Predictors Of Injury Research Articles

The potential Role of CYP2D6*10(c.100 C>T) Gene Polymorphism in Kidney Injury of Patients with Hypertension Complicated with Non-Elevated Cystatin C.

This study aims to investigate the potential role of CYP2D6*10 (c.100 C>T) gene polymorphism in renal function injury among hypertensive patients without elevated cystatin C. A cohort of hypertensive patients without elevated cystatin C was enrolled between 2021 and 2024 in the Fourth Affiliated Hospital of Soochow University, and their peripheral venous blood was used for total RNA extraction and CYP2D6*10 genotype analysis. Based on kidney injury status, patients were categorized into two groups, hypertensive patients with kidney injury (n = 94) and those without (n = 893). General characteristics such as age, gender and hyperlipemia were compared between the two groups. Multiple genotype models were investigated between the two groups, including allele models, dominant models, recessive models, co-dominant models, and super-dominant models. The results revealed that in the co-dominant gene model (CC vs. CT vs. TT), the risk of hypertension combined with renal injury was lower with the CT genotype compared to the CC genotype (Odds Ratio (OR) = 0.55, 95% Confidence Interval (CI) = 0.32-0.93, p = 0.02). In the overdominance model (CC + TT vs. CT), the risk of hypertension and renal injury in CC and CT genotypes was 0.42 times lower than that in the CT genotype (OR = 0.42, 95% CI = 0.27-0.64, p < 0.001). This study proposes CYP2D610 gene polymorphism as a potential predictor of renal function injury in hypertensive patients with normal cystatin C levels.

Effects of different seat inclination angles on lumbar dynamic response and injury during lunar-earth reentry.

The inclination angle of the spacecraft seat is related to the astronaut's reentry angle, which in turn affects the safety of the astronauts. This study quantitatively analyzed the effects of different seat inclination angles on astronauts' lumbar spine injuries using the finite element method during the Lunar-Earth reentry. Firstly, a finite element model of the astronaut's lumbar spine was constructed based on reverse engineering technology, and the effectiveness of the model was verified through mesh sensitivity, vertebral range of motion, and spinal impact experiments. Then, simulation calculations were carried out for different seat inclination angles (0°, 10°, 20°, and 30°) under the typical reentry return loads of Chang'e 5T1 (CE-5T1) and Apollo 10, and the prediction and evaluation of lumbar spine injuries were conducted in conjunction with the biological tissue injury criteria. The results indicated that the stress on the vertebrae and annulus fibrosus increased under both reentry loads with the rise of the seat inclination angle, but the increasing rates decreased. When the acceleration peak of CE-5T1 approached 9G, the risk of tissue injury was higher under the seat angle exceeded 20°. According to the Multi-Axis Dynamic Response Criteria for spinal injury, neither of the two load conditions would directly cause injury to the astronauts' lumbar spine when the seat inclination angle was below 30°. The study findings provide a numerical basis for designing and improving the spacecraft's inclination angle in crewed lunar missions, ensuring the safety of astronauts.

Advancing a revised UB-DBQ: A new insight into driver behaviour and older passenger injuries in urban buses

IntroductionOlder individuals in China face a heightened risk of injuries on urban buses. While the Driving Behaviour Questionnaire (DBQ) effectively gauges drivers' behaviours and predicts injury involvement, the link between aberrant driving and older passenger injuries among urban bus drivers remains underexplored. This study refines the UB-DBQ for urban bus drivers, probing its association with older passenger injuries. MethodsThe Urban Bus Driving Behaviour Questionnaire (UB-DBQ) was adapted, incorporating unique aspects of urban bus driving. An online survey, based on this revised UB-DBQ, was administered to 710 Chinese urban bus drivers in August 2022. Using principal component factor analysis and oblique rotation-promax, a four-factor UB-DBQ emerged. Correlation and difference tests analysed the relationship between driving behaviours and older passenger injury incidents. ResultsThe four-factor UB-DBQ offers a robust fit for Chinese urban bus drivers. Analysis revealed a prevalence of positive driving behaviours, offset by fewer aberrant actions, including aggression, errors, and violations. Notably, higher numbers of older passengers heightened aggressive and erroneous driving behaviours. A notable trend emerged: drivers largely attributed injuries to older passengers, a sentiment growing with more older passengers aboard. Crucially, driver self-assurance in safety and aggressive behaviour were key predictors of injuries involving older adult passengers. ConclusionsThe revised UB-DBQ serves as an effective tool for assessing urban bus driver behaviour and older passenger injury incidents in China. These insights emphasize the need for tailored driver training and risk strategies in line with the unique demands of urban bus driving in China. The UB-DBQ holds potential for shaping more precise and effective transport safety policies.

