Category: Statistics

Prostate Cancer Prostatic Dis. 2025 Sep 19. doi: 10.1038/s41391-025-01026-y. Online ahead of print.

ABSTRACT

BACKGROUND: Digital perioperative programs offer promising solutions to overcome organizational constraints of traditional prehabilitation, potentially improving recovery while reducing healthcare burden and costs. We aimed to assess the impact of adding a surgery-specific module to an optimized digital perioperative program on improving functional outcomes after radical prostatectomy (RP).

METHODS: This was a multicentre, prospective, comparative, non-randomized trial including consecutive robot-assisted RP. Intervention was the implementation of the Betty (Better Surgery) coaching program combined with the activation of a RP-specific pre- and rehabilitation module. The primary endpoint was continence recovery, defined as “0 or 1 safety pad per day” at 6 weeks after surgery. Secondary endpoints were mid-term continence, need for postoperative physiotherapy, erectile function, complications, and readmissions.

RESULTS: A total of 177 and 156 RP cases were included in the control and experimental groups. Baseline and pathological variables were statistically comparable between groups. The mean patient age and PSA were 65.3 years and 11 ng/ml, respectively. At 6 weeks after RP, 83.3% of patients following the digital program were continent, as compared with 68.4% in the control group (p = 0.002). The need for postoperative physiotherapy for persistent incontinence was significantly reduced in the digital program group (27.5%, versus 58.8%, p < 0.001). Patients who followed the digital program experienced lower complications although not statistically significant (p = 0.1), unplanned visits (p = 0.025), reoperation rates (p = 0.025), more same-day discharge surgery (p = 0.030), and higher satisfaction (9.4/10 versus 8.3/10, p < 0.001). The main limitation was the absence of randomization.

CONCLUSIONS: Besides the benefits provided by the perioperative digital program, the addition of a pre- and rehabilitation module, including surgery-specific content, significantly improved functional recovery after RP and perioperative outcomes.

PMID:40973738 | DOI:10.1038/s41391-025-01026-y

J Perinatol. 2025 Sep 19. doi: 10.1038/s41372-025-02432-9. Online ahead of print.

ABSTRACT

Preterm neonates frequently undergo a variety of painful and invasive procedures. Neonatal pain has been associated with changes in hormone levels, tissue damage, and brain development. Nonpharmacological approaches, such as the use of breast milk odor, have been investigated as a strategy to reduce those effects. However, its efficacy remains unclear, and further research is needed to establish its true therapeutic value.

PURPOSE: Evaluate the analgesic effects of breast milk odor in premature neonates.

METHODS: We systematically searched PubMed, EMBASE, and Cochrane for randomized controlled trials (RCTs) assessing the effects of human milk odor compared with no aromatherapy (control) or placebo (odorless diffuser/distilled water/saline) on pain relief, oxygen saturation (SaO₂), and heart rate in preterm neonates undergoing invasive procedures. Two independent reviewers screened studies and extracted data up to July 5, 2025. Statistical analyses were conducted using RevMan Web (version 8.0.0) for the primary meta-analyses, and R software was used to perform sensitivity analyses.

RESULTS: Among 460 references identified, eight randomized controlled trials met the inclusion criteria, totaling 451 preterm neonates, of whom 225 (49.9%) were exposed to human milk odor. The mean gestational age was 33.56 ± 2.58 weeks, and 226 (50.1%) were male. Two studies were conducted using venipuncture as a painful procedure, four used the heel prick test, one used peripheral catheterization, and one used vaccination. Compared with the control group and the placebo, the group exposed to the milk odor showed a reduction in pain scores during the painful procedure (SMD = -0.95; 95% CI: -1.45 to -0.45; p = 0.0002; I² = 84%). The subgroup analysis of the five RCTs that used the PIPP score for the pain assessment showed similar results; the human milk odor-exposed group showed a lower pain score compared with the control group (MD -2.66; 95% CI -4.25 to -1.06; p = 0.001). Secondary outcomes included physiological parameters such as heart rate and oxygen saturation (SaO₂). Neonates exposed to breast milk odor exhibited lower heart rates compared to control (MD = -9.26; 95% CI: -14.28 to -4.24; p = 0.0003; I² = 27%). Oxygen saturation was also higher in the intervention group (MD = 2.40; 95% CI: 0.68-4.12; p = 0.006; I² = 53%) CONCLUSION: Aromatherapy with human breast milk reduces pain in neonates. Larger studies are needed to validate our results.

