Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Predictive value of inflammatory markers for cancer diagnosis in primary care: a prospective cohort study using electronic health records.

ABSTRACT:

Background

Early identification of cancer in primary care is important and challenging. This study examined the diagnostic utility of inflammatory markers (C-reactive protein, erythrocyte sedimentation rate and plasma viscosity) for cancer diagnosis in primary care.

Methods

Cohort study of 160,000 patients with inflammatory marker testing in 2014, plus 40,000 untested matched controls, using Clinical Practice Research Datalink (CPRD), with Cancer Registry linkage. Primary outcome was one-year cancer incidence.

Results

Primary care patients with a raised inflammatory marker have a one-year cancer incidence of 3.53% (95% CI 3.37-3.70), compared to 1.50% (1.43-1.58) in those with normal inflammatory markers, and 0.97% (0.87-1.07) in untested controls. Cancer risk is greater with higher inflammatory marker levels, with older age and in men; risk rises further when a repeat test is abnormal but falls if it normalises. Men over 50 and women over 60 with raised inflammatory markers have a cancer risk which exceeds the 3% NICE threshold for urgent investigation. Sensitivities for cancer were 46.1% for CRP, 43.6% ESR and 49.7% for PV.

Conclusion

Cancer should be considered in patients with raised inflammatory markers. However, inflammatory markers have a poor sensitivity for cancer and are therefore not useful as 'rule-out' test.

SUBMITTER: Watson J

PROVIDER: S-EPMC6738065 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Comorbid conditions delay diagnosis of colorectal cancer: a cohort study using electronic primary care records.

Project description:Pre-existing non-cancer conditions may complicate and delay colorectal cancer diagnosis.Incident cases (aged ?40 years, 2007-2009) with colorectal cancer were identified in the Clinical Practice Research Datalink, UK. Diagnostic interval was defined as time from first symptomatic presentation of colorectal cancer to diagnosis. Comorbid conditions were classified as 'competing demands' (unrelated to colorectal cancer) or 'alternative explanations' (sharing symptoms with colorectal cancer). The association between diagnostic interval (log-transformed) and age, gender, consultation rate and number of comorbid conditions was investigated using linear regressions, reported using geometric means.Out of the 4512 patients included, 72.9% had ?1 competing demand and 31.3% had ?1 alternative explanation. In the regression model, the numbers of both types of comorbid conditions were independently associated with longer diagnostic interval: a single competing demand delayed diagnosis by 10 days, and four or more by 32 days; and a single alternative explanation by 9 days. For individual conditions, the longest delay was observed for inflammatory bowel disease (26 days; 95% CI 14-39).The burden and nature of comorbidity is associated with delayed diagnosis in colorectal cancer, particularly in patients aged ?80 years. Effective clinical strategies are needed for shortening diagnostic interval in patients with comorbidity.

| S-EPMC5518856 | biostudies-other

Predictive value of a series of inflammatory markers in COPD for lung cancer diagnosis: a case-control study.

Project description:BackgroundThere is a relationship between Chronic Obstructive Pulmonary Disease (COPD) and the development of lung cancer (LC). The aim of this study is to analyse several blood markers and compare their concentrations in patients with only COPD and LC?+?COPD.MethodsCase-control study with cases presenting combined LC and COPD and two control groups (patients presenting only COPD and patients presenting only LC). We also included LC patients with descriptive purposes. In both groups, peripheral blood analyses of TNF-?, IL-6, IL-8, total leukocyte, lymphocyte and neutrophil counts, neutrophil-to-lymphocyte ratio, total platelet count, mean platelet volume, platelet-to-lymphocyte ratio, alpha 1-antitripsin (A1AT), IgE, C-reactive protein, fibrinogen, cholesterol and bilirubin were performed. We developed univariate and multivariate analyses of these markers, as well as a risk score variable, and we evaluated its performance through ROC curves.ResultsWe included 280 patients, 109 cases (LC?+?COPD), 83 controls (COPD) and 88 LC without COPD. No differences were observed in the distribution by sex, age, BMI, smoking, occupational exposure, lung function, GOLD stage or comorbidity. Patients with LC?+?COPD had significantly higher levels of neutrophils [OR 1.00 (95%CI 1.00-1.00), p?=?0.03] and A1AT [OR 1.02 (95%CI 1.01-1.03), p?=?0.003] and lower cholesterol levels [OR 0.98 (95%CI 0.97-0.99), p?=?0.009] than COPD controls. We developed a risk score variable combining neutrophils, A1AT and cholesterol, achieving a sensitivity of 80%, a negative predictive value of 90.7% and an area under the curve of 0.78 (95%CI 0.71-0.86).ConclusionsCOPD patients who also have LC have higher levels of neutrophils and A1AT and lower of cholesterol. These parameters could be potentially predicting biomarkers of LC in COPD patients.

| S-EPMC6712782 | biostudies-literature

Predictive value of genomic screening: cross-sectional study of cystic fibrosis in 50,788 electronic health records.

