同一医疗中心不同年龄组患者的多病模式及相关住院费用

Q2 Medicine
Tao Li, Xiaolin Xu, Yangyang Cheng, Kai Lin
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引用次数: 0

摘要

目的:分析不同年龄组住院患者的多病模式和核心疾病,探讨其对住院费用的影响。方法:收集浙江省某三级医院2018年1月1日至2023年6月30日成人住院患者(年龄≥18岁)的电子病历。分析了不同年龄组53种特殊疾病的多发病状况。关联规则挖掘用于识别常见的多发病模式。使用复杂网络分析来识别多发病网络中的核心疾病。采用广义估计方程(GEE)分析不同多病模式对住院费用的影响。结果:359402例成人住院患者多病患病率为38.52%,其中男性高(43.60%),老年人高(58.28%)。关联规则挖掘确定了15种常见的多病模式,这些模式在不同年龄组中表现出差异。总体而言,最普遍的多病模式是“糖尿病-高血压”(支持度=7.04%,置信度=62.17%,提升率=2.17)。青壮年组以“血脂异常-慢性肝病”为主(支持度=1.19%,置信度=53.17%,提升率=6.04)。中年组为“糖尿病-高血压”(支持度=4.84%,置信度=50.28%,提升度=2.15)。老年组为“冠心病、糖尿病、高血压”(支持度=2.38%,置信度=77.43%,提升度=1.63)。复杂网络分析显示,多病网络中的核心疾病在不同年龄组之间存在差异。青壮年组的核心疾病为慢性肝病(度中心性=50,中间中心性=0.055,接近中心性=0.963)。中年组核心疾病包括高血压、慢性肝病、糖尿病(度中心性=52,中间中心性=0.022,接近中心性=1.000)。老年组核心疾病为高血压、糖尿病、恶性肿瘤、慢性肝病、甲状腺疾病、贫血、心律失常(度中心性为52,中间中心性为0.009,接近中心性为1.000)。广义估计方程表明,与没有53种目标疾病中的任何一种的患者相比,大多数多病模式与住院费用增加显著相关。然而,在不同的多病模式中,成本增加的幅度差异很大。具体而言,“心力衰竭-高血压”、“中风-高血压”、“恶性肿瘤-糖尿病-高血压”、“中风-糖尿病-高血压”、“糖尿病-心力衰竭-高血压”等模式患者的住院费用是无目标疾病患者的两倍以上。结论:不同年龄组住院患者的多病模式和核心疾病存在显著差异,不同模式对住院费用的影响也不同。应根据患者的年龄和特定的多病模式实施个性化的管理和治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

[Multimorbidity patterns and associated hospitalization costs among different age groups of patients in a single medical center].

[Multimorbidity patterns and associated hospitalization costs among different age groups of patients in a single medical center].

[Multimorbidity patterns and associated hospitalization costs among different age groups of patients in a single medical center].

Objectives: To analyze the multimorbidity patterns and core diseases among hospitalized patients in different age groups and to explore the impacts of multimorbidity patterns on hospitalization costs.

Methods: Electronic medical records of adult inpatients (aged ≥18 years) from Ningbo Medical Center Lihuili Hospital between January 1, 2018, and June 30, 2023 were collected. The multimorbidity status involving 53 specific diseases was analyzed across different age groups. Association rule mining was used to identify common multimorbidity patterns. Complex network analysis was used to identify core diseases within the multimorbidity networks. Generalized estimating equations (GEE) were used to analyze the impact of different multimorbidity patterns on hospitalization costs.

Results: The prevalence of multimorbidity among the 359 402 adult inpatients was 38.51%, with higher rates observed in males (43.60%) and elderly patients (58.29%). Association rule mining identified 15 common multimorbidity patterns, which exhibited differences across age groups. The most prevalent multimorbidity pattern overall was "diabetes→hypertension" (support=7.04%, confidence=62.17%, lift=2.17). In the young adult group, the most prevalent pattern was "dyslipidemia→chronic liver disease" (support=1.19%, confidence=53.17%, lift=6.04). In the middle-aged group, it was "diabetes→hypertension" (support=4.84%, confidence=50.28%, lift=2.15). In the elderly group, it was "coronary heart disease, diabetes→hypertension" (support=2.38%, confidence=77.43%, lift=1.63). Complex network analysis revealed that the core diseases within multimorbidity networks differed across age groups. The core disease identified in the young adult group was chronic liver disease (degree centrality=50, betweenness centrality=0.055, closeness centrality=0.963). Core diseases in the middle-aged group included hypertension, chronic liver disease, and diabetes (all with degree centrality=52, betweenness centrality=0.022, closeness centrality=1.000). Core diseases in the elderly group comprised hypertension, diabetes, malignant tumors, chronic liver disease, thyroid disease, anemia, and arrhythmia (all with degree centrality=52, betweenness centrality=0.009, closeness centrality=1.000). Generalized estimating equations analysis indicated that, most multimorbidity patterns were significantly associated with increased hospitalization costs. However, the magnitude of cost increase varied across different multimorbidity patterns. Specifically, hospitalization costs for patients with patterns such as "heart failure→hypertension", "stroke→hypertension", "malignant tumor, diabetes→hypertension", "stroke, diabetes→hypertension", and "diabetes, heart failure→hypertension" were more than double those of patients without any target diseases.

Conclusions: Multimorbidity patterns and core diseases among hospitalized patients differ significantly across age groups, and different patterns exert varying impacts on hospitalization costs. These findings underscore the necessity for age-stratified and multimorbidity pattern specific management strategies.

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