Nevin Manimala

JMIR Med Inform. 2024 Oct 15;12:e56343. doi: 10.2196/56343.

ABSTRACT

BACKGROUND: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

OBJECTIVE: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes.

METHODS: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains.

RESULTS: A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited.

CONCLUSIONS: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support.

PMID:39405525 | DOI:10.2196/56343

JMIR Aging. 2024 Oct 15;7:e63024. doi: 10.2196/63024.

ABSTRACT

BACKGROUND: The co-occurrence of chronic illnesses and substance use presents complex challenges for health care systems. Understanding the interplay between these factors, compounded by the context of the COVID-19 pandemic, is essential for effective intervention strategies.

OBJECTIVE: This study aims to investigate the relationships among chronic illness, substance use, and COVID-19 infection in adults aged 50 years and older.

METHODS: Participants were 1196 adults aged 50 years and older. Descriptive statistics were used to describe demographic information. Logistic regressions and multiple regression analyses were used to determine associations between chronic illnesses, substance use, and COVID-19 infection. Mediation analysis was used to determine the effect of chronic illness mediators in the association between COVID-19 concerns and substance use.

RESULTS: The mean age was 68 (SD 10.3) years, with 58.6% (701/1196) being women. Adjusted analysis revealed that age and sex (women) significantly predicted a lower level of substance use (P<.05). However, marital status (separated or widowed) and chronic illness significantly predicted a higher level of substance use (P<.05). Furthermore, having dementia, arthritis, and high cholesterol significantly predicted a higher level of concern about the COVID-19 pandemic (P<.05). Logistic regression analysis indicated that individuals with hypertension (odds ratio [OR] 1.91, 95% CI 1.37-2.66; P<.001), lung disease (OR 2.42, 95% CI 1.23-4.75; P=.01), heart condition (OR 1.99, 95% CI 1.28-3.10; P=.002), stroke (OR 2.35, 95% CI 1.07-5.16; P=.03), and arthritis (OR 1.72, 95% CI 1.25-2.37; P=.001) were more likely to have their work affected by the COVID-19 pandemic. The mediation analysis showed a significant effect of COVID-19 concern on substance use through the mediation of chronic illness, with a 95% CI of -0.02 to -0.01 and an indirect effect of -0.01.

CONCLUSIONS: Our study reveals complex associations among chronic illnesses, substance use, and COVID-19 infection among adults aged 50 years and older. It underscores the impact of demographics and specific chronic conditions on substance use behaviors and COVID-19 concerns. In addition, certain chronic illnesses were linked to heightened vulnerability in employment status during the pandemic. These findings emphasize the need for targeted interventions addressing physical health and substance use in this population during the COVID-19 pandemic.

PMID:39405517 | DOI:10.2196/63024

BJS Open. 2024 Sep 3;8(5):zrae117. doi: 10.1093/bjsopen/zrae117.

ABSTRACT

BACKGROUND: A surgeon’s daily performance may be affected by operating room organizational factors, potentially impacting patient outcomes. The aim of this study was to investigate the link between a surgeon’s exposure to delays in starting scheduled operations and patient outcomes.

METHODS: A prospective observational study was conducted from 1 November 2020 to 31 December 2021, across 14 surgical departments in four university hospitals, covering various surgical disciplines. All elective surgeries by 45 attending surgeons were analysed, assessing delays in starting operations and inter-procedural wait times exceeding 1 or 2 h. The primary outcome was major adverse events within 30 days post-surgery. Mixed-effect logistic regression accounted for operation clustering within surgeons, estimating adjusted relative risks and outcome rate differences using marginal standardization.

RESULTS: Among 8844 elective operations, 4.0% started more than 1 h late, associated with an increased rate of adverse events (21.6% versus 14.4%, P = 0.039). Waiting time surpassing 1 h between procedures occurred in 71.4% of operations and was also associated with a higher frequency of adverse events (13.9% versus 5.3%, P < 0.001). After adjustment, delayed operations were associated with an elevated risk of major adverse events (adjusted relative risk 1.37 (95% c.i. 1.06 to 1.85)). The standardized rate of major adverse events was 12.1%, compared with 8.9% (absolute difference of 3.3% (95% c.i. 0.6% to 5.6%)), when a surgeon experienced a delay in operating room scheduling or waiting time between two procedures exceeding 1 h, as opposed to not experiencing such delays.

