Concordancia entre un algoritmo y el diagnóstico clínico en la gravedad de descompensación de enfermedades crónicas de pacientes en un programa de telemonitorización

IF 1.7 4区医学 Q2 MEDICINE, GENERAL & INTERNAL

Revista clinica espanola Pub Date : 2026-05-01 Epub Date: 2026-04-09 DOI:10.1016/j.rce.2026.502517

J. Fernández-Rivera , Y. Marín-Fernández , M. Gómez-Hernández , J. Ortíz-Romero , A. Fernández-Moyano

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引用次数: 0

Abstract

Background

Telemonitoring of biological variables can facilitate the early detection of decompensation in chronic disease. Data processing through digital tools enables the delivery of care via automated decision-support systems, such as diagnostic algorithms or alerts in high-rink situations. These tools may improve health outcomes and optimize healthcare management.

Methods

A diagnostic accuracy study with a retrospective cross-sectional design was conducted. Agreement in classifying the severity of decompensation in patients with chronic diseases was compared between the algorithm used in the Tele-COMPARTE program and the consensus of three observers, which was considered the reference standard. The weighted Kappa index was used to assess agreement.

Results

The final version of our algorithm achieved a Kappa index (95% CI) of 0,76 (0,7-0.82). By subgroups, the values were 0,84 (0,74-0,94) for alerts in patients with heart failure, 0,71 (0,57-0,85) in COPD, and 0,69 (0,6-0,79) in alerts involving both conditions, all with p < 0,001. The positive likelihood ratios were 6,3 in low-level and moderate alerts, 10 for high-level alerts, and 186,3 for critical alerts. Predictive values exceeded 75% in all categories.

Conclusions

The level of agreement between the Tele-COMPARTE algorithm and clinical consensus in stratifying the severity of decompensation in chronic disease was “good”, reaching a “very good” level in patients with heart failure. The application of the algorithm is a valid and safe procedure that supports its use within our remote monitoring program.

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远程监测项目中患者慢性疾病失配严重程度的算法与临床诊断之间的一致性

背景：远程监测生物变量有助于早期发现慢性疾病的代偿失代偿。通过数字工具进行数据处理，可以通过自动决策支持系统提供护理，例如诊断算法或高冰场情况下的警报。这些工具可以改善健康结果并优化医疗保健管理。方法采用回顾性横断面设计进行诊断准确性研究。将Tele-COMPARTE程序中使用的算法与三名观测者的一致意见作为参考标准，对慢性疾病患者失代偿严重程度的分类进行一致性比较。加权Kappa指数用于评估一致性。结果最终版本的算法Kappa指数（95% CI）为0.76（0.7 -0.82）。按亚组划分，心力衰竭患者的预警值为0.84 (0.74 - 0.94)，COPD患者的预警值为0.71(0.57 - 0.85)，两种情况的预警值均为0.69(0.6 - 0.79)，均p <； 001。低级别和中等级别警报的正似然比为6,3，高级别警报为10，严重警报为186,3。所有类别的预测值均超过75%。结论远程比较算法对慢性疾病失代偿严重程度的分级与临床共识的一致性水平为“好”，在心力衰竭患者中达到“非常好”水平。该算法的应用是一个有效和安全的程序，支持其在我们的远程监控程序中使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Revista clinica espanola 医学-医学：内科

CiteScore

4.40

自引率

6.90%

发文量

审稿时长

28 days

期刊介绍： Revista Clínica Española published its first issue in 1940 and is the body of expression of the Spanish Society of Internal Medicine (SEMI). The journal fully endorses the goals of updating knowledge and facilitating the acquisition of key developments in internal medicine applied to clinical practice. Revista Clínica Española is subject to a thorough double blind review of the received articles written in Spanish or English. Nine issues are published each year, including mostly originals, reviews and consensus documents.