Likelihood Ratio Research Articles

A nursing note-aware deep neural network for predicting mortality risk after hospital discharge

BackgroundICU readmissions and post-discharge mortality pose significant challenges. Previous studies used EHRs and machine learning models, but mostly focused on structured data. Nursing records contain crucial unstructured information, but their utilization is challenging. Natural language processing (NLP) can extract structured features from clinical text. This study proposes the Crucial Nursing Description Extractor (CNDE) to predict post-ICU discharge mortality rates and identify high-risk patients for unplanned readmission by analyzing electronic nursing records. ObjectiveDeveloped a deep neural network (NurnaNet) with the ability to perceive nursing records, combined with a bio-clinical medicine pre-trained language model (BioClinicalBERT) to analyze the electronic health records (EHRs) in the MIMIC III dataset to predict the death of patients within six month and two year risk. DesignA cohort and system development design was used. Setting(s)Based on data extracted from MIMIC-III, a database of critically ill in the US between 2001 and 2012, the results were analyzed. ParticipantsWe calculated patients' age using admission time and date of birth information from the MIMIC dataset. Patients under 18 or over 89 years old, or who died in the hospital, were excluded. We analyzed 16,973 nursing records from patients' ICU stays. MethodsWe have developed a technology called the Crucial Nursing Description Extractor (CNDE), which extracts key content from text. We use the logarithmic likelihood ratio to extract keywords and combine BioClinicalBERT. We predict the survival of discharged patients after six months and two years and evaluate the performance of the model using precision, recall, the F1-score, the receiver operating characteristic curve (ROC curve), the area under the curve (AUC), and the precision–recall curve (PR curve). ResultsThe research findings indicate that NurnaNet achieved good F1-scores (0.67030, 0.70874) within six months and two years. Compared to using BioClinicalBERT alone, there was an improvement in performance of 2.05 % and 1.08 % for predictions within six months and two years, respectively. ConclusionsCNDE can effectively reduce long-form records and extract key content. NurnaNet has a good F1-score in analyzing the data of nursing records, which helps to identify the risk of death of patients after leaving the hospital and adjust the regular follow-up and treatment plan of relevant medical care as soon as possible.

Assessing MRI Sensitivity and Specificity for Detecting Occult Nipple-Areolar Complex Malignancy in Nipple-Sparing Mastectomy Candidates: A Systematic Review and Meta-Analysis

Background: Approximately 8% of breast cancers originate within the central ducts near the nipple-areolar complex(NAC), which revealed higher rates of occult involvement than previously thought. Precise preoperative imaging to assess NAC and subjacent tumor involvement has become critical to identify candidates for nipple-sparing mastectomy(NSM) while ensuring oncological safety. The aim of our study is to determine whether MRI could be used for the detection of subtle neoplastic infiltration. Method: Electronic databases including PubMed, Scopus, Web of Science, and EMBASE were systematically searched to identify all relevant studies on diagnostic performance of Magnetic Resonance Imaging(MRI) in malignant extension to the nipple published before 2024. The keywords include MRI, NAC, breast cancer, NSM. True Positive(TP), True Negative(TN), False Positive(FP), False Negative(FN) were extracted for analysis. Finally, 5 articles were selected for our meta-analysis. STATA version 15 was used to analysis data. Result: Our meta-analysis showed a pooled sensitivity of 89%(95% CI: 81%-94%)with low heterogeneity(I2 = 17.51%), and a pooled specificity of 89% (95% CI: 71%-96%), with significant heterogeneity(I2 = 95.87%). The pooled positive diagnostic likelihood ratio(pDLR) was 7.81(95% CI: 2.82-21.61, I2 = 92.66%), and the negative diagnostic likelihood ratio(nDLR) was 0.12(95% CI: 0.07-0.22, I2 = 11.94%). Conclusion: Diagnostic MRI characteristics such as NAC enhancement, non-mass enhancement(NME) type, mass size (greater than 20 mm), and tumor-to-nipple distance(TND) were assocaited with the presence of NAC involement. Because of high diagnostic accuracy, MRI imaging can be used as a helpful tool for NAC diagnosis.