Retraction Note: Secure prediction and assessment of sports injuries using deep learning based convolutional neural network

Retraction Note: Secure prediction and assessment of sports injuries using deep learning based convolutional neural network

The role of biomarkers in early identification of acute kidney injury among non-critically ill patients.

Prediction and/or early identification of acute kidney injury (AKI) and individuals at greater risk remains of great interest in clinical medicine. Acute kidney injury continues to be a common complication among hospitalized patients, with an incidence ranging from 6 to 58%, depending on the setting.Aim of this study was to determine the performance of Insulin-like growth factor binding protein-7 (IGFBP7), tissue metallopeptidase inhibitor 2 (TIMP2), and urinary neutrophil gelatinase-associated lipocalin (uNGAL) in early detection of AKI among non-critically ill patients. In this prospective observational study at Mayo Clinic Hospitals in Rochester, Minnesota, USA, non-critically ill patients admitted from the emergency department between October 31st, 2016 and May 1st, 2018, who had an acute kidney injury (AKI) probability of 5% or higher were included. Biomarkers were measured in residual urine samples collected in the emergency department. The primary outcome was biomarker performance in predicting AKI development within the first 72h. Among 368 included patients, the mean age was 79 ± 12years, and 160 (43%) were male. Acute kidney injury occurred in 62 (17%) patients; 11.5% stage 1, 2.5% stage 2, and 3% stage 3. Twelve patients (3%) died during hospitalization and 102 (28%) within nine months after admission. The median uNGAL and IGFBP7-TIMP2 were 57 [20-236ng/ml], and 0.3 [0.1-0.8], respectively. The C-statistic of uNGAL and IGFBP7-TIMP2 of > 0.3 and > 2.0 for AKI prediction were 0.56, 0.54, and 0.53, respectively. In a model where one point is assigned to each marker of AKI (elevated serum creatinine, IGFBP7-TIMP2 > 0.3, and uNGAL), a higher score correlated with higher nine-month mortality [OR of 1.32 per point (95% CI 1.02-1.71)]. Among non-critically ill hospitalized patients, the performance of uNGAL and IGFBP7-TIMP2 for AKI prediction within 72h of admission was modest. This suggests a limited role for these biomarkers in AKI risk stratification among non-critically ill patients. Key learning points What was known Acute kidney injury (AKI) is a common complication among hospitalized patients. It is associated with increased morbidity and mortality. Various clinical prediction models and biomarkers have been developed to identify patients in special populations (such as ICU and cardiac surgery) who are at risk of AKI and diagnose AKI early. This study adds The performance of the biomarkers uNGAL, TIMP-2, and IGFBP-7 in predicting AKI within 72h of admission in non-critically ill patients was modest. However, these biomarkers were found to have a prognostic value for predicting 9-month mortality. One potential application of these biomarkers is identifying patients at higher AKI risk before exposing them to nephrotoxic agents. Potential impact This study provides evidence regarding the real-world performance of current FDA-approved biomarkers (uNGAL, TIMP-2, and IGFBP-7) for predicting acute kidney injury (AKI) within 72h of hospital admission among noncritically ill patients. While the performance of these biomarkers for predicting short-term AKI was modest, they may have a prognostic value for predicting 9-month mortality.

Diagnostic Accuracy of Somatosensory Evoked Potential and Transcranial Motor Evoked Potential in Detection of Neurological Injury in Intradural Extramedullary Spinal Cord Tumor Surgeries: A Short-Term Follow-Up Prospective Interventional Study Experience from Tertiary Care Center of India.

