predictive value of the systemic inflammatory response index for the occurrence of multiple organ dysfunction syndrome in patients with wasp sting injury. Toxicon 234 , 107269 (2023). Gong, J., Yuan, H., Gao, Z. & Hu, F. Wasp venom and acute kidney injury: The mechanisms and therapeutic role of renal replacement therapy. Toxicon 163 , 1–7 (2019). Kim, Y. O. et al. Severe rhabdomyolysis and acute renal failure due to multiple wasp stings. Nephrology Dialysis Transplantation 18 , 1235–1235 (2003). Tang, X. et al. Development and validation of a model to predict acute kidney injury following wasp stings: A multicentre study. Toxicon 209 , 43–49 (2022). Liu, Y. et al. Development and internal validation of a Wasp Sting Severity Score to assess severity and indicate blood purification in persons with Asian wasp stings. Clinical Kidney Journal 15 , 320–327 (2022). Chinese Society Of Toxicology Poisoning And Treatment Of Specialized Committee, null, Hubei Emergency Medicine Committee Of Chinese Medical Association, null, Hubei Provincial Poisoning And Occupational Disease Union, null, Yang, X. & Xiao, M. [Expert consensus statement on standardized diagnosis and treatment of wasp sting in China]. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 30 , 819–823 (2018). Khwaja, A. KDIGO Clinical Practice Guidelines for Acute Kidney Injury. Nephron Clin Pract 120 , c179–c184 (2012). Marshall, J. C., Cook, D. J. & Christou, N. V. multiple organ dysfunction score a reliable descriptor of a complex clinical outcome. Critical Care Medicine 10, 1638-1652(1995). Wang, M. et al. Macroscopic hematuria in wasp sting patients: a retrospective study. Renal Failure 43 , 500–509 (2021). Yu, F. et al. Wasp venom-induced acute kidney injury: current progress and prospects. Renal Failure 45 , 2259230 (2023). Xie, C. et al. Clinical Features of Severe Wasp Sting Patients with Dominantly Toxic Reaction: Analysis of 1091 Cases. PLoS ONE 8 , e83164 (2013). Dhanapriya, J. et al. Wasp sting-induced acute kidney injury. Clin Kidney J 9 , 201–204 (2016). Yu, F. et al. A rat model of acute kidney injury caused by multiple subcutaneous injections of Asian giant hornet (Vespa mandarina Smith) venom. Toxicon 213 , 23–26 (2022). Yuan, H., Chen, S., Hu, F. & Zhang, Q. Efficacy of Two Combinations of Blood Purification Techniques for the Treatment of Multiple Organ Failure Induced by Wasp Stings. Blood Purif 42 , 49–55 (2016). Zhang, L. et al. Recovery from AKI Following Multiple Wasp Stings: A Case Series. Clinical Journal of the American Society of Nephrology 8 , 1850–1856 (2013). Zhang, X. et al. The association between procalcitonin and acute kidney injury in patients stung by wasps. Front. Physiol. 14 , 1199063 (2023). Yuan, H., Lu, L., Gao, Z. & Hu, F. Risk factors of acute kidney injury induced by multiple wasp stings. Toxicon 182 , 1–6 (2020). Bhuiyan, M. A. A. et al. Animal-related injuries and fatalities: evidence from a large-scale population-based cross-sectional survey in rural Bangladesh. BMJ Open 9 , e030039 (2019). Singh, D. H. Title: Rhabdomyolysis and acute kidney injury following multiple wasp stings. Oliveira, N. A. D., Cardoso, S. C., Barbosa, D. A. & Fonseca, C. D. D. Acute kidney injury caused by venomous animals: inflammatory mechanisms. J. Venom. Anim. Toxins incl. Trop. Dis 27 , 20200189 (2021). Li, F. et al. Elevated cytokine levels associated with acute kidney injury due to wasp sting. Eur Cytokine Netw 30 , 34–38 (2019). Sun, Y. et al. Interleukin-6 Gene Polymorphism and the Risk of Systemic Inflammatory Response Syndrome Caused by Wasp Sting Injury. DNA and Cell Biology 37 , 967–972 (2018). Schaer, D. J., Buehler, P. W., Alayash, A. I., Belcher, J. D. & Vercellotti, G. M. Hemolysis and free hemoglobin revisited: exploring hemoglobin and hemin scavengers as a novel class of therapeutic proteins. Blood 121 , 1276–1284 (2013). Lv, Y. et al. STING deficiency protects against wasp venom-induced acute kidney injury. Inflamm. Res. 72 , 1427–1440 (2023). Quan, Z., Liu, M., Zhao, J. & Yang, X. Correlation between early changes of serum lipids and clinical severity in patients with wasp stings. Journal of Clinical Lipidology 16 , 878–886 (2022). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4502096","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":313737618,"identity":"1e5b21b3-f7e5-41b3-942f-a050e872c298","order_by":0,"name":"Wen Wu","email":"","orcid":"","institution":"Yichang Central People’s Hospital, The First College of Clinical Medical Science of China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Wu","suffix":""},{"id":313737619,"identity":"6342c353-a17a-4493-b6f9-9347b4cce208","order_by":1,"name":"Yupei Zhang","email":"","orcid":"","institution":"China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Yupei","middleName":"","lastName":"Zhang","suffix":""},{"id":313737620,"identity":"de1ecd5b-51a1-401e-b707-55c058917c51","order_by":2,"name":"Yilan Zhang","email":"","orcid":"","institution":"Yichang Central People’s Hospital, The First College of Clinical Medical Science of China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Yilan","middleName":"","lastName":"Zhang","suffix":""},{"id":313737621,"identity":"756e54b0-2387-433a-8612-e98313dd19aa","order_by":3,"name":"Xingguang Qu","email":"","orcid":"","institution":"China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Xingguang","middleName":"","lastName":"Qu","suffix":""},{"id":313737622,"identity":"f289a1ea-5884-42da-a004-c0a4043eaeff","order_by":4,"name":"Zhaohui Zhang","email":"","orcid":"","institution":"China Three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Zhang","suffix":""},{"id":313737623,"identity":"2be26cfa-d12e-4210-9e7b-e3422cd198d3","order_by":5,"name":"Rong Zhang","email":"data:image/png;base64,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","orcid":"","institution":"China Three Gorges University","correspondingAuthor":true,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-05-30 09:55:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4502096/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4502096/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58385422,"identity":"f194781e-644f-45d5-ada6-ee5f8e47bc26","added_by":"auto","created_at":"2024-06-14 18:40:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":363978,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the study showing the selection of wasp stings patients who were included in the analysis. \u003cem\u003eED emergency department.