Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS)
Abstract Background Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate...
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| Format: | Article |
| Language: | English |
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BMC
2018-08-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | http://link.springer.com/article/10.1186/s12911-018-0652-4 |
| _version_ | 1819011600529489920 |
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| author | Zhaoyi Chen Victoria Y. Bird Rupam Ruchi Mark S. Segal Jiang Bian Saeed R. Khan Marie-Carmelle Elie Mattia Prosperi |
| author_facet | Zhaoyi Chen Victoria Y. Bird Rupam Ruchi Mark S. Segal Jiang Bian Saeed R. Khan Marie-Carmelle Elie Mattia Prosperi |
| author_sort | Zhaoyi Chen |
| collection | DOAJ |
| description | Abstract Background Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate a ‘multi-domain’ personalized diagnostic acute care algorithm for KS (DACA-KS), merging demographic, vital signs, clinical, and laboratory information. Methods We utilized a large, single-center database of patients admitted to acute care units in a large tertiary care hospital. Patients diagnosed with KS were compared to groups of patients with acute abdominal/flank/groin pain, genitourinary diseases, and other conditions. We analyzed multiple information domains (several thousands of variables) using a collection of statistical and machine learning models with feature selectors. We compared sensitivity, specificity and area under the receiver operating characteristic (AUROC) of our approach with the STONE score, using cross-validation. Results Thirty eight thousand five hundred and ninety-seven distinct adult patients were admitted to critical care between 2001 and 2012, of which 217 were diagnosed with KS, and 7446 with acute pain (non-KS). The multi-domain approach using logistic regression yielded an AUROC of 0.86 and a sensitivity/specificity of 0.81/0.82 in cross-validation. Increase in performance was obtained by fitting a super-learner, at the price of lower interpretability. We discussed in detail comorbidity and lab marker variables independently associated with KS (e.g. blood chloride, candidiasis, sleep disorders). Conclusions Although external validation is warranted, DACA-KS could be integrated into electronic health systems; the algorithm has the potential used as an effective tool to help nurses and healthcare personnel during triage or clinicians making a diagnosis, streamlining patients’ management in acute care. |
| first_indexed | 2024-12-21T01:30:44Z |
| format | Article |
| id | doaj.art-b9a0001fdc6141ce95094a4101931e5e |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-21T01:30:44Z |
| publishDate | 2018-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-b9a0001fdc6141ce95094a4101931e5e2022-12-21T19:20:22ZengBMCBMC Medical Informatics and Decision Making1472-69472018-08-0118111410.1186/s12911-018-0652-4Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS)Zhaoyi Chen0Victoria Y. Bird1Rupam Ruchi2Mark S. Segal3Jiang Bian4Saeed R. Khan5Marie-Carmelle Elie6Mattia Prosperi7Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of FloridaDepartment of Urology, University of FloridaDivision of Nephrology, Hypertension, & Renal Transplantation, University of FloridaDivision of Nephrology, Hypertension, & Renal Transplantation, University of FloridaDepartment of Health Outcomes and Biomedical Informatics, University of FloridaDepartment of Pathology, Immunology, and Laboratory MedicineDepartment of Emergency Medicine, University of FloridaDepartment of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of FloridaAbstract Background Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Diagnostics algorithms of KS only use a few variables with a limited sensitivity and specificity. In this study, we tested a big data approach to infer and validate a ‘multi-domain’ personalized diagnostic acute care algorithm for KS (DACA-KS), merging demographic, vital signs, clinical, and laboratory information. Methods We utilized a large, single-center database of patients admitted to acute care units in a large tertiary care hospital. Patients diagnosed with KS were compared to groups of patients with acute abdominal/flank/groin pain, genitourinary diseases, and other conditions. We analyzed multiple information domains (several thousands of variables) using a collection of statistical and machine learning models with feature selectors. We compared sensitivity, specificity and area under the receiver operating characteristic (AUROC) of our approach with the STONE score, using cross-validation. Results Thirty eight thousand five hundred and ninety-seven distinct adult patients were admitted to critical care between 2001 and 2012, of which 217 were diagnosed with KS, and 7446 with acute pain (non-KS). The multi-domain approach using logistic regression yielded an AUROC of 0.86 and a sensitivity/specificity of 0.81/0.82 in cross-validation. Increase in performance was obtained by fitting a super-learner, at the price of lower interpretability. We discussed in detail comorbidity and lab marker variables independently associated with KS (e.g. blood chloride, candidiasis, sleep disorders). Conclusions Although external validation is warranted, DACA-KS could be integrated into electronic health systems; the algorithm has the potential used as an effective tool to help nurses and healthcare personnel during triage or clinicians making a diagnosis, streamlining patients’ management in acute care.http://link.springer.com/article/10.1186/s12911-018-0652-4Diagnostic algorithmKidney stonesBig data analysis |
| spellingShingle | Zhaoyi Chen Victoria Y. Bird Rupam Ruchi Mark S. Segal Jiang Bian Saeed R. Khan Marie-Carmelle Elie Mattia Prosperi Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS) BMC Medical Informatics and Decision Making Diagnostic algorithm Kidney stones Big data analysis |
| title | Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS) |
| title_full | Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS) |
| title_fullStr | Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS) |
| title_full_unstemmed | Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS) |
| title_short | Development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical, demographics and laboratory data: the diagnostic acute care algorithm - kidney stones (DACA-KS) |
| title_sort | development of a personalized diagnostic model for kidney stone disease tailored to acute care by integrating large clinical demographics and laboratory data the diagnostic acute care algorithm kidney stones daca ks |
| topic | Diagnostic algorithm Kidney stones Big data analysis |
| url | http://link.springer.com/article/10.1186/s12911-018-0652-4 |
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