School of Health Sciences, Department of Health and Biostatistics, Swinburne University, John Street Hawthorn, Victoria, 3122, Australia.
Cabrini Health, Melbourne, Australia.
Eur J Med Res. 2022 Jul 25;27(1):128. doi: 10.1186/s40001-022-00754-4.
Patients who exceed their expected length of stay in the hospital come at a cost to stakeholders in the healthcare sector as bed spaces are limited for new patients, nosocomial infections increase and the outcome for many patients is hampered due to multimorbidity after hospitalization.
This paper develops a technique for predicting Extended Length of Hospital Stay (ELOHS) at preadmission and their risk factors using hospital data.
A total of 91,468 records of patient's hospital information from a private acute teaching hospital were used for developing a machine learning algorithm relaying on Recursive Feature Elimination with Cross-Validation and Extra Tree Classifier (RFECV-ETC). The study implemented Synthetic Minority Oversampling Technique (SMOTE) and tenfold cross-validation to determine the optimal features for predicting ELOHS while relying on multivariate Logistic Regression (LR) for computing the risk factors and the Relative Risk (RR) of ELOHS at a 95% confidence level.
An estimated 11.54% of the patients have ELOHS, which increases with patient age as patients < 18 years, 18-40 years, 40-65 years and ≥ 65 years, respectively, have 2.57%, 4.33%, 8.1%, and 15.18% ELOHS rates. The RFECV-ETC algorithm predicted preadmission ELOHS to an accuracy of 89.3%. Age is a predominant risk factors of ELOHS with patients who are > 90 years-PAG (> 90) {RR: 1.85 (1.34-2.56), P: < 0.001} having 6.23% and 23.3%, respectively, higher likelihood of ELOHS than patient 80-90 years old-PAG (80-90) {RR: 1.74 (1.34-2.38), P: < 0.001} and those 70-80 years old-PAG (70-80) {RR: 1.5 (1.1-2.05), P: 0.011}. Those from admission category-ADC (US1) {RR: 3.64 (3.09-4.28, P: < 0.001} are 14.8% and 70.5%, respectively, more prone to ELOHS compared to ADC (UC1) {RR: 3.17 (2.82-3.55), P: < 0.001} and ADC (EMG) {RR: 2.11 (1.93-2.31), P: < 0.001}. Patients from SES (low) {RR: 1.45 (1.24-1.71), P: < 0.001)} are 13.3% and 45% more susceptible to those from SES (middle) and SES (high). Admission type (ADT) such as AS2, M2, NEWS, S2 and others {RR: 1.37-2.77 (1.25-6.19), P: < 0.001} also have a high likelihood of contributing to ELOHS while the distance to hospital (DTH) {RR: 0.64-0.75 (0.56-0.82), P: < 0.001}, Charlson Score (CCI) {RR: 0.31-0.68 (0.22-0.99), P: < 0.001-0.043} and some VMO specialties {RR: 0.08-0.69 (0.03-0.98), P: < 0.001-0.035} have limited influence on ELOHS.
Relying on the preadmission assessment of ELOHS helps identify those patients who are susceptible to exceeding their expected length of stay on admission, thus, making it possible to improve patients' management and outcomes.
患者在医院的住院时间超过预期，会给医疗保健部门的利益相关者带来成本，因为床位有限，新患者无法入住，医院获得性感染增加，许多患者在住院后因多种疾病而受到阻碍。
本文开发了一种使用医院数据预测预入院患者延长住院时间（ELOHS）及其风险因素的技术。
使用来自一家私立急性教学医院的 91468 份患者住院信息记录，使用递归特征消除与交叉验证和 Extra Tree 分类器（RFECV-ETC）开发机器学习算法。该研究实施了合成少数过采样技术（SMOTE）和 10 倍交叉验证，以确定预测 ELOHS 的最佳特征，同时依赖多元逻辑回归（LR）计算 ELOHS 的风险因素和相对风险（RR），置信水平为 95%。
估计有 11.54%的患者有 ELOHS，随着患者年龄的增加而增加，患者年龄分别为<18 岁、18-40 岁、40-65 岁和≥65 岁，ELOHS 发生率分别为 2.57%、4.33%、8.1%和 15.18%。RFECV-ETC 算法预测预入院 ELOHS 的准确率为 89.3%。年龄是 ELOHS 的主要危险因素，年龄>90 岁的患者-PAG（>90）{RR：1.85（1.34-2.56），P：<0.001}比 80-90 岁的患者-PAG（80-90）{RR：1.74（1.34-2.38），P：<0.001}和 70-80 岁的患者-PAG（70-80）{RR：1.5（1.1-2.05），P：0.011}发生 ELOHS 的可能性分别高 6.23%和 23.3%。入院类别-ADC（US1）{RR：3.64（3.09-4.28，P：<0.001}比 ADC（UC1）{RR：3.17（2.82-3.55），P：<0.001}和 ADC（EMG）{RR：2.11（1.93-2.31），P：<0.001}更容易发生 ELOHS。SES（低）{RR：1.45（1.24-1.71），P：<0.001)}的患者比 SES（中）和 SES（高）的患者更容易发生 ELOHS，发病率分别为 13.3%和 45%。入院类型（ADT），如 AS2、M2、NEWS、S2 和其他{RR：1.37-2.77（1.25-6.19），P：<0.001}也有很高的可能性导致 ELOHS，而到医院的距离（DTH）{RR：0.64-0.75（0.56-0.82），P：<0.001}，Charlson 评分（CCI）{RR：0.31-0.68（0.22-0.99），P：<0.001-0.043}和一些 VMO 专科{RR：0.08-0.69（0.03-0.98），P：<0.001-0.035}对 ELOHS 的影响有限。
依赖于预入院评估 ELOHS 有助于识别那些容易超过预期住院时间的患者，从而有可能改善患者的管理和结果。
