Complete Data Research Articles

Alternative statistical interpretation for the apparent plateaus in the duration distributions of gamma-ray bursts

The existence of a plateau in the short-duration tail of the observed distribution of cosmological long-soft gamma ray bursts (LGRBs) is posited to be the first direct evidence of collapsars. A similar plateau in the short-duration tail of the observed duration distribution of short-hard gamma ray bursts (SGRBs) has been suggested as evidence of compact binary mergers. We present an equally plausible alternative interpretation for this evidence, based on a purely statistical approach. Specifically, we show that the observed plateau in the short-duration tail of the duration distribution of LGRBs can naturally occur in the statistical distributions of strictly positive physical quantities, exacerbated by the effects of mixing with the duration distribution of SGRBs, observational selection effects, and data aggregation (e.g., binning) methodologies. The observed plateau in the short-duration tail of the observed distributions of SGRBs may likewise result from a combination of sample incompleteness and inhomogeneous binning of data. We further confirm the impact of these factors on the observation of a plateau in the duration distributions of GRBs through extensive numerical Monte Carlo simulations. This analysis corroborates and strengthens a purely statistical and sample-incompleteness interpretation of the observed plateau in the duration distribution of LGRBs and SGRBs, without invoking the physics of collapsars or jet-propagation through the stellar envelope.

Skyline query under multidimensional incomplete data based on classification tree

A method for skyline query of multidimensional incomplete data based on a classification tree has been proposed to address the problem of a large amount of useless data in existing skyline queries with multidimensional incomplete data, which leads to low query efficiency and algorithm performance. This method consists of two main parts. The first part is the proposed incomplete data weighted classification tree algorithm. In the first part, an incomplete data weighted classification tree is proposed, and the incomplete data set is classified using this tree. The data classified in the first part serves as the basis for the second step of the query. The second part proposes a skyline query algorithm for multidimensional incomplete data. The concept of optimal virtual points has been recently introduced, effectively reducing the number of comparisons of a large amount of data, thereby improving the query efficiency for incomplete data. Theoretical research and experimental analysis have shown that the proposed method can perform skyline queries for multidimensional incomplete data well, with high query efficiency and accuracy of the algorithm.

MANAJEMEN PENGELOLAAN APLIKASI ZAKAT SiMBA PADA BADAN AMIL ZAKAT NASIONAL DI KALIMANTAN TIMUR

This research was motivated by the existence of the SiMBA zakat application at BAZNAS in East Kalimantan, however the operation of this zakat application has not been effective and efficient and is only operated by four daily implementers, so there is no special section that manages this application. This research is also qualitative field research. with data collection techniques in the form of observations at 3 (three) research loci, interviews with SiMBA zakat application managers, and documents related to the research. The results of this research are, first: management of the SiMBA zakat application at BAZNAS Balikpapan City, second: obstacles to the management of the SiMBA zakat application come from two factors, namely internal and external, the internal factor is the incomplete data entered by the SiMBA zakat application operator.

Efficacy and safety of CO2 fractional laser versus Er:YAG fractional laser in the treatment of atrophic acne scar: A meta-analysis and systematic review.

To date, a consensus on the relative efficacy and safety of CO2 fractional laser versus erbium-doped yttrium aluminum garnet (Er:YAG) fractional laser treatments for atrophic acne scars has not been reached. This meta-analysis aims to systematically assess and compare their effectiveness and safety in clinical practice. For this meta-analysis, we conducted comprehensive searches in Pubmed, Embase, and Cochrane databases, covering publications from their inception up to August 2023. Our focus was on studies comparing fractional CO2 laser with Er:YAG fractional laser treatments for atrophic acne scars. We excluded duplicate publications, research lacking full-text access, incomplete data, or cases where data extraction was not feasible. Additionally, animal experiments, reviews, and systematic reviews were not considered. Data analysis was performed using STATA 15.1. Eight studies (seven randomized controlled trials (RCTs) and a retrospective study) were included in this meta-analysis. The sample size ranged from 28 to 106 with a total of 418 patients, including 210 in the CO2 fractional group and 208 in Er:YAG fractional group. The pooled results showed that the effective rate of CO2 fractional laser in treating atrophic acne scar was significantly higher than that of Er:YAG fractional laser (OR = 1.81, 95% CI: 1.08-3.01) and the downtime of CO2 fractional laser in treating atrophic acne scar was significantly shorter than that of Er:YAG fractional laser (Weighted Mean Difference (WMD) = -2.11, 95% CI: -3.11 to -1.10). In addition, VAS of CO2 fractional laser in treating atrophic acne scar was significantly higher than that of Er:YAG fractional laser (WMD = 1.77, 95% CI: 1.32-2.21) and the duration of erythema of CO2 fractional laser in treating atrophic acne scar was significantly longer than that of Er:YAG fractional laser (WMD = 1.85, 95% CI: 1.63-2.07). However, there was no significant difference in the duration of pain and incidence of PIHbetween CO2 fractional laser and of Er:YAG fractional laser. When it comes to treating atrophic acne scars, CO2 fractional laser demonstrates superior efficacy and leads to shorter downtime. However, it is important to note that CO2 fractional laser treatments tend to result in higher pain intensity and may carry a higher risk of post-treatment pigmentation compared to Er:YAG fractional laser procedures.

