Prognostic model of in-hospital ischemic stroke mortality based on an electronic health record cohort in Indonesia
Stroke patients rarely have satisfactory survival, which worsens further if comorbidities develop in such patients. Limited data availability from Southeast Asian countries, especially Indonesia, has impeded the disentanglement of post-stroke mortality determinants. This study aimed to investigate p...
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| Veröffentlicht in: | PloS one 2024-06, Vol.19 (6), p.e0305100 |
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| description | Stroke patients rarely have satisfactory survival, which worsens further if comorbidities develop in such patients. Limited data availability from Southeast Asian countries, especially Indonesia, has impeded the disentanglement of post-stroke mortality determinants. This study aimed to investigate predictors of in-hospital mortality in patients with ischemic stroke (IS). This retrospective observational study used IS medical records from the National Brain Centre Hospital, Jakarta, Indonesia. A theoretically driven Cox's regression and Fine-Gray models were established by controlling for age and sex to calculate the hazard ratio of each plausible risk factor for predicting in-hospital stroke mortality and addressing competing risks if they existed. This study finally included 3,278 patients with IS, 917 (28%) of whom had cardiovascular disease and 376 (11.5%) suffered renal disease. Bivariate exploratory analysis revealed lower blood levels of triglycerides, low density lipoprotein, and total cholesterol associated with in-hospital-stroke mortality. The average age of patients with post-stroke mortality was 64.06 ± 11.32 years, with a mean body mass index (BMI) of 23.77 kg/m2 and a median Glasgow Coma Scale (GCS) score of 12 and an IQR of 5. Cardiovascular disease was significantly associated with IS mortality risk. NIHSS score at admission (hazard ratio [HR] = 1.04; 95% confidence interval [CI]: 1.00-1.07), male sex (HR = 1.51[1.01-2.26] and uric acid level (HR = 1.02 [1.00-1.03]) predicted survivability. Comorbidities, such as cardiovascular disease (HR = 2.16 [1.37-3.40], pneumonia (HR = 2.43 [1.42-4.15] and sepsis (HR = 2.07 [1.09-3.94, had higher hazards for post-stroke mortality. Contrarily, the factors contributing to a lower hazard of mortality were BMI (HR = 0.94 [0.89-0.99]) and GCS (HReye = 0.66 [0.48-0.89]. In summary, our study reported that male sex, NIHSS, uric acid level, cardiovascular diseases, pneumonia, sepsis. BMI, and GCS on admission were strong determinants of in-hospital mortality in patients with IS. |
| doi_str_mv | 10.1371/journal.pone.0305100 |
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Limited data availability from Southeast Asian countries, especially Indonesia, has impeded the disentanglement of post-stroke mortality determinants. This study aimed to investigate predictors of in-hospital mortality in patients with ischemic stroke (IS). This retrospective observational study used IS medical records from the National Brain Centre Hospital, Jakarta, Indonesia. A theoretically driven Cox's regression and Fine-Gray models were established by controlling for age and sex to calculate the hazard ratio of each plausible risk factor for predicting in-hospital stroke mortality and addressing competing risks if they existed. This study finally included 3,278 patients with IS, 917 (28%) of whom had cardiovascular disease and 376 (11.5%) suffered renal disease. Bivariate exploratory analysis revealed lower blood levels of triglycerides, low density lipoprotein, and total cholesterol associated with in-hospital-stroke mortality. The average age of patients with post-stroke mortality was 64.06 ± 11.32 years, with a mean body mass index (BMI) of 23.77 kg/m2 and a median Glasgow Coma Scale (GCS) score of 12 and an IQR of 5. Cardiovascular disease was significantly associated with IS mortality risk. NIHSS score at admission (hazard ratio [HR] = 1.04; 95% confidence interval [CI]: 1.00-1.07), male sex (HR = 1.51[1.01-2.26] and uric acid level (HR = 1.02 [1.00-1.03]) predicted survivability. Comorbidities, such as cardiovascular disease (HR = 2.16 [1.37-3.40], pneumonia (HR = 2.43 [1.42-4.15] and sepsis (HR = 2.07 [1.09-3.94, had higher hazards for post-stroke mortality. Contrarily, the factors contributing to a lower hazard of mortality were BMI (HR = 0.94 [0.89-0.99]) and GCS (HReye = 0.66 [0.48-0.89]. In summary, our study reported that male sex, NIHSS, uric acid level, cardiovascular diseases, pneumonia, sepsis. BMI, and GCS on admission were strong determinants of in-hospital mortality in patients with IS.