提示: 手机请竖屏浏览!

自动识别有院内病情恶化风险的成人患者
Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration


Gabriel J. Escobar ... 其他 • 2020.11.12

摘要


背景

在普通病房内(重症监护病房[ICU]外)病情恶化的成人住院患者的发病率和死亡率高。要早期识别有病情恶化风险的患者,我们需要依赖于人工计算的评分。关于自动检出即将发生的病情恶化之后患者的结局,目前尚无广泛报道。

 

方法

我们根据经过验证的模型(该模型利用电子病历中的信息识别病情恶化风险高的患者,因此可以自动实时计算风险评分)开发了一个干预程序,在该干预程序中,由负责审核高危患者病历的护士进行远程监测;然后将监测结果发送给医院的快速响应团队。我们比较了以下两组患者的结局(包括主要结局,即报警后30日内死亡):一组是在运行该系统的医院(干预医院,因报警做出临床响应)内,病情达到报警阈值的住院患者(不包括ICU内的患者);另一组是在未部署该系统的医院(对照医院,假如运行了该系统的话,报警后,患者病情将启动临床响应)住院的患者。多变量分析针对人口统计学特征、疾病严重程度和合并症负担进行了校正。

 

结果

从2016年8月1日至2019年2月28日,我们以交错方式在19家医院部署了这一程序。我们识别出326,816例患者的548,838次非ICU住院。共计43,949次住院(涉及35,669例患者)中的患者病情达到报警阈值;15,487次住院被纳入干预队列,28,462次住院被纳入对照队列。干预队列在报警后30日内的死亡率低于对照队列(校正后的相对危险度,0.84;95%置信区间[CI],0.7~80.90;P<0.001)。

 

结论

利用自动预测模型识别快速响应团队可以实施干预的高危患者与死亡率降低相关(由戈登和贝蒂·摩尔基金会[Gordon and Betty Moore Foundation]等资助)。





作者信息

Gabriel J. Escobar, M.D., Vincent X. Liu, M.D., Alejandro Schuler, Ph.D., Brian Lawson, Ph.D., John D. Greene, M.A., and Patricia Kipnis, Ph.D.
From the Systems Research Initiative, Kaiser Permanente Division of Research, Oakland (G.J.E., V.X.L., A.S., B.L., J.D.G., P.K.), the Intensive Care Unit, Kaiser Permanente Medical Center, Santa Clara (V.X.L.), and Unlearn.AI, San Francisco (A.S.) — all in California. Address reprint requests to Dr. Escobar at the Systems Research Initiative, Kaiser Permanente Division of Research, 2000 Broadway Ave., Oakland, CA 94612, or at gabriel.escobar@kp.org.

 

参考文献

1. Bapoje SR, Gaudiani JL, Narayanan V, Albert RK. Unplanned transfers to a medical intensive care unit: causes and relationship to preventable errors in care. J Hosp Med 2011;6:68-72.

2. Escobar GJ, Greene JD, Gardner MN, Marelich GP, Quick B, Kipnis P. Intra-hospital transfers to a higher level of care: contribution to total hospital and intensive care unit (ICU) mortality and length of stay (LOS). J Hosp Med 2011;6:74-80.

3. Delgado MK, Liu V, Pines JM, Kipnis P, Gardner MN, Escobar GJ. Risk factors for unplanned transfer to intensive care within 24 hours of admission from the emergency department in an integrated healthcare system. J Hosp Med 2013;8:13-19.

4. Liu V, Kipnis P, Rizk NW, Escobar GJ. Adverse outcomes associated with delayed intensive care unit transfers in an integrated healthcare system. J Hosp Med 2012;7:224-230.

5. Churpek MM, Wendlandt B, Zadravecz FJ, Adhikari R, Winslow C, Edelson DP. Association between intensive care unit transfer delay and hospital mortality: a multicenter investigation. J Hosp Med 2016;11:757-762.

6. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation 2013;84:465-470.

7. Subbe CP, Duller B, Bellomo R. Effect of an automated notification system for deteriorating ward patients on clinical outcomes. Crit Care 2017;21:52-52.

8. Bedoya AD, Clement ME, Phelan M, Steorts RC, O’Brien C, Goldstein BA. Minimal impact of implemented early warning score and best practice alert for patient deterioration. Crit Care Med 2019;47:49-55.

9. Evans RS, Kuttler KG, Simpson KJ, et al. Automated detection of physiologic deterioration in hospitalized patients. J Am Med Inform Assoc 2015;22:350-360.

10. Escobar GJ, LaGuardia JC, Turk BJ, Ragins A, Kipnis P, Draper D. Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record. J Hosp Med 2012;7:388-395.

11. Rothman MJ, Rothman SI, Beals J IV. Development and validation of a continuous measure of patient condition using the Electronic Medical Record. J Biomed Inform 2013;46:837-848.

12. Churpek MM, Yuen TC, Winslow C, et al. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med 2014;190:649-655.

13. Kollef MH, Chen Y, Heard K, et al. A randomized trial of real-time automated clinical deterioration alerts sent to a rapid response team. J Hosp Med 2014;9:424-429.

14. Kipnis P, Turk BJ, Wulf DA, et al. Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU. J Biomed Inform 2016;64:10-19.

