Tag: statistics

Obstet Gynecol. 2021 May 6. doi: 10.1097/AOG.0000000000004394. Online ahead of print.

ABSTRACT

OBJECTIVE: To evaluate the outcomes of the first pregnancy after fertility-sparing surgery in patients treated for early-stage ovarian cancer.

METHODS: We performed a retrospective study of women aged 18-45 years with a history of stage IA or IC ovarian cancer reported to the California Cancer Registry for the years 2000-2012. These data were linked to the 2000-2012 California Office of Statewide Health Planning and Development birth and discharge data sets to ascertain oncologic characteristics and obstetric outcomes. We included in the case group ovarian cancer patients who conceived at least 3 months after fertility-sparing surgery. The primary outcome was preterm birth, and only the first pregnancy after cancer diagnosis was considered. Secondary outcomes included small-for-gestational-age (SGA) neonates, neonatal morbidity (respiratory support within 72 hours after birth, hypoxic-ischemic encephalopathy, seizures, infection, meconium aspiration syndrome, birth trauma, and intracranial or subgaleal hemorrhage), and severe maternal morbidity as defined by the Centers for Disease Control and Prevention. Propensity scores were used to match women in a 1:2 ratio for the case group and the control group. Wald statistics and logistic regressions were used to evaluate outcomes.

RESULTS: A total of 153 patients who conceived after fertility-sparing surgery were matched to 306 women in a control group. Histologic types included epithelial (55%), germ-cell (37%), and sex-cord stromal (7%). Treatment for ovarian cancer was not associated with preterm birth before 37 weeks of gestation (13.7% vs 11.4%; odds ratio [OR] 1.23, 95% CI 0.69-2.20), SGA neonates (birth weight less than the 10th percentile: 11.8% vs 12.7%; OR 0.91, 95% CI 0.50-1.66), severe maternal morbidity (2.6% vs 1.3%; OR 2.03, 95% CI 0.50-8.25), or neonatal morbidity (both 5.9% OR 1.00, 95% CI 0.44-2.28).

CONCLUSION: Patients who conceived at least 3 months after surgery for early-stage ovarian cancer did not have an increased risk of adverse obstetric outcomes.

PMID:33957660 | DOI:10.1097/AOG.0000000000004394

Adv Nutr. 2021 May 6:nmab045. doi: 10.1093/advances/nmab045. Online ahead of print.

ABSTRACT

Appropriate planning, execution, and reporting of statistical methods and results is critical for research transparency, validity, and reproducibility. This paper provides an overview of best practices for developing a statistical analysis plan a priori, conducting statistical analyses, and reporting statistical methods and results for human nutrition randomized controlled trials (RCTs). Readers are referred to the other NURISH (NUtrition inteRventIon reSearcH) publications for detailed information about the preparation and conduct of human nutrition RCTs. Collectively, the NURISH series outlines best practices for conducting human nutrition research.

PMID:33957665 | DOI:10.1093/advances/nmab045

Dtsch Med Wochenschr. 2021 May;146(10):677-682. doi: 10.1055/a-1380-1436. Epub 2021 May 6.

ABSTRACT

Specifying a singular specific cause of death or an appropriate causal chain in the death certificate can be challenging, especially in cases of elderly, multimorbid deceased persons.The German cause of death statistics suggest that mental illnesses, including dementia, are beneath the most frequent causes of death. But when looking at death certificates in the context of dementia considerable information gaps and a lack of plausibility in the causal chain can be observed quite regularly.In this article we give recommendations for the correct designation of the cause of death and underlying diseases in the death certificate. These recommendations are not only to be seen against an academic background. The correct registration of dementia in the causes of death statistics may be a basis for decision making in health politics and is hence in the interest of optimal patient care.

PMID:33957690 | DOI:10.1055/a-1380-1436

Psychother Psychosom. 2021 May 6:1-6. doi: 10.1159/000516185. Online ahead of print.

NO ABSTRACT

PMID:33957635 | DOI:10.1159/000516185

Obstet Gynecol. 2021 May 6. doi: 10.1097/AOG.0000000000004409. Online ahead of print.

