Prediction Intervals Research Articles

The prediction of RULs for Weibull or gamma k-out-of-n system with nonidentical components based on component data

ABSTRACT The paper investigates the prediction procedures of the remaining useful lifetime (RUL) of k -out-of- n system with nonidentical Weibull or gamma components based on component data. The distributions of the offline and online RULs of system are derived. Using the generalized pivotal quantity (GPQ) method, the prediction intervals (PIs) of the offline and online RULs for the Weibull k -out-of- n system are derived. For the gamma k -out-of- n system, the PIs of the offline and online RULs are also obtained based on the Cornish-Fisher expansion and the GPQ method. In addition, the bootstrap PIs of the offline and online RULs for the Weibull or gamma k -out-of- n system are proposed. The performance of the proposed PIs is assessed in terms of coverage probability by using the Monte Carlo simulation. Finally, two examples are used to illustrate the proposed procedures.

Prediction models for ischemic stroke and bleeding in dialysis patients: a systematic review and meta-analysis

Abstract Background Patients with kidney failure on maintenance dialysis have a high stroke and bleeding risk. Multivariable prediction models can be used to estimate the risk of ischemic stroke and bleeding. A systematic review and meta-analysis was performed to determine the performance of the existing models in patients on dialysis. Methods MEDLINE and Embase databases were searched, from inception through January 12, 2024, for studies of prediction models for stroke or bleeding, derived or validated in dialysis cohorts. Discrimination measures for models with c-statistic data from ≥ 3 cohorts were pooled by random effects meta-analysis and a 95% prediction interval (PI) was calculated. Risk of bias was assessed using PROBAST. The review was conducted according to the PRISMA statement and the CHARMS checklist. Results Eight studies were included in this systematic review. All the included studies validated pre-existing models that were derived in cohorts from the general population. None of the identified studies reported the development of a new dialysis specific prediction model for stroke, while dialysis specific risk scores for bleeding were proposed by two studies. In meta-analysis of c-statistics, the CHA2DS2-VASc, CHADS2, ATRIA, HEMORR(2)HAGES and HAS-BLED scores showed very poor discriminative ability in the dialysis population. Six of the eight included studies were at low or unclear risk of bias and certainty of evidence was moderate. Conclusions The existing prediction models for stroke and bleeding have very poor performance in the dialysis population. New dialysis-specific risk scores should be developed to guide clinical decision making in these patients.

Interpreting Population Mean Treatment Effects in the Kansas City Cardiomyopathy Questionnaire: A Patient-Level Meta-Analysis.

The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a commonly used outcome in heart failure trials. While comparing means between treatment groups improves statistical power, mean treatment effects do not necessarily reflect the clinical benefit experienced by individual patients. To evaluate the association between mean KCCQ treatment effects and the proportions of patients experiencing clinically important improvements across a range of clinical trials and heart failure etiologies. A patient-level analysis of 11 randomized clinical trials, including 9977 patients, was performed to examine the association between mean treatment effects and the KCCQ Overall Summary Score (OSS) and the absolute differences in the proportions of patients experiencing clinically important (≥5 points) and moderate to large (≥10 points) improvements. There was no target date range, and included studies were those for which patient-level data were available. Validation was performed in 7 additional trials. The data were analyzed between July 1 and September 15, 2023. Proportion of patients experiencing an improvement of 5 or more and 10 or more points in their KCCQ score (with each domain transformed to a range of 0 to 100 points, where higher scores represent better health status). Group mean KCCQ-OSS differences were strongly correlated with absolute differences in clinically important changes (Spearman correlations 0.76-0.92). For example, a mean KCCQ-OSS treatment effect of 2.5 points (half of a minimally important difference for an individual patient) was associated with an absolute difference of 6.0% (95% prediction interval [PI], 4.0%-8.1%) in the proportion of patients improving 5 or more points and 5.0% (95% PI, 3.1%-7.0%) in the proportion improving 10 or more points, corresponding to a number needed to treat of 17 (95% PI, 12-25) and 20 (95% PI, 14-33), respectively. Inferences about clinical impacts based on population-level mean treatment effects may be misleading, since even small between-group differences may reflect clinically important treatment benefits for individual patients. Results of this study suggest that clinical trials should explicitly describe the distributions of KCCQ change at the patient level within treatment groups to support the clinical interpretation of their results.

