Tool For Early Identification Research Articles

#2508 Urinary [TIMP-2]*[IGFBP7] as the ultimate potential biomarker of subclinical Acute Kidney Injury after kidney surgery: a multicentric study

Abstract Background and Aims Nephron-sparing surgery (NSS) represents the preferred technique to treat localized renal lesions and better preserve kidney function. Still it is not exempted from the risk of postoperative acute kidney injury (AKI) to happen, with a reported incidence ranging from 5.5% to 34%. Patients experiencing postoperative AKI, either clinical or subclinical, are more susceptible to chronic kidney disease (CKD). The latter represents a difficult-to-identify subpopulation characterized by tubular damage biomarker's rising without changes in serum creatinine (scr), that after surgery is less likely to be included among the ones deserving a closer follow-up, but actually needing one, as this subpopulation's long term outcomes slightly differ from individuals recovered from clinical postoperative AKI. This is the reason why a standardized tool for early identification of patients suffering from subclinical AKI is required, to allow a customized postoperative surveillance, promote strategies of nephroprotection and reduce the incidence of CKD and end-stage kidney disease. Recent evidences show that by exclusively following outdated consensus criteria for AKI (changes in scr, urine output), 15-20% of patients undergoing through acute tubular injury and suffering from its adverse outcomes are likely to be missed. Only recently, FDA approved NephroCheck® biomarker, which combines two urinary markers, tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7). It represents the first effective test for AKI's risk assessment. In a previous monocentric study [1], our group presented NephroCheck® as a biomarker able to efficiently identify subclinical AKI and predict long-term eGFR decline after robotic NSS. In order to confirm our previous results, we recruited a larger cohort and performed a multicentric study. Method We prospectively observed all patients scheduled for robotic NSS in suspected localized renal cell carcinoma in two different Centers from 2016 to 2018. Samples were collected preoperatively and postoperatively (4 h, 10 h, 24 h, 48 h), while kidney function was re-assessed up to 24 months. A multivariate logistic analysis was performed to evaluate the association between different parameters and eGFR decline at 24 months. Results A total number of 131 patients were included; 55/131 (42%) developed clinical AKI. A significant association between NephroCheck® at 4 h and clinical AKI, as stated by KDIGO Guidelines, is confirmed (p = 0.0055). In the overall cohort, the multivariate analysis shows that only clinical AKI appears as an independent factor for predicting eGFR decline at 24 months (p < 0.0003), while postoperative NephroCheck® alone did not (p = 0.92). Based on NephroCheck® measured 4 h after surgery and clinical AKI, patients can be divided in four groups with significantly different eGFR at 24 months (p = 0.0003) (Fig. 1). Not considering patients who experienced clinical AKI, the independent role of different parameters in predicting severe eGFR decline at 24 months was evaluated by multivariate logistic analysis; in this subgroup of patients, NephroCheck® evaluated at 4 h postoperatively appears as an independent factor able to predict a severe eGFR decline at 24 months (OR 3.76, p = 0.02) (Fig. 2). Conclusion NephroCheck®, in combination with scr, represents an accurate and easy-to-use test, likely able to early identify long-term eGFR decline in patients undergoing NSS surgery that don't develop a clinically evident postoperative AKI (subclinical AKI), making it possible to identify those in need of closer-up surveillance, reducing the incidence and progression of CKD. Still, clinical postoperative AKI is confirmed as the most important prognostic parameter and predictive factor for eGFR decline in the long-term. Further studies are needed, particularly focusing on application of NephroCheck® in setting other than NSS.

#88 Establishment of machine learning-based risk prediction model for acute kidney injury after acute myocardial infarction