ABSTRACT

OBJECTIVE: To estimate the efficacy of fractionated carbon dioxide (CO2) laser therapy for vulvar lichen sclerosus.

METHODS: We conducted a prospective, double-blind, randomized, sham-controlled, trial conducted in a clinic specializing in vulvar disorders. The study participants were 40 women with active vulvar lichen sclerosus confirmed with biopsy who were abstaining from topical and systemic treatments for at least 4 weeks before enrollment. Women were randomized in a 1:1 ratio to receive either five sham laser treatments or five fractionated CO2 treatments in a 24-week period. Study participants, treating clinicians, and the evaluating pathologist were blinded. The primary endpoint was the change in the histopathology scale score between pretreatment and posttreatment biopsies. We estimated 20 per group for 80% power to detect a 40% reduction in the histopathology scale score with up to 10% attrition. A secondary endpoint was the change in the validated CSS (Clinical Scoring System for Vulvar Lichen Sclerosus).

RESULTS: From November 2018 to June 2020, 40 women were randomized to participate in the trial, and 37 women (19 fractionated CO2, 18 sham) were included in an intention-to-treat (ITT) analysis. Three women were excluded from the ITT analysis because they did not have posttreatment biopsies and, therefore, a posttreatment histopathology scale score could not be obtained. There was a 0.20 reduction (improvement) in histopathology scale score from baseline in the active treatment group (95% CI -1.1, 0.80, P=.74) and a 0.1 increase from baseline in the sham treatment group (95% CI -0.90, 1.0, P=.91). The change in histopathology scale score between the active and sham arm was not statistically significant (95% CI -1.14, 1.06, P=.76).

CONCLUSION: Fractionated CO2 is not an effective monotherapy treatment for vulvar lichen sclerosus.

CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT03665584.

FUNDING SOURCE: Additional funding for this study was supplied by El.En Group, Florence, Italy, the manufacturer of the laser used in this study. In addition, El.En Group supplied the laser used in the study.

PMID:33957648 | DOI:10.1097/AOG.0000000000004409

Oncology. 2021 May 6:1-9. doi: 10.1159/000516168. Online ahead of print.

ABSTRACT

PURPOSE: Glioblastoma, the most common malignant brain tumor, was associated with a median survival of <1 year in the pre-temozolomide (TMZ) era. Despite advances in molecular and genetic profiling studies identifying several predictive biomarkers, none has been translated into routine clinical use. Our aim was to investigate the prognostic significance of a panel of diverse cellular molecular markers of tumor formation and growth in an annotated glioblastoma tissue microarray (TMA).

METHODS AND MATERIALS: A TMA composed of archived glioblastoma tumors from patients treated with surgery, radiation, and non-TMZ chemother-apy, was provided by RTOG. RAD51, BRCA-1, phosphatase and tensin homolog tumor suppressor gene (PTEN), and miRNA-210 expression levels were assessed using quantitative in situ hybridization and automated quantitative protein analysis. The objectives of this analysis were to determine the association of each biomarker with overall survival (OS), using the Cox proportional hazard model. Event-time distributions were estimated using the Kaplan-Meier method and compared by the log-rank test.

RESULTS: A cohort of 66 patients was included in this study. Among the 4 biomarkers assessed, only BRCA1 expression had a statistically significant correlation with survival. From univariate analysis, patients with low BRCA1 protein expression showed a favorable outcome for OS (p = 0.04; hazard ratio = 0.56) in comparison with high expressors, with a median survival time of 18.9 versus 4.8 months.

CONCLUSIONS: BRCA1 protein expression was an important survival predictor in our cohort of glioblastoma patients. This result may imply that low BRCA1 in the tumor and the consequent low level of DNA repair cause vulnerability of the cancer cells to treatment.

PMID:33957633 | DOI:10.1159/000516168

J Am Med Inform Assoc. 2021 May 6:ocab077. doi: 10.1093/jamia/ocab077. Online ahead of print.

ABSTRACT

OBJECTIVE: We introduce Medical evidence Dependency (MD)-informed attention, a novel neuro-symbolic model for understanding free-text clinical trial publications with generalizability and interpretability.

