Tag: statistics

Pharmacoepidemiol Drug Saf. 2026 Jun;35(6):e70398. doi: 10.1002/pds.70398.

ABSTRACT

BACKGROUND: Direct oral anticoagulants (DOACs) offer advantages over warfarin; however, concerns exist regarding their association with acute interstitial lung diseases (ILDs). This study investigated the risk of acute-onset ILDs associated with DOAC use.

METHODS: We conducted a case-crossover study to assess the risk of hospitalization for acute-onset ILDs following DOAC initiation using the JMDC database, a Japanese administrative claims database. Patients aged ≥ 18 years hospitalized for acute-onset ILDs (April 2011-February 2023) were included. DOAC exposure was defined as ≥ 14 days within predefined 30-day windows: 1-30 days before admission (exposure period) and 60-90 and 120-150 days before admission (reference periods). The primary outcome was hospitalizations for acute-onset ILD, identified using a validated algorithm. Conditional logistic regression estimated odds ratios (ORs). Sensitivity analyses included a bidirectional case-crossover design, a case-crossover analysis with warfarin as an active comparator, weighted case-crossover analysis, and a case-case-time-control design. A descriptive cohort analysis of new DOAC and warfarin users examined ILD frequency and prognosis.

RESULTS: The main case-crossover analysis included 178 patients, showing an association between DOAC use and acute-onset ILDs (adjusted OR, 4.44 [95% CI, 1.58-12.5]). Sensitivity analyses demonstrated a consistent direction of association (adjusted ORs: 3.37-6.85). In descriptive cohort analysis (52 021 DOAC and 12 026 warfarin initiators), ILD incidence was low (0.24% vs. 0.20%), but 90-day mortality was higher in the DOAC group (21% vs. 0%).

CONCLUSIONS: DOAC use was associated with an increased risk of hospitalization for acute-onset ILDs. Clinicians should monitor patients on DOACs for ILD symptoms.

PMID:42175652 | DOI:10.1002/pds.70398

J Clin Lab Anal. 2026 May 22:e70249. doi: 10.1002/jcla.70249. Online ahead of print.

ABSTRACT

BACKGROUND: The International Consensus on ANA Patterns (ICAP) recently codified the AC-30 nuclear pattern, characterized by fine speckled nuclear texture and metaphase chromatin staining. Although formally recognized, its clinical significance remains unclear. Quantitative methods and objective outcome measures for characterizing AC-30 have not been established.

METHODS: A retrospective analysis was conducted using archived HEp-2 ANA images. AC-30 was defined by unanimous scoring from three blinded experts. AC-1 and AC-2 served as comparator sets. Clinical diagnoses and serological data were retrieved from hospital records. Pixel classification was performed using ilastik, and per-nucleus intensity features were extracted with CellProfiler. Image-level separation was assessed by partitioning nuclei into high- and low-intensity groups via Isolation Forest. Statistical comparisons used Welch’s t-tests and Spearman’s rank correlation.

RESULTS: The AC-30 group included 183 images (AC-1, n = 183; AC-2, n = 207). Within AC-30, 57.4% had non-AID or unknown diagnoses, 26.8% had other autoimmune diseases, and 15.8% had ANA-associated rheumatic disease (AARD). In ENA-negative AC-30, AARD accounted for 7.7% (6/78), compared to 18.3% (21/115) in ENA-negative AC-2. RA accounted for 16.9% (31/183) in AC-30, consistent across ENA strata. AC-30 displayed lower per-nucleus intensities than AC-2 (all p < 0.0001) and reduced per-image ΔMaxIntensity (0.177 vs. 0.252, p < 0.0001), while ΔMeanIntensity was similar (0.068 vs. 0.067, p = 0.549).

CONCLUSIONS: AC-30 is quantitatively dimmer than AC-2, with reduced nuclear brightness and peak-intensity separation. In this retrospective cohort, ENA-negative AC-30 was associated with fewer AARD cases. RA accounted for ~17% of AC-30, and anti-CCP and RF remained informative markers. The ilastik-CellProfiler workflow enables auditable ANA quantification.