Abstract Background and Aims Acute kidney injury (AKI) is a common complication of acute myocardial infarction (AMI) with high morbidity, mortality and lack of effective treatment, so prevention is particularly important. This study intends to use machine learning to establish a precise prediction model for AKI after AMI. Method AMI patients were consecutively collected from July 2011 to December 2016 in Beijing Anzhen Hospital, Capital Medical University. First, SelectFromModel and Lasso regression model were used to select features. A predictive model (Model A) was then created using a multivariate logistic regression model in the training set and validated in the test set. At the same time, the prediction model (Model B) is created by machine learning algorithm in the training set. The process of model construction and evaluation is implemented by Python. The algorithm includes MLP, SVM, KNN and SimpleRNN, from which the model with the largest area under the ROC curve (Model B) is selected and verified in the test set. DeLong method was used to compare the model B with model A to compare whether the area under the ROC curve was better than multivariate logistic regression model and select the best model. Results A total of 6014 AMI patients were included in this study, 70% were randomly selected as the training set, and the remaining 30% were used as the test set. Males comprised 80.5% of the total population with a mean age of 58.4 ± 11.7 years. The incidence of AKI in the overall population was 11.2% (674/6014) (Fig. 1). A total of 12 important characteristics were included in the model, including the number of myocardial infarctions, ST-segment elevation myocardial infarction, ventricular tachycardia, third-degree atrioventricular block, decompensated heart failure at admission, admission serum creatinine value, admission urea nitrogen value, admission CK-MB peak value, whether diuretics were used, maximum daily dose of diuretics, days of diuretic use, and whether statins were used. Logistic regression analysis resulted in an area under the ROC curve of 0.80 (95% CI, 0.76-0.84) in the test set (Fig. 2). The models were constructed by MLP, SVM, KNN and SimpleRNN algorithms, respectively, and validated in the test set to calculate the area under the ROC curve of each model in the test set (Fig. 3), and finally the model constructed by MLP algorithm was selected as model B, and its area under the ROC curve was 0.82 (95% CI, 0.78–0.85). The area under the ROC curve of the two models was compared at p=0.363, but the machine learning algorithm constructed models with higher absolute values (Fig. 4). Conclusion The prediction model of AKI risk after AMI constructed based on machine learning is similar to that constructed by logistic regression analysis in terms of prediction ability, but the model constructed by machine learning algorithm has a better trend, indicating that the machine learning algorithm may improve the prediction ability of prediction model and provide an effective tool for early identification of these high-risk patients in clinical practice, early preventive measures, and reduction of morbidity.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

BackgroundThe emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.MethodsRelevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.ResultsA total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629–0.828) for PFS in the training set, 0.760 (95%CI: 0.728–0.792) and 0.819 (95%CI: 0.757–0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721–0.771) and 0.700 (95%CI: 0.677–0.724) for OS in the training and validation sets, respectively.ConclusionMachine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

Early Detection and Intervention for Children with High Risk of Cerebral Palsy: A Survey of Physical Therapists and Occupational Therapists in Brazil

Aims The purpose of this study was to assess the current clinical practice of physiotherapists and occupational therapists on early detection and early intervention for children with cerebral palsy (CP) in Brazil. Methods This was a cross-sectional study. A purpose-developed electronic survey was disseminated across the country to physiotherapists and occupational therapists working with young children with or at risk of CP. Results A total of 205 anonymous respondents were included. Most participants (64.4%) agree that the diagnosis of CP can be made before 6 months of age. General Movements Assessment (26.8%) and Hammersmith Infant Neurological Examination (37.1%) were used infrequently. Infants at risk for CP receive therapy twice a week or more by 58.5% of therapists, 93.2% identified parents’ goals as the most important factor in customizing the early intervention program. The most frequent intervention strategies for this age group were active stimulation of the child (n = 182), family training (n = 161), strategies to optimize the environment (n = 143), and neurodevelopmental treatment/Bobath (n = 99). Conclusions Currently, pediatric physiotherapists and occupational therapists in Brazil do not fully incorporate best practice tools for early identification of children with CP, nor sufficient best evidence-based interventions.

Perfusion index: could it be a new tool for early identification of pulmonary embolism severity?

Perfusion index (PI) is a promising indicator for monitoring peripheral perfusion. The present study aimed to compare the efficiency of PI and PESI score in estimating the 30-day mortality and treatment needs of patients diagnosed with pulmonary embolism in the emergency department. This study was prospective and observational. The demographic features of the patients, comorbidities, vital signs, PESI score, PI, treatment applied to the patient and airway management, right ventricular diameter/left ventricular diameter ratio, length of hospital stay, outcome, and 30-day mortality were recorded. A total of 94 patients were included. All patients' vital signs and PI values were recorded on admission. The mean pulse rate (p = 0.001) and shock index (p = 0.017) values of deceased patients were statistically significantly higher, while the mean PI (p = 0.034) was statistically significantly lower. PESI score and PI were statistically significant to predict the need for mechanical ventilation (PI, p = 0.004; PESI score, p < 0.001), inotropic treatment (PI, p = 0.047; PESI score p = 0.005), and thrombolytic therapy (PI, p = 0.035; PESI score p = 0.003). According to the ROC curve, the mortality prediction power of both PESI (AUC: 0.787, 95% CI 0.688-0.886, cutoff: 109.5, p < 0.001) and PI index (AUC: 0.668, 95% CI 0.543-0.793, cutoff: 1, p = 0.011) were determined as statistically significant. PI might be helpful in clinical practice as a tool that can be applied to predict mortality and treatment needs in PE.

