Value Likelihood Ratio Research Articles

Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study.

Preeclampsia is a potentially life-threatening pregnancy complication. Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML] model, and the logistic regression-based fullPIERS model) accurately identify individuals at greatest or least risk of adverse maternal outcomes within 48 h following admission. Both models were developed and validated to be used as part of initial assessment. In the United Kingdom, the National Institute for Health and Care Excellence (NICE) recommends repeated use of such static models for ongoing assessment beyond the first 48 h. This study evaluated the models' performance during such consecutive prediction. This multicountry prospective study used data of 8,843 women (32% white, 30% black, and 26% Asian) with a median age of 31 years. These women, admitted to maternity units in the Americas, sub-Saharan Africa, South Asia, Europe, and Oceania, were diagnosed with preeclampsia at a median gestational age of 35.79 weeks between year 2003 and 2016. The risk differentiation performance of the PIERS-ML and fullPIERS models were assessed for each day within a 2-week post-admission window. The PIERS adverse maternal outcome includes one or more of: death, end-organ complication (cardiorespiratory, renal, hepatic, etc.), or uteroplacental dysfunction (e.g., placental abruption). The main outcome measures were: trajectories of mean risk of each of the uncomplicated course and adverse outcome groups; daily area under the precision-recall curve (AUC-PRC); potential clinical impact (i.e., net benefit in decision curve analysis); dynamic shifts of multiple risk groups; and daily likelihood ratios. In the 2 weeks window, the number of daily outcome events decreased from over 200 to around 10. For both PIERS-ML and fullPIERS models, we observed consistently higher mean risk in the adverse outcome (versus uncomplicated course) group. The AUC-PRC values (0.2-0.4) of the fullPIERS model remained low (i.e., close to the daily fraction of adverse outcomes, indicating low discriminative capacity). The PIERS-ML model's AUC-PRC peaked on day 0 (0.65), and notably decreased thereafter. When categorizing women into multiple risk groups, the PIERS-ML model generally showed good rule-in capacity for the "very high" risk group, with positive likelihood ratio values ranging from 70.99 to infinity, and good rule-out capacity for the "very low" risk group where most negative likelihood ratio values were 0. However, performance declined notably for other risk groups beyond 48 h. Decision curve analysis revealed a diminishing advantage for treatment guided by both models over time. The main limitation of this study is that the baseline performance of the PIERS-ML model was assessed on its development data; however, its baseline performance has also undergone external evaluation. In this study, we have evaluated the performance of the fullPIERS and PIERS-ML models for consecutive prediction. We observed deteriorating performance of both models over time. We recommend using the models for consecutive prediction with greater caution and interpreting predictions with increasing uncertainty as the pregnancy progresses. For clinical practice, models should be adapted to retain accuracy when deployed serially. The performance of future models can be compared with the results of this study to quantify their added value.

DIGITAL APPLICATION FOR LIFESTYLE PROMOTION IN PREDIABETES MANAGEMENT: A CASE STUDY IN BANDUNG CITY

Background: Prediabetes is a condition where blood glucose levels are elevated but not high enough to be classified as diabetes. This condition is a significant global health challenge, especially in urban areas like Bandung. Effective prevention relies on lifestyle changes, which can be supported through accessible digital applications. Objectives: To develop an accurate digital app model to support lifestyle changes in prediabetes and be able to differentiate well between various user groups. Method: Data were collected from respondents regarding age, diabetes history, daily activity, and Body Mass Index (BMI). The model's effectiveness was assessed using Omnibus Tests of Model Coefficients, with accuracy evaluated through the Likelihood Ratio Test and ROC Curve analysis. Results: The majority of respondents were under 45 years of old (88.1%) and female (72.2%). Most had no diabetes history (92.1%) and were active daily (71.4%), with normal BMI in 57.9% of participants. Model testing revealed significant outcomes, with a p-value of 0.00 and an Asymptotic Significance of 0.006, alongside a likelihood ratio value of 81.962, underscoring the model’s accuracy and reliability. Conclusion: The developed digital application is both accurate and reliable for promoting lifestyle changes in prediabetes.

