Tag: pubmed

Rev Med Inst Mex Seguro Soc. 2024 Jan 8;62(1):1-8. doi: 10.5281/zenodo.10278099.

ABSTRACT

BACKGROUND: Obesity creates a burden of disease that affects the health-related quality of life (HRQoL) of women and in those between 20 to 59 years of age it implies greater morbidity and mortality compared to men or other age groups.

OBJECTIVE: To evaluate the HRQoL of Mexican women aged 20 to 59 years with obesity.

MATERIAL AND METHODS: Observational, cross-sectional, prospective, and retrospective study. It was obtained a sample of 104 women from 20 to 59 years of age diagnosed with obesity according to the NOM-008-SSA3-2017 Standard. The participants’ main clinical and sociodemographic characteristics were collected, and their HRQoL was evaluated with the SF-36 questionnaire. For the analysis of the collected variables, descriptive statistics were used. To identify the association of these variables with HRQoL, the Kruskal-Wallis test was used.

RESULTS: 104 women with a median age of 40.0 years participated. Of these, 66.3% had grade I obesity, 21.2% grade II, and 12.5% grade III. In the overall sample, general health and vitality were the lowest dimensions. In the comparison by groups, the physical role and the emotional role had statistically significant differences (p = 0.007 and p = 0.009, respectively), with the most affected group being obesity grade II.

CONCLUSIONS: Obesity mainly affected the perception of general health and vitality; likewise, those with grade II had a greater impact on the physical role and the emotional role.

PMID:39106487 | DOI:10.5281/zenodo.10278099

J Med Internet Res. 2024 Aug 6;26:e51317. doi: 10.2196/51317.

ABSTRACT

BACKGROUND: Early identification is critical for mitigating the impact of medicine shortages on patients. The internet, specifically social media, is an emerging source of health data.

OBJECTIVE: This study aimed to explore whether a routine analysis of data from the Twitter social network can detect signals of a medicine shortage and serve as an early warning system and, if so, for which medicines or patient groups.

METHODS: Medicine shortages between January 31 and December 1, 2019, were collected from the Dutch pharmacists’ society’s national catalog Royal Dutch Pharmacists Association (KNMP) Farmanco. Posts on these shortages were collected by searching for the name, the active pharmaceutical ingredient, or the first word of the brand name of the medicines in shortage. Posts were then selected based on relevant keywords that potentially indicated a shortage and the percentage of shortages with at least 1 post was calculated. The first posts per shortage were analyzed for their timing (median number of days, including the IQR) versus the national catalog, also stratified by disease and medicine characteristics. The content of the first post per shortage was analyzed descriptively for its reporting stakeholder and the nature of the post.

RESULTS: Of the 341 medicine shortages, 102 (29.9%) were mentioned on Twitter. Of these 102 shortages, 18 (5.3% of the total) were mentioned prior to or simultaneous to publication by KNMP Farmanco. Only 4 (1.2%) of these were mentioned on Twitter more than 14 days before. On average, posts were published with a median delay of 37 (IQR 7-81) days to publication by KNMP Farmanco. Shortages mentioned on Twitter affected a greater number of patients and lasted longer than those that were not mentioned. We could not conclusively relate either the presence or absence on Twitter to a disease area or route of administration of the medicine in shortage. The first posts on the 102 shortages were mainly published by patients (n=51, 50.0%) and health care professionals (n=46, 45.1%). We identified 8 categories of nature of content. Sharing personal experience (n=44, 43.1%) was the most common category.

CONCLUSIONS: The Twitter social network is not a suitable early warning system for medicine shortages. Twitter primarily echoes already-known information rather than spreads new information. However, Twitter or potentially any other social media platform provides the opportunity for future qualitative research in the increasingly important field of medicine shortages that investigates how a larger population of patients is affected by shortages.

PMID:39106483 | DOI:10.2196/51317

J Am Acad Orthop Surg Glob Res Rev. 2024 Aug 1;8(8). doi: 10.5435/JAAOSGlobal-D-24-00182. eCollection 2024 Aug 1.

