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Implementing direct access to low-dose computed tomography in general practice--method, adaption and outcome.

ABSTRACT: BACKGROUND:Early detection of lung cancer is crucial as the prognosis depends on the disease stage. Chest radiographs has been the principal diagnostic tool for general practitioners (GPs), but implies a potential risk of false negative results, while computed tomography (CT) has a higher sensitivity. The aim of this study was to describe the implementation of direct access to low-dose CT (LDCT) from general practice. METHODS:We conducted a cohort study nested in a randomised study. A total of 119 general practices with 266 GPs were randomised into two groups. Intervention GPs were offered direct access to chest LDCT combined with a Continuing Medical Education (CME) meeting on lung cancer diagnosis. RESULTS:During a 19-month period, 648 patients were referred to LDCT (0.18/1000 adults on GP list/month). Half of the patients needed further diagnostic work-up, and 15 (2.3%, 95% CI: 1.3-3.8%) of the patients had lung cancer; 60% (95% CI: 32.3-83.7%) in a localised stage. The GP referral rate was 61% higher for CME participants compared to non-participants. CONCLUSION:Of all patients referred to LDCT, 2.3% were diagnosed with lung cancer with a favourable stage distribution. Half of the referred patients needed additional diagnostic work-up. There was an association between participation in CME and use of CT scan. The proportion of cancers diagnosed through the usual fast-track evaluation was 2.2 times higher in the group of CME-participating GPs. The question remains if primary care case-finding with LDCT is a better option for patients having signs and symptoms indicating lung cancer than a screening program. Whether open access to LDCT may provide earlier diagnosis of lung cancer is yet unknown and a randomised trial is required to assess any effect on outcome. TRIAL REGISTRATION:Clinicaltrials.gov NCT01527214.

SUBMITTER: Guldbrandt LM

PROVIDER: S-EPMC4226510 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Implementing direct access to low-dose computed tomography in general practice--method, adaption and outcome.

Guldbrandt Louise Mahncke LM Rasmussen Torben Riis TR Rasmussen Finn F Vedsted Peter P

PloS one 20141110 11

<h4>Background</h4>Early detection of lung cancer is crucial as the prognosis depends on the disease stage. Chest radiographs has been the principal diagnostic tool for general practitioners (GPs), but implies a potential risk of false negative results, while computed tomography (CT) has a higher sensitivity. The aim of this study was to describe the implementation of direct access to low-dose CT (LDCT) from general practice.<h4>Methods</h4>We conducted a cohort study nested in a randomised stud ...[more]

PMID: 25383780

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Project description:BackgroundFew patients with lower bowel symptoms who consult their general practitioner need a specialist opinion. However data from referred patients suggest that those who are referred would benefit from detailed assessment before referral.MethodsA cluster randomised factorial trial. 44 general practices in North Trent, UK. Practices were offered either an electronic interactive referral pro forma, an educational outreach visit by a local colorectal surgeon, both or neither. The main outcome measure was the proportion of cases with severe diverticular disease, cancer or precancerous lesions and inflammatory bowel disease in those referred by each group. A secondary outcome was a referral letter quality score. Semi-structured interviews were conducted to identify key themes relating to the use of the softwareResultsFrom 150 invitations, 44 practices were recruited with a total list size of 265,707. There were 716 consecutive referrals recorded over a six-month period, for which a diagnosis was available for 514. In the combined software arms 14% (37/261) had significant pathology, compared with 19% (49/253) in the non-software arms, relative risk 0.73 (95% CI: 0.46 to 1.15). In the combined educational outreach arms 15% (38/258) had significant pathology compared with 19% (48/256) in the non-educational arms, relative risk 0.79 (95% CI: 0.50 to 1.24). Pro forma practices documented better assessment of patients at referral.ConclusionThere was a lack of evidence that either intervention increased the proportion of patients with organic pathology among those referred. The interactive software did improve the amount of information relayed in referral letters although we were unable to confirm if this made a significant difference to patients or their health care providers. The potential value of either intervention may have been diminished by their limited uptake within the context of a cluster randomised clinical trial. A number of lessons were learned in this trial of novel innovations.

