Essay Patrick, Mosier Jarrod, Subbian Vignesh
College of Engineering, The University of Arizona, Tucson, AZ, United States.
College of Medicine, The University of Arizona, Tucson, AZ, United States.
JMIR Med Inform. 2020 Apr 15;8(4):e18402. doi: 10.2196/18402.
Acute respiratory failure is generally treated with invasive mechanical ventilation or noninvasive respiratory support strategies. The efficacies of the various strategies are not fully understood. There is a need for accurate therapy-based phenotyping for secondary analyses of electronic health record data to answer research questions regarding respiratory management and outcomes with each strategy.
The objective of this study was to address knowledge gaps related to ventilation therapy strategies across diverse patient populations by developing an algorithm for accurate identification of patients with acute respiratory failure. To accomplish this objective, our goal was to develop rule-based computable phenotypes for patients with acute respiratory failure using remotely monitored intensive care unit (tele-ICU) data. This approach permits analyses by ventilation strategy across broad patient populations of interest with the ability to sub-phenotype as research questions require.
Tele-ICU data from ≥200 hospitals were used to create a rule-based algorithm for phenotyping patients with acute respiratory failure, defined as an adult patient requiring invasive mechanical ventilation or a noninvasive strategy. The dataset spans a wide range of hospitals and ICU types across all US regions. Structured clinical data, including ventilation therapy start and stop times, medication records, and nurse and respiratory therapy charts, were used to define clinical phenotypes. All adult patients of any diagnoses with record of ventilation therapy were included. Patients were categorized by ventilation type, and analysis of event sequences using record timestamps defined each phenotype. Manual validation was performed on 5% of patients in each phenotype.
We developed 7 phenotypes: (0) invasive mechanical ventilation, (1) noninvasive positive-pressure ventilation, (2) high-flow nasal insufflation, (3) noninvasive positive-pressure ventilation subsequently requiring intubation, (4) high-flow nasal insufflation subsequently requiring intubation, (5) invasive mechanical ventilation with extubation to noninvasive positive-pressure ventilation, and (6) invasive mechanical ventilation with extubation to high-flow nasal insufflation. A total of 27,734 patients met our phenotype criteria and were categorized into these ventilation subgroups. Manual validation of a random selection of 5% of records from each phenotype resulted in a total accuracy of 88% and a precision and recall of 0.8789 and 0.8785, respectively, across all phenotypes. Individual phenotype validation showed that the algorithm categorizes patients particularly well but has challenges with patients that require ≥2 management strategies.
Our proposed computable phenotyping algorithm for patients with acute respiratory failure effectively identifies patients for therapy-focused research regardless of admission diagnosis or comorbidities and allows for management strategy comparisons across populations of interest.
急性呼吸衰竭一般采用有创机械通气或无创呼吸支持策略进行治疗。各种策略的疗效尚未完全明确。需要基于准确治疗方法的表型分析来对电子健康记录数据进行二次分析，以回答有关每种策略的呼吸管理及预后的研究问题。
本研究的目的是通过开发一种用于准确识别急性呼吸衰竭患者的算法，来填补不同患者群体在通气治疗策略方面的知识空白。为实现这一目标，我们的目的是利用远程监测重症监护病房（远程ICU）的数据，为急性呼吸衰竭患者开发基于规则的可计算表型。这种方法允许根据通气策略对广泛感兴趣的患者群体进行分析，并能够根据研究问题的需要进行亚表型分析。
使用来自≥200家医院的远程ICU数据创建一种基于规则的算法，用于对急性呼吸衰竭患者进行表型分析，急性呼吸衰竭定义为需要有创机械通气或无创策略的成年患者。该数据集涵盖了美国所有地区的广泛医院及ICU类型。结构化临床数据，包括通气治疗开始和停止时间、用药记录以及护士和呼吸治疗图表，用于定义临床表型。纳入所有有通气治疗记录的任何诊断的成年患者。根据通气类型对患者进行分类，并使用记录时间戳对事件序列进行分析来定义每种表型。对每种表型中5%的患者进行人工验证。
我们开发了7种表型：（0）有创机械通气，（1）无创正压通气，（2）高流量鼻导管吸氧，（3）随后需要插管的无创正压通气，（4）随后需要插管的高流量鼻导管吸氧，（5）有创机械通气拔管后改为无创正压通气，以及（6）有创机械通气拔管后改为高流量鼻导管吸氧。共有27734名患者符合我们的表型标准，并被分类到这些通气亚组中。对每种表型随机抽取5%的记录进行人工验证，所有表型的总准确率为88%，精确率和召回率分别为0.8789和0.8785。个体表型验证表明，该算法对患者的分类效果特别好，但对于需要≥2种管理策略的患者存在挑战。
我们提出的用于急性呼吸衰竭患者的可计算表型算法能够有效地识别适合以治疗为重点研究的患者，无论其入院诊断或合并症如何，并允许对感兴趣的人群的管理策略进行比较。