ABSTRACT

BACKGROUND: Accurate and precise templating is paramount for anatomic total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RSA) to enhance preoperative planning, streamline surgery, and improve implant positioning. We aimed to evaluate the predictive potential of readily available patient demographic data in TSA and RSA implant sizing, independent of implant design.

METHODS: A total of 578 consecutive, primary, noncemented shoulder arthroplasty cases were retrospectively reviewed. Demographic variables and implant characteristics were recorded. Multivariate linear regressions were conducted to predict implant sizes using patient demographic variables.

RESULTS: Linear models accurately predict TSA implant sizes within 2 millimeters of humerus stem sizes 75.3% of the time, head diameter 82.1%, head height 82.1%, and RSA glenosphere diameter 77.6% of the time. Linear models predict glenoid implant sizes accurately 68.2% and polyethylene thickness 76.6% of the time and within one size 100% and 95.7% of the time, respectively.

CONCLUSION: Linear models accurately predict shoulder arthroplasty implant sizes from demographic data. No significant statistical differences were observed between linear models and machine learning algorithms, although the analysis was underpowered. Future sufficiently powered studies are required for more robust assessment of machine learning models in predicting primary shoulder arthroplasty implant sizes based on patient demographics.

PMID:39106479 | DOI:10.5435/JAAOSGlobal-D-24-00182

Chirurgia (Bucur). 2024 Aug;119(Ahead of print):1-11. doi: 10.21614/chirurgia.2999.

ABSTRACT

The efficacy and safety of video-assisted thoracoscopic surgery (VATS) versus open thoracotomy in the treatment of non-small cell lung cancer (NSCLC) were evaluated with a focus on mediastinal lymph node dissection, postoperative recovery, and longterm outcomes including survival rates and disease-free intervals. Materials and Methods: This retrospective study analyzed data from 228 NSCLC patients treated at the Institute of Oncology Bucharest from 2016 to 2022. Both VATS and open surgical approaches were compared, with variables including demographic data, comorbidities, surgical outcomes, and postoperative complications meticulously recorded. Statistical significance was assessed using chi-square and independent samples t-tests. Results: Among the findings, VATS demonstrated significantly better two-year progression-free survival rates for patients in early stages (Stages 1-3) of NSCLC compared to open surgery, with p-values 0.01 and 0.001, respectively. In contrast, no significant difference was observed in Stage 4. Furthermore, VATS resulted in shorter operative times (mean 299 vs. 347 minutes, p 0.001), less estimated blood loss (98.68 mL vs. 160.88 mL, p 0.001), reduced chest tube duration (5.78 days vs. 12.17 days, p 0.001), and decreased hospital stays (12.0 days vs. 27.7 days, p 0.001). Conclusions: VATS is associated with improved long-term disease-free survival for early-stage NSCLC and more favorable short-term surgical outcomes, highlighting its advantages over open thoracotomy. Despite its benefits, VATS did not significantly reduce postoperative complications compared to open surgery.

PMID:39106471 | DOI:10.21614/chirurgia.2999

Neural Comput. 2024 Jul 18:1-27. doi: 10.1162/neco_a_01692. Online ahead of print.

ABSTRACT

Adam-type algorithms have become a preferred choice for optimization in the deep learning setting; however, despite their success, their convergence is still not well understood. To this end, we introduce a unified framework for Adam-type algorithms, termed UAdam. It is equipped with a general form of the second-order moment, which makes it possible to include Adam and its existing and future variants as special cases, such as NAdam, AMSGrad, AdaBound, AdaFom, and Adan. The approach is supported by a rigorous convergence analysis of UAdam in the general nonconvex stochastic setting, showing that UAdam converges to the neighborhood of stationary points with a rate of O(1/T). Furthermore, the size of the neighborhood decreases as the parameter β1 increases. Importantly, our analysis only requires the first-order momentum factor to be close enough to 1, without any restrictions on the second-order momentum factor. Theoretical results also reveal the convergence conditions of vanilla Adam, together with the selection of appropriate hyperparameters. This provides a theoretical guarantee for the analysis, applications, and further developments of the whole general class of Adam-type algorithms. Finally, several numerical experiments are provided to support our theoretical findings.