Criterion-Related Validity and Reliability of the Six-Minute Run Walk Test Among Children: Findings from a Systematic Review and Meta-Analysis

ABSTRACT The aim of this study is to examine the available scientific evidence on the reliability and criterion validity of 6-minute run walk field-based test when administered to children and adolescents. Systematic searches were performed in three electronic databases (MEDLINE/PubMed, SPORTDiscuss and Scopus) from their inception until February 2024, with an update on August 15, 2024. Studies were considered eligible if they provided information on the criterion-related validity and/or reliability of the 6-minute run walk test when conducted by children (aged 12 years and under) and/or adolescents (aged 13 to 17 years). To examine reliability and validity, two separate meta-analyses were implemented. In the meta-analysis focused on criterion-related validity, the five studies included indicated a fair validity (r = 0.56 [95% CI: 0.44–0.66]) and high homogeneity (I2 0%). In the meta-analysis focused on reliability, the pooled data from the five studies employing Pearson’s correlation coefficient (n = 585) revealed strong reliability values (r = 0.82 [95% CI: 0.75–0.87]), albeit with a moderate level of heterogeneity (I2 71% prediction interval 0.55–0.94). The evidence suggests that the 6-minute run walk field-based test demonstrated fair criterion-related validity and strong test-retest reliability when conducted with children and adolescent.

Practical cut-off value for radon concentration in indoor air using an activated-charcoal radon collector.

The World Health Organization (WHO) recommends that countries adopt the reference level of 100Bqm-3 for indoor radon gas. Adopting the reference level requires a preliminary assessment of the indoor radon concentration. In the preliminary investigation, a cut-off value is useful, which is a value for selecting samples that can be reliably determined to be below the reference level (in this paper, the WHO reference level) using a straightforward method. If the true value was the WHO reference level, then the cut-off value of the PicoRad collector for selecting samples was determined as 80Bqm-3 through the analysis of the 95% prediction interval.

Prediction intervals with controlled length in the heteroscedastic Gaussian regression

We tackle the problem of building a prediction interval in heteroscedastic Gaussian regression. We focus on prediction intervals with constrained expected length in order to guarantee interpretability of the output. In this framework, we derive a closed-form expression of the optimal prediction interval that allows for the development of a data-driven prediction interval based on plug-in. The construction of the proposed algorithm is based on two samples, one labelled and another unlabelled. Under mild conditions, we show that our procedure is asymptotically as good as the optimal prediction interval both in terms of expected length and error rate. In particular, the control of the expected length is distribution-free. We also derive rates of convergence under smoothness and the Tsybakov noise conditions. We conduct a numerical analysis that exhibits the good performance of our method. It also indicates that even with a few amount of unlabelled data, our method is very effective in enforcing the length constraint.

Abstract 4136785: Interpreting Population Mean Treatment Effects in the Kansas City Cardiomyopathy Questionnaire

Introduction: The Kansas City Cardiomyopathy Questionnaire (KCCQ) is a common outcome in heart failure trials. While comparing means between treatment groups improves statistical power, mean effects do not necessarily reflect the clinical benefit experienced by patients. Objectives: To evaluate the relationship between mean KCCQ treatment effects in clinical trials and the proportions of patients experiencing clinically important improvements across a range of heart failure etiologies and interventions. Methods: We performed a patient-level meta-analysis of 11 randomized trials (9,977 patients) to examine the relationship between mean KCCQ Overall Summary Score (OSS) treatment effects and the absolute differences in the proportions of patients experiencing clinically important (≥5 points) and moderate or larger (≥10 points) improvements. Validation was performed in 7 additional trials. Results: Group mean KCCQ-OSS differences were strongly correlated with absolute differences in clinically important changes (Spearman correlations 0.76-0.92). For example, a mean KCCQ-OSS treatment effect of 2.5 points (half of a minimally important difference for an individual patient) was associated with an absolute difference of 6.0% (95% prediction interval [PI] 4.0-8.1) in the proportion of patients improving ≥5 points and 5.0% (95% PI 3.1-7.0) in the proportion improving ≥10 points, corresponding to NNTs of 17 (95% PI 12-25) and 20 (95% PI 14-33), respectively. Similar relationships were observed in the validation studies. Conclusions: Inferences on clinical impact based on population-level mean treatment effects are misleading since even small between group differences may reflect clinically important treatment benefits at the patient level. Clinical trials should explicitly describe the distributions of patient-level KCCQ changes to support clinical interpretation of their results.

