Goltz Daniel E, Burnett Robert A, Levin Jay M, Helmkamp Joshua K, Wickman John R, Hinton Zoe W, Howell Claire B, Green Cynthia L, Simmons J Alan, Nicholson Gregory P, Verma Nikhil N, Lassiter Tally E, Anakwenze Oke A, Garrigues Grant E, Klifto Christopher S
Department of Orthopaedic Surgery, Duke University Medical Center, Durham, NC, USA.
Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
J Shoulder Elbow Surg. 2023 May;32(5):1032-1042. doi: 10.1016/j.jse.2022.10.016. Epub 2022 Nov 16.
Recent work has shown inpatient length of stay (LOS) following shoulder arthroplasty to hold the second strongest association with overall cost (after implant cost itself). In particular, a preoperative understanding for the patients at risk of extended inpatient stays (≥3 days) can allow for counseling, optimization, and anticipating postoperative adverse events.
A multicenter retrospective review was performed of 5410 anatomic (52%) and reverse (48%) total shoulder arthroplasties done at 2 large, tertiary referral health systems. The primary outcome was extended inpatient LOS of at least 3 days, and over 40 preoperative sociodemographic and comorbidity factors were tested for their predictive ability in a multivariable logistic regression model based on the patient cohort from institution 1 (derivation, N = 1773). External validation was performed using the patient cohort from institution 2 (validation, N = 3637), including area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values.
A total of 814 patients, including 318 patients (18%) in the derivation cohort and 496 patients (14%) in the validation cohort, experienced an extended inpatient LOS of at least 3 days. Four hundred forty-five (55%) were discharged to a skilled nursing or rehabilitation facility. Following parameter selection, a multivariable logistic regression model based on the derivation cohort (institution 1) demonstrated excellent preliminary accuracy (AUC: 0.826), with minimal decrease in accuracy under external validation when tested against the patients from institution 2 (AUC: 0.816). The predictive model was composed of only preoperative factors, in descending predictive importance as follows: age, marital status, fracture case, ASA (American Society of Anesthesiologists) score, paralysis, electrolyte disorder, body mass index, gender, neurologic disease, coagulation deficiency, diabetes, chronic pulmonary disease, peripheral vascular disease, alcohol dependence, psychoses, smoking status, and revision case.
A freely-available, preoperative online clinical decision tool for extended inpatient LOS (≥ 3 days) after shoulder arthroplasty reaches excellent predictive accuracy under external validation. As a result, this tool merits consideration for clinical implementation, as many risk factors are potentially modifiable as part of a preoperative optimization strategy.
近期研究表明，肩关节置换术后的住院时长（LOS）与总成本的关联强度位居第二（仅次于植入物成本本身）。特别是，对于有延长住院时间（≥3天）风险的患者，术前了解情况有助于进行咨询、优化并预测术后不良事件。
对在2个大型三级转诊医疗系统中进行的5410例解剖型（52%）和反置型（48%）全肩关节置换术进行了多中心回顾性研究。主要结局是住院时长延长至少3天，在基于机构1的患者队列（推导组，N = 1773）建立的多变量逻辑回归模型中，对40多个术前社会人口统计学和合并症因素的预测能力进行了测试。使用机构2的患者队列（验证组，N = 3637）进行外部验证，包括受试者操作特征曲线下面积（AUC）、敏感性、特异性以及阳性和阴性预测值。
共有814例患者，包括推导队列中的318例患者（18%）和验证队列中的496例患者（14%），住院时长延长至少3天。其中445例（55%）出院后前往专业护理或康复机构。经过参数选择，基于推导队列（机构1）建立的多变量逻辑回归模型显示出出色的初步准确性（AUC：0.826），在针对机构2的患者进行外部验证时，准确性下降最小（AUC：0.816）。预测模型仅由术前因素组成，按预测重要性从高到低依次为：年龄、婚姻状况、骨折病例、美国麻醉医师协会（ASA）评分、瘫痪、电解质紊乱、体重指数、性别、神经系统疾病、凝血功能缺陷、糖尿病、慢性肺病、外周血管疾病、酒精依赖、精神病、吸烟状况和翻修病例。
一种免费的、用于预测肩关节置换术后住院时长延长（≥3天）的术前在线临床决策工具在外部验证中达到了出色的预测准确性。因此，该工具值得考虑用于临床实践，因为许多风险因素作为术前优化策略的一部分可能是可改变的。
