Model-Based Monitoring of Patient Response to Staged Thyroidectomy

Document Type : Research Paper


1 Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.

2 Skull Base Research Center, Loghman Hakim Hospital, Shahid Beheshti University of Medical Science, Tehran, Iran.

3 Industrial Engineering Department, K.N. Toosi University of Technology, Tehran, Iran.


The goal of this study is to develop a model-based control chart for monitoring patient behavior in a staged thyroidectomy considering risk factors and clinical prescription. prospectively collected data are gathered from thyroid surgery unit of a hospital located in Tehran, Iran for 80 staged thyroidectomy patients discharged from 2009 to 2013. A risk adjusted state space model is developed based on the staged thyroidectomy. Variables to be included in the model are determined as a part of the model building process. Performance criteria, clinical prescription and patient risk factors are three variable components for the model. The appropriate risk factors are directly involved in the model and no scoring system is used for the model construction. Model identification is performed in two steps; model order selection and parameter estimation. In the first step, Hankel singular value decomposition (HSVD) is used for detecting the model order and in the second step, unknown parameters are estimated by the prediction error minimization (PEM) method. For monitoring patient responses, a group individual (GI) control chart is introduced and applied to a real-world problem. Results indicate that the suggested control chart can monitor the staged thyroidectomy patient’s behavior with an acceptable accuracy. Also, computer aided diagnosis (CAD) systems can be developed based on the proposed identification and monitoring method.