Low left ventricular outflow tract velocity time integral predicts normotensive shock in patients with acute pulmonary embolism

Abstract Funding Acknowledgements None. Introduction In patients with intermediate-risk acute pulmonary embolism (PE) outcomes vary widely, with mortality rates ranging from 3-15% and rates of normotensive shock up to 30%.1-3 Current risk models don't fully capture those at higher risk and identify patients who may benefit from advanced therapies. The left ventricular outflow tract (LVOT) velocity time integral (VTI), a surrogate marker of cardiac output, has been associated with increased mortality in acute PE.4 Our objective was to determine the association between LVOT VTI and its predictive value in identifying normotensive shock in this patient population. Methods Patients who underwent percutaneous mechanical thrombectomy (MT) between August 2020 and March 2023 at a large academic public hospital were considered for the study. Inclusion criteria comprised of normotensive patients (SBP ≥ 90 mmHg) with invasive measures of cardiac index (CI). VTI was obtained by pulsed wave doppler at the LVOT in either the apical 3 or apical 5 chamber view. The primary outcome was the association between LVOT VTI and normotensive shock and a cut off of 15 cm was used to examine this association based on prior studies.4 Results A total of 49 patients with intermediate-risk PE underwent MT, 16 patients had incomplete data, resulting in a final cohort of 33 patients (mean age: 57 ± 13yrs; 38% female). Patients with LVOT VTI of <15 cm were younger (53 vs 62 years p=0.02), had lower systolic blood pressure at presentation (122 vs 134 mmHg, p=0.047), higher heart rate (112.6 vs 96.2 beats/min, p=0.008), a higher composite shock score (CPES) (5 vs 3, p=0.012), lower cardiac index (1.9 vs 3.1 L/min/m2, p<0.001), a higher PASP (61.4 vs 48.9 mmHg, p=0.018), and higher rates of elevated lactate (90% vs 42%, p=0.027). LVOT VTI threshold of <15cm effectively identified patients with normotensive shock (90% vs 8% p<0.001). There was no statistically significant difference in the rates of saddle emboli, left ventricular ejection fraction, sPESI and BOVA scores in patients with VTI above or below 15cm. In patients with normotensive shock, 19/20 (95%) had LVOT VTI ≤ 15cm with a median of 13 cm (IQR 12, 14) (Figure). In patients without normotensive shock, 11/13 (84%) had LVOT VTI >15 with a median of 18 (IQR 17, 21). The area under the curve for LVOT VTI ≤ 15 cm in predicting normotensive shock was 0.89. The sensitivity and specificity of LVOT VTI ≤ 15 cm in predicting normotensive shock was 91.6% and 90.4% respectively. Conclusion Our study suggests that LVOT VTI is a reliable surrogate for cardiac output in patients with acute PE and can effectively identify those in the intermediate-risk group who are at risk for normotensive shock. A LVOT VTI <15cm may identify intermediate-risk patients at higher risk for hemodynamic decompensation and further studies are needed to evaluate whether these patients would benefit from upfront catheter-based therapies.

