Tag: statistics

JMIRx Med. 2026 Jun 5;7:e81547. doi: 10.2196/81547.

ABSTRACT

BACKGROUND: Chaotic dynamics has been the subject of both theoretical and empirical research in epidemiology, with the most recent research strongly focusing on SARS-CoV-2. However, few empirical studies have been undertaken with respect to influenza, even though evidence of chaos has also been found in influenza surveillance data. Furthermore, empirical studies on chaos are focused on reconstructing hidden attractors in epidemiological time series to filter out noise; however, dynamical noise affecting chaotic dynamics can have relevant epidemiological features that are, in this way, left unresearched and that can be used for epidemiological surveillance and risk analysis by capturing the main underlying nonlinear processes associated with epidemiological dynamics.

OBJECTIVE: This study aimed to reinforce empirical research on chaotic dynamics in influenza surveillance and the study of the dynamical noise affecting that chaotic dynamics, addressing the consequences for epidemiological risk analysis and surveillance.

METHODS: Working with the weekly share of positive influenza tests for the Northern Hemisphere from January 2009 to March 2025 compiled by Our World in Data using FluNet data from the World Health Organization, we applied a recent method based on topological data analysis for reconstructing underlying attractors from time series and decomposing the dynamics down to independent and identically distributed noise. We adapted the method to the epidemiological context so that it can be used for predictive decomposition with direct application to epidemiological risk analysis and surveillance.

RESULTS: We found evidence of a low-dimensional chaotic attractor in the researched surveillance data, with a fractal dimension between 1 and 2, and a positive statistically significant largest Lyapunov exponent. The chaotic dynamics had power law scaling associated with long-wave influenza outbreaks, and it is affected by a stochastic component that is nonstationary in variance, leading to turbulent bursts in the outbreak dynamics. Testing different machine learning algorithms using the attractor as input for prediction and a 10-week rolling window, we found the following largest R2 scores for the prediction of the target series: 92.11% (1 week ahead), 85.95% (2 weeks ahead), 81.75% (3 weeks ahead), 77.59% (4 weeks ahead), and 73.35% (5 weeks ahead).

CONCLUSIONS: The main results reinforce previous theoretical and empirical studies on chaos in epidemiology. Our findings showed that there is a 2-dimensional chaotic attractor that can support up to a 1-month prediction of the target surveillance series with high prediction scores and that the attractor plus noise can be modeled in a way that supports uncertainty quantification and epidemiological risk analysis.

PMID:42247685 | DOI:10.2196/81547

JMIR Pediatr Parent. 2026 Jun 5;9:e88772. doi: 10.2196/88772.

ABSTRACT

BACKGROUND: Hemolytic disease of the fetus and newborn (HDFN) is a life-threatening condition resulting from maternal-fetal erythrocyte antigen incompatibility. Although anti-Rhesus D (RhD) prophylaxis has reduced RhD-associated cases, HDFN persists due to non-RhD antibodies and gaps in prevention. Population-based data on maternal and neonatal outcomes and recurrence patterns are limited.

OBJECTIVE: This study aimed to characterize maternal and neonatal outcomes, health care use patterns, and recurrence rates of HDFN across pregnancies.

METHODS: We conducted a retrospective cohort study of 464,711 pregnancies within the Kaiser Permanente Southern California system from January 1, 2008, to June 30, 2022. HDFN diagnoses were confirmed using validated natural language processing-assisted manual chart review and followed through 2023. Maternal characteristics, neonatal outcomes, and health care use were compared by HDFN status, and recurrence patterns were evaluated among individuals with ≥2 pregnancies. Chi-square tests and Wilcoxon rank-sum tests were used to compare characteristics between HDFN and non-HDFN pregnancies. Statistical significance was defined as P<.05.