PMID:39106463 | DOI:10.1162/neco_a_01692

Prehosp Emerg Care. 2024 Aug 6:1-15. doi: 10.1080/10903127.2024.2389551. Online ahead of print.

ABSTRACT

OBJECTIVES: The National Emergency Medical Services Information Services (NEMSIS) provides a robust set of data to evaluate prehospital care. However, a major limitation is that the vast majority of the records lack a definitive outcome. We aimed to evaluate the performance of a recently proposed method (‘MLB’ method) to impute missing end-of-EMS-event outcomes (“dead” or “alive”) for patient care reports in the NEMSIS public research dataset.

METHODS: This study reproduced the recently published method for patient outcome imputation in the NEMSIS database and replicated the results for years 2017 through 2022 (n = 686,075). We performed statistical analyses leveraging an array of established performance metrics for binary classification in the machine learning literature. Evaluation metrics included overall accuracy, true positive rate, true negative rate, balanced accuracy, precision, F1 score, Cohen’s Kappa coefficient, Matthews’ coefficient, Hamming loss, the Jaccard similarity score, and the receiver operating characteristic/area under the curve.

RESULTS: Extended metrics show consistently good imputation performance from year-to-year but reveal weakness in accurately indicating the minority class: e.g., after adjustments for conflicting labels, “dead” prediction accuracy was 77.7% for 2018 and 61.8% over the six-year NEMSIS sub-sample, even though overall accuracy was 98.8%. Slight over-fitting is also present.

CONCLUSIONS: We found that the recently published MLB method produced reasonably good “dead” or “alive” indicators. We recommend reporting of True Positive Rate (“dead” prediction accuracy) and True Negative Rate (“alive” prediction accuracy) when applying the imputation method for analyses of NEMSIS data. More attention by EMS clinicians to complete documentation of target NEMSIS elements can further improve the method’s performance.

PMID:39106451 | DOI:10.1080/10903127.2024.2389551

Retina. 2024 Aug 5. doi: 10.1097/IAE.0000000000004230. Online ahead of print.

ABSTRACT

PURPOSE: To evaluate the effects of pupil dilation caused by topical applications of 2.5% phenylephrine and 0.5% tropicamide on retinal microvascularization using optical coherence tomography angiography (OCTA).

METHODS: Healthy children were included in this prospective observational study. Baseline OCTA measurements were taken for all children before dilatation. Then they were randomly divided into two groups, the tropicamide group given 0.5% tropicamide solution and the phenylephrine group given 2.5% phenylephrine solution. After dilation OCTA images were taken for the second time from all children.

RESULTS: The effect of dilation using two different mydriatic agents caused a decrease in mean radial peripapillary capillary density (RPC-VD) and superior RPC-VD (p=0.008 and p=0.001). Remarkably, this reduction due to dilatation was also determined to be caused by the combined effect of both mydriatic agents (p=0.016 and p=0.013). Although phenylephrine showed a slightly greater decrease than tropicamide, the effects of the two mydriatic drugs were not superior to each other (p=0.166 and p=0.167).

CONCLUSION: Dilation with 2.5% phenylephrine or 0.5% tropicamide significantly decreased mean RPC-VD and superior RPC-VD. Although there was no statistically significant difference between the two mydriatic agents, phenylephrine caused a greater reduction than tropicamide. This effect of dilation and phenylephrine on VD should be considered in studies using OCTA and focusing on peripapillary areas.

PMID:39106442 | DOI:10.1097/IAE.0000000000004230

J Med Internet Res. 2024 Aug 6;26:e56316. doi: 10.2196/56316.

ABSTRACT

BACKGROUND: This study demonstrates that digital maturity contributes to strengthened quality and safety performance outcomes in US hospitals. Advanced digital maturity is associated with more digitally enabled work environments with automated flow of data across information systems to enable clinicians and leaders to track quality and safety outcomes. This research illustrates that an advanced digitally enabled workforce is associated with strong safety leadership and culture and better patient health and safety outcomes.