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig1Flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-4502096/v1/5971c39ed6b6de147db5d845.png"},{"id":58385424,"identity":"c51c7efa-5d2d-4e77-a294-40aa74d6f6e7","added_by":"auto","created_at":"2024-06-14 18:40:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198464,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of predictors for nomogram prediction model. (A) Plot of partial likelihood deviance; (B) plot of LASSO coefficient profiles. Each curve represents the LASSO coefficient profile of a feature against the log (lambda) sequence. When the optimal lambda value was 0.05, retention variables were screen, and (C, D) is variable selection based on optimal subset regression.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4502096/v1/46339a2094d82843fb43e2dd.png"},{"id":58385425,"identity":"de3ad6cd-b620-4f94-8e47-3033676d734f","added_by":"auto","created_at":"2024-06-14 18:40:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31771,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram to predict the probability of AKI in patients with multiple wasp sting. \u0026nbsp;According to nomogram points for wasp sting numbers, SIRI, PLT and gross hematuria can be calculated from first line. Total points were the sum of the four points. plotted on the “Total Points” line, corresponds to prediction of AKI with multiple wasp sting in the “Risk of Event” line. \u003cem\u003eSIRI systemic inflammatory response index, PLT platelet count.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3nomogram.png","url":"https://assets-eu.researchsquare.com/files/rs-4502096/v1/dd89002c470766d1501a7676.png"},{"id":58385426,"identity":"538a9df7-6f90-455d-9844-cc29f35ecdd5","added_by":"auto","created_at":"2024-06-14 18:40:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157369,"visible":true,"origin":"","legend":"\u003cp\u003eThe discrimination and calibration assessment of the model. (A) ROC curve and AUC of the nomogram in the training cohort. (B) Decision curve for the predictive nomogram. The net benefits were measured at different threshold probabilities. The blue line represents the predictive nomogram. The gray line represents the assumption that all patients have AKI. The black line represents the assumption that no patients have AKI. (C) Calibration curve for the nomogram to predict the probability of AKI with bootstrap sampling validation.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4502096/v1/568875ed51d1d940cbac560f.png"},{"id":58385428,"identity":"15bb529a-6c6e-45ff-8292-f77cf0a1b953","added_by":"auto","created_at":"2024-06-14 18:40:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":138726,"visible":true,"origin":"","legend":"\u003cp\u003eCompare the discrimination ability of the two Nomogram models with predict AKI. Model 1 is the nomogram of this study, and Model 2 is the nomogram of Tang et al\u003csup\u003e [1] \u003c/sup\u003e.(A) The ROC curves with AUCs of 0.941 and 0.917 to demonstrate the discriminatory ability of the nomogram and Tang et al.’s model; (B) Calibration plot of the nomogram showing predicted AKI; (C) Decision curve analysis comparing the clinical performance of the nomogram and Tang et al.’s model. For the risk of AKI, the nomogram showed the highest net benefit for all potential thresholds. The black dotted line represents the nomogram, and the red dotted line represents the Tang et al.’s model. The blue line represents the assumption that all patients have been treated, and the black line represents the assumption that no patients have been treated.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4502096/v1/e23351bf4554ed023cd7ce5f.png"},{"id":61278947,"identity":"65bd375c-3dd4-4cea-9916-9648f689a100","added_by":"auto","created_at":"2024-07-29 04:29:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1994274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4502096/v1/db063445-b0ec-4a96-86d6-17791db03ff6.pdf"},{"id":58387396,"identity":"c7a6b7f0-b90c-4f4d-b4ac-5e127601f6e3","added_by":"auto","created_at":"2024-06-14 18:48:41","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22192,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4502096/v1/a5e166f9947dcbd6dada1e66.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWasp stings represent a significant public health issue in tropical regions, particularly in Southern China, Southeast Asia, and Latin America\u003csup\u003e1,2\u003c/sup\u003e. The venom from wasps, a complex concoction of peptides, enzymes, proteins, and other chemicals, can cause cellular damage, ranging from localized allergic reactions to multiorgan dysfunction\u003csup\u003e3,4\u003c/sup\u003e. Life-threatening conditions have been associated with multiple wasp stings\u003csup\u003e5\u003c/sup\u003e, leading to a distinct syndrome characterized by intravascular hemolysis, rhabdomyolysis, hepatic dysfunction, coagulopathy, acute respiratory distress syndrome, encephalopathy, and acute kidney injury (AKI) \u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWasp venom-induced AKI is defined as potentially fatal complication that follows mass attacks\u003csup\u003e7\u003c/sup\u003e. Our previous findings\u003csup\u003e8\u003c/sup\u003e, aligning with studies from Gong et al. \u003csup\u003e9\u003c/sup\u003e and Sitprija et al.\u003csup\u003e3\u003c/sup\u003e indicate that between 20\u0026ndash;50% of individuals subjected to wasp stings may experience rapid progression to kidney failure and multiple organ dysfunction syndrome (MODS). The nephrotoxic effects of wasp venom, including rhabdomyolysis, intravascular hemolysis\u003csup\u003e10\u003c/sup\u003e, and diminished renal blood flow, are implicated in renal ischemia and damage, ultimately leading to acute tubular necrosis (ATN)\u003csup\u003e4,9\u003c/sup\u003e. Moreover, it's essential to recognize that the mechanisms behind wasp venom-induced AKI include both inflammatory and toxic effects. Additionally, our studies\u003csup\u003e8\u003c/sup\u003e have identified that the systemic inflammatory response index (SIRI), which gauges the immune system's inflammatory reaction and overall immune status, is associated with MODS after multiple wasp stings.