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Background  Even small variations of serum sodium concentration may be associated with mortality. Our objective was to confirm the impact of borderline dysnatremia for patients admitted to hospital on in-hospital mortality using real life care data from our electronic health record (EHR) and a phenome-wide association analysis (PheWAS).    Methods  Retrospective observational study based on patient data admitted to Hôpital Européen George Pompidou, between 01/01/2008 and 31/06/2014; including 45,834 patients with serum sodium determinations on admission. We analyzed the association between dysnatremia and in-hospital mortality, using a multivariate logistic regression model to adjust for classical potential confounders. We performed a PheWAS to identify new potential confounders.    Results  Hyponatremia and hypernatremia were recorded for 12.0% and 1.0% of hospital stays, respectively. Adjusted odds ratios (ORa) for severe, moderate and borderline hyponatremia were 3.44 (95% CI, 2.41–4.86), 2.48 (95% CI, 1.96–3.13) and 1.98 (95% CI, 1.73–2.28), respectively. ORa for severe, moderate and borderline hypernatremia were 4.07 (95% CI, 2.92–5.62), 4.42 (95% CI, 2.04–9.20) and 3.72 (95% CI, 1.53–8.45), respectively. Borderline hyponatremia (ORa = 1.57 95% CI, 1.35–1.81) and borderline hypernatremia (ORa = 3.47 95% CI, 2.43–4.90) were still associated with in-hospital mortality after adjustment for classical and new confounding factors identified through the PheWAS analysis.    Conclusion  Borderline dysnatremia on admission are independently associated with a higher risk of in-hospital mortality. By using medical data automatically collected in EHR and a new data mining approach, we identified new potential confounding factors that were highly associated with both mortality and dysnatremia.    Electronic supplementary material  The online version of this article (10.1186/s12911-017-0549-7) contains supplementary material, which is available to authorized users.

Association between borderline dysnatremia and mortality insight into a new data mining approach