ANALISIS BACKTESTING UNTUK VALUE AT RISK METODE EKSPANSI CORNISH-FISHER DENGAN UJI KUPIEC

Returns and risks are important to pay attention before investing. Value at Risk (VaR) is a tool for measuring risk. VaR measures the worst-case loss at a certain level of confidence. Methodology and return distribution assumptions are important in estimating VaR. The classic VaR method can only be used for normally distributed returns. Cornish-Fisher Expansion (CFE) VaR doesn’t require distributional assumptions. VaR CFE takes into account skewness and kurtosis. The Covid-19 pandemic had a negative impact, but several companies still carrying out Initial Public Offerings (IPO). This research uses data on closing prices of PT Saraswanti Anugerah Makmur Tbk with returns that are not normally distributed. This company IPO during the Covid-19 pandemic. ECF VaR calculation with an initial investment of IDR. 1,000,000.00, a 95% confidence level results in a risk of Rp. 98,490.7 the next day. Backtesting uses the Kupiec Test to test the validity of the VaR model. The VaR obtained is valid for calculating the risk of SAMF because the probability of a violation occurring equal to the specified α. The number of violations according the Kupiec criteria table is between 16 and 36, namely 34 violations and the Likelihood Ratio value, namely LR=1,11735 < X^2(1;alpha) = 3,84 or Pvalue = 0,07095102 > alpha=0,05.

Evaluating the effects of silent genes on pairwise kinship testing

Short tandem repeat (STR) loci are frequently utilized in kinship testing, and mutations of a single base occurring in the primer-binding region of the STR locus can result in the failure of allelic amplification and the emergence of silent genes. Silent genes are not observable and, therefore, are excluded from the genotypes assessed. Pedigree likelihood ratios (LRs) are often employed in kinship testing to determine the likelihood of different kinship scenarios. LR values are derived from various types of genotypes. LRexact values are based on the exact or actual genotypes, which may include silent genes. Conversely, LRobserve values are based on observed genotypes that exclude silent genes, while LRadjust values incorporate all potential genotypes, including both observed and those with silent genes. Initially, the formulae for LRs in 1st degree, 2nd degree, and 3rd degree kinship testing are presented according to different genotype forms of pairwise individuals. The correctness of these formulae is then verified using the Familias software, and the results are compared with those from the GeneVisa software (www.genevisa.net). Lastly, the simulation modules of GeneVisa are used to assess the impact of silent genes on pairwise kinship testing. The findings indicate that the overall impact of silent genes is minimal, although in some cases, the effects can be relatively significant. The influence of silent genes generally decreases as the kinship relationship becomes more distant. In specific kinship tests, the effect of silent genes is reduced when the individuals are unrelated compared to when there is a kinship relationship. Utilizing the LRadjust value for 1st degree and 2nd degree kinship testing can substantially mitigate the effects of silent genes.

The Evidence Base for Circulating Tumor DNA-Methylation in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis.

Background: Non-Small Cell Lung Cancer (NSCLC) remains a challenging disease to manage with effectiveness. Early detection and precise monitoring are crucial for improving patient outcomes. Circulating tumor DNA (ctDNA) offers a non-invasive cancer detection and monitoring method. Emerging biomarkers, such as ctDNA methylation, have shown promise in enhancing diagnostic accuracy and prognostic assessment in NSCLC. In this review, we examined the current evidence regarding ctDNA methylation's role in NSCLC detection through a systematic review of the existing literature and meta-analysis. Methods: We systematically searched PubMed, Medline, Embase, and Web of Science databases up to 26 June 2024 for studies on the role of ctDNA methylation analysis in NSCLC patients. We included studies from 2010 to 2024 on NSCLC patients. We excluded case reports, non-English articles, studies on cell lines or artificial samples, those without cfDNA detection, prognostic studies, and studies with non-extractable data or mixed cancer types. Funnel plots were visually examined for potential publication bias, with a p value < 0.05 indicating bias. Meta-analysis was conducted using R packages (meta, forestplot, and mada). Combined sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive and negative predictive values, diagnostic odds ratio (DOR), and 95% confidence intervals (95% CI) were calculated. A summary receiver operating characteristic curve (SROC) and area under the curve (AUC) with related Standard Error (SE) were used to evaluate the overall diagnostic performance. Additionally, RASSF1A, APC, SOX17, SEPT9, and RARβ2 were analyzed, since their methylation was assessed in two or more studies. Results: From 38 candidate papers, we finally identified 12 studies, including 472 NSCLC patients. The pooled sensitivity was 0.62 (0.47-0.77) and the specificity was 0.90 (0.85-0.94). The diagnostic odds ratio was 15.6 (95% CI 9.36-26.09) and the area under the curve was 0.249 (SE = 0.138). The positive and negative predictive values were 5.38 (95% CI 3.89-7.44) and 0.34 (95% CI 0.22-0.54), respectively. For single genes, the specificity reached 0.83~0.96, except for RARβ2, but the sensitivity was relatively low for each gene. Significant heterogeneity across the included studies, the potential publication bias for specificity (p = 0.0231), and the need to validate the clinical utility of ctDNA methylation for monitoring treatment response and predicting outcomes in NSCLC patients represent the main limitations of this study. Conclusions: These results provide evidence of the significant potential of ctDNA methylation as a valuable biomarker for improving the diagnosis of NSCLC, advocating for its integration into clinical practice to enhance patient management.

