Abstract
Background and Aims
Secondary hyperparathyroidism (SHPT) is common in patients suffering from chronic kidney disease (CKD) and worsens over time in patients undergoing haemodialysis (HD). The clinical management of SHPT in HD patients is challenging. In this context, the SENEFRO-BD-SHPT study aims to access and analyse the free-text narratives in the electronic health records (EHRs) of patients with SHPT undergoing HD to characterize their demographic and clinical characteristics, including comorbidities, medication use, and control of relevant biochemical parameters.
Method
SENEFRO-BD-SHPT was an observational, retrospective, and multicentre study based on the secondary analysis of EHRs from 8 hospitals from the Spanish National Healthcare Network (Figure 1A). The unstructured clinical data in patients’ EHRs between January 1st 2014 and December 31st 2018 were analysed using the EHRead® technology, based on Natural Language Processing (NLP) and machine learning. We conducted a cross-sectional analysis of all patients at the time of inclusion, hereafter referred to as index date (Figure 1A). For HD patients, the index date was defined as the timepoint when diagnostic criteria for either HD or SHPT were first met, namely a) PTH > 300 pg/ml and/or b) documented use of drugs for the management of SHPT such as calcimimetics, vitamin D or vitamin-D analogues. Follow-up analyses were performed at 6- and 12-months following the index date. Crucially, to guarantee the homogeneity and quality of the data, we only considered HD patients with SHPT with available PTH values at baseline and at least at one time point during follow up.
Results
From a source population of 3,290,365 EHRs in the hospitals catchment area, a total of 623 patients with SHPT undergoing HD were found. Of these, 282 patients had available PTH data (Figure 1A). Regarding demographic characteristics, 68.4% patients (n = 193) were male, with a mean (±SD) age of 67.1 (±15.4) years. The most common causes of CKD were diabetic nephropathy (29%; n = 81), hypertensive/renal vascular disease (24.4%; n = 68), tubulointerstitial disease (19.3%; n = 54), and glomerular disease (12.5%; n = 35) (Figure 1B). The most frequent comorbidities in patients’ EHRs at index date were hypertension (83.7%, n = 236), type 2 diabetes (43.6%; n = 123), and heart failure (34.8%; n = 98) (Figure 1B). The percentage of patients with controlled PTH, calcium (Ca), or phosphorus (P) values at index date and during follow up is shown in Figure 1C; overall, these values remained stable across the study period. Finally, Figure 1D displays the use of selected SHPT-related medications at index date, namely vitamin-D and analogues (63.1%; n = 178), phosphate binders (46.8%; n = 132), and calcimimetics (9.6%; n = 27).
Conclusion
SENEFRO-BD-SHPT represents the first attempt to use clinical NLP and big data analytics to offer an updated picture of patients with SHPT undergoing HD in Spain based on unstructured clinical data. NLP holds great potential to characterize the epidemiology and clinical management of CKD using real-world evidence in EHRs. However, free-text narratives in the EHRs may be suboptimal to study analytical variables. Funding: Unrestricted grant from Amgen to SENEFRO
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