\u003c/p\u003e \u003cp\u003eThe factors associated with AKI have been extensively studied, but only a limited number of studies have developed prediction models. To our knowledge, few studies have developed the prediction models for wasp sting induced AKI\u003csup\u003e11,12\u003c/sup\u003e. These models, though comprehensive in incorporating laboratory tests for risk factors such as myoglobin, urinary monocyte chemotactic protein-1, aspartate transaminase (AST), total bilirubin (TBIL), and lactate dehydrogenase (LDH), are not practical for patients in suburban or rural areas with limited medical resources.\u003c/p\u003e \u003cp\u003eConsequently, we aim to develop and validate a simple, cost-effective, and clinical characteristics-based risk prediction model for early detection of AKI caused by wasp stings. This model is designed for broad applicability across various healthcare settings, especially in resource-constrained environments.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting and design\u003c/h2\u003e \u003cp\u003eThis was a retrospective cohort study involving patients diagnosed with wasp stings from July 2013 to April 2023 at Yichang Central People\u0026rsquo;s Hospital, the First College of Clinical Medicine Science, China Three Gorges University.\u003c/p\u003e \u003cp\u003eWe initially identified 314 consecutive admissions of patients with wasp stings to our hospital between July 1, 2013 and April 30, 2023.Cases diagnosed with wasp stings were extracted from the hospital\u0026rsquo;s electronic chart database. Patients were selected in strict accordance with predetermined exclusion criteria to ensure the relative homogeneity of the selected patients. After excluding patients who were unable to follow-up and incomplete data collection due to missing information, 214 eligible patients were included in the analyses (Fig.\u0026nbsp;1). A standard wasp sting management protocol was followed for the emergent management. Patients were treated according to the Chinese expert consensus for wasp sting\u003csup\u003e13\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e The study, conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Yichang Central People\u0026rsquo;s Hospital (Ethics NO. 2023-055-01), waived the need for informed consent due to de-identification of patient data. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes and other definitions\u003c/h2\u003e \u003cp\u003eDetailed history was recorded and clinical examinations and investigations were performed to provide complete clinical characteristics. The primary outcomes were as follows: acute kidney injury (AKI). AKI was defined by Kidney Disease Improving Global Outcomes (KDIGO) definition criteria as any of the following: increase in serum creatinine by \u0026ge;\u0026thinsp;0.3 mg/dl (\u0026ge;\u0026thinsp;26.4 \u0026micro;mol/L ) within 48 h; or increase in serum creatinine by \u0026ge;\u0026thinsp;1.5 times baseline, which is known or presumed to have occurred within the prior 7 days; or Urine volume\u0026thinsp;\u0026le;\u0026thinsp;0.5 ml/kg/h for 6 h\u003csup\u003e14\u003c/sup\u003e.MODS was diagnosed according to the criteria\u003csup\u003e15\u003c/sup\u003e, including confirmed wasp stings, and the occurrence of dysfunction or failure of 2 or more organs successively or simultaneously 24 h after the wasp stings. An organ dysfunction score\u0026thinsp;\u0026ge;\u0026thinsp;2 was defined as organ failure. Gross hematuria was defined as the presence of dark or reddish-brown or tea-colored urine in patients who have experienced severe wasp stings\u003csup\u003e12,16\u003c/sup\u003e.This indicates a significant amount of hemoglobin or myoglobin in the urine. Complete blood count analysis including white blood cell count, hemoglobin content, neutrophil count (NEU), lymphocyte count (LYM), monocyte count (MONO), and platelet count (PLT) were also collected. Systemic inflammatory response index (SIRI) was calculated by NEU \u0026times; MONO / LYM\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor all admissions, a comprehensive review of all medical records was conducted to gather information on the characteristics of the patients, including gender, age, co-morbidities (such as hypertension, coronary heart disease, chronic obstructive pulmonary disease and diabetes), clinical symptoms at admission (such as gross hematuria, nausea, vomiting, chest tightness, palpitations), time of visit Emergency Department after injury, number of sting wounds, mortality, lengths of hospitalization, and acute physiology and chronic health evaluation II (APACHE II) score, sequential organ failure assessment (SOFA) score within 24 h of admission. Furthermore, we only included the laboratory test results from the first 24h hospitalization for patients. Biochemical parameters including albumin (ALB), blood urea nitrogen (BUN), creatinine (CRE), alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), creatine kinase (CK), creatine kinase isoenzyme (CK-MB), hydroxybutyrate dehydrogenase (HBDH), cardiac troponin I (cTnI), myoglobin (MYO), prothrombin time (PT), activated partial thromboplastin time (APTT), C-reactive protein (CRP).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables with normal distribution were expressed as mean with standard deviation (SD), and differences between groups were compared by an independent sample \u003cem\u003et\u003c/em\u003e-test. Continuous variables that were not normally distributed were represented by median and range, and differences between groups were compared using the Mann-Whitney U-test. Categorical variables were presented as frequencies or percentages, and the Chi-square test was used to compare differences between groups.