Accuracy of the international growth charts to diagnose obesity according to the body composition analysis in US children and adolescents.

This study verified the accuracy of the international BMI references and the allometric BMI reference to diagnose obesity in children and adolescents from the USA. Data from 17313 subjects were obtained from the National Health and Nutrition Examination Survey between the years 1999-2006 and 2011-2018. Fat Mass Index, Allometric Fat Mass Index and fat mass/fat-free mass were calculated. Receiver operating characteristic curve, AUC, sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were estimated to evaluate the accuracy of the growth references for diagnosing obesity. The International Obesity Task Force, MULT BMI 17 years, MULT BMI 18 years and allometric BMI 19 years achieved the best sensitivity-specificity trade-off for boys, with sensitivities ranging from 0·92 to 0·96 and specificities of 0·94, with positive likelihood ratio of 15·51, 16·17, 13·46 and 18·01, respectively. The negative likelihood ratios were notably low, ranging from 0·04 to 0·08. In girls, the International Obesity Task Force, MULT BMI 17 years and MULT allometric BMI 17 years also demonstrated high sensitivity (0·95-0·97) and specificity (0·92), with positive likelihood ratio values of 11·54, 11·82 and 11·77, respectively and low negative likelihood ratio values (0·03-0·05). In summary, these international growth references presented satisfactory performance to diagnose obesity. However, the MULT growth reference performed better, and the MULT allometric BMI was the only indicator capable of detecting that girls have a higher proportion of fat mass than boys for the same index values. These findings suggest that the MULT growth reference may be a better tool to assess the nutritional status of children and adolescents internationally.

Performance comparison of a previously validated microhaplotype panel and a forensic STR panel for DNA mixture analysis

Short Tandem Repeats (STRs) are the most widespread markers in forensic genetics. However, STR stutter peaks can mask alleles from a minor contributor when analysing mixtures, hindering the interpretation of complex profiles. In this study we compared the performance of a previously described panel of microhaplotypes (MHs), an alternative type of forensic marker, against a standard STR kit. The parameters evaluated included: capability of determining the minimum number of contributors in the mixture; percentages of allele drop-outs and drop-ins; retrieval of alleles belonging to the minor contributor, and estimation of likelihood ratio (LR) values. In addition, the capacity of EuroForMix software to estimate each donor's percentage of contribution was tested, as well as the impact on results when using manually, or automatically prepared libraries. The MH panel showed better performance than STRs for the detection of 2-contributor mixtures, but the lower degree of polymorphism per MH marker hindered the task of deconvolution with multiple contributors. MHs presented higher drop-in rates and lower drop-out rates, a higher capability to recover the minor contributor's alleles and provided higher LR values than STRs, likely due to the much higher number of loci combined in the panel. Estimations of contributor ratios using EuroForMix showed promising results and marginal differences were found in these values between manually and automatically prepared libraries. Overall, results showed that the mixture detection performance of the MH panel was better or equal to the standard forensic autosomal STR panel, indicating microhaplotypes are informative markers for this purpose.

