Category: Statistics

J Matern Fetal Neonatal Med. 2023 Dec;36(2):2241100. doi: 10.1080/14767058.2023.2241100.

ABSTRACT

OBJECTIVE: The objective of this study is to create a new screening for spontaneous preterm birth (sPTB) based on artificial intelligence (AI).

METHODS: This study included 524 singleton pregnancies from 18th to 24th-week gestation after transvaginal ultrasound cervical length (CL) analyzes for screening sPTB < 35 weeks. AI model was created based on the stacking-based ensemble learning method (SBELM) by the neural network, gathering CL < 25 mm, multivariate unadjusted logistic regression (LR), and the best AI algorithm. Receiver Operating Characteristics (ROC) curve to predict sPTB < 35 weeks and area under the curve (AUC), sensitivity, specificity, accuracy, predictive positive and negative values were performed to evaluate CL < 25 mm, LR, the best algorithms of AI and SBELM.

RESULTS: The most relevant variables presented by LR were cervical funneling, index straight CL/internal angle inside the cervix (≤ 0.200), previous PTB < 37 weeks, previous curettage, no antibiotic treatment during pregnancy, and weight (≤ 58 kg), no smoking, and CL < 30.9 mm. Fixing 10% of false positive rate, CL < 25 mm and SBELM present, respectively: AUC of 0.318 and 0.808; sensitivity of 33.3% and 47,3%; specificity of 91.8 and 92.8%; positive predictive value of 23.1 and 32.7%; negative predictive value of 94.9 and 96.0%. This machine learning presented high statistical significance when compared to CL < 25 mm after T-test (p < .00001).

CONCLUSION: AI applied to clinical and ultrasonographic variables could be a viable option for screening of sPTB < 35 weeks, improving the performance of short cervix, with a low false-positive rate.

PMID:37518185 | DOI:10.1080/14767058.2023.2241100

J Egypt Natl Canc Inst. 2023 Jul 31;35(1):23. doi: 10.1186/s43046-023-00183-2.

ABSTRACT

OBJECTIVE: Immunohistochemical expression of P53 protein is so closely related to status of mutation of P53 gene which is tightly linked with pathogenesis of nephroblastoma or Wilms tumor. This study aims to determine the immunohistochemical expression of P53 protein and its predictors in formalin-fixed paraffin-embedded tissue blocks of patients with nephroblastoma.

MATERIALS AND METHODS: A series of 83 histologically diagnosed cases of nephroblastoma from formalin-fixed paraffin-embedded tissue blocks archived at the Department of Pathology, Makerere University, in Kampala, Uganda, were analyzed. Monoclonal anti-p53 antibody (DO-7, DAKO) was used to assess the expression of P53 protein expression. Multivariable logistic regression analysis was performed to determine the predictors of P53 protein immunohistochemical expression, and statistical significance was considered when p-value was less than 0.05.

RESULTS: Most (42.2%, n = 35) of the cases were in advanced tumor stages (III-V), and almost one-quarter (21.7%, n = 18) of the cases were in high-risk group. The immunohistochemical expression of P53 protein was (8.4%, n = 7), and there were more (83.3%, n = 5) positive anaplastic cases for P53 protein compared with (2.6%, n = 2) of P53 expression for non-anaplastic cases. High risk (AOR = 3.42, 95% CI = 7.91-12.55, p = 0.037) and anaplasia (AOR = 1.41, 95% CI = 13.85-4.46, p = 0.001) were potential predictors of immunohistochemical expression of P53 protein.

CONCLUSION: Most of patients with nephroblastoma in resources-limited settings are diagnosed with advanced clinical stages. Association of P53 protein with anaplasia found in this study indicates the possibility of having novel target therapy for treatment of patients with anaplastic form of nephroblastoma with a focus of identifying molecules that lead to its suppression in such subpopulations of patients with nephroblastoma.

PMID:37518096 | DOI:10.1186/s43046-023-00183-2

BMJ Open. 2023 Jul 30;13(7):e075142. doi: 10.1136/bmjopen-2023-075142.

ABSTRACT

INTRODUCTION: Peer online mental health forums are commonly used and offer accessible support. Positive and negative impacts have been reported by forum members and moderators, but it is unclear why these impacts occur, for whom and in which forums. This multiple method realist study explores underlying mechanisms to understand how forums work for different people. The findings will inform codesign of best practice guidance and policy tools to enhance the uptake and effectiveness of peer online mental health forums.