McConnell's sign predicts normotensive shock in patients with acute pulmonary embolism

Abstract Funding Acknowledgements None. Background Patients with intermediate-risk acute pulmonary embolism (PE) have mortality rates ranging from 3-15%, with incidence of normotensive shock occurring in up to 30% of patients.1-3 Existing risk models often fall short in accurately identifying those at heightened risk of normotensive shock and those who might derive benefit from advanced therapeutic interventions including catheter based therapies. Diagnosis of normotensive shock typically necessitates invasive right heart catheterization, however, McConnell's sign has been associated with higher risk PEs.4 Consequently, our objective was to assess the relationship between McConnell's sign, a readily available bedside point-of-care tool, and its predictive value in identifying normotensive shock in patients diagnosed with acute intermediate-risk PE. Methods All patients with intermediate-risk PE who underwent percutaneous mechanical thrombectomy (MT) between August 2020 and April 2023 at a large academic public hospital were included in the study. Normotensive shock was defined as patients with systolic blood pressure (SBP) ≥ 90mmHg in the absence of vasopressor support with pre-procedural invasive measures of CI £ 2.2 L/min/m2 and clinical evidence of hypoperfusion (i.e. elevated lactate, oliguria). The primary outcome was the association between McConnell’s sign and normotensive shock. Results A total of 49 patients with intermediate-risk PE underwent MT, 9 patients had incomplete data, resulting in a final cohort of 40 patients (mean age: 57 ± 13 years; 45% female). A total of 29/40 (72.5%) patients had McConnell’s sign. Patients with McConnell’s sign had higher rates of obesity (59% vs 18% p=0.02), higher heart rate (114 vs 99 beats/min, p=0.008), higher rates of elevated lactate (86% vs 55%, p=0..038), lower cardiac index (1.9 vs 3.1 L/min/m2, p=0.003), and higher rates of normotensive shock (76% vs 27%, p=0.005). There was no statistically significant difference in age, sex, race, rates of saddle emboli, left ventricular ejection fraction, pulmonary artery systolic pressure, presenting blood pressure, sPESI and composite pulmonary embolism shock score scores in patients with or without McConnell’s sign. In patients with normotensive shock, 22/25 (88%) had McConnell’s sign and only 53.3% had McConnell’s sign without shock (Figure 1). Patients with McConnell’s sign had an increased odds (OR 8.38, CI: 1.73-40.53; p=0.008, AUC 0.70, 95% CI: 0.56-0.85) of normotensive shock. Conclusion This is the first study to suggest that McConnell’s sign can effectively identify those in the intermediate-risk group who are at risk for normotensive shock. Further studies are needed to validate this non-invasive technique that can be rapidly performed at bedside with high reproducibility and whether it can help risk-stratify patients who may benefit from advanced PE therapies.

Methods for implementing integrated step-selection functions with incomplete data

Integrated step-selection analyses (iSSAs) are versatile and powerful frameworks for studying habitat and movement preferences of tracked animals. iSSAs utilize integrated step-selection functions (iSSFs) to model movements in discrete time, and thus, require animal location data that are regularly spaced in time. However, many real-world datasets are incomplete due to tracking devices failing to locate an individual at one or more scheduled times, leading to slight irregularities in the duration between consecutive animal locations. To address this issue, researchers typically only consider bursts of regular data (i.e., sequences of locations that are equally spaced in time), thereby reducing the number of observations used to model movement and habitat selection. We reassess this practice and explore four alternative approaches that account for temporal irregularity resulting from missing data. Using a simulation study, we compare these alternatives to a baseline approach where temporal irregularity is ignored and demonstrate the potential improvements in model performance that can be gained by leveraging these additional data. We also showcase these benefits using a case study on a spotted hyena (Crocuta crocuta).

Predictors and Prevalence of Alcohol and Cannabis Co-use Among Filipino Adolescents: Evidence From a School-based Student Health Survey.