Digital health interventions for remote follow-up after mild traumatic brain injury

Background After a mild traumatic brain injury (mild TBI,) a significant number of patients may experience persistent symptoms and disabilities for months to years. Early identification and timely management of persistent symptoms may help to reduce the long-term impacts of mild TBIs. There is currently no formalised method for identifying patients with persistent symptoms after mild TBI once they are discharged from emergency department. Objective Assess the feasibility of a remote monitoring tool for early identification of persistent symptoms after mild TBI in the outpatient setting using digital tools. Methods Electronic surveys were sent to patients with mild TBI who presented to the emergency department at a Major Trauma Centre in England. The surveys were completed at three different timepoints (within days of injury (S1), 1 month (S2), and 3 months (S3) after injury). The indicators used to assess feasibility were engagement, number of eligible patients for follow-up evidence of need for the intervention, and consistency with the literature. Feedback was sought from participants. Results Of the 200 people invited to participate, 134 (67.0%) completed S1, 115 (57.5%) completed S2, and 95 (47.5%) completed S3. The rates of persistent symptoms ranged from 17.9%–62.6% depending on the criteria used, and we found a significant proportion of the participants experienced morbidity 1 and 3 months after injury. The electronic follow-up tool was deemed an acceptable and user-friendly method for service delivery by participants. Conclusion Using digital tools to monitor and screen mild TBI patients for persistent symptoms is feasible. This could be a scalable, cost-effective, and convenient solution which could improve access to healthcare and reduce healthcare inequalities. This could enable early identification of patients with further medical needs and facilitate timely intervention to improve the clinical workflows, patient satisfaction, and health outcomes for people with persistent morbidities after mild TBIs.

Role of Personalized Medicine in Myocardial Infarction With Nonobstructive Coronary Artery Disease (MINOCA): An Updated Review

Cardiovascular diseases, notably myocardial infarction (MI), contribute significantly to global mortality. While traditional MIs are often associated with notable coronary artery blockages, a subgroup experiences MI without such blockages, termed myocardial infarction with nonobstructive coronary arteries (MINOCA). Current diagnostic methods, like invasive coronary angiograms, are inadequate in diagnosing MINOCA due to the absence of visible vessel blockages. Personalized medicine endeavors to introduce novel diagnostic tools for early identification and tailored management of MINOCA. Unlike percutaneous coronary intervention, which lacks efficacy in MINOCA, personalized medical management aligned with the specific underlying pathophysiological cause is essential. Treatments may include statins, agents modulating the renin-angiotensin system, and antiplatelet therapies if linked to plaque disruption. At the same time, calcium channel blockers may be pivotal if coronary artery spasm is the primary cause. Research is ongoing to refine tailored therapies and discover new biomarkers, highlighting the crucial need for individualized approaches in managing MINOCA.

Relationship between gingival and peri-implant sulcular fluid active matrix metalloproteinase-8 concentration and clinical indices in healthy and diseased conditions

Aim: The study was to evaluate the active matrix metalloproteinase-8 (aMMP-8) concentration in gingival crevicular fluid (GCF) and in peri-implant sulcular fluid (PISF) in healthy and diseased conditions, before and after non-surgical treatment, and to compare it with the various clinical parameters used to estimate the gingival and peri-implant inflammation. Methods: Plaque index/modified PI (PI/mPI), gingival index/simplified GI (GI/sGI), probing depth (PD), bleeding on probing index/modified BOPI (BOPI/mBOPI), radiographic bone loss/radiographic marginal bone loss (rBL/rMBL), and GCF/PISF samples were evaluated, before and 3 months after non-surgical treatment, GCF/PISF samples were analyzed by a chair-side mouth-rinse test (ImplantSafe®) in combination with a digital reader (ORALyzer®). Results: In all groups, aMMP-8 median levels were statistically higher in the PISF than in GCF and they did not change after treatment. Moreover, it was statistically higher in Group 3 (periodontitis/peri-implantitis) compared to the other groups. A positive correlation of the GCF/PISF and aMMP-8 median concentration was seen with increasing PD and BOPI/mBOPI values. A higher covariation of aMMP-8 mean levels in GCF with PD was found when compared to PISF levels. aMMP-8 mean levels in PISF expressed a higher covariation with increasing grades of sGI, rMBL, and BOPI while aMMP-8 GCF concentration established a better covariation with PD and PI. Conclusions: PISF of sites with peri-implant mucositis and peri-implantitis showed higher levels of aMMP-8 compared to sites with gingivitis and periodontitis. Compared to clinical indices, aMMP-8 concentration in GCF/PISF can be a beneficial adjunctive diagnostic tool for early identification and screening of the risk of peri-implant diseases. After non-surgical therapy, PISF aMMP-8 concentration remained mostly unchanged, while the GCF concentration of aMMP-8 significantly decreased.

