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Assessment of cerebral hemodynamics in patients with acute brain injury: a group-based multivariate trajectory approach.

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  1. [1] § Results › Characterization of clusters ↔ 1002-code/5.2 compare.r, lines 92–135 · score 0.51 · paco2, SpO2, chloride, pH, pairwise, CV

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The authors' code

R · 202 lines · 8.3 KB · no license · 1 match

  1. # //SECTION - 1day mean indicators
  3. # //ANCHOR - preprocess
  5. library(tidyverse)
  7. compareGroupDay1 <- icpBpHr2 %>%
  8. filter(interval < 288) %>%
  9. group_by(icuid) %>%
  10. summarize(
  11. avgicp = round(mean(icp, na.rm = TRUE), 2),
  12. avgisbp = round(mean(isbp, na.rm = TRUE), 2),
  13. avgidbp = round(mean(idbp, na.rm = TRUE), 2),
  14. avghr = round(mean(hr, na.rm = TRUE), 2),
  15. avgmbp = round(mean(mbp, na.rm = TRUE), 2),
  16. avgpp = round(mean(pp, na.rm = TRUE), 2),
  17. avgcpp = round(mean(cpp, na.rm = TRUE), 2),
  18. avgrpp = round(mean(rpp, na.rm = TRUE), 2),
  19. cvicp = round((sd(icp, na.rm = TRUE) / mean(icp, na.rm = TRUE)), 2),
  20. cvisbp = round((sd(isbp, na.rm = TRUE) / mean(isbp, na.rm = TRUE)), 2),
  21. cvidbp = round((sd(idbp, na.rm = TRUE) / mean(idbp, na.rm = TRUE)), 2),
  22. cvhr = round((sd(hr, na.rm = TRUE) / mean(hr, na.rm = TRUE)), 2),
  23. cvmbp = round((sd(mbp, na.rm = TRUE) / mean(mbp, na.rm = TRUE)), 2),
  24. cvpp = round((sd(pp, na.rm = TRUE) / mean(pp, na.rm = TRUE)), 2),
  25. cvcpp = round((sd(cpp, na.rm = TRUE) / mean(cpp, na.rm = TRUE)), 2),
  26. cvrpp = round((sd(rpp, na.rm = TRUE) / mean(rpp, na.rm = TRUE)), 2),
  27. .groups = "drop"
  28. )
  30. # link predictors, group
  31. compareGroupDay1 <- merge(compareGroupDay1, varsImp[, c(1, 31, 67, 69, 71, 77)], by = "icuid", all.y = TRUE)
  33. # //ANCHOR - comparegroups
  35. library(compareGroups)
  37. tableDay1Traj <- descrTable(group ~ . - icuid,
  38. data = compareGroupDay1,
  39. method = NA,
  40. show.all = TRUE
  41. )
  43. # export2word(tableDay1Traj, file = "tableDay1Traj.docx")
  45. # //ANCHOR - pairwise
  47. bonfAvgICPDay1 <- pairwise.t.test(compareGroupDay1$avgicp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  48. print(bonfAvgICPDay1)
  50. bonfAvgIsbpDay1 <- pairwise.t.test(compareGroupDay1$avgisbp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  51. print(bonfAvgIsbpDay1)
  53. bonfAvgIdbpDay1 <- pairwise.t.test(compareGroupDay1$avgidbp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  54. print(bonfAvgIdbpDay1)
  56. bonfAvgHrDay1 <- pairwise.t.test(compareGroupDay1$avghr, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  57. print(bonfAvgHrDay1)
  59. bonfAvgMbpDay1 <- pairwise.t.test(compareGroupDay1$avgmbp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  60. print(bonfAvgMbpDay1)
  62. bonfAvgPPDay1 <- pairwise.t.test(compareGroupDay1$avgpp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  63. print(bonfAvgPPDay1)
  65. bonfAvgCPPDay1 <- pairwise.t.test(compareGroupDay1$avgcpp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  66. print(bonfAvgCPPDay1)
  68. # bonfAvgRPPDay1 <- pairwise.t.test(compareGroupDay1$avgrpp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  69. # print(bonfAvgRPPDay1)
  71. bonfCVICPDay1 <- pairwise.t.test(compareGroupDay1$cvicp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  72. print(bonfCVICPDay1)
  74. bonfCVIsbpDay1 <- pairwise.t.test(compareGroupDay1$cvisbp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  75. print(bonfCVIsbpDay1)
  77. bonfCVIdbpDay1 <- pairwise.t.test(compareGroupDay1$cvidbp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  78. print(bonfCVIdbpDay1)
  80. bonfCVHrDay1 <- pairwise.t.test(compareGroupDay1$cvhr, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  81. print(bonfCVHrDay1)
  83. bonfCVMbpDay1 <- pairwise.t.test(compareGroupDay1$cvmbp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  84. print(bonfCVMbpDay1)
  86. bonfCVPPDay1 <- pairwise.t.test(compareGroupDay1$cvpp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  87. print(bonfCVPPDay1)
  89. bonfCVCPPDay1 <- pairwise.t.test(compareGroupDay1$cvcpp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  90. print(bonfCVCPPDay1)
  92. # bonfCVRPPDay1 <- pairwise.t.test(compareGroupDay1$cvrpp, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  93. # print(bonfCVRPPDay1)
  95. bonfChlorideDay1 <- pairwise.t.test(compareGroupDay1$chloride, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  96. print(bonfChlorideDay1)
