Cost-minimization analysis of sequential genetic testing versus targeted next-generation sequencing gene panels in patients with pheochromocytoma and paraganglioma

IntroductionPheochromocytomas and paragangliomas (PPGLs) are highly heritable tumours, with up to 40% of cases carrying germline variants. Current guidelines recommend genetic testing for all patients with PPGLs. Next-generation sequencing (NGS) enables accurate, fast, and inexpensive genetic testing. This study aimed to compare the costs related to PPGL genetic testing between the sequential testing using the decisional algorithm proposed in the 2014 Endocrine Society guidelines and targeted NGS gene panels.MethodsPatients with proven PPGLs were enrolled. A gene list covering 17 susceptibility genes related to hereditary PPGLs was developed for targeted sequencing. Validation was carried out by Sanger sequencing. We simulated the diagnostic workflow to examine the anticipated costs based on each strategy for genetic testing.ResultsTwenty-nine patients were included, among whom a germline variant was identified in 34.5%. A total of 22.7% with apparently sporadic PPGL carried a variant. Five genes were involved (RET, n = 3; SDHB, n = 3; SDHD, n = 2; EGLN1, n = 1; and NF1, n = 1). According to the diagnostic workflow, the average cost of the targeted NGS (534.7 US dollars per patient) is lower than that of the sequential testing (734.5 US dollars per patient). The targeted NGS can also reduce the number of hospital visits from 4.1 to 1 per person. The cost can be further reduced to 496.24 US dollars per person (32% reduction) if we apply a new syndromic-driven diagnostic algorithm to establish priorities for specific genetic testing for syndromic and selected cases, and targeted NGS for non-syndromic patients.ConclusionsTargeted NGS can reduce both the cost of PPGL genetic testing and the number of hospital visits, compared with the conventional approach. Our proposed algorithm is the preferred approach due to its significant reduction of the cost of genetic testing.

Key message

• Pheochromocytomas and paragangliomas are highly heritable neoplasms.
• The targeted next-generation sequencing (NGS) gene panels have proven to be fast, accurate, and inexpensive for the genetic analysis.
• According to this cost analysis, it is economically reasonable to use targeted NGS gene panels for genetic screening.

     

The validation of smartphone applications for heart rate measurement

Abstract

Background: The smartphone apps provide a user-friendly option for measurement of heart rate (HR) by detecting pulsatile photoplethysmographic signals with built-in cameras from the fingertips, however, the validation study is limited.

Methods: We compared HR detected by the smartphone apps (App1?=?Instant HR, App2?=?Cardiio: HR Monitor and App3?=?Runtastic HR Monitor) with simultaneous standard ECG monitoring in the adult patients at the critical care unit.

Results: HR measurements were obtained from 140 patients with mean age 67.6?±?15.3 years. Mean baseline HR was 89.1?±?19.1 bpm (range, 32–136 bpm). Sinus rhythm was presented in 111 patients (79.3%), atrial fibrillation in 25 patients (17.9%), pacemaker rhythm in 3 patients (2.1%), and high-grade AV block in 1 patient (0.7%). The ECG-derived HR correlated well with App1 (r?=?0.98), App2 (r?=?0.97), and App3 (r?=?0.92). In patients with regular rhythm, mean absolute deviation was 0.8?±?1, 0.7?±?0.9, 1.0?±?1.3 bpm on App1, App2 and App3, respectively. In the patients with irregular rhythm, median absolute deviation (IQR) was 3 (2–5.5), 4 (1.5–11.5), and 6 (2–13) bpm. Skin colour did not affect with the HR measurement.

Conclusions: HR measurements from all applications were correlated well with ECG monitoring. However, it was less accurate in case of irregular rhythm such as atrial fibrillation.

• Key messages

• Several reports on inaccuracy of mobile health apps have been published. We conducted the validation study in the real patients by using popular mobile apps.

• Heart rate measurements from mobile apps were correlated well with standard ECG. The accuracy of HR from apps was worse at irregular rate and tachycardia.