Adaptive target detection for an FDA-MIMO radar in a mainlobe deceptive jamming and a partially homogeneous noise

In this paper, we investigate the adaptive target detection problem for a collocated frequency diverse array multiple-input multiple-output (FDA-MIMO) radar in the presence of mainlobe deceptive jamming and partially homogeneous noise with an unknown covariance matrix and scaling factor. Precisely, we establish a received signal model that includes the true target and false counterparts echoes in the presence of Gaussian noise (it includes thermal noise, interference suppression, and clutter after range compensation.). Furthermore, we consider that the range information of the true target and mainlobe deceptive targets is not identical. Therefore, we project the corresponding transmit steering vectors onto different subspaces with known subspace matrices but unknown coordinates. On the other hand, the true and false targets' receive steering vectors are considered to be from the same subspace. Finally, following the generalized likelihood ratio test (GLRT), the Rao and the Wald criteria, we design three adaptive detectors with two-step criteria, i.e., TGLRT, TRao, and TWald. We note that the proposed detectors maintain a constant false alarm rate (CFAR) against the scaling factor and the noise covariance matrix. The extensive numerical simulation results have validated the effectiveness of the proposed detectors.

Prediction of the treatment response and local failure of patients with brain metastasis treated with stereotactic radiosurgery using machine learning: A systematic review and meta-analysis.

Stereotactic radiosurgery (SRS) effectively treats brain metastases. It can provide local control, symptom relief, and improved survival rates, but it poses challenges in selecting optimal candidates, determining dose and fractionation, monitoring for toxicity, and integrating with other modalities. Practical tools to predict patient outcomes are also needed. Machine learning (ML) is currently used to predict treatment outcomes. We aim to investigate the accuracy of ML in predicting treatment response and local failure of brain metastasis treated with SRS. PubMed, Scopus, Web of Science (WoS), and Embase were searched until April 16th, which was repeated on October 17th, 2023 to find possible relevant papers. The study preparation adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. The statistical analysis was performed by the MIDAS package of STATA v.17. A total of 17 articles were reviewed, of which seven and eleven were related to the clinical use of ML in predicting local failure and treatment response. The ML algorithms showed sensitivity and specificity of 0.89 (95% CI: 0.84-0.93) and 0.87 (95% CI: 0.81-0.92) for predicting treatment response. The positive likelihood ratio was 7.1 (95% CI: 4.5-11.1), the negative likelihood ratio was 0.13 (95% CI: 0.08-0.19), and the diagnostic odds ratio was 56 (95% CI: 25-125). Moreover, the pooled estimates for sensitivity and specificity of ML algorithms for predicting local failure were 0.93 (95% CI: 0.76-0.98) and 0.80 (95% CI: 0.53-0.94). The positive likelihood ratio was 4.7 (95% CI: 1.6-14.0), the negative likelihood ratio was 0.09 (95% CI: 0.02-0.39), and the diagnostic odds ratio was 53 (95% CI: 5-606). ML holds promise in predicting treatment response and local failure in brain metastasis patients receiving SRS. However, further studies and improvements in the treatment process can refine the models and effectively integrate them into clinical practice.

Prediction quality of patients’ composited outcomes of interest and how much variations on the outcomes explained by the patients’ sodium levels

Background: Hypernatremia can be caused by medications, glucose/urea-related hyperosmolarity, overdiuresis, compartmental fluid distribution imbalances, iatrogenic hypertonic saline excess, renal concentrating performance defects, overdiuresis, and hypermetabolic states. Patients who are in critical condition, encompassing ailments of the heart, liver, lungs, brain, and kidneys, frequently manifest hyponatremia (sodium < 135 mEq/L). Similar to hypernatremia, hyponatremia signifies the severity of the illness and a bleak prognosis. Hyponatremia is less lethal than central pontine myelinolysis, which overcorrects sodium concentrations urgently. Aim: The objective of this research endeavour was to investigate the strength of correlations, the extent to which the independent variables account for the overall variability in the dependent variable, and the accuracy of the dependent variable's predictions. In addition, the purpose of this test was to extract the coefficients required to present the Binary Logistic Regression models that were investigated. Methods: Between May 2018 and May 2021, this study examined two Na-based prognosticators for overall mortality in critically ill patients admitted to the King Hussein Medical Centre. An investigation was conducted on 2155 cases utilising Receiver Operating Characteristic (ROC) and Sensitivity Indices Test analyses to ascertain the most effective cut-off points, specificities, sensitivity, predictive values, likelihood ratios, Youden and accuracy indices, and mortality rates. The research discovered that serial termination points with lower values suggested more robust evidence for a positive actual state, whereas higher values suggested less robust evidence for a negative actual state. A Binary Logistic Regression (BLgR) test was performed to examine correlations, the extent of total variations in the dependent variable, and the accuracy of the prediction for each Na-related mortality prognosticator in relation to overall mortality. The analysis was conducted utilising version 25 of SPSS for Windows. Results: The research revealed that the AUROC for cNa was considerably greater in magnitude compared to its prognosticator that is genealogically related to Na. 129.35 mEq/l and 131.05 mEq/l were the optimal cut-off points, TPRs, TNRs, PPVs, NLRs, YIs, and AIs for the two comparative Na investigated statutes. The BLgR models were developed in order to simulate the relationship between the corrected and measured Na levels of patients and their overall mortality rate. For critically ill patients, the probabilities of overall mortality at the optimal cutoff operating Na levels were 79.59 and 81.62 percent, respectively.

