Improvements to the US Preventive Services Task Force Recommendation Statement

Epling, John W.; Borsky, Amanda; Gerteis, Jessie

doi:10.1001/jama.2019.11311

Cited by 90 publications

(119 citation statements)

References 2 publications

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“…The authors mention that it is important to improve these services so that they can provide more comprehensive information and timely help from trained professionals [28]. On the other hand, the United States Preventive Services Task Force recommends that counseling interventions should be applied in women with risk factors for depression, since they found convincing evidence that they help prevent perinatal depression, among the most effective they included cognitive behavioral therapy and interpersonal therapy [29].…”

Section: Discussionmentioning

confidence: 99%

Depression Associated With Intrafamily Violence in Pregnant Women Treated at a Peruvian Health Center, 2022

Cardenas-Barrios¹,

Zagaceta-Guevara²,

Moquillaza-Alcántara³

2023

Preprint

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Violence can promote various mental complications such as depression, however, the type of violence that promotes it has not been estimated in detail. The purpose of this study was to assess whether depression is associated with domestic violence in pregnant women treated at a Peruvian health center, 2022. A quantitative, observational, cross-sectional analytical study. A total of 180 pregnant adults who attend the health facility in person were selected. Intrafamily violence was assessed using a questionnaire from the Ministry of Women and Vulnerable Populations of Peru and depression using the Beck Depression Inventory. The study has the approval of the ethics committee of the university and the permission of the head of the health establishment. 36.11% [95%CI: 29.39-43.43] of the pregnant women presented depression and 1.11% severe de-pression; likewise, 41.67% [95%CI: 36.64-49.05] reported having experienced domestic violence. It was identified that presenting domestic violence increases the probability of presenting depression during pregnancy (aPR: 9.89; p&lt;0.001). The associated types of violence were psychological (aPR: 10.44; p&lt;0.001) and physical (aPR: 1.78; p=0.007). There is an association between domestic violence and depression during pregnancy, the types of violence associated being psychological and physical.

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Section: Discussionmentioning

confidence: 99%

Depression Associated With Intrafamily Violence in Pregnant Women Treated at a Peruvian Health Center, 2022

Cardenas-Barrios¹,

Zagaceta-Guevara²,

Moquillaza-Alcántara³

2023

Preprint

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“…Despite the potential advantages of early detection, population-based asymptomatic pancreatic cancer screening is not considered to be cost-effective because of the low incidence (4). However, screening within high-risk populations could be preferable, but research to identify people at high enough risk to warrant screening is needed (5,6). People who are currently considered to be at a higher risk include individuals with a family history of pancreatic cancer (7), certain germline mutations (7), cystic lesions in the pancreas (8,9), new-onset diabetes mellitus (10), and postpancreatitis diabetes mellitus (11).…”

Section: Introductionmentioning

confidence: 99%

Clinical Prediction Models for Pancreatic Cancer in General and At-Risk Populations: A Systematic Review

et al. 2022

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Identifying high-risk individuals using a risk prediction model could be a crucial first stage of screening pathways to improve the early detection of pancreatic cancer. A systematic review was conducted to critically evaluate the published primary literature on the development or validation of clinical risk prediction models for pancreatic cancer risk. METHODS:MEDLINE, Embase, and Web of Science were searched for relevant articles from the inception of each database up to November 2021. Study selection and data extraction were conducted by 2 independent reviewers. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to assess risk of bias. RESULTS:In total, 33 studies were included, describing 38 risk prediction models. Excluding studies with an overlapping population, this study consist of 15,848,100 participants, of which 58,313 were diagnosed with pancreatic cancer. Eight studies externally validated their model, and 13 performed internal validation. The studies described risk prediction models for pancreatic cancer in the general population (n 5 14), patients with diabetes (n 5 8), and individuals with gastrointestinal (and other) symptoms (symptoms included abdominal pain, unexplained weight loss, jaundice, and change in bowel habits and indigestion; n 5 11). The commonly used clinical risk factors in the model were cigarette smoking (n 5 27), age (n 5 25), diabetes history (n 5 22), chronic pancreatitis (n 5 18), and body mass index (n 5 14). In the 25 studies that assessed model performance, C-statistics ranged from 0.61 to 0.98. Of the 33 studies included, 6 were rated as being at a low risk of bias based on PROBAST. DISCUSSION:Many clinical risk prediction models for pancreatic cancer had been developed for different target populations. Although low risk-of-bias studies were identified, these require external validation and implementation studies to ensure that these will benefit clinical decision making.

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“…PC is often diagnosed in an advanced stage and as such has very poor survival, with the overall 5-year survival reaching only 11.5% (1). Because of the low incidence of PC in the general population (13.2 per 100,000 person-years) (1), widespread population-based screening is not currently recommended by the US Preventive Services Task Force (2). Therefore, a targeted approach to screening among higher-risk populations represents a key opportunity to alter the natural history of this disease.…”

Section: Introductionmentioning

confidence: 99%

Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer

et al. 2022

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INTRODUCTION: There is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for pancreatic ductal adenocarcinoma (PDAC), the most common form of PC, across 2 health systems using electronic health records. METHODS:This retrospective cohort study consisted of patients aged 50-84 years having at least 1 clinic-based visit over a 10-year study period at Kaiser Permanente Southern California (model training, internal validation) and the Veterans Affairs (VA, external testing). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict risk of PDAC within 18 months of cohort entry. RESULTS:The Kaiser Permanente Southern California cohort consisted of 1.8 million patients (mean age 61.6) with 1,792 PDAC cases. The 18-month incidence rate of PDAC was 0.77 (95% confidence interval 0.73-0.80)/1,000 person-years. The final main model contained age, abdominal pain, weight change, HbA1c, and alanine transaminase change (c-index: mean 5 0.77, SD 5 0.02; calibration test: P value 0.4, SD 0.3). The final early detection model comprised the same features as those selected by the main model except for abdominal pain (c-index: 0.77 and SD 0.4; calibration test: P value 0.3 and SD 0.3). The VA testing cohort consisted of 2.7 million patients (mean age 66.1) with an 18-month incidence rate of 1.27 (1.23-1.30)/1,000 person-years. The recalibrated main and early detection models based on VA testing data sets achieved a mean c-index of 0.71 (SD 0.002) and 0.68 (SD 0.003), respectively. DISCUSSION:Using widely available parameters in electronic health records, we developed and externally validated parsimonious machine learning-based models for detection of PC. These models may be suitable for real-time clinical application.

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Improvements to the US Preventive Services Task Force Recommendation Statement

Cited by 90 publications

References 2 publications

Depression Associated With Intrafamily Violence in Pregnant Women Treated at a Peruvian Health Center, 2022

Depression Associated With Intrafamily Violence in Pregnant Women Treated at a Peruvian Health Center, 2022

Clinical Prediction Models for Pancreatic Cancer in General and At-Risk Populations: A Systematic Review

Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer

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