\u003c/p\u003e \u003cp\u003eSeveral steps were performed to develop and validate nomograms for predicting wasp stings associated AKI. First, three methods of best subsets regression (BSR), the forward stepwise regression (FSR), and least absolute shrinkage and selection operator (LASSO) regression was conducted to screen the potential prognostic factors. The least absolute contraction and selection operator (LASSO) regression model was used to deal with the collinearity problem of candidate variables to select the optimal predictor variables. Second, multivariable logistic regression analysis was performed to identify the significant prognostic factors associated with AKI. A logistic regression analysis was used to estimate univariate and multivariate odds ratios and 95% confidence intervals. Third, all potential prognostic factors were used to build the model, and a nomogram was used to visualize the model. Receiver operating characteristic (ROC), concordance index (C-index), and calibration curve analyses were used to evaluate the discrimination and calibration of the model. Furthermore, the corrected ROC, C-index, and calibration curve analyses were calculated using 1000 bootstrap sampling. The C-index indicates the discriminative ability of the model; 0.5\u0026ndash;0.7 suggests low discriminative ability, 0.7\u0026ndash;0.9 indicates moderate discriminative ability, and \u0026gt;\u0026thinsp;0.9 indicates high diagnostic value.\u003c/p\u003e \u003cp\u003ePrevious AKI associated with wasp stings prediction model developed by Tang et al. was calculated based on the number of stings, time from stings to admission (\u0026gt;\u0026thinsp;12h), and two biochemistry parameters\u0026mdash;aspartate aminotransferase (\u0026gt;\u0026thinsp;147 U/L) and lactate dehydrogenase (\u0026gt;\u0026thinsp;477 U/L)\u003csup\u003e11\u003c/sup\u003e. The predictive performance of the nomogram and Tang\u0026rsquo;s model were evaluated by NRI and IDI, the comparison of the Harrell\u0026rsquo;s C index and AUC values. Finally, the decision curve analysis (DCA) was used to evaluate the clinical utility and net benefits of the model for both our center and Tang et al.\u0026rsquo;s cohorts\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMissing data were processed by multiple imputations. Imputation for missing variables was considered if missing values were less than 10%.\u003c/p\u003e \u003cp\u003eAll analyzes were performed with the statistical software package R version 3.3.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation) and the Free Statistics software package version 1.9. Two-tailed tests were performed, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 being considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of patients and incidence of AKI\u003c/h2\u003e \u003cp\u003e214 cases were included in the analysis, with 140 (65.4%) cases of non-AKI outcomes and 74 (34.6%) cases of AKI outcomes. The median age of the included patients was 49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4 years, and 60.3% (129/214) were male. In total, 79 (36.9%) were admission to ICU and 14 were dead.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eshows the characteristics of the non-AKI and AKI groups. Details of the missing data are listed in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. There was no significant difference in gender and the time from wasp sting to hospitalization between the two groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but patients who suffered from AKI tended to be older, have more comorbidities, a higher number of sting wounds, higher SOFA and APACHE II scores, longer hospital stays, and higher rates of MODS, myocardial damage, hemopurification, mechanical ventilation, complications, and death. They also had elevated levels of WBC, Hb, PLT, NEU, MONO, AST, ALT, CRP, CK, CK-MB, LDH, HBDH, PT, APTT, BUN, CRE, SIRI, and lower levels of LYM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;214)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-AKI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAKI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU admission (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (70.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost injury time (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.0 (6.0, 30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.0 (5.0, 30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 (7.0, 43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50 (67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sting wounds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0 (1.2, 25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5 (1.0, 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.0 (20.0, 40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of sting wounds (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100 (46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99 (70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest tightness and Palpitation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRash (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171 (79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Hematuria, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (64.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical Ventilation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMODS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105 (49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial Damage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReceived Hemopurification (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (85.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplication (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo complication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e164 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (93.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute ischemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0 (0.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.0 (5.0, 13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHEII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.0 (2.0, 10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 (0.0, 4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0 (8.0, 20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU LOS (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0 (0.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0, 0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 (0.0, 6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital LOS (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.0 (3.0, 11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0 (3.0, 8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0 (3.0, 25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131.4\u0026thinsp;\u0026plusmn;\u0026thinsp;26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (bpm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.5\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.5\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.7 (9.3, 20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (8.7, 16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.9 (15.1, 26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121.5 (107.2, 129.