Abstract：
OBJECTIVE: To describe the characteristics and publication outcomes of clinical prediction model studies registered on clinicaltrials.gov since 2000.
METHODS: Observational studies registered on clinicaltrials.gov between January 1, 2000, and March 2, 2022, describing the development of a new clinical prediction model or the validation of an existing model for predicting individual-level prognostic or diagnostic risk were analyzed. Eligible clinicaltrials.gov records were classified by modeling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings, and plans to share individual participant data. Publication outcomes were analyzed by linking National Clinical Trial numbers for eligible records with PubMed abstracts.
RESULTS: Nine hundred twenty-eight records were analyzed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with 1 or more matching publications after accounting for time since study start was 2.8% at 2 years (95% CI: 1.7%, 3.9%), 12.3% at 5 years (9.8% to 14.9%) and 27% at 10 years (23% to 33%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritize the development of new prediction models over the validation of existing models (76%; 704/928 vs. 24%; 182/928). At the time of download, 27% of records were marked as complete, 35% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data.
CONCLUSIONS: Published clinical prediction model studies are only a fraction of overall research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.
METHODS: Observational studies registered on clinicaltrials.gov between January 1, 2000, and March 2, 2022, describing the development of a new clinical prediction model or the validation of an existing model for predicting individual-level prognostic or diagnostic risk were analyzed. Eligible clinicaltrials.gov records were classified by modeling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings, and plans to share individual participant data. Publication outcomes were analyzed by linking National Clinical Trial numbers for eligible records with PubMed abstracts.
RESULTS: Nine hundred twenty-eight records were analyzed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with 1 or more matching publications after accounting for time since study start was 2.8% at 2 years (95% CI: 1.7%, 3.9%), 12.3% at 5 years (9.8% to 14.9%) and 27% at 10 years (23% to 33%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritize the development of new prediction models over the validation of existing models (76%; 704/928 vs. 24%; 182/928). At the time of download, 27% of records were marked as complete, 35% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data.
CONCLUSIONS: Published clinical prediction model studies are only a fraction of overall research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.
摘要：
目的：描述自2000年以来在clinicaltrials.gov上注册的临床预测模型研究的特征和发表结果。
方法：分析了2000年1月1日至2022年3月2日在clinicaltrials.gov上注册的观察性研究，描述了新的临床预测模型的开发或现有预测个体水平预后或诊断风险模型的验证。符合条件的临床试验记录按建模研究类型分类(开发，验证)和预测的模型结果(预后，诊断)。记录的特征包括研究状态，样本量信息，医学主题词（MeSH），并计划共享个人参与者数据。通过将符合条件的记录的国家临床试验（NCT）编号与PubMed摘要相关联来分析发表结果。
结果：从可能的89,896个观察性研究记录中分析了928个记录。出版物搜索发现170个匹配的同行评审出版物，包含137个clinicaltrials.gov记录。自研究开始以来考虑时间后，具有一个或多个匹配出版物的记录的估计比例在2年为2.8％（95％置信区间；CI：1.7％至3.9％），五年为12.3%（9.8%至14.9%），十年为27%（23%至33%）。按研究开始年份进行分层记录表明，随着时间的推移，出版物比例有所改善。记录倾向于将新预测模型的开发优先于现有模型的验证（76％；704/928vs24％；182/928）。在下载时，27%的记录被标记为完整,35%仍在招聘，14.7%的患者状态未知。只有7.4%的记录表示计划共享个人参与者数据。
结论：已发表的临床预测模型研究只是整体研究工作的一小部分，许多研究计划，但没有完成或发表。改进研究预注册和后续行动的吸收将增加计划研究的可见性。引入额外的注册特征和指导可以改善发布到临床注册中心的临床预测模型研究的识别。
方法：分析了2000年1月1日至2022年3月2日在clinicaltrials.gov上注册的观察性研究，描述了新的临床预测模型的开发或现有预测个体水平预后或诊断风险模型的验证。符合条件的临床试验记录按建模研究类型分类(开发，验证)和预测的模型结果(预后，诊断)。记录的特征包括研究状态，样本量信息，医学主题词（MeSH），并计划共享个人参与者数据。通过将符合条件的记录的国家临床试验（NCT）编号与PubMed摘要相关联来分析发表结果。
结果：从可能的89,896个观察性研究记录中分析了928个记录。出版物搜索发现170个匹配的同行评审出版物，包含137个clinicaltrials.gov记录。自研究开始以来考虑时间后，具有一个或多个匹配出版物的记录的估计比例在2年为2.8％（95％置信区间；CI：1.7％至3.9％），五年为12.3%（9.8%至14.9%），十年为27%（23%至33%）。按研究开始年份进行分层记录表明，随着时间的推移，出版物比例有所改善。记录倾向于将新预测模型的开发优先于现有模型的验证（76％；704/928vs24％；182/928）。在下载时，27%的记录被标记为完整,35%仍在招聘，14.7%的患者状态未知。只有7.4%的记录表示计划共享个人参与者数据。
结论：已发表的临床预测模型研究只是整体研究工作的一小部分，许多研究计划，但没有完成或发表。改进研究预注册和后续行动的吸收将增加计划研究的可见性。引入额外的注册特征和指导可以改善发布到临床注册中心的临床预测模型研究的识别。