Predictive value of the Ranson and BISAP scoring systems for the severity and prognosis of acute pancreatitis: A systematic review and meta-analysis.

To systematically assess and compare the predictive value of the Ranson and Bedside Index of Severity in Acute Pancreatitis (BISAP) scoring systems for the severity and prognosis of acute pancreatitis (AP). PubMed, Embase, Cochrane Library, and Web of Science were systematically searched until February 15, 2023. Outcomes in this analysis included severity and prognosis [mortality, organ failure, pancreatic necrosis, and intensive care unit (ICU) admission]. The revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate the quality of diagnostic accuracy studies. The threshold effect was evaluated for each outcome. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic (SROC) curve (AUC) as well as 95% confidence intervals (CI) were calculated. The DeLong test was used for AUC comparisons. For the outcome evaluated by over 9 studies, publication bias was assessed using the Deeks' funnel plot asymmetry test. Totally 17 studies of 5476 AP patients were included. For severity, the pooled sensitivity of the Ranson and BISAP was 0.95 (95%CI: 0.87, 0.98) and 0.67 (95%CI: 0.27, 0.92); the pooled specificity of the Ranson and BISAP was 0.74 (0.52, 0.88) and 0.95 (95%CI: 0.85, 0.98); the pooled AUC of the Ranson and BISAP was 0.95 (95%CI: 0.93, 0.97) and 0.94 (95%CI: 0.92, 0.96) (P = 0.480). For mortality, the pooled sensitivity of the Ranson and BISAP was 0.89 (95%CI: 0.73, 0.96) and 0.77 (95%CI: 0.58, 0.89); the pooled specificity of the Ranson and BISAP was 0.79 (95%CI: 0.68, 0.87) and 0.90 (95%CI: 0.86, 0.93); the pooled AUC of the Ranson and BISAP was 0.91 (95%CI: 0.88, 0.93) and 0.92 (95%CI: 0.90, 0.94) (P = 0.480). For organ failure, the pooled sensitivity of the Ranson and BISAP was 0.84 (95%CI: 0.76, 0.90) and 0.78 (95%CI: 0.60, 0.90); the pooled specificity of the Ranson and BISAP was 0.84 (95%CI: 0.63, 0.94) and 0.90 (95%CI: 0.72, 0.97); the pooled AUC of the Ranson and BISAP was 0.86 (95%CI: 0.82, 0.88) and 0.90 (95%CI: 0.87, 0.93) (P = 0.110). For pancreatic necrosis, the pooled sensitivity of the Ranson and BISAP was 0.63 (95%CI: 0.35, 0.84) and 0.63 (95%CI: 0.23, 0.90); the pooled specificity of the Ranson and BISAP was 0.90 (95%CI: 0.77, 0.96) and 0.93 (95%CI: 0.89, 0.96); the pooled AUC of the Ranson and BISAP was 0.87 (95%CI: 0.84, 0.90) and 0.93 (95%CI: 0.91, 0.95) (P = 0.001). For ICU admission, the pooled sensitivity of the Ranson and BISAP was 0.86 (95%CI: 0.77, 0.92) and 0.63 (95%CI: 0.52, 0.73); the pooled specificity of the Ranson and BISAP was 0.58 (95%CI: 0.55, 0.61) and 0.84 (95%CI: 0.81, 0.86); the pooled AUC of the Ranson and BISAP was 0.92 (95%CI: 0.81, 1.00) and 0.86 (95%CI: 0.67, 1.00) (P = 0.592). The Ranson score was an applicable tool for predicting severity and prognosis of AP patients with reliable diagnostic accuracy in resource and time-limited settings. Future large-scale studies are needed to verify the findings.