PMID:42175642 | DOI:10.1002/jcla.70249

Crit Rev Anal Chem. 2026 May 22:1-14. doi: 10.1080/10408347.2026.2675511. Online ahead of print.

ABSTRACT

Nuclear magnetic resonance (NMR) spectroscopy is a fundamental tool of metabolomics, valued for its reproducibility, quantitative accuracy and broad applicability across biological, chemical and clinical sciences. However, methodological inconsistencies, insufficient protocol reporting and limited infrastructure continue to hinder reproducibility and data sharing. To assess the current state of NMR metabolomics practice, we developed a comprehensive questionnaire and distributed it worldwide to researchers engaged in NMR-based metabolomics. We received 75 responses from a diverse cohort of investigators from academia, clinics and core facilities. The survey focused on Quality Assurance (QA) and Quality Control (QC) practices and provides an overview of the current status of NMR metabolomics and its implementation. Results reveal that while 86% of laboratories have Standard Operating Procedures (SOPs), deviations from these protocols are common and often undocumented, undermining reproducibility. QC practices, including pooled samples and system suitability checks, are widely recognized, but their implementation is inconsistent. Data accessibility remains limited, with fewer than 10% of respondents routinely depositing raw or processed spectral data in public repositories. Formal regulatory oversight and dedicated QA personnel are uncommon. Training is largely informal, with substantial gaps in areas such as data analysis and statistics, raising concerns about knowledge transfer and methodological consistency. Our findings describe a technically skilled community that is constrained by variations in NMR infrastructure and inconsistent implementation of best practices. Addressing these issues through adaptive standardization, structured training programs, and stronger institutional support is critical for advancing transparency, reproducibility and impact of NMR in metabolomics.

PMID:42175625 | DOI:10.1080/10408347.2026.2675511

Psychooncology. 2026 May;35(5):e70490. doi: 10.1002/pon.70490.

ABSTRACT

BACKGROUND: Immune-checkpoint Inhibitors (ICIs) are increasingly used in advanced cancers and can be associated with immune-related adverse events (irAEs).

AIMS: This study examined the association between anxiety and depression, irAEs, and overall survival in a national cohort of ICI-treated cancer survivors.

METHODS: This retrospective cohort study used data from 8671 2-year survivors following ICI treatment with advanced melanoma, lung, colorectal, head and neck, and bladder cancers from a nationwide de-identified electronic health record-derived database. Bivariate tests, Kaplan-Meier curves, and multivariable logistic regressions were conducted using R.

RESULTS: The majority (79%, N = 6820) did not have irAEs, anxiety, or depression. The landmark analysis observed that accounting for covariates, irAEs were associated with anxiety or depression (aOR: 1.70, 95% CI: 1.41, 2.06, p < 0.001) in the first 2 years after ICIs. Female sex (aOR: 1.46, 95% CI: 1.24, 1.73, p < 0.001), poorer functional status (ECOG = 2: 1.47, 95% CI: 1.06, 2.04, p = 0.022), Medicaid coverage (aOR: 1.54, 95% CI: 1.08, 2.18, p = 0.016), and being seen in an academic practice setting (aOR: 3.62, 95% CI: 3.02, 4.35, p < 0.001) were associated with greater odds of anxiety/depression diagnoses compared with male sex, ECOG = 0, Medicare, and community practice settings. Non-white survivors who experienced an irAE had lower odds of experiencing anxiety/depression (aOR: 0.58, 95% CI: 0.45, 0.46, p < 0.001) compared with White survivors.

CONCLUSIONS: Few studies have examined the relationship between anxiety and depression and irAEs in a national sample. Findings have implications for survivorship quality of life and integrated mental health care delivery.

PMID:42175622 | DOI:10.1002/pon.70490

J Nurs Manag. 2026;2026(1):e5593996. doi: 10.1155/jonm/5593996.

ABSTRACT

BACKGROUND: With the rapid development of science and technology, the application of artificial intelligence in the field of healthcare is becoming increasingly widespread. As the executors and responsible persons of nursing work, nurses’ understanding and attitude toward AI technology determine whether AI can be deeply integrated and successfully applied in the field of nursing.

