Unmet needs and distress in people with inoperable lung cancer at the commencement of treatment

Purpose : People with lung cancer report a higher burden of unmet needs, specifically psychological and daily living unmet needs. They experience more psychological distress and more physical hardship than other tumour sites. This study examined the levels of unmet need and psychological distress in inoperable lung cancer patients at the start of treatment.

Methods : A cross-section survey methodology was employed using baseline data from a randomised controlled trial designed to evaluate a supportive care intervention. Eligible lung cancer patients were approached to participate at the start of treatment. Consenting patients completed questionnaires prior to or just after the commencement of treatment. Reliable and valid measures included Needs Assessment for Advanced Lung Cancer Patients, Hospital Anxiety and Depression Scale and Brief Distress Thermometer.

Results : Of the 108 patients participating, the top unmet need was ‘Dealing with concerns about your family’s fears and worries’ (62%); with the next four also coming from the psychological/emotional domain, but, on average, most needs related to medical communication. Thirty two percent of patients reported clinical or subclinical anxiety and 19% reported HADS scores suggestive of clinical or subclinical depression. Moreover, 39.8% of the sample reported distress above the cut-off on the distress thermometer and this was associated with higher needs for each need subscale (p < 0.05).

Conclusions : People with lung cancer have high levels of unmet needs especially regarding psychological/emotional or medical communication. People with lung cancer who are classified as distressed have more unmet needs.

Patient expectations and preferences for follow-up after treatment for lung cancer: a pilot study

Introduction

There is no robust evidence to indicate the most appropriate models of follow-up care for patients who have completed treatment for lung cancer. This pilot study aimed to assess expectations and preferences for follow-up care in a sample of patients who had completed treatment for lung cancer.

Method

Thirty-one patients who had completed treatment for primary lung cancer were recruited. A 13 item self-report survey was developed to elicit patient's preferences and expectations for follow-up. Participants completed the developed survey and clinical and demographic variables were collected.

Results

Factors scored as extremely important by over 80% of respondents focused on care coordination: Being able to see the same doctor or health care professional at each visit (24/83%); Knowing which doctor or nurse to contact if queries arise between follow-up appointments (23/82%); and Knowing the patient can book an appointment or contact a health care professional involved in their care regarding health concerns between visits (25/89%). Patients were supportive of nurse-led follow-up when offered in the context of a model of shared care (21/78%).

Conclusion

This study offers new insight into the expectations and preferences for follow-up of patients with lung cancer, with participants indicated preference for intensive follow-up after the completion of treatment.

Validation of the needs assessment for advanced lung cancer patients (NA-ALCP)

Objective: The Needs Assessment for Advanced Cancer Patients (NA-ACP) is a 132-item self-report questionnaire designed to assess the seven needs domains of patients with advanced, incurable cancer. This study aimed to evaluate the short derivative form of that questionnaire with advanced lung cancer patients.

Methods: Item factor loadings, item test–retest data and response distributions were used to retain or reject items from the original NA-ACP scale. This resulted in 38 items being maintained, preserving the original subscales. The response scale was simplified following feedback from patients. 108 people with advanced lung cancer completed the shortened NA-ALCP along with measures of psychological distress (HADS, DT) and quality of life (EORTC QLQ-C30). A-priori predictions were made for divergent and convergent validity.

Results: Internal consistency coefficients were satisfactory for six of seven subscales, range 0.71–0.95. Correlations between NA-ALCP and HADS, DT and EORTC-QLQ-C30 provided support for 11 of the 22 divergent (r = 0.13–0.27) and convergent predictions (r = 0.45–0.71).

Conclusions: Subscales of the NA-ALCP demonstrated internal reliability consistent with the original scale. Results provided supporting evidence for divergent and convergent validity. This study indicates that the NA-ALCP is psychometrically robust, easily understood and one-quarter the length of the original version

Lung cancer: challenges and solutions for supportive care intervention research

Lung cancer is the leading cause of cancer death. It is associated with a high level of morbidity, particularly fatigue, pain, breathlessness, and coughing. These symptoms can have a substantial impact on psychosocial functioning. It is critical to have effective interventions demonstrated to improve quality of life particularly for those with advanced disease. However there is a paucity of high quality intervention research to guide practice in this area. This article discusses the challenges in conducting supportive care research in this group, including the patient's level of literacy in English, poor performance status, rapidly fluctuating health status, and familial or professional “gate-keeping.” Many of these challenges can be overcome by broadening eligibility criteria, permitting some flexibility in relation to recruitment and data collection procedures, working closely with the treatment team, involving the patient's family, minimizing practical difficulties associated with intervention delivery, and reducing study burden in other ways, such as limiting the amount of data collected from the patient and shortening follow-up time intervals. We explore these potential solutions drawing on the experience of conducting a randomized controlled trial of a support intervention for people with lung cancer and their family.

The marsupial: a unique model for understanding the regulation of lung development

 The project has exploited an Australian marsupial, tammar wallaby, as an experimental model to understand lung development. This research has focused on identifying the factors that regulate lung development and to develop new intervention therapies to improve health outcomes in human premature and low birth weight babies.

The influence of sighing respirations on infant lung function measured using multiple breath washout gas mixing techniques

There is substantial interest in studying lung function in infants, to better understand the early life origins of chronic lung diseases such as asthma. Multiple breath washout (MBW) is a technique for measuring lung function that has been adapted for use in infants. Respiratory sighs occur frequently in young infants during natural sleep, and in accordance with current MBW guidelines, result in exclusion of data from a substantial proportion of testing cycles. We assessed how sighs during MBW influenced the measurements obtained using data from 767 tests conducted on 246 infants (50% male; mean age 43 days) as part of a large cohort study. Sighs occurred in 119 (15%) tests. Sighs during the main part of the wash-in phase (before the last 5 breaths) were not associated with differences in standard MBW measurements compared with tests without sighs. In contrast, sighs that occurred during the washout were associated with a small but discernible increase in magnitude and variability. For example, the mean lung clearance index increased by 0.36 (95% CI: 0.11-0.62) and variance increased by a multiplicative factor of 2 (95% CI: 1.6-2.5). The results suggest it is reasonable to include MBW data from testing cycles where a sigh occurs during the wash-in phase, but not during washout, of MBW. By recovering data that would otherwise have been excluded, we estimate a boost of about 10% to the final number of acceptable tests and 6% to the number of individuals successfully tested.

A robust approach for automated lung segmentation in thoracic CT

Lung segmentation in thoracic computed tomography (CT) scans is an important preprocessing step for computer-aided diagnosis (CAD) of lung diseases. This paper focuses on the segmentation of the lung field in thoracic CT images. Traditional lung segmentation is based on Gray level thresholding techniques, which often requires setting a threshold and is sensitive to image contrasts. In this paper, we present a fully automated method for robust and accurate lung segmentation, which includes a enhanced thresholding algorithm and a refinement scheme based on a texture-aware active contour model. In our thresholding algorithm, a histogram based image stretch technique is performed in advance to uniformly increase contrasts between areas with low Hounsfield unit (HU) values and areas with high HU in all CT images. This stretch step enables the following threshold-free segmentation, which is the Otsu algorithm with contour analysis. However, as a threshold based segmentation, it has common issues such as holes, noises and inaccurate segmentation boundaries that will cause problems in future CAD for lung disease detection. To solve these problems, a refinement technique is proposed that captures vessel structures and lung boundaries and then smooths variations via texture-aware active contour model. Experiments on 2,342 diagnosis CT images demonstrate the effectiveness of the proposed method. Performance comparison with existing methods shows the advantages of our method.