Diagnostic accuracy of the Enferplex Bovine TB antibody test using individual milk samples from cattle.

Bovine tuberculosis is usually diagnosed using tuberculin skin tests or at post-mortem. Recently, we have developed a serological test for bovine tuberculosis in cattle which shows a high degree of accuracy using serum samples. Here, we have assessed the performance of the test using individual bovine milk samples. The diagnostic specificity estimate using the high sensitivity setting of the test was 99.7% (95% CI: 99.2-99.9). This estimate was not altered significantly by tuberculin boosting. The relative sensitivity estimates of the test using the high sensitivity setting in milk samples from comparative skin test positive animals was 90.8% (95% CI: 87.1-93.6) with boosting. In animals with lesions, the relative sensitivity was 96.0% (95% CI: 89.6-98.7). Analysis of paired serum and milk samples from skin test positive animals showed correlation coefficients ranging from 0.756-0.955 for individual antigens used in the test. Kappa analysis indicated almost perfect agreement between serum and milk results, while McNemar marginal homogeneity analysis showed no statistically significant differences between the two media. The positive and negative likelihood ratio were 347.8 (95% CI: 112.3-1077.5) and 0.092 (95% CI: 0.07-0.13) respectively for boosted samples from skin test positive animals. The results show that the test has high sensitivity and specificity in individual milk samples and thus milk samples could be used for the diagnosis of bovine tuberculosis.

Blood Urea Nitrogen-Serum Albumin Ratio As a Predictor of Clinical Outcome in Acute Ischemic Stroke

Stroke is a global health issue with high morbidity and fatality rates. Treatment decisions for ischemic stroke rely on clinical outcome prediction. The NIHSS score is commonly used to predict the AIS's clinical outcomes. However, its use is hindered by its complexity, which requires the assessment of many variables. Therefore, there is an urgent need to develop a simpler yet still effective evaluation tool in assessing the AIS’s clinical outcome. The study aimed to predict AIS outcomes using the blood urea nitrogen to albumin serum ratio (RBA). This study was a prospective cohort study design, with subjects being first-time AIS patients hospitalized at RSUP Dr. Wahidin Sudirohusodo and several hospitals in Makassar from December 2022–April 2023. Demographic and clinical data were collected, assessing RBA and comparing it according to the good (mRS 0-2) and poor (mRS 3-6) outcome groups. RBA performance was assessed by receiver operating characteristic curve (ROC) analysis to assess the AIS's clinical outcome. The study's findings on 62 participants meeting the criteria revealed a significant association between RBA and the AIS's clinical outcome (p-value=0,0004). The mean RBA differed significantly between the good and poor AIS outcome groups (p-value =0,006). In the ROC analysis, the area under the curve (AUC) was 0,75 with an optimal cut-off value was 2,05, yielding a sensitivity of 41,67%, a specificity of 98%, and a likelihood ratio of 20,83 (p-value=0,007). In conclusion, RBA can be used as a simple and objective tool to determine the clinical outcome predictors of AIS.

The Distribution of Risk Across Healthcare Providers and Reducing Misclassification