AIMS: To investigate nurses’ negative and positive attitudes toward the use of artificial intelligence and influencing factors, explore the mediating effect of artificial intelligence literacy and anxiety between hospital hierarchy differences and negative and positive attitudes toward the use of artificial intelligence, and provide basis for improving nurses’ attitudes toward the use of artificial intelligence.

METHODS: In November 2025, the convenience sample of 436 nurses from different hospitals in Shandong Province was surveyed. Data were collected using the general information questionnaire, the attitude scale toward the use of artificial intelligence technologies in nursing, the artificial intelligence anxiety scale, and the artificial intelligence literacy scale. Multiple linear regression analyzed the influencing factors of nurses’ negative and positive attitudes toward the use of artificial intelligence. Mediation analyses explored the mediating effect of artificial intelligence literacy and anxiety between the hospital hierarchy differences of nurses and their negative and positive attitudes toward the use of artificial intelligence.

RESULTS: The score of negative attitude was 14.55 ± 6.63, and the score of positive attitude was 38.21 ± 3.87. Artificial intelligence literacy and anxiety partially mediated the relationship between hospital hierarchy differences and the negative and positive attitudes toward the use of artificial intelligence, with the total mediating effects being 3.067 and -1.011, respectively.

CONCLUSION: Hospital hierarchy differences could directly positively predict the negative and positive attitudes toward the use of artificial intelligence and could also indirectly positively predict the negative attitude toward the use of artificial intelligence through mediation by artificial intelligence literacy and anxiety and negatively predict the positive attitude toward the use of artificial intelligence.

IMPLICATIONS FOR NURSING MANAGEMENT: Providing personalized artificial intelligence training based on the needs of hospitals could improve nurses’ attitudes toward the use of artificial intelligence, increase their artificial intelligence literacy, and reduce their anxiety.

PMID:42175619 | DOI:10.1155/jonm/5593996

Nurs Open. 2026 May;13(5):e70590. doi: 10.1002/nop2.70590.

ABSTRACT

AIM: To investigate the current status of discharge readiness in post-surgical patients with obstructive sleep apnoea hypopnoea syndrome (OSAHS) and analyse the influencing factors.

DESIGN: A cross-sectional survey.

METHODS: The study was conducted on 99 postoperative patients with OSAHS who were hospitalised in our department from October 2022 to November 2023. Data were collected using a general information questionnaire, the Chinese version of the Discharge Readiness Scale, the Chinese version of the Discharge Guidance Quality Scale and the Social Support Rating Scale.

RESULTS: The total score for discharge readiness in OSAHS post-surgical patients was 98.63 ± 9.08, with the highest scores in the dimensions of adaptive capacity (8.38 ± 0.77), anticipatory support (8.34 ± 1.21) and personal status (7.80 ± 1.10). Factors influencing discharge readiness scores included living arrangements, quality of discharge guidance, educational level and social support.

PATIENT OR PUBLIC CONTRIBUTION: On the day of discharge, OSAHS post-surgical patients fill in the questionnaire required by our study under the premise of informed consent.

TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) identifier: ChiCTR2600122402.

PMID:42175597 | DOI:10.1002/nop2.70590

Environ Int. 2026 May 15;212:110306. doi: 10.1016/j.envint.2026.110306. Online ahead of print.

ABSTRACT

p-Phenylenediamines (PPDs) are widely used as antioxidants in tire formulations and are released to the environment via tire wear, where they can be transformed into more toxic p-phenylenediamine-derived quinones (PPD-Qs). Despite growing recognition of their environmental importance, information on the occurrence and behavior of these compounds in farmland soils remains limited. In this study, 122 surface soil samples were collected from agricultural regions across China to quantify six PPDs and five PPD-Qs and to examine their spatial patterns, sources, transformation drivers, and potential risks. The PPD and PPD-Q compounds were detected in all samples, with national mean concentrations of 1.02 and 0.28 ng/g, respectively. Spatially, the ΣPPD concentrations were 0.19‒4.93 ng/g in southern China and 0.13‒4.13 ng/g in northern China, while the ΣPPD-Q concentrations were 0.06‒1.37 ng/g in southern China and 0.03‒1.48 ng/g in northern China. 6PPD and 6PPD-Q were the dominant congeners, indicating strong inheritance from parent compounds to transformation products. Source apportionment implicates both direct inputs from agricultural rubber materials and indirect inputs via atmospheric transport of industrial rubber-derived emissions. Conventional statistical approaches together with non-linear dependence analyses identified sunshine duration and soil total organic carbon (TOC) as key factors influencing their composition and transformation. Risk assessment showed that current non-carcinogenic human and ecological risks were within acceptable limits. These findings advance understanding of the environmental occurrence and transformation of PPDs and PPD-Qs in agricultural soils and highlight key factors relevant to their management and risk control.

