Prediction of clinical deterioration within one year in chronic obstructive pulmonary disease using the systemic coagulation-inflammation index: a retrospective study employing multiple machine learning method
Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD. A cohort o...
Saved in:
| Published in | PeerJ (San Francisco, CA) Vol. 13; p. e18989 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
PeerJ. Ltd
25.02.2025
PeerJ, Inc PeerJ Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2167-8359 2167-8359 |
| DOI | 10.7717/peerj.18989 |
Cover
| Abstract | Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD.
A cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC).
Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients.
The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction. |
|---|---|
| AbstractList | BackgroundInflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD. MethodsA cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). ResultsOur study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients. ConclusionThe SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction. Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD.BackgroundInflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD.A cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC).MethodsA cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC).Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients.ResultsOur study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients.The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction.ConclusionThe SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction. Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD. A cohort of 957 COPD patients (mean age: 68.4±7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients. The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction. Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD. A cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients. The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction. Background Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD. Methods A cohort of 957 COPD patients (mean age: 68.4 ± 7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Results Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients. Conclusion The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction. Background Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD), prompting us to hypothesize that the systemic coagulation-inflammation (SCI) index is associated with clinical deterioration in COPD. Methods A cohort of 957 COPD patients (mean age: 68.4±7.8 years; 74.4% male) from January 2018 to December 2021 was analyzed. Six machine learning models (XGBoost, logistic regression, Random Forest, elastic net (ENT), support vector machine (SVM), and K-nearest neighbors (KNN)) were evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). Results Our study encompassed 957 patients, out of which 171 were classified in the clinical deterioration of COPD (cd-COPD) cohort. Significant disparities in age, comorbidities like respiratory failure, C-reactive protein, lymphocyte count, red blood cell distribution width (RDW), SCI, procalcitonin (PCT), and D-dimer were depicted between the cd-COPD and non-cd-COPD groups. Concerning machine learning and model comparison, the SVM model showcased consistent performance and strong generalization capabilities on both the training and testing sets compared to the other five machine learning (ML) models. The SCI index, as the most influential predictor, demonstrated a median of 93.08 in cd-COPD compared to 81.67 in non-cd-COPD patients. Conclusion The SCI is markedly elevated in cd-COPD patients compared to COPD patients, and SVM demonstrates reliable performance in cd-COPD prediction. |
