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Critical Care (London, England) Nov 2023Bacteria are the main pathogens that cause sepsis. The pathogenic mechanisms of sepsis caused by gram-negative and gram-positive bacteria are completely different, and... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Bacteria are the main pathogens that cause sepsis. The pathogenic mechanisms of sepsis caused by gram-negative and gram-positive bacteria are completely different, and their prognostic differences in sepsis remain unclear.
METHODS
The PubMed, Web of Science, Cochrane Library, and Embase databases were searched for Chinese and English studies (January 2003 to September 2023). Observational studies involving gram-negative (G (-))/gram-positive (G (+)) bacterial infection and the prognosis of sepsis were included. The stability of the results was evaluated by sensitivity analysis. Funnel plots and Egger tests were used to check whether there was publication bias. A meta-regression analysis was conducted on the results with high heterogeneity to identify the source of heterogeneity. A total of 6949 articles were retrieved from the database, and 45 studies involving 5586 subjects were included after screening according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Twenty-seven high-quality studies and 18 moderate-quality studies were identified according to the Newcastle‒Ottawa Scale score. There was no significant difference in the survival rate of sepsis caused by G (-) bacteria and G (+) bacteria (OR 0.95, 95% CI 0.70-1.28). Subgroup analysis according to survival follow-up time showed no significant difference. The serum concentrations of C-reactive protein (CRP) (SMD = 0.39, 95% CI 0.02-0.76), procalcitonin (SMD = 1.95, 95% CI 1.32-2.59) and tumor necrosis factor-alpha (TNF-α) (MD = 0.31, 95% CI 0.25-0.38) in the G (-) bacterial infection group were significantly higher than those in the G (+) bacterial infection group, but there was no significant difference in IL-6 (SMD = 1.33, 95% CI - 0.18-2.84) and WBC count (MD = - 0.15, 95% CI - 0.96-00.66). There were no significant differences between G (-) and G (+) bacteria in D dimer level, activated partial thromboplastin time, thrombin time, international normalized ratio, platelet count, length of stay or length of ICU stay. Sensitivity analysis of the above results indicated that the results were stable.
CONCLUSION
The incidence of severe sepsis and the concentrations of inflammatory factors (CRP, PCT, TNF-α) in sepsis caused by G (-) bacteria were higher than those caused by G (+) bacteria. The two groups had no significant difference in survival rate, coagulation function, or hospital stay. The study was registered with PROSPERO (registration number: CRD42023465051).
Topics: Humans; Prognosis; Tumor Necrosis Factor-alpha; Sepsis; Bacterial Infections; Gram-Negative Bacteria; C-Reactive Protein; Bacteria; Gram-Positive Bacteria
PubMed: 38037118
DOI: 10.1186/s13054-023-04750-w -
World Journal of Surgical Oncology Jun 2023Although several studies have confirmed the prognostic value of the consolidation to tumor ratio (CTR) in non-small cell lung cancer (NSCLC), there still remains... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Although several studies have confirmed the prognostic value of the consolidation to tumor ratio (CTR) in non-small cell lung cancer (NSCLC), there still remains controversial about it.
METHODS
We systematically searched the PubMed, Embase, and Web of Science databases from inception to April, 2022 for eligible studies that reported the correlation between CTR and prognosis in NSCLC. Hazard ratios (HRs) with 95% confidence intervals (95% CIs) were extracted and pooled to assess the overall effects. Heterogeneity was estimated by I statistics. Subgroup analysis based on the cut-off value of CTR, country, source of HR and histology type was conducted to detect the sources of heterogeneity. Statistical analyses were performed using STATA version 12.0.
RESULTS
A total of 29 studies published between 2001 and 2022 with 10,347 patients were enrolled. The pooled results demonstrated that elevated CTR was associated with poorer overall survival (HR = 1.88, 95% CI 1.42-2.50, P < 0.01) and disease-free survival (DFS)/recurrence-free survival (RFS)/progression-free survival (PFS) (HR = 1.42, 95% CI 1.27-1.59, P < 0.01) in NSCLC. According to subgroup analysis by the cut-off value of CTR and histology type, both lung adenocarcinoma and NSCLC patients who had a higher CTR showed worse survival. Subgroup analysis stratified by country revealed that CTR was a prognostic factor for OS and DFS/RFS/PFS in Chinese, Japanese, and Turkish patients.
CONCLUSIONS
In NSCLC patients with high CTR, the prognosis was worse than that with low CTR, indicating that CTR may be a prognostic factor.