Machine learning algorithms to predict colistin-induced nephrotoxicity from electronic health records in patients with multidrug-resistant gram-negative infection

ObjectivesColistin-induced nephrotoxicity prolongs hospitalisation and increases mortality. The study aimed to construct machine learning models to predict colistin-induced nephrotoxicity in patients with multidrug-resistant Gram-negative infection. MethodsPatients receiving colistin from three hospitals in the Clinical Research Database were included. Data were divided into a derivation cohort (2011–2017) and a temporal validation cohort (2018–2020). Fifteen machine learning models were established by categorical boosting, light gradient boosting machine and random forest. Classifier performances were compared by the sensitivity, F1 score, Matthews correlation coefficient (MCC), area under the receiver operating characteristic (AUROC) curve, and area under the precision-recall curve (AUPRC). SHapley Additive exPlanations plots were drawn to understand feature importance and interactions. ResultsThe study included 1392 patients, with 360 (36.4%) and 165 (40.9%) experiencing nephrotoxicity in the derivation and temporal validation cohorts, respectively. The categorical boosting with oversampling achieved the highest performance with a sensitivity of 0.860, an F1 score of 0.740, an MCC of 0.533, an AUROC curve of 0.823, and an AUPRC of 0.737. The feature importance demonstrated that the days of colistin use, cumulative dose, daily dose, latest C-reactive protein, and baseline haemoglobin were the most important risk factors, especially for vulnerable patients. A cutoff colistin dose of 4.0 mg/kg body weight/d was identified for patients at higher risk of nephrotoxicity. ConclusionsMachine learning techniques can be an early identification tool to predict colistin-induced nephrotoxicity. The observed interactions suggest a modification in dose adjustment guidelines. Future geographic and prospective validation studies are warranted to strengthen the real-world applicability.

An Illustrative Case Example in a K-12 School District: Considering Sustainable Mental Health Practices within Systematic, Equitable, and Collaborative Approaches

With the increased social-emotional needs of students since the COVID-19 pandemic, schools offer an opportunity to support student wellness and address needs. This article describes one district’s efforts to develop a systematic, equitable, and collaborative continuum of supports. The district used Elementary and Secondary School Emergency Relief funding from the U.S. Department of Education to establish new initiatives and support for students experiencing mental health struggles. These initiatives and supports include hiring a school social worker, a multi-tiered systems of support (MTSS) framework, implementing universal screening tools for early identification and intervention, using data-driven decision-making, and incorporating progress monitoring efforts to document the impact of services and supports that have been provided to their students. As new initiatives with time limited funding often fail to make a lasting impact, using sustainability strategies is critical. This article describes the district’s systematic, equitable, and collaborative approaches with sustainability considerations. We also describe barriers and next steps to assist other districts in planning for change. We conclude with a discussion of implications for practice and research.

Plasmalogens and Octanoylcarnitine Serve as Early Warnings for Central Retinal Artery Occlusion.

Central retinal artery occlusion (CRAO) is a kind of ophthalmic emergency which may cause loss of functional visual acuity. However, the limited treatment options emphasize the significance of early disease prevention. Metabolomics has the potential to be a powerful tool for early identification of individuals at risk of CRAO. The aim of the study was to identify potential biomarkers for CRAO through a comprehensive analysis. We employed metabolomics analysis to compare venous blood samples from CRAO patients with cataract patients for the venous difference, as well as arterial and venous blood from CRAO patients for the arteriovenous difference. The analysis of metabolites showed that PC(P-18:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PC(P-18:0/20:4(5Z,8Z,11Z,14Z)) and octanoylcarnitine were strongly correlated with CRAO. We also used univariate logistic regression, random forest (RF), and support vector machine (SVM) to screen clinical parameters of patients and found that HDL-C and ApoA1 showed significant predictive efficacy in CRAO patients. We compared the predictive performance of the clinical parameter model with combined model. The prediction efficiency of the combined model was significantly better with area under the receiver operating characteristic curve (AUROC) of 0.815. Decision curve analysis (DCA) also exhibited a notably higher net benefit rate. These results underscored the potency of these three substances as robust predictors of CRAO occurrence.

