Risk Score to Predict Hospital-Acquired Pneumonia After Spontaneous Intracerebral Hemorrhage

Author:

Ji Ruijun1,Shen Haipeng1,Pan Yuesong1,Du Wanliang1,Wang Penglian1,Liu Gaifen1,Wang Yilong1,Li Hao1,Zhao Xingquan1,Wang Yongjun1,Bi Qi2,Zhang Weiwei3,Cui Liying4,Sun Yuheng5,He Maolin6,Fan Dongsheng7,Ji Xunming8,Li Jimei9,Zhang Fang10,Feng Kai11,Zhang Xiaojun12,Li Yansheng13,Wang Shaoshi14,Fan Wei15,Liu Zhenguo16,Sun Xiaojiang17,Li Wei18,Liu Jianrong19,Chen Xu20,Bai Qingke21,Gu Dexiang22,Li Xin23,Dong Qiang24,Cheng Yan25,Yu Lan26,Li Bin27,Wang Tongyu28,Zhao Kun29,Zhang Chaodong30,Tao Dingbo31,Yin Lin32,Qu Fang33,Zhang Jingbo34,Wang Jianfeng35,Lian Ying36,Qu Fang37,Fan Jun38,Gao Ying39,Cheng Mingdong40,Wu Jiang41,Sun Huashan42,Li Jinying43,Li Guozhong44,Zhu Yulan45,Yang Zichao46,Yang Fengmin47,Zhou Jun48,Guo Minxia49,Li Zhengyi50,Ma Qilin51,Huang Renbin52,Xiao Bo53,Chen Kangning54,Qin Xinyue55,Hu Changlin56,Gao Li57,Zeng Jinsheng58,Xu Anding59,Zhang Xiong60,Shao Ming61,Qi Feng62,Xiao Weimin63,Zhang Suping64,Pan Xiaoping65,Pan Suyue66,Cai Yefeng67,Wan Qi68,Xu Yun69,Ke KaiFu70,Kong Yuenan71,Di Qing72,Shao Fengyang73,Jiang Yajun74,Wang Daming75,Guo Li76,Xue Wencui77

Affiliation:

1. From the Tiantan Comprehensive Stroke Center, Tiantan Hospital, Capital Medical University, Beijing, China (R.J., Y.P., W.D., P.W., G.L., Yilong Wang, H.L., X.Z., Yongjun Wang); China National Clinical Research Center for Neurological Diseases, Beijing, China (R.J., Y.P., W.D., P.W., G.L., Yilong Wang, H.L., X.Z., Yongjun Wang); and Department of Statistics and Operation Research, University of North Carolina, Chapel Hill (H.S.).

