Σφακιανάκης Αλέξανδρος
ΩτοΡινοΛαρυγγολόγος
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Σάββατο 8 Δεκεμβρίου 2018

From reactive to proactive tube feeding during chemoradiotherapy for head and neck cancer: A clinical prediction model-based approach

Publication date: January 2019

Source: Oral Oncology, Volume 88

Author(s): R.T. Karsten, M.M. Stuiver, L. van der Molen, A. Navran, J.P. de Boer, F.J.M. Hilgers, W.M.C. Klop, L.E. Smeele

Abstract
Objectives

Feeding tubes are placed unnecessarily in a proportion of head and neck cancer (HNC) patients treated with chemoradiotherapy (CRT) when prophylactic tube placement protocols are used. This may have a negative impact on the risk of long-term dysphagia. Reactive tube placement protocols, on the other hand, might result in weight loss and treatment interruption. The objective of this study is to identify patients at risk for prolonged tube dependency in order to implement a personalized strategy regarding proactive tube placement.

Materials and methods

A retrospective study was performed in a consecutive cohort of HNC patients treated with primary CRT for whom a reactive tube placement protocol was used. A prediction model was developed to predict prolonged (> 90 days) feeding tube dependency. Model performance and clinical net benefit of the model were assessed.

Results

Of the 336 included patients, 229 (68%) needed a feeding tube during CRT and 151 (45%) were prolonged feeding tube dependent. The prediction model includes the predictors pretreatment BMI, weight loss, Functional Oral Intake Scale and T-stage. Discriminatory ability is fair (area under the ROC-curve of 0.69) and calibration is adequate (Hosmer and Lemeshow test p = .254). The model shows net benefit over current practice for probability thresholds from 35 to 80%.

Conclusion

The developed model can be used to select patients for proactive feeding tube placement during primary CRT for HNC. The nomogram with easily obtainable parameters is a useful tool for clinicians to support shared decision making regarding proactive tube placement.

Graphical abstract

Graphical abstract for this article



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