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Hollenbeak, Christopher S.

https://orcid.org/0000-0002-3362-814X

Mayo Clinic

Pennsylvania State University

Citations 4

Quality of life following surgery for head and neck cancer: Evidence from ACRIN 6685
Hollenbeak, Christopher S. , Duan, Fenghai , Subramaniam, Rathan M. , Taurone, Alexander, Sicks, JoRean D., Lowe, Val J. , Stack, Brendan C.

Head & Neck | February 14, 2024 | Volume: 46 | Issue: 8 | Pages: 1988-1998

10.1002/hed.27673 Get It

AbstractBackgroundThis study examined the trajectory of health‐related quality of life (HRQoL) for patients with clinical stage N0 HNSCC enrolled in ACRIN 6685 who underwent elective neck dissection(s).MethodsHRQoL of 230 patients in the ACRIN 6685 trial was measured prospectively up to 2 years following surgery using the University of Washington Quality of Life instrument.ResultsGeneral Health Within the Last 7 Days did not differ significantly from baseline at any follow‐up. General Health Relative to Before Cancer fell significantly by 5.8 points following surgery (p = 0.048), and then returned to 3.0 points above baseline at 1 year (p = 0.65). For Overall Quality of Life, HRQoL fell significantly by 4.3 points following surgery (p = 0.031) and then returned to levels not significantly different from baseline.ConclusionsPatients with stage N0 HNSCC experience significant declines in HRQoL immediately following surgery, including neck dissection, which recovers to near or better than baseline within 1–2 years.
Abstract 13353: Sleep Health Phenotypes Predict Symptoms, Cognitive Function, and Adverse Events in Patients With Heart Failure
O'Connell, Meghan , Tocchi, Christine , Wang, Zequan , Jeon, Sangchoon , Conley, Samantha , Hollenbeak, Christopher S. , Yaggi, Henry Klar , Redeker, Nancy S.

Circulation | December 19, 2023 | Volume: 148 | Issue: Suppl_1 | Pages: No page available

10.1161/circ.148.suppl_1.13353 Get It

Introduction: Poor sleep contributes to outcomes in heart failure (HF). Phenotypes reflecting multi-dimensional sleep health may better predict outcomes and support more precise interventions than single measures, but few studies have used this approach. Aims: 1) Describe phenotypes of sleep health and 2) Evaluate whether sleep health phenotypes predict symptoms, cognitive function, and time to first hospitalization and emergency department (ED) visits among HF patients. Methods: We performed secondary analysis of a randomized controlled trial of cognitive behavioral therapy for insomnia compared with HF self-management education with HF patients. We measured sleep [rest-activity rhythms, sleep duration, and efficiency (2-week wrist actigraphy/follow-ups); insomnia; CPAP use; and daytime sleepiness (Epworth Scale)]; perceived stress; fatigue; anxiety; depression; pain; and cognitive ability (Patient-Reported Outcomes Measurement System: PROMIS) at baseline and 3, 6, and 12 months post-intervention and tracked hospitalizations and ED visits. We used K-means clustering to identify sleep phenotypes and generalized linear mixed models and Cox models to predict outcomes, adjusted for demographic factors. Results: Among 166 participants (M age = 63.2 (SD = 12.8) years; 57% female; 23% NYHA Class 3/4 HF; 35% EF less than 45%), there were 4 phenotypes [poor sleep (14%); short sleep/high sleep efficiency (39%); long sleep/low sleep efficiency (25%); and healthy sleep (21%)] at baseline. The healthy sleep group was older and had lower BMIs and less comorbidity and had better outcomes compared to all other phenotypes. In particular, this group had better fatigue (-2.43±0.87), pain (-1.86±1.05), anxiety (-1.50±0.89), stress (-1.46±0.69), and cognitive ability (1.76±0.80) than the poor sleep group. The poor sleep group had shorter time to adverse events compared to the other phenotypes (Hazard ratios of 0.41 to 0.56 for hospitalizations and 0.35 to 0.60 for ED visits). Conclusions: Multidimensional aspects of sleep predict HF outcomes and may improve on prediction with single measures. Future tailored sleep interventions focused on phenotypes may more precisely improve outcomes among HF patients than traditional approaches.
Costs and Resource Utilization of People with Stable Heart Failure and Insomnia: Evidence from a Randomized Trial of Cognitive Behavioral Therapy for Insomnia
Jeon, Sangchoon , Connell, Meghan O’, Jeon, Sangchoon , Conley, Samantha , Hollenbeak, Christopher S. , Yaggi, Henry Klar , Redeker, Nancy S.

Behavioral Sleep Medicine | August 2, 2023 | Volume: 22 | Issue: 3 | Pages: 263-274

10.1080/15402002.2023.2241589 Get It

0395 Effects of Insomnia and Rest-Activity on Hospitalizations and Emergency Department Visits in People with Stable Heart Failure
O’Connell, Meghan , Tocchi, Christine , Jeon, Sangchoon , Conley, Samantha , Hollenbeak, Christopher S. , Yaggi, Henry Klar , Jacoby, Daniel Louis , Wang, Zequan , Redeker, Nancy S.

SLEEP | May 29, 2023 | Volume: 46 | Issue: Supplement_1 | Pages: A175-A175

10.1093/sleep/zsad077.0395 Get It

Abstract Introduction People with heart failure (HF) have frequent hospitalizations, and emergency department (ED) visits. We examined the effects of insomnia and rest-activity rhythms (RARs) on time to hospitalizations and ED visits after controlling for other known risk factors among participants in a randomized controlled trial of cognitive behavioral therapy for insomnia compared to heart failure self-management education (attention control). Methods The analysis included 168 HF patients who had available medical records for dates of hospitalization, ED visit, transplant, and death from a randomized clinical trial for insomnia of people with HF and insomnia. The known risk factors included age, sex, New York Heart Association (NYHA) Class, ejection fraction, and comorbid conditions. We used the insomnia severity index (ISI) and evaluated the strength of RAR using the ratio of the amplitude to the MESOR estimated with cosinor analysis with activity counts from a wrist-actigraph worn for at least five consecutive days. Cox proportional hazard models were used to examine ­insomnia and the RAR metrics with the known risk factors on time to first hospitalization, ED visit, transplant, and death. Results Eighty-five (50.1%) had at least one hospitalization, and ninety-one (54.2%) participants had an ED visit. Approximately 42% of the participants were admitted to the hospital due to cardiac events. Older age, male sex, higher NYHA, and greater comorbidity were associated with earlier hospitalization or ED visits. After controlling for those risk factors, stronger RAR was associated with later time to hospitalization, and ED visit with hazard ratios (HRs) of 0.26 (95% CI=[0.07, 0.91]) and 0.25 (95% CI=[0.07, 0.89]); higher insomnia severity (ISI ≥22) was associated with shorter time to ED visits, with an HR of 2.28 (95% CI=[1.19, 4.38]). Conclusion In addition to known demographic and clinical risk factors, insomnia severity and RAR predict hospitalizations and ED visits in people with HF. Further research is necessary to assess the biological mechanism of the associations and examine whether the effects of the risk factors could be modified by improving insomnia severity and RAR in people with HF. Support (if any) This study was financially supported by grants R01NR016191 and P20NR014126 from NIH/NINR.