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Symposium: Circadian Rhythms in Acute & Chronic Illness Mary Anne Vincent, PhD, RN, ACNS-BC PowerPoint PPT Presentation

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ICU Patient Core Body & Ambient Temperature 24-Hr Patterns & Illness Severity Authors : M.A. Vincent, S.K. Hanneman, N.S. Padhye, M.H . Smolensky, D-H Kang. Symposium: Circadian Rhythms in Acute & Chronic Illness Mary Anne Vincent, PhD, RN, ACNS-BC

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Symposium: Circadian Rhythms in Acute & Chronic Illness Mary Anne Vincent, PhD, RN, ACNS-BC

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Symposium circadian rhythms in acute chronic illness mary anne vincent phd rn acns bc

ICU Patient Core Body & Ambient Temperature 24-Hr Patterns & Illness SeverityAuthors: M.A. Vincent, S.K. Hanneman, N.S. Padhye, M.H. Smolensky, D-H Kang


Circadian Rhythms in Acute & Chronic Illness

Mary Anne Vincent, PhD, RN, ACNS-BC

Sam Houston State University, Department of Nursing

Huntsville, Texas

Background significance

Background & Significance

  • > 2 million Americans per year receive ICU care (Angus et al., 2006).

  • Acute stress: ICU environment and critical illness (Christensen, 2007; Denniset al., 2010; Meyer et al., 1994).

  • Altered circadian rhythms have shown to reliably diminish the well-being of young and healthy individuals (Litinski, Scheer, & Shea, 2009).

  • We wonder what altered circadian rhythms can do to ICU patients.

  • Mechanically ventilated ICU patients:

    • among the most critically ill

    • little is known about the normal pattern of their circadian rhythms

  • Knowing the normal pattern of mechanically ventilated ICU patients will:

    • inform interventions (e.g., timing of ventilator weaning)

    • that offer chronotherapeuticadvantage

Sam Houston State University

State of the science

State of the Science

Notes. %R = Percent statistically significant rhythms.

All segments were either abnormal or abolished circadian rhythms.

All studies only looked at 24-hr segments.

Sam Houston State University

Aim of the study

Aim of the Study

  • To measure 24-hr patterns of core body temperature and ambient temperature in mechanically ventilated ICU patients for up to 7 days to determine how patterns influence patient outcome.

Sam Houston State University



  • Time-series design

  • Recorded in 1-minute intervals continuously

  • For up to 7 days

  • Adult mechanically ventilated ICU patients

  • Variables:

    • Core body temperature

    • Ambient temperature

Sam Houston State University



  • Single cosinor analysis: linear regression fit to cosine curve

    • Serial sections (Padhye & Hanneman, 2007)

    • Length set to 24-hrs

  • Correlation of segment parameters with daily clinical outcomes

    • Days on mechanical ventilation

    • Patient mortality

    • APACHE II illness severity scores

Sam Houston State University

Cosinor analysis

Cosinor Analysis

  • Linear regression model using a cosine curve

  • Produces parameters for each 24-hr serial segment:

    • Amplitude (A, ½ peak-to-trough difference)

    • Peak time (P, referenced to local midnight)

    • Mesor (M, 24-h rhythm-adjusted mean)

    • Period (Time length of a complete cycle)

  • Significant rhythm patterns have:

    • Zero amplitude test: A ≠ 0 test, p < 0.05

      Meaning: Probability less than 5% that the cosine curve is a flat horizontal line.

    • Linear regression goodness-of-fit test: R2 ≥ 0.3

      Meaning: 30% or more of the data fit the curve.

Sam Houston State University

Apache ii


  • Illness severity index

    • 0-72 possible

    • Higher scores

      indicate greater illness


    • Parameters

      • Age

      • Clinical vital signs & labs

        • Acute physiology

        • Chronic health

Sam Houston State University



  • Significant patterns occurred in:

    • 83% of core body temperature segments

    • 76% of ambient temperature segments

  • All core body temperature cosinor parameters were abnormal.

  • Primary medical diagnoses:

    • H1N1 pneumonia

    • ARDS

    • Sepsis

    • CHF

    • Delirium Tremens

  • 9 patients completed the study

    • 10 patients enrolled

    • 1 patient dropped due to expiring 8 hrsfrom enrollment

  • Age 43 to 87 years

  • Duration in study: 2 to 7 days

  • APACHE II average: 17±5

  • Mortality: 50% of all enrolled

  • Days on mechanical ventilation: 1.6-36

Sam Houston State University



  • Mean(SD) core body temperature cosinor parameters were:

    • Amplitude = 0.3 (0.2) oC

    • Peak time = 12:52 (07:08)

    • Mesor = 37.6 (0.5) oC

  • Mean (SD) ambient temperature cosinor parameters were:

    • Amplitude = .7 (.6) oC

    • Peak time = 11:31 (5:56)

    • Mesor = 20.9 (1.3)

  • Normal core body temperature cosinor parameters are:

    • Amplitude: 0.167 - 0.28 oC

    • Peak time: 16:48 to 18:08

    • Mesor: 36.4 to 37.1oC

Normal values from "Reference Values for Chronopharmacology,“ by E. Haus et al., 1988, Annual Review of Chronopharmacology: Vol. 4, pp. 333-424.

