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The suboptimal clinical applicability of prognostic prediction models for severe postpartum hemorrhage: a
Chunrong Liu1, Yiquan Xiong1, Peng Zhao1
1Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, Sichuan 610041, China; Sichuan Center of Technology Innovation for Real World Data, Chengdu, Sichuan 610041, China.
Objectives:
To systematically investigate clinical applicability of the current prognostic prediction models for severe postpartum hemorrhage (SPPH).
Study Design And Setting:
A meta-epidemiological study of prognostic prediction models was conducted for SPPH. A pre-designed structured questionnaire was adopted to extract the study characteristics, predictors and the outcome, modeling methods, predictive performance, the classification ability for high-risk individuals, and clinical use scenarios. The risk of bias among studies was assessed by the Prediction model Risk Of Bias ASsessment Tool (PROBAST).
Results:
Twenty-two studies containing 27 prediction models were included. The number of predictors in the final models varied from 3 to 53. However, one-third of the models (11) did not clearly specify the timing of predictor measurement. Calibration was found to be lacking in 10 (37.0%) models. Among the 20 models with an incidence rate of predicted outcomes below 15.0%, none of the models estimated the area under the precision-recall curve, and all reported positive predictive values were below 40.0%. Only two (7.4%) models specified the target clinical setting, while seven (25.9%) models clarified the intended timing of model use. Lastly, all 22 studies were deemed to be at high risk of bias.
Conclusion:
Current SPPH prediction models have limited clinical applicability due to methodological flaws, including unclear predictor measurement, inadequate calibration assessment, and insufficient evaluation of classification ability. Additionally, there is a lack of clarity regarding the timing for model use, target users, and clinical settings. These limitations raise concerns about the reliability and usefulness of these models in real-world clinical practice.
