Tool For Early Identification Research Articles

Development, Reliability, and Validity of the Preschool Learning Skills Scale: A Tool for Early Identification of Preschoolers at Risk of Learning Disorder in Mainland China

BackgroundEarly identification of children at risk of learning disorders (LD) may mitigate the adverse effects of delayed intervention by guiding children to receive preventive services at an earlier age. However, there is no assessment tool for the early identification of children at risk of LD in Mainland China. Therefore, this study aimed to create a Chinese version of the Preschool Learning Skills Scale and investigate its validity and reliability.MethodsFirstly, a pilot scale was designed based on literature review and expert review. Secondly, a pre-survey of the pilot scale was conducted. In phase 3, a formal survey was carried out to test the reliability and validity of the scale by involving 2,677 preschool children from 7 kindergartens. Data were collected using a checklist for demographic characteristics, the preschool learning skills scale, the Behavior Rating Inventory of Executive Function-Preschool Version (BRIEF-P), and Conners' Rating Scales.ResultsThe final scale included 38 items under seven factors. The reliability and validity tests confirmed that the Cronbach's alpha, split-half reliability, and test–retest reliability coefficients of the scale were 0.946, 0.888, and 0.941, respectively. The Spearman correlations of factor-total score ranged from 0.685 to 0.876. The results of criterion-related validity showed a direct and significant association between the preschool learning skills scale with the BRIEF-P (r = 0.641, P < 0.001) and the cognitive problems factor of Conners' Rating Scales (r = 0.564, P < 0.001). The model had a good fit (χ2/df = 3.489, RMSEA = 0.047, RMR = 0.024, CFI = 0.912, TLI = 0.900, and IFI = 0.912). Multigroup confirmatory factor analysis supported the structural and measurement invariance on the preschool learning skills scale across gender and grade.ConclusionsThe developed preschool learning skills scale has good reliability and validity, indicating that the scale can be used to identify preschool children at risk of LD and can be recommended for use in clinical research and practice.

Elemental analysis of hair provides biomarkers of maternal hardship linked to adverse behavioural outcomes in 4-year-old children: The QF2011 Queensland Flood Study

BackgroundExposure to adverse experiences during pregnancy, such as a natural disaster, can modify development of the child with potential long-term consequences. Elemental hair analysis may provide useful indicators of cellular homeostasis and child health. The present study investigated (1) if flood-induced prenatal maternal stress is associated with altered hair elemental profiles in 4-year-old children, and (2) if hair elemental profiles are associated with behavioural outcomes in children. MethodsParticipants were 75 children (39 boys; 36 girls) whose mothers were exposed to varying levels of stress due to a natural disaster (2011 Queensland Flood, Australia) during pregnancy. At 4 years of age, language development, attention and internalizing and externalizing problems were assessed and scalp hair was collected. Hair was analyzed by inductively coupled plasma mass spectrometry (ICP-MS) for 28 chemical elements. ResultsA significant curvilinear association was found between maternal objective hardship and copper levels in boys, as low and high maternal objective hardship levels were associated with the highest hair copper levels. Mediation analysis revealed that low levels of maternal objective hardship and high levels of copper were associated with lower vocabulary scores. Higher levels of maternal objective hardship were associated with higher magnesium levels, which in turn were associated with attention problems and aggression in boys. In girls, high and low maternal objective hardship levels were associated with high calcium/potassium ratios. ConclusionElemental hair analysis may provide a sensitive biomonitoring tool for early identification of health risks in vulnerable children.

Legislating Mandatory Reporting of Child Abuse in India and China: A Divergence of Legislative Choice

Mandatory reporting is used as a tool for early identification of child abuse by countries around the world. However, there is variation in terms of the legislative models followed. The two models most commonly followed are universal mandatory reporting and stakeholder specific mandatory reporting. India and China joined the bandwagon and introduced legislation on mandatory reporting over a decade ago. While India has adopted the ambitious model of universal mandatory reporting, China has taken a more incremental and experimental approach with stakeholder-specific mandatory reporting. The paper aims to differentiate the legislative approaches taken by the two countries for introducing mandatory reporting for child abuse, by delving into the legislative history, legislative provisions, and implementation challenges for mandatory reporting in both jurisdictions. The paper does not comment on the sanctity of mandatory reporting, but is limited to a comparative analysis of the legislative strategies taken by India and China.