Four linear multilevel mixed-risk models were compared using model assumption tests and predictions. Models varied by the number of random intercepts from 1 to 4, producing 2-level through 5-level models of the same measure, operative time. Normality of the dependent variable and residuals, variance homoscedasticity, level-1, and level-2 exogeneity were tested using the robust test of the level-1 residuals variance by surgeon, estimates of density, skew, and the Hausman test. Measure (operative time by hospital and surgeon) aberrancy and risk classification were evaluated using traditional methods and used to assess distribution measures. The dependent variable and the level-1 residuals required transformation for linearity and variance stabilization, respectively. Normality criteria were met for both level-1 and level-2 residuals and standardized residuals. The likelihood ratio comparing the four models was significantly larger for the 5-level (1016.1; P<0.00005) model than the likelihood ratio for the four-level and other models. Shrinkage was greatest for the 2-level model (0.039; P<0.00005) and least for the 5-level model (0.028; P<0.00005). Level-1 variance homoscedasticity was confirmed by the robust variance test across all models (P>F=1). Aberrant value detection did not require the exclusion of any observations, while prediction intervals revealed low or high risk for 54.2% of surgeons for the 2-level model and 8.6% for the 5-level model. The traditional (c2 = -11.01; P=1) and instrumental variable (c2 = 21.06; P=1) Hausman tests show that the null hypothesis cannot be rejected for level-1 or level-2 exogeneity. Once level-1 and level-2 exogeneity was confirmed, and since deconfounding was a model consideration, causal inferential capacity was assumed. The likelihood ratio, residual variance, shrinkage, and predictions show that the 5-level model is preferred to the other models.

CAVaLRi: An Algorithm for Rapid Identification of Diagnostic Germline Variation

Clinical exome and genome sequencing (ES/GS) have become indispensable diagnostic tools for rare genetic diseases (RGD). However, the interpretation of ES/GS presents a substantial operational challenge in clinical settings. Test interpretation requires the review of hundreds of genetic variants, a task that has become increasingly challenging given the rising use of ES/GS. In response, we present Clinical Assessment of Variants by Likelihood Ratios (CAVaLRi), which employ a modified likelihood ratio (LR) framework to assign diagnostic probabilities to candidate germline disease genes. CAVaLRi models aspects of the clinical variant assessment process, taking into consideration the predicted impact of the variant, the proband and parental genotypes, and the proband’s clinical characteristics. It also factors in computational phenotype noise and weighs the relative significance of genotype, phenotype, and variant segregation information. We trained and tested CAVaLRi on variant and phenotype data from an internal cohort of 655 clinical ES cases. For validation, CAVaLRi’s performance was benchmarked against four leading gene prioritization algorithms (Exomiser’s hiPHIVE and PhenIX prioritizers, LIRICAL, and XRare) using a distinct cohort of 12,832 ES cases. Our findings reveal that CAVaLRi significantly outperforms its counterparts when clinician-curated phenotype sets are used, as evidenced by its superior precision-recall curve (PR AUC: 0.701) and average diagnostic gene rank (1.59). Notably, even when substituting highly focused clinician-curated phenotype sets with large and potentially nonspecific computationally derived phenotypes, CAVaLRi retains its precision (PR AUC: 0.658; diagnostic gene average rank: 1.68) and markedly outperforms other tools. In a large, heterogeneous validation cohort, CAVaLRi stood out as the most precise prioritization algorithm (PR AUC: 0.335; average diagnostic rank: 1.91). In conclusion, CAVaLRi presents a robust solution for prioritizing diagnostic genes, surpassing current methods. It demonstrates resilience to noisy, computationally-derived phenotypes, providing a scalable strategy to help labs focus on the most diagnostically relevant variants, thus addressing the growing demand for ES/GS interpretation.

Serum Ferritin Level Linked to Male Type-2 Diabetes Mellitus Patients Amongst Residents of Hyderabad, Sindh

Introduction: Diabetes mellitus is a metabolic disorder which is caused by either deficiency of insulin or its disturbed action on peripheral receptors. Globally it is estimated that 451 million peoples were suffering from Diabetes mellitus type 2 in 2017 and this burden will rise to 693million in 2045.The relationship between diabetes mellitus and serum ferritin levels is intricately linked, sharing a common intersection in the liver where both glucose metabolism and iron metabolism converge. Aims and Objectives: To measure the serum ferritin levels among residents of Hyderabad with type 2 Diabetes Mellitus. Place and Duration of study: The study was conducted in the Outpatient Department (OPD) of Liaquat university Hospital from June 2022 to August 2022 (3 months). Material and Methods: The study consists of 100 subjects divided into two groups, a control group consists of 50 normal healthy people with normal fasting glucose below 100mg/dl and a Case group consists of 50 patients with fasting glucose level more than 125 mg/dl according to World Health Organization criteria. Their Serum ferritin levels were evaluated using the ELFA method, employing a commercially available kit supplied by Roche Cobas Integra. The results were analyzed on the software Graph Pad Prism 9, p-value ? 0.05was taken as significant. Results: The frequency of Type 2 Diabetes mellitus was significantly higher in patients with increased serum ferritin levels (P=0.001) with an odds ratio of 1.838 , a sensitivity/specifity value of (0.50/0.56) and a robust likelihood ratio of 2.25. Conclusion: The study underscores a potential link between elevated serum ferritin and Diabetes Mellitus Type-2.