This study explored the prevalence and predictors of alcohol and cannabis co-use among 9263 Filipino adolescents, using data from the 2019 Global School-based Student Health Survey (GSHS). We conducted a cross-sectional secondary analysis of the GSHS, targeting adolescents aged 13-17 years and excluding cases with incomplete data on alcohol and cannabis use. Our analysis employed the bivariate chi-square test of independence and multivariable logistic regression using Stata version 18 to identify significant predictors of co-use, with a p-value threshold set at 0.05. The weighted prevalence of co-users was 4.2% (95% confidence interval [CI], 3.4 to 5.3). Significant predictors included male gender (adjusted odds ratio [aOR], 4.50; 95% CI, 3.31 to 6.10; p<0.001) and being in a lower academic year, specifically grade 7 (aOR, 4.08; 95% CI, 2.39 to 6.99; p<0.001) and grade 8 (aOR, 2.20; 95% CI, 1.30 to 3.72; p=0.003). Poor sleep quality was also a significant predictor (aOR, 1.77; 95% CI, 1.29 to 2.44; p<0.001), as was a history of attempted suicide (aOR, 5.31; 95% CI, 4.00 to 7.06; p<0.001). Physical inactivity was associated with lower odds of co-use (aOR, 0.45; 95% CI, 0.33 to 0.62; p<0.001). Additionally, non-attendance of physical education classes (aOR, 1.48; 95% CI, 1.06 to 2.05; p=0.021), infrequent unapproved parental checks (aOR, 1.37; 95% CI, 1.04 to 1.80; p=0.024), and lower parental awareness of free-time activities (aOR, 0.63; 95% CI, 0.45 to 0.87; p=0.005) were associated with higher odds of co-use. Factors not significantly linked to co-use included age group, being in grade 9, always feeling lonely, having no close friends, being bullied outside school, and whether a parent or guardian understood the adolescent's worries. The findings highlight the critical need for comprehensive interventions in the Philippines, addressing not only physical inactivity and parental monitoring but also focusing on gender, academic grade, participation in physical education classes, sleep quality, and suicide attempt history, to effectively reduce alcohol and cannabis co-use among adolescents.

Data Completion-guided Unified Graph Learning for Incomplete Multi-View Clustering

Due to its heterogeneous property, multi-view data has been widely concerned over single-view data for performance improvement. Unfortunately, some instances may be with partially available information because of some uncontrollable factors, for which the incomplete multi-view clustering (IMVC) problem is raised. IMVC aims to partition unlabeled incomplete multi-view data into their clusters by exploiting the heterogeneity of multi-view data and overcoming the difficulty of data loss. However, most existing IMVC methods like BSV, MIC, OMVC, and IVC tend to conduct basic completion processing on the input data, without taking advantage of the correlation between samples and information redundancy. To overcome the above issue, we propose one novel IMVC method named Data Completion-guided Unified Graph Learning (DCUGL), which could complete the data of missing views and fuse multiple learned view-specific similarity matrices into one unified graph. Specifically, we first reduce the dimension of the input data to learn multiple view-specific similarity matrices. By stacking all view-specific similarity matrices, DCUGL constructs a third-order tensor with the low-rank constraint, such that sample correlation within and between views can be well explored. Finally, by dividing the original data into observed data and unobserved data, DCUGL can infer and complete the missing data according to the view-specific similarity matrices, and obtain a unified graph, which can be directly used for clustering. To solve the proposed model, we design an iterative algorithm, which is based on the alternating direction method of multipliers (ADMM) framework. The proposed model proves to be superior by benchmarking on six challenging datasets compared with state-of-the-art IMVC methods.

Validation of postnatal growth and retinopathy of prematurity (G-ROP) screening guidelines in a tertiary care hospital of Pakistan: A report from low-middle income country.

Retinopathy of Prematurity (ROP) significantly contributes to childhood blindness globally, with a disproportionately high burden in low- and middle-income countries (LMICs) due to improved neonatal care alongside inadequate ROP screening and treatment facilities. This study aims to validate the performance of Postnatal Growth and Retinopathy of Prematurity (G-ROP) screening criteria in a cohort of premature infants presenting at a tertiary care setting in Pakistan. This cross-sectional study utilized retrospective chart review of neonates admitted to the neonatal intensive care unit (NICU) at The Aga Khan University Hospital, Pakistan from January 2018 to February 2022. The complete G-ROP criteria were applied as prediction tool for infants with type 1 ROP, type 2 ROP, and no ROP outcomes. Out of the 166 cases, 125 cases were included in the final analysis, and remaining cases were excluded due to incomplete data. ROP of any stage developed in 83 infants (66.4%), of whom 55 (44%) developed type 1 ROP, 28 (22.4%) developed type 2 ROP, and 19 (15.2%) were treated for ROP. The median BW was 1060 gm (IQR = 910 to 1240 gm) and the median gestational age was 29 wk (IQR = 27 to 30 wk). The G-ROP criteria demonstrated a sensitivity of 98.18% (95% CI: 90.28-99.95%) for triggering an alarm for type 1 ROP. The G-ROP criteria achieved 100% sensitivity (95% CI: 87.66 to 100%) for type 2 ROP. The overall sensitivity of G-ROP criteria to trigger an alarm for any type of ROP was 98.8% (95% CI: 93.47 to 99.97%). Thus, the G-ROP screening model is highly sensitive in detecting at-risk infants for ROP in a Pakistani tertiary care setting, supporting its use in LMICs where standard screening criteria may not suffice.