  98. bonfPhDay1 <- pairwise.t.test(compareGroupDay1$ph, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  99. print(bonfPhDay1)
  101. bonfPaco2Day1 <- pairwise.t.test(compareGroupDay1$paco2, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  102. print(bonfPaco2Day1)
  104. bonfSpo2Day1 <- pairwise.t.test(compareGroupDay1$spo2_cv, compareGroupDay1$group, p.adj = "bonf", data = compareGroupDay1)
  105. print(bonfSpo2Day1)
  107. # //!SECTION
  109. # //SECTION - 5day mean indicators
  111. # //ANCHOR - preprocess
  113. library(tidyverse)
  115. compareGroupDay5 <- icpBpHr2 %>%
  116. group_by(icuid) %>%
  117. summarize(
  118. avgicp = round(mean(icp, na.rm = TRUE), 2),
  119. avgisbp = round(mean(isbp, na.rm = TRUE), 2),
  120. avgidbp = round(mean(idbp, na.rm = TRUE), 2),
  121. avghr = round(mean(hr, na.rm = TRUE), 2),
  122. avgmbp = round(mean(mbp, na.rm = TRUE), 2),
  123. avgpp = round(mean(pp, na.rm = TRUE), 2),
  124. avgcpp = round(mean(cpp, na.rm = TRUE), 2),
  125. avgrpp = round(mean(rpp, na.rm = TRUE), 2),
  126. cvicp = round((sd(icp, na.rm = TRUE) / mean(icp, na.rm = TRUE)), 2),
  127. cvisbp = round((sd(isbp, na.rm = TRUE) / mean(isbp, na.rm = TRUE)), 2),
  128. cvidbp = round((sd(idbp, na.rm = TRUE) / mean(idbp, na.rm = TRUE)), 2),
  129. cvhr = round((sd(hr, na.rm = TRUE) / mean(hr, na.rm = TRUE)), 2),
  130. cvmbp = round((sd(mbp, na.rm = TRUE) / mean(mbp, na.rm = TRUE)), 2),
  131. cvpp = round((sd(pp, na.rm = TRUE) / mean(pp, na.rm = TRUE)), 2),
  132. cvcpp = round((sd(cpp, na.rm = TRUE) / mean(cpp, na.rm = TRUE)), 2),
  133. cvrpp = round((sd(rpp, na.rm = TRUE) / mean(rpp, na.rm = TRUE)), 2),
  134. .groups = "drop"
  135. )
  137. # link group
  138. compareGroupDay5 <- merge(compareGroupDay5, varsImp[, c(1, 77)], by = "icuid", all.y = TRUE)
  140. # //ANCHOR - comparegroups
  142. library(compareGroups)
  144. tableDay5Traj <- descrTable(group ~ . - icuid,
  145. data = compareGroupDay5,
  146. method = NA,
  147. show.all = TRUE
  148. )
  150. # export2word(tableDay5Traj, file = "tableDay5Traj.docx")
  152. # //ANCHOR - pairwise
  154. bonfAvgICPDay5 <- pairwise.t.test(compareGroupDay5$avgicp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  155. print(bonfAvgICPDay5)
  157. bonfAvgIsbpDay5 <- pairwise.t.test(compareGroupDay5$avgisbp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  158. print(bonfAvgIsbpDay5)
  160. bonfAvgIdbpDay5 <- pairwise.t.test(compareGroupDay5$avgidbp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  161. print(bonfAvgIdbpDay5)
  163. bonfAvgHrDay5 <- pairwise.t.test(compareGroupDay5$avghr, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  164. print(bonfAvgHrDay5)
  166. bonfAvgMbpDay5 <- pairwise.t.test(compareGroupDay5$avgmbp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  167. print(bonfAvgMbpDay5)
  169. bonfAvgPPDay5 <- pairwise.t.test(compareGroupDay5$avgpp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  170. print(bonfAvgPPDay5)
  172. bonfAvgCPPDay5 <- pairwise.t.test(compareGroupDay5$avgcpp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  173. print(bonfAvgCPPDay5)
  175. # bonfAvgRPPDay5 <- pairwise.t.test(compareGroupDay5$avgrpp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  176. # print(bonfAvgRPPDay5)
  178. bonfCVICPDay5 <- pairwise.t.test(compareGroupDay5$cvicp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  179. print(bonfCVICPDay5)
  181. bonfCVIsbpDay5 <- pairwise.t.test(compareGroupDay5$cvisbp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  182. print(bonfCVIsbpDay5)
  184. bonfCVIdbpDay5 <- pairwise.t.test(compareGroupDay5$cvidbp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  185. print(bonfCVIdbpDay5)
  187. bonfCVHrDay5 <- pairwise.t.test(compareGroupDay5$cvhr, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  188. print(bonfCVHrDay5)
  190. bonfCVMbpDay5 <- pairwise.t.test(compareGroupDay5$cvmbp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  191. print(bonfCVMbpDay5)
  193. bonfCVPPDay5 <- pairwise.t.test(compareGroupDay5$cvpp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  194. print(bonfCVPPDay5)
  196. bonfCVCPPDay5 <- pairwise.t.test(compareGroupDay5$cvcpp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  197. print(bonfCVCPPDay5)
  199. # bonfCVRPPDay5 <- pairwise.t.test(compareGroupDay5$cvrpp, compareGroupDay5$group, p.adj = "bonf", data = compareGroupDay5)
  200. # print(bonfCVRPPDay5)
  202. # //!SECTION