CONCLUSION: A surgeon’s exposure to delay before starting elective procedures was associated with an increased occurrence of major adverse events. Optimizing operating room turnover to prevent delayed operations and waiting time is critical for patient safety.

PMID:39405502 | DOI:10.1093/bjsopen/zrae117

J Phys Chem A. 2024 Oct 15. doi: 10.1021/acs.jpca.4c05677. Online ahead of print.

ABSTRACT

A transient carbon-centered hydroperoxyalkyl intermediate (•QOOH) in the oxidation of cyclopentane is identified by IR action spectroscopy with time-resolved unimolecular decay to hydroxyl (OH) radical products that are detected by UV laser-induced fluorescence. Two nearly degenerate •QOOH isomers, β- and γ-QOOH, are generated by H atom abstraction of the cyclopentyl hydroperoxide precursor. Fundamental and first overtone OH stretch transitions and combination bands of •QOOH are observed and compared with anharmonic frequencies computed by second-order vibrational perturbation theory. An OH stretch transition is also observed for a conformer arising from torsion about a low-energy CCOO barrier. Definitive identification of the β-QOOH isomer relies on its significantly lower transition state (TS) barrier to OH products, which results in rapid unimolecular decay and near unity branching to OH products. A benchmarking approach is utilized to compute high-accuracy stationary point energies, most importantly TS barriers, for cyclopentane oxidation (C₅H₉O₂), building on higher level reference calculations for ethane oxidation (C₂H₅O₂). The experimental OH product appearance rates are compared with computed statistical microcanonical rates using RRKM theory, including heavy-atom tunneling, thereby validating the computed TS barrier. The results are extended to thermal unimolecular decay rate constants at temperatures and pressures relevant to cyclopentane combustion via master-equation modeling. The various torsional and ring puckering states of the wells and transition states are explicitly considered in these calculations.

PMID:39405476 | DOI:10.1021/acs.jpca.4c05677

Angew Chem Int Ed Engl. 2024 Oct 15:e202410308. doi: 10.1002/anie.202410308. Online ahead of print.

ABSTRACT

With the rise of machine learning (ML), the modeling of chemical systems has reached a new era and has the potential to revolutionize how we understand and predict chemical reactions. Here, we probe the historic dependence on utilizing enantiomeric excess (ee) as a target variable and discuss the benefits of using relative Gibbs free activation energies (ΔΔG^≠), grounded firmly in transition-state theory, emphasizing practical benefits for chemists. This perspective is intended to discuss best practices that enhance modeling efforts especially for chemists with an experimental background in asymmetric catalysis that wish to explore modelling of their data. We outline the enhanced modeling performance using ΔΔG^≠, escaping physical limitations, addressing temperature effects, managing non-linear error propagation, adjusting for data distributions and how to deal with unphysical predictions,in order to streamline modeling for the practical chemist and provide simple guidelines to strong statistical tools. For this endeavor, we gathered ten datasets from the literature covering very different reaction types. We evaluated the datasets using fingerprint-, descriptor-, and graph neural network-based models. Our results highlight the distinction in performance among varying model complexities with respect to the target representation, emphasizing practical benefits for chemists.

PMID:39405467 | DOI:10.1002/anie.202410308

J Trauma Acute Care Surg. 2024 Oct 15. doi: 10.1097/TA.0000000000004446. Online ahead of print.

ABSTRACT

BACKGROUND: Rib fracture pain is a major issue but likely underappreciated, given that patients avoid activity due to the pain. Pain is one criterion used to determine if someone is a candidate for surgical stabilization of rib fractures (SSRF). The purpose of this study was to assess pain for rib fracture patients, hypothesizing pain from rib fractures is underappreciated in current practice.