Objective Intraoperative neuromonitoring (IONM) is an acknowledged tool for real-time neuraxis assessment during surgery. Somatosensory evoked potential (SSEP) and transcranial motor evoked potential (MEP) are commonest deployed modalities of IONM. Role of SSEP and MEP in intradural extramedullary spinal cord tumor (IDEMSCT) surgery is not well established. The aim of this study was to evaluate sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of SSEP and transcranial MEP, in detection of intraoperative neurological injury in IDEMSCT patients as well as their postoperative limb-specific neurological improvement assessment at fixed intervals till 30 days. Materials and Methods Symptomatic patients with IDEMSCTs were selected according to the inclusion criteria of study protocol. On modified McCormick (mMC) scale, their sensory-motor deficit was assessed both preoperatively and postoperatively. Surgery was done under SSEP and MEP (transcranial) monitoring using appropriate anesthetic agents. Gross total/subtotal resection of tumor was achieved as per IONM warning alarms. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of SSEP and MEP were calculated considering postoperative neurological changes as "reference standard." Patients were followed up at postoperative day (POD) 0, 1, 7, and 30 for convalescence. Statistical Analysis With appropriate tests of significance, statistical analysis was carried out. Receiver-operating characteristic curve was used to find cutoff point of mMC for SSEP being recordable in patients with higher neurological deficit along with calculation of sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of SSEP and MEP for prediction of intraoperative neurological injury. Results Study included 32 patients. Baseline mean mMC value was 2.59. Under neuromonitoring, gross total resection of IDEMSCT was achieved in 87.5% patients. SSEP was recordable in subset of patients with mMC value less than or equal to 2 with diagnostic accuracy of 100%. MEP was recordable in all patients and it had 96.88% diagnostic accuracy. Statistically significant neurological improvement was noted at POD-7 and POD-30 follow-up. Conclusion SSEP and MEP individually carry high diagnostic accuracy in detection of intraoperative neurological injuries in patients undergoing IDEMSCT surgery. MEP continues to monitor the neuraxis, even in those subsets of patients where SSEP fails to record.

Predictive Characteristics of Childhood Emergency Injury Presentations in a Non-Urban Area of Victoria, Australia

Injury is the leading cause of death and disability amongst those between 0 and 16 years of age. Routinely associated with a social gradient of health, childhood injury is reportedly higher in rural and regional areas. A retrospective cross-sectional study collected deidentified emergency injury presentation data from nine health services in regional Victoria for children aged 0–14 years between 2017 and 2022. Emergency injury presentation incidence rates and predictor variables were analysed using hierarchical multiple regression. Significance was determined at p &lt; 0.05. Amongst the 15,884 emergency injury presentations, 35.2% (n = 5597) occurred at health services that did not routinely report data to government datasets. Although some findings are consistent with current research, this study highlights specific factors that are predictive of injury amongst 0–4-year-old children who require deeper investigation, including general practitioner access and health literacy-related elements such as preschool health assessment attendance rates, parent education levels, and overseas birth. These findings provide more comprehensive insights for healthcare workers and policymakers as they seek to support people with injuries and accurately address health inequities.

Prevalence and Correlates of Poor Safety Awareness and Accidental Injury in ASD, ADHD, ASD + ADHD, and Neurotypical Youth Samples.

The purpose of the present study is to compare risk and predictors of poor safety awareness and accidental injuries in ASD, ADHD, and neurotypical samples. Neurodivergent groups (ADHD-I n = 309; ADHD-C n = 747; ASD-only n = 328; ASD + ADHD n = 1,108) were 2-17 years old. The neurotypical group (n = 186) was 6-12 years of age. Maternal ratings on the Pediatric Behavior Scale examined safety awareness, accidental injury, and psychological problems. Children with ASD + ADHD had significantly poorer safety awareness and accidental injury ratings than all other groups. Predictors of poor safety awareness in the total ASD and/or ADHD sample were: impulsivity, younger age, lower IQ, and hyperactivity. Predictors of accidental injuries were: incoordination, hyperactivity, and conduct problems. Clinicians working with children who have ASD and ADHD are encouraged to screen for poor safety awareness, discuss child safety measures, and provide evidence-based intervention to improve safety awareness and mitigate the risk of injury.

A Prospective Longitudinal Cohort Study of Serum Stanniocalcin-1 as a Potential Prognostic Biomarker of Severe Traumatic Brain Injury.

