Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease.
Can subjective pain be inferred from objective physiological data? Evidence from patients with sickle cell disease.
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主观疼痛能从客观生理数据中推断出来吗？来自镰状细胞病患者的证据。
DOI：
10.1371/journal.pcbi.1008542
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Shah NR
中科院分区：
文献类型：
--
作者：
Panaggio MJ;Abrams DM;Yang F;Banerjee T;Shah NR
镰状细胞病（SCD）患者一生都在与慢性和急性疼痛作斗争，通常需要医疗干预。疼痛可以用药物来控制，但剂量必须平衡疼痛缓解的目标与耐受性，成瘾和其他不良反应的风险。设定适当的剂量需要了解患者的主观疼痛，但从患者那里收集疼痛报告对于临床医生来说可能很困难，并且对患者来说可能会造成干扰，并且只有当患者清醒且善于沟通时才有可能。在这里，我们调查的方法，估计SCD患者的疼痛程度间接使用的生命体征，定期收集和记录在医疗记录。使用机器学习，我们开发了顺序和非顺序概率模型，可用于从这些生理测量的序列中推断疼痛水平或疼痛变化。我们证明，这些模型优于空模型，客观的生理数据可以用来告知主观疼痛的估计。理解人类的主观疼痛仍然是一个重大挑战。如果可以使用客观数据来代替报告的疼痛水平，它可以减轻患者的负担，并能够收集更大的数据集，从而加深我们对疼痛原因的理解，并允许准确预测和更有效的疼痛管理。在这里，我们将两种机器学习方法应用于镰状细胞病患者的数据，这些患者经常经历使人衰弱的疼痛危机。使用在医院环境中常规收集的生命体征数据，包括呼吸率，心率和血压，以及不规则时间，缺失数据和患者间变化的现实挑战，我们证明这些模型在估计主观疼痛，区分典型和非典型疼痛水平以及检测疼痛变化方面优于基线模型。一旦经过训练，这些类型的模型可以用于在没有直接疼痛报告的情况下改善真实的疼痛估计。
Patients with sickle cell disease (SCD) experience lifelong struggles with both chronic and acute pain, often requiring medical interventMaion. Pain can be managed with medications, but dosages must balance the goal of pain mitigation against the risks of tolerance, addiction and other adverse effects. Setting appropriate dosages requires knowledge of a patient’s subjective pain, but collecting pain reports from patients can be difficult for clinicians and disruptive for patients, and is only possible when patients are awake and communicative. Here we investigate methods for estimating SCD patients’ pain levels indirectly using vital signs that are routinely collected and documented in medical records. Using machine learning, we develop both sequential and non-sequential probabilistic models that can be used to infer pain levels or changes in pain from sequences of these physiological measures. We demonstrate that these models outperform null models and that objective physiological data can be used to inform estimates for subjective pain. Understanding subjective human pain remains a major challenge. If objective data could be used in place of reported pain levels, it could reduce patient burdens and enable the collection of much larger data sets that could deepen our understanding of causes of pain and allow for accurate forecasting and more effective pain management. Here we apply two machine learning approaches to data from patients with sickle cell disease, who often experience debilitating pain crises. Using vital sign data routinely collected in hospital settings including respiratory rate, heart rate, and blood pressure and amidst the real-world challenges of irregular timing, missing data, and inter-patient variation, we demonstrate that these models outperform baseline models in estimating subjective pain, distinguishing between typical and atypical pain levels, and detecting changes in pain. Once trained, these types of models could be used to improve pain estimates in real time in the absence of direct pain reports.
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DOI：
10.5811/westjem.2017.9.35422
发表时间：
2018-03
期刊：
The western journal of emergency medicine
影响因子：
--
作者：
Cline DM;Silva S;Freiermuth CE;Thornton V;Tanabe P
通讯作者：
Tanabe P
影响因子：
--
作者：
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通讯作者：
Bateman, Alex
DOI：
10.1016/j.smhl.2018.01.002
发表时间：
2018-06-01
期刊：
Smart health (Amsterdam, Netherlands)
影响因子：
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作者：
Yang, Fan;Banerjee, Tanvi;Shah, Nirmish
通讯作者：
Shah, Nirmish
影响因子：
10.6
作者：
He, Xin;Frey, Eric. C.
通讯作者：
Frey, Eric. C.
影响因子：
7.4
作者：
Lötsch J;Ultsch A
通讯作者：
Ultsch A