What does this research mean for the field?

Phase-Rectified Signal Averaging (PRSA) parameters, specifically an Acceleration Capacity (AC) ≥ -7 and a Deceleration Capacity (DC) ≤ 7, accurately identify Cardiac Autonomic Neuropathy with performance comparable to conventional diagnostic methods. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to compare Phase-Rectified Signal Averaging (PRSA) methods with conventional cardiac autonomic testing to identify cardiac autonomic neuropathy (CAN) accurately.

May 27, 20260 citationsOpen Access

Cardiac Autonomic Neuropathy: Comparative performance of Phase-Rectified Signal Averaging methods with Cardiac Autonomic Reflex Tests and Time Domain Heart rate variability

SJStigi Joseph ASAbish Sudhakar JMJK Mukkadan

Key Result

The provided text is a manuscript preparation guide for the Nigerian Medical Journal and contains no clinical study data.

Key Points

This research aims to compare Phase-Rectified Signal Averaging (PRSA) methods with conventional cardiac autonomic testing to identify cardiac autonomic neuropathy (CAN) accurately.
Studied two cohorts: 126 individuals with normal ventricular function and 143 with left ventricular dysfunction.
Utilized CARTs and ECG recordings, categorizing patients based on TD-HRV parameters and CART results.
Determined cut-off values for AC and DC using the ROC curve method.
AC cut-off ≥ -7 and DC cut-off ≤ 7 show high accuracy for identifying CAN.
Both AC and DC cut-offs achieved an AUC of nearly 0.9, indicating excellent diagnostic performance.
Kappa values for reclassification of CAN based on cut-offs ranged from 0.5 to 0.7.

Structured PICO

Do Phase-Rectified Signal Averaging methods (Acceleration and Deceleration Capacity) accurately diagnose Cardiac Autonomic Neuropathy compared to conventional methods in patients with and without left ventricular dysfunction?

Population

269 individuals, comprising 126 with normal ventricular function (derivation cohort) and 143 with Left Ventricular Dysfunction (validation cohort).

Intervention

Phase-Rectified Signal Averaging (PRSA) methods, specifically Deceleration Capacity (DC) and Acceleration Capacity (AC), derived from 2 to 3 minutes of supine, resting ECG recordings.

Comparator

Conventional methods including Time Domain Heart Rate Variability (TD-HRV) and Cardiac Autonomic Reflex Tests (CARTs) using AIIMS-AFT criteria and 2011 Toronto Consensus recommendations.

Outcome

Diagnostic accuracy (cut-off values, AUC, and kappa agreement) of PRSA methods for detecting Cardiac Autonomic Neuropathy (CAN).surrogate

Phase-Rectified Signal Averaging methods (AC ≥ -7 and DC ≤ 7) derived from short resting ECGs offer a highly accurate alternative to conventional tests for diagnosing cardiac autonomic neuropathy.

Abstract

Background:Phase-Rectified Signal Averaging (PRSA) methods, Deceleration (DC), and Acceleration Capacity (AC), provide a comprehensive assessment of cardiac autonomic function (CAN). There are no published studies comparing PRSA methods with conventional methods such as Time Domain Heart Rate Variability (TD-HRV) or Cardiac Autonomic Reflex Tests (CARTs), nor have they described a cut-off value for AC and DC to distinguish patients with CAN (+ve) from those without CAN (-ve). Our study compares PRSA methods with conventional methods and defines cut-off values for AC and DC to diagnose CAN. Methodology:We studied two cohorts: 126 individuals with normal ventricular function (derivation cohort) and 143 individuals with Left Ventricular Dysfunction (validation cohort). These patients underwent CARTs and supine, resting ECG recordings for 2 to 3 minutes. The patients were categorized as CAN +Ve and CAN -Ve based on TD-HRV parameters and the CARTs. Two different CART criteria were studied: the All-India Institute of Medical Sciences (AIIMS-AFT) criteria and the 2011 Toronto Consensus recommendations. Patients with and without CAN were segregated by AC and DC values, and the methods were compared. The cutoff values for DC and AC were calculated using the ROC curve method from the derivation cohort and verified in the validation cohort. Results:A reduction in DC values and an increase in AC values indicate a higher chance of CAN. The cut-off values of -7 for AC and 7 for DC provide the highest accuracy in detecting CAN prevalence. Both values have an AUC of nearly 0.9. Reclassifying both cohorts as CAN +Ve based on the derived cut-offs and comparing with the prevalence determined by conventional methods results in kappa values ranging from 0.5 to 0.7. Conclusions:A decrease in DC value and an increase in AC value are associated with a higher probability of CAN. An AC value ≥ -7 and a DC value ≤ 7 indicate good accuracy in identifying CAN.

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