Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care
Abstract Background Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients’ medical background. Given the aging population, not only are diseases becoming incr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-07-01
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| Series: | Critical Care |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13054-019-2486-6 |
| _version_ | 1819113170241847296 |
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| author | Zsolt Zador Alexander Landry Michael D. Cusimano Nophar Geifman |
| author_facet | Zsolt Zador Alexander Landry Michael D. Cusimano Nophar Geifman |
| author_sort | Zsolt Zador |
| collection | DOAJ |
| description | Abstract Background Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients’ medical background. Given the aging population, not only are diseases becoming increasingly prevalent but occur more frequently in combinations (“multimorbidity”). We hypothesized the existence of patient subgroups in critical care with distinct multimorbidity states. We further hypothesize that certain multimorbidity states associate with higher rates of organ failure, sepsis, and mortality co-occurring with these clinical problems. Methods We analyzed 36,390 patients from the open source Medical Information Mart for Intensive Care III (MIMIC III) dataset. Morbidities were defined based on Elixhauser categories, a well-established scheme distinguishing 30 classes of chronic diseases. We used latent class analysis to identify distinct patient subgroups based on demographics, admission type, and morbidity compositions and compared the prevalence of organ dysfunction, sepsis, and inpatient mortality for each subgroup. Results We identified six clinically distinct multimorbidity subgroups labeled based on their dominant Elixhauser disease classes. The “cardiopulmonary” and “cardiac” subgroups consisted of older patients with a high prevalence of cardiopulmonary conditions and constituted 6.1% and 26.4% of study cohort respectively. The “young” subgroup included 23.5% of the cohort composed of young and healthy patients. The “hepatic/addiction” subgroup, constituting 9.8% of the cohort, consisted of middle-aged patients (mean age of 52.25, 95% CI 51.85–52.65) with the high rates of depression (20.1%), alcohol abuse (47.75%), drug abuse (18.2%), and liver failure (67%). The “complicated diabetics” and “uncomplicated diabetics” subgroups constituted 9.4% and 24.8% of the study cohort respectively. The complicated diabetics subgroup demonstrated higher rates of end-organ complications (88.3% prevalence of renal failure). Rates of organ dysfunction and sepsis ranged 19.6–69% and 12.5–46.7% respectively in the six subgroups. Mortality co-occurring with organ dysfunction and sepsis ranges was 8.4–23.8% and 11.7–27.4% respectively. These adverse outcomes were most prevalent in the hepatic/addiction subgroup. Conclusion We identify distinct multimorbidity states that associate with relatively higher prevalence of organ dysfunction, sepsis, and co-occurring mortality. The findings promote the incorporation of multimorbidity in healthcare models and the shift away from the current single-disease paradigm in clinical practice, training, and trial design. |
| first_indexed | 2024-12-22T04:25:09Z |
| format | Article |
| id | doaj.art-cf1de3df5cb940d298668d30978216cb |
| institution | Directory Open Access Journal |
| issn | 1364-8535 |
| language | English |
| last_indexed | 2024-12-22T04:25:09Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Critical Care |
| spelling | doaj.art-cf1de3df5cb940d298668d30978216cb2022-12-21T18:39:11ZengBMCCritical Care1364-85352019-07-0123111110.1186/s13054-019-2486-6Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical careZsolt Zador0Alexander Landry1Michael D. Cusimano2Nophar Geifman3Division of Neurosurgery, Department of Surgery, St. Michael’s HospitalDivision of Neurosurgery, Department of Surgery, St. Michael’s HospitalDivision of Neurosurgery, Department of Surgery, St. Michael’s HospitalDivision of Informatics, Imaging and Data Sciences, University of ManchesterAbstract Background Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients’ medical background. Given the aging population, not only are diseases becoming increasingly prevalent but occur more frequently in combinations (“multimorbidity”). We hypothesized the existence of patient subgroups in critical care with distinct multimorbidity states. We further hypothesize that certain multimorbidity states associate with higher rates of organ failure, sepsis, and mortality co-occurring with these clinical problems. Methods We analyzed 36,390 patients from the open source Medical Information Mart for Intensive Care III (MIMIC III) dataset. Morbidities were defined based on Elixhauser categories, a well-established scheme distinguishing 30 classes of chronic diseases. We used latent class analysis to identify distinct patient subgroups based on demographics, admission type, and morbidity compositions and compared the prevalence of organ dysfunction, sepsis, and inpatient mortality for each subgroup. Results We identified six clinically distinct multimorbidity subgroups labeled based on their dominant Elixhauser disease classes. The “cardiopulmonary” and “cardiac” subgroups consisted of older patients with a high prevalence of cardiopulmonary conditions and constituted 6.1% and 26.4% of study cohort respectively. The “young” subgroup included 23.5% of the cohort composed of young and healthy patients. The “hepatic/addiction” subgroup, constituting 9.8% of the cohort, consisted of middle-aged patients (mean age of 52.25, 95% CI 51.85–52.65) with the high rates of depression (20.1%), alcohol abuse (47.75%), drug abuse (18.2%), and liver failure (67%). The “complicated diabetics” and “uncomplicated diabetics” subgroups constituted 9.4% and 24.8% of the study cohort respectively. The complicated diabetics subgroup demonstrated higher rates of end-organ complications (88.3% prevalence of renal failure). Rates of organ dysfunction and sepsis ranged 19.6–69% and 12.5–46.7% respectively in the six subgroups. Mortality co-occurring with organ dysfunction and sepsis ranges was 8.4–23.8% and 11.7–27.4% respectively. These adverse outcomes were most prevalent in the hepatic/addiction subgroup. Conclusion We identify distinct multimorbidity states that associate with relatively higher prevalence of organ dysfunction, sepsis, and co-occurring mortality. The findings promote the incorporation of multimorbidity in healthcare models and the shift away from the current single-disease paradigm in clinical practice, training, and trial design.http://link.springer.com/article/10.1186/s13054-019-2486-6SepsisMultimorbidityData analyticsMachine learningLatent class analysis |
| spellingShingle | Zsolt Zador Alexander Landry Michael D. Cusimano Nophar Geifman Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care Critical Care Sepsis Multimorbidity Data analytics Machine learning Latent class analysis |
| title | Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care |
| title_full | Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care |
| title_fullStr | Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care |
| title_full_unstemmed | Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care |
| title_short | Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care |
| title_sort | multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis a data driven analysis in critical care |
| topic | Sepsis Multimorbidity Data analytics Machine learning Latent class analysis |
| url | http://link.springer.com/article/10.1186/s13054-019-2486-6 |
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