研究人群结局参数数据集
包含293例PUUV检测患者的临床参数和结局，用于构建预测PUUV阳性风险的评分工具，指导血清学检测。
基本信息
资源简介
研究人群结局参数数据集
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 receiver operating characteristic、positive test result、four admission parameters、causes nephropathia epidemica、independent historic cohort，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Study population outcome parameters.》。源站首次发布：2024-05-31。
【数据内容】
源站原始描述：Background Puumala hantavirus (PUUV) causes nephropathia epidemica (NE), an endemic form of transient acute renal injury (AKI). Serological testing is the mainstay of diagnosis. It was the aim of the present study to assist decision-making for serological testing by constructing a simple tool that predicts the likelihood of PUUV positivity. Methods We conducted a comparative cohort study of all PUUV-tested cases at Aachen University tertiary care center in Germany between mid-2013 and mid-2021.…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0304500.t002。
【主题与分类】
源站学科分类：Medicine、Microbiology、Cell Biology、Biotechnology、Developmental Biology、Infectious Diseases。主题标签：receiver operating characteristic、positive test result、four admission parameters、causes nephropathia epidemica、independent historic cohort、partial external validation、41 positive cases、suspected puuv infection、aachen puuv score、comparative cohort study。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Study_population_outcome_parameters_/25946939
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Brockmann, Justus; Kleines, Michael; Laleh, Narmin Ghaffari; Kather, Jakob Nikolas; Wied, Stephanie; Floege, Jürgen; et al. (2024). Study population outcome parameters.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Study population outcome parameters.
- DOI：10.1371/journal.pone.0304500.t002
- 发布日期：2024-05-31
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Study_population_outcome_parameters_/25946939
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/46753322"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Background
Puumala hantavirus (PUUV) causes nephropathia epidemica (NE), an endemic form of transient acute renal injury (AKI). Serological testing is the mainstay of diagnosis. It was the aim of the present study to assist decision-making for serological testing by constructing a simple tool that predicts the likelihood of PUUV positivity.
Methods
We conducted a comparative cohort study of all PUUV-tested cases at Aachen University tertiary care center in Germany between mid-2013 and mid-2021. N = 293 qualified for inclusion; N = 30 had a positive test result and clinical NE; N = 263 were negative. Two predictive point scores, the Aachen PUUV Score (APS) 1 and 2, respectively, were derived with the aid of logistic regression and receiver operating characteristic (ROC) analysis by determining the presence of four admission parameters. For internal validation, the internal Monte Carlo method was applied. In addition, partial external validation was performed using an independent historic cohort of N = 41 positive cases of NE.
Results
APS1 is recommended for clinical use as it estimated the probability of PUUV positivity in the entire medical population tested. With a range from 0 to 6 points, it yielded an area under the curve of 0.94 by allotting 2 points each for fever or headache and 1 point each for AKI or LDH>300 U/L. A point sum of 0–2 safely predicted negativity for PUUV, as was confirmed in the NE validation cohort.
Conclusion
Here, we present a novel, easy-to-use tool to guide the diagnostic management of suspected PUUV infection/NE and to safely avoid unnecessary serological testing, as indicated by point sum class 0–2. Since 67% of the cohort fell into this stratum, half of the testing should be avoidable in the future.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0304500.t002（source: https://plos.figshare.com/articles/dataset/Study_population_outcome_parameters_/25946939）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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