METHODS AND ANALYSIS: In workstream 1, we will conduct a realist synthesis, based on existing literature and interviews with approximately 20 stakeholders, to generate initial programme theories about the impacts of forums on members and moderators and mechanisms driving these. Initial theories that are relevant for forum design and implementation will be prioritised for testing in workstream 2.Workstream 2 is a multiple case study design with mixed methods with several online mental health forums differing in contextual features. Quantitative surveys of forum members, qualitative interviews and Corpus-based Discourse Analysis and Natural Language Processing of forum posts will be used to test and refine programme theories. Final programme theories will be developed through novel triangulation of the data.Workstream 3 will run alongside workstreams 1 and 2. Key stakeholders from participating forums, including members and moderators, will be recruited to a Codesign group. They will inform the study design and materials, refine and prioritise theories, and codesign best policy and practice guidance.

ETHICS AND DISSEMINATION: Ethical approval was granted by Solihull Research Ethics Committee (IRAS 314029). Findings will be reported in accordance with RAMESES (Realist And MEta-narrative Evidence Syntheses: Evolving Standards) guidelines, published as open access and shared widely, along with codesigned tools.

TRIAL REGISTRATION NUMBER: ISRCTN 62469166; the protocol for the realist synthesis in workstream one is prospectively registered at PROSPERO CRD42022352528.

PMID:37518092 | DOI:10.1136/bmjopen-2023-075142

BMJ Open. 2023 Jul 30;13(7):e066876. doi: 10.1136/bmjopen-2022-066876.

ABSTRACT

INTRODUCTION: In Aotearoa New Zealand (NZ), socioeconomic status and being of Māori ethnicity are often associated with poorer health outcomes, including after surgery. Inequities can be partially explained by differences in health status and health system biases are hypothesised as important factors for remaining inequities. Previous work identified inequities between Māori and non-Māori following cardiovascular surgery, some of which have been identified in studies between 1990 and 2012. Days Alive and Out of Hospital (DAOH) is an emerging surgical outcome metric. DAOH is a composite measure of outcomes, which may reflect patient experience and longer periods of DAOH may also reflect extended interactions with the health system. Recently, a 1.1-day difference in DAOH was observed between Māori and non-Māori at a hospital in NZ across a range of operations.

METHODS AND ANALYSIS: We will conduct a secondary data analysis using data from the National Minimum Data Set, maintained by the Ministry of Health. We will report unadjusted and risk-adjusted DAOH values between Māori and non-Māori using direct risk standardisation. We will risk adjust first for age and sex, then for each of deprivation (NZDep18), levels of morbidity (M3 score) and rurality. We will report DAOH values across three time periods, 30, 90 and 365 days and across nine deciles of the DAOH distribution (0.1-0.9 inclusive). We will interpret all results from a Kaupapa Māori research positioning, acknowledging that Māori health outcomes are directly tied to the unequal distribution of the social determinants of health.

ETHICS AND DISSEMINATION: Ethics approval for this study was given by the Auckland Health Research Ethics Committee. Outputs from this study are likely to interest a range of audiences. We plan to disseminate our findings through academic channels, presentations to interested groups including Māori-specific hui (meetings), social media and lay press.

PMID:37518091 | DOI:10.1136/bmjopen-2022-066876

BMJ Open. 2023 Jul 30;13(7):e070509. doi: 10.1136/bmjopen-2022-070509.

ABSTRACT

OBJECTIVES: Chronic pain (CP) is a poorly recognised and frequently inadequately treated condition affecting one in five adults. Reflecting on sociodemographic disparities as barriers to CP care in Canada was recently established as a federal priority. The objective of this study was to assess sex and gender differences in healthcare utilisation trajectories among workers living with CP.

DESIGN: Retrospective cohort study.

PARTICIPANTS: This study was conducted using the TorSaDE Cohort which links the 2007-2016 Canadian Community Health Surveys and Quebec administrative databases (longitudinal claims). Among 2955 workers living with CP, the annual number of healthcare contacts was computed during the 3 years after survey completion.

OUTCOME: Group-based trajectory modelling was used to identify subgroups of individuals with similar patterns of healthcare utilisation over time (healthcare utilisation trajectories).

