Tag: statistics

JNCI Cancer Spectr. 2024 Nov 25:pkae117. doi: 10.1093/jncics/pkae117. Online ahead of print.

ABSTRACT

BACKGROUND: In non-small cell lung cancer (NSCLC), social determinants of health (SDOHs) influence treatment, but SDOHs with geographic precision are infrequently used in real-world research due to privacy considerations. This research aims to characterize the influence of census-tract level SDOHs on treatment for stage I and IIa NSCLC.

METHODS: Patients diagnosed between 1/1/17 and 9/30/22 with stages I and IIa NSCLC in the Syapse Learning Health Network had their addresses geocoded and linked to five census tract-level indicators of SDOH (social vulnerability index (SVI), percent (%) housing burden, % broadband internet access, primary care shortage area, and rurality). Clinical and demographic characteristics were ascertained from medical records. Nested multinomial logistic regression models estimated associations between SDOHs and initial treatment using two-sided Wald tests. The collective statistical significance of SDOHs was assessed with a likelihood ratio test (LRT) comparing nested models. Descriptive statistics described time-to-treatment-initiation.

RESULTS: Among 3595 patients, 58% were initially treated with surgery, 29% with radiation, and 12% with “other.” Two SDOH variables were associated with increased relative risk ratios (RRR) for radiation therapy compared to surgery: living in primary care shortage areas (RRR 1.61, 95% CI: (1.23-2.10)) and living in non-metropolitan areas (RRR 1.45 (1.02-2.07)). The LRT suggested that the five SDOH variables collectively improved the treatment model. Further, patients in areas with high SVI, low internet access, and high housing-burden initiated treatment later.

CONCLUSION: When using precise estimates of geospatial SDOHs, these measures were associated with treatment, and should be considered in analyses of cancer outcomes.

PMID:39585653 | DOI:10.1093/jncics/pkae117

Qual Life Res. 2024 Nov 25. doi: 10.1007/s11136-024-03855-1. Online ahead of print.

ABSTRACT

PURPOSE: The Myelofibrosis Symptom Assessment Form version 4.0 (MFSAF v4.0) comprises 7 common MF symptom items (fatigue, night sweats, pruritus, abdominal discomfort, pain under the left ribs, early satiety, bone pain) and is the first patient-reported outcome (PRO) instrument designed to assess MF symptom burden. Given that information on the psychometric properties of this instrument has been limited, we sought to evaluate its measurement properties and validate its use in the phase 3 MOMENTUM trial.

METHODS: Data were pooled to assess MFSAF item distribution, structural validity, reliability (test-retest and internal consistency), construct validity (convergent, divergent, and known-groups), and sensitivity to change. Other PRO measures included Patient Global Impression of Severity/Change (PGIS/PGIC), EORTC QLQ-C30, PROMIS Physical Function Short Form 10b, and ECOG performance status.

RESULTS: Participants (N = 195) showed high completion rates (> 93%) across 24 weeks. Moderate to strong Spearman correlation coefficients among items were mostly observed at baseline (range, 0.289-0.772) and week 24 (range, 0.391-0.829), which supported combining items into a multi-item scale and total score. Internal consistency (Cronbach’s α, 0.877 at baseline and 0.903 at week 24) and test-retest reliability (intraclass correlation coefficient, > 0.829) were satisfactory across selected time intervals. Reliability was also supported by McDonald’s omega (ω) coefficient (> 0.875). MFSAF moderately correlated with PRO measures of similar content, differentiated between PGIS and ECOG groups (P < .001), and was able to detect change over time.

CONCLUSIONS: The MFSAF v4.0 is a valid tool to assess MF symptom burden, supporting its use in future trials in similar populations.

PMID:39585603 | DOI:10.1007/s11136-024-03855-1

Bull Exp Biol Med. 2024 Nov 25. doi: 10.1007/s10517-024-06301-x. Online ahead of print.