OBJECTIVE: This study aimed to examine the relationship between digital maturity and quality and safety outcomes in US hospitals.

METHODS: The data sources were hospital safety letter grades as well as quality and safety scores on a continuous scale published by The Leapfrog Group. We used the digital maturity level (measured using the Electronic Medical Record Assessment Model [EMRAM]) of 1026 US hospitals. This was a cross-sectional, observational study. Logistic, linear, and Tweedie regression analyses were used to explore the relationships among The Leapfrog Group’s Hospital Safety Grades, individual Leapfrog safety scores, and digital maturity levels classified as advanced or fully developed digital maturity (EMRAM levels 6 and 7) or underdeveloped maturity (EMRAM level 0). Digital maturity was a predictor while controlling for hospital characteristics including teaching status, urban or rural location, hospital size measured by number of beds, whether the hospital was a referral center, and type of hospital ownership as confounding variables. Hospitals were divided into the following 2 groups to compare safety and quality outcomes: hospitals that were digitally advanced and hospitals with underdeveloped digital maturity. Data from The Leapfrog Group’s Hospital Safety Grades report published in spring 2019 were matched to the hospitals with completed EMRAM assessments in 2019. Hospital characteristics such as number of hospital beds were obtained from the CMS database.

RESULTS: The results revealed that the odds of achieving a higher Leapfrog Group Hospital Safety Grade was statistically significantly higher, by 3.25 times, for hospitals with advanced digital maturity (EMRAM maturity of 6 or 7; odds ratio 3.25, 95% CI 2.33-4.55).

CONCLUSIONS: Hospitals with advanced digital maturity had statistically significantly reduced infection rates, reduced adverse events, and improved surgical safety outcomes. The study findings suggest a significant difference in quality and safety outcomes among hospitals with advanced digital maturity compared with hospitals with underdeveloped digital maturity.

PMID:39106100 | DOI:10.2196/56316

Clin Cancer Res. 2024 Aug 6. doi: 10.1158/1078-0432.CCR-24-1215. Online ahead of print.

ABSTRACT

PURPOSE: In radiotherapy (RT) for brain tumors, patient heterogeneity masks treatment effects, complicating the prediction and mitigation of radiation-induced brain necrosis. Therefore, understanding this heterogeneity is essential for improving outcome assessments and reducing toxicity.

EXPERIMENTAL DESIGN: We developed a clinically practical pipeline to clarify the relationship between dosimetric features and outcomes by identifying key variables. We processed data from a cohort of 130 patients treated with proton therapy for brain and head and neck tumors, utilizing an expert-augmented Bayesian network to understand variable interdependencies and assess structural dependencies. Critical evaluation involved a 3-level grading system for each network connection and a Markov blanket analysis to identify variables directly impacting necrosis risk. Statistical assessments included log-likelihood ratio (LLR), integrated discrimination index (IDI), net reclassification index (NRI), and receiver operating characteristic (ROC).

RESULTS: The analysis highlighted tumor location and proximity to critical structures like white matter and ventricles as major determinants of necrosis risk. The majority of network connections were clinically supported, with quantitative measures confirming the significance of these variables in patient stratification (LLR=12.17, p=0.016; IDI=0.15; NRI=0.74). The ROC curve area was 0.66, emphasizing the discriminative value of non-dosimetric variables.

CONCLUSIONS: Key patient variables critical to understanding brain necrosis post-RT were identified, aiding the study of dosimetric impacts and providing treatment confounders and moderators. This pipeline aims to enhance outcome assessments by revealing at-risk patients, offering a versatile tool for broader applications in RT to improve treatment personalization in different disease sites.

PMID:39106090 | DOI:10.1158/1078-0432.CCR-24-1215

JAMA Netw Open. 2024 Aug 1;7(8):e2426830. doi: 10.1001/jamanetworkopen.2024.26830.

NO ABSTRACT

PMID:39106071 | DOI:10.1001/jamanetworkopen.2024.26830