Preferences help
enabled [disable] Abstract
Number of results
2019 | 119 | 181-191
Article title

Cardiac Index Predicted Factors for Shock Patients

Title variants
Languages of publication
The report aims to formulate an appropriate cardiac index (CI) model with 19 explanatory variables for 113 shock patients. Determinants of CI are focused from the fitted model. There is little study for the predicted factors of CI based on probabilistic modeling. CI model has been developed in the report with 19 explanatory variables for 113 shock patients, and the data site is: Statistical method of joint generalized linear models (JGLMs) is adopted. Mean CI is negatively associated with age (P = 0.0044), shock type (SHOCKT) at level 3 (P = 0.0586), diastolic blood pressure (DBP) (P = 0.0032), mean circulation time (MCT) (P<0.0001), hemoglobin (HG) (P = 0.0053), while it is positively with mean arterial pressure (MAP) (P<0.0001), heart rate (HR) (P<0.0001), body surface index (BSI) (P<0.0001), appearance time (AT) (P<0.0001), plasma volume index (PVI) (P<0.0001). Variance of CI is negatively associated with age (P = 0.0020), height (P = 0.0255), sex (P = 0.0582), SHOCKT at level 2 (P = 0.0356), MAP (P = 0.0664), AT (P = 0.0080), hematocrit (HCT) (P = 0.0039), card sequence order (CSO) (P<0.0001), while it is positively associated with systolic blood pressure (SBP) (P = 0.0241) and MCT (P = 0.0039). CI is high if MAP, or HR, or BSI, or AT, or PVI rises, or age, or DBP, or MCT, or HG decreases. Variance of CI depends on many explanatory factors. These findings are completely a new addition in medical literature.
Physical description
  • Department of Applied Statistics, Dongguk University, Gyeongju, Korea
  • Department of Statistics, The University of Burdwan, Burdwan, West Bengal, India
  • Department of Statistics, College of Natural Science, Seoul National University, Seoul, 151-747, Korea
  • [1] Carlsson et al. Cardiac output and cardiac index measured with cardiovascular magnetic resonance in healthy subjects, elite athletes and patients with congestive heart failure. Journal of Cardiovascular Magnetic Resonance 2012; 14: 51
  • [2] Geerts BF, Aarts LP, Jansen JR. Methods in pharmacology: measurement of cardiac output. Br J Clin Pharmacol 2011, 71(3): 316–330
  • [3] Åstrand PO, Rodahl K. Textbook or work physiology. McGraw Hill; 1970.
  • [4] Hillis LD, Firth BG, Winniford MD. Analysis of factors affecting the variability of Fick versus indicator dilution measurements of cardiac output. Am J Cardiol 1985, 56(12): 764–768
  • [5] Cigarroa RG, Lange RA, Hillis LD. Oximetric quantitation of intra cardiac left-to-right shunting: limitations of the Qp/Qs ratio. Am J Cardiol 1989; 64 (3): 246–247
  • [6] Critchley LA, Critchley JA. A meta-analysis of studies using bias and precision statistics to compare cardiac output measurement techniques. J Clin Monit Comput 1999; 15(2): 85–91
  • [7] Muthurangu V, Taylor A, Andriantsimiavona R, Hegde S, Miquel ME, Tulloh R,Baker E, Hill DL, Razavi RS. Novel method of quantifying pulmonary vascular resistance by use of simultaneous invasive pressure monitoring and phase-contrast magnetic resonance flow. Circulation 2004; 110(7): 826–834
  • [8] Arheden H, Holmqvist C, Thilen U, Hanseus K, Bjorkhem G, Pahlm O, LaurinS, Stahlberg F. Left-to-right cardiac shunts: comparison of measurementsobtained with MR velocity mapping and with radionuclide angiography. Radiology 1999; 211(2): 453–458
  • [9] Romano SM, Pistolesi M. Assessment of cardiac output from systemic arterial pressure in humans. Crit Care Med 2002; 30: 1834–1841
  • [10] Saxena R, Durward A, Puppala NK, Murdoch IA, Tibby SM. Pressure recording analytical method for measuring cardiac output in critically ill children: a validation study. Br J Anaesth 2013; 110: 425–431
  • [11] Bojan M, Gerelli S, Gioanni S, Pouard P, Vouhé P. Comparative study of the Aristotle comprehensive complexity and the risk adjustment in congenital heart surgery scores. Ann Thorac Surg 2011; 92: 949–956
  • [12] Carlsson M, Ugander M, Heiberg E, Arheden H. The quantitative relationship between longitudinal and radial function in left, right, and total heart pumping in humans. Am J Physiol Heart Circ Physiol 2007; 293(1): H636–H644
  • [13] Das RN. Cardiac Index Determinants. EC Cardiology 2017; 3(4): 112-114
  • [14] Afifi AA, Azen SP. Statistical analysis: A computer oriented approach, 2nd ed. Academic Press, New York 1979.
  • [15] Lee Y, Nelder JA, Pawitan Y. Generalized Linear Models with Random Effects (Unified Analysis via H–likelihood). London: Chapman & Hall 2006.
  • [16] Das RN, Lee Y. Log-normal versus gamma models for analyzing data from quality-improvement experiments. Quality Engineering 2009; 21(1): 79-87.
  • [17] Das RN, Lee Y. Analysis strategies for multiple responses in quality improvement experiments. International Journal of Quality Engineering and Technology 2010; 1(4): 395-409.
  • [18] Das RN. Robust Response Surfaces, Regression, and Positive Data Analyses. London: Chapman & Hall 2014.
  • [19] Das RN. Hypertension risk factors who underwent Dobutamine stress echocardiography. Interventional Cardiology: Open Access 2016; 8(1):595-605.
  • [20] Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning, Springer-Verlag, 2001.
  • [21] Das RN. Discrepancy in fitting between log-normal and gamma models: An illustration. Model Assisted Statistics and Applications 2012; 7 (1), 23–32.
  • [22] Carlsson M, Andersson R, Bloch KM, et al. Cardiac output and cardiac index measured with cardiovascular magnetic resonance in healthy subjects, elite athletes and patients with congestive heart failure. Journal of Cardiovascular Magnetic Resonance 2012; 14: 51
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.