Phase Angle and Impedance Ratio as Indicators of Physical Function and Fear of Falling in Older Adult Women: Cross-Sectional Analysis.

Older adults experience a significant decline in muscle integrity and function with aging. Early detection of decreased muscle quality can pave the way for interventions to mitigate the progression of age-related physical declines. Phase angle (PhA) and impedance ratio (IR) are measures of muscle integrity, which can be assessed quickly via bioelectrical impedance analysis (BIA) and may be indicative of physical function. This study aimed to characterize the relationships among handgrip strength (HGS), sit-to-stand (STS), BTrackS balance scores, fear of falling (evaluated using the Short Falls Efficacy Scale-International [Short FES-I]), and IR among community-dwelling older adult women classified as having a low or high PhA. A cross-sectional analysis was conducted with 85 older women (mean age 75.0, SD 7.2 years; mean weight 71.0, SD 15.0 kg; mean height 162.6, SD 6.1 cm). To examine the influence of PhA on performance measures, participants were divided into 2 PhA groups: high (>4.1°; n=56) and low (≤4.1°; n=29). Data were nonnormative; hence, the Mann-Whitney U test was used to evaluate between-group differences, and Kendall τ coefficients were used to determine the partial correlations. The low PhA group had a significantly higher IR (mean 0.85, SD 0.03) than the high PhA group (mean 0.81, SD 0.03; r=.92; P<.001). The high PhA group had superior HGS (mean 21.4, SD 6.2 kg; P=.007; r=0.36), BTrackS balance scores (mean 26.6, SD 9.5 cm; P=.03; r=0.30), and STS scores (mean 16.0, SD 5.5; P<.001; r=0.49) than the low PhA group (mean HGS 17.6, SD 4.7 kg; mean BTrackS balance score 37.1, SD 21.1 cm; mean STS score 10.7, SD 6.2). Both PhA and IR were significantly correlated with HGS and BTrackS balance, STS, and Short FES-I scores (P<.05). However, on adjusting for the whole sample's age, only PhA was strongly correlated with HGS (τb=0.75; P=.003) and STS scores (τb=0.76; P=.002). Short FES-I scores were moderately correlated with IR (τb=0.46; P=.07) after controlling for age. No significant between-group differences were observed for height, weight, or BMI. PhA and IR are associated with physical function and the fear of falling in older women. However, only PhA was significantly associated with physical function (HGS and STS) independent of age. Conversely, only IR was significantly associated with the fear of falling. Diminished physical function and increased IR appear to be characteristics of older women with a PhA of ≤4.1°. These findings suggest that PhA and IR measured through BIA together may serve as a valuable tool for early identification of older women at the risk of functional decline and a heightened fear of falling.

Estimated glomerular filtration rate as a tool for early identification of patients with insufficient exposure to beta-lactam antibiotics in intensive care units

Background Only about 50% of intensive care unit (ICU) patients reach a free trough concentration above MIC (100% fT > MIC) of beta-lactam antibiotics. Although dose adjustments based on therapeutic drug monitoring (TDM) could be beneficial, TDM is not widely available. We investigated serum creatinine-based estimated GFR (eGFR) as a rapid screening tool to identify ICU patients at risk of insufficient exposure. Method Ninety-three adult patients admitted to four ICUs in southeast Sweden treated with piperacillin/tazobactam, meropenem, or cefotaxime were included. Beta-lactam trough concentrations were measured. The concentration target was set to 100% fT > MICECOFF (2, 4, and 16 mg/L based on calculated free levels for meropenem, cefotaxime, and piperacillin, respectively). eGFR was primarily determined via Chronic Kidney Disease–Epidemiology Collaboration (CKD-EPI) and compared to three other eGFR equations. Data was analysed using logistic regression and receiver operative characteristic (ROC) curves. Results With intermittent standard dosing, insufficient exposure was common in patients with a relative eGFR ≥48mL/min/1.73m2 [85%, (45/53)], particularly when treated with cefotaxime [96%, (24/25)]. This eGFR cut-off had a sensitivity of 92% and specificity of 82% (AUC 0.871, p < 0.001) in identifying insufficient exposure. In contrast, patients with eGFR <48mL/min/1.73m2 had high target attainment [90%, (36/40)] with a wide variability in drug exposure. There was no difference between the four eGFR equations (AUC 0.866–0.872, cut-offs 44–51 ml/min/1.73m2). Conclusion Serum creatinine-based eGFR is a simple and widely available surrogate marker with potential for early identification of ICU patients at risk of insufficient exposure to piperacillin, meropenem, and cefotaxime.

