Systematic Review of Prediction Models for Preterm Birth Using CHARMS

2021 ◽  
pp. 109980042110256
Author(s):  
Jeung-Im Kim ◽  
Joo Yun Lee
Keyword(s):  
Systematic Review ◽  
Body Mass Index ◽  
Chronic Disease ◽  
Preterm Birth ◽  
Prediction Models ◽  
Target Population ◽  
Economic Factors ◽  
Behavioral Factors ◽  
Inclusion Criteria ◽  
Socio Economic Factors

Objective: This study sought to evaluate prediction models for preterm birth (PTB) and to explore predictors frequently used in PTB prediction models. Methods: A systematic review was conducted. We selected studies according to the PRISMA, classified studies according to TRIPOD, appraised studies according to the PROBAST, and extracted and synthesized the data narratively according to the CHARMS. We classified the predictors in the models into socio-economic factors with demographic, psychosocial, biomedical, and health behavioral factors. Results: Twenty-one studies with 27 prediction models were selected for the analysis. Only 16 models (59.3%) defined PTB outcomes as 37 weeks or less, and seven models (25.9%) defined PTB as 32 weeks or less. The PTB rates varied according to whether high-risk pregnant women were included and according to the outcome definition used. The most frequently included predictors were age (among demographic factors), height, weight, body mass index, and chronic disease (among biomedical factors), and smoking (among behavioral factors). Conclusion: When using the PTB prediction model, one must pay attention to the outcome definition and inclusion criteria to select a model that fits the case. Many studies use the sub-categories of PTB; however, some of these sub-categories are not correctly indicated, and they can be misunderstood as PTB (≤ 37 weeks). To develop further PTB prediction models, it is necessary to set the target population and identify the outcomes to predict.

scholarly journals The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

2020 ◽  
Vol 17 (9) ◽  
pp. 3048 ◽  
Author(s):  
Shamil D. Cooray ◽  
Lihini A. Wijeyaratne ◽  
Georgia Soldatos ◽  
John Allotey ◽  
Jacqueline A. Boyle ◽  
...  
Keyword(s):  
Systematic Review ◽  
Body Mass Index ◽  
High Risk ◽  
Gestational Diabetes ◽  
Body Mass ◽  
Pregnancy Complications ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Risk Of Bias ◽  
Prognostic Prediction

Gestational diabetes (GDM) increases the risk of pregnancy complications. However, these risks are not the same for all affected women and may be mediated by inter-related factors including ethnicity, body mass index and gestational weight gain. This study was conducted to identify, compare, and critically appraise prognostic prediction models for pregnancy complications in women with gestational diabetes (GDM). A systematic review of prognostic prediction models for pregnancy complications in women with GDM was conducted. Critical appraisal was conducted using the prediction model risk of bias assessment tool (PROBAST). Five prediction modelling studies were identified, from which ten prognostic models primarily intended to predict pregnancy complications related to GDM were developed. While the composition of the pregnancy complications predicted varied, the delivery of a large-for-gestational age neonate was the subject of prediction in four studies, either alone or as a component of a composite outcome. Glycaemic measures and body mass index were selected as predictors in four studies. Model evaluation was limited to internal validation in four studies and not reported in the fifth. Performance was inadequately reported with no useful measures of calibration nor formal evaluation of clinical usefulness. Critical appraisal using PROBAST revealed that all studies were subject to a high risk of bias overall driven by methodologic limitations in statistical analysis. This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, at present, a lack of external validation and high risk of bias limit clinical application. Future model development and validation should utilise the latest methodological advances in prediction modelling to achieve the evolution required to create a useful clinical tool. Such a tool may enhance clinical decision-making and support a risk-stratified approach to the management of GDM. Systematic review registration: PROSPERO CRD42019115223.

scholarly journals Development, validation and effectiveness of diagnostic prediction tools for colorectal cancer in primary care: a systematic review

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bogdan Grigore ◽  
Ruth Lewis ◽  
Jaime Peters ◽  
Sophie Robinson ◽  
Christopher J. Hyde
Keyword(s):  
Colorectal Cancer ◽  
Systematic Review ◽  
Primary Care ◽  
Cost Effectiveness ◽  
Prediction Model ◽  
Prediction Models ◽  
Diagnostic Tools ◽  
Inclusion Criteria ◽  
Impact Studies ◽  
The Impact