A novel approach for multivariate time series interval prediction of water quality at wastewater treatment plants

ABSTRACT This study proposes a novel approach for predicting variations in water quality at wastewater treatment plants (WWTPs), which is crucial for optimizing process management and pollution control. The model combines convolutional bi-directional gated recursive units (CBGRUs) with Adaptive Bandwidth Kernel Function Density Estimation (ABKDE) to address the challenge of multivariate time series interval prediction of WWTP water quality. Initially, wavelet transform (WT) was employed to smooth the water quality data, reducing noise and fluctuations. Linear correlation coefficient (CC) and non-linear mutual information (MI) techniques were then utilized to select input variables. The CBGRU model was applied to capture temporal correlations in the time series, integrating the Multiple Heads of Attention (MHA) mechanism to enhance the model's ability to comprehend complex relationships within the data. ABKDE was employed, supplemented by bootstrap to establish upper and lower bounds of the prediction intervals. Ablation experiments and comparative analyses with benchmark models confirmed the superior performance of the model in point prediction, interval prediction, the analysis of forecast period, and fluctuation detection for water quality data. Also, this study verifies the model's broad applicability and robustness to anomalous data. This study contributes significantly to improved effluent treatment efficiency and water quality control in WWTPs.

EPID-01. PEDIATRIC BRAIN TUMORS IN SUB-SAHARAN AFRICA:A SYSTEMATIC REVIEW AND META-ANALYSIS

Abstract OBJECTIVE brain tumors are a global problem, leading to higher cancer-related morbidity and mortality rates in children. Despite the progressive though slow advances in neuro-oncology care, research, and diagnostics in sub-Saharan Africa (SSA), the epidemiological landscape of pediatric brain tumors (PBTs) remains underestimated. This study aimed to systematically analyze the distribution of PBT types in SSA. METHODS major databases and grey literature were searched for literature on PBTs in SSA published before October 29, 2022. A proportional meta-analysis was performed. RESULTS forty-nine studies, involving 2360 children, met the inclusion criteria for review; only 20 (40.82%) were included in the quantitative analysis. South Africa and Nigeria were the countries with the most abundant data. Glioma not otherwise specified (NOS) was the common PBT in the 4 SSA regions combined. However, medulloblastoma was more commonly reported in Southern-SSA(p=0.01) than in other regions. The prevalence and the overall pooled proportion of the 3 common PBTs was estimated at 46.27% and 0.41 (95%CI 0.32–0.50, 95% prediction interval [PI] 0.11–0.79), 25.34% and 0.18 (95%CI 0.14–0.21, 95%PI 0.06–0.40), and 12.67% and 0.12 (95%CI 0.09–0.15, 95%PI 0.04–0.29) for glioma-NOS, medulloblastoma, and craniopharyngioma, respectively. Sample size moderated the estimated proportion of glioma-NOS (p = 0.02). The highest proportion of craniopharyngiomas was in Western-SSA, and medulloblastoma and glioma-NOS in Central-SSA. CONCLUSION these findings provide insight into the trends of PBT types and the proportion of the top 3 most common tumors across SSA. Although statistical conclusions are difficult due to the inconsistency in the data. We identify critical areas for policy development and collaborations that can facilitate improved outcomes in PBTs in SSA. More accurate epidemiological studies are needed to better understand the burden of the disease and the geographic variation in their distribution, and to raise awareness in their subsequent management.

Conformal prediction with local weights: randomization enables robust guarantees

Abstract In this work, we consider the problem of building distribution-free prediction intervals with finite-sample conditional coverage guarantees. Conformal prediction (CP) is an increasingly popular framework for building such intervals with distribution-free guarantees, but these guarantees only ensure marginal coverage: the probability of coverage is averaged over both the training and test data, meaning that there might be substantial undercoverage within certain subpopulations. Instead, ideally we would want to have local coverage guarantees that hold for each possible value of the test point’s features. While the impossibility of achieving pointwise local coverage is well established in the literature, many variants of conformal prediction algorithm show favourable local coverage properties empirically. Relaxing the definition of local coverage can allow for a theoretical understanding of this empirical phenomenon. We propose randomly localized conformal prediction (RLCP), a method that builds on localized CP and weighted CP techniques to return prediction intervals that are not only marginally valid but also offer relaxed local coverage guarantees and validity under covariate shift. Through a series of simulations and real data experiments, we validate these coverage guarantees of RLCP while comparing it with the other local conformal prediction methods.