Psychometric Evaluation of a Tablet-Based Tool to Detect Mild Cognitive Impairment in Older Adults: Mixed Methods Study.

With the rapid aging of the global population, the prevalence of mild cognitive impairment (MCI) and dementia is anticipated to surge worldwide. MCI serves as an intermediary stage between normal aging and dementia, necessitating more sensitive and effective screening tools for early identification and intervention. The BrainFx SCREEN is a novel digital tool designed to assess cognitive impairment. This study evaluated its efficacy as a screening tool for MCI in primary care settings, particularly in the context of an aging population and the growing integration of digital health solutions. The primary objective was to assess the validity, reliability, and applicability of the BrainFx SCREEN (hereafter, the SCREEN) for MCI screening in a primary care context. We conducted an exploratory study comparing the SCREEN with an established screening tool, the Quick Mild Cognitive Impairment (Qmci) screen. A concurrent mixed methods, prospective study using a quasi-experimental design was conducted with 147 participants from 5 primary care Family Health Teams (FHTs; characterized by multidisciplinary practice and capitated funding) across southwestern Ontario, Canada. Participants included health care practitioners, patients, and FHT administrative executives. Individuals aged ≥55 years with no history of MCI or diagnosis of dementia rostered in a participating FHT were eligible to participate. Participants were screened using both the SCREEN and Qmci. The study also incorporated the Geriatric Anxiety Scale-10 to assess general anxiety levels at each cognitive screening. The SCREEN's scoring was compared against that of the Qmci and the clinical judgment of health care professionals. Statistical analyses included sensitivity, specificity, internal consistency, and test-retest reliability assessments. The study found that the SCREEN's longer administration time and complex scoring algorithm, which is proprietary and unavailable for independent analysis, presented challenges. Its internal consistency, indicated by a Cronbach α of 0.63, was below the acceptable threshold. The test-retest reliability also showed limitations, with moderate intraclass correlation coefficient (0.54) and inadequate κ (0.15) values. Sensitivity and specificity were consistent (63.25% and 74.07%, respectively) between cross-tabulation and discrepant analysis. In addition, the study faced limitations due to its demographic skew (96/147, 65.3% female, well-educated participants), the absence of a comprehensive gold standard for MCI diagnosis, and financial constraints limiting the inclusion of confirmatory neuropsychological testing. The SCREEN, in its current form, does not meet the necessary criteria for an optimal MCI screening tool in primary care settings, primarily due to its longer administration time and lower reliability. As the number of digital health technologies increases and evolves, further testing and refinement of tools such as the SCREEN are essential to ensure their efficacy and reliability in real-world clinical settings. This study advocates for continued research in this rapidly advancing field to better serve the aging population. RR2-10.2196/25520.

Genomic surveillance of carbapenem-resistant Klebsiella pneumoniae reveals a prolonged outbreak of extensively drug-resistant ST147 NDM-1 during the COVID-19 pandemic in the Apulia region (Southern Italy)

The recent worldwide spread of New Delhi metallo-beta-lactamase-producing Klebsiella pneumoniae (NDM-KP) in health-care settings remains a concern. The aim of the study was to describe an outbreak of extensively drug-resistant ST147 NDM-1-KP in the Apulia region of Southern Italy that occurred between 2020 and 2022 through genomic surveillance of carbapenem-resistant Enterobacterales. A total of 459 carbapenem-resistant KP isolates collected from patients hospitalised with bloodstream infections were tested using a commercial multiplex real-time polymerase chain reaction to identify carbapenemase genes. A subset of 27 isolates was subjected to whole-genome sequencing. Core-genome multilocus sequence typing was performed by analysing a panel of 4884 genes. Molecular testing revealed that 104 (22.6%) isolates carried the carbapenemase NDM gene. Phylogenetic analysis of the 27 isolates subjected to whole-genome sequencing revealed high genetic relatedness among strains. All isolates were resistant to all first-line antibiotics. Virulome analysis identified the ybt locus, the two well-recognised virulence factors iucABCDiutA and rmpA, and the genes encoding the type 3 pilus virulence factor. Plasmids IncFIB(pkPHS1), IncFIB(pNDM-Mar), IncFIB(pQil), IncHI1B(pNDM-MAR), IncR, and Col(pHAD28) were identified in all isolates. Moreover, further analysis identified the IncFIB-type plasmid carrying the NDM-1 genes. The increasing circulation of extensively drug-resistant NDM-1 ST147 KP strains in Southern Italy in recent years is worrisome, because these clones pose a real risk, particularly in hospital settings. Genomic surveillance is a crucial tool for early identification of emerging threats such as the spread of high-risk pathogens. Rapid infection control measures and antimicrobial stewardship are key to preventing further spread of hypervirulent KP strains.