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.0 (114.8, 131.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.5 (90.5, 129.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e232.5 (161.0, 294.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e252.0 (200.2, 316.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e169.0 (92.0, 238.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEU (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.5 (5.8, 18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1 (4.8, 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.6 (13.4, 24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5 (0.7, 2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9 (1.0, 3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.5, 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMONO (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.4, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.4, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7 (0.5, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.0 (30.0, 441.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.0 (26.0, 102.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e975.0(236.5, 3462.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.0 (17.8, 135.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.0 (15.0, 50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245.0 (68.2, 822.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.5 (34.9, 43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.9 (38.4, 44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.5 (31.6, 37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.6 (1.5, 13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (0.9, 7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5 (5.8, 38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e403.5 (149.0, 4685.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179.5 (122.2, 853.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4223.5 (994.8, 14500.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK-MB (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.0 (18.0, 140.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5 (16.8, 43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173.0 (59.3, 352.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e447.5 (289.2, 1848.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e322.5 (258.2, 459.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2462.5 (1363.0, 5219.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBDH (IU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324.0 (218.0, 1085.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243.0 (188.0, 343.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1388.0 (780.0, 3007.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.4 (4.5, 10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2 (4.2, 6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4 (9.3, 15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRE (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.0 (61.0, 163.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.0 (57.0, 74.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253.0 (160.5, 419.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.5 (12.6, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3 (12.3, 14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.7 (12.9, 16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT (s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.9 (35.2, 94.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.7 (33.8, 45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.6 (51.7, 155.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.9 (1.3, 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3 (0.9, 5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.1 (5.4, 29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Clinical characteristics in the AKI and non-AKI groups. Mean and interquartile range for continuous variables: P value was calculated by weighted linear regression model. % for categorical variables: P value was calculated by weighted chi-square test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAKI acute kidney injury, ICU intensive care unit, MODS multiple organ dysfunction syndrome, SOFA sequential organ failure assessment, APACHE II acute physiology and chronic health evaluation II, LOS length of stay, SBP systolic blood pressure, HR heart rate, WBC white blood cell count, Hb hemoglobin level, PLT platelet count, NEU neutrophil count in peripheral blood, LYM lymphocyte count in peripheral blood, MONO monocyte count in peripheral blood, AST aspartate aminotransferase, ALT alanine aminotransferase, ALB albumin, CRP C-reactive protein, CK creatine kinase, CK-MB creatine kinase isoenzyme, LDH lactate dehydrogenase, HBDH alpha-hydroxybutyrate dehydrogenase, BUN blood urea nitrogen, CRE serum creatinine, PT prothrombin time, APTT activated partial thromboplastin time, SIRI systemic inflammatory response index.