Dataset Information

Independent test of a model to predict severe acute esophagitis.

ABSTRACT:

Purpose

Treatment planning factors are known to affect the risk of severe acute esophagitis during thoracic radiation therapy. We tested a previously published model to predict the risk of severe acute esophagitis on an independent data set.

Methods and materials

The data set consists of data from patients who had recoverable treatment plans and received definitive radiation therapy for non-small cell carcinoma of the lung at a single institution between November 2004 and January 2010. Complete esophagus dose-volume and available clinical information was extracted using our in-house software. The previously published model was a logistic function with a combination of mean esophageal dose and use of concurrent chemotherapy. In addition to testing the previous model, we used a novel, machine learning-based method to build a maximally predictive model.

Results

Ninety-four patients (81.7%) developed Common Terminology Criteria for Adverse Events, Version 4, Grade 2 or more severe esophagitis (Grade 2: n = 79 and Grade 3: n = 15). Univariate analysis revealed that the most statistically significant dose-volume parameters included percentage of esophagus volume receiving ?40 to 60 Gy, minimum dose to the highest 20% of esophagus volume (D20) to D35, and mean dose. Other significant predictors included concurrent chemotherapy and patient age. The previously published model predicted risk effectively with a Spearman's rank correlation coefficient (r_s) of 0.43 (P < .001) with good calibration (Hosmer-Lemeshow goodness of fit: P = .537). A new model that was built from the current data set found the same variables, yielding an r_s of 0.43 (P < .001) with a logistic function of 0.0853 × mean esophageal dose [Gy] + 1.49 × concurrent chemotherapy [1/0] - 1.75 and Hosmer-Lemeshow P = .659. A novel preconditioned least absolute shrinkage and selection operator method yielded an average r_s of 0.38 on 100 bootstrapped data sets.

Conclusions

The previously published model was validated on an independent data set and determined to be nearly as predictive as the best possible two-parameter logistic model even though it overpredicted risk systematically. A novel, machine learning-based model using a bootstrapping approach showed reasonable predictive power.

SUBMITTER: Huang EX

PROVIDER: S-EPMC5514225 | biostudies-literature | 2017 Jan-Mar

REPOSITORIES: biostudies-literature

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Publications

Independent test of a model to predict severe acute esophagitis.

Huang Ellen X EX Robinson Clifford G CG Molotievschi Alerson A Bradley Jeffrey D JD Deasy Joseph O JO Oh Jung Hun JH

Advances in radiation oncology 20161116 1

<h4>Purpose</h4>Treatment planning factors are known to affect the risk of severe acute esophagitis during thoracic radiation therapy. We tested a previously published model to predict the risk of severe acute esophagitis on an independent data set.<h4>Methods and materials</h4>The data set consists of data from patients who had recoverable treatment plans and received definitive radiation therapy for non-small cell carcinoma of the lung at a single institution between November 2004 and January ...[more]

PMID: 28740914

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Project description:IntroductionSeveral studies demonstrated that the hypothalamic-pituitary-adrenal (HPA) axis is dysregulated in suicide attempters. Prospective studies found that people with an abnormal response at the dexamethasone suppression test (DST) are more likely to commit suicide. However, whether DST may predict suicide attempts remains less clear. A possible strategy to address this question is to consider the suicide attempt lethality.Objectives(1) To compare the pre- and post-DST cortisol levels in serious/violent suicide attempters and in non-serious/non-violent suicide attempters, and (2) to investigate whether cortisol level can predict new suicide attempts or their lethality.MethodsThe study included 70 recent suicide attempters (25 with a serious or violent attempt) who were followed for two years. Three saliva samples for cortisol measurement were obtained at 8a.m., 3p.m., and 9p.m. before the DST (pre-DST). Then, at 11 p.m., 1 mg of dexamethasone was given orally. The following day (post-DST), three saliva samples were collected at the same hours as before. The post-DST-pre-DST salivary cortisol Δ index was calculated for each collection time. The Risk-Rescue Ratio Scale (RRRS) and the Suicidal Intent Scale (SIS) were used to characterize the suicide attempt at inclusion and those occurring during the follow-up.ResultsPost-DST cortisol level at 9 p.m. was higher in patients with an initial violent or serious suicide attempt than in non-violent/non-serious attempters (p < .010). Higher post-DST cortisol at 9p.m. was associated with lower RRRS rescue score and higher clinical impression of suicide severity at inclusion. Among the 66 patients who completed the follow-up, 26 attempted suicide again at least once. Higher pre-DST cortisol at 8a.m. predicted new suicide attempts during the follow-up (OR = 2.15 [1.11, 4.15]), and higher cortisol Δ index at 9p.m. was associated with higher suicide intent during the follow-up.ConclusionsOur results suggest that HPA axis hyper-reactivity monitored with the DST is a marker of violent/serious suicide attempt with lower rescue possibility. Furthermore, higher changes between pre-DST and post-DST cortisol levels may predict higher suicide intent. These findings might help to characterize the biological features of nearest suicide phenotypes.

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Dataset Information

Independent test of a model to predict severe acute esophagitis.

Purpose

Methods and materials

Results

Conclusions

Publications

Independent test of a model to predict severe acute esophagitis.

Similar Datasets

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