A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study
Acute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related...
Gespeichert in:
| Veröffentlicht in: | PloS one 2016-11, Vol.11 (11), p.e0165280-e0165280 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0165280 |
|---|---|
| container_issue | 11 |
| container_start_page | e0165280 |
| container_title | PloS one |
| container_volume | 11 |
| creator | Liu, Xing Ye, Yongkai Mi, Qi Huang, Wei He, Ting Huang, Pin Xu, Nana Wu, Qiaoyu Wang, Anli Li, Ying Yuan, Hong |
| description | Acute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors.
This retrospective cohort study included 24,451 hypertensive patients aged ≥18 years who underwent general surgery between 2007 and 2015. The endpoints for AKI classification utilized by the KDIGO (Kidney Disease: Improving Global Outcomes) system were assessed. The most discriminative predictor was selected using Fisher scores and was subsequently used to construct a stepwise multivariate logistic regression model, whose performance was evaluated via comparisons with models used in other published works using the net reclassification index (NRI) and integrated discrimination improvement (IDI) index.
Surgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86-0.89) and (0.89; 95% CI 0.88-0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88-0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101).
We developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery. |
| doi_str_mv | 10.1371/journal.pone.0165280 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1834921038</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A471797701</galeid><doaj_id>oai_doaj_org_article_99141aa3ca09407dbc598b05673aa613</doaj_id><sourcerecordid>A471797701</sourcerecordid><originalsourceid>FETCH-LOGICAL-c795t-652f9aed3f4b5f5eec1261e8161c11f227a779df53b5d417a37feeb8a897d4b03</originalsourceid><addsrcrecordid>eNqNk1Fv0zAQxyMEYmPwDRBYQkLw0GLHSRzzgBRVwCqGNrXAq-XYl9YljYvtTPSNj45Du6lFe5j8EMv53f_u_vYlyXOCx4Qy8m5le9fJdryxHYwxKfK0xA-SU8JpOipSTB8e7E-SJ96vMM5pWRSPk5OUlTilOD1N_lToyoE2KphrQF-thhY11qHKe_DedAs0790C3HY0g1YG0KhSfQD0xegOtmjarXq3RTPjfyLTofPtBlyAzg9iVzIY6IJ_jyo0g-Cs38AuzcQurQtoHnq9fZo8amTr4dn-e5Z8__Tx2-R8dHH5eTqpLkaK8TyMYnsNl6Bpk9V5kwMokhYESlIQRUiTpkwyxnWT0zrXGWGSsgagLmXJmc5qTM-SlzvdTWu92JvnBSlpxlOCaRmJ6Y7QVq7Expm1dFthpRH_DqxbCOmCUS0IzklGpKRKYp5hpmuV87LGecGolAWhUevDPltfr0Gr6IOT7ZHo8Z_OLMXCXoscx4IZjwJv9gLO_urBB7E2XkHbyg5sP9SdFZSXWUbugcYWOaPFYMKr_9C7jdhTCxl7NV1jY4lqEBVVxgiLBeIh7fgOKi4Na6Pio2xMPD8KeHsUEJkAv8NC9t6L6Xx2f_byxzH7-oBdgmzD0tu2D8Z2_hjMdqCKb9E7aG7vg2AxzNSNG2KYKbGfqRj24vAub4Nuhoj-BckgHB4</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1834921038</pqid></control><display><type>article</type><title>A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><source>Public Library of Science (PLoS)</source><creator>Liu, Xing ; Ye, Yongkai ; Mi, Qi ; Huang, Wei ; He, Ting ; Huang, Pin ; Xu, Nana ; Wu, Qiaoyu ; Wang, Anli ; Li, Ying ; Yuan, Hong</creator><creatorcontrib>Liu, Xing ; Ye, Yongkai ; Mi, Qi ; Huang, Wei ; He, Ting ; Huang, Pin ; Xu, Nana ; Wu, Qiaoyu ; Wang, Anli ; Li, Ying ; Yuan, Hong</creatorcontrib><description>Acute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors.
This retrospective cohort study included 24,451 hypertensive patients aged ≥18 years who underwent general surgery between 2007 and 2015. The endpoints for AKI classification utilized by the KDIGO (Kidney Disease: Improving Global Outcomes) system were assessed. The most discriminative predictor was selected using Fisher scores and was subsequently used to construct a stepwise multivariate logistic regression model, whose performance was evaluated via comparisons with models used in other published works using the net reclassification index (NRI) and integrated discrimination improvement (IDI) index.
Surgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86-0.89) and (0.89; 95% CI 0.88-0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88-0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101).
We developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0165280</identifier><identifier>PMID: 27802302</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acute Kidney Injury - blood ; Acute Kidney Injury - etiology ; Adult ; Aged ; Albumin ; Area Under Curve ; Aspartate ; Biomarkers ; Biomarkers - blood ; Blood tests ; Cardiology ; Cardiovascular disease ; Cholesterol ; Chronic illnesses ; Cohort analysis ; Cytokines ; Diabetes ; Epidermal growth factor receptors ; Family medical history ; Female ; Health aspects ; Health risks ; Heart diseases ; Heart failure ; Heart surgery ; Hemoglobin ; Hemoglobins ; Hospital patients ; Hospitalization ; Hospitals ; Humans ; Hypertension ; Hypertension - blood ; Hypertension - complications ; Hypertension - surgery ; Kidney diseases ; Kidneys ; Laboratories ; Male ; Medical research ; Medicine and Health Sciences ; Middle Aged ; Neutrophils ; Patients ; Performance prediction ; Pharmacology ; Physical Sciences ; Postoperative Complications - blood ; Postoperative Complications - etiology ; Potassium ; Prediction models ; Prothrombin ; Reclassification ; Regression models ; Research and Analysis Methods ; Retrospective Studies ; Risk analysis ; Risk assessment ; Risk Factors ; ROC Curve ; Serum albumin ; Surgery ; Surgical Procedures, Operative - adverse effects ; Thrombin ; Treatment Outcome ; Uric acid ; Urine</subject><ispartof>PloS one, 2016-11, Vol.11 (11), p.e0165280-e0165280</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2016 Liu et al 2016 Liu et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c795t-652f9aed3f4b5f5eec1261e8161c11f227a779df53b5d417a37feeb8a897d4b03</citedby><cites>FETCH-LOGICAL-c795t-652f9aed3f4b5f5eec1261e8161c11f227a779df53b5d417a37feeb8a897d4b03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089779/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089779/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27802302$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Xing</creatorcontrib><creatorcontrib>Ye, Yongkai</creatorcontrib><creatorcontrib>Mi, Qi</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>He, Ting</creatorcontrib><creatorcontrib>Huang, Pin</creatorcontrib><creatorcontrib>Xu, Nana</creatorcontrib><creatorcontrib>Wu, Qiaoyu</creatorcontrib><creatorcontrib>Wang, Anli</creatorcontrib><creatorcontrib>Li, Ying</creatorcontrib><creatorcontrib>Yuan, Hong</creatorcontrib><title>A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Acute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors.
This retrospective cohort study included 24,451 hypertensive patients aged ≥18 years who underwent general surgery between 2007 and 2015. The endpoints for AKI classification utilized by the KDIGO (Kidney Disease: Improving Global Outcomes) system were assessed. The most discriminative predictor was selected using Fisher scores and was subsequently used to construct a stepwise multivariate logistic regression model, whose performance was evaluated via comparisons with models used in other published works using the net reclassification index (NRI) and integrated discrimination improvement (IDI) index.
Surgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86-0.89) and (0.89; 95% CI 0.88-0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88-0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101).
We developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery.</description><subject>Acute Kidney Injury - blood</subject><subject>Acute Kidney Injury - etiology</subject><subject>Adult</subject><subject>Aged</subject><subject>Albumin</subject><subject>Area Under Curve</subject><subject>Aspartate</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Blood tests</subject><subject>Cardiology</subject><subject>Cardiovascular disease</subject><subject>Cholesterol</subject><subject>Chronic illnesses</subject><subject>Cohort analysis</subject><subject>Cytokines</subject><subject>Diabetes</subject><subject>Epidermal growth factor receptors</subject><subject>Family medical history</subject><subject>Female</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Heart diseases</subject><subject>Heart failure</subject><subject>Heart surgery</subject><subject>Hemoglobin</subject><subject>Hemoglobins</subject><subject>Hospital patients</subject><subject>Hospitalization</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Hypertension - blood</subject><subject>Hypertension - complications</subject><subject>Hypertension - surgery</subject><subject>Kidney diseases</subject><subject>Kidneys</subject><subject>Laboratories</subject><subject>Male</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Neutrophils</subject><subject>Patients</subject><subject>Performance prediction</subject><subject>Pharmacology</subject><subject>Physical Sciences</subject><subject>Postoperative Complications - blood</subject><subject>Postoperative Complications - etiology</subject><subject>Potassium</subject><subject>Prediction models</subject><subject>Prothrombin</subject><subject>Reclassification</subject><subject>Regression models</subject><subject>Research and Analysis Methods</subject><subject>Retrospective Studies</subject><subject>Risk analysis</subject><subject>Risk assessment</subject><subject>Risk Factors</subject><subject>ROC Curve</subject><subject>Serum albumin</subject><subject>Surgery</subject><subject>Surgical Procedures, Operative - adverse effects</subject><subject>Thrombin</subject><subject>Treatment Outcome</subject><subject>Uric acid</subject><subject>Urine</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk1Fv0zAQxyMEYmPwDRBYQkLw0GLHSRzzgBRVwCqGNrXAq-XYl9YljYvtTPSNj45Du6lFe5j8EMv53f_u_vYlyXOCx4Qy8m5le9fJdryxHYwxKfK0xA-SU8JpOipSTB8e7E-SJ96vMM5pWRSPk5OUlTilOD1N_lToyoE2KphrQF-thhY11qHKe_DedAs0790C3HY0g1YG0KhSfQD0xegOtmjarXq3RTPjfyLTofPtBlyAzg9iVzIY6IJ_jyo0g-Cs38AuzcQurQtoHnq9fZo8amTr4dn-e5Z8__Tx2-R8dHH5eTqpLkaK8TyMYnsNl6Bpk9V5kwMokhYESlIQRUiTpkwyxnWT0zrXGWGSsgagLmXJmc5qTM-SlzvdTWu92JvnBSlpxlOCaRmJ6Y7QVq7Expm1dFthpRH_DqxbCOmCUS0IzklGpKRKYp5hpmuV87LGecGolAWhUevDPltfr0Gr6IOT7ZHo8Z_OLMXCXoscx4IZjwJv9gLO_urBB7E2XkHbyg5sP9SdFZSXWUbugcYWOaPFYMKr_9C7jdhTCxl7NV1jY4lqEBVVxgiLBeIh7fgOKi4Na6Pio2xMPD8KeHsUEJkAv8NC9t6L6Xx2f_byxzH7-oBdgmzD0tu2D8Z2_hjMdqCKb9E7aG7vg2AxzNSNG2KYKbGfqRj24vAub4Nuhoj-BckgHB4</recordid><startdate>20161101</startdate><enddate>20161101</enddate><creator>Liu, Xing</creator><creator>Ye, Yongkai</creator><creator>Mi, Qi</creator><creator>Huang, Wei</creator><creator>He, Ting</creator><creator>Huang, Pin</creator><creator>Xu, Nana</creator><creator>Wu, Qiaoyu</creator><creator>Wang, Anli</creator><creator>Li, Ying</creator><creator>Yuan, Hong</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20161101</creationdate><title>A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study</title><author>Liu, Xing ; Ye, Yongkai ; Mi, Qi ; Huang, Wei ; He, Ting ; Huang, Pin ; Xu, Nana ; Wu, Qiaoyu ; Wang, Anli ; Li, Ying ; Yuan, Hong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c795t-652f9aed3f4b5f5eec1261e8161c11f227a779df53b5d417a37feeb8a897d4b03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Acute Kidney Injury - blood</topic><topic>Acute Kidney Injury - etiology</topic><topic>Adult</topic><topic>Aged</topic><topic>Albumin</topic><topic>Area Under Curve</topic><topic>Aspartate</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Blood tests</topic><topic>Cardiology</topic><topic>Cardiovascular disease</topic><topic>Cholesterol</topic><topic>Chronic illnesses</topic><topic>Cohort analysis</topic><topic>Cytokines</topic><topic>Diabetes</topic><topic>Epidermal growth factor receptors</topic><topic>Family medical history</topic><topic>Female</topic><topic>Health aspects</topic><topic>Health risks</topic><topic>Heart diseases</topic><topic>Heart failure</topic><topic>Heart surgery</topic><topic>Hemoglobin</topic><topic>Hemoglobins</topic><topic>Hospital patients</topic><topic>Hospitalization</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Hypertension - blood</topic><topic>Hypertension - complications</topic><topic>Hypertension - surgery</topic><topic>Kidney diseases</topic><topic>Kidneys</topic><topic>Laboratories</topic><topic>Male</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Neutrophils</topic><topic>Patients</topic><topic>Performance