  • [1] Cardoso J, Van der Aalst W. (2009). Handbook of research on business process modelling. Information Science Reference.
  • [2] Hu, J., Zhao, N., Kong, R., Wang, D., Sun, B., & Wu, L. (2016). Total thyroidectomy as primary surgical management for thyroid disease: surgical therapy experience from 5559 thyroidectomies in a less-developed region. World Journal of Surgical Oncology, 14(1), 1-7.
  • [3] Dionigi, G., Frattini, F. (2013). Staged thyroidectomy: Time to consider intraoperative neuromonitoring as standard of care. Thyroid, 23(7), 906-908.
  • [4] Rosen, K., Reid, R., Broemeling, A., & Rakovsky, C. (2003). Applying a risk-adjusted framework to primary care: Can we improve on existing measures? Annals of Family Medicine, 1(1), 44-55.
  • [5] Juhnke, C., Bethge, S., & Muhlbacher, A. (2016). A review on methods of risk adjustment and their use in integrated healthcare systems. International Journal of Integrated Care, 16(4), 1-18.
  • [6] Knaus, W.A., Zimmerman, J.E., Wagner, D.P., Draper, E.A., & Lawrence, D.E. (1981). APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Critical Care Medicine, 9(8), 591-597.
  • [7] Knaus,A., Draper, E.A., Wagner, D.P., & Zimmerman, J.E. (1985). APACHE II: a severity of disease classification system. Critical Care Medicine, 13(10), 818-829.
  • [8] Zimmerman, J.E., Kramer, A.A., McNair, D.S., & Malila, F.M. (2006). Acute physiology and chronic health evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Critical Care Medicine, 34(5), 1297-1310.
  • [9] Or, Z., Renaud, T., & Thuilliez, J. (2012). Diagnosis related groups and variations in resource use for child delivery across 10 European countries. Health Economy, 21, 55-65.
  • [10] Paat-Ahi, G., Swiderek, M., & Sakowski, P. (2012). DRGs in Europe: a cross country analysis for cholecystectomy. Health Economy, 21, 66-76.
  • [11] De Cassai, A., Boscolo, A., Tonetti, T., Ban, I., & Ori, C. (2019). Assignment of ASA-physical status relates to anesthesiologists' experience: a survey-based national-study. Korean Journal of Anesthesiology, 72(1), 53-59.
  • [12] Howard, R., Yin, S., McCandless, L., Wang, S., Englesbe, M., & Machado-Aranda, D. (2019). Taking Control of Your Surgery: Impact of a Prehabilitation Program on Major Abdominal Surgery. Journal of the American College of Surgeons, 228(1), 72-80.
  • [13] Knuf, M., Maani, V., & Cummings, K. (2018). Clinical agreement in the American Society of Anesthesiologists physical status classification. Perioperative Medicine, 7, 7-14.
  • [14] Zeng, L. (2016). Risk-adjusted performance monitoring in healthcare quality control, In Quality and Reliability Management and its Applications, Pham H (ed). Springer, London.
  • [15] Verbeke, G., Fieuws, S., Molenberghs, G., & Davidian, M. (2015). The analysis of multivariate longitudinal data: A review. Statistical Methods in Medical Research, 23(1), 42-59.
  • [16] Koetsier, A., De Keizer, N.F., De Jong, E., Cook, D.A., & Peek N. (2012). Performance of risk-adjusted control charts to monitor in-hospital mortality of intensive care unit patients: A simulation study. Critical Care Medicine, 40(6), 1799-1807.
  • [17] Lie, R.T., Heuch, I., & Irgens, L.M. (1993). A New Sequential Procedure for Surveillance of Down’s Syndrome. Statistics in Medicine, 12, 13-25.
  • [18] Alemi, F., Olivier, D. (2001). Tutorial on Risk-adjusted Quality Management in Healthcare, 10, 1-9.
  • [19] Cook, D.A., Steiner, S.H., Cook, R.J., Farewell, V.T., & Morton, A.P. (2003). Monitoring the Evolutionary Process of Quality: Risk-adjusted Charting to Track Outcomes in Intensive Cares. Critical Care Medicine, 31(6), 1676-1682.
  • [20] Sego, L.H., Woodall, W.H., & Reynolds, M.R Jr. (2008). A Comparison of Methods for Small Incidence Rate. Statistics in Medicine, 27(8), 1225-1247.
  • [21] Grigg, O., Spiegelhalter, D.J. (2007). Simple Risk-adjusted Exponentially Weighted Moving average. Journal of the American Statistical Association, 102, 140-152.
  • [22] Steiner, S.H., Jones, M. (2010). Risk-adjusted survival Time Monitoring with an Updating Exponentially Weighted Moving Average (EWMA) Control Chart. Statistics in Medicine, 29, 444-454.
  • [23] Szarka, J.L., Woodall, W. (2011). A Review and Perspective on Surveillance of Bernoulli Processes. Quality and Reliability Engineering International, 27, 735-752.
  • [24] Paynabar, K., Jin, J. (2012). Phase I Risk-adjusted Control Charts for Monitoring Surgical Performance by Considering Categorical Covariates. Journal of Quality Technology, 44(1), 39-53.
  • [25] Shojaei, S.N., Niaki, T.A. (2013). A Risk-adjusted Multi-Attribute Cumulative Sum Control Scheme in Healthcare System. The 2013 IEEE International Conference of Industrial Engineering and Engineering Management (IEEM). Singapore.
  • [26] Tian, W., Sun, H., Zhang, X., & Woodall, W. (2014). The Impact of Varying Patient Populations on the In-control Performance of the Risk-adjusted CUSUM Chart. International Journal of Quality in Healthcare, 27(1), 31-36.
  • [27] Zhang, X., Woodall, W. (2015). Dynamic Probability Control Limits for Risk-adjusted Bernoulli CUSUM Charts. Statistics in Medicine, 34(25), 3336-3348.
  • [28] Zhang, X., Woodall, W. (2016). Reduction of the Effect of Estimation Error on In-Control Performance for Risk-adjusted Bernoulli CUSUM Chart with Dynamic Probability Control Limits. Quality and Reliability Engineering International, 33(2), 381-386.
  • [29] Zhang, X., Woodall, W.H. (2016). Dynamic Probability Control Limits for Lower and Two-Sided Risk-Adjusted Bernoulli CUSUM Charts. Quality and Reliability Engineering International, 33(3), 607-616.
  • [30] Sparks, R. (2016). Linking EWMA p Charts and the Risk-Adjusted Control Charts. Quality and Reliability Engineering International, 33(3), 617-636.
  • [31] Sachlas, A., Bersimis, S., & Psarakis, S. (2019). Risk-adjusted control charts: theory, methods, and applications in health. Statistics in Bioscience, 11(1), 1-29.