ABSTRACT

In adult male mice with high (HR) and low (LR) resistance to hypoxia, on days 21 and 28 after subcutaneous injection of Lewis lung carcinoma cells, a morphological and morphometric study of the primary tumor nodes and metastases in the lungs was carried out. Peripheral blood parameters and subpopulation composition of blood cells, the expression of genes responsible for the development of inflammation (Nfkb, Il1b, Il6, Tnfa, Il10, and Tgfb) and the response to hypoxia (Hif1a) in the liver were also assessed. The tumors were detected in 84.6% HR and 91.7% LR mice. The mitotic index of tumor cells in the subcutaneous nodes of HR animals was statistically significantly higher. The metastases area on days 21 and 28 did not differ. In animals of both groups, an increase in the absolute number of leukocytes, monocytes, and granulocytes, a decrease in the hemoglobin content and the absolute number of erythrocytes in the peripheral blood were detected on day 28 of the experiment. Only in LR animals, an increase in the absolute number of CD11b⁺ monocytes was found on day 28 of the experiment in comparison with the control group. The expression of Hif1a, Nfkb, Tnfa, and Tgfb genes in the liver of LR animals was higher than in HR mice, which attested to more pronounced systemic inflammatory response. These data should be taken into account when developing new approaches for the treatment of neoplastic disorders.

PMID:39585596 | DOI:10.1007/s10517-024-06301-x

Bull Exp Biol Med. 2024 Nov 25. doi: 10.1007/s10517-024-06288-5. Online ahead of print.

ABSTRACT

We studied the effect of bone marrow cells (BMC) and nanosecond repetitively pulsed microwaves (RPMs, 10 GHz, pulse duration 100 nsec, pulse repetition rate 8 Hz, peak power flux density (pPFD) 140 W/cm²) on the regeneration of thermally damaged skin in rats. The combined use of BMC and RPMs accelerates separation of an eschar with its complete rejection on day 14 of the experiment. Histological analysis demonstrated a statistically significant increase in the relative area of the granulation tissue and thickness of newly formed epidermis in the group with combined exposure. Combined therapy ensured completion of wound epithelialization in 100% of rats by day 30 of the study.

PMID:39585592 | DOI:10.1007/s10517-024-06288-5

Clin Rheumatol. 2024 Nov 25. doi: 10.1007/s10067-024-07244-4. Online ahead of print.

ABSTRACT

OBJECTIVE: To develop a simplified predictive model for identifying psoriatic arthritis (PsA) in psoriasis patients.

METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) database were analyzed, including patients with psoriasis without arthritis (PsC) or PsA. The least absolute shrinkage and selection operator, Boruta algorithm, random forest, and stepwise regression were employed to select key variables from 38 potential predictors. Logistic regression models were constructed for each combination of selected variables and evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, Brier scores, and decision curve analysis (DCA).

RESULTS: The study included 587 patients with psoriasis, 238 of whom had PsA. The variable combinations proposed by the Boruta algorithm exhibited the best overall performance. Key predictors in the Borutamodel included age, fasting glucose, education level, thyroid disease, hypertension, and chronic bronchitis. This model achieved area under the curve (AUC) of 0.781 (95% CI, 0.737-0.826) for the training set and 0.780 (95% CI, 0.712-0.848) for the testing set in the ROC curve analyses. The AUC values in the PR curves were 0.687 (95% CI, 0.611-0.757) and 0.653 (95% CI, 0.535-0.770), respectively. The Brier scores of 0.186 and 0.191 for the testing and training sets indicated a good fit, further supported by the calibration curves. DCA showed a net clinical benefit for decision thresholds ranging from 0.2 to 0.8 in both datasets.

CONCLUSION: The Borutamodel represents a promising tool for early risk assessment of PsA. Key Points • National Database Utilization: This study leverages the NHANES database to predict psoriatic arthritis risk, addressing previous limitations tied to regional or ethnic constraints. • Comprehensive Variable Analyses: The research examines 38 variables, including demographics, health conditions, laboratory results, and lifestyle factors, using four distinct screening methods and thorough evaluations of model performance. • Innovative Risk Model: The study introduces a novel risk assessment model that integrates age, fasting glucose, education, and comorbidities including hypertension, thyroid disease, and chronic bronchitis, thus moving beyond traditional focus on skin lesions and joint symptoms.

PMID:39585569 | DOI:10.1007/s10067-024-07244-4

Environ Monit Assess. 2024 Nov 25;196(12):1248. doi: 10.1007/s10661-024-13444-x.