METHODS: A prospective study analyzing patients with one or more rib fractures admitted to our Level I trauma center from March 2023 through February 2024. Exclusion criteria included refusal to participate, ventilator dependent, younger than 18 years, moderate/severe traumatic brain injury, spinal cord injury, pregnancy, or incarceration. Basic demographics were obtained. Participants rated their pain on an 11-point Numerical Rating Scale while resting in bed and performing a series of movements (0, no pain; 10, worst pain imaginable). Movements included incentive spirometer, flexion, extension, bilateral side bending, bilateral rotation, and holding a 5-pound dumbbell. Patients undergoing SSRF were surveyed pre- and postoperatively. Outcomes included the difference between pain scores at rest versus performing all movements, difference between pain scores pre- and post-SSRF, and incentive spirometry pre- and postoperatively. Nonparametric analysis was completed with the Wilcoxon signed-rank test with statistical significance set at p < 0.05.

RESULTS: One-hundred two patients were enrolled. The mean age was 60 ± 15 years; 57.8% were male. The median pain score at rest was 3 (interquartile range [IQR], 2-5.5). Pain scores significantly increased to >5 for all movements. Thirty-one patients underwent SSRF. Resting pain prior to SSRF was 3 (IQR, 1-6) and postoperatively was 2 (IQR, 1.5-3) (p = 0.446). For all movements, median Numerical Rating Scale score was significantly less after SSRF (p < 0.001). The median incentive spirometry was 1,100 mL (IQR, 625-1,600 mL) preoperatively and 2,000 mL (IQR, 1,475-2,250 mL) postoperatively.

CONCLUSION: Traditional assessment of pain in patients with rib fractures significantly underappreciates true pain severity caused by movements involving the chest wall and should be considered when evaluating for SSRF.

LEVEL OF EVIDENCE: Therapeutic/Care Management; Level III.

PMID:39405440 | DOI:10.1097/TA.0000000000004446

JBRA Assist Reprod. 2024 Oct 15. doi: 10.5935/1518-0557.20240055. Online ahead of print.

ABSTRACT

OBJECTIVE: The deleterious effects of caffeine consumption on reproductive functions of female Wistar rats were investigated in this study.

METHODS: In this experimental study, 35 female Wistar rats (180-200g) were divided into 7 groups: Control, II-IV received oral caffeine (10, 20, and 40mg/kg/day respectively) for 21 days. V-VII received similar caffeine doses for 21 days, followed by a 21-day withdrawal period. The ovaries, fallopian tubes, and uteri were assessed for levels of malondialdehyde (MDA), nitric oxide (NO), reduced glutathione (GSH), superoxide dismutase (SOD), and catalase activity using spectrophotometry. Serum luteinizing hormone (LH), follicle-stimulating hormone (FSH), and estradiol levels were measured by ELISA. Organ histology was performed using microscopy. Statistical analysis employed ANOVA with significance at p<0.05.

RESULTS: Caffeine caused dose-dependent increases in MDA, NO, and catalase activity in the ovaries, fallopian tubes, and uteri which decreased upon withdrawal. GSH levels in the ovary and fallopian tubes decreased with caffeine intake but recovered during withdrawal. Caffeine reduced estradiol levels in a dose-dependent manner, its withdrawal led to reductions in serum LH at 20 and 40mg/kg/day and FSH at 40mg/kg/day. Histology revealed dose-dependent alterations in ovarian architecture with congested connective tissues. Caffeine caused sloughing of plicae in the muscularis of the fallopian tubes, degenerated epithelial layer in the uterus, and severe inflammation of the myometrial stroma cells that persisted during caffeine withdrawal.

CONCLUSIONS: Caffeine consumption adversely impacted the female reproductive functions of rats, altering hormonal balance and organ structure which persisted even after caffeine withdrawal.

PMID:39405421 | DOI:10.5935/1518-0557.20240055

Traffic Inj Prev. 2024 Oct 15:1-10. doi: 10.1080/15389588.2024.2404713. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptrons (MLP). This approach leverages advanced feature analysis capabilities, offering a more direct and interpretable method than traditional models.

METHODS: High-resolution optical cameras recorded detailed pedestrian and vehicle movements at study sites, with data processed to extract trajectories and convert them into real-world coordinates via precise georeferencing. Trained observers categorized interactions into safe passage, critical event, and conflict based on movement patterns, speeds, and accelerations. Fleiss Kappa statistic measured inter-rater agreement to ensure evaluator consistency. This study introduces a novel deep-learning model combining Transformer-based time series data capabilities with the classification strengths of a Multilayer Perceptron (MLP). Unlike traditional models, this approach focuses on feature analysis for greater interpretability. The model, trained on dynamic input variables from trajectory data, employs attention mechanisms to evaluate the significance of each input variable, offering deeper insights into factors influencing interaction severity.