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eModel derivation\u003c/h2\u003e \u003cp\u003eFirst, we adopted LASSO regression penalty to streamline the dimension and select the most meaningful prognostic indicators. Subsequently, a tenfold cross-validation of the lasso model was performed for tuning parameter selection via the minimum criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The trajectory of each prognostic indicator coefficient was observed in the LASSO coefficient profiles with the changing of the log-transformed lambda in LASSO algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). When the lambda optimal value was 0.05 (1 standard error of the minimum criteria), four non-zero coefficient variables were selected as potential prognosis-related indicators, including wasp sting numbers, gross hematuria, SIRI, and PLT. Furthermore, we also used Best Subsets Regression (BSR) to filter the variables of the minimum Bayesian Information Criterion (BIC) to be retained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), and the results were consistent with the LASSO regression. Significant variables (P value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) of the univariate analysis were entered into a multivariate logistic regression model, and showed that wasp sting numbers, gross hematuria, SIRI, and PLT affected AKI significantly (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to multivariate logistic regression analyses, four prognostic factors were used to establish the nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariable logistic analyses of the risk factors for AKI. \u003cem\u003eSBP systolic blood pressure, HR heart rate, SIRI systemic inflammatory response index, Hb hemoglobin level, PLT platelet count, OR odds ratio, CI confidence interval.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.251 (0.705\u0026thinsp;~\u0026thinsp;2.218)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.062 (1.038\u0026thinsp;~\u0026thinsp;1.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.002 (0.965\u0026thinsp;~\u0026thinsp;1.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost injury time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.003 (1\u0026thinsp;~\u0026thinsp;1.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.32 (2.069\u0026thinsp;~\u0026thinsp;9.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.508 (0.401\u0026thinsp;~\u0026thinsp;5.675)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.038 (1.024\u0026thinsp;~\u0026thinsp;1.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.013 (0.991\u0026thinsp;~\u0026thinsp;1.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.964 (0.949\u0026thinsp;~\u0026thinsp;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994 (0.967\u0026thinsp;~\u0026thinsp;1.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWasp sting numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.133 (1.096\u0026thinsp;~\u0026thinsp;1.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.068 (1.024\u0026thinsp;~\u0026thinsp;1.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross hematuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (10.774\u0026thinsp;~\u0026thinsp;53.461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.09 (1.939\u0026thinsp;~\u0026thinsp;19.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial Damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.838 (2.639\u0026thinsp;~\u0026thinsp;8.867)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38 (0.467\u0026thinsp;~\u0026thinsp;4.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest tightness Palpitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.136 (2.983\u0026thinsp;~\u0026thinsp;12.623)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.89 (0.58\u0026thinsp;~\u0026thinsp;6.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.141 (1.927\u0026thinsp;~\u0026thinsp;13.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.085 (0.267\u0026thinsp;~\u0026thinsp;4.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.215 (1.063\u0026thinsp;~\u0026thinsp;1.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.102 (1.036\u0026thinsp;~\u0026thinsp;1.362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.969 (0.955\u0026thinsp;~\u0026thinsp;0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996 (0.969\u0026thinsp;~\u0026thinsp;1.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.988 (0.984\u0026thinsp;~\u0026thinsp;0.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991 (0.985\u0026thinsp;~\u0026thinsp;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eModel validation\u003c/h2\u003e \u003cp\u003eThe nomogram was assessed using the area under the curve (AUC), concordance index (C-index), and calibration curve. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, the AUC for predicting AKI was 0.941 (95% CI 0.909 to 0.974); when the Youden index was 1.810, it had a sensitivity of 0.946 and specificity of 0.864. The concordance index (C-index) of the model was 0.939.The decision curve analysis showed that patients could benefit from the model when the threshold probabilities were 0.02\u0026ndash;0.89 in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB.As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, the decision curve analysis indicated that when the threshold probabilities ranged between 2% and 89% in the cohort, the use of the nomogram to predict AKI provided greater net benefit than the \u0026ldquo;treat all\u0026rdquo; or \u0026ldquo;treat none\u0026rdquo; strategies, which indicates the clinical usefulness of the nomogram. Bootstrap sampling validation was performed, and the corrected C-index was 0.934. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, to assess the accuracy of the model, a calibration curve was drawn, and the slope of the calibrate was 0.968, close to 1.0.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTang et al.\u003csup\u003e11\u003c/sup\u003edeveloped a prediction model by recruited 508 wasp sting Chinese patients from 18 hospitals in Sichuan Province from 2015 to 2019. The nomogram exhibited a better prognostic performance (C index\u0026thinsp;=\u0026thinsp;0.939) compared with the Tang et al.\u0026rsquo;s model (C index\u0026thinsp;=\u0026thinsp;0.917), but no statistical difference between previous Tang et al.\u0026rsquo;s model and model in nomogram (P\u0026thinsp;=\u0026thinsp;0.173) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Calibration curve also indicated that the nomogram in the present cohort showed a better prognostic performance for AKI than the previous Tang et al.\u0026rsquo;s model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Moreover, the nomogram also improved the ability of predicting AKI (0.08, 0.958 and 0.113, IDI, NRI- Continuous and NRI- Categorical respectively, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to Tang et al.\u0026rsquo;s model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).Furthermore, DCA was also conducted and indicated that the net benefit of the nomogram model was higher than that of Tang et al.\u0026rsquo;s AKI models over a wide range of threshold probabilities, suggesting that the superior clinical utility of the nomogram in the present study was generalizable (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction improvement with nomogram compared to Tang et al.