Robust optimal power flow considering uncertainty in wind power probability distribution

This paper proposes an optimal power flow model that takes into account the uncertainty in the probability distribution of wind power. The model can schedule controllable generators under any possible distribution of wind power to ensure the safe and economic operation of the system. Firstly, considering the incompleteness of historical wind power data, the paper models the uncertainty of wind power using second-order moments of probability distribution and their fluctuation intervals. Subsequently, a robust optimal power flow model based on probability distribution model and joint chance constraints is established. The Lagrangian duality theorem is then employed to eliminate random variables from the optimization model, transforming the uncertainty model into a deterministic linear matrix inequality problem. Finally, a convex optimization algorithm is used to solve the deterministic problem, and the results are compared with traditional chance-constrained optimal power flow model. The feasibility and effectiveness of the proposed method are validated through case study simulations.

Join multiple Riemannian manifold representation and multi‐kernel non‐redundancy for image clustering

AbstractImage clustering has received significant attention due to the growing importance of image recognition. Researchers have explored Riemannian manifold clustering, which is capable of capturing the non‐linear shapes found in real‐world datasets. However, the complexity of image data poses substantial challenges for modelling and feature extraction. Traditional methods such as covariance matrices and linear subspace have shown promise in image modelling, and they are still in their early stages and suffer from certain limitations. However, these include the uncertainty of representing data using only one Riemannian manifold, limited feature extraction capacity of single kernel functions, and resulting incomplete data representation and redundancy. To overcome these limitations, the authors propose a novel approach called join multiple Riemannian manifold representation and multi‐kernel non‐redundancy for image clustering (MRMNR‐MKC). It combines covariance matrices with linear subspace to represent data and applies multiple kernel functions to map the non‐linear structural data into a reproducing kernel Hilbert space, enabling linear model analysis for image clustering. Additionally, the authors use matrix‐induced regularisation to improve the clustering kernel selection process by reducing redundancy and assigning lower weights to identical kernels. Finally, the authors also conducted numerous experiments to evaluate the performance of our approach, confirming its superiority to state‐of‐the‐art methods on three benchmark datasets.

Predictive capability of rough set machine learning in tetracycline adsorption using biochar

Machine learning algorithms investigate relationships in data to deliver useful outputs. However, past models required complete datasets as a prerequisite. In this study, rough set-based machine learning was applied using real-world incomplete datasets to generate a prediction model of biochar’s adsorption capacity based on key attributes. The predictive model consists of if–then rules classifying properties by fulfilling certain conditions. The rules generated from both complete and incomplete datasets exhibit high certainty and coverage, along with scientific coherence. Based on the complete dataset model, optimal pyrolysis conditions, biomass characteristics and adsorption conditions were identified to maximize tetracycline adsorption capacity (> 200 mg/g) by biochar. This study demonstrates the capabilities of rough set-based machine learning using incomplete practical real-world data without compromising key features. The approach can generate valid predictive models even with missing values in datasets. Overall, the preliminary results show promise for applying rough set machine learning to real-world, incomplete data for generating biomass and biochar predictive models. However, further refinement and testing are warranted before practical implementation.

Ventriculoperitoneal shunting obstruction: a multicentre clinical audit for cerebrospinal fluid parameters and its prediction role.

