Effect of rapid response team combined with early warning scoring system in treatment of obstetric patients with severe diseases

doi:10.3760/cma.j.issn.1007-1245.2023.08.008

Abstract

Abstract:

Objective　To explore the effect of rapid response team combined with early warning scoring system in the treatment of obstetric patients with severe diseases. Methods　One hundred and two obstetric patients with severe diseases admitted to Zhengzhou Maternal and Child Health Hospital from November 2019 to November 2021 were selected for the randomized control led trial, and were divided into a combined group and a control group by the random number table method, with 51 cases in each group. The combined group were 21-40 (32.36±5.25) years old, and the control group 22-42 (33.58±5.35). The control group were given routine nursing care; in addition, the combined group adopted rapid response team combined with early warning scoring system. The occupancy rates of intensive care unit (ICU) were compared between the two groups. The treatment times and effective treatment rates in the two groups were recorded. The nursing satisfactions of the patients' family members in the two groups were compared. t and χ² tests were applied. Results　The ICU occupancy rate after surgery in the combined group was lower than that in the control group [11.76% (6/51) vs. 29.41% (15/51); t=4.857, P=0.028]. The treatment time and effective treatment rate in the combined group were better than those in the control group (t=11.692, P<0.001; χ²=5.718, P=0.017). The satisfaction of the combined group was higher than that of the control group [94.12% (48/51) vs. 76.47% (39/51); χ²=6.331, P=0.012]. Conclusion　Rapid response team combined with early warning scoring system in the treatment of obstetric patients with severe diseases can effectively reduce the occupancy rate of ICU, shorten the treatment time of emergency early warning, and improve the effective treatment rate and their family members' satisfaction, so it is worthy of further promotion.

Key words:

ICU, Rapid response team, Early warning scoring system, Nursing satisfaction, Obstetric patients with severe diseases, Effective treatment rate

摘要：

目的　探究快速反应团队协同预警评分系统治疗产科重症患者的效果。方法　选取2019年11月至2021年11月郑州市妇幼保健院收治的产科重症患者102例进行随机对照试验，随机数字表法将其分为联合组和对照组，各51例。联合组年龄21～40（32.36±5.25）岁，对照组年龄22～42（33.58±5.35）岁。对照组进行常规护理，联合组在对照组基础上采用快速反应团队协同预警评分系统。比较两组患者重症监护室（ICU）入住率；记录两组患者病情意外预警情况处理时间及有效处理率；比较两组患者家属护理满意度。采用t检验和χ²检验。结果　联合组患者术后ICU入住率低于对照组［11.76%（6/51）比29.41%（15/51），t=4.857，P=0.028］。联合组病情意外预警情况处理时间、有效处理率优于对照组（t=11.692，P<0.001，χ²=5.718，P=0.017）。联合组满意度高于对照组［94.12%（48/51）比76.47%（39/51），χ²=6.331，P=0.012］。结论　快速反应团队协同预警评分系统应用于产科重症患者可有效降低ICU入住率，缩短病情意外预警情况处理时间，提高有效处理率，提升患者家属满意度，值得进一步推广。

关键词:

重症监护室, 快速反应团队, 预警评分系统, 护理满意度, 产科重症患者, 有效处理率

Xu Yi, Zhang Xiaodan, Li Xiujuan.

Effect of rapid response team combined with early warning scoring system in treatment of obstetric patients with severe diseases [J]. International Medicine and Health Guidance News, 2023, 29(8): 1067-1070.

徐毅张晓丹李秀娟.

快速反应团队协同预警评分系统治疗产科重症患者的效果研究 [J]. 国际医药卫生导报, 2023, 29(8): 1067-1070.

