Construction and evaluation of networks among multiple postoperative complications

Shen, Yubing; Zhang, Luwen; Wu, Peng; Huang, Yuguang; Xin, Shijie; Zhang, Qiang; Zhao, Shengxiu; Sun, Huawei; Lei, Guanghua; Zhang, Taiping; Han, Wei; Wang, Zixing; Jiang, Jingmei; Yu, Xinmin

doi:10.1016/j.cmpb.2023.107439

Cited by 1 publication

(2 citation statements)

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“…In terms of the contribution to prediction, the interdependence between complications was greater than the preoperative factors. Even mild complications can lead to a cascade of complications 11 ; however, this is typically poorly captured and ignored by most surgeons 39 . By assessing whether early complications may predict severe morbidity, Staiger et al .…”

Section: Discussionmentioning

confidence: 99%

“…However, there is still a lack of insight into how different complications interact. Complications are not independent but related to each other in recognizable and clinically relevant patterns 10 , and form complex network relationships 11 . Severe complications are a common end pathway after multiple less severe complications and tend to co-occur in a cluster 12 , 13 .…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of an interpretable markov-embedded multi-label model for predicting risks of multiple postoperative complications among surgical inpatients: A multicenter prospective cohort study

Yu,

Zhang,

et al. 2023

International Journal of Surgery

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Background: When they encounter various highly related postoperative complications, existing risk evaluation tools that focus on single or any complications are inadequate in clinical practice. This seriously hinders complication management because of the lack of a quantitative basis. An interpretable multi-label model framework that predicts multiple complications simultaneously is urgently needed. Materials and Methods: We included 50,325 inpatients from a large multicenter cohort (2014–2017). We separated patients from one hospital for external validation and randomly split the remaining patients into training and internal validation sets. A MARKov-EmbeDded (MARKED) multi-label model was proposed, and three models were trained for comparison: binary relevance (BR), a fully connected network (FULLNET), and a deep neural network (DNN). Performance was mainly evaluated using the area under the receiver operating characteristic curve (AUC). We interpreted the model using Shapley Additive Explanations. Complication-specific risk and risk source inference were provided at the individual level. Results: There were 26,292, 6574, and 17,459 inpatients in the training, internal validation, and external validation sets, respectively. For the external validation set, MARKED achieved the highest average AUC (0.818, 95% confidence interval: 0.771–0.864) across eight outcomes (compared with BR, 0.799 [0.748–0.849], FULLNET, 0.806 [0.756–0.856], and DNN, 0.815 [0.765–0.866]). Specifically, the AUCs of MARKED were above 0.9 for cardiac complications (0.927 [0.894–0.960]), neurological complications (0.905 [0.870–0.941]), and mortality (0.902 [0.867–0.937]). Serum albumin, surgical specialties, emergency case, American Society of Anesthesiologists score, age, and sex were the six most important preoperative variables. The interaction between complications contributed more than the preoperative variables, and formed a hierarchical chain of risk factors, mild complications, and severe complications. Conclusion: We demonstrated the advantage of MARKED in terms of performance and interpretability. We expect that the identification of high-risk patients and inference of the risk source for specific complications will be valuable for clinical decision-making.

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Section: Discussionmentioning

confidence: 99%