PMID:40973724 | DOI:10.1038/s41372-025-02432-9

Int J Obes (Lond). 2025 Sep 19. doi: 10.1038/s41366-025-01895-2. Online ahead of print.

ABSTRACT

BACKGROUND/OBJECTIVES: Tracking excess adiposity at population scale is essential for managing the obesity pandemic in human populations. New formulas based on weight, height, waist and hip measurements have been suggested as better alternatives to the classic body mass index and waist-hip ratio, but the lack of systematic benchmarking on how these formulas reflect adiposity, metabolic dysfunction and clinical sequelae causes confusion on how to best monitor the health of populations.

SUBJECTS/METHODS: Participants from the Northern Finland Birth Cohort 1966 were included based on data availability at the 46-year visit (2511 women and 1908 men). Cross-sectional sex-adjusted Spearman correlations with clinical biomarkers and serum and urine NMR metabolomics were calculated for body mass index (BMI), waist-hip ratio (WHR), waist-height ratio (WHER), abdominal volume index, body adiposity index, body roundness index, body shape index, conicity index and impedance-based body fat. UK biobank participants were selected based on available data at initial visit (244,947 women and 205,949 men). Prevalent and incident cases of type 2 diabetes, hypertension, liver disease and heart disease were ascertained through register linkage. Prevalent cases were predicted from adiposity measures by age- and sex-adjusted logistic regression and incident cases by age- and sex-adjusted Cox regression.

RESULTS: Adiposity measures were highly collinear and exhibited low biomolecular specificity. BMI and WHR together captured almost all body shape information related to cardiometabolic diseases. For instance, the c-statistic of the BMI & WHR model for diabetes (0.8012; CI95: 0.7963, 0.8061) was near the theoretical maximum of 0.8047. Diabetes was also predicted by WHER (0.7951; CI95: 0.7903, 0.8000). Other adiposity measures showed equal or worse prediction accuracy. This pattern repeated across multiple disease diagnoses.

CONCLUSIONS: We did not observe sufficient benefits from the more recent body adiposity formulas over body mass index, waist-hip or waist-height ratio to warrant their widespread application in cardiometabolic epidemiology.

PMID:40973720 | DOI:10.1038/s41366-025-01895-2

Eur Heart J. 2025 Sep 19:ehaf702. doi: 10.1093/eurheartj/ehaf702. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Antiplatelet treatment is recommended to start 24 h after intravenous thrombolysis due to concerns about haemorrhagic transformation. This study aimed to investigate the potential benefit of early antiplatelet after intravenous thrombolysis in minor stroke.

METHODS: A multicentre, double-blind, randomized trial was conducted in China between 7 August 2022 and 1 August 2024, to evaluate the efficacy and safety of early antiplatelet in acute ischaemic stroke patients presenting with mild neurological deficits, as indicated by a National Institutes of Health Stroke Scale (NIHSS) score of 0-5, who received intravenous thrombolysis. Patients were randomly assigned to receive either clopidogrel and aspirin or placebo within 6 h after intravenous thrombolysis. The primary endpoint was an excellent functional outcome at 90 days, indicated by a modified Rankin Scale (mRS) score of 0-1. Statistical analysis was based on a modified intention-to-treat population. Symptomatic intracranial haemorrhage, any intracranial haemorrhage, and major systemic bleeding were safety endpoints.

RESULTS: The primary endpoint was not met in this study. Of the randomly assigned 1022 patients, 995 patients were included in the modified intention-to-treat analysis (503 with early antiplatelet treatment and 492 with placebo). The primary endpoint occurred in 89.7% (451/503) of patients receiving early antiplatelet vs 89.6% (441/492) of those receiving placebo with no significant difference (odds ratio 1.00, 95% confidence interval .67-1.51, P = .99). Similar safety profiles were found between the two groups.