5.2 compare.r at commit 752e922, no license · at the source

Overview

Authors: Hai Zhou1, Changcun Chen2,3, Hui Zheng2, Yuheng Wu4, Li Zhu1, Changqing Zhou1
  1. Department of Sleep Medicine, Bayi Orthopedic Hospital, China RongTong Medical Healthcare Group Co. Ltd., Chengdu, Sichuan, China
  2. Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
  3. Department of Neurosurgery, Longquan Hospital, Chengdu, Sichuan, China
  4. Department of Pain Management, Bayi Orthopedic Hospital, China RongTong Medical Healthcare Group Co. Ltd., Chengdu, Sichuan, China
Institutions: Sichuan University (China); West China Hospital of Sichuan University (China)
Journal: Frontiers in neurology, volume 17, article 1783194
Dates: received 8 January 2026; accepted 29 May 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fneur.2026.1783194 · PMID 42422201 · PMCID PMC13341458 · OpenAlex W7165814848
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), clinical / translational (subfield)
Methods: Statistics, Machine learning
Keywords: acute brain injury, cerebral hemodynamics, group-based multivariate trajectory, group-based trajectory modeling, longitudinal analysis
Topic: Traumatic Brain Injury and Neurovascular Disturbances (Neurology, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 27 references in the paper

Abstract

Objective: This study aimed to describe the evolution patterns of cerebral hemodynamics in acute brain injury (ABI) patients.

Methods: ABI patients with intracranial pressure (ICP), invasive arterial blood pressure, and heart rate (HR) records were identified from Medical Information Mart for the Intensive Care (MIMIC)-IV 3.0, eICU Collaborative Research Database (eICU-CRD) 2.0, and the Department of Neurosurgery of Longquan Hospital. Group-based multivariate trajectory (GBMT) modeling was employed to identify clusters of participants with similar evolution patterns of cerebral hemodynamics. Multivariate logistic regression analysis was used to examine the association between the GBMT clusters and outcomes. Features were selected using a random forest (RF) algorithm based on recursive feature elimination (RFE), and feature importance was interpreted using SHAP values. Furthermore, subgroup analyses were performed.