Abstract Background Tools based on diagnostic prediction models are available to help general practitioners (GP) diagnose colorectal cancer. It is unclear how well they perform and whether they lead to increased or quicker diagnoses and ultimately impact on patient quality of life and/or survival. The aim of this systematic review is to evaluate the development, validation, effectiveness, and cost-effectiveness, of cancer diagnostic tools for colorectal cancer in primary care. Methods Electronic databases including Medline and Web of Science were searched in May 2017 (updated October 2019). Two reviewers independently screened titles, abstracts and full-texts. Studies were included if they reported the development, validation or accuracy of a prediction model, or assessed the effectiveness or cost-effectiveness of diagnostic tools based on prediction models to aid GP decision-making for symptomatic patients presenting with features potentially indicative of colorectal cancer. Data extraction and risk of bias were completed by one reviewer and checked by a second. A narrative synthesis was conducted. Results Eleven thousand one hundred thirteen records were screened and 23 studies met the inclusion criteria. Twenty-studies reported on the development, validation and/or accuracy of 13 prediction models: eight for colorectal cancer, five for cancer areas/types that include colorectal cancer. The Qcancer models were generally the best performing. Three impact studies met the inclusion criteria. Two (an RCT and a pre-post study) assessed tools based on the RAT prediction model. The third study looked at the impact of GP practices having access to RAT or Qcancer. Although the pre-post study reported a positive impact of the tools on outcomes, the results of the RCT and cross-sectional survey found no evidence that use of, or access to, the tools was associated with better outcomes. No study evaluated cost effectiveness. Conclusions Many prediction models have been developed but none have been fully validated. Evidence demonstrating improved patient outcome of introducing the tools is the main deficiency and is essential given the imperfect classification achieved by all tools. This need is emphasised by the equivocal results of the small number of impact studies done so far.

scholarly journals Risk Factors for Cesarean Section Surgical Site Infections: A Systematic Review

2020 ◽  
Vol 41 (S1) ◽  
pp. s377-s377
Author(s):  
Feah Visan ◽  
Jenalyn Castro ◽  
Yousra Siam Shahada ◽  
Naser Al Ansari ◽  
Almunzer Zakaria
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Body Mass Index ◽  
Risk Factor ◽  
Cesarean Section ◽  
Surgical Site Infection ◽  
Body Mass ◽  
Surgical Site Infections ◽  
Site Infection ◽  
Inclusion Criteria

Background: According to the CDC NHSN, surgical site infections (SSI) are wound infections that develop within 30 days postoperatively for nonimplanted surgeries such as cesarean sections. SSIs is shown to manifest in a continuum of a purulent discharge from surgical site to severe sepsis. It contributes to rising morbidity, mortality and prolonged length of stay. Objective: To describe risk factors to the development of SSI in cesarean section in descriptive studies. Methods: The Preferred Reporting Items for Systematic Reviews (PRISMA) reporting guidelines is used as method for this systematic review. A PubMed literature search was conducted, limited to published articles in English from 1998 to 2016 using the broad key terms “cesarean section,” “surgical site infection,” and “risk factor.” The following inclusion criteria were applied to all reviews: (1) peer-reviewed journal, (2) computed risk factor for SSI development, and (3) calculated SSI rate. Reviews of references of the include studies were conducted, and 7 studies were appraised, with only 1 accepted. Results: After extracting data from 52 article reviews, 23 were finally accepted based on the inclusion criteria. Most studies were multivariate studies (n = 8) followed by cohort studies (n = 6). Unique numerators and denominators for SSI reviews were mentioned in all 23 studies, of which 22 studies followed the CDC NHSN definitions for SSI. Within the 23 studies, most studies showed that obesity (11.46%) is a common maternal risk factor for the development of postoperative cesarean section SSI. Conclusions: Identifying that obesity is a major contributor of surgical site infection in postoperative cesarean section women is a topic that warrants exploration. The relationship of cesarean section SSI to obesity should be investigated, specifically highlighting the level of obesity based on the WHO international body mass index (BMI) classification and the development of SSI. A correlation between increasing wound infection rates and increasing body mass index should be studied further. Published recommendations for preventing SSIs in this population should be reviewed.Funding: NoneDisclosures: None

scholarly journals Food environment near schools and body weight—A systematic review of associations by race/ethnicity, gender, grade, and socio‐economic factors