Implications of leukocytosis following distal pancreatectomy splenectomy (DPS) and association with postoperative complications.

Early identification of complications after distal pancreatectomy splenectomy (DPS) poses challenges, as white blood cell count (WBC) is confounded by physiologic leukocytosis. We examined WBC patterns associated with complications after DPS. Clinicopathologic data were collected for patients who underwent DPS in our system from 2009 to 2016. We examined WBC, temperature, platelet count (PC), and ratios of these variables as potential early indicators of patients at risk of infections or major complications (MCs). 348 patients met study inclusion, of whom 206 (59%) were women and the median patient age was 59 ± 15 years. Infectious and MC rates were 11% and 16%, respectively, with <1% 30-day mortality. Postoperative WBC peaks were higher in patients with infections and MCs compared with no complication (23 vs. 17, p < 0.0001). WBC peak timing occurred postoperative day (POD) 2-3 for uncomplicated cases while peaks occurred POD9 for patients with infections and MCs. These data define patterns of leukocytosis following DPS. Although differences in infection markers were identified for patients with and without complications, no obvious thresholds were identified. Clinical suspicion for complications after DPS remains our best tool for early identification.

Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury.

BackgroundSepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients, which is associated with significantly increased mortality. Existing mortality prediction tools showed insufficient predictive power or failed to reflect patients' dynamic clinical evolution. Therefore, the study aimed to develop and validate machine learning-based models for real-time mortality prediction in critically ill patients with SA-AKI.MethodsThe multi-center retrospective study included patients from two distinct databases. A total of 12,132 SA-AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) were randomly allocated to the training, validation, and internal test sets. An additional 3,741 patients from the eICU Collaborative Research Database (eICU-CRD) served as an external test set. For every 12 h during the ICU stays, the state-of-the-art eXtreme Gradient Boosting (XGBoost) algorithm was used to predict the risk of in-hospital death in the following 48, 72, and 120 h and in the first 28 days after ICU admission. Area under the receiver operating characteristic curves (AUCs) were calculated to evaluate the models' performance.ResultsThe XGBoost models, based on routine clinical variables updated every 12 h, showed better performance in mortality prediction than the SOFA score and SAPS-II. The AUCs of the XGBoost models for mortality over different time periods ranged from 0.848 to 0.804 in the internal test set and from 0.818 to 0.748 in the external test set. The shapley additive explanation method provided interpretability for the XGBoost models, which improved the understanding of the association between the predictor variables and future mortality.ConclusionsThe interpretable machine learning XGBoost models showed promising performance in real-time mortality prediction in critically ill patients with SA-AKI, which are useful tools for early identification of high-risk patients and timely clinical interventions.

11-LB: A Prediction Model on Incident CKD among Individuals with T2D in the United States

CKD is highly prevalent and costly in individuals with diabetes. Clinicians need to have a tool for early identification and prediction of incident CKD in individuals with diabetes. This study aimed to predict incident CKD among individuals with diabetes. A time-varying Cox model was derived from the ACCORD clinical trial data to predict the risk of incident CKD, defined as eGFR&amp;lt;60 mL/min/1.73 m2 or UACR&amp;gt;30 mg/g lasting for 3 months. A list of candidate variables was chosen based on literature reviews and experts’ consultation, including demographic characteristics, physical exam, laboratory, medical history, drug use, and healthcare utilization. Predictors were selected from candidate variables using a stepwise algorithm. Data were split into training and validation set. Model performance was evaluated by Brier score (BS) and C statistics. Confidence intervals (CI) was calculated using a bootstrap method. Decomposition analysis was conducted to assess the predictor contribution. External validation was performed on patient-level data of HARMONY Outcome clinical trial. A total of 6,006 diabetes patients free of CKD at baseline were used for model development, with a median follow-up of 3 years and 2,257 new onset CKD events. The CKD risk model identified age at T2D diagnosed, smoking status, BMI, HDL, VLDL, ALT, eGFR, UACR, hypoglycemia event, retinopathy event, CHF event, CHD history, antihyperlipidemic drug use, antihypertensive drug use, and hospitalization. The model demonstrated good discrimination (C-statistics 0.828 [95% CI 0.812-0.845]) and calibration (BS 0.0552 [95% CI 0.0474-0.0593]) performance. UACR, eGFR, and CHF event were the top 3 factors that contributed most to the prediction. A total of 8,221 patients free of CKD was selected from HARMONY Outcome trial with 1,288 CKD events and median follow-up of 2 years. The model demonstrated acceptable discrimination (C-statistics: 0.772 [95% CI 0.767-0.805]) and calibration (BS: 0.0504 [95% CI 0.0477-0.0531]) in external data. A risk prediction of incident CKD among T2D patients was developed and validated for use in decision support of CKD prevention. Disclosure Y. Lin: None. H. Shao: Board Member; BRAVO4HEALTH, LLC. A. H. Anderson: None. V. Fonseca: Consultant; Abbott, Asahi Kasei Corporation, Bayer AG, Novo Nordisk, Sanofi, Research Support; Fractyl Health, Inc., Jaguar Gene Therapy, Stock/Shareholder; Abbott, Amgen Inc., BRAVO4Health, Mellitus Health. V. Batuman: None. L. Shi: None.