References

[1] Teberik K, Eski MT. Association of ABO blood groups and Rh factor with retinal and choroidal thickness[J]. Eur J Ophthalmol, 2019, 29(2): 234-238. DOI: 10.1177/1120672118783629.
[2] Ayenew AA. Prevalence of rhesus D-negative blood type and the challenges of rhesus D immunoprophylaxis among obstetric population in Ethiopia: a systematic review and meta-analysis [J]. Matern Health Neonatol Perinatol, 2021, 7(1): 8. DOI: 10.1186/s40748-021- 00129-3.
[3] Villalba A, Santiago M, Freiria C, et al. Anti-D alloimmunization after RhD-positive platelet transfusion in RhD-negative women under 55 years diagnosed with acute leukemia: results of a retrospective study[J]. Transfus Med Hemother, 2018, 45(3):162-166. DOI: 10.1159/000488804.
[4] Seo MH, Won EJ, Hong YJ, et al. An effective diagnostic strategy for accurate detection of RhD variants including Asian DEL type in apparently RhD-negative blood donors in Korea [J]. Vox Sang, 2016, 111(4): 425-430. DOI: 10. 1111/vox.12450.
[5] Sorensen K, Kjeldsen-Kragh J, Husby H, et al. Determination of fetal RHD type in plasma of RhD negative pregnant women [J]. Scand J Clin Lab Invest, 2018, 78(5): 411-416. DOI: 10.1080/00365513. 2018.1475681.
[6] Molano A, Apraiz I, Espana P, et al. Performance evaluation study of ID RHD XT, a new genotyping assay for the detection of high-prevalence RhD negative and weak D types [J]. Vox Sang, 2018, 113(7):694-700. DOI: 10.1111/vox.12701.
[7] Kulkarni SS, Gogri H, Parchure D, et al. RHD-positive alleles among D-C/E+ individuals from India[J]. Transfus Med Hemother,2018,45(3):173-177. DOI: 10.1159/000479239.
[8] Dauber EM, Mayr WR, Hustinx H, et al. Somatic mosaicisms of chromosome 1 at two different stages of ontogenetic development detected by Rh blood group discrepancies[J]. Haematologica,2019,104(3):632-638. DOI: 10.3324/haematol.2018.201293.
[9] Karafin MS, Westlake M, Hauser RG, et al. Risk factors for red blood cell alloimmunization in the Recipient Epidemiology and Donor Evaluation Study (REDS-Ⅲ) database[J]. Br J Haematol, 2018,181(5):672-681. DOI: 10.1111/bjh.15182.
[10] Allard S, Massey E. Fetal RHD genotyping is a cost-effective option for supporting targeted anti-D prophylaxis in D-negative pregnancies [J]. BJOG, 2018, 125(11): 1423. DOI: 10.1111/1471-0528.15259.
[11] 张婷,严文萍,阎仿,等. 快速反应团队护理模式在产科危急重症手术护理中的应用效果及对DDI、新生儿预后的影响[J]. 临床医学研究与实践,2022,7(25):148-151,158. DOI:10.19347/j.cnki.2096-1413.202225041.
[12] Saab A, Abi Khalil C, Jammal M, et al. Early prediction of all-cause clinical deterioration in general wards patients: development and validation of a biomarker-based machine learning model derived from rapid response team activations [J]. J Patient Saf, 2022, 18(6): 578-586. DOI: 10.1097/PTS.0000000000001069.
[13] Schramko L, Paterson T, Anstey MH. Duration and management of sepsis-associated hypotension at rapid response team call-outs to patients subsequently admitted to the intensive care unit: a case series [J]. Aust Crit Care, 2022, 35(4): 450-453. DOI: 10.1016/j.aucc. 2021.06.003.
[14] Kim T, Shatzel J, Ornstein DL. Characteristics of upper extremity deep vein thrombosis in hospitalized patients. a review of 8350 admissions from the 2012 national inpatient sample[J]. Blood, 2019, 126(23): 2-7. DOI: 10.1182/bloodadvances.2021004727.