CONCLUSIONS: Among Chinese patients with acute minor ischaemic stroke who received intravenous thrombolysis, early antiplatelet treatment with clopidogrel plus aspirin was safe but did not improve already excellent functional outcome (mRS 0-1) at 90 days.

PMID:40973702 | DOI:10.1093/eurheartj/ehaf702

J Public Health (Oxf). 2025 Sep 19:fdaf117. doi: 10.1093/pubmed/fdaf117. Online ahead of print.

ABSTRACT

BACKGROUND: The increasing prevalence of caesarian section (C-section) births in South and Southeast Asia poses potential public health challenges by influencing maternal and child nutrition. These changes may contribute to the growing double burden of malnutrition (DBM), where maternal overweight/obesity coexists with child undernutrition. This study explores how C-section deliveries are linked to household-level DBM in three countries in this region. Understanding this link is key for developing effective interventions to improve maternal and child nutrition and reduce health burdens.

METHODS: We analysed 2022 Demographic and Health Survey (DHS) data from Bangladesh, Cambodia, and Nepal, including women aged 15-49 with at least one child, with available nutritional and delivery mode data. Chi-square tests, analysis of variance, and two-level logistic regression were used to assess the association between C-sections and DBM.

RESULTS: C-section deliveries were linked to a significantly higher risk of DBM in Bangladesh and Nepal. Delayed breastfeeding initiation after C-section further increased this risk. Urban households showed higher DBM rates, while longer breastfeeding duration was protective.

CONCLUSION: To reduce DBM, policies should focus on limiting unnecessary C-sections, promoting early and sustained breastfeeding, and supporting maternal postpartum health-especially in urban areas where risks are higher. Understanding local factors is crucial for effective interventions.

PMID:40973686 | DOI:10.1093/pubmed/fdaf117

Fam Pract. 2025 Aug 14;42(5):cmaf071. doi: 10.1093/fampra/cmaf071.

ABSTRACT

BACKGROUND: The aim of the study was to assess the effect of an interdisciplinary, digital consultation platform on the proportion of appropriate referrals from general practitioners (GPs) to an orthopaedic outpatient hospital.

METHODS: We performed a stepped wedge, cluster, randomized controlled trial. Sixty GP practices in the catchment area of a large teaching hospital in the Netherlands were randomized. Groups of GP practices entered the trial in four steps at 13-week intervals, at which point they received access to the Prisma platform. The platform allowed them to post questions about anonymized cases to a multidisciplinary group of specialists. During the control condition, GPs did not receive platform access. In both conditions, GPs provided care as usual. The proportion of appropriate referrals, defined as referrals for which a patient had either (i) more than one consultation with an orthopaedic surgeon or (ii) one consultation with additional diagnostics or interventions, was the primary outcome.

RESULTS: Participating GPs referred 4928 patients to hospital. Intention-to-treat analysis showed a 4.4% estimated increase in the proportion of appropriate referrals among GP practices randomized to have access to the platform compared to the control group, with an odds ratio (OR) of 1.22 [95% confidence interval (CI), 1.01-1.46; P = 0.037]. Per-protocol analysis showed a smaller, but non-significant, 2.2% difference between interventions, with an OR of 1.11 (95% CI, of 0.96%-1.28%; P = 0.178).

CONCLUSIONS: We observed a modest increase in appropriate referrals for orthopaedic review among GP practices randomized to the platform. On a larger scale, this could contribute to more sustainable access to specialist care.

PMID:40973675 | DOI:10.1093/fampra/cmaf071

Med Phys. 2025 Oct;52(10):e70019. doi: 10.1002/mp.70019.

ABSTRACT

BACKGROUND: Prostate cancer (PCa) presents a significant global health challenge affecting men. Accurate segmentation and grading of PCa lesions in multiparametric Magnetic Resonance Imaging (mp-MRI) are essential for effective diagnosis and treatment planning.

PURPOSE: This study aimed to develop and validate an automated model for PCa lesion segmentation and Prostate Imaging Reporting and Data System (PI-RADS) grading in mp-MRI.