Shunt obstruction is a type of ventriculoperitoneal shunt (VPS) failure. Whether changes in cerebrospinal fluid (CSF) parameters can influence shunt outcomes or not is debatable. In this study, we retrospectively included adult hydrocephalus patients who received VPS from 6 general hospitals in different provinces of China from November 2013 to September 2021. The inclusion criteria: Patients with hydrocephalus of all etiologies underwent shunt surgery from 6 general hospitals in different provinces of China were included in the study. The exclusion criteria: 1.Patients under the age of 18; 2.Patients who had previous shunt surgery; 3. Shunt failurefromother factors; 4.Patients died from other causes; 5. Patients with incomplete data. The CSF of shunt patients had been analyzed at the time of shunt insertion. The CSF samples were collected and analyzed when the shunt was implanted. The relationship between CSF parameters and the incidence rate of shunt obstruction in one year was analyzed. A total of 717 eligible patients from 6 hospitals were included, of whom 59(8.23%) experienced obstruction. Multivariate logistic regression analysis identified that protein level(odds ratio [OR] 1.161, 95% CI 1.005 ~ 1.341, p = 0.043), decreased glucose level(< 2.5 mmol/L)(odds ratio 3.784, 95% confidence interval 1.872 ~ 7.652, p = 0.001) and protein level increase(> 0.45 g/L) (odds ratio 3.653, 95% confidence interval 1.931 ~ 6.910, p = 0.001)were independent risk factors of shunt obstruction. This study suggested that increased protein level (> 0.45 g/L) and decreased glucose level (< 2.5 mmol/L) in CSF indicated an increased risk of shunt obstruction in a patient with hydrocephalus. Thus, shunt surgery should be more carefully considered when the CSF glucose and protein were abnormal.

Deep embedding based tensor incomplete multi-view clustering

The majority of multi-view data are extracted from real life, which often lose information in some views. To solve this problem, existing incomplete multi-view clustering algorithms explore the valuable information from incomplete data while populating the missing information. Nevertheless, they suffer from the following two limitations: 1) The non-linear relationships of high-dimensional available data are not considered. 2) Although some methods utilize information from different views to fill in missing information, they still cannot precisely explore the missing information of each view. To this end, this article proposes a novel one-step incomplete multi-view framework, referred to as deep embedding based tensor incomplete multi-view clustering (DETIMC). Concretely, in this framework, the high-dimensional available data are projected into the low-dimensional embedding space by deep non-negative matrix factorization (NMF), which can obtain a clean space while capturing the complex non-linear relationship of available data. Moreover, a novel double-enhanced missing-view inferring strategy is developed, in which the weighted higher-order information of different views and the clustering structure information are simultaneously exploited. Comprehensive experiments on several benchmark datasets exhibit the superiority of DETIMC over conventional and state-of-the-art algorithms.

Unravelling the therapeutic landscape of bile acid-based therapies in gastrointestinal disorders.

Bile acids serve as endogenous ligands for nuclear and cell membrane receptors and play a crucial role in bile acid and lipid metabolism. These detergent-like compounds promote bile flow and aid in the absorption of dietary fats and fat-soluble vitamins in the intestine. Synthesized in the liver as end products of cholesterol catabolism, bile acids exhibit a chemical structure comprising a nucleus and a side chain featuring a carboxyl group, with diverse steric arrangements and potential polar substituents. Critical interactions occur between bile acid species and various nuclear and cell membrane receptors, including the farnesoid X receptor and G-protein-coupled bile acid receptor 1. This research aimed to review the literature on bile acids and their roles in treating different diseases. Currently, numerous investigations are concentrating on specific bile acid species that target nuclear receptors in the gastrointestinal system, aiming to improve the treatment of conditions such as nonalcoholic fatty liver disease. Given the global attention this topic has garnered from research groups, it is considered relatively new, thus anticipating some gaps or incomplete data. Bile acid species have a significant therapeutic promise, especially in their ability to activate or inhibit nuclear receptors, such as farnesoid X receptor. This research provides to offer essential information for scientists and medical practitioners interested in discovering new studies that underscore the importance of bile acids in ameliorating and impeding the progression of disorders. Furthermore, it opens avenues for previously overlooked bile acid-based therapies.

Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains a significant challenge. This review explores the effectiveness and challenges of using machine learning (ML) for forecasting HIV spread in high-risk areas. ML models have shown promise in identifying patterns and trends in HIV data, enabling more accurate predictions and targeted interventions. ML insights into HIV outbreak predictions leverage various data sources, including demographic, epidemiological, and behavioural data. By analysing these data, ML algorithms can identify high-risk populations and geographical areas susceptible to HIV transmission. This information is crucial for public health authorities to allocate resources efficiently and implement preventive measures effectively. Despite the potential benefits, several challenges exist in using ML for HIV outbreak predictions. These include data quality issues, such as incomplete or inaccurate data, which can affect the reliability of predictions. Additionally, the complexity of HIV transmission dynamics and the need for real-time data pose challenges for ML models. To address these challenges, researchers and practitioners are exploring innovative approaches, such as integrating multiple data sources and using advanced ML techniques. Collaborations between researchers, public health officials, and technology experts are also crucial for developing robust ML models for HIV outbreak predictions. In conclusion, while ML offers valuable insights into HIV outbreak predictions in Sub-Saharan Africa, addressing challenges such as data quality and model complexity is essential for its effective use. By overcoming these challenges, ML has the potential to significantly improve HIV prevention efforts and ultimately reduce the burden of the epidemic in the region. Keywords:   Machine Learning, AI, HIV Outbreaks: Predictions, Insights.

