リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiac arrest」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。
リケラボ

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiac arrest

Nishioka, Norihiro 京都大学 DOI:10.14989/doctor.k23798

2022.03.23

概要

背景：
院外心停止患者の転帰を改善するために，蘇生後の集中治療を効果が期待される症例に実施することが求められる．高額かつ侵襲度の高い治療をどの症例に用いるか蘇生治療の早い段階で決定しなければならないが，院外心停止患者の神経学的予後は，患者背景や蘇生過程の多様な要因が関与することから，早期に精度の高い予測を行うことは困難である．院外心停止患者に対する予測モデルの報告は多く報告されているが，院内での詳細な情報を用いた予後予測モデルの検討は不十分である．

目的：
非外傷性院外心停止患者の長期的な神経学的転帰を来院後早期に予測するモデルを開発し，検証する．

方法：
大阪府下の救命救急センター並びに 2 次救急病院の 16 施設が参加する多施設前向きコホート研究のデータを用いた．2013 年1 月1 日から2017 年12 月1 日に，自己心拍が再開し集中治療室に入院した成人（18 歳以上）の非外傷性院外心停止患者2354 例を抽出した．予測モデルの開発セットとモデルの妥当性の検証セットとして，それぞれ 1329 人（2013～2015 年）と 1025 人（2016～2017 年）に分割した．アウトカムは90 日後のcerebral performance category（CPC）を二分し，良好（CPC 1−2；社会復帰可能な状態）または不良（CPC 3−5；死亡または神経学的な機能低下を伴う生存）と定義した．予測モデルの構築には，正則化と変数選択を行うLeast Absolute Shrinkageand Selection Operator（LASSO）を用いたロジスティック回帰を採用した．自己心拍再開後早期までに得られる変数を予後予測因子の候補とし，モデル1 は病院前の情報と血液検査データを除く病院収容後の院内情報から 12 の変数を，モデル 2 はモデル 1 の変数に自己心拍再開直後の血液検査データを追加し19 の変数を予測変数の候補として含め，２つのモデルを作成した．予測モデルの性能を識別能，較正，臨床的有用性の指標（C 統計量，Brier スコア，較正プロット，ネットベネフィット）を用いて検証セットで評価した．

結果：
患者背景は，開発セットと検証セットでそれぞれ，年齢中央値は72 歳と73 歳，男性はそれぞれ887 人（66.7％）と681 人（66.4％）であった．心停止の原因は，50％以上の患者が心原性であり [開発セット；755 人（56.8％），検証セット；588人（57.4％）]，心停止の目撃がある症例 [開発セット；888 人（63.3％），検証セット；681 人（66.1％）] であった．90 日後の神経学的予後不良は，開発セットで1105 人（83.1%），検証セットでは867 人（84.5%）であった．LASSOを用いたロジスティック回帰により，モデル1 では10 の変数，モデル2 では15 の変数が予測因子として選択され，2 つの予測モデルを構築した．モデルの妥当性検証について，検証セットのモデル1 とモデル2 の識別能は，C 統計量（95％信頼区間）がそれぞれ0.947（0.930-0.964）と0.950（0.934-0.966）であった（DeLong テスト: p = 0.344）．検証セットにおけるモデル1 とモデル2 のBrier スコアは，それぞれ0.0622 と0.0606 であり，較正プロットの視覚的な評価では，両モデルとも予測確率と実際に観察された頻度は近似しており，よく較正されていることが示された．また，ネットベネフィットから決定曲線分析を描出し，臨床的有用性がモデル1 とモデル2 とで，ほぼ同等であることが示された．本研究で開発した予測モデルによる神経学的予後不良確率の算出には，ウェブ版計算機が利用可能である．

結論：
自己心拍再開した院外心停止患者の予後予測モデルの開発と検証を行い，90 日後の神経学的転帰を高い精度で予測することができた．心停止患者の院内での詳細な情報を用いた本研究の予後予測モデルは，自己再開後の早期に神経学的予後を迅速かつ客観的に予測することができ，臨床現場で治療選択の一助となる．

論文の公開元へ

この論文で使われている画像

関連論文

関連論文をもっと見る

参考文献

1. Merchant RM, Topjian AA, Panchal AR, et al. Part 1: Executive summary: 2020 American heart association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation 2020;142. https://doi.org/10.1161/ CIR.0000000000000918.