ABSTRACT

Temperature accelerates the deterioration processes affecting grotto temples. As such, studies of the temperature distribution characteristics of grotto temples can provide an important basis for their protection. In this paper, the hourly surface temperatures of 123 grotto temples in China were studied using ERA5-Land hourly data from 1981 to 2020, obtained through the AI Earth platform. Using the local Python development environment, the daily surface temperature difference and highest and lowest temperatures of grotto temples were linearly fitted for each year, after which the monthly average temperature difference distribution was statistically analyzed to determine trends in temperature change. Then, the GIS Spatially Constrained Multivariate Clustering method was used to cluster the surface temperature characteristics. The results showed that the grotto temples in China can be mainly divided into seven regions, namely Xinjiang, Qinghai-Xizang Plateau, Hexi, Longdong, Shaanxi and North China, Southwest, and East and Southeast. The highest average surface temperature, greater than 15 °C, occurred in South China, and the lowest, close to 0 °C, occurred in the Qinghai-Xizang Plateau. The average surface temperature of the seven regions identified showed an increasing trend. The Qinghai-Xizang Plateau was affected by severe temperature differences throughout the year, with annual average daily temperature differences approaching 30 °C, followed by Xinjiang and Hexi region, with a perennial temperature difference of approximately 25 °C. The Longdong, Shaanxi, and North China regions had annual average daily temperature differences of 15-20 °C, whereas values for the South China region were less than 15 °C. The daily surface temperature differences of grotto temples reached their maximum values in April to May and their minimum values in December to January. All studied regions are subject to temperature-induced challenges: Xinjiang region faces particularly severe high-temperature influences, with a mean daily surface temperature of almost 45 °C in summer, followed by Hexi region with 35 °C or above, and the other regions with approximately 30 °C. The Qinghai-Xizang Plateau exhibits perennially low temperatures, with a mean daily minimum temperature below – 25 °C in winter; less than – 10 °C in the Xinjiang, Hexi, Longdong, Shaanxi, and North China regions; and approximately 0 °C in southern China. The relative impacts of temperature on grotto temples in each region are as follows: Xinjiang and Hexi > Qinghai-Xizang Plateau > Longdong, Shaanxi, and North China > Southwest China > East and Southeast China. This study has revealed the characteristics of surface temperature distribution in grotto temples in China and proposes appropriate protection measures, which will help improve national-scale practical mitigation of the threats facing these important cultural heritage sites.

PMID:39585564 | DOI:10.1007/s10661-024-13444-x

Jpn J Radiol. 2024 Nov 25. doi: 10.1007/s11604-024-01710-4. Online ahead of print.

ABSTRACT

PURPOSE: So far, there is no gold standard to diagnosis dysthyroid optic neuropathy (DON). Diffusion kurtosis imaging (DKI) has the potential to provide imaging biomarkers for the timely and accurate diagnosis of DON. This study aimed to explore the white matter (WM) alterations in thyroid-associated ophthalmopathy (TAO) patients with and without DON using DKI with tract-based spatial statistics method.

MATERIALS AND METHODS: Fifty-three TAO patients (21 DON and 32 non-DON) and 30 healthy controls (HCs) were recruited in this cross-sectional study. DKI data were analyzed and compared among groups. The correlations between diffusion parameters and clinical variables were assessed. Receiver-operating characteristic curve analysis was used to evaluate the feasibility of using DKI parameters to distinguish DON and non-DON.

RESULTS: Compared with HCs, both DON and non-DON groups exhibited significantly decreased radial kurtosis (RK), mean kurtosis (MK), axial kurtosis (AK), kurtosis fractional anisotropy, and fractional anisotropy values in several WM tracts. No significant differences were observed in mean diffusivity values among groups. Meanwhile, DON patients exhibited lower RK, MK, and AK values than non-DON patients mainly in the visual system. Significant correlations were observed between RK values of posterior thalamic radiation (PTR) and best-corrected visual acuity. For distinguishing DON, the RK values of PTR exhibited decent diagnostic performance.

CONCLUSION: Microstructural abnormalities in WM, especially in the visual system, could provide novel insights into the potential neural mechanisms of the disease, thereby contributing to the timely diagnosis of DON and the development of neuroprotective therapy.

PMID:39585557 | DOI:10.1007/s11604-024-01710-4

3D Print Med. 2024 Nov 25;10(1):39. doi: 10.1186/s41205-024-00245-8.

ABSTRACT

AIM: This study compares the precision, accuracy, and user experience of 3D body surface scanning of human subjects using the Artec Leo hand-held scanner and the iPad Pro as 3D scanning devices for capturing cervical and craniofacial data. The investigation includes assessing methods for correcting ‘dropped head syndrome’ during scanning, to demonstrate the ability of the scanner to be used to reconstruct body surface of patients.