15. Escobar GJ, Dellinger RP. Early detection, prevention, and mitigation of critical illness outside intensive care settings. J Hosp Med 2016;11:Suppl 1:S5-S10.

16. Escobar GJ, Turk BJ, Ragins A, et al. Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals. J Hosp Med 2016;11:Suppl 1:S18-S24.

17. Dummett BA, Adams C, Scruth E, Liu V, Guo M, Escobar GJ. Incorporating an early detection system into routine clinical practice in two community hospitals. J Hosp Med 2016;11:Suppl 1:S25-S31.

18. Granich R, Sutton Z, Kim YS, et al. Early detection of critical illness outside the intensive care unit: clarifying treatment plans and honoring goals of care using a supportive care team. J Hosp Med 2016;11:Suppl 1:S40-S47.

19. Escobar GJ, Liu V, Kim YS, et al. Early detection of impending deterioration outside the ICU: a difference-in-differences (DiD) study. Am J Respir Crit Care Med 2016;93:A7614-A7614. abstract.

20. Escobar GJ, Greene JD, Scheirer P, Gardner MN, Draper D, Kipnis P. Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases. Med Care 2008;46:232-239.

21. Escobar GJ, Gardner MN, Greene JD, Draper D, Kipnis P. Risk-adjusting hospital mortality using a comprehensive electronic record in an integrated health care delivery system. Med Care 2013;51:446-453.

22. Escobar GJ, Ragins A, Scheirer P, Liu V, Robles J, Kipnis P. Nonelective rehospitalizations and postdischarge mortality: predictive models suitable for use in real time. Med Care 2015;53:916-923.

23. Escobar GJ, Plimier C, Greene JD, Liu V, Kipnis P. Multiyear rehospitalization rates and hospital outcomes in an integrated health care system. JAMA Netw Open 2019;2(12):e1916769-e1916769.

24. Paulson SS, Dummett BA, Green J, Scruth E, Reyes V, Escobar GJ. What do we do after the pilot is done? Implementation of a hospital early warning system at scale. Jt Comm J Qual Patient Saf 2020;46:207-216.

25. Volchenboum SL, Mayampurath A, Göksu-Gürsoy G, Edelson DP, Howell MD, Churpek MM. Association between in-hospital critical illness events and outcomes in patients on the same ward. JAMA 2016;316:2674-2675.

26. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613-619.

27. Hussey MA, Hughes JP. Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials 2007;28:182-191.

28. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer-Verlag, 2009.

29. Harrell F Jr. Regression modeling strategies with applications to linear models, logistic regression, and survival analysis. New York: Springer-Verlag, 2010.

30. Davey C, Hargreaves J, Thompson JA, et al. Analysis and reporting of stepped wedge randomised controlled trials: synthesis and critical appraisal of published studies, 2010 to 2014. Trials 2015;16:358-358.

31. McNutt L-A, Wu C, Xue X, Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. Am J Epidemiol 2003;157:940-943.

32. Kalbfleisch JD, Prentice RL. The statistical analysis of failure time data. 2nd ed. New York: John Wiley, 2011.

33. Austin PC, Fine JP. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat Med 2017;36:4391-4400.

34. Harrell FE Jr. Semiparametric modeling of health care cost and resource utilization. Presented at the 24th Annual Midwest Biopharmaceutical Statistics Workshop, Muncie, IN, May 21–23 2001.

35. Basu A, Manning WG, Mullahy J. Comparing alternative models: log vs Cox proportional hazard? Health Econ 2004;13:749-765.

36. Hosmer DW, Lemeshow S. Applied survival analysis: regression modelling of time to event data. New York: Wiley, 2008.

37. Mihaylova B, Briggs A, O’Hagan A, Thompson SG. Review of statistical methods for analysing healthcare resources and costs. Health Econ 2011;20:897-916.

38. Collett D. Modelling survival data in medical research. 3rd ed. Boca Raton, FL: Chapman and Hall/CRC Press, 2014.

39. Goldstein H, Browne W, Rasbash J. Multilevel modelling of medical data. Stat Med 2002;21:3291-3315.

40. Chen J, Ou L, Flabouris A, Hillman K, Bellomo R, Parr M. Impact of a standardized rapid response system on outcomes in a large healthcare jurisdiction. Resuscitation 2016;107:47-56.

41. Priestley G, Watson W, Rashidian A, et al. Introducing Critical Care Outreach: a ward-randomised trial of phased introduction in a general hospital. Intensive Care Med 2004;30:1398-1404.

42. Horwitz LI, Kuznetsova M, Jones SA. Creating a learning health system through rapid-cycle, randomized testing. N Engl J Med 2019;381:1175-1179.

43. Linnen DT, Escobar GJ, Hu X, Scruth E, Liu V, Stephens C. Statistical modeling and aggregate-weighted scoring systems in prediction of mortality and ICU transfer: a systematic review. J Hosp Med 2019;14:161-169.

44. Morgan DJ, Bame B, Zimand P, et al. Assessment of machine learning vs standard prediction rules for predicting hospital readmissions. JAMA Netw Open 2019;2(3):e190348-e190348.

服务条款 | 隐私政策 | 联系我们