| ArticleNumber | e18989 |
| Audience | Academic |
| Author | Min, Ming Liu, Qianfei Tan, Wei Zhang, Minghua Hou, Ling Hou, Rui |
| Author_xml | – sequence: 1 givenname: Ling surname: Hou fullname: Hou, Ling organization: Department of Central Hospital of Tujia and Miao Autonomous Prefecture, Hubei University of Medicine, Hubei, China – sequence: 2 givenname: Ming surname: Min fullname: Min, Ming organization: Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China – sequence: 3 givenname: Rui surname: Hou fullname: Hou, Rui organization: Hubei Enshi College, Enshi, China – sequence: 4 givenname: Wei surname: Tan fullname: Tan, Wei organization: Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China – sequence: 5 givenname: Minghua surname: Zhang fullname: Zhang, Minghua organization: Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China – sequence: 6 givenname: Qianfei surname: Liu fullname: Liu, Qianfei organization: Department of Pulmonary and Critical Care Medicine, Central Hospital of Tujia and Miao Autonomous Prefecture, Enshi, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40028201$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kktv1DAUhSNUREvpij2yhISQYAY_8rDZVRWPSpVgAevoTnI945FjB9uhzM_kH-HMlNIiRLyIZX_3-NjnPi6OnHdYFE8ZXTYNa96MiGG7ZFJJ9aA44axuFlJU6ujO_Lg4i3FL8yd5TaV4VByXlHLJKTspfn4O2JsuGe-I16SzxpkOLOkxYTA-wH7n2qSNyYBDskMIJM-7TfAZJX4VU5iywHck42QH7yDsSG8iQkQyRePWJG2QxF1MOOSCzsN6snvdhXHawjAcDjGuxx9vCZCAKfg44kE0pqnfERxG63ez2DDZZEaLZIAum0JisyO338G08f2T4qEGG_Hs5n9afH3_7svFx8XVpw-XF-dXi65s6rTQgLRksulKJrjUUleSKQq8U0KUfa2hhEqCLCtZS6YbpRnvQa-0rqiq6mYlTovLg27vYduOwQz54q0H0-4XfFi3EJLpLLYlVbRZMURV6hI1yB65goYpxrlQmmat1wetyY2wuwZrbwUZbeeg233Q7T7ojL884GPw3yaMqR1M7NBacOin2ArWiBxxJcqMPv8L3fopuPwwrchni1oJ3vyh1pDt5lR8CtDNou255LLhopazy-U_qDz6OdjcHNrk9XsFL-4UbBBs2kRvpznueB98duNyWg3Y317-d59m4NUB6HJnxID6v-_zC2Wu_cA |
| Cites_doi | 10.1016/j.arbres.2011.04.011 10.1177/2045894020948470 10.1152/ajplung.00121.2021 10.1371/journal.pone.0037483 10.1016/j.ccm.2020.06.007 10.1164/rccm.201509-1722PP 10.3389/fimmu.2023.1131292 10.1016/j.jaci.2016.05.011 10.1016/j.jacasi.2022.06.007 10.3389/fphar.2017.00512 10.1016/s0140-6736(18)30841-9 10.1136/thoraxjnl-2012-201871 10.1016/j.modpat.2024.100680 10.3238/arztebl.2014.0825 10.1111/resp.14000 10.4046/trd.2017.80.3.313 10.7326/aitc202008040 10.1161/jaha.121.023021 10.1016/j.micres.2022.127244 10.1164/rccm.200707-1037OC 10.3324/haematol.2019.217463 10.1042/cs20160718 10.1183/09059180.00003609 10.1016/j.eclinm.2023.101936 10.1016/j.redox.2022.102587 10.1080/10937404.2023.2208886 10.18087/cardio.2023.10.n2586 10.1016/j.alit.2018.01.001 10.1161/jaha.121.022277 10.1186/s12931-015-0240-4 10.5543/tkda.2023.30344 10.1186/s12989-024-00614-5 |
| ContentType | Journal Article |
| Copyright | 2025 Hou et al. COPYRIGHT 2025 PeerJ. Ltd. 2025 Hou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2025 Hou et al. – notice: COPYRIGHT 2025 PeerJ. Ltd. – notice: 2025 Hou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7XB 88I 8FE 8FH 8FK ABUWG AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO GNUQQ HCIFZ LK8 M2P M7P PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 ADTOC UNPAY DOA |
