The CAPER studies: five case-control studies aimed at identifying and quantifying the risk of cancer in symptomatic primary care patients.
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ABSTRACT:
SUBMITTER: Hamilton W
PROVIDER: S-EPMC2790706 | biostudies-other | 2009 Dec
REPOSITORIES: biostudies-other
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Project description:Over 8000 new pancreatic cancers are diagnosed annually in the UK; most at an advanced stage, with only 3% 5-year survival. We aimed to identify and quantify the risk of pancreatic cancer for features in primary care.A case-control study using electronic primary care records identified and quantified the features of pancreatic cancer. Cases, aged ≥40 in the General Practice Research Database, UK, with primary pancreatic cancer were matched with controls on age, sex and practice. Putative features of pancreatic cancer were identified in the year before diagnosis. Odds ratios (OR) were calculated for features of cancer using conditional logistic regression. Positive predictive values (PPV) were calculated for consulting patients.In all, 3635 cases and 16,459 controls were studied. Nine features were associated with pancreatic cancer (all P<0.001 except for back pain, P=0.004); jaundice, OR 1000 (95% confidence interval (CI) 4,302,500); abdominal pain, 5 (4.4, 5.6); nausea/vomiting, 4.5 (3.5, 5.7); back pain, 1.4 (1.1, 1.7); constipation, 2.2 (1.7, 2.8); diarrhoea, 1.9 (1.5, 2.5); weight loss, 15 (11, 22); malaise, 2.4 (1.6, 3.5); new-onset diabetes 2.1 (1.7, 2.5). Positive predictive values for patients aged ≥60 were <1%, apart from jaundice at 22% (95% CI 14, 52), though several pairs of symptoms had PPVs >1%.Most previously reported symptoms of pancreatic cancer were also relevant in primary care. Although predictive values were small - apart from jaundice - they provide a basis for selection of patients for investigation, especially with multiple symptoms.
| S-EPMC3388562 | biostudies-other
Project description:Over 15 000 new oesophago-gastric cancers are diagnosed annually in the United Kingdom, with most being advanced disease. We identified and quantified features of this cancer in primary care.Case-control study using electronic primary-care records of the UK patients aged ≥40 years was performed. Cases with primary oesophago-gastric cancer were matched to controls on age, sex and practice. Putative features of cancer were identified in the year before diagnosis. Odds ratios (ORs) were calculated for these features using conditional logistic regression, and positive predictive values (PPVs) were calculated.A total of 7471 cases and 32 877 controls were studied. Sixteen features were independently associated with oesophago-gastric cancer (all P<0.001): dysphagia, OR 139 (95% confidence interval 112-173); reflux, 5.7 (4.8-6.8); abdominal pain, 2.6 (2.3-3.0); epigastric pain, 8.8 (7.0-11.0); dyspepsia, 6 (5.1-7.1); nausea and/or vomiting, 4.9 (4.0-6.0); constipation, 1.5 (1.2-1.7); chest pain, 1.6 (1.4-1.9); weight loss, 8.9 (7.1-11.2); thrombocytosis, 2.4 (2.0-2.9); low haemoglobin, 2.4 (2.1-2.7); low MCV, 5.2 (4.2-6.4); high inflammatory markers, 1.7 (1.4-2.0); raised hepatic enzymes, 1.3 (1.2-1.5); high white cell count, 1.4 (1.2-1.7); and high cholesterol, 0.8 (0.7-0.8). The only PPV >5% in patients ≥55 years was for dysphagia. In patients <55 years, all PPVs were <1%.Symptoms of oesophago-gastric cancer reported in secondary care were also important in primary care. The results should inform guidance and commissioning policy for upper GI endoscopy.
| S-EPMC3553533 | biostudies-other
Project description:BackgroundIdentifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP.MethodsWe used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5?years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination.ResultsThe final analysis included data on 93,120 patients, with a median age of 82.6?years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing.ConclusionsOur model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time.