RESULTS: Among all pregnancies, 139 of 464,711 (0.03%) were diagnosed with HDFN. Women with HDFN were more likely than those without HDFN to be older (aged ≥35 years; n=42, 30.2% vs n=97,146, 20.9%) and multiparous (n=121, 87.1% vs n=264,766, 57%). Infants affected by HDFN had higher rates of preterm birth (n=40, 28.4% vs n=42,240, 9.5%), low birth weight (<2500 g; n=22, 15.6% vs n=31,740, 7.1%), and neonatal jaundice (n=92, 65.2% vs n=162,465, 36.4%) than non-HDFN infants. Delivery hospitalizations (median 5.0, IQR 2.0-7.5 days vs median 2.0, IQR 1.0-2.0 days) and neonatal intensive care unit stays (median 4.0, IQR 0.0-7.0 days vs median 0.0, IQR 0.0-0.0 days) were longer, and maternal nondelivery hospitalizations were more frequent (n=27, 19.4% vs n=23,228, 5%) among pregnancies complicated by HDFN. Among women with a prior HDFN-affected pregnancy, 83.3% (n=25) experienced recurrence in a subsequent pregnancy. Of these recurrent cases, 32% (n=8) were severe, and 75% (n=6) involved fetal anemia requiring at least 1 intrauterine transfusion.

CONCLUSIONS: HDFN was rare but was associated with substantial maternal and neonatal morbidity, including higher rates of preterm birth, increased neonatal intensive care unit admissions, and greater health care use. Recurrence was frequent and clinically significant, underscoring the importance of early surveillance and proactive management strategies.

PMID:42247681 | DOI:10.2196/88772

Biodemography Soc Biol. 2026 Jun 5:1-12. doi: 10.1080/19485565.2026.2684735. Online ahead of print.

ABSTRACT

Women have a higher risk of Long COVID, defined as symptoms persisting for three or more months after SARS-CoV-2 infection. This study examines whether the elevated risk of Long COVID among women varies across income subgroups in a nationally representative sample of the U.S. population. Using data from the 2023 Behavioral Risk Factor Surveillance System (BRFSS), we estimated adjusted odds ratios for Long COVID associated with female sex, stratified by four age categories and 11 income groups. We conducted random-effects meta-analyses of income subgroup estimates within each age category and assessed heterogeneity using Cochran’s Q, I² statistics, prediction intervals, and Galbraith plots. Among younger age groups (18-34, 35-49, and 50-64 years), Cochran’s Q ranged from 7.70 to 10.98 (p > 0.10), and I² was 0.00%, indicating no significant heterogeneity across income groups. In the ≥65 age group, Cochran’s Q was 18.35 (p = 0.0494), and I² was 21.96%, suggesting modest heterogeneity. The 95% prediction interval for the ≥65 group (1.121-1.978) was wider than those for younger groups: 1.437-1.975 (18-34 years), 1.551-2.019 (35-49 years), and 1.355-1.766 (50-64 years).

PMID:42247671 | DOI:10.1080/19485565.2026.2684735

Sci Prog. 2026 Apr-Jun;109(2):368504261457365. doi: 10.1177/00368504261457365. Epub 2026 Jun 5.

ABSTRACT

ObjectiveDelirium is an established predictor of adverse outcomes in general ICU populations, but its specific prognostic impact in critically ill patients with acute pancreatitis (AP) remains unclear. This study aimed to evaluate the independent association between ICU-acquired delirium and clinical outcomes in this high-risk population.MethodsThis retrospective cohort study utilized data from the MIMIC-IV database (2008-2022). Critically ill adults with AP were included and stratified by the presence of ICU-acquired delirium, assessed using the CAM-ICU. The primary outcome was 90-day all-cause mortality. Secondary outcomes included 90-day unplanned readmission, emergency department revisits, and a composite adverse outcome. Propensity score matching (PSM) was performed to balance baseline characteristics, generating 178 matched pairs. Multivariable Cox regression with four sequential models and sensitivity analyses were conducted to assess robustness.ResultsAmong 594 included patients, 44.6% (265/594) developed delirium. After PSM, baseline characteristics were well-balanced. Delirium was independently associated with increased 90-day all-cause mortality (aHR=1.91, 95% CI: 1.04-3.50; P=0.038) and a higher risk of the composite adverse outcome (aHR=1.84, 95% CI: 1.24-2.73; P=0.002). The association with unplanned readmission remained significant after full adjustment (aHR=1.87, 95% CI: 1.20-2.92; P=0.006), while the association with ED revisits did not reach statistical significance. Sensitivity analyses confirmed the robustness of the primary findings.ConclusionsIn critically ill patients with AP, ICU-acquired delirium was an independent predictor of increased 90-day mortality, unplanned readmission and composite adverse outcomes. These findings highlight delirium as a significant prognostic factor, underscoring the importance of routine screening and targeted management in this vulnerable population.

PMID:42247663 | DOI:10.1177/00368504261457365

JMIR Form Res. 2026 Jun 5;10:e68004. doi: 10.2196/68004.

