PDF(Pubmed)
Abstract：
BACKGROUND: Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department \"boarding\" and hospital \"exit block\" and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes.
OBJECTIVE: To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital\'s electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval).
METHODS: This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data.
RESULTS: The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted.
CONCLUSIONS: This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.
OBJECTIVE: To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital\'s electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval).
METHODS: This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data.
RESULTS: The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted.
CONCLUSIONS: This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.
摘要：
背景：急诊科拥挤继续威胁患者的安全并导致患者预后不良。先前设计用于预测住院的模型存在偏见。成功估计患者入院概率的预测模型将有助于减少或预防急诊科“登机”和医院“出口障碍”，并通过提前入院和避免旷日持久的床位采购流程来减少急诊科的拥挤。
目的：通过利用现有的临床描述符，开发一种模型来预测即将从急诊科住院的成年患者在患者就诊早期（即，患者生物标志物)在分诊时常规收集并记录在医院的电子病历中。生物标志物有利于建模，因为它们在分诊时的早期和常规收集；瞬时可用性；标准化定义，测量,和解释；以及他们摆脱患者病史的限制(即，他们不会受到不准确的病史患者报告的影响，不可用的报告，或延迟报告检索)。
方法：这项回顾性队列研究评估了急诊科成年患者1年的连续数据事件，并开发了一种算法来预测哪些患者需要即将入院。评估了八个预测变量在患者急诊科就诊结果中的作用。采用Logistic回归对研究数据进行建模。
结果：8预测模型包括以下生物标志物：年龄，收缩压,舒张压,心率,呼吸频率,温度,性别,和敏锐度水平。该模型使用这些生物标志物来识别需要住院的急诊科患者。我们的模型表现很好，观察到的和预测的录取之间有很好的一致性，这表明了一个很好的拟合和校准良好的模型，显示出很好的能力来区分谁会入院和不会入院。
结论：这个基于主要数据的预测模型确定了急诊科患者入院风险增加。这些可操作的信息可用于改善患者护理和医院运营，特别是通过预测分诊后哪些患者可能入院，从而减少急诊科的拥挤，从而提供所需的信息，以在护理连续体中更早地启动复杂的入院和床位分配过程。
目的：通过利用现有的临床描述符，开发一种模型来预测即将从急诊科住院的成年患者在患者就诊早期（即，患者生物标志物)在分诊时常规收集并记录在医院的电子病历中。生物标志物有利于建模，因为它们在分诊时的早期和常规收集；瞬时可用性；标准化定义，测量,和解释；以及他们摆脱患者病史的限制(即，他们不会受到不准确的病史患者报告的影响，不可用的报告，或延迟报告检索)。
方法：这项回顾性队列研究评估了急诊科成年患者1年的连续数据事件，并开发了一种算法来预测哪些患者需要即将入院。评估了八个预测变量在患者急诊科就诊结果中的作用。采用Logistic回归对研究数据进行建模。
结果：8预测模型包括以下生物标志物：年龄，收缩压,舒张压,心率,呼吸频率,温度,性别,和敏锐度水平。该模型使用这些生物标志物来识别需要住院的急诊科患者。我们的模型表现很好，观察到的和预测的录取之间有很好的一致性，这表明了一个很好的拟合和校准良好的模型，显示出很好的能力来区分谁会入院和不会入院。
结论：这个基于主要数据的预测模型确定了急诊科患者入院风险增加。这些可操作的信息可用于改善患者护理和医院运营，特别是通过预测分诊后哪些患者可能入院，从而减少急诊科的拥挤，从而提供所需的信息，以在护理连续体中更早地启动复杂的入院和床位分配过程。