P300 An Expert Delphi Consensus: Early Identification of Patients at Risk of Crohn's Disease in Perianal Fistulas and Abscesses (PREFAB) and Identification and Management of Isolated Perianal Crohn's Disease (ipCD) - Part A, PREFAB

Abstract Background In ± 10% of all Crohn’s disease (CD) patients a perianal abscess (PAA) or fistula (PAF) is the manifesting symptom. The delay in diagnosis of CD, from fistula or abscess onset, is very long and associated with worse outcomes. The aim of this Delphi study was to reach consensus on a clinical decision tool to help select patients with raised suspicion of CD in PAA/PAF patients, to identify underlying CD earlier and reduce the diagnostic delay. Methods A panel of international experts in the field of proctology and/or Inflammatory Bowel Disease (IBD), consisting of surgeons, gastroenterologists and radiologists were invited to participate in the PREFAB part of this Delphi study. The first round was an electronic survey and the second round a virtual consensus meeting. In the first round, participants were asked to anonymously provide their opinion probing 1) the relevance and use of clinical characteristics (“Red Flags”) suggestive of underlying CD, 2) the use of faecal calprotectin (FCP) as an adjunct for screening for CD and 3) on the diagnostic work-up for IBD in PAA/PAF patients with raised clinical suspicion. In the second round, statements were paired/revised based on the feedback from the first round and presented in a final set of statements. Consensus was predefined as ≥70% agreement. Results A total of 30 experts participated in the first round and 25 in the second round (83.3%). Final consensus was reached for 29% of all statements in the first Delphi round and, after adjustments/merging, for all 11 statements (100%) in the second round. Ninety-seven percent of participants agreed that shortening of the delay in diagnosis by using a clinical decision tool could improve outcomes in patients with perianal disease as a first symptom. Consensus was reached on the red flags to be included in the clinical decision tool, on screening of all patients with any perianal fistula (regardless of the complexity, biological behaviour and co-existent perianal symptoms) to identify CD in an early (sub)clinical phase, on referral criteria and on the diagnostic workup and follow up of patients with raised suspicion. Conclusion A clinical decision tool for early identification of CD in patients with PAA/PAF as a first symptom could shorten delay in diagnosis and improve outcomes. Global consensus for this tool, including a practical and relevant algorithm for finding or excluding CD in patients with PAA/PAF as a manifesting sign, was reached within two rounds. This clinical decision tool will be validated in 2024 in a large, prospective multicenter study.

Cause for Concern? The Relationship Between Language Skills in Toddlers and Later Reading Skills in School

AbstractThe purpose of this longitudinal study is to investigate the association between poor language skills in toddlers and later reading skills. In contrast to earlier research, the present study used an authentic assessment of language skills conducted by staff in early childhood education and care as a tool for early identification. The participants were 515 Norwegian monolingual toddlers who were assessed when they were 33 months, 7 years, and 10 years. The results showed a small correlation between language skills identified at 33 months and later reading skills. The toddlers with the 10% poorest language skills were overrepresented in the 10% with the poorest decoding skills in 2nd grade and reading comprehension in 5th grade. Furthermore, 67.3% of toddlers with poor language skills attained below-average reading scores in 5th grade. These findings support the importance of paying attention to children in the lowest 10%, even at the age of 33 months. Furthermore, the results show that authentic assessments in the early years conducted by staff in early childhood education and care can make an important contribution to identifying toddlers in need of extra language stimulation in early childhood education.