Likelihood Ratio Test and the Evidential Approach for 2 × 2 Tables.

Categorical data analysis of 2 × 2 contingency tables is extremely common, not least because they provide risk difference, risk ratio, odds ratio, and log odds statistics in medical research. A χ2 test analysis is most often used, although some researchers use likelihood ratio test (LRT) analysis. Does it matter which test is used? A review of the literature, examination of the theoretical foundations, and analyses of simulations and empirical data are used by this paper to argue that only the LRT should be used when we are interested in testing whether the binomial proportions are equal. This so-called test of independence is by far the most popular, meaning the χ2 test is widely misused. By contrast, the χ2 test should be reserved for where the data appear to match too closely a particular hypothesis (e.g., the null hypothesis), where the variance is of interest, and is less than expected. Low variance can be of interest in various scenarios, particularly in investigations of data integrity. Finally, it is argued that the evidential approach provides a consistent and coherent method that avoids the difficulties posed by significance testing. The approach facilitates the calculation of appropriate log likelihood ratios to suit our research aims, whether this is to test the proportions or to test the variance. The conclusions from this paper apply to larger contingency tables, including multi-way tables.

Comparison of the Applied Biosystems® SeqStudio™ and 3500 Genetic Analyzers

ABSTRACT We report on a comparison of the performance of the Applied Biosystems® SeqStudioTM Genetic Analyzer with the 3500 Genetic Analyzer when analysing trace DNA samples. Totally, 162 profiles generated from ‘trace DNA’ were analysed on both instruments with comparable number of alleles detected, relative fluorescence units (RFU) and likelihood ratio (LR) values. The SeqStudioTM was found to be suitable for the analysis of trace DNA samples.

An Adaptive Tracking Method for Moving Target in Fluctuating Reverberation Environment

In environments with a low signal-to-reverberation ratio (SRR) characterized by fluctuations in clutter number and distribution, particle filter-based tracking methods may experience significant fluctuations in the posterior probability of existence. This can lead to interruptions or even loss of the target trajectory. To address this issue, an adaptive PF-based tracking method (APF) with joint reverberation suppression is proposed. This method establishes the state space model under the Bayesian framework and implements it through particle filtering. To keep the weak target echoes, all the non-zero entries contained in the sparse matrix processed by the low-rank and sparsity decomposition (LRSD) are treated as the measurements. The prominent feature of this approach is introducing an adaptive measurement likelihood ratio (AMLR) into the posterior update step, which solves the problem of unstable tracking due to the strong fluctuation in the number of point measurements per frame. The proposed method is verified by four shallow water experimental datasets obtained by an active sonar with a uniform horizontal linear array. The results demonstrate that the tracking frame success ratio of the proposed method improved by over 14% compared with the conventional PF tracking method.

Association of whole-person eigen-polygenic risk scores with Alzheimer's disease.

Late-Onset Alzheimer's Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors. Here we describe the "whole person" genetic risk landscape of polygenic risk scores for 2218 traits in 2044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve the prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) from these clusters significantly improved LOAD-related phenotypes prediction over current state-of-the-art LOAD PRS models. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of the brain-wide beta-amyloid burden by 1.7% (likelihood ratio test P = 9.02 × 10-7). All associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci. However, our association analysis identified modules characterized by PRSs of high cholesterol and LOAD. We believe this is due to the influence of the APOE region from both PRSs. We found significantly higher mean SNP effects for LOAD in the intersecting APOE region SNPs. Combining genetic risk factors for vascular traits and dementia could improve current single-trait PRS models of LOAD, enhancing the use of PRS in risk stratification. Our results are catalogued for the scientific community, to aid in generating new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes.