Modernising infectious disease surveillance and an early-warning system: The need for China's action

The severe acute respiratory syndrome (SARS) outbreak in 2003 gave impetus to the establishment of an internet-based timely reporting system in China, that has improved the efficiency of the Infectious Disease Surveillance System. The re-analysis of SARS data and proposed use of hotspot mapping as a tool for early identification of outbreak area(s) for quarantine provided a rationale for locking down1 when the coronavirus disease 2019 (COVID-19) entered Wuhan, China. The implementation of the Chinese Infectious Disease Automated-Alert and Response System since 2008 has further facilitated the timely response to disease outbreaks.

Non-Invasive Liver Fibrosis Scores Are Associated With Contrast-Associated Acute Kidney Injury in Patients Undergoing Elective Percutaneous Coronary Intervention.

Previous studies have demonstrated that non-invasive liver fibrosis scores (LFSs) are associated with kidney function deterioration. This study aimed to assess the predictive performance of LFSs in contrast-associated acute kidney injury (CA-AKI) in coronary artery disease (CAD) patients undergoing elective percutaneous coronary intervention (PCI). This retrospective study involved 5627 patients. The frequency of CA-AKI was 6.3% (n = 353). In a multivariate logistic analysis after adjustment, non-invasive LFSs, including fibrosis-5 score (FIB-5), fibrosis-4 score (FIB-4), aspartate aminotransferase to alanine aminotransferase ratio (AAR), and aspartate aminotransferase to platelet ratio index were independent risk factors for CA-AKI (all P < .05), whereas the Forns score was not (P > .05). The highest predictive performance was observed for FIB-5 (area under the curve [AUC] = .644) compared to other LFSs. A restricted cubic spline analysis confirmed approximately linear relationships between LFSs and risks of CA-AKI. Furthermore, adding FIB-5 (AUC = .747; net reclassification improvement [NRI] = .441, P < .001; integrated discrimination improvement [IDI] = .008, P < .001) or AAR (AUC = .747; NRI = .419, P < .001; IDI = .006, P = .010) to an established clinical risk model could significantly improve the prediction of CA-AKI. The LFSs were significantly associated with CA-AKI, possibly serving as predictive tools for early identification of CAD patients undergoing elective PCI that are at high risk of CA-AKI.

Development and validation of a predictive model for acute kidney injury in patients with moderately severe and severe acute pancreatitis.