Margin of safety for needle puncture of a radial artery in children: Recommendation for ultrasound-guided cannulation.

The radial artery is commonly selected for arterial puncture and cannulation, but radial nerve palsy may occur. To minimize possible damage to the nerve, needle puncture should be made within the margin of safety (between the wrist to the distal end of the radial artery and the radial nerve running in parallel). In adults, the margin of safety for radial artery puncture is approximately 6.8cm from the wrist in men and approximately 5.4cm in women, but the margin of safety is not known in children of different age groups. Using an ultrasound device, we measured the margin of safety in 100 anesthetized patients aged 0months to 15yr. Polynomial quadratic regression models were made, and the lower limit of the prediction interval was regarded as the margin of safety. These results were then compared with the results obtained in adults. The margin of safety became wider as a child grows older, and the height, weight, and age were all suitable explanatory variables to predict the margin of safety, providing fairly a constant predicted margin of safety from a few millimeters in neonates to approximately 4cm in adolescents (much narrower than in adults). In children and adolescents, the margin of safety for radial artery puncture is much narrower than in adults, and these findings support the recommendation to use ultrasound guidance during radial artery puncture in children and adolescents, to minimize the risk of associated complications. jRCT1032230243.

Vetting the Extrapolation Caution of Tamhane & Dunlop: A Time Series Forecasting Analysis

Context In forecasting there are only two classes of Projections: Extrapolations or Interpolations. Interestingly, Interpolations usually, all but guarantee, that the theoretical expectations that rationalize the veracity of the forecasting profiles are the likely State of Nature. Simply: The [1-a]% Forecasting Prediction Interval [FPI] provides the expected-intel with a frequency of [1-a]%. However, for Extrapolations, [T&D] [8 p. 363] offer the following “Forecasting Bump in the Road” T&D Conjecture: “- - -extrapolation beyond the range of the data is a risky business should be avoided.” If T&D are correct that: Forecasting Extrapolations somehow compromise the linkages of the Projection of the Past into a Relevant Future, Then, indeed, Extrapolations should be avoided. T&D’s theoretical and experientially based caution has garnered very little vetting interest—Voilà: The motivation for our research report. Research Elements Initially, we offer a detailed analysis of the mathematical nature of an Extrapolation as it affects the Precision of the 95% Time Series [TS] Forecasting Prediction Interval [95%TSFPIs]. This is critical as, interestingly, T&D offer no detailed support for their risky business musing. For the inferential testing stage, we will use as the Y-Response Variate: The Capture Rate of the 95%TSFPIs. As we are using a DOE-Inferential Model, we are opting for two-Category Covariates: Extrapolations—[As they are the focus of our inquiry.] & Panel-Variation—measured as the Coefficient of Determination [CoD]—[As variation is the bane of forecasting.]. For DOE: testing, we have selected a random sample of 202 Panels from the S&P500; for each Panel there are Three TS-Extrapolations and also Three [CoD-screens]. Results We found clear inferential evidence that as the Extrapolations move away from the mean of the TS time-index & The CoD increases that certain DOE-{Ext & CoD} Tukey-Kramer HSD-interaction-profiles suggest a failure to realizes the theoretical expectations of the Capture Rate of the 95%TSFPIs. We offer, for the first time, a deconstruction of these very interesting inferential DOE-results that rationalize T&D’s risky business musing.

Factors Associated With Ocular Perfusion Measurements as Obtained With Laser Speckle Contrast Imaging.

