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However, mapping functional connectivity for individual patients has been challenging due to the lack of analytical methods for evaluating physiological networks using routine clinical and laboratory data. This study explored the use of parenclitic network mapping to assess organ connectivity and predict sepsis outcomes based on routine laboratory data. Data from 162 sepsis patients meeting Sepsis\u20103 criteria were retrospectively analyzed from the MIMIC\u2010III database. Fifteen physiological variables representing organ systems were used to construct organ network connectivity through correlation analysis. Correlation analysis identified 7 interactions linked to 30\u2010day survival. Parenclitic network analysis was used to measure deviations in individual patients' correlations between organ systems from the reference physiological interactions observed in survivors. Parenclitic deviations in the pH\u2010bicarbonate axis (hazard ratio\u2009=\u20092.081,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.001) and pH\u2010lactate axis (hazard ratio\u2009=\u20092.773,\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.024) significantly predicted 30\u2010day mortality, independent of the Sequential Organ Failure Assessment (SOFA) score and ventilation status. This study highlights the potential of parenclitic network mapping to provide insights into sepsis pathophysiology and differences in organ system connectivity between survivors and non\u2010survivors independent of sepsis severity and mechanical ventilation status.\n                  <\/jats:p>","DOI":"10.14814\/phy2.70407","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T04:25:17Z","timestamp":1749183917000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Parenclitic network mapping predicts survival in critically ill patients with sepsis"],"prefix":"10.14814","volume":"13","author":[{"given":"Emily","family":"Ito","sequence":"first","affiliation":[{"name":"Network Physiology Lab, Division of Medicine UCL  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1151-0295","authenticated-orcid":false,"given":"Tope","family":"Oyelade","sequence":"additional","affiliation":[{"name":"Network Physiology Lab, Division of Medicine UCL  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Wikner","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, UCL  London UK"},{"name":"Department of Perioperative Medicine and Pain Barts Health NHS Trust  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Network Physiology Lab, Division of Medicine UCL  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Watjana","family":"Lilaonitkul","sequence":"additional","affiliation":[{"name":"Institute of Health Informatics, UCL  London UK"},{"name":"Global Business School for Health, UCL  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0830-2022","authenticated-orcid":false,"given":"Ali R.","family":"Mani","sequence":"additional","affiliation":[{"name":"Network Physiology Lab, Division of Medicine UCL  London UK"},{"name":"Institute for Liver and Digestive Health (ILDH), Division of Medicine UCL  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.04500422"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0000000000001354"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms1705"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/1469\u20108986.3510127"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2017.00555"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(02)09459\u2010X"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0005\u20102728(99)00025\u20100"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11908\u2010012\u20100282\u20104"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1164\/rccm.201003\u20100326OC"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ki.5002340"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467\u2010019\u201012399\u20109"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.3892\/etm.2024.12589"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1152\/ajpregu.1983.244.6.R794"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.95.13.7631"},{"key":"e_1_2_12_16_1","volume-title":"Statistical power analysis for the behavioral sciences","author":"Cohen J.","year":"1988"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0021\u20109258(18)83822\u20104"},{"key":"e_1_2_12_18_1","volume-title":"IBM SPSS statistics for Macintosh","author":"Corp., I. 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