Acute kidney injury is a serious complication of moderately severe and severe acute pancreatitis, which significantly increases mortality. There are currently no reliable tools for early identification of AKI especially severe AKI in these patients. We aim to develop a predictive model so that physicians can assess the risk of AKI and severe AKI, thus take further preventive measures. Patients with a diagnosis of MSAP and SAP admitted to our hospital from January 2018 to December 2021 were retrospectively included in the study. The participants were divided into the training and validation cohorts randomly, in a 2:1 ratio. A clinical signature was built based on reproducible features, using the least absolute shrinkage and selection operator method and machine learning. Multivariate logistic regression analysis was used to develop the prediction model. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. A total of 996 eligible patients were enrolled. 698 patients were allocated in the training cohort and 298 in the validation cohort. AKI occurred in 148 patients (21%) in the training cohort and 54 (18%) in the validation cohort, respectively. The clinical features, including C-reactive protein, intra-abdominal pressure and serum cysC, were significantly associated with AKI as well as severe AKI. The nomogram showed favorable discrimination, calibration and clinical usefulness. The novel risk score model has good performance for predicting AKI and severe AKI in MSAP and SAP patients. Application of this model can help clinicians stratify patients for primary prevention, surveillance and early therapeutic intervention to improve care and prognosis.

Risk Prediction of Cerebral Infarction after Anterior Circulation Aneurysm Rupture in an Under-Equipped Centre.

BackgroundMorbidity and mortality is high among aneurysm rupture patients. Despite surviving the initial rupture, morbidity is high as they suffer from vasospasm and cerebral infarction (CI). Most prediction tools for CI after aneurysmal subarachnoid haemorrhage (SAH) are complex and are not routinely available in all neurosurgical centres. Current therapies for prevention of CI are still debatable and selective usage among high-risk patients is advised. These factors necessitate a simple prediction model for identifying patients in the high risk group to initiate early preventive treatment of CI.MethodsPatients with anterior circulation aneurysm rupture who underwent surgical clipping were included. Demographic data and factors related to CI were collected to determine significance and were used to develop VINODH score (VS).ResultsTwo hundred patients were included with a median age of 51 years old. Multivariate analysis proved only four predictors were significant (P < 0.01) for developing CI. These predictors were used for the development of VS which was named after the main author and the model’s sensitivity was 79.0% and specificity was 83.0%. This highly predictive score (receiver operating characteristic [ROC]: 0.902) was internally validated.ConclusionVS is a reliable tool for early identification of patients at risk of CI after aneurysmal SAH.

First-in-Human Study of [68Ga]Ga-NODAGA-E[c(RGDyK)]2 PET for Integrin αvβ3 Imaging in Patients with Breast Cancer and Neuroendocrine Neoplasms: Safety, Dosimetry and Tumor Imaging Ability.

Arginine-Glycine-Aspartate (RGD)-recognizing cell surface integrins are involved in tumor growth, invasiveness/metastases, and angiogenesis, and are therefore an attractive treatment target in cancers. The subtype integrin αvβ3 is upregulated on endothelial cells during angiogenesis and on tumor cells. In vivo assessment of integrin αvβ3 is possible with positron emission tomography (PET). Preclinical data on radiochemical properties, tumor uptake and radiation exposure identified [68Ga]Ga-NODAGA-E[c(RGDyK)]2 as a promising candidate for clinical translation. In this first-in-human phase I study, we evaluate [68Ga]Ga-NODAGA-E[c(RGDyK)]2 PET in patients with neuroendocrine neoplasms (NEN) and breast cancer (BC). The aim was to investigate safety, biodistribution and dosimetry as well as tracer uptake in tumor lesions. A total of 10 patients (5 breast cancer, 5 neuroendocrine neoplasm) received a single intravenous dose of approximately 200 MBq [68Ga]Ga-NODAGA-E[c(RGDyK)]2. Biodistribution profile and dosimetry were assessed by whole-body PET/CT performed at 10 min, 1 h and 2 h after injection. Safety assessment with vital parameters, electrocardiograms and blood tests were performed before and after injection. In vivo stability of [68Ga]Ga-NODAGA-E[c(RGDyK)]2 was determined by analysis of blood and urine. PET images were analyzed for tracer uptake in tumors and background organs. No adverse events or pharmacologic effects were observed in the 10 patients. [68Ga]Ga-NODAGA-E[c(RGDyK)]2 exhibited good in vivo stability and fast clearance, primarily by renal excretion. The effective dose was 0.022 mSv/MBq, equaling a radiation exposure of 4.4 mSv at an injected activity of 200 MBq. The tracer demonstrated stable tumor retention and good image contrast. In conclusion, this first-in-human phase I trial demonstrated safe use of [68Ga]Ga-NODAGA-E[c(RGDyK)]2 for integrin αvβ3 imaging in cancer patients, low radiation exposure and favorable uptake in tumors. Further studies are warranted to establish whether [68Ga]Ga-NODAGA-E[c(RGDyK)]2 may become a tool for early identification of patients eligible for treatments targeting integrin αvβ3 and for risk stratification of patients.