To study the ocular and systemic factors affecting optic nerve head (ONH) perfusion data as obtained using a commercially available laser speckle flowgraphy (LSFG) device in a cohort of Caucasian subjects without ocular diseases. Also, to assess the intrasession repeatability and intersession reproducibility of ONH, macular, retinal, and choroidal perfusion. Seventy-five healthy eyes of 75 Caucasian participants underwent LSFG and spectral-domain optical coherence tomography (SD-OCT) on the same visit. Perfusion of the ONH was assessed with LSFG, and SD-OCT was used to measure peripapillary retinal nerve fiber layer thickness (RNFLT) and macular ganglion cell plus inner plexiform layer thickness (GCIPLT). The intrasession repeatability and intersession reproducibility of ONH and macular perfusion and retinal and choroidal relative flow volume (RFV) were evaluated in 20 participants measured on three different days over a 6-month period. Intrasession and intersession intraclass correlation coefficients of LSFG parameters ranged from 0.787 to 0.967 and from 0.776 to 0.935, respectively. Intersession 95% prediction intervals for the ratio of two measurements were wider for RFV indices than for ONH and macular perfusion parameters. The multiple regression analysis indicated that higher ONH perfusion was associated with younger age, female sex, smaller optic disc area, and higher RNFLT. RNFLT was an independent predictor of ONH perfusion, whereas GCIPLT was not. Each 1-µm increase in RNFLT was associated with a 0.272 arbitrary unit increase in ONH perfusion. LSFG measurements of optic disc perfusion are influenced by sex, age, and anatomical variations in optic disc area and RNFLT. Better evaluation of ocular blood flow will result in better diagnosis and treatment of various ocular diseases.

Risk-Prediction Model of Restenosis after Endovascular Treatment for Peripheral Arterial Disease: A Systematic Review and Meta-analysis.

Peripheral artery disease (PAD) patients after endovascular treatment (EVT) have a relatively high restenosis rate. However, this risk can be mitigated through precise risk assessment and individualized self-management intervention plans. Moreover, the number of predictive models for restenosis risk in PAD patients after EVT is gradually increasing, yet these results of study exhibit certain discrepancies, raising uncertainties regarding the quality and applicability in clinical practice and future research. The objective of this study was to systematically evaluate risk-predictive models for restenosis in patients with PAD after EVT. A systematic review and meta-analysis of predictive model construction and validation using observational studies was undertaken. The China National Knowledge Infrastructure, China Science and Technology Journal Database, Wanfang Database, SinoMed, PubMed, Web of Science, Embase, and the Cochrane Library were searched from inception to January 1, 2024. Two researchers independently conducted literature screening and data extraction, encompassing study design, data sources, outcome definition, sample size, predictive factors, model development, and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used for risk of bias and applicability assessment of the models. A total of 4275 studies were retrieved, ultimately resulting in the inclusion of 7 articles comprising 7 predictive models for restenosis in PAD patients after EVT, with a restenosis incidence ranging from 21.8% to 39.7%. The total sample size of the included models ranged from 137 to 1578 cases, with logistic regression analysis being the most commonly used modeling method. All models were built using R software. Only 2 models underwent external validation, and the reported area under the curve ranged from 0.728 to 0.864. The summary area-under-the-curve statistic was 0.80 (95% confidence interval [CI], 0.74-0.86), with an approximate prediction interval of 0.80 (95% CI, 0.62-0.91) . The number of included predictive factors ranged from 3 to 10, with the most common factors being age, Trans-Atlantic Inter-Society Consensus Ⅱ classification, hypertension, diabetes, high-sensitivity C-reactive protein, and surgical approach. All studies exhibited high risk of bias, primarily attributed to inappropriate sources of data and poor reporting of the analysis domain. Predictive models for restenosis after EVT in PAD patients demonstrate overall good predictive performance but are still in the developmental stage with higher risk of bias. Future studies should follow the TRIPOD statement, focusing on the development of new models with larger samples, rigorous study designs, and multicenter external validation. This systematic review adheres to the PRISMA 2020 statement, offering the most recent systematic assessment of risk prediction models for restenosis following endovascular treatment in peripheral arterial disease.The newly developed PROBAST tool was employed to assess the risk of bias and the applicability of the existing evidence.This review emphasizes the practical utility, limitations of the current evidence, and recommendations for future research, with the goal of providing valuable information for clinicians and patients in their decision-making process, while also supporting the advancement of future research endeavors.