Biochemical risk factors and outcomes of acute promyelocytic leukemia patients with thrombotic events: a matched pair analysis.

Acute promyelocytic leukemia (APL) stands out as a distinctive form of acute leukemia, exhibiting a higher occurrence of thrombotic events when contrasted with other leukemia subtypes. Since thrombosis is a relatively rare but unfavorable condition with poor prognostic implications, it is crucial to determine the risk factors for thrombotic events in APL(thrombosis in large venous or arterial from onset to differentiation therapy in 30d). We performed a retrospective study involving 950 APL patients between January 2000 and October 2022, from which 123 were excluded by younger than 16years of age, 95 were excluded by incomplete data, and 6 were excluded by thrombosis related to CVC or PICC. A total of 23 APL patients with thrombosis for inclusion in our analysis were performed a 1:5 ratio matching based on sex (perfect match) and age (within 5years) to patients without thrombosis. These patients were continuously monitored in the outpatient department over a period of 5years. We meticulously examined clinical and laboratory data to pinpoint the risk factors related to thrombotic events in APL. Our primary clinical endpoints were all-cause mortality and achieving complete remission, while secondary clinical outcomes included APL relapse. Thrombotic events were observed in 2.4% (23/950) of APL patients. Compared to patients without thrombosis, patients with thrombosis had higher lactate dehydrogenase (LDH) [313 (223, 486) vs. 233 (188, 367) U/L, p = 0.020], higher indirect bilirubin [11.2 (7.4, 18.6) vs.8.3 (6.0, 10.7) umol/L, p = 0.004], higher creatinine [72 (62, 85) vs. 63 (54, 74) umol/L, p = 0.026], higher CD2 expression (65.2 vs. 15.2%, p < 0.001), higher CD15 expression (60.9 vs. 24.3%, p = 0.001), and PML/RARαisoforms (p < 0.001). Multivariate-logistic-regression analysis revealed several factors that were markedly related to thrombosis, including LDH (OR≈1.003, CIs≈1.000-1.006, p = 0.021), indirect bilirubin (OR≈1.084, CIs≈1.000-1.188, p = 0.043), CD2 expression positive (OR≈16.629, CIs≈4.001-62.832, p < 0.001), and CD15 expression positive (OR≈7.747, CIs≈2.005-29.941, p = 0.003). The S-type (OR≈0.012, CIs≈0.000-0.310, p = 0.008) and L-type (OR≈0.033, CIs≈0.002-0.609, p = 0.022) PML/RARα isoforms were negatively associated with thrombosis. Kaplan-Meier curves indicated that the survival rates were remarkably varied between APL patients with and without thrombosis (HR:21.34, p < 0.001). LDH and indirect bilirubin are variables significantly associated with thrombosis in APL, S-type and L-type PML/RARαisoforms exhibit a negative association with thrombotic events. The thrombotic events of APL can predict the subsequent survival of thrombosis. The findings of our study have the potential to facilitate early detection of thrombosis and enhance the prognosis for individuals with APL who develop thrombosis. Further validation of our findings will be essential through future prospective or multicenter studies.