Prognostic nutritional index as a predictive marker for acute kidney injury in adult critical illness population: a systematic review and diagnostic test accuracy meta-analysis

BackgroundThe prognostic nutritional index (PNI), integrating nutrition and inflammation markers, has been increasingly recognized as a prognostic predictor in diverse patient cohorts. Recently, its effectiveness as a predictive marker for acute kidney injury (AKI) in various clinical settings has gained attention. This study aims to assess the predictive accuracy of the PNI for AKI in critically ill populations through systematic review and meta-analysis.MethodsA systematic review was conducted using the databases MEDLINE, EMBASE, PubMed, and China National Knowledge Infrastructure up to August 2023. The included trials reported the PNI assessment in adult population with critical illness and its predictive capacity for AKI. Data on study characteristics, subgroup covariates, and diagnostic performance of PNI, including sensitivity, specificity, and event rates, were extracted. A diagnostic test accuracy meta-analysis was performed. Subgroup analyses and meta-regression were utilized to investigate the sources of heterogeneity. The GRADE framework evaluated the confidence in the meta-analysis’s evidence.ResultsThe analysis encompassed 16 studies with 17 separate cohorts, totaling 21,239 patients. The pooled sensitivity and specificity of PNI for AKI prediction were 0.67 (95% CI 0.58–0.74) and 0.74 (95% CI 0.67–0.80), respectively. The pooled positive likelihood ratio was 2.49 (95% CI 1.99–3.11; low certainty), and the negative likelihood ratio was 0.46 (95% CI 0.37–0.56; low certainty). The pooled diagnostic odds ratio was 5.54 (95% CI 3.80–8.07), with an area under curve of summary receiver operating characteristics of 0.76. Subgroup analysis showed that PNI’s sensitivity was higher in medical populations than in surgical populations (0.72 vs. 0.55; p < 0.05) and in studies excluding patients with chronic kidney disease (CKD) than in those including them (0.75 vs. 0.56; p < 0.01). Overall, diagnostic performance was superior in the non-chronic kidney disease group.ConclusionOur study demonstrated that PNI has practical accuracy for predicting the development of AKI in critically ill populations, with superior diagnostic performance observed in medical and non-CKD populations. However, the diagnostic efficacy of the PNI has significant heterogeneity with different cutoff value, indicating the need for further research.

Incidence and prevalence of dementia: A 2015-2020 population-based study in the Campania Region of Italy.

To provide population-based estimates of prevalence and incidence of any dementia and Alzheimer's dementia in the Campania Region (South Italy), and to validate towards a clinical registry. Population-based study, using routinely collected healthcare data of individuals living in the Campania Region (South Italy) from 2015 to 2020. We included individuals aged ≥65 years alive at the prevalence day (1 January 2021) who had at least one administrative record for dementia and/or Alzheimer's dementia from 2015 to 2020. Age-and sex-standardised prevalence rates were calculated using direct standardisation method (European population in 2020 as reference population). To estimate incidence, we tested three possible algorithms, which differed for the duration of the time interval between study baseline (Jan 1, 2015) and index date (first record for dementia and/or Alzheimer's dementia in administrative databases). We employed a clinical database for the validation of our algorithms towards neuropsychological test results. Among individuals aged over 65, 80,392 had dementia, of which 35,748 had Alzheimer's dementia. The age- and sex-standardized prevalence rates per 1,000 individuals for any dementia and Alzheimer's dementia were 77.64 (95%CI = 77.57; 77.68) and 34.05 (95%CI = 34.01; 34.09), respectively. There were 82.10 incident of any dementia cases per 100,000 per year (0.79 sensitivity and 0.62 specificity), and 59.89 incident cases of any dementia per 100,000 per year (0.80 sensitivity and 0.59 specificity). The capture-recapture method showed very low number of undetected cases (1.7% for any dementia and 3.0% for Alzheimer's dementia). Our algorithms showed acceptable performance with AUC ranging from 0.59 to 0.72, and double likelihood ratio of correctly identifying individuals above and below MMSE standard cut-offs (24 and 26). Prevalence and incidence of any dementia and Alzheimer's dementia in the Campania Region (South Italy) from 2015 to 2020 are in line with previous estimates from other countries. Our algorithm, integrating administrative and clinical data, holds potential for assessing dementia's epidemiological burden, identifying risk factors, planning healthcare access, and developing prevention strategies.