Sam Houston State University



  • Within-subject daily illness severity scores correlated negatively with core body temperature amplitude:

    (r = -0.28, one-sided p = 0.04)

  • No correlation existed among other variables.

  • Results essentially agree with the findings of the state of the science

*Patient 6 suffered from alcohol withdraw

Sam Houston State University



  • Core body temperature circadian patterns

    • All daily rhythms were abnormal

    • Persisted for up to 7 days

    • Findings agree with the current state of the science

    • Contribution to the science: Abnormal cosinor amplitude was associated with illness severity

Sam Houston State University



  • Small sample

  • Assuming a rhythm period to be 24-hrs long

  • Mechanically ventilated ICU patients

  • Light, sound, relative humidity, and ambient temperature were also measured

  • Further research needed:

    • determine if these findings hold with larger samples

    • offer insight for devising interventions (e.g., circadian light patterns) that improve clinical outcome by synchronizing the central circadian pacemaker.

Sam Houston State University



  • This work was done in 2005-2011 while I was a PhD student at The University of Texas Health Science Center at Houston.

  • Dr. Sandra K. Hanneman – Dissertation chair

  • Committee members: Dr. Duck-Hee Kang, Dr. Nikhil S. Padhye, Dr. Michael H. Smolensky, and Dr. Jack C. Waymire

  • Funding sources:

    • NIH, UTHSC-Houston, CCTS, National Center for Research Resources, NRSA , T32, RR024148

    • STTI, Virginia Henderson Clinical Research Grant

    • STTI, Zeta Pi Chapter, Research Grant

    • Texas Nurses Association, District 9, Research Grant

    • Speros Martel Research Scholarship

    • Florence & Harold Smith Endowed Scholarship

    • Ben Love PARTNERS Endowed Scholarship

Sam Houston State University



  • Angus, D. C., Shon, A. F., White, A., Dremsizov, T. T., Schmitz, R. J., & Kelley, M. A. (2006). Critical care delivery in the United States: Distribution of services and compliance with Leapfrog recommendations. Critical Care Medicine, 34, 1016-24. doi:10.1097/01.CCM.0000206105.05626.15

  • Christensen, M. (2007). Noise levels in a general intensive care unit: a descriptive study. Nursing in Critical Care, 12, 188-197. doi:10.1111/j.1478-5153.2007.00229.x

  • Dennis, C.M., Lee, R., Woodard, E.K., Szalaj, J.J., & Walker, C.A. (2010). Benefits of quiet time for neuro-intensive care patients. Journal of Neuroscience Nursing, 42, 217-224. doi:10.1097/JNN.0b013e3181e26c20

  • Haus, E., Nicolau, G. Y., Lakatua, D. & Sackett-Lundeen, L. (1988). Reference values for chronopharmacology. In A. Reinberg, M. Smolensky & G. Labrecque, (Eds.), Annual Review of Chronopharmacology: Vol. 4 (pp. 333-424). New York: Pergamon.

  • Litinski, M., Scheer, F.A., & Shea, S.A. (2009). Influence of circadian system on disease severity. Sleep Medicine Clinics, 4, 143-163. doi:10.1016/j.jsmc.2009.02.005

Sam Houston State University



  • Meyer, T. J., Eveloff, S. E., Bauer, M. S., Schwartz, W. A., Hill, N. S. & Millman, R. P. (1994). Adverse environmental conditions in the respiratory and medical ICU settings. Chest, 105, 1211-1217. doi:10.1378/chest.105.4.1211

  • Nuttall, G. A., Kumar, M. & Murray, M. J. (1998). No difference exists in the alteration of circadian rhythm between patients with and without intensive care unit psychosis. Critical Care Medicine, 26, 1351-1355. doi:10.1097/00003246-199808000-00019

  • Padhye, N.S. & Hanneman, S.K. (2007). Cosinor analysis for temperature time series data of long duration. Biological Research for Nursing, 9, 30-41. doi:10.1177/1099800407303509

  • Paul, T. & Lemmer, B. (2007). Disturbance of circadian rhythms in analgosedated intensive care unit patients with and without craniocerebral injury. Chronobiology International, 14, 45-61. doi:10.1080/07420520601142569

Sam Houston State University



  • Pina, G., Brun, J., Tissot, S., & Claustrat, B. (2010). Long-term alteration of daily melatonin, 6-sulfatoxymelatonin, cortisol, and temperature profiles in burn patients: a preliminary report. Chronobiology International, 27, 378-392. doi:10.3109/07420520903502234

  • Tweedie, I. E., Bell, C. F., Clegg, A., Campbell, I. T., Minors, D. S. & Waterhouse, J. M. (1989). Retrospective study of temperature rhythms of intensive care patients. Critical Care Medicine, 17, 1159-1165. doi:10.1097/00003246-198911000-00012

Sam Houston State University

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