RESULTS: Across the study population, three distinct 3-year healthcare utilisation trajectories were found: (1) low healthcare users (59.9%), (2) moderate healthcare users (33.6%) and (3) heavy healthcare users (6.4%). Sex and gender differences were found in the number of distinct trajectories and the stability of the number of healthcare contacts over time. Multivariable analysis revealed that independent of other sociodemographic characteristics and severity of health condition, sex-but not gender-was associated with the heavy healthcare utilisation longitudinal trajectory (with females showing a greater likelihood; OR 2.6, 95% CI 1.6 to 4.1).

CONCLUSIONS: Our results underline the importance of assessing sex-based disparities in help-seeking behaviours, access to healthcare and resource utilisation among persons living with CP.

PMID:37518085 | DOI:10.1136/bmjopen-2022-070509

BMJ Open. 2023 Jul 30;13(7):e071492. doi: 10.1136/bmjopen-2022-071492.

ABSTRACT

INTRODUCTION: Individuals at an inherited high-risk of developing adult-onset disease, such as breast cancer, are rare in the population. These individuals require lifelong clinical, psychological and reproductive assistance. After a positive germline test result, clinical genetic services provide support and care coordination. However, ongoing systematic clinical follow-up programmes are uncommon. Digital health solutions offer efficient and sustainable ways to deliver affordable and equitable care. This paper outlines the codesign and development of a digital health platform to facilitate long-term clinical and psychological care, and foster self-efficacy in individuals with a genetic disease predisposition.

METHODS AND ANALYSIS: We adopt a mixed-methods approach for data gathering and analysis. Data collection is in two phases. In phase 1, 300 individuals with a high-risk genetic predisposition to adult disease will undertake an online survey to assess their use of digital health applications (apps). In phase 2, we will conduct focus groups with 40 individuals with a genetic predisposition to cardiac or cancer syndromes, and 30 clinicians from diverse specialities involved in their care. These focus groups will inform the platform’s content, functionality and user interface design, as well as identify the barriers and enablers to the adoption and retention of the platform by all endusers. The focus groups will be audiorecorded and transcribed, and thematic and content data analysis will be undertaken by adopting the Unified Theory of Acceptance and Use of Technology. Descriptive statistics will be calculated from the survey data. Phase 3 will identify the core skillsets for a novel digital health coordinator role. Outcomes from phases 1 and 2 will inform development of the digital platform, which will be user-tested and optimised in phase 4.

ETHICS AND DISSEMINATION: This study was approved by the Peter MacCallum Human Research Ethics Committee (HREC/88892/PMCC). Results will be disseminated in academic forums, peer-reviewed publications and used to optimise clinical care.

PMID:37518079 | DOI:10.1136/bmjopen-2022-071492

BMC Res Notes. 2023 Jul 30;16(1):159. doi: 10.1186/s13104-023-06438-4.

ABSTRACT

OBJECTIVE: To use genome-wide association study (GWAS) by subtraction, a method for deriving novel GWASs from existing summary statistics, to derive genome-wide summary statistics for paternal smoking.

RESULT: A GWAS by subtraction was implemented using a weighted linear model that defined the child-genotype paternal-phenotype association as the child-genotype child-phenotype association minus the child-genotype maternal-phenotype association. We first use the laws of inherence to derive the weighted linear model. We then implemented the linear model to create a GWAS of paternal smoking by subtracting the summary statistics from a GWAS of maternal smoking from the summary statistics of a GWAS of the index individual’s smoking. We used a Monte-Carlo simulation to validate the model and showed that this approach performed similarly in terms of bias to performing a traditional GWAS of paternal smoking. Finally, we validated the summary statistics in a Mendelian randomisation analysis by demonstrating an association of genetically predicted paternal smoking with paternal lung cancer and emphysema.

PMID:37518004 | DOI:10.1186/s13104-023-06438-4

Aging (Albany NY). 2023 Jul 28;15. doi: 10.18632/aging.204910. Online ahead of print.

ABSTRACT

PURPOSE: Chronic inflammation and lipid peroxidation (LPO) are associated with the pathogenesis of hepatocellular carcinoma (HCC), and γ-hydroxy-1, N²-propanodeoxyguanosine (γ-OHPdG) is a promutagenic DNA adduct derived from LPO. This study aimed to examine the relationship between γ-OHPdG and the progression of liver carcinogenesis.

METHODS: Primary HCC specimens were obtained from 228 patients and cirrhosis specimens from 46 patients. The patients were followed up with after surgery via outpatient visits and telephone calls. The levels of γ-OHPdG were determined by immunohistochemical analysis in the carcinomatous tissues together with adjacent and cirrhosis tissues.

