Analysis of the Status Quo and Influencing Factors of Revisiting Patients After Pituitary Tumor Resection: Based on a Chinese Patient Population.

Wei Wang,Han Xiaoxu
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Abstract

OBJECTIVE To investigate the status of patients' post-pituitary tumor resection and analyze influencing factors, providing evidence for improved long-term management. METHODS The authors screened 1209 patients who underwent pituitary tumor resection at Zhejiang University's Second Affiliated Hospital from August 2020 to July 2022 using electronic medical records. Patients were classified into return visits (≥2 reviews/y or within 6 mo) and missing visit groups. Demographic and disease-related data were extracted from inpatient records, whereas return visits and prognosis data were collected from outpatient records and phone inquiries. Propensity score matching (1:1) was used to balance the groups, followed by univariate and multivariate logistic regression analyses to identify influencing factors. RESULTS Of the 1209 patients, 113 were unreachable. The study included 1095 patients, with 553 (50.5%) in the missing visit group and 542 (49.5%) in the return visit group. The authors matched 421 pairs, achieving balanced baseline data. Univariate analysis revealed significant differences in residence, unplanned readmission history, and current outcomes (P < 0.05). Multivariate analysis identified unplanned readmission history (odds ratio = 0.495, 95% CI: 0.307-0.799) as a protective factor. City residents had higher return visit rates than those from other provinces (odds ratio = 0.269, 95% CI: 0.610-1.579). CONCLUSION Postdischarge return rates for pituitary tumor resection patients are low and influenced by various factors. Improving return visit policies and systems is essential for facilitating outpatient follow-ups.
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垂体瘤切除术后复发患者的现状及影响因素分析:基于中国患者群体。
目的研究垂体瘤切除术后患者的状况并分析影响因素,为改善长期管理提供证据。方法作者利用电子病历对2020年8月至2022年7月期间在浙江大学附属第二医院接受垂体瘤切除术的1209例患者进行了筛查。将患者分为回访组(≥2次复查/年或6个月内)和缺访组。人口统计学和疾病相关数据从住院病历中提取,而回访和预后数据则从门诊病历和电话咨询中收集。采用倾向得分匹配法(1:1)平衡各组,然后进行单变量和多变量逻辑回归分析,以确定影响因素。研究共纳入了 1095 名患者,其中 553 人(50.5%)属于失访组,542 人(49.5%)属于回访组。作者对 421 对患者进行了配对,实现了基线数据的平衡。单变量分析显示,居住地、非计划再入院史和当前结果存在显著差异(P < 0.05)。多变量分析发现,非计划再入院史(几率比=0.495,95% CI:0.307-0.799)是一个保护因素。城市居民的回访率高于其他省份的居民(几率比=0.269,95% CI:0.610-1.579)。改善回访政策和制度对促进门诊随访至关重要。
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