Short-term interval prediction strategy of photovoltaic power based on meteorological reconstruction with spatiotemporal correlation and multi-factor interval constraints

Short-term photovoltaic (PV) power interval prediction provides a basis for day-ahead power dispatching and generation planning. However, the current gridded numerical weather prediction (NWP) has poor matching in specific PV stations, and the lack of consideration of PV power mutation characteristics and historical correlation in interval prediction, which further limit the improvement of PV power prediction accuracy. In this regard, this paper proposes a novel short-term interval prediction strategy for PV power. Based on the second-order extended hidden Markov model (HMM), the key meteorological elements of the PV station with poor matching are reconstructed. In the interval prediction, the trend mutation and historical correlation characteristics of the PV sequence are fully considered, and a PV power interval prediction method that combines three factors such as trend change, time correlation and numerical mutation is proposed. The proposed method is applied to a PV station in Jilin, China. The results show that compared with other methods, the RMSE of the proposed method is reduced by 5.3 % on average, and the CWC is reduced by at least 2.1 %, which verifies the effectiveness of the proposed method.

Systematic review and meta-analysis of human bocavirus as food safety risk in shellfish.

Human bocavirus (HBoV) is an emerging pathogen causing gastroenteritis/respiratory tract infection. Shellfish has been implicated in foodborne HBoV dissemination. The present investigation aimed at synthesising shellfish-associated HBoV data. Shellfish-HBoV data were mined from public repositories using topic-specific algorithm. A total of 30 data sources was identified of which 5 were synthesised. The average HBoV positivity and sample-size was 12 ± 9.2 and 134.2 ± 113.6, respectively. HBoV was studied in mollusc with 3.7-83.3% crude prevalence. The pooled HBoV prevalence in shellfish was 9.2% (7.2-11.8; 5 studies) and 12.9% (1.8-53.9; 5 studies) in common-effects and random-effects model respectively, with 0.12-94.89% prediction interval (PI). Sensitivity analysis yielded 8.7% (6.7-11.2; PI = 1.99-29.48%) prevalence. HBoV1 and HBoV2 pooled prevalence in shellfish was 7.91% (1.61-31.09; 3 studies) and 12.52% (0.01-99.60; 3 studies), respectively. HBoV3 prevalence was reported in one single study as 6.96% (4.41-10.35). In conclusion, the present study revealed high HBoV prevalence in shellfish, signifying the need to characterise HBoV and subtypes circulating in non-mollusc shellfish. Furthermore, there is an urgent need to mitigate the food safety risk that may result from HBoV contaminated shellfish since shellfish-borne HBoV is not routinely assessed and might be underestimated at present.

Transcranial direct current stimulation for Parkinson's disease: systematic review and meta-analysis of motor and cognitive effects.

Transcranial direct current stimulation (tDCS) is a promising noninvasive intervention for Parkinson's disease (PD). However, studies of its motor and cognitive effect have produced mixed results. We conducted a systematic review including 38 studies and meta-analysis of 12 randomized sham-controlled trials with 263 PD patients. No significant differences were found between active and sham tDCS in motor function (UPDRS-III: SMD = -0.14, p = 0.74), gait (SMD = 0.10, p = 0.513), attention and working memory (SMD = 0.24, p = 0.13), executive function (SMD = 0.03, p = 0.854), and memory and learning (SMD: -0.07, p = 0.758). The prediction intervals indicated substantial heterogeneity among studies. Meta-regression showed small positive effects in younger PD patients with milder symptoms. These findings are preliminary but suggest tDCS may benefit some PD patients while being neutral or harmful to others.

Low PAPPA and Its Association with Adverse Pregnancy Outcomes in Twin Pregnancies: A Systematic Review of the Literature and Meta-Analysis