METHODS: The lesion’s perceived characteristics are strongly related to both imaging modalities and lesion locations. Therefore, we propose a Lesion-guided Selective Multi-modal Integration (LeSMI) module. This module incorporates two advanced mechanisms-Dynamic Modality Weighting (DMW) and Localized Lesion Attention (LLA)-to dynamically integrate crucial information across and within imaging modalities. Specifically, DMW operates on the mp-MRI inputs (T2-weighted (T2w) images, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps) to dynamically assign weights to each modality, thereby integrating complementary information and enhancing feature identification across different contexts. LLA, on the other hand, maintains spatial structure information within each modality for precise lesion localization. Inspired by clinical workflows, our framework is employed through a two-stage Prostate Cancer Segmentation and Grading (PCaSG) strategy, leveraging knowledge from segmentation to improve PI-RADS grading performance. We validated our method using two publicly available datasets, namely, Prostate158 and PI-CAI Challenge, to assess its advantages over other methods. For the Prostate158 dataset, we used the officially reported partition with 119 cases for training, 20 for validation, and 19 for testing. In contrast, the PI-CAI Challenge dataset, which lacks predefined splits, was randomly divided into 180 for training, 20 for validation, and 20 for testing. In addition to these dataset partitions, 5-fold cross-validation was conducted on both the Prostate158 and PI-CAI Challenge datasets to provide a more robust and comprehensive statistical evaluation of the model’s performance.

RESULTS: Evaluated on the Prostate158 and PI-CAI Challenge datasets, our method demonstrated superior performance, achieving a Dice Similarity Coefficient (DSC) of 51.30% and a lesion-level quadratic-weighted kappa score ( $QW{K}_l$ ) of 62.48% on Prostate158, and a DSC of 43.81% and a $QW{K}_l$ of 42.98% on PI-CAI. These results represent improvements of up to 2% in DSC and 17% in $QW{K}_l$ over current state-of-the-art models on Prostate158, and enhancements of 4% in DSC and 3% in $QW{K}_l$ on PI-CAI.

CONCLUSION: The proposed model’s robustness in handling diverse lesion presentations, combined with its reliable assessments, underscores its significant clinical applicability. Our model offers substantial advancements in both segmentation accuracy and PI-RADS grading, addressing the challenges of inter-reader variability and the need for high expertise in conventional diagnostic practices. This technological innovation holds promise for enhancing early, accurate detection and risk assessment in prostate cancer management, ultimately improving patient outcomes.

PMID:40973673 | DOI:10.1002/mp.70019

Bioinformatics. 2025 Sep 19:btaf520. doi: 10.1093/bioinformatics/btaf520. Online ahead of print.

ABSTRACT

MOTIVATION: The gut microbiome plays an important role in human health and disease, prompting large-scale studies that generate extensive datasets. A critical preprocessing step in analyzing such datasets is anomaly detection, which aims to identify erroneous samples and prevent misleading statistical outcomes. Microbiome data, however, pose unique challenges such as compositionality, sparsity, interdependencies, and high dimensionality, limiting the effectiveness of conventional methods and highlighting the need for specifically-tailored approaches for anomaly detection in microbiome data.

IMPLEMENTATION: To address this challenge, we introduce KADAIF, a microbiome-specific anomaly detection method that generalizes the common Isolation Forest approach. As in Isolation Forest, KADAIF builds an ensemble of trees, each recursively partitioning the data along randomly selected features, and measures the average depth at which samples are isolated, assuming that anomalous samples will be isolated closer to the root. Unlike Isolation Forest, however, KADAIF partitions samples based on subsets of features (coupled with dimensionality reduction), addressing microbiome-specific properties such as sparsity and species interactions.

RESULTS: We evaluate KADAIF by simulating common scenarios that introduce anomalous behavior, demonstrating that KADAIF outperforms alternative methods across various settings and datasets. Furthermore, we show that KADAIF outperforms Isolation Forest in detecting anomalies also in other types of high dimensional sparse biological data. Finally, we show KADAIF’s application for identifying disease onset in longitudinal microbiome data and for partitioning cases vs controls based on the Anna Karenina principle. Combined, our work highlights KADAIF’s potential to enhance microbiome data processing and downstream analyses, with beneficial implications for precision medicine studies.