Shotgun DNA sequencing for human identification: Dynamic SNP selection and likelihood ratio calculations accounting for errors

Shotgun sequencing is a DNA analysis method that potentially determines the nucleotide sequence of every DNA fragment in a sample, unlike PCR-based genotyping methods that is widely used in forensic genetics and targets predefined short tandem repeats (STRs) or predefined single nucleotide polymorphisms (SNPs). Shotgun DNA sequencing is particularly useful for highly degraded low-quality DNA samples, such as ancient samples or those from crime scenes. Here, we developed a statistical model for human identification using shotgun sequencing data and developed formulas for calculating the evidential weight as a likelihood ratio (LR). The model uses a dynamic set of binary SNP loci and takes the error rate from shotgun sequencing into consideration in a probabilistic manner. To our knowledge, the method is the first to make this possible. Results from replicated shotgun sequencing of buccal swabs (high-quality samples) and hair samples (low-quality samples) were arranged in a genotype-call confusion matrix to estimate the calling error probability by maximum likelihood and Bayesian inference. Different genotype quality filters may be applied to account for genotyping errors. An error probability of zero resulted in the commonly used LR formula for the weight of evidence. Error probabilities above zero reduced the LR contribution of matching genotypes and increased the LR in the case of a mismatch between the genotypes of the trace and the person of interest. In the latter scenario, the LR increased from zero (occurring when the error probability was zero) to low positive values, which allow for the possibility that the mismatch may be due to genotyping errors. We developed an open-source R package, wgsLR, which implements the method, including estimation of the calling error probability and calculation of LR values. The R package includes all formulas used in this paper and the functionalities to generate the formulas.

Tuberculosis in Aceh Province, Indonesia: a spatial epidemiological study covering the period 2019-2021.

The purpose of this study was to determine whether there were any TB clusters in Aceh Province, Indonesia and their temporal distribution during the period of 2019-2021. A spatial geo-reference was conducted to 290 sub-districts coordinates by geocoding each sub-district's offices. By using SaTScan TM v9.4.4, a retrospective space-time scan statistics analysis based on population data and annual TB incidence was carried out. To determine the regions at high risk of TB, data from 1 January 2019 to 31 December 2021 were evaluated using the Poisson model. The likelihood ratio (LLR) value was utilized to locate the TB clusters based on a total of 999 permutations were performed. A Moran's I analysis (using GeoDa) was chosen for a study of both local and global spatial autocorrelation. The threshold for significance was fixed at 0.05. At the sub-district level, the spatial distribution of TB in Aceh Province from 2019-2021 showed 19 clusters (three most likely and 16 secondary ones), and there was a spatial autocorrelation of TB. The findings can be used to provide thorough knowledge on the spatial pattern of TB occurrence, which is important for designing effective TB interventions.

Integrating pretest probability for rheumatoid arthritis with likelihood ratios of RF and ACPA to improve clinical utility of rheumatoid arthritis autoantibody testing

Rheumatoid arthritis (RA) manifests through various symptoms and systemic manifestations. Diagnosis involves serological markers like rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA). Past studies have shown the added value of likelihood ratios (LRs) in result interpretation. LRs can be combined with pretest probability to estimate posttest probability for RA. There is a lack of information on pretest probability. This study aimed to estimate pretest probabilities for RA. This retrospective study included 133 consecutive RA patients and 651 consecutive disease controls presenting at a rheumatology outpatient clinic. Disease characteristics, risk factors associated with RA and laboratory parameters were documented for calculating pretest probabilities and LRs. Joint involvement, erosions, morning stiffness, and positive CRP, ESR tests significantly correlated with RA. Based on these factors, probabilities for RA were estimated. Besides, LRs for RA were established for RF and ACPA and combinations thereof. LRs increased with antibody levels and were highest for double high positivity. Posttest probabilities were estimated based on pretest probability and LR. By utilizing pretest probabilities for RA and LRs for RF and ACPA, posttest probabilities were estimated. Such approach enhances diagnostic accuracy, offering laboratory professionals and clinicians insights in the value of serological testing during the diagnostic process.

Racial Composition of Social Environments Over the Life Course Using the Pictorial Racial Composition Measure: Development and Validation Study.

