M. S. Schaefer, M. Hammer, K. Platzbecker, P. Santer, S. D. Grabitz, K. R. Murugappan, S. Barnett, M. Eikermann, Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
M. S. Schaefer, M. Hammer, K. Platzbecker, P. Santer, S. D. Grabitz, K. R. Murugappan, T. Houle, S. Barnett, E. K. Rodriguez, M. Eikermann Harvard Medical School, Boston, MA, USA.
Clin Orthop Relat Res. 2021 Mar 1;479(3):546-547. doi: 10.1097/CORR.0000000000001532.
Adverse discharge disposition, which is discharge to a long-term nursing home or skilled nursing facility is frequent and devastating in older patients after lower-extremity orthopaedic surgery. Predicting individual patient risk allows for preventive interventions to address modifiable risk factors and helps managing expectations. Despite a variety of risk prediction tools for perioperative morbidity in older patients, there is no tool available to predict successful recovery of a patient's ability to live independently in this highly vulnerable population.
QUESTIONS/PURPOSES: In this study, we asked: (1) What factors predict adverse discharge disposition in patients older than 60 years after lower-extremity surgery? (2) Can a prediction instrument incorporating these factors be applied to another patient population with reasonable accuracy? (3) How does the instrument compare with other predictions scores that account for frailty, comorbidities, or procedural risk alone?
In this retrospective study at two competing New England university hospitals and Level 1 trauma centers with 673 and 1017 beds, respectively; 83% (19,961 of 24,095) of patients 60 years or older undergoing lower-extremity orthopaedic surgery were included. In all, 5% (1316 of 24,095) patients not living at home and 12% (2797 of 24,095) patients with missing data were excluded. All patients were living at home before surgery. The mean age was 72 ± 9 years, 60% (11,981 of 19,961) patients were female, 21% (4155 of 19,961) underwent fracture care, and 34% (6882 of 19,961) underwent elective joint replacements. Candidate predictors were tested in a multivariable logistic regression model for adverse discharge disposition in a development cohort of all 14,123 patients from the first hospital, and then included in a prediction instrument that was validated in all 5838 patients from the second hospital by calculating the area under the receiver operating characteristics curve (ROC-AUC).Thirty-eight percent (5360 of 14,262) of patients in the development cohort and 37% (2184 of 5910) of patients in the validation cohort had adverse discharge disposition. Score performance in predicting adverse discharge disposition was then compared with prediction scores considering frailty (modified Frailty Index-5 or mFI-5), comorbidities (Charlson Comorbidity Index or CCI), and procedural risks (Procedural Severity Scores for Morbidity and Mortality or PSS).
After controlling for potential confounders like BMI, cardiac, renal and pulmonary disease, we found that the most prominent factors were age older than 90 years (10 points), hip or knee surgery (7 or 8 points), fracture management (6 points), dementia (5 points), unmarried status (3 points), federally provided insurance (2 points), and low estimated household income based on ZIP code (1 point). Higher score values indicate a higher risk of adverse discharge disposition. The score comprised 19 variables, including socioeconomic characteristics, surgical management, and comorbidities with a cutoff value of ≥ 23 points. Score performance yielded an ROC-AUC of 0.85 (95% confidence interval 0.84 to 0.85) in the development and 0.72 (95% CI 0.71 to 0.73) in the independent validation cohort, indicating excellent and good discriminative ability. Performance of the instrument in predicting adverse discharge in the validation cohort was superior to the mFI-5, CCI, and PSS (ROC-AUC 0.72 versus 0.58, 0.57, and 0.57, respectively).
The Adverse Discharge in Older Patients after Lower Extremity Surgery (ADELES) score predicts adverse discharge disposition after lower-extremity surgery, reflecting loss of the ability to live independently. Its discriminative ability is better than instruments that consider frailty, comorbidities, or procedural risk alone. The ADELES score identifies modifiable risk factors, including general anesthesia and prolonged preoperative hospitalization, and should be used to streamline patient and family expectation management and improve shared decision making. Future studies need to evaluate the score in community hospitals and in institutions with different rates of adverse discharge disposition and lower income. A non-commercial calculator can be accessed at www.adeles-score.org.
Level III, diagnostic study.
下肢骨科手术后，老年患者的不良出院处置（即长期疗养院或康复护理机构的出院）频繁且具有破坏性。预测个体患者的风险可以进行预防性干预，以解决可改变的风险因素，并有助于管理预期。尽管有多种针对老年患者围手术期发病率的风险预测工具，但没有工具可用于预测这一高度脆弱人群中患者独立生活能力的恢复情况。
问题/目的：在这项研究中，我们提出了以下三个问题：（1）哪些因素可以预测下肢手术后年龄超过 60 岁的患者的不良出院处置？（2）能否将包含这些因素的预测工具应用于另一个具有合理准确性的患者人群？（3）该工具与仅考虑脆弱性、合并症或手术风险的其他预测评分相比如何？
本研究回顾性分析了两家新英格兰大学医院和 1 家 1 级创伤中心的 24095 名 60 岁及以上接受下肢骨科手术的患者。其中 5%（1316 名）不住在自己家里，12%（2797 名）的患者缺失数据，其余所有患者术前均居住在自己家里。患者的平均年龄为 72±9 岁，60%（11981 名）为女性，21%（4155 名）为骨折患者，34%（6882 名）为择期关节置换术患者。在第一个医院的所有 14123 名患者的发展队列中，测试了候选预测因素，以确定不良出院处置的多变量逻辑回归模型，然后将其纳入第二个医院的所有 5910 名患者的验证队列中，通过计算接受者操作特征曲线下的面积（ROC-AUC）来验证预测工具。发展队列中有 38%（5360 名）的患者和验证队列中有 37%（2184 名）的患者有不良出院处置。然后将预测不良出院处置的评分与考虑脆弱性（改良脆弱性指数-5 或 mFI-5）、合并症（Charlson 合并症指数或 CCI）和手术风险（发病率和死亡率的手术严重程度评分或 PSS）的预测评分进行比较。
在控制 BMI、心脏、肾脏和肺部疾病等潜在混杂因素后，我们发现最显著的因素是年龄大于 90 岁（10 分）、髋部或膝关节手术（7 分或 8 分）、骨折管理（6 分）、痴呆（5 分）、未婚状态（3 分）、联邦政府提供的保险（2 分）和根据邮政编码估算的低家庭收入（1 分）。评分值越高，不良出院处置的风险越高。该评分包括 19 个变量，包括社会经济特征、手术管理和合并症，其截断值为≥23 分。评分的 ROC-AUC 在发展队列中为 0.85（95%置信区间为 0.84 至 0.85），在独立验证队列中为 0.72（95%CI 为 0.71 至 0.73），表明具有优异和良好的区分能力。该工具在验证队列中预测不良出院的性能优于 mFI-5、CCI 和 PSS（ROC-AUC 0.72 与 0.58、0.57 和 0.57 相比）。
下肢手术后老年患者不良出院风险评分（ADELES）预测下肢手术后的不良出院处置，反映了独立生活能力的丧失。其鉴别能力优于仅考虑脆弱性、合并症或手术风险的工具。ADELES 评分确定了可改变的风险因素，包括全身麻醉和术前住院时间延长，应用于简化患者和家属的期望管理，改善共同决策。未来的研究需要评估该评分在社区医院和不良出院处置率和收入较低的机构中的应用。可访问一个非商业计算器，网址为：www.adeles-score.org。
三级，诊断研究。
