Atamañuk Andrés Nicolás, Gandino Ignacio Javier, Miranda María Noralí, Cardozo Leandro Martín, Escalante Sergio Exequiel, Villalba Cesar, Abalovich Bernal David, Ross Gustavo, Perna Eduardo, Delgado Diego
División Cardiología, Hospital General de Agudos Juan Antonio Fernández, Ciudad Autónoma de Buenos Aires, Argentina.
División Clínica Médica, Hospital General de Agudos Juan Antonio Fernández, Ciudad Autónoma de Buenos Aires, Argentina.
JACC Adv. 2025 Jul;4(7):101888. doi: 10.1016/j.jacadv.2025.101888. Epub 2025 Jun 19.
Analyzing skin temperature in heart failure is an important medical practice that could assist to identify poor perfusion. Thermography, a technique that captures infrared radiation from tissues, could quantify these temperatures and thermal gradients. It has not been evaluated in patients with acute decompensated heart failure (ADHF) before.
The purpose of this study was to assess the performance of thermography in the diagnosis of ADHF.
A cross-sectional study was performed, including consecutive patients hospitalized with ADHF diagnosed by an expert heart failure team. Patients hospitalized for other cardiac disorders without ADHF were included as controls. Ten thermal photos of each patient were taken within the first 4 hours after admission in a cardiac care unit. Specific thermal spots, averages, and gradients were analyzed. Thermography's diagnostic properties for ADHF detection were evaluated using machine learning with the extreme gradient boosting model.
Sixty patients were included: 30 cases with ADHF and 30 controls. The mean age was 63.4 years (SD: 13.3), and 38 (63.3%) were males. Thermal points and averages showed lower temperature, while gradients were higher in the ADHF group, being all statistically significant between groups. The properties of the blend between thermography and artificial intelligence to detect ADHF had 84% sensitivity and 52% specificity. The area under the curve was 0.82 (95% CI: 0.73-0.91).
Thermography demonstrated differences between patients with ADHF and those with other cardiological disorders. In this proof of concept, combining thermography with artificial intelligence enabled the detection of ADHF in subjects hospitalized in a cardiac care unit.
分析心力衰竭患者的皮肤温度是一项重要的医学实践，有助于识别灌注不足。热成像技术能够捕捉组织发出的红外辐射，可对这些温度和热梯度进行量化。此前尚未在急性失代偿性心力衰竭（ADHF）患者中进行过评估。
本研究旨在评估热成像技术在ADHF诊断中的性能。
进行了一项横断面研究，纳入由专业心力衰竭团队诊断为ADHF并连续住院的患者。将因其他无ADHF的心脏疾病住院的患者作为对照。在心脏监护病房入院后的前4小时内，为每位患者拍摄10张热成像照片。分析特定的热点、平均值和梯度。使用极端梯度提升模型的机器学习方法评估热成像技术检测ADHF的诊断特性。
共纳入60例患者：30例ADHF患者和30例对照。平均年龄为63.4岁（标准差：13.3），男性38例（63.3%）。ADHF组的热点和平均值显示温度较低，而梯度较高，两组之间均具有统计学意义。热成像技术与人工智能相结合检测ADHF的特性具有84%的敏感性和52%的特异性。曲线下面积为0.82（95%置信区间：0.73 - 0.91）。
热成像技术显示出ADHF患者与其他心脏疾病患者之间的差异。在本概念验证中，热成像技术与人工智能相结合能够在心脏监护病房住院的患者中检测出ADHF。