Specific Gravity Improves Identification of Clinically Significant Quantitative Proteinuria from the Dipstick Urinalysis.

CKD is often underdiagnosed during early stages when GFR is preserved due to underutilization of testing for quantitative urine albumin-to-creatinine ratio (UACR) or protein-to-creatinine ratio (UPCR). Semi-quantitative dipstick proteinuria (DSP) on urinalysis is widely obtained but not accurate for identifying clinically significant proteinuria. We identified all patients with a urinalysis and UACR or UPCR obtained on the same day at a tertiary referral center. The accuracy of DSP alone or in combination with specific gravity against a gold-standard of UACR ≥30 mg/g or UPCR ≥0.15 g/g, characterizing clinically significant proteinuria, was evaluated using logistic regression. Models were internally validated using 10-fold cross validation. The specific gravity for each DSP above which significant proteinuria is unlikely was determined. Of 11,229 patients, clinically significant proteinuria was present in 4,073 (36%). The area under the receiver operating characteristic curve (95% confidence interval) was 0.77 (0.76, 0.77) using DSP alone and 0.82 (0.82, 0.83) in combination with specific gravity (P<0.001), yielding a specificity of 0.93 (standard error, SE=0.02) and positive likelihood ratio of 9.52 (SE=0.85). The optimal specific gravity cut-offs to identify significant proteinuria were ≤1.0012, 1.0238, and 1.0442, for DSP of trace, 30, and 100 mg/dL. At any specific gravity, a DSP ≥300 mg/dL was extremely likely to represent significant proteinuria. Adding specific gravity to DSP improves recognition of clinically significant proteinuria and can be easily used to identify patients with early-stage CKD who may not have otherwise received a quantified proteinuria measurement for both clinical and research purposes.

Effectiveness of MRI in Detecting and Diagnosing Brain Tumors in Children: A Meta-Analysis

Introduction: Brain tumors are one of the most common types of cancer in children. Accurate and timely diagnosis is essential to determine optimal treatment and improve patient prognosis. Magnetic resonance imaging (MRI) has become one of the main imaging modalities for detecting and diagnosing brain tumors in children. Methods: A meta-analysis was conducted to evaluate the effectiveness of MRI in detecting and diagnosing brain tumors in children. Twelve studies that met the inclusion criteria were evaluated for sensitivity, specificity, positive predictive value (NPV), negative predictive value (NPV), and positive likelihood ratio (LR+). Results: The results of the meta-analysis showed that MRI had a sensitivity of 92.5% (95% CI: 86.4% - 96.7%) and a specificity of 97.3% (95% CI: 94.2% - 99.0%) for detecting brain tumors in children. NPV and LRP+ were 97.0% (95% CI: 93.8% - 99.2%) and 33.1 (95% CI: 11.8 - 117.2), respectively. Conclusion: MRI is recommended as the imaging modality of choice for detecting and diagnosing brain tumors in children.

Evaluation of magnetic resonance imaging to detect cholesteatoma in clinical healthcare

Background Non-Echo-Planar Diffusion Weighed Magnetic Resonance Imaging (non-EPI DW MRI) plays a role in the planning of cholesteatoma surgery. Objectives To outline the usage of Non-EPI DW MRI in cholesteatoma diagnostics and to determine its accuracy, using otosurgery diagnostics as reference standard. Material and Methods A retrospective descriptive study. All subjects operated, with suspicion of cholesteatoma as indication, that had a preceding examination with non-EPI DW MRI, between October 2010 and March 2019. Calculating sensitivity, specificity, predicative values, and likelihood ratios, using non-EPI DW MRI as index test and diagnosis from otosurgery as reference standard. Results Fifty-two subject episodes were included. Non-EPI DW MRI had a sensitivity of 0.50, specificity of 0.75, positive and negative predictive values of 0.74 and 0.52 and, positive and negative likelihood ratios of 2.0 and 0.67 respectively. Conclusions and Significance There is a clear annual trend with increased numbers of executed examinations during the study period. The diagnostic accuracy of non-EPI DW MRI does not reach acceptable levels in the existing everyday routine practice conditions. The accuracy of the examination increases when interpreted by an experienced radiologist and when using the definition of cholesteatoma recommended by EAONO/JOS.