Project description:ObjectiveTo investigate the feasibility of providing general practitioners (GPs) direct and fast referral access to transvaginal ultrasound (TVUS).DesignA prospective cohort study.SettingA total of 232 Danish general practices in parts of the Central Denmark Region.SubjectsWomen aged ≥40 years who consulted their GP for vague and non-specific symptoms (n = 479).Main outcome measuresThe feasibility assessment included the GPs' referral rate, indications for referral, management of test results, and findings from TVUS.ResultsA total of 479 women were referred to TVUS. The examinations revealed abnormalities in 104 (21.7%) women. Additional investigations were needed in 68 (14.2%) women of whom seven (1.5%) underwent major surgery. No case of ovarian cancer was diagnosed during the study period or the 6-month follow-up. However, three (0.6%) women with an abnormal transvaginal ultrasound were diagnosed with urogynecological cancer; this yielded a PPV of 4.4% (95% confidence interval: 1.5-12.2) and an NPV of 100.0% (95% confidence interval: 96.7-100.0) for urogynecological cancer.ConclusionProviding GPs with direct access to transvaginal ultrasound was feasible; 80% of the investigated women were referred back to the GP, 14% were further investigated, 0.6% were diagnosed with urogynecological cancer, and 1.5% had major procedures performed without complications.ImplicationsDirect access to TVUS could be an important pathway to ensure fast evaluation of women presenting with vague non-specific symptoms of potential ovarian cancer. Future studies should explore the patient experience, cancer outcomes, and health economics issues.KEY POINTS Current awareness • GPs have no fast referral option for women presenting with vague non-specific symptoms that could indicate underlying ovarian cancer. Key findings • We offered GPs direct and fast referral access to TVUS; 51.7% of practices used the opportunity. • The GPs referred 479 women to TVUS; 104 had an abnormal TVUS and 68 needed additional investigations. • Seven women underwent major surgery, leading to three cases of urogynecological cancer. No woman had a false negative TVUS result.

Project description:In current computed tomography (CT) examinations, the associated x-ray radiation dose is of a significant concern to patients and operators. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) or kVp parameter (or delivering less x-ray energy to the body) as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and the noise would propagate into the CT image if no adequate noise control is applied during image reconstruction. Since a normal-dose high diagnostic CT image scanned previously may be available in some clinical applications, such as CT perfusion imaging and CT angiography (CTA), this paper presents an innovative way to utilize the normal-dose scan as a priori information to induce signal restoration of the current low-dose CT image series.Unlike conventional local operations on neighboring image voxels, nonlocal means (NLM) algorithm utilizes the redundancy of information across the whole image. This paper adapts the NLM to utilize the redundancy of information in the previous normal-dose scan and further exploits ways to optimize the nonlocal weights for low-dose image restoration in the NLM framework. The resulting algorithm is called the previous normal-dose scan induced nonlocal means (ndiNLM). Because of the optimized nature of nonlocal weights calculation, the ndiNLM algorithm does not depend heavily on image registration between the current low-dose and the previous normal-dose CT scans. Furthermore, the smoothing parameter involved in the ndiNLM algorithm can be adaptively estimated based on the image noise relationship between the current low-dose and the previous normal-dose scanning protocols.Qualitative and quantitative evaluations were carried out on a physical phantom as well as clinical abdominal and brain perfusion CT scans in terms of accuracy and resolution properties. The gain by the use of the previous normal-dose scan via the presented ndiNLM algorithm is noticeable as compared to a similar approach without using the previous normal-dose scan.For low-dose CT image restoration, the presented ndiNLM method is robust in preserving the spatial resolution and identifying the low-contrast structure. The authors can draw the conclusion that the presented ndiNLM algorithm may be useful for some clinical applications such as in perfusion imaging, radiotherapy, tumor surveillance, etc.

Project description:BACKGROUND:Reliably abstracting outcomes from free-text electronic health records remains a challenge. While automated classification of free text has been a popular medical informatics topic, performance validation using real-world clinical data has been limited. The two main approaches are linguistic (natural language processing [NLP]) and statistical (machine learning). The authors have developed a hybrid system for abstracting computed tomography (CT) reports for specified outcomes. OBJECTIVES:The objective was to measure performance of a hybrid NLP and machine learning system for automated outcome classification of emergency department (ED) CT imaging reports. The hypothesis was that such a system is comparable to medical personnel doing the data abstraction. METHODS:A secondary analysis was performed on a prior diagnostic imaging study on 3,710 blunt facial trauma victims. Staff radiologists dictated CT reports as free text, which were then deidentified. A trained data abstractor manually coded the reference standard outcome of acute orbital fracture, with a random subset double-coded for reliability. The data set was randomly split evenly into training and testing sets. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for certainty and temporal status. Findings were filtered for low certainty and past/future modifiers and then combined with the manual reference standard to generate decision tree classifiers using data mining tools Waikato Environment for Knowledge Analysis (WEKA) 3.7.5 and Salford Predictive Miner 6.6. Performance of decision tree classifiers was evaluated on the testing set with or without NLP processing. RESULTS:The performance of machine learning alone was comparable to prior NLP studies (sensitivity = 0.92, specificity = 0.93, precision = 0.95, recall = 0.93, f-score = 0.94), and the combined use of NLP and machine learning showed further improvement (sensitivity = 0.93, specificity = 0.97, precision = 0.97, recall = 0.96, f-score = 0.97). This performance is similar to, or better than, that of medical personnel in previous studies. CONCLUSIONS:A hybrid NLP and machine learning automated classification system shows promise in coding free-text electronic clinical data.

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Implementing direct access to low-dose computed tomography in general practice--method, adaption and outcome.

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Implementing direct access to low-dose computed tomography in general practice--method, adaption and outcome.

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