Background: It is well established in the literature that pregnancy-associated plasma protein-A (PAPP-A) is linked to several adverse pregnancy outcomes, including pre-eclampsia (PE), fetal growth restriction (FGR), and preterm birth (PTB) in singleton pregnancies. However, data regarding such an association in twin pregnancies are lacking. The primary goal of this systematic review and meta-analysis was to assess the potential value of low PAPP-A levels in the prediction of the subsequent development of hypertensive disorders of pregnancy (HDPs), PTB, and small for gestational age (SGA)/FGR fetuses in twin pregnancies and investigate its association with the development of gestational diabetes, intrauterine death (IUD) of at least one twin, and birth weight discordance (BWD) among the fetuses. Methods: Medline, Scopus, CENTRAL, Clinicaltrials.gov, and Google Scholar databases were systematically searched from inception until 31 July 2024. All observational studies reporting low PAPP-A levels after the performance of the first-trimester combined test as part of the screening for chromosomal abnormalities with reported adverse pregnancy outcomes were included. Results: The current systematic review encompassed a total of 11 studies (among which 6 were included in the current meta-analysis) that enrolled a total of 3741 patients. Low PAPP-A levels were not associated with HDPs (OR 1.25, 95% CI 0.78, 2.02, I-square test: 13%). Low PAPP-A levels were positively associated with both the development of preterm birth prior to 32 (OR 2.85, 95% CI 1.70, 4.77, I-square test: 0%) and 34 weeks of gestational age (OR 2.09, 95% CI 1.34, 3.28, I-square test: 0%). Furthermore, low PAPP-A levels were positively associated with SGA/FGR (OR 1.58, 95% CI 1.04, 2.41, I-square test: 0%). Prediction intervals indicated that the sample size that was used did not suffice to support these findings in future studies. Conclusions: Our study indicated that low PAPP-A levels are correlated with an increased incidence of adverse perinatal outcomes in twin pregnancies. Identifying women at elevated risk for such adversities in twin pregnancies may facilitate appropriate management and potential interventions, but additional studies are required to identify the underlying mechanism linking PAPP-A with those obstetrical complications.

Multi-Step Ahead Water Level Forecasting Using Deep Neural Networks

Stream gauge height (water level) is a significant indicator for forecasting future floods. Flooding occurs when the water level exceeds the flood stage. Predicting imminent floods can save lives, protect infrastructure, and improve road traffic management and transportation. Deep neural networks have been increasingly used in this domain due to their predictive capabilities in capturing complex features and interdependencies. This study employs four distinct models—Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), transformer, and LSTNet—with MLP serving as the baseline model to forecast water levels. The models are trained using data from 20 distinct river gages across the state of Missouri to ensure consistent performance. Random search optimization is employed for hyperparameter tuning. The prediction intervals are set at 4, 6, 8, and 10 (each interval equivalent to 30 min) to ensure that performance results are robust and not due to random weight initialization or suboptimal hyperparameters and are consistent throughout different prediction intervals. The findings of this study indicate that the LSTNet model leads to a better performance than the other models, with a median RMSE of 0.00724, 0.00959, 0.01204, and 0.01230 for the 4, 6, 8, and 10 intervals, respectively. As climate change leads to localized storms driven by atmospheric shifts, water level fluctuations are becoming increasingly extreme, further exacerbating data drift in real-world datasets. The LSTNet model demonstrates superior performance in terms of RMSE, MAE, and the correlation coefficient across all prediction intervals when forecasting water levels under data drift conditions.

Efficacy of 8-week daclatasvir-sofosbuvir regimen in chronic hepatitis C: a systematic review and meta-analysis

BackgroundThe high rates of the sustained virologic response 12 weeks after treatment (SVR12) in real world settings provoked the adoption of shortened courses of the costly direct-acting antivirals (DAAs) regimens. This study provides, to our knowledge, the first systematic review and meta-analysis for the efficacy of the shortened 8-week course of sofosbuvir (SOF) plus daclatasvir (DCV), the most accessible DAAs in the low-middle income countries (LMICs).MethodsWe performed a proportion meta-analysis to determine a reliable rate of SVR12 by pooling all studies that evaluated the results of the 8-week regimen of DCV + SOF. In addition, we applied sensitivity analyses using two imputation paradigms: a conservative approach, and a pragmatic approach to avoid overestimating the efficacy of the 8-week regimen in studies that followed a response-guided treatment (RGT) approach.ResultsSix studies with a total of 159 patients were included. The pooled SVR12 rate ranged from 91 to 97% in the included scenarios. The pragmatic scenario showed that the pooled SVR12 was 97% (95% confidence interval (CI) 91%; 100%) with lower variability as assessed by the prediction interval. The conservative approach revealed an SVR12 of 93% (95% CI 84%; 95%).ConclusionThe 8-week course of 60 mg DCV with SOF provided a comparable SVR12 to the standard 12-week regimen in treatment-naïve, non-HIV co-infected patients with a minimum estimated efficacy of 90%.