Project description:Doubts have been raised about the value of DNA-based screening for low-prevalence monogenic conditions following reports of testing this approach using available electronic health record (EHR) as the sole phenotyping source. We hypothesized that a better model for EHR-focused examination of DNA-based screening is Cystic Fibrosis (CF) since the diagnosis is proactively sought within the healthcare system. We reviewed CFTR variants in 50,778 exomes. In 24 cases with bi-allelic pathogenic CFTR variants, there were 21 true-positives. We considered three cases "potential" false-positives due to limitations in available EHR phenotype data. This genomic screening exhibited a positive predictive value of 87.5%, negative predictive value of 99.9%, sensitivity of 95.5%, and a specificity of 99.9%. Despite EHR-based phenotyping limitations in three cases, the presence or absence of pathogenic CFTR variants has strong predictive value for CF diagnosis when EHR data is used as the sole phenotyping source. Accurate ascertainment of the predictive value of DNA-based screening requires condition-specific phenotyping beyond available EHR data.

| S-EPMC6726623 | biostudies-literature

Investigating concordance in diabetes diagnosis between primary care charts (electronic medical records) and health administrative data: a retrospective cohort study.

Project description:BACKGROUND: Electronic medical records contain valuable clinical information not readily available elsewhere. Accordingly, they hold important potential for contributing to and enhancing chronic disease registries with the goal of improving chronic disease management; however a standard for diagnoses of conditions such as diabetes remains to be developed. The purpose of this study was to establish a validated electronic medical record definition for diabetes. METHODS: We constructed a retrospective cohort using health administrative data from the Institute for Clinical Evaluative Sciences Ontario Diabetes Database linked with electronic medical records from the Deliver Primary Healthcare Information Project using data from 1 April 2006-31 March 2008 (N = 19,443). We systematically examined eight definitions for diabetes diagnosis, both established and proposed. RESULTS: The definition that identified the highest number of patients with diabetes (N = 2,180) while limiting to those with the highest probability of having diabetes was: individuals with ?2 abnormal plasma glucose tests, or diabetes on the problem list, or insulin prescription, or ?2 oral anti-diabetic agents, or HbA1c ?6.5%. Compared to the Ontario Diabetes Database, this definition identified 13% more patients while maintaining good sensitivity (75%) and specificity (98%). CONCLUSIONS: This study establishes the feasibility of developing an electronic medical record standard definition of diabetes and validates an algorithm for use in this context. While the algorithm may need to be tailored to fit available data in different electronic medical records, it contributes to the establishment of validated disease registries with the goal of enhancing research, and enabling quality improvement in clinical care and patient self-management.

| S-EPMC3022877 | biostudies-other

Molecular markers of predictive value associated with low birth weight

Project description:Sub-optimal fetal development is associated with an increased risk of developing cardiovascular disease, type 2 diabetes (T2D) and adiposity later in life. However, definitions of intrauterine growth restriction (IUGR) and small for gestational age (SGA) are based on simple statistical approaches that may misclassify infants with a normal developmental profile and vice versa. We used an unbiased global profiling approach to identify gene expression patterns in umbilical cord tissue from 38 infants and identified a set of 466 genes which separated the subjects into 2 distinct groups – one biased towards lower birth weight and one biased towards normal birth weight. The data suggest that approximately 30% of children of normal size have a molecular profile more typical of impaired fetal development and who may be on a programmed trajectory. Differences in expression between the two groups encompassed 384 upregulated and 82 downregulated genes. Molecular profiling at birth may have utility in identifying markers that potentially reflect antenatal developmental and may be predictive of future phenotypic development after birth. Importantly, it may provide an alternative to the current classification of infants using birth weights.

2011-10-21 | GSE24818 | GEO

Molecular markers of predictive value associated with low birth weight

2011-10-20 | E-GEOD-24818 | biostudies-arrayexpress

Predictive Value of Combining Inflammatory Biomarkers and Rapid Decline of FEV1 for COPD in Chinese Population: A Prospective Cohort Study.