\u0026rsquo;s model. \u003cem\u003eIDl, integrated discrimination improvement; NRI, net reclassification improvement.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIDI (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNRI (Continuous) (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNRI\u003c/p\u003e \u003cp\u003e(Categorical)\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003cp\u003e(0.017\u0026ndash;0.143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003cp\u003e(0.708\u0026ndash;1.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003cp\u003e(0.005\u0026ndash;0.221)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWasp sting-induced AKI complicates the management of multiple wasp stings by potentially delaying renal recovery, impacting patient outcomes. This study investigated the clinical characteristics and prognostic indicators of wasp sting-induced AKI using real-world data, examining susceptibility factors including patient demographics, inflammation biomarkers, and rhabdomyolysis signs. We identified four critical prognostic factors to construct a user-friendly prediction model, represented as a nomogram for straightforward visualization and application. The model underwent comprehensive evaluation and validation through ROC and calibration curves, and decision curve analysis (DCA). Additionally, its predictive accuracy was compared with a previous model, confirming its effectiveness in forecasting wasp sting-induced AKI\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn our study, 33.6% of wasp sting victims developed AKI, a figure slightly higher than the 21% reported in a Chinese multi-center survey\u003csup\u003e18\u003c/sup\u003e but consistent with the 30\u0026ndash;50% incidence range found in other studies\u003csup\u003e19,20\u003c/sup\u003e. Despite advancements in managing wasp sting-induced AKI, particularly with renal replacement therapy\u003csup\u003e21,22\u003c/sup\u003e, the mortality rate remains high at 5.1\u0026ndash;8.6%\u003csup\u003e1,9,11,18\u003c/sup\u003e. Our in-hospital mortality rate was 6.5%, similar to rates reported in the same western China region, reflecting regional consistency in outcomes\u003csup\u003e18,23\u003c/sup\u003e. Furthermore, in our study, 37.4% of patients underwent blood hemopurification, aligning with the 23.19\u0026ndash;39.9% range reported by Zhang et al. and Tang et al. in similar regions of China\u003csup\u003e11,23\u003c/sup\u003e, and significantly exceeding the 12.4% reported by Wang et al\u003csup\u003e16\u003c/sup\u003e. Additionally, 13.6% of our patients required intubation and mechanical ventilation, higher than the 7.25% observed by Zhang et al.\u003csup\u003e23\u003c/sup\u003e, suggesting a more critical condition in our patient cohort.\u003c/p\u003e \u003cp\u003eSeveral prediction models exist to identify patients at elevated risk of acute kidney injury (AKI) following wasp stings. A study of 112 patients pinpointed leukocytes, myoglobin, and urinary monocyte chemotactic protein-1 as independent risk factors for post-wasp sting AKI\u003csup\u003e24\u003c/sup\u003e. Tang et al. developed a model incorporating sting count, AST, LDH, APTT, and time to hospital admission as predictors for wasp sting-induced AKI\u003csup\u003e11\u003c/sup\u003e. The Wasp Sting Severity Score (WSS), assessing factors such as tea-colored urine, sting count, serum LDH, and TBIL, facilitates early identification of patients needing blood purification\u003csup\u003e12\u003c/sup\u003e. However, the reliance on complex laboratory tests may delay patient assessment and intensive care unit (ICU) admission. The accuracy of recalling the wasp sting attack time further complicates the application of these models. The complexity and reliance on extensive laboratory testing limit the practicality of these models in emergency situations, especially in resource-limited settings typical of developing countries\u003csup\u003e23,25\u003c/sup\u003e. In response, our study presents an accessible, cost-effective risk prediction model based on simple biomarkers, including Complete Blood Count and clinical features. This model facilitates early detection of individuals at risk of AKI, enabling prompt initiation of organ support therapy to improve renal recovery. Moreover, our nomogram outperforms Tang et al.'s model in discrimination capability, as demonstrated by superior concordance index (C index), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI) metrics. Decision Curve Analysis (DCA) further confirms its enhanced predictive benefits for AKI, underscoring its value for application across various healthcare settings, particularly where resources are constrained.\u003c/p\u003e \u003cp\u003eThe kidney's susceptibility to wasp venom, due to its high vascularity and excretory function\u003csup\u003e3\u003c/sup\u003e. The severity of AKI and associated mortality rates escalate with the number of stings, particularly beyond ten stings, underlining the venom's dose-dependent toxicity\u003csup\u003e4,18\u003c/sup\u003e. The Wasp Sting Severity Score (WSS), which includes sting count and biochemical markers, categorizing sting severity and its impact on patient outcomes\u003csup\u003e12\u003c/sup\u003e. Our research confirms the critical role of sting count in determining the risk and severity of AKI, emphasizing the importance of sting number in patient prognosis.