PMID:42172717 | DOI:10.1016/j.envint.2026.110306

Vaccine. 2026 May 22;86:128737. doi: 10.1016/j.vaccine.2026.128737. Online ahead of print.

ABSTRACT

BACKGROUND: Pregnant and lactating women face heightened risk of mortality due to Ebola disease during outbreaks, yet have historically been excluded from vaccine trials and emergency vaccination campaigns. This study assessed willingness to receive an Ebola vaccine and identified associated factors among pregnant and lactating women in Kampala-Uganda, in the aftermath of the 2022 Sudan ebolavirus outbreak and amid the COVID-19 pandemic.

METHODS: A cross-sectional survey was conducted in 2023 among pregnant/lactating women receiving antenatal/postnatal services at Kawempe National Referral Hospital. Data were collected using a structured questionnaire adapted from the WHO SAGE Working Group on Vaccine Hesitancy and included additional items on Ebola vaccine attitudes, socio-demographics, prior vaccination experiences, vaccine preferences (oral vs injectable) and health status, including chronic illness. The primary outcome was willingness to receive an Ebola vaccine. Modified Poisson regression was done using STATA version 15 and a p-value <0.05 was considered statistically significant.

RESULTS: Among 424 participants (212 pregnant, 212 lactating), 252(59.4%) expressed willingness to receive an Ebola vaccine. In multivariable analysis, willingness was significantly associated with previous COVID-19 vaccination (adjusted Prevalence Ratio [aPR] = 1.32; 95%CI:1.02-1.70) and a preference for injectable vaccines only, compared to those open to oral or injectable modes (aPR = 1.54; 95%CI:1.18-2.00). Among those who were unwilling or unsure (n = 172), the most cited reasons for hesitancy were fear of side effects (22.1%) and lack of awareness about Ebola vaccines (20.3%).

CONCLUSION: While just over half of the pregnant and lactating women in this study were willing to receive an Ebola vaccine, concerns about safety and limited awareness remain important barriers. Prior COVID-19 vaccination and preference for injectable vaccines were positively associated with willingness, suggesting that trust and familiarity with vaccination influence acceptance. Tailored risk communication and community engagement, and inclusion in vaccine planning are essential to ensuring equitable protection during EVD outbreaks.

PMID:42172692 | DOI:10.1016/j.vaccine.2026.128737

JMIR Form Res. 2026 May 22;10:e83918. doi: 10.2196/83918.

ABSTRACT

BACKGROUND: Unplanned hospital readmissions represent a critical operational and financial challenge for health care systems in the United States, with 3.8 million 30-day all-cause readmissions in 2018 at an average cost of US $15,200 each, totaling US $58 billion in costs. Many published prediction models rely on comprehensive information (eg, full billing abstractions, discharge summaries, laboratory tests, and vitals) that becomes available only late in the encounter, limiting usefulness for real-time, in-hospital intervention. This creates a timeliness-accuracy trade-off: models that are most accurate retrospectively may arrive too late to act upon.

OBJECTIVE: This study tests whether a clinically meaningful predictive signal for 30-day all-cause readmission is present within a limited set of structured clinical codes recorded during the patient’s hospital stay. This approach evaluates whether predictive signals are retained when using a restricted set of structured clinical codes.