Studies investigating the impact of racial segregation on health have reported mixed findings and tended to focus on the racial composition of neighborhoods. These studies use varying racial composition measures, such as census data or investigator-adapted questions, which are currently limited to assessing one dimension of neighborhood racial composition. This study aims to develop and validate a novel racial segregation measure, the Pictorial Racial Composition Measure (PRCM). The PRCM is a 10-item questionnaire of pictures representing social environments across adolescence and adulthood: neighborhoods and blocks (adolescent and current), schools and classrooms (junior high and high school), workplace, and place of worship. Cognitive interviews (n=13) and surveys (N=549) were administered to medically underserved patients at a primary care clinic at the Barnes-Jewish Hospital. Development of the PRCM occurred across pilot and main phases. For each social environment and survey phase (pilot and main), we computed positive versus negative pairwise comparisons: mostly Black versus all other categories, half Black versus all other categories, and mostly White versus all other categories. We calculated the following validity metrics for each pairwise comparison: sensitivity, specificity, correct classification rate, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, false positive rate, and false negative rate. For each social environment, the mostly Black and mostly White dichotomizations generated better validity metrics relative to the half Black dichotomization. Across all 10 social environments in the pilot and main phases, mostly Black and mostly White dichotomizations exhibited a moderate-to-high sensitivity, specificity, correct classification rate, positive predictive value, and negative predictive value. The positive likelihood ratio values were >1, and the negative likelihood ratio values were close to 0. The false positive and negative rates were low to moderate. These findings support that using either the mostly Black versus other categories or the mostly White versus other categories dichotomizations may provide accurate and reliable measures of racial composition across the 10 social environments. The PRCM can serve as a uniform measure across disciplines, capture multiple social environments over the life course, and be administered during one study visit. The PRCM also provides an added window into understanding how structural racism has impacted minoritized communities and may inform equitable intervention and prevention efforts to improve lives.

A comparison of likelihood ratios calculated from surface DNA mixtures using MPS and CE Technologies

This study evaluates the performance of analysing surface DNA samples using massively parallel sequencing (MPS) compared to traditional capillary electrophoresis (CE). A total of 30 samples were collected from various surfaces in an office environment and were analysed with CE and MPS. These were compared against 60 reference samples (office inhabitants). To identify contributors, likelihood ratios (LRs) were calculated for MPS and CE data using the probabilistic genotyping software MPSproto and EuroForMix respectively. Although a higher number of sequences/peaks were observed per DNA profile in MPS compared to CE, LR values were found to be lower for MPS data formats. This might be the result of the increased complexity of MPS data, along with a possible elevation of unknown alleles and/or artefacts. The study highlights avenues for improving MPS data quality and analysis to facilitate more robust interpretation of challenging casework-like samples.

Clinical validation of "spiritual distress (00066)" in parents of children with chronic diseases.

This study aimed to determine the clinical validation of the nursing diagnosis (ND) of "spiritual distress (00066)" and the sensitivity, specificity, likelihood ratio, and predictive value in parents of children with chronic diseases. This cross-sectional study was conducted using the clinical diagnostic validity method proposed by Fehring. The data were collected through structured interviews and using a researcher-made list that included 5 parts of demographic information, parents' opinions about spiritual distress, the researcher's diagnosis, 74 defining characteristics (DCs) of the ND of spiritual distress, and the Spiritual Well-being Questionnaire. Data were analyzed using descriptive statistics as well as sensitivity, specificity, likelihood ratio, and predictive value. The prevalence of diagnosis was 70% in a sample of 120 parents. Out of the 74 DCs, 39 criteria were validated. Questioning meaning of illness and suffering had the highest sensitivity (98.8%), the highest negative predictive value (88.88%), and the lowest negative likelihood ratio (0.05%). Expressing the lack of meaning in life demonstrated the highest specificity (97.22%), the highest positive predictive value (98.33%), and the highest positive likelihood ratio (25.26%). Parents who search for meaning of illness and suffering related to a lack of meaning in life are in spiritual distress. The ND was validated. These findings can empower clinical nurses to confidently assess and identify patients experiencing spiritual distress, bridging the gaps caused by the absence of standardized tools for assessing spiritual distress in the inpatient setting.

