Andrew P. Reimer, Wei Dai, N. Schiltz, Jiayang Sun, S. Koroukian
{"title":"Patient factors associated with survival after critical care interhospital transfer","authors":"Andrew P. Reimer, Wei Dai, N. Schiltz, Jiayang Sun, S. Koroukian","doi":"10.3389/femer.2023.1339798","DOIUrl":null,"url":null,"abstract":"To identify the factors that predict mortality post-transfer and develop a comprehensive mortality prediction model capable of supporting pre-transfer decision making.Electronic health record data from the Medical Transport Data Repository of a large health system hospital in Northeast Ohio that consists of a main campus and 11 affiliated medical centers. We retrospectively analyzed patient data from the referring hospital encounter prior to interhospital transfer. All patient data including diagnoses, laboratory results, medication, and medical and social history were analyzed to predict in-hospital mortality post-transfer. We employed a multi-method approach including logistic regression, gradient boosting, and multiple correspondence analysis to identify significant predictors of mortality as well as variables that are clinically useful to inform clinical decision support development. We identified all patients aged 21 and older that underwent critical care transfer in the health system between 2010 and 2017.We found that age, laboratory results (albumin, INR, platelets, BUN, leukocyte, hemoglobin, glucose), vital signs (temperature, respirations, pulse, systolic blood pressure, pulse oximetry), and ventilator usage are the most predictive variables of post-interhospital transfer mortality. Using structured data from the EHR we achieved the same performance as APACHE IV within our health system (0.85 vs. 0.85). Lastly, mode of transport alone was not a significant predictor for the general population in any of the outcome models.Our findings provide a foundation for the development of decision support tools to guide transport referrals and identified the need for further inquiry to discern the role of mode of transport to enable future inclusion in decision support approaches. Further inquiry is needed to identify factors that differentiate patients not triaged as time-sensitive transfers but still require helicopter intervention to maintain or improve post-interhospital transfer morbidity and mortality.","PeriodicalId":502453,"journal":{"name":"Frontiers in Disaster and Emergency Medicine","volume":"48 23","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Disaster and Emergency Medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/femer.2023.1339798","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
To identify the factors that predict mortality post-transfer and develop a comprehensive mortality prediction model capable of supporting pre-transfer decision making.Electronic health record data from the Medical Transport Data Repository of a large health system hospital in Northeast Ohio that consists of a main campus and 11 affiliated medical centers. We retrospectively analyzed patient data from the referring hospital encounter prior to interhospital transfer. All patient data including diagnoses, laboratory results, medication, and medical and social history were analyzed to predict in-hospital mortality post-transfer. We employed a multi-method approach including logistic regression, gradient boosting, and multiple correspondence analysis to identify significant predictors of mortality as well as variables that are clinically useful to inform clinical decision support development. We identified all patients aged 21 and older that underwent critical care transfer in the health system between 2010 and 2017.We found that age, laboratory results (albumin, INR, platelets, BUN, leukocyte, hemoglobin, glucose), vital signs (temperature, respirations, pulse, systolic blood pressure, pulse oximetry), and ventilator usage are the most predictive variables of post-interhospital transfer mortality. Using structured data from the EHR we achieved the same performance as APACHE IV within our health system (0.85 vs. 0.85). Lastly, mode of transport alone was not a significant predictor for the general population in any of the outcome models.Our findings provide a foundation for the development of decision support tools to guide transport referrals and identified the need for further inquiry to discern the role of mode of transport to enable future inclusion in decision support approaches. Further inquiry is needed to identify factors that differentiate patients not triaged as time-sensitive transfers but still require helicopter intervention to maintain or improve post-interhospital transfer morbidity and mortality.
我们从俄亥俄州东北部一家大型医疗系统医院的医疗转运数据存储库中获取了电子健康记录数据,该医院由一个主校区和 11 个附属医疗中心组成。我们回顾性地分析了院际转运前转诊医院的患者数据。我们分析了所有患者数据,包括诊断、化验结果、用药、病史和社会史,以预测转院后的院内死亡率。我们采用了多种方法,包括逻辑回归、梯度提升和多重对应分析,以确定死亡率的重要预测因素以及对临床有用的变量,为临床决策支持的开发提供信息。我们发现,年龄、实验室结果(白蛋白、INR、血小板、BUN、白细胞、血红蛋白、葡萄糖)、生命体征(体温、呼吸、脉搏、收缩压、脉搏血氧饱和度)和呼吸机使用情况是院内转运后死亡率的最大预测变量。通过使用电子病历中的结构化数据,我们的医疗系统实现了与 APACHE IV 相同的性能(0.85 vs. 0.85)。我们的研究结果为开发决策支持工具以指导转运提供了基础,并确定了进一步调查的必要性,以确定转运方式的作用,以便将来纳入决策支持方法。我们还需要进一步研究,以确定哪些因素可以区分未被分流为时间敏感转运但仍需要直升机干预的患者,从而维持或改善院内转运后的发病率和死亡率。