2. Gra¨sner J-T, Herlitz J, Tjelmeland IBM, et al. European resuscitation council guidelines 2021: Epidemiology of cardiac arrest in Europe. Resuscitation 2021;161:61–79.

3. Nolan JP, Sandroni C, Bo¨ttiger BW, et al. European resuscitation council and european society of intensive care medicine guidelines 2021: post-resuscitation care. Intensive Care Med 2021;47:369–421.

4. Panchal AR, Bartos JA, Caban˜as JG, et al. Part 3: Adult basic and advanced life support: 2020 American heart association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation 2020;142.

5. Lemiale V, Dumas F, Mongardon N, et al. Intensive care unit mortality after cardiac arrest: the relative contribution of shock and brain injury in a large cohort. Intensive Care Med 2013;39:1972–80.

6. Irisawa T, Matsuyama T, Iwami T, et al. The effect of different target temperatures in targeted temperature management on neurologically favorable outcome after out-of-hospital cardiac arrest: A nationwide multicenter observational study in Japan (the JAAM-OHCA registry). Resuscitation 2018;133:82–7.

7. Yoshimura S, Hirayama A, Kiguchi T, et al. Trends in in-hospital advanced management and survival of out-of-hospital cardiac arrest among adults from 2013 to 2017 - A multicenter, prospective registry in Osaka, Japan -. Circulation J 2021.

8. Adrie C, Cariou A, Mourvillier B, et al. Predicting survival with good neurological recovery at hospital admission after successful resuscitation of out-of-hospital cardiac arrest: the OHCA score. Eur Heart J 2006;27:2840-5.

9. Hayakawa K, Tasaki O, Hamasaki T, et al. Prognostic indicators and outcome prediction model for patients with return of spontaneous circulation from cardiopulmonary arrest: The Utstein Osaka Project. Resuscitation 2011;82:874–80.

10. Aschauer S, Dorffner G, Sterz F, Erdogmus A, Laggner A. A prediction tool for initial out-of-hospital cardiac arrest survivors. Resuscitation 2014;85(9):1225–31.

11. Maupain C, Bougouin W, Lamhaut L, et al. The CAHP (Cardiac Arrest Hospital Prognosis) score: a tool for risk stratification after outof-hospital cardiac arrest. Eur Heart J 2016;37:3222–8.

12. Carrick RT, Park JG, McGinnes HL, et al. Clinical predictive models of sudden cardiac arrest: a survey of the current science and analysis of model performances. J Am Heart Assoc 2020;9. https://doi.org/10.1161/JAHA.119.017625.

13. Kwon J-M, Jeon K-H, Kim HM, et al. Deep-learning-based out-ofhospital cardiac arrest prognostic system to predict clinical outcomes. Resuscitation 2019;139:84–91.

14. Seki T, Tamura T, Suzuki M. Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique. Resuscitation 2019;141:128–35.

15. Park JH, Shin SD, Song KJ, et al. Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis. Resuscitation 2019;142:127–35.

16. Andersson P, Johnsson J, Bjo¨rnsson O, et al. Predicting neurological outcome after out-of-hospital cardiac arrest with cumulative information; development and internal validation of an artificial neural network algorithm. Critical Care 2021;25. https://doi.org/10.1186/s13054-021-03505-9.

17. Steyerberg EW, Eijkemans MJC, Harrell FE, Habbema JDF. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Stat Med 2000;19:1059–79.

18. Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J Royal Stat Soc: Ser B (Statistical Methodol) 2011;73:273–82.

19. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. Ann Internal Med 2015;162(1):55–63.

20. Yamada T, Kitamura T, Hayakawa K, et al. Rationale, design, and profile of Comprehensive Registry of In-Hospital Intensive Care for OHCA Survival (CRITICAL) study in Osaka. Japan J Intensive Care 2016;4. https://doi.org/10.1186/s40560-016-0128-5.