prediction</topic><topic>Pharmacology</topic><topic>Physical Sciences</topic><topic>Postoperative Complications - blood</topic><topic>Postoperative Complications - etiology</topic><topic>Potassium</topic><topic>Prediction models</topic><topic>Prothrombin</topic><topic>Reclassification</topic><topic>Regression models</topic><topic>Research and Analysis Methods</topic><topic>Retrospective Studies</topic><topic>Risk analysis</topic><topic>Risk assessment</topic><topic>Risk Factors</topic><topic>ROC Curve</topic><topic>Serum albumin</topic><topic>Surgery</topic><topic>Surgical Procedures, Operative - adverse effects</topic><topic>Thrombin</topic><topic>Treatment Outcome</topic><topic>Uric acid</topic><topic>Urine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Xing</creatorcontrib><creatorcontrib>Ye, Yongkai</creatorcontrib><creatorcontrib>Mi, Qi</creatorcontrib><creatorcontrib>Huang, Wei</creatorcontrib><creatorcontrib>He, Ting</creatorcontrib><creatorcontrib>Huang, Pin</creatorcontrib><creatorcontrib>Xu, Nana</creatorcontrib><creatorcontrib>Wu, Qiaoyu</creatorcontrib><creatorcontrib>Wang, Anli</creatorcontrib><creatorcontrib>Li, Ying</creatorcontrib><creatorcontrib>Yuan, Hong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Xing</au><au>Ye, Yongkai</au><au>Mi, Qi</au><au>Huang, Wei</au><au>He, Ting</au><au>Huang, Pin</au><au>Xu, Nana</au><au>Wu, Qiaoyu</au><au>Wang, Anli</au><au>Li, Ying</au><au>Yuan, Hong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2016-11-01</date><risdate>2016</risdate><volume>11</volume><issue>11</issue><spage>e0165280</spage><epage>e0165280</epage><pages>e0165280-e0165280</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Acute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors.
This retrospective cohort study included 24,451 hypertensive patients aged ≥18 years who underwent general surgery between 2007 and 2015. The endpoints for AKI classification utilized by the KDIGO (Kidney Disease: Improving Global Outcomes) system were assessed. The most discriminative predictor was selected using Fisher scores and was subsequently used to construct a stepwise multivariate logistic regression model, whose performance was evaluated via comparisons with models used in other published works using the net reclassification index (NRI) and integrated discrimination improvement (IDI) index.
Surgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86-0.89) and (0.89; 95% CI 0.88-0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88-0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101).
We developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27802302</pmid><doi>10.1371/journal.pone.0165280</doi><tpages>e0165280</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2016-11, Vol.11 (11), p.e0165280-e0165280 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1834921038 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Acute Kidney Injury - blood Acute Kidney Injury - etiology Adult Aged Albumin Area Under Curve Aspartate Biomarkers Biomarkers - blood Blood tests Cardiology Cardiovascular disease Cholesterol Chronic illnesses Cohort analysis Cytokines Diabetes Epidermal growth factor receptors Family medical history Female Health aspects Health risks Heart diseases Heart failure Heart surgery Hemoglobin Hemoglobins Hospital patients Hospitalization Hospitals Humans Hypertension Hypertension - blood Hypertension - complications Hypertension - surgery Kidney diseases Kidneys Laboratories Male Medical research Medicine and Health Sciences Middle Aged Neutrophils Patients Performance prediction Pharmacology Physical Sciences Postoperative Complications - blood Postoperative Complications - etiology Potassium Prediction models Prothrombin Reclassification Regression models Research and Analysis Methods Retrospective Studies Risk analysis Risk assessment Risk Factors ROC Curve Serum albumin Surgery Surgical Procedures, Operative - adverse effects Thrombin Treatment Outcome Uric acid Urine |
| title | A Predictive Model for Assessing Surgery-Related Acute Kidney Injury Risk in Hypertensive Patients: A Retrospective Cohort Study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T22%3A40%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Predictive%20Model%20for%20Assessing%20Surgery-Related%20Acute%20Kidney%20Injury%20Risk%20in%20Hypertensive%20Patients:%20A%20Retrospective%20Cohort%20Study&rft.jtitle=PloS%20one&rft.au=Liu,%20Xing&rft.date=2016-11-01&rft.volume=11&rft.issue=11&rft.spage=e0165280&rft.epage=e0165280&rft.pages=e0165280-e0165280&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0165280&rft_dat=%3Cgale_plos_%3EA471797701%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1834921038&rft_id=info:pmid/27802302&rft_galeid=A471797701&rft_doaj_id=oai_doaj_org_article_99141aa3ca09407dbc598b05673aa613&rfr_iscdi=true |