Topics: Humans; Carcinoma, Non-Small-Cell Lung; Prognosis; Lung Neoplasms; Proportional Hazards Models; Tomography
PubMed: 37349739
DOI: 10.1186/s12957-023-03081-y -
International Journal of Medical... Aug 2023As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the... (Review)
Review
BACKGROUND
As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx.
METHOD
A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).
RESULTS
Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center.
CONCLUSIONS
While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.
Topics: Humans; Artificial Intelligence; Big Data; Heart Transplantation; Prognosis; Retrospective Studies
PubMed: 37285695
DOI: 10.1016/j.ijmedinf.2023.105110 -
Cancer Medicine Aug 2022The prognostic significance of insulin-like growth factor binding protein 2 (IGFBP2) expression has been explored in plenty of studies in human cancers. Because of the... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
The prognostic significance of insulin-like growth factor binding protein 2 (IGFBP2) expression has been explored in plenty of studies in human cancers. Because of the controversial results, the meta-analysis was carried out to evaluate the relevance of IGFBP2 expression with the prognosis in various tumors.
METHODS
The data searched from four databases (Pubmed, Embase, Cochrane library, and Web of science) was used to calculate pooled hazard ratios (HRs) in this meta-analysis. Subgroup analyses were stratified by ethnicity, cancer type, publication year, Newcastle-Ottawa Scale score, treatments, and populations.
RESULTS
Twenty-one studies containing 5560 patients finally met inclusion criteria. IGFBP2 expression was associated with lower overall survival (HR = 1.57, 95% CI = 1.31-1.88) and progression-free survival (HR = 1.18, 95% CI = 1.04-1.34) in cancer patients, but not with disease-free survival (HR = 1.50, 95% CI = 0.91-2.46) or recurrence-free survival (HR = 1.50, 95% CI = 0.93-2.40). The subgroup analyses indicated IGFBP2 overexpression was significantly correlated with overall survival in Asian patients (HR = 1.42, 95% CI = 1.18-1.72), Caucasian patients (HR = 2.20, 95% CI = 1.31-3.70), glioma (HR = 1.36, 95% CI = 1.03-1.79), and colorectal cancer (HR = 2.52, 95% CI = 1.43-4.44) and surgery subgroups (HR = 1.97, 95% CI = 1.50-2.58).
CONCLUSION
The meta-analysis showed that IGFBP2 expression was associated with worse prognosis in several tumors, and may serve as a potential prognostic biomarker in cancer patients.
Topics: Disease-Free Survival; Humans; Insulin-Like Growth Factor Binding Protein 2; Neoplasms; Prognosis; Proportional Hazards Models
PubMed: 35546443
DOI: 10.1002/cam4.4680 -
Clinical Microbiology and Infection :... Apr 2023Prognostic models are typically developed to estimate the risk that an individual in a particular health state will develop a particular health outcome, to support... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Prognostic models are typically developed to estimate the risk that an individual in a particular health state will develop a particular health outcome, to support (shared) decision making. Systematic reviews of prognostic model studies can help identify prognostic models that need to further be validated or are ready to be implemented in healthcare.
OBJECTIVES
To provide a step-by-step guidance on how to conduct and read a systematic review of prognostic model studies and to provide an overview of methodology and guidance available for every step of the review progress.
SOURCES
Published, peer-reviewed guidance articles.
CONTENT
We describe the following steps for conducting a systematic review of prognosis studies: 1) Developing the review question using the Population, Index model, Comparator model, Outcome(s), Timing, Setting format, 2) Searching and selection of articles, 3) Data extraction using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist, 4) Quality and risk of bias assessment using the Prediction model Risk Of Bias ASsessment (PROBAST) tool, 5) Analysing data and undertaking quantitative meta-analysis, and 6) Presenting summary of findings, interpreting results, and drawing conclusions. Guidance for each step is described and illustrated using a case study on prognostic models for patients with COVID-19.
IMPLICATIONS
Guidance for conducting a systematic review of prognosis studies is available, but the implications of these reviews for clinical practice and further research highly depend on complete reporting of primary studies.
Topics: Humans; Prognosis; COVID-19; Bias
PubMed: 35934199
DOI: 10.1016/j.cmi.2022.07.019 -
Journal of Otolaryngology - Head & Neck... Sep 2021Head and neck cutaneous squamous cell carcinoma (HNCSCC) is a non-melanoma skin cancer that is mostly caused by solar ultraviolet radiation exposure. While it usually... (Review)
Review
BACKGROUND
Head and neck cutaneous squamous cell carcinoma (HNCSCC) is a non-melanoma skin cancer that is mostly caused by solar ultraviolet radiation exposure. While it usually has an excellent prognosis, a subset of patients (5%) develops nodal metastasis and has poor outcomes. The aim of this study was to systematically review the literature and evaluate the prognostic factors of HNCSCC in order to better understand which patients are the most likely to develop metastatic disease.