| DOI | 10.7717/peerj.18989 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) ProQuest Central (purchase pre-March 2016) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Journals ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central ProQuest Central Student SciTech Premium Collection Biological Sciences Science Database (Proquest) Biological Science Database (Proquest) ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic Unpaywall for CDI: Periodical Content Unpaywall DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Central (New) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition Biological Science Database ProQuest SciTech Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 4 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 5 dbid: BENPR name: ProQuest Central url: http://www.proquest.com/pqcentral?accountid=15518 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2167-8359 |
| ExternalDocumentID | oai_doaj_org_article_40907b1ee94f4efa8de29a71912239f0 10.7717/peerj.18989 A828723680 40028201 10_7717_peerj_18989 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | 53G 5VS 88I 8FE 8FH AAFWJ AAYXX ABUWG ADBBV ADRAZ AENEX AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ CCPQU CITATION DIK DWQXO ECGQY GNUQQ GROUPED_DOAJ GX1 HCIFZ HYE IAO IEA IHR IHW ITC KQ8 LK8 M2P M7P M~E OK1 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC PUEGO RPM W2D YAO CGR CUY CVF ECM EIF H13 M48 NPM 3V. 7XB 8FK PKEHL PQEST PQUKI PRINS Q9U 7X8 ADTOC UNPAY |
| ID | FETCH-LOGICAL-c476t-fae04187c41328f8f58190a2c9334d6fa4a58a8458681f79f12dafbff509567b3 |
| IEDL.DBID | UNPAY |
| ISSN | 2167-8359 |
| IngestDate | Fri Oct 03 12:45:48 EDT 2025 Sun Oct 26 04:09:23 EDT 2025 Thu Oct 02 12:06:30 EDT 2025 Tue Sep 30 12:10:40 EDT 2025 Mon Oct 20 22:45:03 EDT 2025 Mon Oct 20 16:58:17 EDT 2025 Thu May 22 21:23:47 EDT 2025 Mon May 12 02:38:49 EDT 2025 Wed Oct 01 06:52:05 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Chronic obstructive pulmonary disease Predictor Clinical deterioration Machine learning Systemic coagulation-inflammation index |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 2025 Hou et al. cc-by |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c476t-fae04187c41328f8f58190a2c9334d6fa4a58a8458681f79f12dafbff509567b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://doi.org/10.7717/peerj.18989 |
| PMID | 40028201 |
| PQID | 3239369327 |
| PQPubID | 2045935 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_40907b1ee94f4efa8de29a71912239f0 unpaywall_primary_10_7717_peerj_18989 proquest_miscellaneous_3173400534 proquest_journals_3239369327 gale_infotracmisc_A828723680 gale_infotracacademiconefile_A828723680 gale_healthsolutions_A828723680 pubmed_primary_40028201 crossref_primary_10_7717_peerj_18989 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2025-02-25 |
| PublicationDateYYYYMMDD | 2025-02-25 |
| PublicationDate_xml | – month: 02 year: 2025 text: 2025-02-25 day: 25 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Diego |
| PublicationTitle | PeerJ (San Francisco, CA) |
| PublicationTitleAlternate | PeerJ |
| PublicationYear | 2025 |
| Publisher | PeerJ. Ltd PeerJ, Inc PeerJ Inc |
| Publisher_xml | – name: PeerJ. Ltd – name: PeerJ, Inc – name: PeerJ Inc |
| References | Pantanowitz (10.7717/peerj.18989/ref-20) 2024; 38 Bazzan (10.7717/peerj.18989/ref-3) 2023; 14 Raherison (10.7717/peerj.18989/ref-23) 2009; 18 Sandström (10.7717/peerj.18989/ref-26) 2024; 21 Hikichi (10.7717/peerj.18989/ref-9) 2018; 67 Liu (10.7717/peerj.18989/ref-15) 2011; 47 Mkorombindo (10.7717/peerj.18989/ref-17) 2021; 26 Agustí (10.7717/peerj.18989/ref-1) 2012; 7 Solinc (10.7717/peerj.18989/ref-27) 2022; 11 Zengin (10.7717/peerj.18989/ref-33) 2023; 63 Barnes (10.7717/peerj.18989/ref-2) 2016; 138 Dey (10.7717/peerj.18989/ref-5) 2022; 322 Özkan (10.7717/peerj.18989/ref-18) 2024; 52 Papakonstantinou (10.7717/peerj.18989/ref-21) 2015; 16 Upadhyay (10.7717/peerj.18989/ref-28) 2023; 26 Liu (10.7717/peerj.18989/ref-14) 2022; 2 Perros (10.7717/peerj.18989/ref-22) 2008; 178 Wang (10.7717/peerj.18989/ref-29) 2018; 391 GBD 2019 Chronic Respiratory Diseases Collaborators (10.7717/peerj.18989/ref-8) 2023; 59 Chaurasia (10.7717/peerj.18989/ref-4) 2019; 104 Miller (10.7717/peerj.18989/ref-16) 2016; 193 Pandey (10.7717/peerj.18989/ref-19) 2017; 8 R Core Team (10.7717/peerj.18989/ref-24) 2023 Karhunen (10.7717/peerj.18989/ref-10) 2021; 10 Li (10.7717/peerj.18989/ref-13) 2017; 131 Kim (10.7717/peerj.18989/ref-11) 2017; 80 Fan (10.7717/peerj.18989/ref-7) 2023; 59 Labaki (10.7717/peerj.18989/ref-12) 2020; 173 Welte (10.7717/peerj.18989/ref-30) 2014; 111 Yu (10.7717/peerj.18989/ref-32) 2023; 266 Wu (10.7717/peerj.18989/ref-31) 2020; 10 Duvoix (10.7717/peerj.18989/ref-6) 2013; 68 Ritchie (10.7717/peerj.18989/ref-25) 2020; 41 |
| References_xml | – volume: 47 start-page: 427 year: 2011 ident: 10.7717/peerj.18989/ref-15 article-title: High value of combined serum C-reactive protein and BODE score for mortality prediction in patients with stable COPD publication-title: Archivos de Bronconeumología doi: 10.1016/j.arbres.2011.04.011 – volume: 10 start-page: 2045894020948470 year: 2020 ident: 10.7717/peerj.18989/ref-31 article-title: Endothelial platelet-derived growth factor-mediated activation of smooth muscle platelet-derived growth factor receptors in pulmonary arterial hypertension publication-title: Pulmonary Circulation doi: 10.1177/2045894020948470 – year: 2023 ident: 10.7717/peerj.18989/ref-24 article-title: R: a language and environment for statistical computing – volume: 322 start-page: L64 year: 2022 ident: 10.7717/peerj.18989/ref-5 article-title: Pathogenesis, clinical features of asthma COPD overlap, and therapeutic modalities publication-title: American Journal of Physiology-Lung Cellular and Molecular Physiology doi: 10.1152/ajplung.00121.2021 – volume: 7 start-page: e37483 year: 2012 ident: 10.7717/peerj.18989/ref-1 article-title: Persistent systemic inflammation is associated with poor clinical outcomes in COPD: a novel phenotype publication-title: PLOS ONE doi: 10.1371/journal.pone.0037483 – volume: 41 start-page: 421 year: 2020 ident: 10.7717/peerj.18989/ref-25 article-title: Definition, causes, pathogenesis, and consequences of chronic obstructive pulmonary disease exacerbations publication-title: Clinics in Chest Medicine doi: 10.1016/j.ccm.2020.06.007 – volume: 193 start-page: 607 year: 2016 ident: 10.7717/peerj.18989/ref-16 article-title: Plasma fibrinogen qualification as a drug development tool in chronic obstructive pulmonary disease. perspective of the chronic obstructive pulmonary disease biomarker qualification consortium publication-title: American Journal of Respiratory and Critical Care Medicine doi: 10.1164/rccm.201509-1722PP – volume: 14 start-page: 1131292 year: 2023 ident: 10.7717/peerj.18989/ref-3 article-title: Macrophages-derived factor XIII links coagulation to inflammation in COPD publication-title: Frontiers in Immunology doi: 10.3389/fimmu.2023.1131292 – volume: 138 start-page: 16 year: 2016 ident: 10.7717/peerj.18989/ref-2 article-title: Inflammatory mechanisms in patients with chronic obstructive pulmonary disease publication-title: Journal of Allergy and Clinical Immunology doi: 10.1016/j.jaci.2016.05.011 – volume: 2 start-page: 763 year: 2022 ident: 10.7717/peerj.18989/ref-14 article-title: Prognostic impact of systemic coagulation-inflammation index in acute type A aortic dissection surgery publication-title: JACC Asia doi: 10.1016/j.jacasi.2022.06.007 – volume: 8 start-page: 512 year: 2017 ident: 10.7717/peerj.18989/ref-19 article-title: Role of proteases in chronic obstructive pulmonary disease publication-title: Frontiers in Pharmacology doi: 10.3389/fphar.2017.00512 – volume: 391 start-page: 1706 year: 2018 ident: 10.7717/peerj.18989/ref-29 article-title: Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study