‘Low’ LRs obtained from DNA mixtures: On calibration and discrimination performance of probabilistic genotyping software

The validity of a probabilistic genotyping (PG) system is typically demonstrated by following international guidelines for the developmental and internal validation of PG software. These guidelines mainly focus on discriminatory power. Very few studies have reported with metrics that depend on calibration of likelihood ratio (LR) systems. In this study, discriminatory power as well as various calibration metrics, such as Empirical Cross-Entropy (ECE) plots, pool adjacent violator (PAV) plots, log likelihood ratio cost (Cllr and Cllrcal), fiducial calibration discrepancy plots, and Turing' expectation were examined using the publicly-available PROVEDIt dataset. The aim was to gain deeper insight into the performance of a variety of PG software in the 'lower' LR ranges (∼LR 1-10,000), with focus on DNAStatistX and EuroForMix which use maximum likelihood estimation (MLE). This may be a driving force for the end users to reconsider current LR thresholds for reporting. In previous studies, overstated 'low' LRs were observed for these PG software. However, applying (arbitrarily) high LR thresholds for reporting wastes relevant evidential value. This study demonstrates, based on calibration performance, that previously reported LR thresholds can be lowered or even discarded. Considering LRs >1, there was no evidence for miscalibration performance above LR ∼1000 when using Fst 0.01. Below this LR value, miscalibration was observed. Calibration performance generally improved with the use of Fst 0.03, but the extent of this was dependent on the dataset: results ranged from miscalibration up to LR ∼100 to no evidence of miscalibration alike PG software using different methods to model peak height, HMC and STRmix. This study demonstrates that practitioners using MLE-based models should be careful when low LR ranges are reported, though applying arbitrarily high LR thresholds is discouraged. This study also highlights various calibration metrics that are useful in understanding the performance of a PG system.

Uterocervical angle and cervical length measurements for spontaneous preterm birth prediction in low-risk singleton pregnant women: a prospective cohort study.

Preterm birth is the leading cause of early neonatal morbidity and mortality. Strategies to predict preterm birth risk can help improve pregnancy outcomes. Even pregnant women without known risk factors for preterm birth can also experience it. This study aimed to evaluate the ability of the uterocervical angle and cervical length to predict spontaneous preterm birth in low-risk singleton pregnant women. A prospective study on 1107 singleton pregnant women between 16+0 and 23+6weeks gestation at low risk for spontaneous preterm birth who were treated at the Haiphong Hospital of Obstetrics and Gynecology, Vietnam, between September 2020 and September 2021 was conducted. A single sonographer assessed the cervical length and the uterocervical angle using transvaginal ultrasonography. The patients were followed up until delivery to determine the main pregnancy outcome (spontaneous preterm birth before 37weeks gestation). The cut-off points for the uterocervical angle and cervical length were established by analyzing the receiver operating characteristic curve. The sensitivity, specificity, likelihood ratio, positive and negative predictive values, and accuracy of the uterocervical angle and cervical length for predicting spontaneous preterm birth were determined. A uterocervical angle ≥ 99° predicted spontaneous preterm birth at < 37weeks, with a sensitivity and specificity of 91% and 76%, respectively. A cervical length ≤ 33.8mm predicted preterm birth at < 37weeks with a sensitivity and specificity of 25% and 66%, respectively. A uterocervical angle ≥ 99° combined with a cervical length ≤ 33.8mm yielded the sensitivity, specificity, positive predictive value, likelihood ratio, and accuracy of spontaneous preterm birth prediction of 66%, 93%, 36%, 9, and 91%, respectively; thus provided a significant increase of specificity with an acceptable reduction of sensitivity as compared to cervical length alone. Besides the cervical length, the uterocervical angle can be considered a valuable ultrasound parameter for predicting spontaneous preterm birth in low-risk singleton pregnant women. Combining the uterocervical angle and cervical length yielded stronger spontaneous preterm birth prediction values.