Stanniocalcin-1 (STC1) may harbor anti-inflammatory and anti-oxidative properties, thereby exerting neuroprotective effects. This study was done with the intent to determine the role of serum STC1 in severity assessment and prognosis prediction of severe traumatic brain injury (sTBI). In this prospective longitudinal cohort study of 104 sTBI patients and 104 healthy individuals (controls), serum STC1 levels were quantified. Severity indicators were Glasgow Coma Scale (GCS) and Rotterdam computed tomography classification. Follow-up time was 180 days and extended Glasgow outcome scale (GOSE) score 1-4 was deemed as poor prognosis. Multivariate analyses were applied to assess severity correlations and prognosis associations. Discriminative efficiencies were estimated in terms of area under receiver operating characteristic curve (AUC). Patients exhibited significantly higher serum STC1 levels than controls. Serum STC1 levels were substantially elevated in order of GCS scores from 8 to 3, Rotterdam scores from 3 to 6 and 180-day GOSE scores from 8 to 1. Also, serum STC1 levels were independently correlated with GCS scores, Rotterdam scores and 180-day GOSE scores. Serum STC1 levels were independently associated with 180-day death, overall survival and poor prognosis, as well as were efficiently predictive of death and poor prognosis. Prediction model containing GCS scores, Rotterdam scores and serum STC1 levels, as opposed to any of them, showed higher discriminative ability for the risks of death and poor prognosis. Alternatively, serum STC1 levels were linearly correlated with risk of death, overall survival and poor prognosis under restricted cubic spline. Subgroup analysis showed that serum STC1 levels non-statistically significantly interacted with age, gender, hypertension, diabetes mellitus, etc. A significant elevation of serum STC1 levels is highly related to severity and clinical outcome, suggesting that serum STC1 may be a potential prognostic biomarker of sTBI.

Brain Deformation Estimation With Transfer Learning for Head Impact Datasets Across Impact Types.

The machine-learning head model (MLHM) to accelerate the calculation of brain strain and strain rate, which are the predictors for traumatic brain injury (TBI), but the model accuracy was found to decrease sharply when the training/test datasets were from different head impacts types (i.e., car crash, college football), which limits the applicability of MLHMs to different types of head impacts and sports. Particularly, small sizes of target dataset for specific impact types with tens of impacts may not be enough to train an accurate impact-type-specific MLHM. To overcome this, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR). The strategies were tested on American football (338), mixed martial arts (457), reconstructed car crash (48) and reconstructed American football (36) and we found that the MLHMs developed with transfer learning are significantly more accurate in estimating MPS and MPSR than other models, with a mean absolute error (MAE) smaller than 0.03 in predicting MPS and smaller than [Formula: see text] in predicting MPSR on all target impact datasets. High performance in concussion detection was observed based on the MPS and MPSR estimated by the transfer-learning-based models. The MLHMs can be applied to various head impact types for rapidly and accurately calculating brain strain and strain rate. This study enables developing MLHMs for the head impact type with limited availability of data, and will accelerate the applications of MLHMs.

Design of a novel deep network model for spinal cord injury prediction

Degenerative cervical myelopathy must be diagnosed with magnetic resonance imaging (MRI) which predicts spinal cord injury (SCI). The growing volume of medical imaging data can be managed by deep learning models, which provide a preliminary interpretation of images taken in basic care settings. Our main goal was to create a deep-learning approach that could identify SCI using MRI data. This work concentrates on modeling a novel 2D-convolutional neural networks (2D-CNN) approach for predicting SCI. For holdouts, training, and validation, various datasets of patients were created. Two experts assigned labels to the images. The holdout dataset was used to evaluate the performance of our deep convolutional neural network (DCNN) over the image data from the available dataset. The dataset is acquired from the online resource for training and validation purpose. With the available dataset, the anticipated model attains 94% AUC, 0.1 p-value, and 92.2% accuracy. The anticipated model might make cervical spine MRI scan interpretation more accurate and reliable.

#1810 The association between circulating fibroblast growth factor 23 (FGF23) and serum aldosterone level in chronic kidney disease patients