  • [32] Begun, A., Kulinsakaya, E., & MacGregor, A.J. (2019). Risk-adjusted CUSUM control charts for shared frailty survival models with application to hip replacement outcomes: a study using the NJR dataset. BMC Medical Research Methodology, 19, 217.
  • [33] Roy, A., Cutright, D., Gopalakrishnan, M., Yeh, A.B., & Mittal, B.B. (2020). A risk-adjusted control chart to evaluate intensity modulated radiation therapy plan quality. Advances in Radiation Oncology, 5(5), 1032-1041.
  • [34] Ali, S., Altaf, N., Shah, I., Wang, L., & Raza, S.M.M. (2020). On the effect of estimation error for the risk-adjusted charts. Complexity, ID: 6258010.
  • [35] Ding, N., He, , Shi, L., & Qu, L. (2020). A new risk‐adjusted EWMA control chart based on survival time for monitoring surgical outcome quality. Quality and Reliability Engineering International, Published Online.
  • [36] Rafiei, N., Asadzadeh, S. (2020). Designing a risk-adjusted CUSUM control chart based on DEA and NSGA-II approaches (a case study in healthcare: Cardiovascular patients). Scientia Iranica, In Press.
  • [37] Keshavarz, M., Asadzadeh, S., & Niaki, S.T.A. (2021). Risk-adjusted frailty-based CUSUM control chart for phase I monitoring of patients’ lifetime. Journal of Statistical Computation and Simulation, 91(2), 334-352.
  • [38] Keshavarz, M., Asadzadeh, S. (2021). Phase II monitoring of survival times with categorical covariates. Quality and Reliability Engineering International, 37(2), 451-463.
  • [39] Kazemi, S., Noorossana, R., Rasouli, M., Nayebpour, M., & Heidari, K. (2021). Monitoring therapeutic processes using risk-adjusted multivariate Tukey’s CUSUM control chart. Quality and Reliability Engineering International, In Press.
  • [40] Grigg, O., Farewell, V. (2004). An Overview of Risk-Adjusted Charts. Journal of the Royal Statistical Society, 167(3), 523-539.
  • [41] Woodall, W. (2006). The Use of Control Charts in Healthcare and Public Health Surveillance. Journal of Quality Technology, 38(2), 89-104.
  • [42] Cook, D.A., Duke, G., Hart, G.K., Pilcher, D., & Mullany, D. (2008). Review of the Application of Risk-adjusted Charts to analyze Mortality Outcomes in Critical Care. Critical Care Resuscitation, 10(3), 239-251.
  • [43] Woodall, W.H., Fogel, S.L., & Steiner, S.H. (2015). The Monitoring and Improvement of Surgical-Outcome Quality. Journal of Quality Technology, 47(4), 383-399.
  • [44] Kalaei, M., Atashgar, K., Niaki, S.T.A., & Soleimani, P. (2018). Phase I monitoring of simple linear profiles in multistage process with cascade property. International Journal of Advanced Manufacturing and Technology, 94, 1745-1757.
  • [45] Funatogawa, I., Funatogawa, T., & Ohashi, Y. (2007). An autoregressive linear mixed effects model for the analysis of longitudinal data which show profiles approaching asymptotes. Statistics in Medicine, 26, 2113-2130.
  • [46] Funatogawa, I., Funatogawa, T., & Ohashi, Y. (2008). A bivariate autoregressive linear mixed effects model for the analysis of longitudinal data. Statistics in Medicine, 26, 6367-6378.
  • [47] Funatogawa, I., Funatogawa, T., & Takeuhi, M. (2008). An autoregressive linear mixed effects model for the analysis of longitudinal data which include dropouts and show profiles asymptotes. Statistics in Medicine, 27, 6351-6366.
  • [48] Funatogawa, I., Funatogawa, T. (2011). An aoutregressive linear mixed effect model for the analysis of unequally spaced longitudinal data with dose modification. Statistics in Medicine, 31, 589-599.
  • [49] Funatogawa, I., Funatogawa, T. (2012). Dose-response relationship from longitudinal data with response-dependent dose modification using likelihood methods. Biometrics Journal, 54(4), 1-13.
  • [50] Sibanda, N. (2014). Graphical model based O/E control Chart for Monitoring Multiple Outcomes from a Multistage Healthcare Process. Statistical Methods in Medicine Research, 0(0), 1-20.
  • [51] Rastgoomoghadam, A., Samimi, Y., & Nasiri, S. (2016). A method for monitoring the quality characteristic for two-stage thyroid cancer surgery using risk-adjusted model. Journal of Quality Engineering and Management, 6, 92-102, In Persian.
  • [52] Kazemian, P., Lavieri, M.S., Van Oyen, M.P., Andrews, C.N., & Stein, J. (2017). Personalized prediction of glaucoma progression under different target intraocular pressure levels using filtered forecasting methods. Ophtalmology, 125(4), 569-577.
  • [53] Sogandi, F., Aminnayeri, M., Mohammadpour, A., Amiri, A. (2019). Risk-adjusted Bernoulli chart in multi-stage healthcare processes based on state-space model with a latent risk variable and dynamic probability control limits. Computer and Industrial Engineering, 130, 699-713.
  • [54] Shi, J., Zhou, S. (2009). Quality control and improvement multistage systems: A survey. IIE Transactions, 41(9), 744-753.
  • [55] The Royal College of Pathologists of Australasia (RCPA). (2020). Thyroid cancer structured reporting protocol, Second Edition.
  • [56] The British Association of Endocrine and Thyroid Surgeons. (2017). Fifth national audit report, prepared by Chadwick, D., Kinsman, R., Walton, P.
  • [57] Commandeur, J., Koopman, S. (2007). An introduction to state space time series analysis. New York, Oxford University Press Inc.
  • [58] Kung, Y. (1978). A new identification and model reduction algorithm via singular value decompositions. 12th Asilomar Conference on Circuits, Systems and Computers, November 6-8.
  • [59] Danilov, D., Zhigljavsky, A. (1997). Principle component of time series: the ‘caterpillar’ method. St. Petersburg, Russia, University of St. Petersburg.
  • [60] Al-Saggaf, M., Franklin, F. (1987). An error bound for a discrete reduced order model of a linear multivariable system. IEEE Transaction, 32, 815-819.
  • [61] Ljung, L. (1999). System identification: theory for the users. Second Edition. New York, Prentice Hall PTR.