British Journal of General Practice最新文献

Background: Diagnosing cancer in general practice is complex, given the non-specific nature of many presenting symptoms and the overlap of potential diagnoses.

Aim: This trial aimed to evaluate the effectiveness of Future Health Today (FHT) - a technology that provides clinical decision support, auditing, and quality-improvement monitoring - on the appropriate follow-up of patients at risk of undiagnosed cancer.

Design and setting: Pragmatic, cluster randomised trial undertaken in general practices in Victoria and Tasmania, Australia.

Method: Practices were randomly assigned to receive recommendations for follow-up investigations for cancer (FHT cancer module) or the active control. Algorithms were applied to the electronic medical record, and used demographic information and abnormal test results that are associated with a risk of undiagnosed cancer (that is, anaemia/iron deficiency, thrombocytosis, and raised prostate-specific antigen) to identify patients requiring further investigation and provide recommendations for care. The intervention consisted of the FHT cancer module, a case-based learning series, and ongoing practice support. Using the intention-to-treat approach, the between-arm difference in the proportion of patients with abnormal test results who were followed up according to guidelines was determined at 12 months.

Results: In total, 7555 patients were identified as at risk of undiagnosed cancer. At 12 months post-randomisation, 76.0% of patients in the intervention arm had received recommended follow-up (21 practices, n = 2820/3709), compared with 70.0% in the control arm (19 practices, n = 2693/3846; estimated between-arm difference = 2.6% [95% confidence interval (CI)] = -2.8% to 7.9%; odds ratio = 1.15 [95% CI = 0.87 to 1.53]; P = 0.332).

Conclusion: The FHT cancer module intervention did not increase the proportion of patients receiving guideline-concordant care. The proportion of patients receiving recommended follow-up was high, suggesting a possible ceiling effect for the intervention.

Background: The journey of >80% of patients diagnosed with lung cancer starts in general practice. About 75% of patients are diagnosed when it is at an advanced stage (3 or 4), leading to >80% mortality within 1 year at present. The long-term data in GP records might contain hidden information that could be used for earlier case finding of patients with cancer.

Aim: To develop new prediction tools that improve the risk assessment for lung cancer.

Design and setting: Text analysis of electronic patient data using natural language processing and machine learning in the general practice files of four networks in the Netherlands.

Method: Files of 525 526 patients were analysed, of whom 2386 were diagnosed with lung cancer. Diagnoses were validated by using the Dutch cancer registry, and both structured and free-text data were used to predict the diagnosis of lung cancer 5 months before diagnosis (4 months before referral).

Results: The algorithm could facilitate earlier detection of lung cancer using routine general practice data. Discrimination, calibration, sensitivity, and specificity were established under various cut-off points of the prediction 5 months before diagnosis. Internal validation of the best model demonstrated an area under the curve of 0.88 (95% confidence interval [CI] = 0.86 to 0.89), which shrunk to 0.79 (95% CI = 0.78 to 0.80) during external validation. The desired sensitivity determines the number of patients to be referred to detect one patient with lung cancer.

Conclusion: Artificial intelligence-based support enables earlier detection of lung cancer in general practice using readily available text in the patient files of GPs, but needs additional prospective clinical evaluation.

Background: Evidence is equivocal on whether general practice rates of investigation in symptomatic patients using chest X-ray (CXR) affect outcomes.

Aim: To determine whether there is an association between rates of CXR requested in general practice and lung cancer outcomes.

Design and setting: Observational study using data on English general practices.

Method: Cancer registry data for patients diagnosed with lung cancer in 2014-2018 were linked to data on general practice CXRs from 2013 until 2017. Cancer stage at diagnosis (I/II versus III/IV) and 1-year and 5-year survival rates (conditional on survival to 1 year) post-diagnosis were reported by general practice quintile of CXR rate, with adjustment for population differences (age, smoking, prevalence of chronic obstructive pulmonary disease and heart failure, ethnicity, and deprivation) and by unadjusted category (low, medium, and high).

Results: In total, 192 631 patient records and CXR rates for 7409 practices were obtained. Practices in the highest quintile of CXR rate had fewer cancers diagnosed at stage III/IV compared with those in the lowest quintile (odds ratio [OR] 0.87, 95% confidence interval [CI] = 0.83 to 0.92, P<0.001). The association was weaker for the high unadjusted CXR category (OR 0.94, 95% CI = 0.91 to 0.97). For the highest adjusted quintile, hazard ratios (HRs) for death within 1 year and 5 years were 0.92 (95% CI = 0.90 to 0.95, P<0.001) and 0.95 (95% CI = 0.91 to 0.99, P = 0.023), respectively. For the high unadjusted CXR category, the HR for 1-year survival was 0.98 (95% CI = 0.96 to 0.99, P = 0.004), with no association demonstrated for 5-year survival.

Conclusion: Patients registered at general practices with higher CXR use have a favourable stage distribution and slightly better survival. This supports the use of CXR in promoting earlier diagnosis of symptomatic lung cancer in general practice.