Abstract Background and Aims Chronic kidney disease (CKD) remains a major health problem, till now, diagnosis of CKD depends on radiology and estimated glomerular filtration rate (eGFR). Therefore, there is an ultimate need for new sensitive prognostic and diagnostic biomarkers for diagnosis and progression of CKD. CKD is accompanied by increased levels of circulating fibroblast growth factor 23 (FGF23) and aldosterone levels. In the current study, we aim to evaluate the association between circulating FGF23 and serum aldosterone levels in CKD patients and to test the hypothesis that aldosterone may be a direct driving factor of increased FGF23 secretion in CKD patients. Method This cross section observational study was carried out at Kafr El Sheikh University Hospital's internal medicine department which enrolled 140 subjects that were divided into two groups: group I: 70 patients with various stages of newly diagnosed chronic kidney disease who did not receive renin-angiotensin-aldosterone system (RAAS) blocker yet (CKD stage 1 –5) &amp; group II: 70 apparently healthy subjects (of corresponding age and sex as a control group). We detected serum FGF-23 and Serum aldosterone using enzyme-linked immunosorbent assay (ELISA). Results A strong correlation was identified between FGF-23 and serum aldosterone in group I (p = &amp;lt; 0.001), there was remarkably stage dependent strong associations in CKD patients. High FGF-23 and serum aldosterone significantly independently increase risk of CKD by 1.15 and 1.043 folds respectively. The best cutoff of FGF-23 in prediction of kidney injury is ≥59.735 with area under curve 0.977, sensitivity 91.4%, specificity 90%, positive predictive value 90.1%, negative predictive value 91.3% and overall accuracy 90.7% (p &amp;lt; 0.001). The best cutoff of aldosterone in prediction of kidney injury is ≥117.59 with area under curve 0.978, sensitivity 92.9%, specificity 88.6%, positive predictive value 89%, negative predictive value 92.5% and overall accuracy 90.7% (p &amp;lt; 0.001). Conclusion FGF23 and aldosterone may be used as markers in CKD progression. aldosterone may be a direct driving factor of increased FGF23 secretion in CKD patients.

#959 Developing and validating a risk score model for prediction of AKI in non-ICU hospitalized patients

Abstract Background and aim : Acute kidney injury (AKI) is an increasingly common and potentially catastrophic complication in hospitalized patients. Several risk score models have been designed for intensive care unit (ICU) patients. Aim in this work was to establish a new risk prediction score for AKI patients in general wards. Methods All hospitalized ward patients from the first of July 2019 to 31st of December 2019 who developed AKI 24 hours after admission were included. To create a new prediction score model of AKI in hospitalized patients we used the data collected from 107 patients who developed AKI. We used our prospective cohort to develop and validate this prediction score model of AKI in patients admitted to general wards. Results Only 107 patients of 10,243 (1%) developed AKI 24 hours after admission to the general wards. Mortality rate was 26.2%. A score model of 15 points, based on a combination of clinical and laboratory data, was developed for prediction of AKI. We demonstrated that a cutoff value ≥ 4 out of 15 was a predictor of Acute Kidney Injury in non-ICU hospitalized patients. The area under the receiver operating characteristic (AUC ROC) value of the score model was 0.950, 95% CI (confidence interval) 0.922–0.978 and the P-value &amp;lt;0.001 with sensitivity of 94.39 and specificity of 81.43. Applying the score model in another group of patients (validation group n = 122) showed that the AUC ROC value in the validation group was 0.826. Conclusion We developed and validated a new risk score model with a cutoff value ≥ 4 out of 15 for prediction of Acute Kidney Injury in non-ICU hospitalized patients with a good sensitivity and specificity. It will help the early prediction of Acute Kidney Injury in non-ICU patients.

#668 Combined biomarker testing for the assessment of acute kidney injury among myocardial infarction patients

Abstract Background and aims The utility of different biomarkers for the prediction of acute kidney injury following ST-elevation myocardial infarction (STEMI) has been evaluated in several studies however, very few data exist on the prognostic value of combining biomarkers. Among numerous biomarkers examined, C-reactive protein (CRP) and Neutrophil gelatinase associated Lipocalin (NGAL) stand high in their utility to assess AKI risk. The aim of this study was to assess the prognostic value of combined CRP and NGAL measurement for the assessment of the risk of AKI in reperfused STEMI patients Methods Blood samples for plasma NGAL and CRP were drawn 24 hours after admission. High NGAL and CRP were defined as values within the 4th quartile (&amp;gt;75percentile). Patients were stratified into 4 study groups: Low NGAL/Low CRP, LOW NGAL/high CRO, High NGAL /low CRP and high NGAL/high CRP. Patients were assessed for the occurrence of in-AKI based on the KDIGO criteria. Results A total of 311 patients were included (mean age 66 ± 14 years, 81% males). According to the 4 study groups, there was a stepwise increase in the proportion of AKI (9% vs. 21% vs. 40% vs. 68%; p 0.001; Fig. 1). In a multivariate regression model, compared to patients having low NGAL/low CRP we observed a graded, independent increase in the risk for AKI for low NGAL/high CRP, high NGAL/low CRP and high NGAL/high CRP(OR 2.89, p = 0.02 vs. OR 3.29, p &amp;lt; 0.001 vs. OR 17.6, p &amp;lt; 0.001 respectively, Table 1). Peak concentrations showed significant areas under the curves (AUCs) for the prediction of AKI for both CRP AUC (0.692, 95% CI 0.62-0.76) and NGAL (AUC 0.778, 95% CI 0.72-0.84). The combination of NGAL and CRP yielded a significant increase in AUC to 0.822 (95% CI 0.77 to 0.88) (combined biomarkers vs CRP: p &amp;lt; 0.001 and combined vs. NGAL: p = 0.02; Fig. 2). Conclusions In patients with reperfused STEMI, the combined assessment of NGAL and CRP provided incremental prognostic information for the prediction AKI when compared with single-biomarker measurement.