METHODS: We conducted a retrospective comparative modeling study using a large, deidentified electronic health record dataset of 50,000 inpatient encounters from the 2019 New York State Emergency Department Database. Two feature sets were constructed: (1) a limited set consisting of the first 5 ICD-10 (International Classification of Diseases, 10th Revision) diagnosis codes, the first 5 Current Procedural Terminology (CPT) codes, and Charlson Comorbidity Index (CCI; 11 input features); and (2) a rich set using all available ICD-10 and CPT codes plus CCI (up to 135 input features). We trained 4 models: random forest, CatBoost, multilayer perceptron, and DistilBERT (a distilled Bidirectional Encoder Representations from Transformers [BERT] model; structured codes mapped to text and tokenized with DistilBERT-base-uncased). Evaluation used an untouched hold-out set of 10,000 encounters, preserving the natural 21.1% readmission prevalence. Primary metrics were area under the receiver operating characteristic curve (AUROC), F1-score, and accuracy. To address class imbalance, the training split only was balanced via undersampling of the majority class and bootstrap oversampling of the minority class; validation/test distributions were left unchanged.

RESULTS: Models trained on the limited feature set achieved AUROC values ranging from 0.5369 to 0.5596 and F1-scores from 0.2555 to 0.3434. Across 3 of 4 architectures, models trained on the limited feature set matched or exceeded the discrimination of their rich counterparts. The best model (random forest, limited) achieved an area under the curve AUROC 0.5596 (95% CI 0.5440-0.5739) compared to the best performing rich model (DistilBERT) at 0.5703 (95% CI 0.5565-0.5842), an absolute difference of 0.0107. The highest F1-score (0.3434) was achieved by DistilBERT on the limited feature set. Differences across architectures were small in absolute terms, with threshold-dependent metrics (eg, F1-score) being comparable.

CONCLUSIONS: The findings suggest that models using a limited set of structured clinical codes can achieve performance comparable to those using more comprehensive coding information.

PMID:42172660 | DOI:10.2196/83918

J Trauma Acute Care Surg. 2026 Jun 1;100(6):915-921. doi: 10.1097/TA.0000000000004941. Epub 2026 May 22.

ABSTRACT

OBJECTIVES: Pediatric thoracolumbar spinal injuries (TLSIs) are rare and uncommonly require intervention. In adults, plain x-ray is not adequate to screen for spinal injury. In children, plain film utilization for screening is variable and supportive evidence is lacking. Liberal CT screening for TLSI in children results in significant unnecessary radiation exposure. We investigated the utility of plain x-rays and physical exam (PE) to screen children for TLSI.

METHODS: Children aged 1 to 18 years with MRI or CT-confirmed TLSI presenting to one of five Level 1 pediatric trauma centers between 2017 and 2022, who had a plain film involving the injured spine, were identified. ICD10 codes for injuries, imaging types, intervention (surgery or bracing), age, mechanism, BMI, fracture type (thoracic, lumbar, both), comorbidities, PE findings, and type of radiograph (spine, chest, abdomen) were identified and compared.

RESULTS: Two hundred thirty-two children with MRI or CT-confirmed TLSI and plain x-rays were identified (46% thoracic, 34.9% lumbar, 18.1% both). 57.3% (n=133) of patients underwent dedicated spine radiographs, while the others had only chest (n=87, 37.5%) or only abdominal (n=10, 4.3%) radiographs. In total, 13.79% of patients underwent surgery, 43.9% required bracing, and the remaining had no intervention. Of the patients with dedicated spine films, x-rays alone were 82.7% sensitive for injury and 100% sensitive for injury needing surgery. Patients with lumbar spine injuries who had positive spine x-rays and suspicious PE findings were significantly more likely to require intervention (p<0.0001). No child with a normal spinal x-ray in the lumbar region required intervention, regardless of PE findings.

CONCLUSIONS: In this retrospective study, dedicated spinal x-rays combined with PE reliably excluded >99% of all injuries and 100% of injuries requiring intervention. These findings suggest children can be effectively screened for TLSI with PE and spinal x-rays, reserving cross-sectional imaging for positive x-ray findings and persistent PE findings on repeat exam. (J Trauma Acute Care Surg. 2026;100:915-921.

LEVEL OF EVIDENCE: Prognostic/Epidemiological; Level III.

PMID:42172649 | DOI:10.1097/TA.0000000000004941