Unlocking the potential of HB/RDW ratio as a simple marker for predicting mortality in acute ischemic stroke patients after thrombolysis

Systemic inflammation impairs outcomes in acute ischemic stroke (AIS). There is limited knowledge regarding the prognostic value of inflammatory biomarkers derived from complete blood count in predicting in-hospital mortality (IHM) in AIS patients treated with recombinant tissue plasminogen activator (rt-PA). Our study aims to compare the predictive performance of various inflammatory biomarkers for predicting IHM in AIS patients. This retrospective study included AIS patients treated with rt-PA between January 2015 and July 2022. We identified the following inflammatory biomarkers: white blood cell counts (WBCs), absolute neutrophil count, absolute lymphocyte count, neutrophil to lymphocyte count ratio, platelet to neutrophil ratio, platelet to lymphocyte ratio, red cell distribution width (RDW), RDW to platelet ratio (RPR), and hemoglobin to RDW (HB/RDW) at admission before rt-PA administration. We assessed the predictive value of these biomarkers for IHM by plotting receiver operating characteristic (ROC) curves. The associations between inflammatory biomarkers and IHM were analyzed using multivariable logistic regression (MVLR) analyses. Of 345 AIS patients, IHM occurred in 65 patients (18.84%). HB/RDW and RDW showed better predictive performance compared to other inflammatory biomarkers. In ROC curve analysis, HB/RDW and RDW had an area under ROC of 0.668. HB/RDW outperformed RDW in terms of the positive likelihood ratio (2.733 vs 1.575), accuracy (0.757 vs 0.585), specificity (0.814 vs 0.560), and positive predictive values (0.388 vs 0.267). In MVLR analysis, RDW, RPR, and HB/RDW remained significantly associated with IHM (per 1-unit increases: odds ratios (ORs) = 1.450, 95% CI: [1.178-1.784]; per 1-unit increases: ORs = 1.329, 95% CI [1.103-1.602]; and per 0.1-unit decreases: ORs = 1.412, 95% CI [1.089-1.831], respectively). The association between HB/RDW and IHM in AIS patients treated with rt-PA was significant. HB/RDW exhibited superior predictive performance compared to other inflammatory biomarkers in predicting IHM.

Comparison of Performance Characteristics in Early Warning Scoring Tools for Diagnosis of Intubation and Mortality Among COVID-19 Patients

Early warning scores serve as valuable tools for predicting adverse events in patients. This study aimed to compare the diagnostic performance of National Early Warning Score, Hamilton Early Warning Score, Standardized Early Warning Score, and Triage Early Warning Score in forecasting intubation and mortality among patients with coronavirus disease 2019. This predictive correlation study included 370 patients admitted to the emergency department of 22 Bahman Hospital in Neyshabur, Iran, from December 2021 to March 2022. The aforementioned scores were assessed daily upon patient admission and throughout a 1-month hospitalization period, alongside intubation and mortality occurrences. Data analysis used SPSS 26 and MEDCALC 20.0.13 software. We adhered to the Standards for Reporting of Diagnostic Accuracy Studies guidelines to ensure the accurate reporting of our study. The patients' mean age was 65.03 ± 18.47 years, with 209 (56.5%) being male. Both Standardized Early Warning Score and Hamilton Early Warning Score demonstrated high diagnostic performance, with area under the curve values of 0.92 and 0.95, respectively. For Standardized Early Warning Score, the positive likelihood ratio was 10.81 for intubation and 17.90 for mortality, whereas for Hamilton Early Warning Score, the positive likelihood ratio was 7.88 for intubation and 10.40 for mortality. The negative likelihood ratio values were 0.23 and 0.17 for Standardized Early Warning Score and 0.21 and 0.18 for Hamilton Early Warning Score, respectively, for the 24-hour period preceding intubation events and mortality. Findings suggest that Standardized Early Warning Score, followed by Hamilton Early Warning Score, has superior diagnostic performance in predicting intubation and mortality in patients with coronavirus disease 2019 within 24 hours before these outcomes. Therefore, serial assessments of Hamilton Early Warning Score or Standardized Early Warning Score may be valuable tools for health care providers in identifying high-risk patients with coronavirus disease 2019 who require intubation or are at increased risk of mortality.

Diagnostic performance of the O-RADS MRI system for magnetic resonance imaging in discriminating benign and malignant adnexal lesions: a systematic review, meta-analysis, and meta-regression.