Copper toxicity on Eisenia fetida in a vineyard soil: a combined study with standard tests, genotoxicity assessment and gut metagenomic analysis

Copper (Cu) toxicity is a pressing concern for several soils, especially in organic viticulture. The objective of this work was to assess Cu toxicity on the non-target organism Eisenia fetida, employing both traditional and novel tools for early identification of Cu-induced damages. In addition to traditional tests like avoidance and reproductive toxicity experiments, other tests such as the single cell gel electrophoresis (SCGE) and gut microbiome analysis were evaluated to identify early and more sensitive pollution biomarkers. Four sub-lethal Cu concentrations were studied, and the results showed strong dose-dependent responses by the earthworm avoidance test and the exceeding of habitat threshold limit at the higher Cu doses. An inverse proportionality was observed between reproductive output and soil Cu concentration. Bioaccumulation was not detected in earthworms; soil concentrations of potentially bioavailable Cu were not affected by E. fetida presence or by time. On the contrary, the SCGE test revealed dose-dependent genotoxicity for the ‘tail length’ parameter already at the second day of Cu exposition. Gut microbiome analysis a modulation of microbial composition, with the most aboundant families being Pectobateriaceae, Comamonadaceae and Microscillaceae. Bacillaceae increased over time and showed adaptability to copper up to 165 mg/kg, while at the highest dose even the sensitive Acetobacteriaceae family was affected. The research provided new insights into the ecotoxicity of Cu sub-lethal doses highlighting both alterations at earthworms’ cellular level and changes in their gut microbiota.

Prediction of Subclinical and Clinical Multiple Organ Failure Dysfunction in Breast Cancer Patients-A Review Using AI Tools.

This review explores the interconnection between precursor lesions of breast cancer (typical ductal hyperplasia, atypical ductal/lobular hyperplasia) and the subclinical of multiple organ failure syndrome, both representing early stages marked by alterations preceding clinical symptoms, undetectable through conventional diagnostic methods. Addressing the question "Why patients with breast cancer exhibit a tendency to deteriorate", this study investigates the biological progression from a subclinical multiple organ failure syndrome, characterized by insidious but indisputable lesions, to an acute (clinical) state resembling a cascade akin to a waterfall or domino effect, often culminating in the patient's demise. A comprehensive literature search was conducted using PubMed, Google Scholar, and Scopus databases in October 2023, employing keywords such as "MODS", "SIRS", "sepsis", "pathophysiology of MODS", "MODS in cancer patients", "multiple organ failure", "risk factors", "cancer", "ICU", "quality of life", and "breast cancer". Supplementary references were extracted from the retrieved articles. This study emphasizes the importance of early identification and prevention of the multiple organ failure cascade at the inception of the malignant state, aiming to enhance the quality of life and extend survival. This pursuit contributes to a deeper understanding of risk factors and viable therapeutic options. Despite the existence of the subclinical multiple organ failure syndrome, current diagnostic methodologies remain inadequate, prompting consideration of AI as an increasingly crucial tool for early identification in the diagnostic process.

Development and validation of a nomogram predictive model for cerebral small vessel disease: a comprehensive retrospective analysis.

Cerebral small vessel disease (CSVD) is a significant contributor to stroke, intracerebral hemorrhages, and vascular dementia, particularly in the elderly. Early diagnosis remains challenging. This study aimed to develop and validate a novel nomogram for the early diagnosis of cerebral small vessel disease (CSVD). We focused on integrating cerebrovascular risk factors and blood biochemical markers to identify individuals at high risk of CSVD, thus enabling early intervention. In a retrospective study conducted at the neurology department of the Affiliated Hospital of Hebei University from January 2020 to June 2022, 587 patients were enrolled. The patients were randomly divided into a training set (70%, n = 412) and a validation set (30%, n = 175). The nomogram was developed using multivariable logistic regression analysis, with variables selected through the Least Absolute Shrinkage and Selection Operator (LASSO) technique. The performance of the nomogram was evaluated based on the area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and decision curve analysis (DCA). Out of 88 analyzed biomarkers, 32 showed significant differences between the CSVD and non-CSVD groups. The LASSO regression identified 12 significant indicators, with nine being independent clinical predictors of CSVD. The AUC-ROC values of the nomogram were 0.849 (95% CI: 0.821-0.894) in the training set and 0.863 (95% CI: 0.810-0.917) in the validation set, indicating excellent discriminative ability. Calibration plots demonstrated good agreement between predicted and observed probabilities in both sets. DCA showed that the nomogram had significant clinical utility. The study successfully developed a nomogram predictive model for CSVD, incorporating nine clinical predictive factors. This model offers a valuable tool for early identification and risk assessment of CSVD, potentially enhancing clinical decision-making and patient outcomes.