\u003c/p\u003e \u003cp\u003eWasp venom-induced AKI is primarily attributed to acute tubular necrosis (ATN) resulting from hemolysis and rhabdomyolysis, with venom components disrupting skeletal muscle and red blood cell membranes\u003csup\u003e10\u003c/sup\u003e. This leads to the release of myoglobin and hemoglobin, which, upon reaching the renal parenchyma, cause intratubular obstruction and direct toxicity leading to renal failure\u003csup\u003e10,19,26\u003c/sup\u003e. Gross hematuria, identified by brown to tea-colored urine, indicates significant hemoglobin or myoglobin presence\u003csup\u003e16,18,22\u003c/sup\u003e. Xie et al. reported that 79.5% of patients with rhabdomyolysis following multiple wasp stings developed AKI, while in our cohort, 64.9% of patients exhibited gross hematuria\u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWasp venom initiates allergic and toxic reactions, activating immune and inflammatory pathways that increase inflammatory cytokines (IL-6, IL-8) and coagulation factors\u003csup\u003e27\u0026ndash;29\u003c/sup\u003e,leading to hemoglobin breakdown and enhancing inflammation through hemolysis products (heme, ferrous heme, oxygen free radicals)\u003csup\u003e30\u003c/sup\u003e.This process mediated by the STING-TBK1-p65/IRF3 signaling pathway and involves dysregulated lipid metabolism (HDL-C, Apo-A1)\u003csup\u003e31,32\u003c/sup\u003e. Our study demonstrates that elevated levels of the Systemic Inflammatory Response Index (SIRI), which reflects systemic inflammation and immune status correlate with MODS and AKI in patients stung by wasps\u003csup\u003e8\u003c/sup\u003e .SIRI, easily assessed in emergency settings, offers a timely evaluation of the inflammatory response, proving more immediate than C-Reactive Protein (CRP) and Procalcitonin (PCT) in identifying high-risk patients. Moreover, in individuals affected by wasp stings, decreased platelet counts can lead to coagulation problems, exacerbate hemolysis, intensify bleeding-induced hypovolemia, and diminish perfusion, collectively facilitating AKI onset.\u003c/p\u003e \u003cp\u003eAlthough our study is based on real-world data and provides a comprehensive overview of patient information, there are still some limitations. Firstly, the retrospective nature of our study, being confined to a single institution, introduces the possibility of selection bias. As a result, additional prospective and longitudinal studies are essential to further validate the reliability of the nomogram. Secondly, the number of patients included in the analysis in this study is still limited, so further validation with a multiple center cohort study is necessary. Thirdly, in critical situations, SIRI may be influenced by comorbidities, significant hemolysis, or simultaneous trauma, potentially not reflecting accurately the severity of wasp sting reactions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we developed and validated a predictive nomogram consisting of four independent risk factors: the number of wasp stings, Systemic Inflammatory Response Index (SIRI), gross hematuria, and platelet counts. This nomogram may empower clinicians with earlier and more accurate information regarding the risk of wasp stings induced AKI and aid in clinical decision-making.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no financial support for the research, authorship, and publication of this article.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWen Wu and Rong Zhang conceived and designed the study, while Yupei Zhang, Yilan Zhang and Xingguang Qu acquired the data, which was analyzed by Wen Wu. Wen Wu interpreted the data and results and drafted the manuscript. Zhaohui Zhang and Rong Zhang critically revised the manuscript for intellectual content. All authors contributed to revising the article and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe gratefully thank Dr. Jie Liu of Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital for his contribution to the statistical support and comments regarding the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVikrant, S. \u0026amp; Parashar, A. Acute kidney injury due to multiple Hymenoptera stings\u0026mdash;a clinicopathological study. \u003cem\u003eClinical Kidney Journal\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 532\u0026ndash;538 (2017).\u003c/li\u003e\n\u003cli\u003eYang, L. 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Correlation between early changes of serum lipids and clinical severity in patients with wasp stings. \u003cem\u003eJournal of Clinical Lipidology\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 878\u0026ndash;886 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Wasp sting, Acute kidney injury, Machine learning model, Nomogram, Prediction","lastPublishedDoi":"10.21203/rs.3.rs-4502096/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4502096/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAcute kidney injury (AKI) following multiple wasp stings is a severe complication with potentially poor outcomes. Despite extensive research on AKI's risk factors, predictive models for wasp sting-related AKI are limited. This study aims to develop and validate a machine learning-based clinical prediction model for AKI in individuals with wasp stings. We retrospectively analyzed clinical data from 214 patients with wasp sting injuries. Among these patients, 34.6% (74/214) developed AKI. Using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis, the number of stings, presence of gross hematuria, systemic inflammatory response index (SIRI), and platelet count were identified as prognostic factors. A nomogram was constructed and evaluated for its predictive accuracy, showing an area under the curve (AUC) of 0.757 (95% CI 0.711 to 0.804) and a concordance index (C-index) of 0.75. The model's performance was assessed through internal validation, leave-one-out cross-validation, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Validation confirmed the model's reliability and superior discrimination ability over existing models, as demonstrated by NRI, IDI, and DCA. This nomogram accurately predicts the risk of AKI in wasp sting patients, facilitating early identification and management of those at risk.\u003c/p\u003e","manuscriptTitle":"Machine-Learning Based Prediction Model for Acute Kidney Injury Induced by Multiple Wasp Stings: Incorporating Four Admission Clinical Indices","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-14 18:40:36","doi":"10.21203/rs.3.rs-4502096/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be193625-438c-4c85-8b2a-20defd67bff2","owner":[],"postedDate":"June 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33176478,"name":"Health sciences/Biomarkers"},{"id":33176479,"name":"Health sciences/Diseases"},{"id":33176480,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-07-29T04:21:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-14 18:40:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4502096","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4502096","identity":"rs-4502096","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}