publication-title: Lancet doi: 10.1016/s0140-6736(18)30841-9 – volume: 68 start-page: 670 year: 2013 ident: 10.7717/peerj.18989/ref-6 article-title: Blood fibrinogen as a biomarker of chronic obstructive pulmonary disease publication-title: Thorax doi: 10.1136/thoraxjnl-2012-201871 – volume: 38 start-page: 100680 issue: 3 year: 2024 ident: 10.7717/peerj.18989/ref-20 article-title: Non-generative artificial intelligence (AI) in medicine: advancements and applications in supervised and unsupervised machine learning publication-title: Modern Pathology doi: 10.1016/j.modpat.2024.100680 – volume: 111 start-page: 825 year: 2014 ident: 10.7717/peerj.18989/ref-30 article-title: Chronic obstructive pulmonary disease- a growing cause of death and disability worldwide publication-title: Deutsches Arzteblatt International doi: 10.3238/arztebl.2014.0825 – volume: 26 start-page: 290 year: 2021 ident: 10.7717/peerj.18989/ref-17 article-title: COPD: cOagulation-associated Pulmonary Disease? publication-title: Respirology doi: 10.1111/resp.14000 – volume: 80 start-page: 313 year: 2017 ident: 10.7717/peerj.18989/ref-11 article-title: Systemic white blood cell count as a biomarker for chronic obstructive pulmonary disease: utility and limitations publication-title: Tuberculosis and Respiratory Diseases doi: 10.4046/trd.2017.80.3.313 – volume: 173 start-page: Itc17 year: 2020 ident: 10.7717/peerj.18989/ref-12 article-title: Chronic obstructive pulmonary disease publication-title: Annals of Internal Medicine doi: 10.7326/aitc202008040 – volume: 11 start-page: e023021 year: 2022 ident: 10.7717/peerj.18989/ref-27 article-title: Platelet-derived growth factor receptor type α activation drives pulmonary vascular remodeling via progenitor cell proliferation and induces pulmonary hypertension publication-title: Journal of the American Heart Association: doi: 10.1161/jaha.121.023021 – volume: 266 start-page: 127244 year: 2023 ident: 10.7717/peerj.18989/ref-32 article-title: The association between the respiratory tract microbiome and clinical outcomes in patients with COPD publication-title: Microbiological Research doi: 10.1016/j.micres.2022.127244 – volume: 178 start-page: 81 year: 2008 ident: 10.7717/peerj.18989/ref-22 article-title: Platelet-derived growth factor expression and function in idiopathic pulmonary arterial hypertension publication-title: American Journal of Respiratory and Critical Care Medicine doi: 10.1164/rccm.200707-1037OC – volume: 104 start-page: 2482 year: 2019 ident: 10.7717/peerj.18989/ref-4 article-title: Platelet HIF-2α promotes thrombogenicity through PAI-1 synthesis and extracellular vesicle release publication-title: Haematologica doi: 10.3324/haematol.2019.217463 – volume: 131 start-page: 2847 year: 2017 ident: 10.7717/peerj.18989/ref-13 article-title: What do polymorphisms tell us about the mechanisms of COPD? publication-title: Clinical Science doi: 10.1042/cs20160718 – volume: 18 start-page: 213 year: 2009 ident: 10.7717/peerj.18989/ref-23 article-title: Epidemiology of COPD publication-title: European Respiratory Review doi: 10.1183/09059180.00003609 – volume: 59 start-page: 101936 year: 2023 ident: 10.7717/peerj.18989/ref-8 article-title: Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019 publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2023.101936 – volume: 59 start-page: 102587 year: 2023 ident: 10.7717/peerj.18989/ref-7 article-title: PM2.5 increases susceptibility to acute exacerbation of COPD via NOX4/Nrf2 redox imbalance-mediated mitophagy publication-title: Redox Biology doi: 10.1016/j.redox.2022.102587 – volume: 26 start-page: 275 year: 2023 ident: 10.7717/peerj.18989/ref-28 article-title: Animal