After the introduction of the Ovarian-Adnexal Reporting and Data System (O-RADS) for magnetic resonance imaging (MRI), several studies with diverse characteristics have been published to assess its diagnostic performance. This systematic review and meta-analysis aimed to assess the diagnostic performance of O-RADS MRI scoring for adnexal masses, accounting for the risk of selection bias. The PubMed, Scopus, Web of Science, and Cochrane databases were searched for eligible studies. Borderline or malignant lesions were considered malignant. All O-RADS MRI scores ≥4 were considered positive. The quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The pooled sensitivity, specificity, and likelihood ratio (LR) values were calculated, considering the risk of selection bias. Fifteen eligible studies were found, and five of them had a high risk of selection bias. Between-study heterogeneity was low-to-moderate for sensitivity but substantial for specificity (I2 values were 35.5% and 64.7%, respectively). The pooled sensitivity was significantly lower in the studies with a low risk of bias compared with those with a high risk of bias (93.0% and 97.5%, respectively; P = 0.043), whereas the pooled specificity was not different (90.4% for the overall population). The negative and positive LRs were 0.08 [95% confidence interval (CI) 0.05–0.11] and 10.0 (95% CI 7.7–12.9), respectively, for the studies with low risk of bias and 0.03 (95% CI 0.01–0.10) and 10.3 (95% CI 3.8–28.3), respectively, for those with high risk of bias. The overall diagnostic performance of the O-RADS system is very high, particularly for ruling out borderline/malignant lesions, but with a moderate ruling-in potential. Studies with a high risk of selection bias lead to an overestimation of sensitivity. The O-RADS system demonstrates considerable diagnostic performance, particularly in ruling out borderline or malignant lesions, and should routinely be used in practice. The high between-study heterogeneity observed for specificity suggests the need for improvement in the consistent characterization of the benign lesions to reduce false positive rates.

Comparison of intraoperative imaging with a portable gamma camera with extemporaneous histology in minimally invasive surgery for primary hyperparathyroidism

The curative treatment of primary hyperparathyroidism (PPH) is surgical and today it can be performed by minimally invasive surgery (MIS) and also be radioguided (RG) if a radiopharmaceutical with affinity for the parathyroid tissue that can be detected with gamma-detector probes or with a portable gamma camera (PGC) is injected. The objective is to assess whether intraoperative scintigraphy (GGio) with PGC can replace intraoperative pathological anatomy (APio) to determine if the removed specimen is an abnormal parathyroid. 92 patients underwent CMI RG--HPP with PGC after the administration of a dose of 99 mTc-MIBI. The information provided by the PGC in the analysis of the excised specimens is qualitatively compared (capture yes/no) with the result of the intraoperative pathological anatomy (APio). The Gold standard is the definitive histology. 120 excised pieces are evaluated with GGio and APio. There were 110 agreements (95TP and 15TN) and 10 disagreements (3FP and 7FN). Of the 120 lesions, 102 were parathyroid and 18 were non-parathyroid. There was good agreement between intraoperative scintigraphy imaging (GGio) and PA, 70.1% according to Cohen's Kappa index. The GGio presented the following values ​​of Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Positive Likelihood Ratio, Negative Likelihood Ratio and Overall Value of the Test (93.1%, 83.3%, 96.9%, 68.2%, 5.59, 0.08 and 0.92 respectively). GGio is a rapid and effective surgical aid technique to confirm/rule out the possible parathyroid nature of the lesions removed in PPH surgery, but it cannot replace histological study.

Pairwise kinship inference and pedigree reconstruction using 91 microhaplotypes

Kinship inference has been a major issue in forensic genetics, and it remains to be solved when there is no prior hypothesis and the relationships between multiple individuals are unknown. In this study, we genotyped 91 microhaplotypes from 46 pedigree samples using massive parallel sequencing and inferred their relatedness by calculating the likelihood ratio (LR). Based on simulated and real data, different treatments were applied in the presence and absence of relatedness assumptions. The pedigree of multiple individuals was reconstructed by calculating pedigree likelihoods based on real pedigree samples. The results showed that the 91 MHs could discriminate pairs of second-degree relatives from unrelated individuals. And more highly polymorphic loci were needed to discriminate the pairs of second-degree or more distant relative from other degrees of relationship, but correct classification could be obtained by expanding the suspected relationship searched to other relationships with lower LR values. Multiple individuals with unknown relationships can be successfully reconstructed if they are closely related. Our study provides a solution for kinship inference when there are no prior assumptions, and explores the possibility of pedigree reconstruction when the relationships of multiple individuals are unknown.