A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology.

INTRODUCTION: Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer. METHODS: This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models. RESULTS: Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001). CONCLUSIONS: In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.

Energy expenditure and intake in COPD: The extent of unnoticed unbalance by predicting REE.

Undernourishment is promoted by an unbalance between energy expenditure and intake. Resting energy expenditure (REE) in chronic obstructive pulmonary disease (COPD) is commonly predicted using the Harris-Benedict (HB) and the Angelillo-Moore (AM) formulas, however no study has investigated to which extent COPD patients with an energy unbalance go unnoticed when REE is predicted rather than measured with indirect calorimetry. This study demonstrates that 66% and 25% of negatively unbalanced patients go unnoticed when using HB and AM, respectively, urging to discourage the use of REE predicting formulas in clinical practice, at least in cases at risk of undernourishment.

Clustering of patients with end-stage chronic diseases by symptoms: a new approach to identify health needs.

BACKGROUND: End-stage chronic obstructive pulmonary disease (COPD), chronic heart failure (CHF) and chronic renal failure (CRF) are characterized by a high burden of daily symptoms that, irrespective of the primary organ failure, are widely shared. AIMS: To evaluate whether and to which extent symptom-based clusters of patients with end-stage COPD, CHF and CRF associate with patients' health status, mobility, care dependency and life-sustaining treatment preferences. METHODS: 255 outpatients with a diagnosis of advanced COPD (n = 95), advanced CHF (n = 80) or CRF requiring dialysis (n = 80) were visited in their home environment and underwent a multidimensional assessment: clinical characteristics, symptom burden using Visual Analog Scale (VAS), health status questionnaires, timed "Up and Go" test, Care Dependency Scale and willingness to undergo mechanical ventilation or cardiopulmonary resuscitation. Three clusters were obtained applying K-means cluster analysis on symptoms' severity assessed via VAS. Cluster characteristics were compared using non-parametric tests. RESULTS: Cluster 1 patients, with the least symptom burden, had a better quality of life, lower care dependency and were more willing to accept life-sustaining treatments than others. Cluster 2, with a high presence and severity of dyspnea, fatigue, cough, muscle weakness and mood problems, and Cluster 3, with the highest occurrence and severity of symptoms, reported similar care dependency and life-sustaining treatment preferences, while Cluster 3 reported the worst physical health status. DISCUSSION: Symptom-based clusters identify patients with different health needs and might help to develop palliative care programs. CONCLUSION: Clustering by symptoms identifies patients with different health status, care dependency and life-sustaining treatment preferences.

Defining Resilience in Older People: Does a Subjective Definition of Stressor Work?

BACKGROUND: The operational definition of resilience is elusive and resilient people are difficult to identify. We used self-reported "major health event" (srMHE) to identify resilience and evaluate the functional and mortality trajectories associated with this condition. METHOD: We selected from the InCHIANTI study persons aged 65 or older who could perform the Short Physical Performance Battery at baseline and attended the 3 years follow-up visit. We identified 4 groups: Controls: no srMHE and no decline in physical function; Decliners: no srMHE and decline in physical function; Resilient: srMHE and no decline in physical function; and Non-resilient: srMHE and decline in physical function. Linear mixed models and Cox regression were used to analyze changes in activities of daily living (ADL) score over 9- and 10-year mortality across groups, respectively. RESULTS: The 313 participants that reported a srMHE had worse perceived health status and higher number of GP visits and prescribed drugs at baseline. Of these, 78 were Resilient and 235 Non-resilient; of the remaining, 136 were Controls and 277 Decliners. Compared to the Controls, Resilient had similar change of ADL score over time (ß: -.03, p = .92) and mortality (hazard ratio: 1.31, 95% confidence interval: 0.76-2.23), while Decliners and Non-resilient showed significantly higher mortality and, the latter, worsening of ADL score. Additional srMHE during follow-up affected the rate of change of ADL score and mortality more in the Controls group than in the Resilient group. CONCLUSIONS: A srMHE along with repeated evaluation of physical function may be used to identify resilience in older people, and may complement the standard functional evaluation of geriatric patients.