RESULTS: The model demonstrated high performance across different severity categories: safe interactions achieved a precision of 0.78, recall of 0.91, and F1-score of 0.84. In more severe categories like critical events and conflicts, precision and recall were even higher. Overall accuracy stood at 0.87, with both macro and weighted averages for precision, recall, and F1-score also at 0.87. The variable importance analysis, using attention scores from the proposed transformer model, identified ‘Vehicle Speed’ as the most significant input variable positively influencing severity. Conversely, ‘Approaching Angle’ and ‘Vehicle Distance from Conflict Point’ negatively impacted severity. Other significant factors included ‘Type of Vehicle’, ‘Pedestrian Speed’, and ‘Pedestrian Yaw Rate’, highlighting the complex interplay of behavioral and environmental factors in pedestrian-vehicle interactions.

CONCLUSIONS: This study introduces a deep-learning model that effectively predicts the severity of pedestrian-vehicle interactions at crosswalks, utilizing a Transformer-MLP hybrid architecture with high precision and recall across severity categories. Key factors influencing severity were identified, paving the way for further enhancements in real-time analysis and broader safety assessments in urban settings.

PMID:39405419 | DOI:10.1080/15389588.2024.2404713

J Hosp Palliat Nurs. 2024 Oct 14. doi: 10.1097/NJH.0000000000001068. Online ahead of print.

ABSTRACT

Polypharmacy is commonly encountered by providers caring for patients with medically complex and palliative care needs in many settings. The purpose of this quality improvement project was to measure the impact of an evidence-based educational deprescribing intervention on polypharmacy rate and provider confidence and knowledge in the nursing home. We invited providers working in 52 nursing homes to attend a 1-hour-long educational deprescribing session. Twenty-one nurse practitioners and 1 physician assistant across 11 states participated in the intervention. Provider confidence level related to deprescribing improved in all categories, with statistical significance demonstrated with both paired t test and Wilcoxon signed rank test (P < .001). The polypharmacy rate 3 months after the intervention decreased more in centers where a provider had attended the training. Additional open-ended data about experiences with and barriers to deprescribing were collected and analyzed. The findings from this quality improvement project demonstrate that an educational intervention focused on providers practicing in the nursing home setting can improve deprescribing confidence and reduce polypharmacy rates. These findings may be used to implement similar deprescribing education programs for palliative care nurses and providers that prioritize goals of care for patients living with serious illness.

PMID:39405406 | DOI:10.1097/NJH.0000000000001068

J Sports Sci. 2024 Oct 15:1-8. doi: 10.1080/02640414.2024.2414361. Online ahead of print.

ABSTRACT

Motor competence (MC) is conceptually defined as a multidimensional latent construct that covers the proficient performance in motor skills and its underlying mechanisms This study aimed to statistically provide arguments that MC is a network of interconnected constructs, such as FMS, coordination, and its underlying mechanisms, which are responsible for preschoolers’ proficiency in motor tasks. Participated 102 preschoolers (65 girls, M age = 4.22 ± 0.19) who were assessed for the Test of Gross Motor Development – 2^nd edition, the Motor Competence Assessment, and the Supine-to-Stand. Data were explored using Exploratory Graph Analysis, using the EGAnet package in RStudio. A four-dimensional structure (61.2% of interactions) comprising tasks of the different protocols was underlined, in which all the nodes presented stable and adequate indexes (≥0.65; TEFI = -2.67). Four dimensions of MC were highlighted, namely Dimension 1, which combined movements for locomotor patterns; Dimension 2, comprising three process-oriented measures of object control skills to project objects; Dimension 3, which comprised of skills which require body coordination to displace body through space; and Dimension 4, composed by object control skills evaluated through product-oriented measures. For a better understanding of MC, the assessment of these different aspects that comprises MC should be considered.

PMID:39405381 | DOI:10.1080/02640414.2024.2414361