ABSTRACT

BACKGROUND: Surgeons work in a progressive field where communicating research is vital to advancing health care and enabling meaningful interactions among clinicians. It also contributes to societal impact, increases access to information, and reduces misinformation. Additionally, there can be barriers to accessing papers. Social media enhances research impact through sharing scholarly work and improving its translation into clinical practice, but little is known about how to design specific posts to maximize research impact through language.

OBJECTIVE: The purpose of this study was to determine the linguistic cues that optimize research impact among surgeons through Twitter (subsequently rebranded X). Additionally, this research combines the linguistic features of the posts and article access to determine their unique contributions.

METHODS: An exploratory linguistic analysis of 84 posts extracted from Twitter was conducted, which shared scholarly activity by 17 of the most-followed surgeons. The linguistic cues were measured on a continuous scale, computed from the percentage of each linguistic cue used in the text, and reported as mean (SD). Regression analysis and analysis of covariance were conducted to determine which cues influenced research impact and to estimate the potential association with study accessibility (open vs restricted access).

RESULTS: Analyzed tweets were highly analytic (mean 94.77, SD 9.00), moderate in clout (mean 42.69, SD 19.84), low in tone (mean 20.06, SD 33.91), suggesting negative tone use, and low in authenticity (mean 19.52, SD 24.50). Results suggest that a high use of formal language negatively impacts readership and citations. Analytical language was indirectly associated with readership (β=-0.296, 95% CI -423.57 to -59.95; P=.01) and citations (β=-0.524, 95% CI -0.442 to -0.187; P<.001). Linguistic clout had a positive association with readership (β=0.260, 95% CI 8.58-186.91; P=.03), and tone in tweets had a negative association with readership (β=-0.317, 95% CI -138.52 to -5.39; P=.04). Negative language tone was found to increase the impact of research. With respect to linguistic cues and study accessibility, the results also suggest that the number of citations was impacted by readership (F1,66=4.11, 95% CI 2.459E-06 to 0.003; P=.047) and analytic linguistic cues (F1,66=18.77, 95% CI -0.402 to -0.149; P<.001) used in the post, but the association of open (mean 3.04, SE 1.062) versus restricted access (mean 1.83, SE 0.716) was not statistically significant (F1,66=0.877, 95% CI 0.405-3.266; P=.352).

CONCLUSIONS: This research is the first to explore article accessibility and linguistic cues used in creating posts that share research on social media to determine their influence on research impact, making this study both innovative and unique relative to existing studies in the surgery field. Through language, the medical field can expand its impact and encourage dialogue between scientists and the public, thereby increasing scientific and societal contributions while reducing the negative effects of limited article access.

PMID:42247625 | DOI:10.2196/68004

Adv Clin Exp Med. 2026 Jun 5. doi: 10.17219/acem/220688. Online ahead of print.

ABSTRACT

BACKGROUND: Intracardiac leads commonly produce metal artifacts on computed tomography (CT) images. These artifacts may be reduced using dedicated metal artifact reduction algorithms, such as metal artifact reduction (MAR).

OBJECTIVES: The aim of this study was to develop a method for measuring lead-related artifacts in CT and to assess the suitability of various reconstruction presets for lead visualization.

MATERIAL AND METHODS: Fifty-four patients (mean age: 73.9 ±11.32 years) with implanted cardiac implantable electronic devices (CIEDs) who underwent cardiac CT, chest CT, or pulmonary angio-CT were included in the study. Images were reconstructed using at least 2 kernels (soft tissue and lung) with slice thicknesses of 0.6 mm or 1.0 mm. A tissue density volume >1,000 HU, corresponding to the presumed volume of hyperdense artifacts, was isolated within a manually drawn spherical region of interest (ROI), and the values were recorded. The obtained values for each iterative metal artifact reduction (iMAR) reconstruction preset were compared with native images (without iMAR) to calculate the percentage reduction in hyperdense artifacts.

RESULTS: All tested algorithm variants reduced artifact volume; however, only 2 presets achieved statistically significant reductions: “dental fillings” (p = 0.001) and “neuro coils” (p = 0.000). Pacemaker-dedicated presets reduced metal artifacts in all cases, although the reductions were not statistically significant (p = 0.667), which may limit their reliability in routine clinical practice.

