Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure

Abstract

Abstract Aims Acute decompensated heart failure (ADHF) presents with pulmonary congestion, which is caused by an increased pulmonary arterial wedge pressure (PAWP). PAWP is strongly associated with prognosis, but its quantitative evaluation is often difficult. Our prior work demonstrated that a deep learning approach based on chest radiographs can calculate estimated PAWP (ePAWP) in patients with cardiovascular disease. Therefore, the present study aimed to assess the prognostic value of ePAWP and compare it with other indices of haemodynamic congestion. Methods and results We conducted a post hoc analysis of a single‐centre, prospective, observational heart failure registry and analysed data from 534 patients admitted for ADHF between January 2018 and December 2019. The deep learning approach was used to calculate ePAWP from chest radiographs at admission and discharge. Patients were divided into three groups based on the ePAWP tertiles at discharge, as follows: first tertile group (ePAWP ≤ 11.2 mm Hg, n = 178), second tertile group (11.2 < ePAWP < 13.5 mm Hg, n = 170), and third tertile group (ePAWP ≥ 13.5 mm Hg, n = 186). The third tertile group had a higher prevalence of atrial fibrillation and lower systolic blood pressure at admission; a lower platelet count and higher total bilirubin at both admission and discharge; and a higher left atrial diameter, peak early diastolic transmitral flow velocity, right ventricular end‐diastolic diameter, and maximal inferior vena cava diameter at discharge. During the median follow‐up period of 289 days, 223 (41.7%) patients reached the primary endpoint (a composite of all‐cause mortality or rehospitalization for heart failure). Kaplan–Meier analysis revealed a significantly higher composite event rate in the third tertile group (log‐rank test, P = 0.006). Even when adjusted for clinically relevant factors, a higher ePAWP at discharge and a smaller decrease in ePAWP from admission to discharge were significantly associated with higher event rates [ePAWP at discharge: hazard ratio, 1.10; 95% confidence interval (CI), 1.02–1.19; P = 0.010; and size of ePAWP decrease: hazard ratio, 0.94; 95% CI, 0.89–0.99; P = 0.038]. Conclusions Our study suggests that ePAWP calculated by a deep learning approach may be useful for identifying and monitoring pulmonary congestion during hospitalization for ADHF.

Keywords

• Heart failure
• Artificial intelligence
• Pulmonary congestion