Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

Tian, Dong; Yan, Hang; Huang, Heng; Zuo, Yu-Jie; Liu, Ming-Zhao; Zhao, Jinwei; Wu, Bo; Shi, Lingzhi; Chen, Jingyu

doi:10.1001/jamanetworkopen.2023.12022

Cited by 19 publications

(2 citation statements)

References 31 publications

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“…Gao et al reported that the incidence of PMV (defined as > 72 h) was 31.9%, excluding ECMO-supportive patients 10 . Since PMV is not clearly defined in the literature, the PMV in this study was defined according to the previous large-scale retrospective study, where the mean ventilation time of LTx patients was 5 days 11 . Therefore, PMV was defined as > 5 days in the current research.…”

Section: Discussionmentioning

confidence: 99%

A novel nomogram for predicting prolonged mechanical ventilation in lung transplantation patients using extracorporeal membrane oxygenation

Xuan,

Gu,

et al. 2024

Sci Rep

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Prolonged mechanical ventilation (PMV) is commonly associated with increased post-operative complications and mortality. Nevertheless, the predictive factors of PMV after lung transplantation (LTx) using extracorporeal membrane oxygenation (ECMO) as a bridge remain unclear. The present study aimed to develop a novel nomogram for PMV prediction in patients using ECMO as a bridge to LTx. A total of 173 patients who used ECMO as a bridge following LTx from January 2022 to June 2023 were divided into the training (122) and validation sets (52). A mechanical ventilation density plot of patients after LTx was then performed. The training set was divided in two groups, namely PMV (95) and non-prolonged ventilation (NPMV) (27). For the survival analysis, the effect of PMV was assessed using the log-rank test. Univariate and multivariate logistic regression analyses were performed to assess factors associated with PMV. A risk nomogram was established based on the multivariate analysis, and model performance was further assessed in terms of calibration, discrimination, and clinical usefulness. Internal validation was additionally conducted. The difference in survival curves in PMV and NPMV groups was statistically significant (P < 0.001). The multivariate analysis and risk factors in the nomogram revealed four factors to be significantly associated with PMV, namely the body mass index (BMI), operation time, lactic acid at T0 (Lac), and driving pressure (DP) at T0. These four factors were used to develop a nomogram, with an area under the curve (AUC) of 0.852 and good calibration. After internal validation, AUC was 0.789 with good calibration. Furthermore, goodness-of-fit test and decision-curve analysis (DCA) indicated satisfactory performance in the training and internal validation sets. The proposed nomogram can reliably and accurately predict the risk of patients to develop PMV after LTx using ECMO as a bridge. Four modifiable factors including BMI, operation time, Lac, and DP were optimized, which may guide preventative measures and improve prognosis.

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Section: Discussionmentioning

confidence: 99%

A novel nomogram for predicting prolonged mechanical ventilation in lung transplantation patients using extracorporeal membrane oxygenation

Xuan,

Gu,

et al. 2024

Sci Rep

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“…In addition, Tian et al explored the development and evaluation of a ML-based prognostic model to predict survival outcomes for patients following lung transplantation ( 41 ). Utilizing data from the United Network for Organ Sharing (UNOS), the authors developed a Random Survival Forest (RSF) model, incorporating a comprehensive set of clinical variables.…”

Section: Prognosticationmentioning

confidence: 99%

The transformative potential of artificial intelligence in solid organ transplantation

Al Moussawy,

Lakkis,

Ansari

et al. 2024

Front. Transplant.

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Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

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Posttransplant complications: molecular mechanisms and therapeutic interventions

Liu,

Shen,

Yan

et al. 2024

MedComm

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Posttransplantation complications pose a major challenge to the long‐term survival and quality of life of organ transplant recipients. These complications encompass immune‐mediated complications, infectious complications, metabolic complications, and malignancies, with each type influenced by various risk factors and pathological mechanisms. The molecular mechanisms underlying posttransplantation complications involve a complex interplay of immunological, metabolic, and oncogenic processes, including innate and adaptive immune activation, immunosuppressant side effects, and viral reactivation. Here, we provide a comprehensive overview of the clinical features, risk factors, and molecular mechanisms of major posttransplantation complications. We systematically summarize the current understanding of the immunological basis of allograft rejection and graft‐versus‐host disease, the metabolic dysregulation associated with immunosuppressive agents, and the role of oncogenic viruses in posttransplantation malignancies. Furthermore, we discuss potential prevention and intervention strategies based on these mechanistic insights, highlighting the importance of optimizing immunosuppressive regimens, enhancing infection prophylaxis, and implementing targeted therapies. We also emphasize the need for future research to develop individualized complication control strategies under the guidance of precision medicine, ultimately improving the prognosis and quality of life of transplant recipients.

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Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

Cited by 19 publications

References 31 publications

A novel nomogram for predicting prolonged mechanical ventilation in lung transplantation patients using extracorporeal membrane oxygenation

A novel nomogram for predicting prolonged mechanical ventilation in lung transplantation patients using extracorporeal membrane oxygenation

The transformative potential of artificial intelligence in solid organ transplantation

Posttransplant complications: molecular mechanisms and therapeutic interventions

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