METHODS
A comprehensive literature search was performed on PubMed and EMBASE to identify the studies that evaluated the prognostic factors of HNCSCC. Prognostic factors were deemed significant if they had a reported p-value of < 0.05. Proportions of studies that reported a given factor to be statistically significant were calculated for each prognostic factor.
RESULTS
The search yielded a total of 958 citations. Forty studies, involving a total of 8535 patients, were included in the final analysis. The pre-operative/clinical prognostic factors with the highest proportion of significance were state of immunosuppression (73.3%) and age (53.3%); while post-operative/pathological prognostic factors of importance were number of lymph nodes involved with carcinoma (70.0%), margins involved with carcinoma (66.7%), and tumor depth (50.0%).
CONCLUSION
This systematic review is aimed to aid physicians in assessing the prognosis of HNCSCC and identifying the subsets of patients that are most susceptible to metastasis. It also suggests that immunosuppressed patients with a high-risk feature on biopsy, such as invasion beyond subcutaneous fat, could possibly benefit from a sentinel lymph node biopsy.
Topics: Carcinoma, Squamous Cell; Head and Neck Neoplasms; Humans; Neoplasm Staging; Prognosis; Retrospective Studies; Skin Neoplasms; Ultraviolet Rays
PubMed: 34493343
DOI: 10.1186/s40463-021-00529-7 -
Journal of the American Medical... Jun 2022This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance.
MATERIALS AND METHODS
We searched Embase, MEDLINE, Web of Science, and Google Scholar to identify studies that developed prognostic prediction models using information extracted from unstructured text in a data-driven manner, published in the period from January 2005 to March 2021. Data items were extracted, analyzed, and a meta-analysis of the model performance was carried out to assess the added value of text to structured-data models.
RESULTS
We identified 126 studies that described 145 clinical prediction problems. Combining text and structured data improved model performance, compared with using only text or only structured data. In these studies, a wide variety of dense and sparse numeric text representations were combined with both deep learning and more traditional machine learning methods. External validation, public availability, and attention for the explainability of the developed models were limited.
CONCLUSION
The use of unstructured text in the development of prognostic prediction models has been found beneficial in addition to structured data in most studies. The text data are source of valuable information for prediction model development and should not be neglected. We suggest a future focus on explainability and external validation of the developed models, promoting robust and trustworthy prediction models in clinical practice.
Topics: Machine Learning; Prognosis
PubMed: 35475536
DOI: 10.1093/jamia/ocac058 -
The International Journal of Biological... Mar 2023The relationship between PLIN2 expression and prognosis, and clinicopathological significance of various cancers has been extensively studied, but the results are not... (Meta-Analysis)
Meta-Analysis Review
The relationship between PLIN2 expression and prognosis, and clinicopathological significance of various cancers has been extensively studied, but the results are not completely consistent. This review followed the guidelines for systematic reviews of prognostic factors studies and was reported under the Preferred Reporting Program for Systematic Reviews and Meta-Analysis (PRISMA). We searched PubMed, Embase, Cochrane Library, Web of Science, and Google Academia for relevant articles up to September 2, 2022, and calculated the pooled hazard ratios (HR) with 95% confidence intervals (CI) to determine the association between PLIN2 expression and the prognosis of various cancers. The meta-analysis ultimately included 17 studies. The quality of all included cohort studies was evaluated using the Quality in Prognosis Studies (QUIPS) tool, and an adaptation of Grading of Recommendations Assessment, Development and Evaluation (GRADE) method was used to assess the certainty of the results. High expression of PLIN2 was associated with poorer overall survival (HR = 1.65; 95% CI = 1.14, 2.38; = 0.008), metastasis-free survival (HR = 1.48; 95% CI = 1.12, 1.94; = 0.005), progression-free survival (HR = 2.11; 95% CI = 1.55, 2.87; < 0.0005) and recurrence-free survival/relapse-free survival (HR = 2.21; 95% CI = 1.64, 2.98; < 0.0005) in cancers. The clinicopathological parameters of digestive system malignancies suggested that high expression of PLIN2 was notably associated with distant metastasis ( + ) (odds ratio (OR) = 3.37; 95% CI = 1.31, 8.67; = 0.012), lymph node metastasis ( + ) (OR = 1.61; 95% CI = 1.01, 2.54; = 0.004), and tumor stage (III-IV) (OR = 1.96; 95% CI = 1.24, 3.09; = 0.006). In summary, overexpression of PLIN2 is significantly associated with a poor prognosis in various human cancers, especially in respiratory and digestive malignancies. Thus, PLIN2 expression may be a potential prognostic biomarker in cancer patients.