| S-EPMC6889642 | biostudies-literature
Project description:OBJECTIVE:Analysis of routinely collected electronic health record (EHR) data from primary care is reliant on the creation of codelists to define clinical features of interest. To improve scientific rigour, transparency and replicability, we describe and demonstrate a standardised reproducible methodology for clinical codelist development. DESIGN:We describe a three-stage process for developing clinical codelists. First, the clear definition a priori of the clinical feature of interest using reliable clinical resources. Second, development of a list of potential codes using statistical software to comprehensively search all available codes. Third, a modified Delphi process to reach consensus between primary care practitioners on the most relevant codes, including the generation of an 'uncertainty' variable to allow sensitivity analysis. SETTING:These methods are illustrated by developing a codelist for shortness of breath in a primary care EHR sample, including modifiable syntax for commonly used statistical software. PARTICIPANTS:The codelist was used to estimate the frequency of shortness of breath in a cohort of 28?216 patients aged over 18 years who received an incident diagnosis of lung cancer between 1 January 2000 and 30 November 2016 in the Clinical Practice Research Datalink (CPRD). RESULTS:Of 78 candidate codes, 29 were excluded as inappropriate. Complete agreement was reached for 44 (90%) of the remaining codes, with partial disagreement over 5 (10%). 13?091 episodes of shortness of breath were identified in the cohort of 28?216 patients. Sensitivity analysis demonstrates that codes with the greatest uncertainty tend to be rarely used in clinical practice. CONCLUSIONS:Although initially time consuming, using a rigorous and reproducible method for codelist generation 'future-proofs' findings and an auditable, modifiable syntax for codelist generation enables sharing and replication of EHR studies. Published codelists should be badged by quality and report the methods of codelist generation including: definitions and justifications associated with each codelist; the syntax or search method; the number of candidate codes identified; and the categorisation of codes after Delphi review.
| S-EPMC5719324 | biostudies-other
Project description:COPD remains largely undiagnosed or is diagnosed late in the course of disease. We report findings of a specialist outreach programme to identify undiagnosed COPD in primary care. An electronic case-finding algorithm identified 1602 at-risk patients from 12 practices who were invited to attend the clinic. Three hundred and eighty-three (23.9%) responded and 288 were enrolled into the study. Forty-eight (16.6%) had undiagnosed mild and 28 (9.7%) had moderate airway obstruction, meeting spirometric diagnostic criteria for COPD. However, at 12 months only 8 suspected COPD patients (10.6%) had received a diagnostic label in their primary care record. This constituted 0.38% of the total patient population, as compared with 0.31% of control practices, p = 0.306. However, if all patients with airway obstruction received a coding of COPD, then the diagnosis rate in the intervention group would have risen by 0.84%. Despite the low take-up and diagnostic yield, this programme suggests that integrated case-finding strategies could improve COPD recognition.
| S-EPMC7878732 | biostudies-literature
Project description:Aim: This study aimed to assess the prevalence of symptoms glioma patients may present with to the general practitioner, and whether these can be distinguished from patients with other CNS disorders or any other condition. Methods: Glioma patients were matched to CNS patients and 'other controls' using anonymized general practitioner registries. Prevalences were evaluated in the 5 years prior to diagnosis. Result: CNS patients reported significantly more motor symptoms in the period 60-24 months, (p = 0.039). Moreover, <6 months before diagnosis CNS patients differed significantly in mood disorders/fear compared with 'other controls' (p = 0.012) but not glioma patients (p = 0.816). Conclusion: Glioma patients could not be distinguished from both control groups with respect to the number or type of prediagnostic symptoms.
| S-EPMC6880303 | biostudies-literature
Project description:Introduction Bisphosphonates are becoming a common treatment for osteoporosis particularly after discovery of the association between hormone replacement therapy and increased risk of breast cancer. As osteoporosis develops with age, treatment is a long-term intervention. Randomised control trials typically have limited follow-up times, which restricts investigation of the effects of the drugs on risk of primary cancers. A few observational studies have demonstrated a reduced risk of breast cancer and possibly of endometrial cancer in bisphosphonate users. Two epidemiological studies have studied the effect of the drugs on oesophageal cancer but did not reach any definite conclusions. So far, no effects on colorectal and stomach cancer have been shown. This study will investigate the association of bisphosphonates with risks of the 10 most common primary cancers. Methods and analysis A series of nested case-control studies will be based on the general population using records from 660 UK general practices within the QResearch Database. Cases will be patients with primary cancers diagnosed between 1996 and 2011. Each case will be matched by age, sex, practice and calendar year to five controls, who are alive and registered with the practice at the time of diagnosis of the case. Exposure to bisphosphonates will be defined as at least one prescription during the study period. For the most common cancers with substantial numbers of observations, the effect of the duration of the treatment and different types of bisphosphonates will be studied. Conditional logistic regression will be applied to produce ORs adjusted for smoking status, socioeconomic status, ethnicity, cancer-specific co-morbidities and use of other medications.