MATERIALS AND METHODS: We trained one head in the multi-head self-attention model to attend to the Medical evidence Ddependency (MD) and to pass linguistic and domain knowledge on to later layers (MDinformed). This MD-informed attention model was integrated into BioBERT and tested on 2 public machine reading comprehension benchmarks for clinical trial publications: Evidence Inference 2.0 and PubMedQA. We also curated a small set of recently published articles reporting randomized controlled trials on COVID-19 (coronavirus disease 2019) following the Evidence Inference 2.0 guidelines to evaluate the model’s robustness to unseen data.

RESULTS: The integration of MD-informed attention head improves BioBERT substantially in both benchmark tasks-as large as an increase of +30% in the F1 score-and achieves the new state-of-the-art performance on the Evidence Inference 2.0. It achieves 84% and 82% in overall accuracy and F1 score, respectively, on the unseen COVID-19 data.

CONCLUSIONS: MD-informed attention empowers neural reading comprehension models with interpretability and generalizability via reusable domain knowledge. Its compositionality can benefit any transformer-based architecture for machine reading comprehension of free-text medical evidence.

PMID:33956981 | DOI:10.1093/jamia/ocab077

Cochrane Database Syst Rev. 2021 May 6;5:CD013491. doi: 10.1002/14651858.CD013491.pub2.

ABSTRACT

BACKGROUND: Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence.

OBJECTIVES: To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery.

SEARCH METHODS: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status .

SELECTION CRITERIA: We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model.

DATA COLLECTION AND ANALYSIS: Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews.

MAIN RESULTS: We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model.

AUTHORS’ CONCLUSIONS: Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

PMID:33956992 | DOI:10.1002/14651858.CD013491.pub2

PLoS One. 2021 May 6;16(5):e0250660. doi: 10.1371/journal.pone.0250660. eCollection 2021.

ABSTRACT

Alzheimer’s disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

PMID:33956862 | DOI:10.1371/journal.pone.0250660

PLoS One. 2021 May 6;16(5):e0251322. doi: 10.1371/journal.pone.0251322. eCollection 2021.

ABSTRACT

BACKGROUND: First-trimester pregnancy stage is the fastest developmental period of the fetus, in which all organs become well developed and need special care. Yet, many women make their first antenatal visit with the pregnancy already compromised due to fetomaternal complications. This study aimed to fill this dearth using the 2016 national representative data set to augment early antenatal care visits in Ethiopia.

METHODS: A cross-sectional study design using the 2016 Ethiopia Demographic and Health Survey (EDHS) data set. Kaplan-Meir estimate was used to explain the median survival time of the timing of the first ANC visit. Multivariate Cox-proportional hazard regression analysis was performed to identify the factors related to the timing of the first ANC visit. Adjusted hazard ratios (AHR) with a 95% Confidence interval (CI) plus a p-value of < 0.05 were considered to declare a statistically significant association.

RESULTS: Data for 4666 study participants who had ANC follow-up history during pregnancy were included in the study and analyzed. The overall median survival time in this study was seven months. The timing of the first ANC visit was shorter by 2.5 times (AHR: 2.5; 95% CI: 2.34-3.68), 4.3 times (AHR: 4.3; 95% CI: 2.2-7.66), 4.8 times (AHR: 4.8, 95% CI: 4.56-10.8) among women who attended primary, secondary, and higher education as compared with non-educated one. Similarly, women who were residing in urban areas had 3.6 times (AHR: 3.6; 95% CI: 2.7-4.32) shorter timing of first ANC visit than rural residents. Furthermore, the timing of the first visit among the richest women was 3.2 times (AHR: 3.2; 95% CI: 2.5-9.65) shorter than the poorest women.

CONCLUSION: The median survival time of the first ANC visit was seven months. The timing of the first ANC was longer among younger, poorer women, those who had no access to media, who considered distances as a big challenge to reach a health facility and, those with no education. Therefore, health care providers and community health workers should provide health education to create community awareness regarding the timing of the first ANC visit.

PMID:33956902 | DOI:10.1371/journal.pone.0251322