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Background The effectiveness of Incentive spirometry (IS) in patients undergoing video-assisted thoracic surgery (VATS) remains lacking. We conducted a population-based study to investigate the effectiveness of IS on patients with lung cancers following VATS. Methods We identified patients newly diagnosed with lung cancer who underwent surgical resection by VATS or thoracotomy from the years 2000 to 2008 in the Longitudinal Health Insurance Database. Exposure variable was the use of IS during admission for surgical resection by VATS or thoracotomy. Primary outcomes included hospitalization cost, incidence of pneumonia, and length of hospital stay. Secondary outcomes included the frequency of emergency department (ED) visits and hospitalizations at 3-month, 6-month, and 12-month follow-ups after thoracic surgery. Results We analyzed 7549 patients with lung cancer undergoing surgical resection by VATS and thoracotomy. The proportion of patients who were subjected to IS was significantly higher in those who underwent thoracotomy than in those who underwent VATS (68.4% vs. 53.1%, P < 0.0001). After we controlled for potential covariates, the IS group significantly reduced hospitalization costs (− 524.5 USD, 95% confidence interval [CI] = − 982.6 USD – -66.4 USD) and the risk of pneumonia (odds ratio = 0.55, 95% CI = 0.32–0.95) compared to the non-IS group following VATS. No difference in ED visit frequency and hospitalization frequency at 3-month, 6-month, and 1-year follow-up was noted between the IS and the non-IS groups following VATS. Conclusions The use of IS in patients with lung cancers undergoing VATS may reduce hospitalization cost and the risk of pneumonia.

Factors associated with re-intubation within 14 d after ventilator liberation are related to the level and quality of the care setting; thus, to prevent re-intubation, more attention should be paid to higher-risk ventilator-dependent subjects after they are liberated from mechanical ventilation.

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Cognitive radio appears as a promising technology to allocate wireless spectrum between licensed and unlicensed users in an efficient way. When unlicensed users opportunistically utilize spectrum holes, prediction models that infer the availability of spectrum holes can help to improve the spectrum extraction rate and reduce the collision rate. In this paper, a spectrum occupancy prediction model based on Partial Periodic Pattern Mining (PPPM) is introduced. The mining aims at identifying frequent spectrum occupancy patterns that are hidden in the spectrum usage of a channel. The mined frequent patterns are then used to predict future channel states (i.e., busy or idle). Based on the prediction, unlicensed users are able to utilize spectrum holes aggressively without introducing significant interference to licensed users. PPPM outperforms traditional Frequent Pattern Mining (FPM) by considering real patterns that do not repeat perfectly due to noise, sensing errors, and irregular behaviors. Using real-world Wi-Fi and personal communication service (PCS) activities, we show a significant reduction on miss rate in channel state prediction. With the proposed prediction mechanism, the performance of Dynamic Spectrum Access (DSA) is substantially improved. Further, we extend the three-state PPPM to an N-state PPPM to predict the duration of high/low utilization in a channel. The frequent patterns of channel utilization duration are critical in optimizing channel switch strategies. The high prediction accuracy is validated with data collected in the paging bands.

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