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Mika Matsuzaki ◽  
Brisa N. Sánchez ◽  
Maria Elena Acosta ◽  
Jillian Botkin ◽  
Emma V. Sanchez‐Vaznaugh
Keyword(s):  
Systematic Review ◽  
Body Weight ◽  
Food Environment ◽  
Economic Factors ◽  
Race Ethnicity ◽  
Socio Economic Factors

scholarly journals Prediction Models of Obstructive Sleep Apnea in Pregnancy: A Systematic Review and Meta-Analysis of Model Performance

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1097
Author(s):  
Sukanya Siriyotha ◽  
Visasiri Tantrakul ◽  
Supada Plitphonganphim ◽  
Sasivimol Rattanasiri ◽  
Ammarin Thakkinstian
Keyword(s):  
Systematic Review ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Prediction Models ◽  
Meta Analysis ◽  
Model Performance ◽  
Inclusion Criteria ◽  
Obstructive Sleep ◽  
High Level ◽  
Development And Validation

Background: Gestational obstructive sleep apnea (OSA) is associated with adverse maternal and fetal outcomes. Timely diagnosis and treatment are crucial to improve pregnancy outcomes. Conventional OSA screening questionnaires are less accurate, and various prediction models have been studied specifically during pregnancy. Methods: A systematic review and meta-analysis were performed for multivariable prediction models of both development and validation involving diagnosis of OSA during pregnancy. Results: Of 1262 articles, only 6 studies (3713 participants) met the inclusion criteria and were included for review. All studies showed high risk of bias for the construct of models. The pooled C-statistics (95%CI) for development prediction models was 0.817 (0.783, 0850), I2 = 97.81 and 0.855 (0.822, 0.887), I2 = 98.06 for the first and second–third trimesters, respectively. Only multivariable apnea prediction (MVAP), and Facco models were externally validated with pooled C-statistics (95%CI) of 0.743 (0.688, 0.798), I2 = 95.84, and 0.791 (0.767, 0.815), I2 = 77.34, respectively. The most common predictors in the models were body mass index, age, and snoring, none included hypersomnolence. Conclusions: Prediction models for gestational OSA showed good performance during early and late trimesters. A high level of heterogeneity and few external validations were found indicating limitation for generalizability and the need for further studies.

scholarly journals Obesity and periodontitis: systematic review and meta-analysis

2014 ◽  
Vol 19 (6) ◽  
pp. 1763-1772 ◽  
Author(s):  
Patrícia Garcia de Moura-Grec ◽  
Juliane Avansini Marsicano ◽  
Cristiane Alves Paz de Carvalho ◽  
Silvia Helena de Carvalho Sales-Peres
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Body Mass Index ◽  
Literature Search ◽  
Search Strategy ◽  
Meta Analysis ◽  
Inclusion Criteria ◽  
Paired Samples ◽  
Literature Search Strategy ◽  
Selection Of

The scope of this study was to conduct a systematic review of the studies on the association between obesity and periodontitis. The methods applied included a literature search strategy and selection of studies using inclusion and exclusion in accordance with the criteria for characteristics of the studies and meta-analysis. The research was conducted in the PubMed, Embase and Lilacs databases through 2010. Selected papers were on studies on humans investigating whether or not obesity is a risk factor for periodontitis. Of the 822 studies identified, 31 studies met the inclusion criteria and were included in this meta-analysis. The risk of periodontitis was associated with obesity (or had a tendency for this) in 25 studies, though it was not associated in 6 studies. The meta-analysis showed a significant association with obesity and periodontitis (OR = 1.30 [95% Confidence Interval (CI), 1.25 - 1.35]) and with mean Body Mass Index (BMI) and periodontal disease (mean difference = 2.75). Obesity was associated with periodontitis, however the risk factors that aggravate these diseases should be better clarified to elucidate the direction of this association. Working with paired samples and avoiding confusion factors may contribute to homogeneity between the studies.

Sign in / Sign up

Export Citation Format

Share Document