#767 Utility of cystatin C as a predictor for postoperative acute kidney injury in non-cardiac surgeries [S1

Abstract Background and Aims Serum creatinine has been widely utilized to estimate glomerular filtration rate (eGFR), although various biomarkers have been developed to address its limitations. Among these biomarkers, the clinical use of Cystatin C is increasingly gaining recognition. However, there is a lack of evaluation regarding the utility of cystatin C in predicting the risk of postoperative acute kidney injury (PO-AKI) in non-cardiac surgeries. Method A retrospective cohort study was conducted on patients who underwent non-cardiac surgeries more than 1 hour from five departments (general surgery, neurosurgery, obstetrics and gynecology [OBGY], orthopedics, and urosurgery) at Chung-Ang University Hospital between 2018 and 2020. Patients with missing baseline information, including creatinine and cystatin C, and those without follow-up serum creatinine levels were excluded. PO-AKI was defined as KDIGO-AKI criteria occurring within 7 days after surgery. The estimated glomerular filtration rate (eGFR) was calculated from 2012 and 2021 chronic kidney disease-epidemiology collaboration (CKD-EPI) equations. Logistic regression was used to develop a prediction model, and C-statistics and the Delong test were used to compare the performance. Baseline comorbidities (diabetes mellitus [DM] and hypertension), laboratory findings (hypoalbuminemia [serum albumin &amp;lt;3.5 g/dL], anemia [hemoglobin &amp;lt;12 g/dL for female or &amp;lt;13 g/dL for male], and hyponatremia [sodium &amp;lt;135 mEq/L]), medication usage within 90 days of surgery (nonsteroidal anti-inflammatory drug and renin-angiotensin-system [RAS] blocker), and operation information (Anesthesia type, emergency operation, and operation duration) were adjusted. Results Overall, 339 patients were enrolled, with 49 patients (14.5%) developed PO-AKI. The median age was 73.0 [64.0; 80.0]; eGFR-Cr 86.4 [65.5; 99.1] mL/min/1.73 m2, eGFR-Cystatin C 70.0 [45.2; 92.7] mL/min/1.73 m2; eGFR-Cr/Cystatin C 77.2 [54.5; 100.5] mL/min/1.73 m2. Patients who developed AKI had a higher prevalence of diabetes (AKI 49.0%, no AKI 28.3%; p-value 0.006) and prescription of RAS blockers (AKI 63.3%, no AKI 34.5%; p-value &amp;lt;0.001), longer operation duration (AKI 63.3%, no AKI 34.5%; p-value &amp;lt;0.001), and lower eGFR of all types (AKI: eGFR-Cr 69.8, eGFR-Cystatin C 48.0, eGFR-Cr/Cystatin C 57.2; no-AKI: eGFR-Cr 88.0, eGFR-Cystatin C 74.0, eGFR-Cr/Cystatin C 80.5 mL/min/1.73 m2; p-values &amp;lt;0.001). No statistical differences were observed in other laboratory findings between the two groups (Table 1). The model with eGFR-Cystatin C demonstrated the highest area under the curve (AUC) among the three models (eGFR-Cr: AUC 0.78, 95% confidential interval [CI] 0.702‒0.857; eGFR-Cystatin C: AUC 0.81, 95% CI 0.735‒0.877; eGFR-Cr/Cystatin C: AUC 0.80, 95% CI 0.724‒0.871). Only the eGFR-Cystatin C-based model showed significant improvement in prediction compared to the eGFR-Cr-based model in the Delong test (eGFR-Cystatin C vs eGFR-Cr: p-value 0.033; eGFR-Cr/Cystatin C vs eGFR-Cr: p-value 0.055, Fig. 1). Conclusion Our findings suggest the potential usefulness of cystatin C in predicting the risk of PO-AKI non-cardiac surgery in addition to conventional PO-AKI risk factors. Further studies including larger and external cohorts, are needed for validation.