models and mechanisms of tobacco smoke-induced chronic obstructive pulmonary disease (COPD) publication-title: Journal of Toxicology and Environmental Health: Part B, Critical Reviews doi: 10.1080/10937404.2023.2208886 – volume: 63 start-page: 72 year: 2023 ident: 10.7717/peerj.18989/ref-33 article-title: Systemic coagulation inflammation index associated with bleeding in acute coronary syndrome publication-title: Kardiologiia doi: 10.18087/cardio.2023.10.n2586 – volume: 67 start-page: 179 year: 2018 ident: 10.7717/peerj.18989/ref-9 article-title: Asthma and COPD overlap pathophysiology of ACO publication-title: Allergology International doi: 10.1016/j.alit.2018.01.001 – volume: 10 start-page: e022277 year: 2021 ident: 10.7717/peerj.18989/ref-10 article-title: Modifiable risk factors for intracranial aneurysm and aneurysmal subarachnoid hemorrhage: a mendelian randomization study publication-title: Journal of the American Heart Association: doi: 10.1161/jaha.121.022277 – volume: 16 start-page: 78 year: 2015 ident: 10.7717/peerj.18989/ref-21 article-title: Acute exacerbations of COPD are associated with significant activation of matrix metalloproteinase 9 irrespectively of airway obstruction, emphysema and infection publication-title: Respiratory Research doi: 10.1186/s12931-015-0240-4 – volume: 52 start-page: 36 year: 2024 ident: 10.7717/peerj.18989/ref-18 article-title: A novel potential biomarker for predicting the development of septic embolism in patients with infective endocarditis: systemic coagulation inflammation index publication-title: Turk Kardiyoloji Derneği Arşivi doi: 10.5543/tkda.2023.30344 – volume: 21 start-page: 53 year: 2024 ident: 10.7717/peerj.18989/ref-26 article-title: Acute airway inflammation following controlled biodiesel exhaust exposure in healthy subjects publication-title: Particle and Fibre Toxicology doi: 10.1186/s12989-024-00614-5 |
| SSID | ssj0000826083 |
| Score | 2.3526757 |
| Snippet | Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease (COPD),... Background Inflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease... BackgroundInflammatory response and the coagulation system are pivotal in the pathogenesis of clinical deterioration in chronic obstructive pulmonary disease... |
| SourceID | doaj unpaywall proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | e18989 |
| SubjectTerms | Aged Blood clotting Blood Coagulation C-reactive protein Cardiac arrhythmia Cardiovascular disease Cell number Chronic illnesses Chronic obstructive pulmonary disease Clinical deterioration Coagulation Comorbidity Coronary vessels Development and progression Diabetes Disease Progression Erythrocytes Female Health aspects Hematology Hemoglobin Hospitalization Hospitals Humans Hypertension Inflammation Inflammation - blood Learning algorithms Lung diseases Lung diseases, Obstructive Lymphocytes Machine Learning Male Medical research Medicine, Experimental Methods Middle Aged Mortality Patients Physiological aspects Predictor Procalcitonin Prognosis Pulmonary Disease, Chronic Obstructive - blood Pulmonary Disease, Chronic Obstructive - diagnosis Pulmonary Disease, Chronic Obstructive - physiopathology Python Regression analysis Respiratory failure Retrospective Studies Statistical analysis Support vector machines Systemic coagulation-inflammation index Variables Vein & artery diseases |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQD8AF8SZQYJCKOEXNw4ltbgVRVUhFHKjUW2Q7dlVpm6zCLmh_Jv-IGdsb7QoJLtyi9STaeL7MI5n5hrGjxjhZW1fnXmNugh7D5OjHVd7LxqrCWNX29L7j_Et7dsE_XzaXO6O-qCYs0gPHjTvG_KMQpnROcc-d17J3ldIC0wx0bMqHbL2QaieZCjYYo2YMLmJDnsCU5Xjp3IR2gaYl7rmgwNT_pz3ecUh31sNSb37qxWLH85zeZ_dSyAgn8a8-YLfc8JDdPk8fxR-xX18nOqYdhtHDttcReqp0uU4qBnrheo0Cg4MNohvw2EZiXBhNYpH94WC5XiAw9bSB9OkGqDL-CjBOhMj6jCfYUV-lsV85IhRBFRsgIXAvvgcNk1tN47aLEwKHLbgwXJgutq1ihJtQyukgza7AlTDQ-jG7OP307eNZniY15JaLdoWKdgUvpbDoEivppW8o0NCVVXXN-9ZrrhupJW9kK0svlC-rXnvjfUM8iMLUT9jBgBvwjAFHnyocXUwbbpRVpeatL62wDnPZvszY0VZ53TIScnSYyJCOu6DjLug4Yx9IsbMIsWiHHxBbXcJW9y9sZew1waKLLamzLehOaEpAVbcSJd4FCbIGq0lbnZoa8F6IV2tP8nBPEp9iu7-8hV6XrMj3riZ-uhYjbJGxN_MynUmVcYMb1yhTipqTKeUZexohO980Dxl1gXv2dsbw3zbt-f_YtBfsbkUzkqntvzlkBwhg9xIDt5V5FZ7R31gQRkY priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3da9RAEF_qFdQX8bNGq45Q8Sk0H5vsRhBppaUIPYpY6FvYbHaPwpnEeKfcn-l_5MxmE3sIfQvZSUh2Zudjd-Y3jB1klZGpNmloFcYmaDGqEO14EdYy00VU6SKvab_jfJ6fXfIvV9nVDpuPtTCUVjnqRKeo61bTHvlhSlhdOXob4lP3I6SuUXS6OrbQUL61Qv3RQYzdYbsJIWPN2O7xyfzi67TrggYvR6djKNQTGMocdsb0qC-oi-KWaXII_v_r6RuG6t666dTmt1oub1ik04fsgXcl4Wjg_SO2Y5rH7O65Pyx_wv5c9HRNMw-thbEGEmrKgLn2rAfaiL1GgsbABqUe8FoPgLnQVh5d9peBbr3EGVD9BvyRDlDG_ALQf4QBDRof0K1a-HZgIUouCttQGAkOk_EDKOjNqm_H6k5w2LZgXNNhetmY3QjfXYqnAd_TAkdco-un7PL05Nvns9B3cAg1F_kKBcBEPJZCo6lMpJU2IwdEJbpIU17nVnGVSSV5JnMZW1HYOKmVrazNCB9RVOkzNmtwAp4z4GhrhaGXqYpXhS5ixXMba6ENxrh1HLCDkXllNwB1lBjgEI9Lx-PS8Thgx8TYiYTQtd2Ntl-UfrGWGPNGooqNKbjlxipZm6RQAkNbdKYKGwXsDYlFOZSqTjqiPKLuAUmaS6R47yhIS6x6pZUvdsB_IbytLcr9LUpc3Xp7eBS90muXn-W_tRCwt9MwPUkZc41p10gTi5STiuUB2xtEdvpp7iLtCOfs3STDt03ai9s_4iW7n1BXZCr0z_bZDEXTvEJXbVW99uvvL14mRIs priority: 102 providerName: ProQuest |
| Title | Prediction of clinical deterioration within one year in chronic obstructive pulmonary disease using the systemic coagulation-inflammation index: a retrospective study employing multiple machine learning method |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40028201 https://www.proquest.com/docview/3239369327 https://www.proquest.com/docview/3173400534 https://doi.org/10.7717/peerj.18989 https://doaj.org/article/40907b1ee94f4efa8de29a71912239f0 |
| UnpaywallVersion | publishedVersion |
| Volume | 13 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: KQ8 dateStart: 20130101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVBFR databaseName: Free Medical Journals customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: DIK dateStart: 20130101 isFulltext: true titleUrlDefault: http://www.freemedicaljournals.com providerName: Flying Publisher – providerCode: PRVFQY databaseName: GFMER Free Medical Journals customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: GX1 dateStart: 20130101 isFulltext: true titleUrlDefault: http://www.gfmer.ch/Medical_journals/Free_medical.php providerName: Geneva Foundation for Medical Education and Research – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVAQN databaseName: PubMed Central customDbUrl: eissn: 2167-8359 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: RPM dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.ncbi.nlm.nih.gov/pmc/ providerName: National Library of Medicine – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 2167-8359 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000826083 issn: 2167-8359 databaseCode: BENPR dateStart: 20130212 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fb9MwELZGKwEv_B4URjFiiKeMJnFim7cONiakVRWiUnmKbMeeJkpThRZU_kv-I-4cp2qHBLxUUX2JEvuz786--46Qw0xbkRqbRk6BbwIaQ0egx2VUiszIgTYyL3G_43yUn03Yh2k23SMv2lyYrfN7Dp7G64W1NUxnLHJ4jXTzDAzuDulORuPhZywbh6zdYEPIJvPu6h07usZT8v-58G5pnhur-UKtf6jZbEvFnN4m79qXayJLvhytlvrI_LzC2_iPt79DbgUTkw4bTNwle3Z-j1w_D4fo98mvcY3XOCK0crTNjaQlRsZcBkhQ3KC9BIG5pWuYDRSuTUOkSysdWGe_W7pYzQDIql7TcNRDMZL-goJdSRuWaLjBVOoilAmLANEAwiZhknquxjdU0dou66rN-qSe85ZaX4wYH9ZGPdKvPvTT0lDrAlp8AewHZHJ68untWRQqO0SG8XwJwLADFgtuQIUmwgmXoWGiEiPTlJW5U0xlQgmWiVzEjksXJ6Vy2rkMeRO5TvdJZw4d8IhQBjqYW3yY0kxLI2PFchcbbiz4vmXcI4ctBopFQ-BRgOODw1P44Sn88PTIMeJjI4Ks2_4PGM4iTOICfOEB17G1kjlmnRKlTaTi4PKCkSXdoEeeIbqKJoV1s3YUQ6wqkKS5AIlXXgJXj2WtjApJEPAtyMO1I3mwIwmz3uw2twguwqrzrUiRzy4Hi5z3yPNNM96JkXRzW61AJuYpw6WX9cjDBvmbj2beAx9An73cTIW_ddrj_5R7Qm4mWDYZmQCyA9IBjNqnYMstdZ90j09G4499vxcCv--ncT_M7982sVB2 |