| S-EPMC3278492 | biostudies-literature
Project description:Genome-wide association studies (GWASs) aim to detect genetic risk factors for complex human diseases by identifying disease-associated single-nucleotide polymorphisms (SNPs). The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lack of power in many GWASs. In this article, we proposed a model-based clustering method that transforms the challenging high-dimension-small-sample-size problem to low-dimension-large-sample-size problem and borrows information across SNPs by grouping SNPs into three clusters. We pre-specify the patterns of clusters by minor allele frequencies of SNPs between cases and controls, and enforce the patterns with prior distributions. In the simulation studies our proposed novel model outperforms traditional SNP-wise approach by showing better controls of false discovery rate (FDR) and higher sensitivity. We re-analyzed two real studies to identifying SNPs associated with severe bortezomib-induced peripheral neuropathy (BiPN) in patients with multiple myeloma (MM). The original analysis in the literature failed to identify SNPs after FDR adjustment. Our proposed method not only detected the reported SNPs after FDR adjustment but also discovered a novel BiPN-associated SNP rs4351714 that has been reported to be related to MM in another study.
| S-EPMC6757104 | biostudies-literature
Project description:Factors related to the discrepancy between patient-perceived and actual disease control remain unclear. Identifying patients at risk of overestimation of asthma control remains elusive. This study aimed to (i) investigate the relationship between patient-reported and actual level of asthma control (ii), compare the characteristics between patients who believe their asthma is well controlled that accurately report 'well-controlled' asthma with those that do not, and (iii) identify factors associated with inaccurately reported 'well-controlled' asthma. A historical, multinational, cross-sectional study using data from the iHARP (initiative Helping Asthma in Real-life Patients) review service for adults with asthma prescribed fixed-dose combination therapy. Data from 4274 patients were analysed. A major discrepancy between patient-reported and Global Initiative for Asthma defined asthma control was detected; 71.1% of patients who reported 'well-controlled' asthma were inaccurate in their perception despite receiving regular maintenance therapy. Significant differences were noted in age, gender, body mass index, education level, medication use, side effects, attitudes to preventer inhaler use, inhaler technique review and respiratory specialist review between patients who accurately reported 'well-controlled' asthma and those who did not. Independent risk factors associated with inaccurately reported 'well-controlled' asthma were: having taken a maximum of 5-12 puffs or more of reliever inhaler on at least one day within the previous 4 weeks; being female; having seen a respiratory specialist more than a year ago (rather than in the previous year); and having required oral corticosteroids for worsening asthma in the previous year. The study highlighted the significant hidden burden associated with under-recognition of poor asthma control, on the part of the patient and the need for targeted interventions designed to address the continuing discrepancy between perceived and actual disease control.
| S-EPMC6895161 | biostudies-literature
Project description:ObjectiveChronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality, yet research suggests this disease is greatly underdiagnosed. This literature review sought to summarize the most common and significant variables associated with case-finding or missed cases of COPD to inform more effective and efficient detection of high-risk COPD patients in primary care.MethodsPubMed and EMBASE were searched for articles describing case-finding and epidemiologic research to detect or characterize new cases of COPD. International studies in primary and non-primary care settings, published in English from 2002-2014, were eligible for inclusion. Studies related to risk factors for development of COPD were excluded.ResultsOf the 33 studies identified and reviewed, 21 were case-finding or screening and 12 were epidemiological, including cross-sectional, longitudinal, and retrospective designs. A range of variables were identified within and across studies. Variables common to both screening and epidemiological studies included age, smoking status, and respiratory symptoms. Seven significant predictors from epidemiologic studies did not appear in screening tools. No studies targeted discovery of higher risk patients such as those with reduced lung function or risks for exacerbations.ConclusionVariables used to identify new cases of COPD or differentiate COPD cases and non-cases are wide- ranging, (from sociodemographic to self-reported health or health history variables), providing insight into important factors for case identification. Further research is underway to develop and test the best, smallest variable set that can be used as a screening tool to identify people with undiagnosed, high-risk COPD in primary care.
| S-EPMC4521771 | biostudies-literature