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.
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.
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).
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.
BMC medical informatics and decision making. 2018 Aug 17*** epublish ***
Zhaoyi Chen, Victoria Y Bird, Rupam Ruchi, Mark S Segal, Jiang Bian, Saeed R Khan, Marie-Carmelle Elie, Mattia Prosperi
Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, 2004 Mowry Road, PO Box 100231, Gainesville, Florida, 32610-0231, USA. ., Department of Urology, University of Florida, Gainesville, Florida, USA., Division of Nephrology, Hypertension, & Renal Transplantation, University of Florida, Gainesville, Florida, USA., Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA., Department of Pathology, Immunology, and Laboratory Medicine, Gainesville, Florida, USA., Department of Emergency Medicine, University of Florida, Gainesville, Florida, USA., Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, 2004 Mowry Road, PO Box 100231, Gainesville, Florida, 32610-0231, USA.