Results: Data from 477 eligible patients from the MIMIC-IV and eICU-CRD databases were included in the trajectory analysis. Additionally, data from 519 patients from the Department of Neurosurgery were utilized for external validation. GBMT analyses identified five clusters with distinct cerebral hemodynamics evolution patterns. Compared to Cluster 1, Cluster 5 was the only one associated with an unfavorable outcome. Sensitivity analysis indicated that the effect size and direction in different subgroups were consistent, and the results were stable.

Conclusion: This study identified a unique cerebral hemodynamics evolution pattern in ABI that was associated with unfavorable outcomes. Notably, our study suggested that the longitudinal analysis of multiple hemodynamic indicators may offer a novel perspective for the treatment of ABI patients.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

Nicholas2074/1002-stat

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 752e922d64f23065fadd62cefb0c7e98cba37f62, 9 May 2026
Languages: R (17)
Size: 18 files, 17 scripts
Software Heritage: not archived
Found in: the text
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (13 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
17 files
At the source: github.com/Nicholas2074/1002-stat

Nicholas2074/1002-data

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3a99227eaa9808bcb26a7eeb9a1f5af1474e97b5, 29 April 2026
Size: 9 files, 0 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
At the source: github.com/Nicholas2074/1002-data

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 17 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

Publicly available datasets were analyzed in this study. Data of the trajectory analysis cohort are available from the MIMIC-IV and eICU-CRD databases, which require authorized access via PhysioNet (https://physionet.org). The data extraction code for these two databases and the dataset from the external validation cohort are publicly available in the GitHub repository (https://github.com/Nicholas2074/1002-data).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 26 references.

Cite

This paper

Zhou, H., Chen, C., Zheng, H., Wu, Y., Zhu, L., & Zhou, C. (2026). Assessment of cerebral hemodynamics in patients with acute brain injury: a group-based multivariate trajectory approach. Frontiers in neurology, 17, 1783194. https://doi.org/10.3389/fneur.2026.1783194

BibTeX

@article{zhou2026assessment,
author = {Zhou, Hai and Chen, Changcun and Zheng, Hui and Wu, Yuheng and Zhu, Li and Zhou, Changqing},
title = {{Assessment of cerebral hemodynamics in patients with acute brain injury: a group-based multivariate trajectory approach}},
journal = {Frontiers in neurology},
year = {2026},
month = jun,
volume = {17},
pages = {1783194},
publisher = {Frontiers Media SA},
issn = {1664-2295},
doi = {10.3389/fneur.2026.1783194},
url = {https://doi.org/10.3389/fneur.2026.1783194},
pmid = {42422201},
pmcid = {PMC13341458}
}

RIS

TY - JOUR
AU - Zhou, Hai
AU - Chen, Changcun
AU - Zheng, Hui
AU - Wu, Yuheng
AU - Zhu, Li
AU - Zhou, Changqing
TI - Assessment of cerebral hemodynamics in patients with acute brain injury: a group-based multivariate trajectory approach
T2 - Frontiers in neurology
J2 - Front Neurol
PY - 2026
DA - 2026/06/24
VL - 17
SP - 1783194
SN - 1664-2295
PB - Frontiers Media SA
DO - 10.3389/fneur.2026.1783194
UR - https://doi.org/10.3389/fneur.2026.1783194
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fneur.2026.1783194",
"type": "article-journal",
"title": "Assessment of cerebral hemodynamics in patients with acute brain injury: a group-based multivariate trajectory approach",
"container-title": "Frontiers in neurology",
"author": [
{
"family": "Zhou",
"given": "Hai"
},
{
"family": "Chen",
"given": "Changcun"
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{
"family": "Zheng",
"given": "Hui"
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{
"family": "Wu",
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"given": "Changqing"
}
],
"container-title-short": "Front Neurol",
"volume": "17",
"page": "1783194",
"DOI": "10.3389/fneur.2026.1783194",
"PMID": "42422201",
"PMCID": "PMC13341458",
"ISSN": "1664-2295",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fneur.2026.1783194",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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