Early identification of high-risk patients with recurrent acute pancreatitis progression to chronic pancreatitis.

IntroductionThe aim of the study was to develop a simple tool for early identification of high-risk patients with recurrent acute pancreatitis (RAP) progression to chronic pancreatitis (CP) in primary hospitals or outpatient clinics.MethodsThis retrospective cohort study included 265 patients with RAP.ResultsA nomogram for RAP progression to CP was developed and the C-index of the model was 0.817 (95% CI: 0.72–0.91). Patients were divided into two risk groups according to the nomogram prediction scores and a higher proportion of patients in the high-risk group progressed to CP.ConclusionsThe nomogram provided a means of predicting which patients were at high risk of progression to CP.

Abstract 105: Polygenic Burden Of Hypertension-related Alleles Leads To Non-traumatic Subarachnoid Hemorrhage At Younger Ages

Introduction: Non-traumatic subarachnoid hemorrhage (SAH) is strongly linked to hypertension, a condition highly influenced by common genetic variants. For complex diseases influenced by genetic and environmental factors, genetic predisposition plays a key role in earlier onset. We tested the hypothesis that persons with a higher polygenic burden of hypertension-related alleles have SAH at younger ages. Methods: We analyzed data from the UK Biobank, a large cohort study that enrolled over 500,000 Britons aged 40-69. We included participants of European descent. We constructed two polygenic risk scores (PRS) using 807 independent genetic variants known to associate with higher systolic and diastolic blood pressure (BP). We fitted linear regression to assess the relationship between these PRS and the age-of-onset of SAH, using product terms to test for interaction with sex. We subsequently implemented Mendelian Randomization analyses using the inverse variance weighted and weighted median methods to evaluate causality. Results: We evaluated a total of 1,178 SAH cases (mean age, 58; female sex, 722 [61.3%]). When evaluating all participants jointly, there was no association between the systolic BP PRS and age-of-onset for SAH (p=0.130). There was a significant interaction between the PRS and sex (p=0.002): each additional standard deviation of the systolic BP PRS was associated with an earlier onset of SAH in females (beta, -1.45; 95% CI, -2.31 to -0.58; p=0.001), but not in males (adjusted beta, 0.83; 95% CI, -0.37 to 2.02; p=0.176). In MR analyses, a 10mmHg increase in genetically determined systolic BP was associated with a 5-year earlier onset of SAH in female participants using both the inverse variance weighted (beta, -4.72; 95% CI, -7.34 to -2.10; p&lt;0.001) and weighted median approaches (beta, -5.05; 95% CI, -9.19 to -0.90; p=0.017). Analyses with the diastolic BP SNPs yielded comparable results (all p&lt;0.05). Conclusions: Genetically determined hypertension is associated with earlier onset of non-traumatic SAH in women. These results indicate that genetic predisposition may play a more important causal role in younger patients and point to genetic information as possible tools for early identification of high-risk individuals.

Tracheostomy prediction model in neonatal bronchopulmonary dysplasia via lung and airway MRI.

Clinical management of neonatal bronchopulmonary dysplasia (BPD) is often imprecise and can vary widely between different institutions and providers, due to limited objective measurements of disease pathology severity. There is critical need to improve guidance on the application and timing of interventional treatments, such as tracheostomy. To generate an imaging-based clinical tool for early identification of those patients with BPD who are likely to require later tracheostomy and long-term mechanical ventilation. We conducted a prospective cohort study of n = 61 infants (55 BPD, 6 preterm non-BPD). Magnetic resonance imaging (MRI) scores of lung parenchymal disease were used to create a binomial logistic regression model for predicting tracheostomy requirement. This model was further investigated using clinical variables and MRI-quantified tracheomalacia (TM). A model for predicting tracheostomy requirement was created using MRI parenchymal score. This model had 89% accuracy, 100% positive predictive value (PPV), and 85% negative predictive value (NPV), compared with 84%, 60%, and 83%, respectively, when using only relevant clinical variables. In a subset of patients with airway MRI (n = 36), a model including lung and TM measurements had 83% accuracy, 92% PPV, and 78% NPV. MRI-based measurements of parenchymal disease and TM can be used to predict need for tracheostomy in infants with BPD, more accurately than clinical factors alone. This prediction model has strong potential as a clinical tool for physicians and families for early determination of tracheostomy requirement.