| linkProvider | Unpaywall |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwELYoSKWXqu9uS4srgXqKyMOJnUqogha0FHaFKpC4GcexV0jbzTbsFu2f63_oP-qM46SsKnHjFiUTK_GM52HPzEfIVloYkWiTBFZBbAIWowjAjudBKVKdh4XOsxL3OwbDrH_Ovl2kFyvkd1sLg2mVrU50irqsNO6R7yTYqysDb4N_nv4MEDUKT1dbCA3loRXKXddizBd2HJvFDYRw17tHX4Hf23F8eHD2pR94lIFAM57N4CNNyCLBNajzWFhhUzSSKtYQ6rMys4qpVCjBUpGJyPLcRnGpbGFtij38eJHAuA_IGktYDsHf2v7B8PR7t8sDBjYDJ6cpDOQQOu1MjalBPyFq45IpdIgB_9uFW4ZxfT6ZqsWNGo9vWcDDJ-Sxd13pXiNrT8mKmTwjDwf-cP45-XNa4zVymlaWtjWXtMSMmysvahQ3fq-AYGLoAqaPwrVuGvTSqvDdbH8ZOp2PYcZVvaD-CIlihv6Igr9Km-7T8IKu1MjDjwWwUkC4m0JM6npAfqKK1mZWV201KXW9dKlxIMc4WJtNSX-4lFJDPYYGPHHA2i_I-b3w8iVZncAEvCaUgW3nBgdTBStynUeKZTbSXBuIqcuoR7Za5slp0xhEQkCFPJaOx9LxuEf2kbEdCXbzdjeqeiS9cpAQY4e8iIzJmWXGKlGaOFccQmlw3nIb9sgmioVsSmM7nST3EK0gTjIBFB8dBWqlWa208sUV8C_Y32uJcmOJErSJXn7cip702uxa_lt7PfKhe4xvYobexFRzoIl4wlClsx551Yhs99PMRfYhzNl2J8N3Tdqbuz9ik6z3zwYn8uRoePyWPIoRkRmbDKQbZBXE1LwDN3FWvPdrkZLL-17-fwFVN4DH |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELfGJg1eEN8UBjPSJp6i5sOJHaQJbWzVxlhVISbtzTiOXU0qTclapv6LvPEfcec4YRXS3vYWJRcr8Z3vw777HSE7aWFEok0SWAWxCViMIgA7ngelSHUeFjrPStzvOBtmx-fs80V6sUZ-t7UwmFbZ6kSnqMtK4x55P0Gsrgy8Dd63Pi1idDj4OPsZYAcpPGlt22ko32ah3HNwY77I49QsryGcu9o7OQTe78bx4Ojbp-PAdxwINOPZHD7YhCwSXINqj4UVNkWDqWINYT8rM6uYSoUSLBWZiCzPbRSXyhbWpojnx4sExr1HNvDwC5TExsHRcPS12_EBY5uBw9MUCXIIo_ozY2rQVdjBccUsuu4B_9uIG0by_mI6U8trNZncsIaDR-Shd2PpfiN3j8mamT4hm2f-oP4p-TOq8Rq5TitL2_pLWmL2zaUXO4qbwJdAMDV0CdNH4Vo3YL20Kjyy7S9DZ4sJzLiql9QfJ1HM1h9T8F1pg0QNL-hKjX0rsgBWDQh6U5RJHR7kB6pobeZ11VaWUoerS41reIyDtZmV9IdLLzXU99OAJ67J9jNyfie8fE7WpzABLwllYOe5wcFUwYpc55FimY001wbi6zLqkZ2WeXLWgIRICK6Qx9LxWDoe98gBMrYjQWRvd6Oqx9IrCgnxdsiLyJicWWasEqWJc8UhrAZHLrdhj2yjWMimTLbTT3IfOxfESSaA4r2jQA01r5VWvtAC_gWxvlYot1YoQbPo1cet6Emv2a7kv3XYI--6x_gmZutNTbUAmognDNU765EXjch2P81clB_CnO12MnzbpL26_SO2ySaoAfnlZHj6mjyIsTkz4g2kW2QdpNS8AY9xXrz1S5GS73e9-v8C9YaE9g |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwELdGJwEvfH8UBhxiiKeMJnFim7fyMU1Im_ZApfEU2Y49TZS0Ci2o_Jf8R9w5TtUOCXiL4nOU2D_77uK73zG2Xxgnc-vyxGv0TVBjmAT1uEpqWVg1MlaVNf3vOD4pjyb841lxtsNe9LkwG-f3Aj2N13PnWlzOVOTwCtstCzS4B2x3cnI6_kxl44i1G20I1WXeXe6xpWsCJf-fG--G5rm2bOZ69UNPpxsq5vAme9-_XBdZ8uVguTAH9ucl3sZ_vP0tdiOamDDuMHGb7bjmDrt6HA_R77Jfpy1d04zAzEOfGwk1RcZcREgA_aC9QIHGwQpXA-C17Yh0YWYi6-x3B_PlFIGs2xXEox6gSPpzQLsSOpZo7GBn-jyWCUsQ0QjCLmESAlfjG9DQukU767M-IXDeggvFiOlhfdQjfA2hnw5irQtsCQWw77HJ4YdP746SWNkhsVyUCwSGG_FUCosqNJNe-oIME51Zlee8Lr3mupBa8kKWMvVC-TSrtTfeF8SbKEx-nw0aHICHDDjqYOHoYdpwo6xKNS99aoV16PvW6ZDt9xio5h2BR4WOD01PFaanCtMzZG8JH2sRYt0ON3A6q7iIK_SFR8KkzinuufNa1i5TWqDLi0aW8qMhe0boqroU1vXeUY2pqkCWlxIlXgUJ2j0WrbY6JkHgtxAP15bk3pYkrnq73dwjuIq7zrcqJz67Ei1yMWTP183UkyLpGjdbokwqck5bLx-yBx3y1x_Ngwc-wjF7uV4Kfxu0R_8p95hdz6hsMjEBFHtsgBh1T9CWW5incS3_BhXATPY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Prediction+of+clinical+deterioration+within+one+year+in+chronic+obstructive+pulmonary+disease+using+the+systemic+coagulation-inflammation+index%3A+a+retrospective+study+employing+multiple+machine+learning+method&rft.jtitle=PeerJ+%28San+Francisco%2C+CA%29&rft.au=Hou%2C+Ling&rft.au=Min%2C+Ming&rft.au=Hou%2C+Rui&rft.au=Tan%2C+Wei&rft.date=2025-02-25&rft.issn=2167-8359&rft.eissn=2167-8359&rft.volume=13&rft.spage=e18989&rft_id=info:doi/10.7717%2Fpeerj.18989&rft.externalDBID=n%2Fa&rft.externalDocID=10_7717_peerj_18989 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2167-8359&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2167-8359&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2167-8359&client=summon |