#2206 Predictors of post-operative acute kidney injury after colorectal cancer surgery

Abstract Background and Aims Post-operative acute kidney injury (PO-AKI) is a common complication of colorectal cancer (CRC) surgery associated with increased morbidity and mortality, and higher hospital costs. Specific treatment of AKI is still limited, supporting the importance of preventing AKI. Identification of patients at high risk may allow early interventions, including closer monitoring and perioperative optimization. This study aimed to identify predictors of PO-AKI by assessing the association between preoperative factors and the occurrence of PO-AKI. Method We conducted a nationwide cohort study including adult CRC surgery patients in 2010–2021 using Danish health registries. We defined PO-AKI as AKI within 7 days after the CRC surgery according to the 2012 KDIGO criteria using creatinine measurements. A list of patient characteristics associated with AKI was identified using existing literature. We obtained information on variables using population-based health registries containing data on diagnosis codes, redeemed prescriptions, and laboratory data. We investigated the individual predictive factors by estimating PO-AKI risks and the odds ratios (ORs) with 95% confidence intervals (CIs) adjusting for sex and age. Results We identified 35,650 CRC surgery patients, of whom 24,555 had creatinine measurements during the study period, making them eligible for further analysis. Among these patients, we identified 2,694 cases of PO-AKI, corresponding to a 11% risk of PO-AKI. The risk of PO-AKI was higher for males (13.5% vs. 8.4%, aOR=1.76 [1.62-1.91]), and patients with diabetes (15.4% vs. 10.2%, aOR=1.46 [1.32-1.61]), cardiovascular disease (15.4% vs. 9.9%, aOR=1.38 [1.26-1.52]), CKD (18.9% vs. 9.2%, aOR=1.99 [1.80-2.19]), COPD (16.6% vs. 10.3%, aOR=1.59 [1.42-1.78]), and hypertension (13.6% vs. 7.5%, aOR=1.68 [1.53-1.84]). When examining potentially nephrotoxic medicine, the risk was higher in ACE-I/ARB users (14.5% vs. 9.6%, aOR=1.44 [1.32-1.56]) and similar in NSAID users and nonusers (10.3% vs. 11.0%, aOR=1.00 [0.84-1.19]). Conclusion We found that male gender, diabetes, cardiovascular disease, CKD, COPD, hypertension, and the use of ACE-I/ARB medications were associated with a higher risk of PO-AKI. Interestingly, users and nonusers of NSAIDs had similar risks of AKI. The identification of high-risk groups, such as those with pre-existing comorbidities or users of specific medications, may aid clinicians in identifying patients with a particularly high risk of PO-AKI.

#862 Prevalence and surrogate predictors of acute kidney injury in tropical infections in eastern India

Abstract Background and Aims Acute kidney injury (AKI) in tropical countries is strikingly different from the other countries with a temperate climate. The epidemiology of AKI is largely influenced by environmental factors and climatic conditions and differ significantly its outcome and the surrogate predictors are not much studied. 1. To observe the spectrum and estimate the prevalence of AKI in tropical infections. 2. To evaluate the outcome and surrogate predictor of tropical AKI. Method This was a tertiary level single centre prospective observational study. All patients admitted with tropical infections associated AKI (KDIGO) were enrolled in two years and underwent complete history taking, clinical examinations and necessary laboratory investigations according to situations.. All patients were followed up more than 6 month. All data recorded were analysed in suitable statistical tool. Univariate analysis is followed by multivariate analysis was done for predictors followed by hazards ratio and Neglekers test. Results Out of 132 patients with Tropical AKI M:F was 1.5:1. Mean age was 42.3 ± 14.4 year, About 35.6% had Malaria,31.1% Dengue, 17% Scrub typhus, 10.4% Leptospira, and 5.9% patients had Typhoid fever. Malaria was the most common causative factor followed by Dengue. study reflecting the high dengue incidence occurring in the months of August, September, October &amp; November. Among Malaria AKI (MAKI) 43.75% (21) had Falciparum malaria, 50% (24) had Vivax malaria and 6.25% (3) had dual infection. Out of 132 patients with Tropical AKI about 60% totally recovered at the time of discharge. 40% patients had Shock. Among them 60% Dengue AKI, 33.3% Malarial AKI 5 (21.7%) Scrub typhus, 4 (28.6%) Leptospira AKI &amp; 3 (37%) Typhoid AKI patients had Shock. About 23% patients required RRT. Among them 8 (25%) patients died, 5 (15.6%) patients had CKD progression compared to non-RRT group where only 3 (2.9%) patients died, and 2 (1.94%) patients had progression to CKD. Total 11 (8.1%) patients died during hospital stay and only 7 (6.2%) patients had progressed to CKD. The mean Hospital stay was 5.51 ± 3.8 days. S hock at presentation, requirement of ICU support ( in form of ionotropic and ventilatory support), Need of RRT, prolong hospital stay, late referral was the bad prognostic predictor in terms of death. Though not statistically significant, presence of anaemia, hyponatraemia, deranged liver function &amp; hypoalbuminemia were more in patients who died compared to survival group. Limitations: Small enrolments. non inclusion of all cause including snake bite, less biopsy in this study. Conclusion There are wide spectrum of tropical AKI with high mortality and morbidity. A greater awareness and early intervention is necessary for prompt diagnosis, treatment and favourable outcome.

