PDF(Pubmed)
Abstract：
UNASSIGNED: Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively.
UNASSIGNED: This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.
UNASSIGNED: LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.
UNASSIGNED: Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.
UNASSIGNED: To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.
UNASSIGNED: This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.
UNASSIGNED: LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.
UNASSIGNED: Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.
UNASSIGNED: To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.
摘要：
■不必要的延长住院时间（LOS）会增加医院获得性并发症的风险，发病率，和全因死亡率，需要得到承认和积极解决。
■本系统评价旨在确定有效的预测变量和方法，用于预测所有住院患者，特别是普通医学（GenMed）住院患者的长期LOS风险。
■自2010年以来发布的LOS预测工具在五个主要研究数据库中被确定。主要结果是模型性能指标，预测变量，和验证级别。对已验证的模型进行Meta分析。使用PROBAST检查表评估偏倚风险。
■总的来说，确定了25项所有入院研究和14项GenMed研究。统计和机器学习方法在两组中几乎相等地使用。校准指标不经常报告，39项研究中只有2项进行了外部验证。所有入院验证研究的荟萃分析显示，曲线下面积的θ为0.596至0.798的95％预测间隔。重要的预测指标类别是合并症诊断和疾病严重程度风险评分，人口统计，和录取特点。由于数据处理和分析报告不佳，总体研究质量被认为较低。
■据我们所知,这是首次评估GenMed和所有入院组的住院LOS风险预测模型质量的系统评价.值得注意的是,机器学习和统计建模都表现出良好的预测性能，但模型很少经过外部验证,总体研究质量较差.往前走,在临床应用之前,需要通过采用现有指南和外部验证来关注质量方法.
■https://www.crd.约克。AC.英国/PROSPERO/,标识符：CRD42021272198。
■本系统评价旨在确定有效的预测变量和方法，用于预测所有住院患者，特别是普通医学（GenMed）住院患者的长期LOS风险。
■自2010年以来发布的LOS预测工具在五个主要研究数据库中被确定。主要结果是模型性能指标，预测变量，和验证级别。对已验证的模型进行Meta分析。使用PROBAST检查表评估偏倚风险。
■总的来说，确定了25项所有入院研究和14项GenMed研究。统计和机器学习方法在两组中几乎相等地使用。校准指标不经常报告，39项研究中只有2项进行了外部验证。所有入院验证研究的荟萃分析显示，曲线下面积的θ为0.596至0.798的95％预测间隔。重要的预测指标类别是合并症诊断和疾病严重程度风险评分，人口统计，和录取特点。由于数据处理和分析报告不佳，总体研究质量被认为较低。
■据我们所知,这是首次评估GenMed和所有入院组的住院LOS风险预测模型质量的系统评价.值得注意的是,机器学习和统计建模都表现出良好的预测性能，但模型很少经过外部验证,总体研究质量较差.往前走,在临床应用之前,需要通过采用现有指南和外部验证来关注质量方法.
■https://www.crd.约克。AC.英国/PROSPERO/,标识符：CRD42021272198。