21. Cummins RO, Chamberlain DA, Abramson NS, et al. Recommended guidelines for uniform reporting of data from out-of-hospital cardiac arrest: the Utstein Style. A statement for health professionals from a task force of the American Heart Association, the European Resuscitation Council, the Heart and Stroke. Circulation 1991;84:960–75.

22. Becker LB, Aufderheide TP, Geocadin RG, et al. Primary outcomes for resuscitation science studies. Circulation 2011;124:2158–77.

23. Fernando SM, Tran A, Cheng W, et al. Pre-arrest and intra-arrest prognostic factors associated with survival after in-hospital cardiac arrest: systematic review and meta-analysis. Bmj. 2019;367:l6373.

24. Kragholm K, Wissenberg M, Mortensen RN, et al. Bystander efforts and 1-year outcomes in out-of-hospital cardiac arrest. New England J Med 2017;376:1737-47.

25. Zijlstra JA, Koster RW, Blom MT, et al. Different defibrillation strategies in survivors after out-of-hospital cardiac arrest. Heart 2018;104:1929–36.

26. Matsuyama T, Komukai S, Izawa J, et al. Epinephrine administration for adult out-of-hospital cardiac arrest patients with refractory shockable rhythm: time-dependent propensity score-sequential matching analysis from a nationwide population-based registry. Eur Heart J - Cardiovascular Pharmacotherapy. 2021.

27. Izawa J, Komukai S, Gibo K, et al. Pre-hospital advanced airway management for adults with out-of-hospital cardiac arrest: nationwide cohort study. BMJ. 2019:l430.

28. Luo S, Zhang Y, Zhang W, Zheng R, Tao J, Xiong Y. Prognostic significance of spontaneous shockable rhythm conversion in adult out-of-hospital cardiac arrest patients with initial non-shockable heart rhythms: A systematic review and meta-analysis. Resuscitation. 2017;121:1-8.

29. Kandori K, Okada Y, Okada A, et al. Association between cardiac rhythm conversion and neurological outcome among cardiac arrest patients with initial shockable rhythm: a nationwide prospective study in Japan. Eur Heart J Acute Cardiovascular Care. 2021;10:119-26.

30. Hifumi T, Kuroda Y, Kawakita K, et al. Effect of admission glasgow coma scale motor score on neurological outcome in out-of-hospital cardiac arrest patients receiving therapeutic hypothermia. Circulation J 2015;79(10):2201–8.

31. Adnet F, Triba MN, Borron SW, et al. Cardiopulmonary resuscitation duration and survival in out-of-hospital cardiac arrest patients. Resuscitation 2017;111:74–81.

32. Matsuyama T, Iwami T, Yamada T, et al. Effect of serum albumin concentration on neurological outcome after out-of-hospital cardiac arrest (from the CRITICAL [Comprehensive Registry of Intensive Cares for OHCA Survival] study in Osaka, Japan). Am J Cardiol 2018;121:156–61.

33. Domanovits H, Mu¨llner M, Sterz F, et al. Impairment of renal function in patients resuscitated from cardiac arrest: frequency, determinants and impact on outcome. Wien Klin Wochenschr 2000;112 (4):157–61.

34. Hasper D, von Haehling S, Storm C, Jo¨rres A, Schefold JC. Changes in serum creatinine in the first 24 hours after cardiac arrest indicate prognosis: an observational cohort study. Crit Care 2009;13:R168. https://doi.org/10.1186/cc8144.

35. Shida H, Matsuyama T, Iwami T, et al. Serum potassium level on hospital arrival and survival after out-of-hospital cardiac arrest: The CRITICAL study in Osaka, Japan. Eur Heart J: Acute Cardiovascular Care. 2020;9:S175-S83.

36. Kim SH, Choi SP, Park KN, et al. Association of blood glucose at admission with outcomes in patients treated with therapeutic hypothermia after cardiac arrest. Am J Emerg Med. 2014;32:900-4.