2. Beijing Anzhen Hospital

3. Beijing Military District Gengral hospital of Chinese People’s Liberation Army

4. Peking Union Medical College Hospital of Peking University

5. Beijing Jishuitan Hospital

6. Beijing Shijitan Hospital

7. Peking University Third Hospital

8. Beijing Xuanwu Hospital

9. Beijing Friendship Hospital Affiliated to Capital Medical University

10. Beijing Guangwai Hospital

11. Beijing Shunyi District Hospital

12. Beijing Tongren Hospital

13. Shanghai Renji Hospital

14. Shanghai First Municipal People’s Branch hospital

15. Zhongshan Hospital of Fudan University

16. Xin Hua Hospital Affiliated to Shanghai Jiao Tong University

17. The sixth People’sHospital Affiliated to Shanghai JiaoTong University

18. Shanghai Ninth People’s Hospital Affiliated to Shanghai JiaoTong University

19. Shanghai Ruijin Hospital

20. Shanghai 8th People’s Hospital

21. Pudong New Area People’s Hospital

22. Shanghai Yangpu Area Shidong Hospital

23. Shanghai Yangpu Area Center Hospital

24. Huashan Hospital of Fudan University

25. Tianjin Medical University Gengeal Hospital

26. Tianjin Huanhu Hospital

27. Dagang Oilfield Gengeal Hospital

28. Bohai Oilfield Hospital

29. Baodi District People’s Hospital of Tianjin

30. The First Affiliated Hospital of China Medical University

31. The First Afflicated Hospital of Dlian Medical University

32. The Second Affiliated Hospital of Dlian Medical University

33. Dlian Second People’s hospital

34. Dlian Third People’s hospital

35. Dalian Central hospital

36. Dalian Economic and Technological Development District Hospital

37. Shenying Military District General hospital of Chinese People’s Liberation Army

38. Shenyang Military District 202 Hospital

39. National Traditional Chinese Medicine (TCM)Thrombus Treatment Center of Liaoning Province

40. En’liang hopital of Tai’an County

41. The First Clinical College of Jilin University

42. Jilin Chemical Industrial Group General hopital

43. Jilin Oilfield General Hospital

44. The First Clinical College of Harbin Medical University

45. The Second Clinical College of Harbin Medical University

46. The Fourth Clinical College of Harbin Medical University

47. Daqing Oilfield General Hospital

48. Mudan Jiang Second hospital of Hailongjiang Province

49. Shaanxi Provincial People’s Hospital

50. The First Afflicated Hospital of Medical College of Xian Jiaotong University

51. The First Hospital of Xiamen

52. Chenzhou First People’s Hospital

53. Xiangya Hospital of Centre-south University

54. Southwest Hospital

55. The First Affiliated Hospital of Chongqing Medical University

56. The Second Affiliated Hospital of Chongqing Medical University

57. Chengdu Third Municipal People’s Hospital

58. The First Affiliated Hospital of Sun Yat-Sen University

59. The First Affiliated Hospital of Jinan University

60. Guangdong People’s Hospital

61. The First Affiliated Hospital of Guangzhou Medical University

62. LiWan Hospital of GuangZhou Medical College

63. Dungun Municipal People’s Hospital

64. Guangzhou Red Cross Hospital

65. Guangzhou First TMUNICIPAL People’s Hospital

66. Nan Fang Hospital

67. Guangdong Provincial Hospital of Traditional Chinese Medicine

68. Jiang Su People’s Hospital

69. Drum Tower Hospital Affiliated to Nanjing Medical University Upper First-class Hospital

70. he Affiliated Hospital of Nantong University Upper First class Hospital

71. Wuxi Second People’s Hospital Upper First-class Hospital

72. Neurology Hospital Affiliated to Nanjing Medical University Upper First-class Hospital

73. Jiangsu Province Lianyungang Hospital of TCM Upper First-class Hospital

74. Jiangsu Province Hospital of TCM Upper First-class Hospital

75. The First People’s Hospital of Changzhou Upper First-class Haospital

76. The Second Hospital of Hebei Medical University

77. Qinhuangdao C

Abstract

Background and Purpose— We aimed to develop a risk score (intracerebral hemorrhage–associated pneumonia score, ICH-APS) for predicting hospital-acquired stroke-associated pneumonia (SAP) after ICH. Methods— The ICH-APS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Variables routinely collected at presentation were used for predicting SAP after ICH. For testing the added value of hematoma volume measure, we separately developed 2 models with (ICH-APS-B) and without (ICH-APS-A) hematoma volume included. Multivariable logistic regression was performed to identify independent predictors. The area under the receiver operating characteristic curve (AUROC), Hosmer–Lemeshow goodness-of-fit test, and integrated discrimination index were used to assess model discrimination, calibration, and reclassification, respectively. Results— The SAP was 16.4% and 17.7% in the overall derivation (n=2998) and validation (n=2000) cohorts, respectively. A 23-point ICH-APS-A was developed based on a set of predictors and showed good discrimination in the overall derivation (AUROC, 0.75; 95% confidence interval, 0.72–0.77) and validation (AUROC, 0.76; 95% confidence interval, 0.71–0.79) cohorts. The ICH-APS-A was more sensitive for patients with length of stay >48 hours (AUROC, 0.78; 95% confidence interval, 0.75–0.81) than those with length of stay <48 hours (AUROC, 0.64; 95% confidence interval, 0.55–0.73). The ICH-APS-A was well calibrated (Hosmer–Lemeshow test) in the derivation ( P =0.20) and validation ( P =0.66) cohorts. Similarly, a 26-point ICH-APS-B was established. The ICH-APS-A and ICH-APS-B were not significantly different in discrimination and reclassification for SAP after ICH. Conclusion— The ICH-APSs are valid risk scores for predicting SAP after ICH, especially for patients with length of stay >48 hours.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Advanced and Specialised Nursing,Cardiology and Cardiovascular Medicine,Clinical Neurology

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