Abstract PS10-09: Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium

Abstract Risk prediction models that have been developed for overall breast cancer risk are based on a limited number of premenopausal cases in individual cohorts. As some risk factors differ in their associations with pre- versus postmenopausal breast cancer, a distinct risk prediction model is needed for premenopausal breast cancer. We developed a risk prediction model for premenopausal breast cancer using 779,601 participants and 9,665 incident cases from 18 prospective studies within the Premenopausal Breast Cancer Collaborative Group (PBCCG), across North America (N=9), Europe (N=6), Australia (N=1), and Asia (N=2). Data were split, within each cohort, into training (2/3) and testing (1/3) datasets. Individual risk was assessed in five-year intervals, using variables reported at the start of the time interval. Cox proportional hazards regression was used to model risk factors in a backwards-selection method, stratified by cohort: age at menarche, age at first birth, parity, breastfeeding (months), height (cm), BMI (kg/m2), BMI at age 18, recent weight change (kg), alcohol consumption (drinks/week), first-degree family history of breast cancer, and personal history of benign breast disease. To enable the use of information from all cohorts despite differences in missing variables by design, cohorts were grouped by available variables and risk models were fit for each group. In cohorts with incomplete data, model coefficients were adjusted based on the correlation of covariates with the missing variables in the complete case dataset. Coefficients were meta-analyzed with inverse variance weighting to obtain final coefficients. Discrimination was evaluated in the testing dataset by calculation of the c-index. Work is ongoing to calibrate the model based on five-year absolute risk using GLOBOCAN continent- and age-specific incidence rates to represent baseline risk. The final model included age at menarche, parity, height, BMI, BMI at age 18, first-degree family history of breast cancer, and history of benign breast disease. Young adulthood BMI and BMI (at start of the 5-year risk interval) were associated with a decreased risk (Hazard Ratio (HR) (95% confidence interval (CI)) per 5 kg/m2 = 0.87 (0.81-0.93) and 0.90 (0.86-0.95), respectively) as was parity (HR (95% CI) = 0.92 (0.90-0.94)). Height was associated with increased risk (HR (95% CI) per 10 cm = 1.14 (1.07-1.21)), while history of benign breast disease and family history were associated with larger increases in risk (HR (95% CI) = 1.64 (1.30-2.06) and 1.76 (1.63-1.90), respectively). Model discrimination was higher than that reported for women under 50 years in existing breast cancer risk prediction models considering clinical factors (AUC (95% CI) = 0.61 (0.59-0.62)). Calibration of absolute 5-year risk is ongoing. Several factors driving risk prediction of postmenopausal breast cancer have similar influence on risk of premenopausal breast cancer, while family history has a stronger influence on premenopausal breast cancer risk. Our model demonstrates acceptable discrimination and will enable individual 5-year absolute risk prediction for premenopausal breast cancer. Citation Format: Kristen Brantley, Michael Jones, Minouk Schoemaker, Hazel Nichols, Anthony Swerdlow, Robert MacInnis, Roger Milne, Tess Clenenden, Yu Chen, Xiao-Ou Shu, Wei Zheng, Woon-Puay Koh, Jian-Min Yuan, Cari Kitahara, Martha Linet, Dale Sandler, Bernard A Rosner, Peter Kraft, A. Heather Eliassen. Development of an absolute risk prediction model for premenopausal breast cancer in an international consortium [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-09.

Assessment of BRCA1 and BRCA2 Germline Variant Data From Patients With Breast Cancer in a Real-World Data Registry.

The emergence of large real-world clinical databases and tools to mine electronic medical records has allowed for an unprecedented look at large data sets with clinical and epidemiologic correlates. In clinical cancer genetics, real-world databases allow for the investigation of prevalence and effectiveness of prevention strategies and targeted treatments and for the identification of barriers to better outcomes. However, real-world data sets have inherent biases and problems (eg, selection bias, incomplete data, measurement error) that may hamper adequate analysis and affect statistical power. Here, we leverage a real-world clinical data set from a large health network for patients with breast cancer tested for variants in BRCA1 and BRCA2 (N = 12,423). We conducted data cleaning and harmonization, cross-referenced with publicly available databases, performed variant reassessment and functional assays, and used functional data to inform a variant's clinical significance applying American College of Medical Geneticists and the Association of Molecular Pathology guidelines. In the cohort, White and Black patients were over-represented, whereas non-White Hispanic and Asian patients were under-represented. Incorrect or missing variant designations were the most significant contributor to data loss. While manual curation corrected many incorrect designations, a sizable fraction of patient carriers remained with incorrect or missing variant designations. Despite the large number of patients with clinical significance not reported, original reported clinical significance assessments were accurate. Reassessment of variants in which clinical significance was not reported led to a marked improvement in data quality. We identify the most common issues with BRCA1 and BRCA2 testing data entry and suggest approaches to minimize data loss and keep interpretation of clinical significance of variants up to date.