METHODS: Eighteen volunteers with no prior history of neck weakness were scanned three times in three different positions, using the two different devices. Surface area, scanning time, and participant comfort scores were evaluated for both devices. Precision and accuracy were assessed using Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), and Intra-Class Correlation Coefficients (ICC).

RESULTS: Surface area comparisons revealed no significant differences between devices and positions. Scanning times showed no significant difference between devices or positions. Comfort scores varied across positions. MAD analysis identified chin to chest measurements as having the highest variance, especially in scanning position 3. However, no statistical differences were found. MAPE results confirmed accuracy below 5% error for both devices. ICC scores indicated good reliability for both measurement methods, particularly for chin to chest measurements in positions 1 and 3.

CONCLUSION: The iPad Pro using the Qlone app demonstrates a viable alternative to the Artec Leo, particularly for capturing head and neck surface area within a clinical setting. The scanning resolution, with an error margin within ±5%, is consistent with clinically accepted standards for orthosis design, where padding and final fit adjustments allow for bespoke devices that accommodate patient comfort. This study highlights the comparative performance of the iPad, as well as suggests two methods which can be used within clinics to correct head drop for scanning.

PMID:39585546 | DOI:10.1186/s41205-024-00245-8

Bull Math Biol. 2024 Nov 25;87(1):4. doi: 10.1007/s11538-024-01376-z.

ABSTRACT

Single cell RNA-seq (scRNAseq) workflows typically start with a count matrix and end with the clustering of sampled cells. While a range of methods have been developed to cluster scRNAseq datasets, no theoretical tools exist to explain why a particular cluster exists or why a hypothesized cluster is missing. Recently, several authors have shown that eigenvalues of scRNAseq count matrices can be approximated using random matrix models. In this work, we extend these previous works to the study of a scRNAseq workflow. We model scaled count matrices using random matrices with normally distributed entries. Using these random matrix models, we quantify the differential expression of a cluster and develop predictions for the workflow, and in particular clustering, as a function of the differential expression. We also use results from random matrix theory (RMT) to develop predictive formulas for portions of the scRNAseq workflow. Using simulated and real datasets, we show that our predictions are accurate if certain conditions hold on differential expression, with our RMT based predictions requiring particularly stringent condition. We find that real datasets violate these conditions, leading to bias in our predictions, but our predictions are better than a naive estimator and we point out future work that can improve the predictions. To our knowledge, our formulas represents the first predictive results for scRNAseq workflows.

PMID:39585539 | DOI:10.1007/s11538-024-01376-z

Sci Rep. 2024 Nov 24;14(1):29096. doi: 10.1038/s41598-024-80190-y.

ABSTRACT

Often linked with the risk of various diseases, blood low-density lipoprotein cholesterol (LDL-C) levels are typically deemed more favorable when lower. The objective of this investigation is to elucidate the link between blood LDL-C levels and the risk of prostate cancer (PCa) in middle-aged and older men without hypertension in the United States. Utilizing continuous data from the National Health and Nutrition Examination Survey (NHANES) database spanning 2003-2010, a selection of 1,223 non-hypertensive men aged ≥ 40 years was made from a pool of 41,156 participants, ensuring no missing information. Regression analyses were employed to investigate the correlation between blood LDL-C levels and the PCa risk, while identifying potential inflection points indicative of threshold effects. Additionally, we scrutinized the linkage between cholesterol-lowering prescription drug usage and PCa. In our study of 2,224 participants, we found no significant correlation between blood LDL-C levels and the PCa risk after adjusting for confounding variables (Odds Ratio = 0.99; P-value > 0.05). However, upon conducting a subgroup analysis, we discovered a meaningful correlation between lower blood LDL-C levels and an increased PCa risk in the non-hypertensive population (Odds Ratio = 0.99; P-value < 0.05). Meanwhile, we identified a threshold effect and a tipping point at an LDL-C levels of 67 mg/dl. Furthermore, a significant correlation was identified between cholesterol-lowering prescription drug usage and a heightened PCa risk in the non-hypertensive population (Odds Ratio = 18.87; P-value < 0.05; P for interaction < 0.05). Our results indicate that in non-hypertensive middle-aged and older men residing in the United States, lower blood LDL-C levels are not necessarily better and the PCa risk escalates when blood LDL-C levels drop below 67 mg/dl, which may guide early screening and prognosis of PCa in specific populations. This finding calls for further validation via larger sample sizes and a more in-depth analysis of PCa history.

PMID:39582086 | DOI:10.1038/s41598-024-80190-y