AVAILABILITY: An implementation of KADAIF, as well as all the code used for the analysis, is available on GitHub (https://github.com/borenstein-lab/KADAIF).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40973672 | DOI:10.1093/bioinformatics/btaf520

J Occup Health. 2025 Sep 19:uiaf053. doi: 10.1093/joccuh/uiaf053. Online ahead of print.

ABSTRACT

OBJECTIVES: This study evaluated the impact of transitioning from an 8-hour to a 12-hour shift schedule on sleep patterns and well-being in intensive care unit (ICU) nurses with preexisting sleep disturbances using wearable sensors. We also examined differences in outcome based on chronotype.

METHODS: We conducted an observational study at a university hospital ICU between November 2020 and October 2023, before and after a hospital-wide shift schedule change. Nurses wore wearable sensors and completed daily surveys over five weeks under each shift system. Rotating-shift ICU nurses with a Pittsburgh Sleep Quality Index (PSQI) score>5 were eligible. Sleep metrics and subjective well-being were compared using linear mixed models, adjusting for age. Sleep episodes were categorized relative to shift timing, and chronotype-stratified subgroup analyses were performed.

RESULTS: Eighty nurses completed the study (12-h shift: 37; 8-h shift: 43). The interval between shifts was greater for the 12-h shift group (36.12 vs 26.78 hours). Total sleep duration did not significantly differ between groups(12-h shift: 418.5 minutes; 8-h shift: 398 minutes); however, the 12-h shift group had less fragmented sleep, higher subjective well-being scores, and lower reported stress and fatigue. Evening chronotypes appeared to benefit more from 12-h shifts, with longer sleep duration and higher well-being scores, though these differences were not statistically significant.

CONCLUSIONS: Transitioning to a 12-hour shift schedule was associated with reduced sleep fragmentation and improved well-being, particularly among evening chronotypes. These findings suggest that shift schedule structure and individual chronotype may influence adaptation to shift work in ICU settings.

PMID:40973662 | DOI:10.1093/joccuh/uiaf053

Sleep. 2025 Sep 19:zsaf282. doi: 10.1093/sleep/zsaf282. Online ahead of print.

ABSTRACT

STUDY OBJECTIVES: GGIR is an R package for processing raw acceleration data to estimate sleep health parameters. We aimed to 1) assess the performance of three sleep algorithms within GGIR against PSG for detecting sleep/wake in clinically referred, typically-developing children (criterion validity); and 2) describe GGIR-derived sleep estimates from typically developing children enrolled in multiple cohort studies (face validity).

METHODS: For criterion evaluation, children (8-16y, N=30) wore an actigraphy device for one night during in-lab polysomnography with performance assessed using epoch-by-epoch analyses. For face validity evaluation, four community/free living datasets were used: 1) BMAYC (3-5y, N=310), 2) SSS (5-8y, N=118), 3) S-Grow2 (12-13y; N=291) and 4) ELEMENT (9-18y; N=543). All raw acceleration data were processed using GGIR (v.3.0-0) with the Cole-Kripke (CK), Sadeh (S), and van Hees (vH) algorithm settings.

RESULTS: Following the in-lab test, 60% of children were diagnosed with mild to severe obstructive sleep apnea (OSA). For criterion evaluation, the 30-s epoch-by-epoch analyses revealed that average balanced accuracies were 0.80 (Sensitivity=0.80; Specificity=0.79), 0.76 (Sensitivity=0.86; Specificity=0.65), and 0.67 (Sensitivity=0.95, Specificity=0.39) for GGIR-CK, GGIR-vH, and GGIR-S, respectively. For face validity evaluation, sleep estimates mirrored the in-lab performance metrics (e.g., sleep duration estimates were similar when using GGIR-CK and GGIR-VH but approximately one hour longer when using GGIR-S).

CONCLUSIONS: The in-lab performance metrics, from typically-developing children with and without OSA, and cohort-based descriptive statistics from samples of typically-developing children, provide benchmark data to guide investigators on the suitability of GGIR for automated processing of raw acceleration data for pediatric sleep estimation.

PMID:40973655 | DOI:10.1093/sleep/zsaf282