CONCLUSIONS: We proposed a method for evaluating intracardiac leads that enables precise three-dimensional (3D) assessment of hyperdense artifacts. The metal artifact reduction technique demonstrated promising results, particularly for the “dental fillings” and “neuro coils” presets.

PMID:42247616 | DOI:10.17219/acem/220688

ESC Heart Fail. 2026 Jun 5:xvag148. doi: 10.1093/eschf/xvag148. Online ahead of print.

ABSTRACT

BACKGROUND: The renal effects of sacubitril/valsartan (Sac/Val) in heart failure (HF) remain incompletely defined, partly because kidney outcomes in pivotal HF trials have been variably prespecified and heterogeneously defined. We performed an updated systematic review and meta-analysis to assess whether the apparent renal signal of Sac/Val varies according to endpoint definition.

METHODS: This updated systematic review was conducted in accordance with PRISMA 2020. Building on the pre-existing evidence base from prior meta-analyses, we performed an updated search of PubMed and Web of Science. Randomized controlled trials (RCTs) and observational comparative studies in adults with HF comparing Sac/Val with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, or standard care were included if renal outcome data were extractable. Renal outcomes were analyzed using a hierarchical cross-endpoint approach, including sustained ≥50% estimated glomerular filtration rate (eGFR) decline, end-stage kidney disease (ESKD), composite kidney outcomes, and annualized eGFR decline. Random-effects meta-analysis was performed, with Hartung-Knapp and RCT-only sensitivity analyses.

RESULTS: Overall, 13 study-level reports comprising 34,969 patients were included. Sac/Val was associated with a lower risk of sustained 50% eGFR decline (RR 0.68, 95% CI 0.57-0.82) and composite kidney outcome (RR 0.70, 95% CI 0.58-0.84), with the composite endpoint showing the most robust and consistent signal, including in RCT-only analyses. By contrast, the association for ESKD alone was directionally favorable but not statistically significant (RR 0.80, 95% CI 0.64-1.00). Sac/Val was also associated with a slower annualized eGFR decline (MD 0.52 mL/min/1.73 m2/year, 95% CI 0.35-0.69).

CONCLUSIONS: The renal signal associated with Sac/Val in HF appeared at least partly dependent on endpoint definition. Composite kidney outcomes may best capture its potential nephroprotective effect, with sustained 50% eGFR decline showing a consistent pattern, whereas isolated ESKD remains inconclusive. These findings support a potential nephroprotective role of ARNIs but future research are needed.

PMID:42247581 | DOI:10.1093/eschf/xvag148

Scand J Clin Lab Invest. 2026 Jun 5:1-11. doi: 10.1080/00365513.2026.2684025. Online ahead of print.

ABSTRACT

Standard operating procedures (SOPs) are foundational to quality assurance in ISO-accredited clinical laboratories, but manual SOP development is labor-intensive and prone to error. With the emergence of large language models (LLMs), such as ChatGPT-5, there is growing interest in their potential to generate compliant, context-specific laboratory documentation. To test a regulatory-aligned, clause-based evaluation framework for assessing whether ChatGPT-5 can generate context-aware, fit-for-purpose SOP drafts within a real ISO-aligned laboratory environment. In a single-site, paired proof-of-concept study, 10 high-priority SOPs were generated using ChatGPT-5 and compared with matched SOPs written by experienced laboratory professionals. AI prompts were enriched with laboratory-specific inputs (e.g. operator manuals, reagent inserts and LIS codes). SOPs were evaluated using a seven-domain ISO/CLSI-aligned rubric, a laboratory-specificity audit, clause-mapping checklists, content validity indexing and usability testing by junior staff. Inter-rater reliability and paired non-parametric statistics were applied. AI-assisted SOPs demonstrated higher median quality scores, more complete ISO clause referencing, improved traceability and stronger lifecycle conformity compared with manual SOPs. Drafting time was reduced by approximately 91%. Expert reviewers showed excellent agreement (ICC = 0.91), and content validity indices exceeded established thresholds. Junior staff rated AI-assisted SOPs as clearer and more independently usable. Context-anchored prompts improved laboratory-specific relevance. This study demonstrates a replicable, clause-based framework for evaluating AI-assisted SOP generation within a regulated laboratory context. While findings support the feasibility of AI as a documentation co-author under expert oversight, external multi-center validation is required before broader regulatory or clinical adoption.

