{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T01:46:09Z","timestamp":1792028769078,"version":"4.4.0"},"reference-count":46,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"the National Science Foundation of China","award":["81960664"],"award-info":[{"award-number":["81960664"]}]},{"name":"the Applied Basic Research Program Yunnan Province of China","award":["202401AY070001-367"],"award-info":[{"award-number":["202401AY070001-367"]}]},{"name":"the Applied Basic Research Program Yunnan Province of China","award":["202501AS070163"],"award-info":[{"award-number":["202501AS070163"]}]},{"name":"the Young and Middle-aged Academic and Technical Leaders Reserve Talent Project of Yunnan Province","award":["202405AC350037"],"award-info":[{"award-number":["202405AC350037"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technology and Health Care"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Septic patients with heart failure (HF) have higher mortality and poorer prognosis than patients with either disease alone. Currently, no tool exists for predicting survival rate in such patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>This study aimed to develop an interpretable prediction model to predict survival rate for septic patients with HF.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Severe septic patients with HF were recruited from the MIMIC-IV database (as training and internal validation cohorts) as well as from the MIMIC-III database (as external validation cohorts). Four models including Deep Learning Survival (DeepSurv) were constructed and evaluated. Furthermore, Shapley Additive Explanations (SHAP) method was employed to explain the DeepSurv model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>A total of 11,778 patients were included and 22 features were identified to construct the models. Among the 4 models, the DeepSurv model had the highest area under the curve (AUC) values with an AUC of 0.851 (internal) and 0.801 (external) and C-index of 0.8329 (internal) and 0.7816 (external). The mean cumulative\/dynamic AUC values exceeded 0.85 in both internal and external validations. The Integrated Brier Score values were well below 0.25, at 0.068 and 0.093, respectively. Furthermore, the Decision Curve Analysis showed that the DeepSurv model achieved favorable net benefit. The SHAP method further confirmed the reliability of the DeepSurv model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Our DeepSurv model was the most comprehensive interpretable prediction model specifically developed and validated for septic critically ill patients with HF. It demonstrated good model performance in predicting the 28-day survival rate of such patients and will provide valuable decision support for clinicians.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/09287329251346284","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T03:32:09Z","timestamp":1749612729000},"page":"2404-2415","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting survival rates of critically ill septic patients with heart failure using interpretable machine learning models"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2592-2188","authenticated-orcid":false,"given":"Hai-Ying","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"},{"name":"College of Pharmacy, Dali University, Dali, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7009-8736","authenticated-orcid":false,"given":"Meng-Han","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"},{"name":"College of Pharmacy, Dali University, Dali, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8608-9645","authenticated-orcid":false,"given":"Fang","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9896-7702","authenticated-orcid":false,"given":"Li-Juan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"},{"name":"College of Pharmacy, Dali University, Dali, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2062-4868","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Respiratory, 920th Hospital of Joint Logistics Support Force, Kunming, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9404-1334","authenticated-orcid":false,"given":"De-Min","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"},{"name":"College of Pharmacy, Dali University, Dali, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0473-1284","authenticated-orcid":false,"given":"Yu","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4749-7177","authenticated-orcid":false,"given":"Jia-De","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"},{"name":"College of Pharmacy, Dali University, Dali, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2972-9132","authenticated-orcid":false,"given":"Sun-Jun","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1274-3130","authenticated-orcid":false,"given":"Gong-Hao","family":"He","sequence":"additional","affiliation":[{"name":"Department of Clinical Pharmacy, The 920th Hospital of Joint Logistics Support Force, Kunming, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"e_1_3_4_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-017-4980-0"},{"key":"e_1_3_4_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrc.2020.12.015"},{"key":"e_1_3_4_4_2","doi-asserted-by":"publisher","DOI":"10.1002\/ejhf.592"},{"key":"e_1_3_4_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejheart.2006.01.001"},{"key":"e_1_3_4_6_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2021-058205"},{"key":"e_1_3_4_7_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0072476"},{"key":"e_1_3_4_8_2","doi-asserted-by":"publisher","DOI":"10.1097\/00003246-198510000-00009"},{"key":"e_1_3_4_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF01709751"},{"key":"e_1_3_4_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40121-022-00628-6"},{"key":"e_1_3_4_11_2","doi-asserted-by":"publisher","DOI":"10.2196\/38082"},{"key":"e_1_3_4_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"e_1_3_4_13_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x"},{"key":"e_1_3_4_14_2","article-title":"Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury","volume":"10","author":"Dong L","year":"2024","unstructured":"Dong L, Liu P, Qi Z, et al. Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury. Heliyon 2024; 10: e29985.","journal-title":"Heliyon"},{"key":"e_1_3_4_15_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-024-02265-4"},{"key":"e_1_3_4_16_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.0287"},{"key":"e_1_3_4_17_2","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2011.00771.x"},{"key":"e_1_3_4_18_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-022-03469-6"},{"key":"e_1_3_4_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41398-020-01172-y"},{"key":"e_1_3_4_20_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0274-4"},{"key":"e_1_3_4_21_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq134"},{"key":"e_1_3_4_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"e_1_3_4_23_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurjpc\/zwac122"},{"key":"e_1_3_4_24_2","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2023.1273640"},{"key":"e_1_3_4_25_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-024-05544-6"},{"key":"e_1_3_4_26_2","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.118.009684"},{"key":"e_1_3_4_27_2","doi-asserted-by":"publisher","DOI":"10.1177\/0885066620928299"},{"key":"e_1_3_4_28_2","doi-asserted-by":"publisher","DOI":"10.1186\/s40001-023-01593-7"},{"key":"e_1_3_4_29_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.967758"},{"key":"e_1_3_4_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106040"},{"key":"e_1_3_4_31_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.5842"},{"key":"e_1_3_4_32_2","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp-2022-330230"},{"key":"e_1_3_4_33_2","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.923549"},{"key":"e_1_3_4_34_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-018-0482-1"},{"key":"e_1_3_4_35_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1131859"},{"key":"e_1_3_4_36_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-80575-z"},{"key":"e_1_3_4_37_2","doi-asserted-by":"publisher","DOI":"10.1080\/0886022X.2024.2316267"},{"key":"e_1_3_4_38_2","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0b013e3181958d91"},{"key":"e_1_3_4_39_2","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.121.023535"},{"key":"e_1_3_4_40_2","doi-asserted-by":"publisher","DOI":"10.1186\/cc8026"},{"key":"e_1_3_4_41_2","doi-asserted-by":"publisher","DOI":"10.1111\/hdi.12760"},{"key":"e_1_3_4_42_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCRESAHA.113.301934"},{"key":"e_1_3_4_43_2","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.03681106"},{"key":"e_1_3_4_44_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrneph.2016.113"},{"key":"e_1_3_4_45_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12939-024-02114-6"},{"key":"e_1_3_4_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjmed.2020.07.025"},{"key":"e_1_3_4_47_2","doi-asserted-by":"publisher","DOI":"10.1097\/HCO.0000000000000855"}],"container-title":["Technology and Health Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09287329251346284","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/09287329251346284","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/09287329251346284","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T01:28:20Z","timestamp":1792027700000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/09287329251346284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,11]]},"references-count":46,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1177\/09287329251346284"],"URL":"https:\/\/doi.org\/10.1177\/09287329251346284","relation":{},"ISSN":["0928-7329","1878-7401"],"issn-type":[{"value":"0928-7329","type":"print"},{"value":"1878-7401","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,11]]}}}