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首页> 外文期刊>JMIR Medical Informatics >A Bayesian Network Analysis of the Diagnostic Process and its Accuracy to Determine How Clinicians Estimate Cardiac Function in Critically Ill Patients: Prospective Observational Cohort Study
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A Bayesian Network Analysis of the Diagnostic Process and its Accuracy to Determine How Clinicians Estimate Cardiac Function in Critically Ill Patients: Prospective Observational Cohort Study

机译：贝叶斯网络分析诊断过程及其准确性，以确定临床医生如何估计临床病患者心功能：前瞻性观察队列研究

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Background Hemodynamic assessment of critically ill patients is a challenging endeavor, and advanced monitoring techniques are often required to guide treatment choices. Given the technical complexity and occasional unavailability of these techniques, estimation of cardiac function based on clinical examination is valuable for critical care physicians to diagnose circulatory shock. Yet, the lack of knowledge on how to best conduct and teach the clinical examination to estimate cardiac function has reduced its accuracy to almost that of “flipping a coin.” Objective The aim of this study was to investigate the decision-making process underlying estimates of cardiac function of patients acutely admitted to the intensive care unit (ICU) based on current standardized clinical examination using Bayesian methods. Methods Patient data were collected as part of the Simple Intensive Care Studies-I (SICS-I) prospective cohort study. All adult patients consecutively admitted to the ICU with an expected stay longer than 24 hours were included, for whom clinical examination was conducted and cardiac function was estimated. Using these data, first, the probabilistic dependencies between the examiners’ estimates and the set of clinically measured variables upon which these rely were analyzed using a Bayesian network. Second, the accuracy of cardiac function estimates was assessed by comparison to the cardiac index values measured by critical care ultrasonography. Results A total of 1075 patients were included, of which 783 patients had validated cardiac index measurements. A Bayesian network analysis identified two clinical variables upon which cardiac function estimate is conditionally dependent, namely, noradrenaline administration and presence of delayed capillary refill time or mottling. When the patient received noradrenaline, the probability of cardiac function being estimated as reasonable or good P(ER,G) was lower, irrespective of whether the patient was mechanically ventilated (P[ER,G|ventilation, noradrenaline]=0.63, P[ER,G|ventilation, no noradrenaline]=0.91, P[ER,G|no ventilation, noradrenaline]=0.67, P[ER,G|no ventilation, no noradrenaline]=0.93). The same trend was found for capillary refill time or mottling. Sensitivity of estimating a low cardiac index was 26% and 39% and specificity was 83% and 74% for students and physicians, respectively. Positive and negative likelihood ratios were 1.53 (95% CI 1.19-1.97) and 0.87 (95% CI 0.80-0.95), respectively, overall. Conclusions The conditional dependencies between clinical variables and the cardiac function estimates resulted in a network consistent with known physiological relations. Conditional probability queries allow for multiple clinical scenarios to be recreated, which provide insight into the possible thought process underlying the examiners’ cardiac function estimates. This information can help develop interactive digital training tools for students and physicians and contribute toward the goal of further improving the diagnostic accuracy of clinical examination in ICU patients.
机译：背景技术危重患者的血流动力学评估是一个具有挑战性的努力，通常需要先进的监测技术来指导治疗选择。鉴于这些技术的技术复杂性和偶尔的不可用，基于临床检查的心功能估计对于诊断循环冲击的关键护理医生有价值。然而，缺乏关于如何最好的行为和教导临床检查来估计心脏功能的知识，这使得其准确性降低了“翻转硬币”的准确性。目的本研究的目的是探讨基于使用贝叶斯方法目前的标准化临床检查急剧入院的患者心功能估计的决策过程。方法将患者数据作为简单的重症监护研究的一部分收集 - 我（SICS-I）预期队列研究。所有成年患者均包括预期停留时间超过24小时的ICU，为此，对其进行了临床检查，估计心功能。首先，使用这些数据，审查员估计与临床测量变量之间的概率依赖性，这些级别使用贝叶斯网络分析这些级别的临床测量变量。其次，通过与临界护理超声检查测量的心脏指数值进行评估，评估心功能估计的准确性。结果总共包括1075名患者，其中783名患者验证了心脏指数测量。贝叶斯网络分析鉴定了两种临床变量，该临床变量在有条件依赖性，即去甲肾上腺素给药和延迟毛细血管重新填充时间或斑点的存在。当患者接受去甲肾上腺素时，无论患者是否机械通气，估计为合理或良好的p（ER，G）的心功能估计的概率（P [ER，G | Noradrenaline] = 0.63，P [ ER，G |通风，无去甲肾上腺素] = 0.91，P [ER，G |无通风，去甲肾上腺素] = 0.67，P [ER，G |无通气，无NORADRENALINE] = 0.93）。发现毛细血管重新填充时间或斑点相同的趋势。估计低心脏指数的敏感性分别为26％，39％，特异性分别为学生和医生的83％和74％。阳性和负似然比为1.53（95％CI 1.19-1.97）和0.87（95％CI 0.80-0.95），总体而言。结论临床变量与心功能估计之间的条件依赖性导致网络与已知的生理关系一致。条件概率查询允许重新创建多种临床情景，这提供了对审查员心功能估计的可能思想过程的洞察力。这些信息可以帮助为学生和医生开发互动数字培训工具，并有助于进一步提高ICU患者临床检查诊断准确性的目标。

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