PMID:42247578 | DOI:10.1080/00365513.2026.2684025

JMIR Nurs. 2026 Jun 5;9:e88567. doi: 10.2196/88567.

ABSTRACT

BACKGROUND: For clinical nurses, manually entering information into hospital information systems (HISs) remains time-consuming and prone to omissions. Although speech recognition can reduce the need for manual entry, its use in clinical settings has historically been limited by code-switching, medical terminology, and noisy ward environments. Recent advances in customized automatic speech recognition (ASR) and large language models (LLMs) now make speech-based, structured documentation aligned with nursing frameworks such as DART (data, action, response, and teaching) increasingly feasible.

OBJECTIVE: This study developed and evaluated an integrated ASR and LLM system that transforms spoken nursing input into structured DART notes and evaluated its accuracy, usability, and clinical feasibility within HIS workflows.

METHODS: A code-switching nursing speech corpus from emergency and ward settings was used to fine-tune the Whisper large-v2 model with parameter-efficient adaptation. The LLM generated schema-constrained DART records from ASR transcripts, which were verified by nurses before being uploaded to the corresponding HIS fields. Evaluation included mixed error rate for ASR accuracy, F1-scores, and agreement statistics for DART classification, hallucination assessments based on factual correctness, and analysis of nurse feedback on system use.

RESULTS: The fine-tuned ASR model reduced the mixed error rate from 44.79% to 6.67%. DART generation achieved a macroaveraged F1-score of 0.82 (95% CI 0.80-0.84) and met the noninferiority margin relative to human transcripts (δ=-0.04). The hallucination rate was 2.51%. During deployment, the monthly volume of valid nursing notes generated through voluntary use of the ASR system increased from 32,724 to 65,417, where each note represented a single documentation entry generated per patient care episode. Among 120 participating nurses, 91 (75.8%) reported reduced workload and improved completeness.

CONCLUSIONS: The integrated ASR and LLM system was feasible and showed strong performance, with good acceptance among clinical nurses. It reduced the manual documentation burden, improved record completeness, and demonstrated the value of an ASR- and LLM-supported workflow for nursing documentation.

PMID:42247576 | DOI:10.2196/88567

Biomol Biomed. 2026 Jun 5. doi: 10.17305/bb.2026.14308. Online ahead of print.

ABSTRACT

Early hospital arrival is essential for timely reperfusion therapy in acute stroke. This study aimed to identify clinical, symptom-related, and prehospital factors associated with delayed hospital arrival among patients with first-ever acute stroke, with particular focus on the 4.5-hour therapeutic window. This prospective single-center observational study was conducted at a comprehensive stroke center in Istanbul, Türkiye, between December 2023 and October 2024. Among 362 patients screened for suspected acute cerebrovascular events, 205 adults with first-ever acute ischemic stroke, intracerebral hemorrhage, or transient ischemic attack managed through the acute stroke pathway were included. Data were collected using a structured clinical form and a questionnaire on barriers to accessing acute stroke treatment, administered through face-to-face interviews with patients’ relatives. Prehospital delay was defined as the interval from last known well (LKW) to hospital arrival. Patients were analyzed according to arrival within 1 hour, 2 hours, 3 hours, and 4.5 hours, using appropriate comparative statistical tests. The mean LKW-to-arrival time was 338.66 ± 345.67 minutes, with a median (IQR) of 240 (90-720) minutes. Overall, 29.3% of patients arrived within 1 hour, 41.0% within 2 hours, 48.3% within 3 hours, and 58.5% within 4.5 hours. Facial droop was consistently associated with earlier hospital arrival across multiple time thresholds (p ≤ 0.004), and syncope was more frequent among early presenters (p = 0.001). Conversely, visual symptoms were associated with delayed presentation, including vision loss after 3 hours and diplopia beyond 4.5 hours (p = 0.042 for both). Diabetes mellitus was associated with delayed arrival at the 1-hour and 2-hour thresholds, while hypertension was more common among patients arriving after 4.5 hours. Prehospital delay remains a substantial barrier to timely acute stroke care. Recognizable symptoms such as facial droop may facilitate earlier presentation, whereas less typical symptoms, particularly visual disturbances, may contribute to delayed arrival. These findings support locally tailored public awareness strategies and optimized prehospital stroke pathways that emphasize both typical and atypical stroke symptoms.

PMID:42247575 | DOI:10.17305/bb.2026.14308