Review of a large trauma registry in Addis Ababa, Ethiopia: insights into prehospital care and provider training for trauma quality improvement

BackgroundInjury is a major cause of death and disability in Ethiopia. ALERT Hospital, one of only three designated trauma centers in the country, has employed a basic trauma registry since...

Human and Porcine Lumbar Endplate Injury Risk in Repeated Flexion-Compression.

Low back pain (LBP) affects 50-80% of adults at some pointin their lifetime, yet the etiology of injury is not well understood. Those exposed to repeated flexion-compression are at a higher risk for LBP, such as helicopter pilots and motor vehicle operators. Animal injury models offer insight into in vivo injury mechanisms, but interspecies scaling is needed to relate animal results to human. Human (n = 16) and porcine (n = 20) lumbar functional spinal units (FSUs) were loaded in repeated flexion-compression (1Hz) to determine endplate fracture risk over long loading exposures. Flexion oscillated from 0 to 6° and peak applied compressivestress ranged from 0.65 to 2.38MPa for human and 0.64 to 4.68MPa for porcine specimens. Five human and twelve porcine injuries were observed. The confidence intervals for human and porcine 50% injury risk curves in terms of stress and cycles overlapped, indicating similar failure behavior for this loading configuration. However, porcine specimens were more tolerant to the applied loading compared to human, demonstrated by a longer time-to-failure for the same applied stress. Optimization revealed that time-to-failure in human specimens was approximately 25% that of porcine specimens at a given applied stress within 0.65-2.38MPa. This study determined human and porcine lumbar endplate fracture risks in long-duration repeated flexion-compression that can be directly used for future equipment and vehicle design, injury prediction models, and safety standards. The interspecies scale factor produced in this study can be used for previous and future porcine lumbar injury studies to scale results to relevant human injury.

Association between self-reported functional capacity measures and postoperative myocardial injury in patients undergoing noncardiac surgeries.

Self-reported functional capacity measures have an uncertain role in the pre-operative cardiovascular risk stratification. This substudy aimed to evaluate whether self-reported metabolic equivalent (MET) could improve the prediction of postoperative myocardial injury (MI) over other well-established cardiovascular risk factors. This is a post hoc analysis of an international multicenter prospective cohort study. We recruited patients ≥45 years old who had elective elevated-risk noncardiac surgery in 45 centers across 17 countries between June 2017 and April 2020. The primary outcome was MI defined according to the Fourth Universal Definition of Myocardial Infarction. We measured the proportion of new prognostic information added by self-reported MET using multivariable logistic regression. In total, 860 (41.3%) patients suffered MI. In patients without systematic troponin surveillance, the odds ratio for MI with each 1-point increment in MET equaled 1.03 (95% confidence interval [CI], 0.99-1.07). The new prognostic information, according to the likelihood ratio adequacy index, accounted for 1.5%. Sensitivity analysis, including centers with >90% of patients with routine high-sensitivity troponin T monitoring, showed that MET added 21.8% of new information to the baseline model, and each additional point was associated with a lower risk of MI (odds ratio, 0.86; 95% CI, 0.81-0.91). In elevated-risk noncardiac surgery, self-reported functional capacity measures do not significantly improve the prediction of MI; however, they add new prognostic information in centers with routine perioperative troponin monitoring.