Topics: Humans; Prognosis; Lymphatic Metastasis; Progression-Free Survival; Proportional Hazards Models; Biomarkers, Tumor; Perilipin-2
PubMed: 36604990
DOI: 10.1177/03936155221147536 -
BMJ Open Apr 2022As part of the PIONEER Consortium objectives, we have explored which diagnostic and prognostic factors (DPFs) are available in relation to our previously defined...
OBJECTIVES
As part of the PIONEER Consortium objectives, we have explored which diagnostic and prognostic factors (DPFs) are available in relation to our previously defined clinician and patient-reported outcomes for prostate cancer (PCa).
DESIGN
We performed a systematic review to identify validated and non-validated studies.
DATA SOURCES
MEDLINE, Embase and the Cochrane Library were searched on 21 January 2020.
ELIGIBILITY CRITERIA
Only quantitative studies were included. Single studies with fewer than 50 participants, published before 2014 and looking at outcomes which are not prioritised in the PIONEER core outcome set were excluded.
DATA EXTRACTION AND SYNTHESIS
After initial screening, we extracted data following the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of prognostic factor studies (CHARMS-PF) criteria and discussed the identified factors with a multidisciplinary expert group. The quality of the included papers was scored for applicability and risk of bias using validated tools such as PROBAST, Quality in Prognostic Studies and Quality Assessment of Diagnostic Accuracy Studies 2.
RESULTS
The search identified 6604 studies, from which 489 DPFs were included. Sixty-four of those were internally or externally validated. However, only three studies on diagnostic and seven studies on prognostic factors had a low risk of bias and a low risk concerning applicability.
CONCLUSION
Most of the DPFs identified require additional evaluation and validation in properly designed studies before they can be recommended for use in clinical practice. The PIONEER online search tool for DPFs for PCa will enable researchers to understand the quality of the current research and help them design future studies.
ETHICS AND DISSEMINATION
There are no ethical implications.
Topics: Bias; Humans; Male; Mass Screening; Prognosis; Prostatic Neoplasms
PubMed: 35379637
DOI: 10.1136/bmjopen-2021-058267 -
Academic Emergency Medicine : Official... Mar 2022The objective was to assess the prognostic value of hypertension detected in the emergency department (ED). (Meta-Analysis)
Meta-Analysis Review
OBJECTIVES
The objective was to assess the prognostic value of hypertension detected in the emergency department (ED).
METHODS
The ED presents a unique opportunity to predict long-term cardiovascular disease (CVD) outcomes with its potential for high-footfall, and large-scale routine data collection applied to underserved patient populations. A systematic review and meta-analyses were conducted to assess the prognostic performance and feasibility of ED-measured hypertension as a risk factor for long-term CVD outcomes. We searched MEDLINE and Embase databases and gray literature sources. The target populations were undifferentiated ED patients. The prognostic factor of interest was hypertension. Feasibility outcomes included prevalence, reliability, and follow-up attendance. Meta-analyses were performed for feasibility using a random effect and exact likelihood.
RESULTS
The searches identified 1072 studies after title and abstract review, 53 studies had their full text assessed for eligibility, and 26 studies were included. Significant heterogeneity was identified, likely due to the international populations and differing study design. The meta-analyses estimate of prevalence for ED-measured hypertension was 0.31 (95% confidence interval 0.25-0.37). ED hypertension was persistent outside the ED (FE estimate of 0.50). The proportion of patients attending follow-up was low with an exact likelihood estimate of 0.41. Three studies examined the prognostic performance of hypertension and demonstrated an increased risk of long-term CVD outcomes.
CONCLUSION
Hypertension can be measured feasibly in the ED and consequently used in a long-term cardiovascular risk prediction model. There is an opportunity to intervene in targeted individuals, using routinely collected data.
Topics: Emergency Service, Hospital; Humans; Hypertension; Likelihood Functions; Prognosis; Reproducibility of Results
PubMed: 34553441
DOI: 10.1111/acem.14324