Development of a novel mobile health tool for age-specific dehydration assessment and management in patients with diarrheal disease

项目摘要 据估计，腹泻疾病每年导致24亿起疾病发作和130万人死亡， 这些死亡中的大多数发生在五岁以上的成年人、青少年和儿童。作为严重的 腹泻疾病差异很大，准确评估脱水状况仍然是最关键的 在预防发病率和死亡率方面采取步骤。而严重脱水的患者则需要住院 入院和立即静脉输液复苏以防止血液动力学损害 缺血和死亡，轻度到中度脱水的人可以在门诊环境中使用 相对便宜的口服补液液。然而，尽管有几种工具已被验证可用于儿童 在五岁以下，没有任何临床诊断工具被验证用于评估 患有急性腹泻的成人、青少年或五岁以上儿童的脱水严重程度。 成人生理和腹泻病因的差异可能会影响临床诊断的准确性 为幼儿开发的模型。拟议中的研究将得出第一个特定年龄段的 为评估5岁以上患者的脱水状态而创建的临床诊断模型 对于急性腹泻，将这些模型整合到新的移动健康(MHealth)工具中，并验证 这一工具在新的急性腹泻患者群体中的表现。 为了完成这项任务，我们将招募一批五岁以上的成年人和儿童， 出现在国际腹泻病研究中心补液部的急性腹泻， 孟加拉国(ICDDR，b)在孟加拉国达卡，并收集关于呈现临床体征和症状的数据 在先前的研究中，与脱水严重程度相关。然后，我们将使用机器学习技术来 建立年龄特定的临床诊断模型，评估5岁以上儿童的脱水情况 急性腹泻。我们将在ICDDR，b的临床医生中进行形成性研究，以开发一种 创新的基于手机的平台，将结合这些新的特定年龄诊断模型 被一线卫生工作者快速使用。最后，我们将验证新模型的准确性和可靠性 在患有急性腹泻的成人和五岁以上儿童的新人群中开发了mHealth工具。 一旦开发并得到适当验证，这种新型的mHealth工具有可能帮助医生， 护士和其他医疗保健提供者更准确地诊断脱水严重程度，并更好地 确定急性腹泻患者的最佳治疗策略。改进的诊断 反过来，可以证明这些方法可以降低因错过而导致的发病率和死亡率。 脱水的诊断，以及不良事件和对有限医疗保健的不当利用 脱水诊断不准确可能导致的资源。

Project Summary Diarrheal diseases lead to an estimated 2.4 billion episodes of illness and 1.3 million deaths each year, with the majority of those deaths occurring in adults, adolescents, and children over five years. As the severity of diarrheal diseases can vary widely, accurately assessing dehydration status remains the most crucial step in preventing morbidity and mortality. While patients with severe dehydration require hospital admission and immediate resuscitation with intravenous fluids to prevent hemodynamic compromise, organ ischemia, and death, those with mild to moderate dehydration can be treated in outpatient settings with relatively inexpensive oral rehydration solution. Yet, while several tools have been validated for use in children under five years of age, no clinical diagnostic tool has ever been validated for the assessment of dehydration severity in adults, adolescents or children over five years of age with acute diarrhea. Differences in both adult physiology and diarrhea etiology may compromise the accuracy of clinical diagnostic models developed for use in young children. The proposed research will derive the very first age-specific clinical diagnostic models created for the assessment of dehydration status in patients over five years of age with acute diarrhea, incorporate those models into a new mobile health (mHealth) tool, and validate the performance of this tool in a new population of patients with acute diarrhea. To accomplish this task, we will enroll a prospective cohort of adults and children over five years of age with acute diarrhea presenting to the rehydration unit of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b) in Dhaka, Bangladesh and collect data on presenting clinical signs and symptoms shown to correlate with dehydration severity in prior studies. We will then employ machine learning techniques to derive age-specific clinical diagnostic models for assessing dehydration in patients over five years of age with acute diarrhea. We will conduct formative research among clinicians working at icddr,b to develop an innovative mobile phone based platform which will incorporate these new age-specific diagnostic models for rapid use by frontline health workers. Finally, we will validate both the accuracy and reliability of the newly developed mHealth tool in a new population of adults and children over five years of age with acute diarrhea. Once developed and properly validated, this novel mHealth tool has the potential to help physicians, nurses, and other healthcare providers more accurately diagnose dehydration severity and better determine the optimal management strategy for patients with acute diarrhea. Improved diagnostic approaches may in turn be shown to reduce both the morbidity and mortality that occurs as a result of missed diagnoses of dehydration, as well as the adverse events and inappropriate utilization of limited healthcare resources that can result from inaccurate diagnoses of dehydration.