RESULTS: γ-OHPdG levels in the cancerous tissues were significantly higher compared to adjacent tissues (P < 0.001) and also higher than the ones from the tissues of cirrhosis patients. Along with tumor size, histological grade, MVI grade, T stage, the percentage of ki67-positive cells and HCC progression, γ-OHPdG levels in cancerous tissues showed a gradually increasing trend. Moreover, prognostic analysis showed that higher γ-OHPdG levels in cancerous tissues were strongly correlated with lower overall survival (P < 0.001), lower intrahepatic recurrence-free survival (P < 0.001) and lower distant metastasis-free survival (P < 0.05). There was a trend, although not statistically significant, of increased levels of γ-OHPdG in cirrhosis cases that advanced to HCC, whereas γ-OHPdG levels reversely correlated with the period of time observed for cirrhosis advanced to HCC.

CONCLUSIONS: These results suggest that γ-OHPdG is a prognostic biomarker for predicting outcomes in HCC, and may serve as a prospective indicator for predicting HCC in cirrhosis patients.

PMID:37517089 | DOI:10.18632/aging.204910

Med Phys. 2023 Jul 30. doi: 10.1002/mp.16657. Online ahead of print.

ABSTRACT

BACKGROUND: Due to the nonlinear nature of the logarithmic operation and the stochastic nature of photon counts (N), sinogram data of photon counting detector CT (PCD-CT) are intrinsically biased, which leads to statistical CT number biases. When raw counts are available, nearly unbiased statistical estimators for projection data were developed recently to address the CT number bias issue. However, for most clinical PCD-CT systems, users’ access to raw detector counts is limited. Therefore, it remains a challenge for end users to address the CT number bias issue in clinical applications.

PURPOSE: To develop methods to correct statistical biases in PCD-CT without requiring access to raw PCD counts.

METHODS: (1) The sample variance of air-only post-log sinograms was used to estimate air-only detector counts, ${\overline{N}}_0$ . (2) If the post-log sinogram data, y, is available, then N of each detector pixel was estimated using $N={\overline{N}}_0{e}^{–y}$ . Once N was estimated, a closed-form analytical bias correction was applied to the sinogram. (3) If a patient’s post-log sinogram data are not archived, a forward projection of the bias-contaminated CT image was used to perform a first-order bias correction. Both the proposed sinogram domain- and image domain-based bias correction methods were validated using experimental PCD-CT data.

RESULTS: Experimental results demonstrated that both sinogram domain- and image domain-based bias correction methods enabled reduced-dose PCD-CT images to match the CT numbers of reference-standard images within [-5, 5] HU. In contrast, uncorrected reduced-dose PCD-CT images demonstrated biases ranging from -25 to 55 HU, depending on the material. No increase in image noise or spatial resolution degradation was observed using the proposed methods.

CONCLUSIONS: CT number bias issues can be effectively addressed using the proposed sinogram or image domain method in PCD-CT, allowing PCD-CT acquired at different radiation dose levels to have consistent CT numbers desired for quantitative imaging.

PMID:37517080 | DOI:10.1002/mp.16657

Med Phys. 2023 Jul 30. doi: 10.1002/mp.16597. Online ahead of print.

ABSTRACT

BACKGROUND: Microvascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI-positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics.

PURPOSE: To develop three-class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors.

METHODS: The data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively. The patients were divided into the training (116 cases) and test (50 cases) groups at a ratio of 7:3. Of them, 69 cases were MVI positive in the training group, including 45 cases in the low-risk group (M1) and 24 cases in the high-risk group (M2), and 47 cases were MVI negative (M0). In the training group, the optimal subset features were obtained through feature selection, and the arterial phase radiomics model, portal venous phase radiomics model, delayed phase radiomics model, three-phase radiomics model, clinical imaging model, and combined model were developed using Linear Support Vector Classification. The test group was used for validation, and the efficacy of each model was evaluated through the receiver operating characteristic curve (ROC).

RESULTS: The clinical imaging features of MVI included alpha-fetoprotein, tumor size, tumor margin, peritumoral enhancement, intratumoral artery, and low-density halo. The area under the curve (AUC) of the ROC values of the clinical imaging model for M0, M1, and M2 were 0.831, 0.701, and 0.847, respectively, in the training group and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p = 0.029).

CONCLUSION: The clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, which can provide precise reference value for preoperative clinical diagnosis and treatment. The combined application of the two models had a high predictive efficacy.

PMID:37517073 | DOI:10.1002/mp.16597