Abstract
BackgroundKidney 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.MethodsWe 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.ResultsThirty 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).ConclusionsAlthough 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.
Highlights
Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden
Our study included patients aged 18 years and older, divided into four groups based on the ICD-9 diagnoses during hospitalization: (a) KS cases (ICD-9592, including sub-codes 592.0, 592.1, 592.9); (b) patients diagnosed with genitourinary diseases (GUD) except KS, e.g. patients with nephritis, nephrotic syndrome, nephrosis; (c) patients admitted to acute care with other conditions (OTH) who did not have any KS or GUD diagnosed to represent a general patient population; (d) patients admitted with acute localized pain (ALP) of abdominal (ICD-9 code: 789.0), back (ICD-9 code: 724.2), flank, or groin
We report a series of novel findings in KS that are significantly different than GUD, OTH and ALP populations and which could aid in the triage of patients when they present to the emergency department (ED) or are admitted/transferred into critical care
Summary
Kidney stone (KS) disease has high, increasing prevalence in the United States and poses a massive economic burden. Kidney stone (KS) disease prevalence has increased in the United States from 5.2% (6.3% males and 4.1% females) in 1994 to 8.8% (10.6% males and 7.1% females) in 2012 [1]. Since it is one of the costliest urologic diseases in the United States, an increase in prevalence poses a huge economic burden on society. During the past two decades, a significant increase in ED visits with stone-related symptoms has been observed [5], with over 1.3 million individuals per year presenting to the ED with KS in the United States. The workup may include initial lab tests such as complete blood count with differential, comprehensive metabolic panel, and urine analysis; but often these tests are not promptly measured or are inappropriately interpreted [5]
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