Research On the Construction of Monitoring System for Responding to Major Public Health Emergencies in Border Areas of Yunnan

Background - The special geographical position of Yunnan Province as the key area and frontline of "external prevention and importation", the difficulty and pressure of prevention and control in border areas is high, the prevention and control of the epidemic in Yunnan Province is crucial to the overall situation of the province and even the whole country. Covid-19 exposes alarming gaps in Infectious Disease Surverillance in Border Areas. Objectives - Infectious disease surveillance is an important tool for early identification and prevention and control of infectious diseases. Regular disease surveillance of emerging infectious diseases is essential to be able to respond to pandemics and control them early through the construction of infectious disease surveillance systems to build stronger and better health systems. Method - Summarize and sort out the existing monitoring system and problems using a combination of qualitative research methods such as literature methods, group interviews, and expert consultation. Using a combination of typical and stratified sampling methods, a questionnaire was used to analyze the current status of monitoring and management in border counties in Yunnan Province. Results - There is no statistical difference in surveillance and prevention between the state and county levels in response to major public health emergencies, and the current surveillance system is difficult for epidemic dissemination, prediction and timely and effective follow-up. Conclusion - The border areas of Yunnan Province have not established an effective epidemic surveillance system, lack of diversified channels and ways to disseminate epidemic information, difficulty in accurately grasping the progress and changing trends of the epidemic, and risk awareness has not been strengthened and transformed to the detriment of active prevention, control and management of infectious diseases.

Correlation Between Computed Tomography Severity Score and Mortality in Patients with COVID-19 Chest Infection

Introduction: Computed Tomography (CT) is one of the most essential diagnostic tools for early identification and treatment of COVID -19 pneumonia. The objective of this study was to identify the relationship between CT severity score and mortality in COVID-19 patients. Method: A total of 62 patients with confirmed COVID-19 were included in this study. All these patients underwent High resolution computed tomography (HRCT) chest using a standard protocol. The CT severity score in all of these patients was calculated and the results were analyzed by SPSS version 21. Results: A higher CT severity score was associated with higher chances of mortality in the patients of COVID-19. There was no significant association of mortality with the age, sex, and number of lobes involvement Conclusion: HRCT chest can help in the early identification of the patients who need to be admitted to the Intensive care unit. Early admission and intervention to patients with high CT severity scores can help to reduce mortality.

Information processing by community health nurses using mobile health (mHealth) tools for early identification of suicide and depression risks in Fiji Islands

ObjectivesHigh rates of depression and suicide and a lack of trained psychiatrists have emerged as significant concerns in the low-income and middle-income countries (LMICs) such as the Pacific Island Countries...

Mobile Phones: Vital Addiction or Lethal Addiction? Mobile Phone Usage Patterns and Assessment of Mobile Addiction among Undergraduate Medical Students in Telangana, India.

Background Increased mobile phone usage among undergraduate medical students causes a detrimental effect on their health. The main focus of this study is to determine the pattern of mobile phone usage among undergraduate medical students in Hyderabad, India, and the detrimental effect on their health due to excess mobile use. Materials and Methods A cross-sectional study was conducted among undergraduate medical students from various medical colleges in Hyderabad, India, from September 2020 to January 2021. Data were collected from 626 respondents using a semistructured, pretested questionnaire. Smartphone Addiction Scale-Short Version (SAS-SV) was used to assess the risk of smartphone addiction. Microsoft Excel and SAS were employed to analyze the data. Associations were examined using Fisher's exact test. Results 100% of the respondents were using mobiles, with 83.2% spending more than 4 hours on them. Only 22% reported that no mobile use during classes. Half (51.6%) admitted to keeping their mobiles close by while sleeping. 84.3% used social networking apps via their mobiles. Common symptoms arising from prolonged mobile usage included eye strain (67.9%), blurred vision (31.4%), and numbness or tingling in palms (30.9%). 52.70% of the respondents were at high risk of mobile addiction according to SAS-SV. Screen time more than 4 hours was associated with high risk of mobile addiction (p < 0.0001). Significant association was found between high risk of mobile addiction and eye strain (p < 0.0001), blurry vision (p=0.0115), numbness/tingling in palms (p < 0.0001), and heat/tingling in the auditory area (p < 0.0001). Conclusion The study shows the alarming rate of risk of smartphone addiction among medical students. Students can be encouraged to assess their mobile addiction status and become aware of the issue. More research may be performed to develop standardized tools for early identification of mobile addiction and appropriate therapies for its rectification.

