Estimation of Physiological Stability Risk Score Change in Transport of Newborn Infants by Monte Carlo Simulation

Yıl 2024, Cilt: 39 Sayı: 3, 753 - 758, 03.10.2024

Müfide Narlı

https://doi.org/10.21605/cukurovaumfd.1560178

Öz

Transportation of neonatal intensive care unit patients has high risk and mortality. Therefore, the transport process should be handled and evaluated carefully. There are transport scoring systems that evaluate the performance of this process. Among these, Transport Risk Index of Physiologic Stability (TRIPS) scoring, which measures the change in the patient's physiologic stability risk score, is widely used. The TRIPS score value is measured at least twice, in the first 15 minutes and at the end of transport. In this study, the initial TRIPS score and TRIPS score values measured just before delivery and the registered weight parameter value of the infants were obtained from 1117 patients between 2011 and 2022. In this retrospective study, the difference between the two TRIPS score values measured depending on the patient's weight was estimated with the Monte Carlo simulation model. By determining the average TRIPS score difference for each patient weight group separately, the transport process of different patient groups was compared with each other, the differences between the groups were determined and the process was analyzed. This analysis will contribute to the improvement of the process, planning and decision making.

Anahtar Kelimeler

Monte Carlo simulation, Forecasting, Transport, Scoring

Monte Carlo Simülasyonu ile Yenidoğan Transportunda Fizyolojik Stabilite Risk Skor Değişiminin Tahminlemesi

Öz

Yenidoğan yoğun bakım hastalarının transportu yüksek risk ve mortaliteye sahiptir. Bu nedenle transport süreci dikkatle ele alınmalı ve değerlendirilmelidir. Bu sürecin performansının değerlendirildiği transport skorlama sistemleri bulunmaktadır. Bunlardan hastanın fizyolojik stabilite risk skoru değişimini ölçen Transport Risk Index of Physiologic Stability (TRIPS) skorlaması yaygın olarak kullanılmaktadır. TRIPS skor değeri transportun ilk 15 dakika ve sonunda olmak üzere en az iki defa ölçülür. Bu çalışmada 2011-2022 yılları arasında 1117 sayıda hastanın teslim alındığında ilk TRIPS skoru ve teslim edilmeden hemen önce ölçülen TRIPS skor değerleri ile bebeklerin kayıtlı ağırlık parametre değeri alınmıştır. Retrospektif olarak yapılan bu çalışmada hastanın ağırlığına bağlı olarak ölçülen iki TRIPS skor değeri arasındaki fark, Monte Carlo simülasyon modeli ile tahminlenmiştir. Her hasta ağırlık grubu için ayrı ayrı ortalama TRIPS skor farkı taminlenerek, farklı hasta gruplarının transport süreci birbirleri ile kıyaslanarak gruplar arası farklar tespit edilmiş ve süreç analiz edilmiştir. Bu analiz sürecin iyileştirilmesi, planlanması ve kararların alınmasına katkı sağlayacaktır.

Anahtar Kelimeler

Monte Carlo simülasyonu, Tahminleme, Transport, Skorlama

Kaynakça

• 1. Narli, N., Kırımi, E., Uslu, S., 2018. Turkish neonatal society guideline on the safe transport of newborn. Turkish Archives of Pediatrics/Türk Pediatri Arşivi, 53(Suppl 1), 18.
• 2. Lee, Sk., Zupancic, Ja., Pendray, M., Tiessen, P., Schimidt, B., Whyte, R., Shorten, D., Stewart, S., 2001. Transport risk index of physiologic stability: a pratical system for assessing infant transport care. The Journal of Pediatrics, 139(2), 220-226.
• 3. Broughton, S.J., Berry, A., Jacobe, S., Cheeseman, P., Tarnow-Mordi, W.O., Neonatal intensive care unit study group, & Greenough, A., (2004). The mortality index for neonatal transportation score: a new mortality prediction model for retrieved neonates. Pediatrics, 114(4), e424-e428.
• 4. Perinatal care of the extremely preterm baby, https://www.health.qld.gov.au/_data/assets/pdf_file /0023/142259/g-viability.pdf., Erişim tarihi: 02.05.2024.
• 5. https://emedicine.medscape.com/article/975909-overview., Erişim tarihi: 02.05.2024.
• 6. Hogue, C.J., Buehler, J.W., Strauss, L.T., Smith, J.C., 1987. Overview of the national ınfant mortality surveillance (NIMS) project--design, methods, results. Public Health Reports, 102(2), 126.
• 7. Beggs, C.B., Shepherd, S.J., Kerr, K.G., 2010. Potential for airborne transmission of infection in the waiting areas of healthcare premises: stochastic analysis using a Monte Carlo model. BMC Infectious Diseases, 10, 1-8.
• 8. Muthoni, G.J., Kimani, S., Wafula, J., 2014. Review of predicting number of patients in the queue in the hospital using Monte Carlo simulation. International Journal of Computer Science Issues (IJCSI), 11(2), 219.
• 9. Goswami, M., Daultani, Y., Paul, S.K., Pratap, S., 2023. A framework for the estimation of treatment costs of cardiovascular conditions in the presence of disease transition. Annals of Operations Research, 328(1), 577-616.
• 10. Cooper, N.J., Lambert, P.C., Abrams, K.R., Sutton, A.J., 2007. Predicting costs over time using Bayesian Markov chain Monte Carlo methods: an application to early inflammatory polyarthritis. Health Economics, 16(1), 37-56.
• 11. Richter, A., Mauskopf, J.A., 1998. Mm1 Monte Carlo simulation in health care models. Value in Health, 1(1), 84-85.
• 12. Cooper, N.J., Sutton, A.J., Mugford, M., Abrams, K.R., 2003. Use of Bayesian Markov chain Monte Carlo methods to model cost-of-illness data. Medical Decision Making, 23(1), 38-53.
• 13. Krajewski, L.J., Ritzman, L.P., Malhotra, M.K., 2014. Operations management: processes and supply Chains (9. Baskı). Ankara: Nobel Yayınevi.
• 14. Di Leo, G., Sardanelli, F., 2020. Statistical significance: p value, 0.05 threshold, and applications to radiomics-reasons for a conservative approach. European Radiology Experimental, 4, 1-8.
• 15. Kaneko, M., Yamashita, R., Kai, K., Yamada, N., Sameshima, H., Ikenoue, T., 2015. Perinatal morbidity and mortality for extremely low‐birthweight infants: a population‐based study of regionalized maternal and neonatal transport. Journal of Obstetrics and Gynaecology Research, 41(7), 1056-1066.
• 16. McPherson, M.L., Jefferson, L.S., Graf, J.M., 2008. A validated pediatric transport survey: How is your team performing? Air Medical Journal, 27(1), 40-45.
• 17. De Vries, S., Wallis, L.A., Maritz, D., 2011. A retrospective evaluation of the impact of a dedicated obstetric and neonatal transport service on transport times within an urban setting. International Journal of Emergency Medicine, 4, 1-6.