37. Jamme M, Ben Hadj Salem O, Guillemet L, et al. Severe metabolic acidosis after out-of-hospital cardiac arrest: risk factors and association with outcome. Ann Intensive Care 2018;8. https://doi.org/10.1186/s13613-018-0409-3.

38. Mckenzie N, Finn J, Dobb G, et al. Arterial carbon dioxide tension has a non-linear association with survival after out-of-hospital cardiac arrest: A multicentre observational study. Resuscitation 2021;162:82–90.

39. Lee DH, Cho IS, Lee SH, et al. Correlation between initial serum levels of lactate after return of spontaneous circulation and survival and neurological outcomes in patients who undergo therapeutic hypothermia after cardiac arrest. Resuscitation 2015;88:143–9.

40. Harrell FE. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. New York: Springer; 2015

41. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020:m441.

42. Stekhoven DJ, Buhlmann P. MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics. 2012;28 (1):112-8.

43. Waljee AK, Mukherjee A, Singal AG, Zhang Y, Warren J, Balis U, Marrero J, Zhu Ji, Higgins PDR. Comparison of imputation methods for missing laboratory data in medicine. BMJ Open 2013;3(8): e002847. https://doi.org/10.1136/bmjopen-2013-002847.

44. Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H. Comparison of random forest and parametric imputation models for imputing missing data using MICE: A CALIBER study. Am J Epidemiology 2014;179:764–74.

45. Groenwold RHH, Donders ART, Roes KCB, Harrell FE, Moons KGM. Dealing with missing outcome data in randomized trials and observational studies. Am J Epidemiology. 2012;175:210-7.

46. R Core Team. R: A language and environment for statistical computing. Austria: R Foundation for Statistical Computing, Vienna; 2018 URL https://www.R-project.org/.

47. Pavlou M, Ambler G, Seaman SR, et al. How to develop a more accurate risk prediction model when there are few events. BMJ 2015. h3868.

48. glmnet: lasso and elastic-net regularized generalized linear models.2021. (Accessed May 22, 2021, at https://cran.r-project.org/web/ packages/glmnet/index.html.)

49. Noma H, Shinozaki T, Iba K, Teramukai S, Furukawa TA. Confidence intervals of prediction accuracy measures for multivariable prediction models based on the bootstrap-based optimism correction methods. Statistics Med 2021.

50. Steyerberg EW, Vickers AJ, Cook NR, et al. Assessing the performance of prediction models. Epidemiology. 2010;21:128-38.

51. Steyerberg EW. Clinical prediction models : a practical approach to development, validation, and updating, vol.: hardcover. New York; London: Springer; 2009.

52. Vickers AJ, Van Calster B, Steyerberg EW. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 2016. i6.

53. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837. https://doi.org/10.2307/2531595.

54. Zhou SE, Maciel CB, Ormseth CH, Beekman R, Gilmore EJ, Greer DM. Distinct predictive values of current neuroprognostic guidelines in post-cardiac arrest patients. Resuscitation 2019;139:343–50.

55. Geocadin RG, Callaway CW, Fink EL, et al. Standards for studies of neurological prognostication in comatose survivors of cardiac arrest: a scientific statement from the American heart association. Circulation 2019;140. e517-e42.

56. Leisman DE, Harhay MO, Lederer DJ, et al. Development and reporting of prediction models: guidance for authors from editors of respiratory, sleep, and critical care journals. Crit Care Med 2020;48 (5):623–33.

57. Soar J, Berg KM, Andersen LW, et al. Adult advanced life support: 2020 international consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations. Resuscitation 2020;156:A80–A119.

58. Rittenberger JC, Martin JR, Kelly LJ, Roth RN, Hostler D, Callaway CW. Inter-rater reliability for witnessed collapse and presence of bystander CPR. Resuscitation 2006;70(3):410–5.

59. Hong S, Lynn HS. Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction. BMC Med Res Methodology 2020;20(1). https://doi.org/10.1186/s12874-020-01080-1.

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文
を
一発検索！

この論文の関連論文を見る