ADVANCED LUNG CANCER INFLAMMATORY INDEX AS A PROGNOSTIC MARKER FOR OXYGEN REQUIREMENT IN PATIENTS ADMITTED WITH SARS-COV-2 INFECTION

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Severe acute respiratory syndrome coronavirus (SARS-COV-2) infection continues to spread at exponential rates globally with more than 145 million confirmed cases worldwide and over 3 million deaths. SARS-COV-2 presents with different clinical manifestations ranging from asymptomatic to severe infection, requiring various levels of O2 support, facing high rates of mortality. Systemic inflammatory response to the viral infection contributes to a variety of clinical manifestations and to the degree of severity of illness. Limited evidence exists on potential biomarkers to quantify severity and outcomes of SARS-COV-2 infection. Our objective is to see if Advanced lung cancer inflammation index (ALI) calculated at the time of admission to the hospital can be used as a prognostic marker for oxygen requirements and adverse clinical outcomes. METHODS: We conducted a retrospective chart review of all patients ages 18 and above admitted to Memorial Hermann Hospitals between March 1, 2020 to June 30, 2020 who tested positive for SARS-COV-2 by RT-PCR. The ALI was calculated using patients Body mass index (BMI), serum albumin and neutrophil/lymphocyte ratio (NLR) from the admission labs using the formula ALI= BMI (kg/m2) × albumin (g/dL)/NLR. Patient related information was obtained by reviewing medical records after obtaining Institutional review board approval. RESULTS: A total of 700 patients were identified out of which 417 patients had complete medical records for analysis. Patients were stratified into group A (high inflammation (ALI < 18) n = 205) and group B (low inflammation (ALI ≥ 18) n = 218). 67% of the patients were aged 45 or older. 52.5% of the patients had a BMI < 30. 22.3% of patients in group A, 22.3% and 16.7% patients in group B, 16.7% were of white race. ALI < 18 was associated with higher oxygen requirements (73% vs 52%, p ≥ 0.0001), higher mechanical ventilation requirements (16.59% vs 9.91%, p = 0.04) and higher mortality rates (14.15% vs 7.08%, p = 0.02). In multi-variate analysis, ALI < 18 was significantly associated with oxygen requirements, (OR 2.62 (1.64 - 4.17), p ≤ 0.0001). CONCLUSIONS: In patients diagnosed with SARS-CoV-2 infection, an ALI < 18, representing high systemic inflammation, calculated at the time of admission is associated with significantly increased oxygen requirements, severe disease and mortality. CLINICAL IMPLICATIONS: Advanced lung cancer inflammation index can be easily calculated from basic clinical parameters available in most clinical settings. It can be used as a tool in early identification of patients who are likely to have more severe respiratory disease and poorer outcomes in patients with SARS-COV-2 infections. Early use of steroids can be considered in this population of patients with an ALI <18. DISCLOSURES: No relevant relationships by Shailesh Advani, source=Web Response No relevant relationships by Syed Jafri, source=Web Response No relevant relationships by Pushan Jani, source=Web Response No relevant relationships by Arthi Sridhar, source=Web Response No relevant relationships by Wei Yang, source=Web Response

Implementation of a Detection Tool to Aid Early Identification of Necrotizing Enterocolitis

The purpose of this poster is to explore initial signs and symptoms of necrotizing enterocolitis (NEC) in the preterm infant and the use of an early identification tool, the Neonatal Necrotizing Enterocolitis Early Detection Score (NeoNEEDS).