Project description:Background:In China, the high prevalence and mortality rate of Chronic Obstructive Pulmonary Disease (COPD) and the poor intervention effect makes it into a heavy social burden. The main reason is that the current diagnosis of COPD mainly based on the static lung function, which is difficult for early intervention. Through matching a predictive model for high-risk groups of COPD that rewards FEV1 rapid decline as the core, we will establish the early warning model and prove its validity and socio-economic value. Methods:This is a multi-center, prospective, cohort study. A total of 10,000 people aged 40?75 without lung disease will be recruited and followed for 3 years. Some questionnaires such as St George's Respiratory Questionnaire (SGRQ), income class, educational level, comorbidity, smoking habit, and biomass smoke exposure history will be collected. The baseline level of Interleukin 6 (IL-6), high-sensitivity C-reactive Protein (hs-CRP), microRNAs-23a (miR-23a) in peripheral blood and pH value in exhaled breath condensate (EBC) will be measured, lung spirometry will be tested in the first, second, and fourth years. Primary outcome is the incidence of COPD, multivariate regression analysis will be used to establish the predictive model for COPD in China. Discussion:With the rapid decline of lung function as the core and the baseline inflammatory biomarkers in peripheral blood and pH of the exhaled breath condensate as affecting factors, a predictive model to achieve early detection of high-risk COPD groups will be established and promoted. Trial registration:This study has been registered at www.ClinicalTrials.gov (registration identifier: NCT03532893) on 21 May 2018, https://register.clinicaltrials.gov.

| S-EPMC6901061 | biostudies-literature

electronic Medical Records & Genomics (eMERGE)

Project description:electronic Medical Records & Genomics (eMERGE)

| PRJNA158491 | ENA

Sepsis recording in primary care electronic health records, linked hospital episodes and mortality records: Population-based cohort study in England.

Project description:BackgroundSepsis is a growing concern for health systems, but the epidemiology of sepsis is poorly characterised. We evaluated sepsis recording across primary care electronic records, hospital episodes and mortality registrations.Methods and findingsCohort study including 378 general practices in England from Clinical Practice Research Datalink (CPRD) GOLD database from 2002-2017 with 36,209,676 patient-years of follow-up with linked Hospital Episode Statistics (HES) and Office for National Statistics (ONS) mortality registrations. Incident sepsis episodes were identified for each source. Concurrent records from different sources were identified and age-standardised and age-specific incidence rates compared. Logistic regression analysis evaluated associations of gender, age-group, fifth of deprivation and period of diagnosis with concurrent sepsis recording. There were 20,206 first episodes of sepsis from primary care, 20,278 from HES and 13,972 from ONS. There were 4,117 (20%) first HES sepsis events and 2,438 (17%) mortality records concurrent with incident primary care sepsis records within 30 days. Concurrent HES and primary care records of sepsis within 30 days before or after first diagnosis were higher at younger or older ages and for patients with the most recent period of diagnosis. Those diagnosed during 2007:2011 were less likely to have a concurrent HES record given CPRD compared to those diagnosed during 2012-2017 (odd ratio 0.65, 95% confidence interval 0.60-0.70). At age 85 and older, primary care incidence was 5.22 per 1,000 patient years (95% CI 1.75-11.97) in men and 3.55 (0.87-9.58) in women which increased to 10.09 (4.86-18.51) for men and 7.22 (2.96-14.72) for women after inclusion of all three sources.ConclusionExplicit recording of 'sepsis' is inconsistent across healthcare sectors with a high proportion of non-concurrent records. Incidence estimates are higher when linked data are analysed.

| S-EPMC7774940 | biostudies-literature

Antibiotic prescribing frequency amongst patients in primary care: a cohort study using electronic health records.

Project description:BackgroundReducing inappropriate antibiotic prescribing in primary care is a public health priority.ObjectivesWe hypothesized that a subset of patients account for the majority of antibiotic prescriptions in primary care. We investigated the relationship between the total amount of antibiotics prescribed, individual-level antibiotic use and comorbidity.MethodsThis was a cohort study using electronic health records from 1?948?390 adults registered with 385 primary care practices in the UK in 2011-13. We estimated the average number of antibiotic prescriptions per patient and the association between prescribing and comorbidity. We modelled the impact on total prescribing of reducing antibiotic use in those prescribed antibiotics most frequently.ResultsOn average 30.1% (586?194/1?948?390) of patients were prescribed at least one antibiotic per year. Nine percent (174?602/1?948?390) of patients were prescribed 53% (2?091?496/3?922?732) of the total amount of antibiotics, each of whom received at least five antibiotic prescriptions over 3?years. The presence of any comorbidity increased the prescribing rate by 44% [adjusted incidence rate ratio (IRR) 1.44, 95% CI 1.43-1.45]; rates of prescribing to women exceeded those in men by 62% (adjusted IRR 1.62, 95% CI 1.62-1.63).ConclusionsHalf of antibiotics prescribed to adults in primary care were for <10% of patients. Efforts to tackle antimicrobial resistance should consider the impact of this on total prescribing.

| S-EPMC5437523 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data