[15] Di Martino A, Quattrocchi CC, Scarciolla L, et al. Estimating the risk for symptomatic adjacent segment degeneration after lumbar fusion: analysis from a cohort of patients undergoing revision surgery [J]. Eur Spine J, 2014, 23 Suppl 6: 693-698. DOI: 10.1007/s00586-014- 3551-0.
[16] Zhu S, Wang Z, Wu X, et al. Apolipoprotein E polymorphism is associated with lower extremity deep venous thrombosis: color-flow Doppler ultrasound evaluation [J]. Lipids Health Dis, 2014, 13:21. DOI: 10.1186/1476-511X-13-21.
[17] Robertson JJ, Long B, Koyfman A. Emergency medicine myths: ectopic pregnancy evaluation, risk factors, and presentation [J]. J Emerg Med, 2017, 53(6):819-828. DOI: 10.1016/j.jemermed.2017.08.074.
[18] Kasper S, Müller WE, Volz HP, et al. Silexan in anxiety disorders: clinical data and pharmacological background [J]. World J Biol Psychiatry, 2018, 19(6): 412-420. DOI: 10.1080/15622975.2017.1331046.
[19] 王玉丽,刘洁. 妊娠期孕妇营养状况与新生儿体质量的相关性研究[J]. 国际医药卫生导报,2020,26(3):354-357. DOI: 10.3760/cma.j.issn.1007-1245.2020.03.019.
[20] Malik AT, Drain JP, Karnes JM. Letter to the editor regarding "risk of postoperative complications and revision surgery following robot-assisted posterior lumbar spinal fusion" [J]. Spine (Phila Pa 1976), 2021, 46(6): E411-E412. DOI: 10.1097/BRS. 000000000- 0003915.
[21] Yang DS, Li NY, Kleinhenz DT, et al. Risk of postoperative complications and revision surgery following robot-assisted posterior lumbar spinal fusion [J]. Spine (Phila Pa 1976), 2020, 45(24): E1692-E1698. DOI: 10.1097/BRS.0000000000003701.
[22] Rokosh RS, Ranganath N, Yau P, et al. High prevalence and mortality associated with upper extremity deep venous thrombosis in hospitalized patients at a tertiary care center [J]. Ann Vasc Surg, 2020, 65: 55-65. DOI: 10.1016/j.avsg.2019.10.055.
[23] Karnuta JM, Golubovsky JL, Haeberle HS, et al. Can a machine learning model accurately predict patient resource utilization following lumbar spinal fusion? [J]. Spine J, 2020, 20(3): 329-336. DOI: 10.1016/j.spinee. 2019.10.007.
[24] Zhang C, Li Q, Yu H, et al. Relationship between the site of thrombosis and the prevalence of pulmonary embolism in acute lower extremity deep venous thrombosis [J]. J Vasc Surg Venous Lymphat Disord, 2020, 8(5): 725-733. DOI: 10.1016/j.jvsv.2019.11.010.
[25] Huang H, Gu JP, Shi HF, et al. Assessment of the probability of post-thrombotic syndrome in patients with lower extremity deep venous thrombosis [J]. Sci Rep, 2018, 8(1): 12663. DOI: 10.1038/s41598-018-30645-w.
[26] 蒋晓蘅,方心怡,马敏湖,等. 快速反应团队工作模式在急性左心衰竭患者抢救中的应用[J]. 齐鲁护理杂志,2022,28(18):127-129. DOI: 10.3969/j.issn.1006-7256.2022. 18.045.
[27] Dunham CM, Huang GS. Lethal trauma pulmonary embolism is a black swan event in patients at risk for deep vein thrombosis: an evidence-based review [J]. Am Surg, 2017, 83(4):403-413.
[28] Shi WY, Wang LW, Wang SJ, et al. Combined direct and indirect CT venography (combined CTV) in detecting lower extremity deep vein thrombosis [J]. Medicine (Baltimore), 2016, 95(11):e3010. DOI: 10.1097/MD.0000000000003010.
[29] Seki K, Sumino H, Nara M, et al. Relationships between blood rheology and age, body mass index, blood cell count, fibrinogen, and lipids in healthy subjects [J]. Clin Hemorheol Microcirc, 2006, 34(3): 401-410.