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0305100</identifier><identifier>PMID: 38865423</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aged ; Biology and Life Sciences ; Bivariate analysis ; Blood levels ; Body mass index ; Body size ; Cardiac arrhythmia ; Cardiovascular disease ; Cardiovascular diseases ; Cholesterol ; Clinical outcomes ; Codes ; Comorbidity ; Consciousness ; Datasets ; Diabetes ; Disability ; Electronic health records ; Electronic Health Records - statistics & numerical data ; Electronic medical records ; Electronic records ; Fatalities ; Female ; Health hazards ; Health risks ; Heart failure ; Hospital Mortality ; Hospital patients ; Hospitalization ; Hospitals ; Humans ; Hypertension ; Indonesia ; Indonesia - epidemiology ; Ischemia ; Ischemic Stroke - blood ; Ischemic Stroke - epidemiology ; Ischemic Stroke - mortality ; Kidney diseases ; Low density lipoprotein ; Male ; Males ; Medical prognosis ; Medical records ; Medical research ; Medicine and Health Sciences ; Medicine, Experimental ; Middle Aged ; Missing data ; Mortality ; Mortality risk ; Observational studies ; Patients ; Physical Sciences ; Pneumonia ; Prognosis ; Proportional Hazards Models ; Regression analysis ; Retrospective Studies ; Risk Factors ; Sepsis ; Sex ; Statistical analysis ; Stroke ; Stroke patients ; Survivability ; Survival analysis ; Triglycerides ; Uric acid ; Variables</subject><ispartof>PloS one, 2024-06, Vol.19 (6), p.e0305100</ispartof><rights>Copyright: © 2024 Yamanie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Yamanie 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>2024 Yamanie et al 2024 Yamanie et al</rights><rights>2024 Yamanie 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><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c576t-2427c7f4568cb313e0017a90eb20123d5d52b7969ed88731725a8f7108d4852c3</cites><orcidid>0000-0002-1379-2399 ; 0000-0002-5224-0183</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168658/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168658/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38865423$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yamanie, Nizar</creatorcontrib><creatorcontrib>Felistia, Yuli</creatorcontrib><creatorcontrib>Susanto, Nugroho Harry</creatorcontrib><creatorcontrib>Lamuri, Aly</creatorcontrib><creatorcontrib>Sjaaf, Amal Chalik</creatorcontrib><creatorcontrib>Miftahussurur, Muhammad</creatorcontrib><creatorcontrib>Santoso, Anwar</creatorcontrib><title>Prognostic model of in-hospital ischemic stroke mortality based on an electronic health record cohort in Indonesia</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Stroke patients rarely have satisfactory survival, which worsens further if comorbidities develop in such patients. Limited data availability from Southeast Asian countries, especially Indonesia, has impeded the disentanglement of post-stroke mortality determinants. This study aimed to investigate predictors of in-hospital mortality in patients with ischemic stroke (IS). This retrospective observational study used IS medical records from the National Brain Centre Hospital, Jakarta, Indonesia. A theoretically driven Cox's regression and Fine-Gray models were established by controlling for age and sex to calculate the hazard ratio of each plausible risk factor for predicting in-hospital stroke mortality and addressing competing risks if they existed. This study finally included 3,278 patients with IS, 917 (28%) of whom had cardiovascular disease and 376 (11.5%) suffered renal disease. Bivariate exploratory analysis revealed lower blood levels of triglycerides, low density lipoprotein, and total cholesterol associated with in-hospital-stroke mortality. The average age of patients with post-stroke mortality was 64.06 ± 11.32 years, with a mean body mass index (BMI) of 23.77 kg/m2 and a median Glasgow Coma Scale (GCS) score of 12 and an IQR of 5. Cardiovascular disease was significantly associated with IS mortality risk. NIHSS score at admission (hazard ratio [HR] = 1.04; 95% confidence interval [CI]: 1.00-1.07), male sex (HR = 1.51[1.01-2.26] and uric acid level (HR = 1.02 [1.00-1.03]) predicted survivability. Comorbidities, such as cardiovascular disease (HR = 2.16 [1.37-3.40], pneumonia (HR = 2.43 [1.42-4.15] and sepsis (HR = 2.07 [1.09-3.94, had higher hazards for post-stroke mortality. Contrarily, the factors contributing to a lower hazard of mortality were BMI (HR = 0.94 [0.89-0.99]) and GCS (HReye = 0.66 [0.48-0.89]. In summary, our study reported that male sex, NIHSS, uric acid level, cardiovascular diseases, pneumonia, sepsis. BMI, and GCS on admission were strong determinants of in-hospital mortality in patients with IS.</description><subject>Aged</subject><subject>Biology and Life Sciences</subject><subject>Bivariate analysis</subject><subject>Blood levels</subject><subject>Body mass index</subject><subject>Body size</subject><subject>Cardiac arrhythmia</subject><subject>Cardiovascular disease</subject><subject>Cardiovascular diseases</subject><subject>Cholesterol</subject><subject>Clinical outcomes</subject><subject>Codes</subject><subject>Comorbidity</subject><subject>Consciousness</subject><subject>Datasets</subject><subject>Diabetes</subject><subject>Disability</subject><subject>Electronic health records</subject><subject>Electronic Health Records - statistics & numerical data</subject><subject>Electronic medical records</subject><subject>Electronic records</subject><subject>Fatalities</subject><subject>Female</subject><subject>Health hazards</subject><subject>Health risks</subject><subject>Heart failure</subject><subject>Hospital Mortality</subject><subject>Hospital patients</subject><subject>Hospitalization</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Indonesia</subject><subject>Indonesia - epidemiology</subject><subject>Ischemia</subject><subject>Ischemic Stroke - blood</subject><subject>Ischemic Stroke - epidemiology</subject><subject>Ischemic Stroke - mortality</subject><subject>Kidney diseases</subject><subject>Low density lipoprotein</subject><subject>Male</subject><subject>Males</subject><subject>Medical prognosis</subject><subject>Medical records</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Medicine, Experimental</subject><subject>Middle Aged</subject><subject>Missing data</subject><subject>Mortality</subject><subject>Mortality risk</subject><subject>Observational studies</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Pneumonia</subject><subject>Prognosis</subject><subject>Proportional Hazards Models</subject><subject>Regression analysis</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Sepsis</subject><subject>Sex</subject><subject>Statistical analysis</subject><subject>Stroke</subject><subject>Stroke patients</subject><subject>Survivability</subject><subject>Survival analysis</subject><subject>Triglycerides</subject><subject>Uric 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Nizar</au><au>Felistia, Yuli</au><au>Susanto, Nugroho Harry</au><au>Lamuri, Aly</au><au>Sjaaf, Amal Chalik</au><au>Miftahussurur, Muhammad</au><au>Santoso, Anwar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prognostic model of in-hospital ischemic stroke mortality based on an electronic health record cohort in Indonesia</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2024-06-12</date><risdate>2024</risdate><volume>19</volume><issue>6</issue><spage>e0305100</spage><pages>e0305100-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Stroke patients rarely have satisfactory survival, which worsens further if comorbidities develop in such patients. Limited data availability from Southeast Asian countries, especially Indonesia, has impeded the disentanglement of post-stroke mortality determinants. This study aimed to investigate predictors of in-hospital mortality in patients with ischemic stroke (IS). This retrospective observational study used IS medical records from the National Brain Centre Hospital, Jakarta, Indonesia. A theoretically driven Cox's regression and Fine-Gray models were established by controlling for age and sex to calculate the hazard ratio of each plausible risk factor for predicting in-hospital stroke mortality and addressing competing risks if they existed. This study finally included 3,278 patients with IS, 917 (28%) of whom had cardiovascular disease and 376 (11.5%) suffered renal disease. Bivariate exploratory analysis revealed lower blood levels of triglycerides, low density lipoprotein, and total cholesterol associated with in-hospital-stroke mortality. The average age of patients with post-stroke mortality was 64.06 ± 11.32 years, with a mean body mass index (BMI) of 23.77 kg/m2 and a median Glasgow Coma Scale (GCS) score of 12 and an IQR of 5. Cardiovascular disease was significantly associated with IS mortality risk. NIHSS score at admission (hazard ratio [HR] = 1.04; 95% confidence interval [CI]: 1.00-1.07), male sex (HR = 1.51[1.01-2.26] and uric acid level (HR = 1.02 [1.00-1.03]) predicted survivability. Comorbidities, such as cardiovascular disease (HR = 2.16 [1.37-3.40], pneumonia (HR = 2.43 [1.42-4.15] and sepsis (HR = 2.07 [1.09-3.94, had higher hazards for post-stroke mortality. Contrarily, the factors contributing to a lower hazard of mortality were BMI (HR = 0.94 [0.89-0.99]) and GCS (HReye = 0.66 [0.48-0.89]. In summary, our study reported that male sex, NIHSS, uric acid level, cardiovascular diseases, pneumonia, sepsis. BMI, and GCS on admission were strong determinants of in-hospital mortality in patients with IS.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38865423</pmid><doi>10.1371/journal.pone.0305100</doi><orcidid>https://orcid.org/0000-0002-1379-2399</orcidid><orcidid>https://orcid.org/0000-0002-5224-0183</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2024-06, Vol.19 (6), p.e0305100 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_3069265254 |
| 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 | Aged Biology and Life Sciences Bivariate analysis Blood levels Body mass index Body size Cardiac arrhythmia Cardiovascular disease Cardiovascular diseases Cholesterol Clinical outcomes Codes Comorbidity Consciousness Datasets Diabetes Disability Electronic health records Electronic Health Records - statistics & numerical data Electronic medical records Electronic records Fatalities Female Health hazards Health risks Heart failure Hospital Mortality Hospital patients Hospitalization Hospitals Humans Hypertension Indonesia Indonesia - epidemiology Ischemia Ischemic Stroke - blood Ischemic Stroke - epidemiology Ischemic Stroke - mortality Kidney diseases Low density lipoprotein Male Males Medical prognosis Medical records Medical research Medicine and Health Sciences Medicine, Experimental Middle Aged Missing data Mortality Mortality risk Observational studies Patients Physical Sciences Pneumonia Prognosis Proportional Hazards Models Regression analysis Retrospective Studies Risk Factors Sepsis Sex Statistical analysis Stroke Stroke patients Survivability Survival analysis Triglycerides Uric acid Variables |
| title | Prognostic model of in-hospital ischemic stroke mortality based on an electronic health record cohort in Indonesia |
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