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Incorporating preoperative frailty to assist in early prediction of postoperative pneumonia in elderly patients with hip fractures: an externally validated online interpretable machine learning model.
Dai, Anran; Liu, Hao; Shen, Po; Feng, Yue; Zhong, Yi; Ma, Mingtao; Hu, Yuping; Huang, Kaizong; Chen, Chen; Xia, Huaming; Yan, Libo; Si, Yanna; Zou, Jianjun.
Afiliación
  • Dai A; Department of Pharmacy, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Liu H; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
  • Shen P; School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
  • Feng Y; Department of Pharmacy, Shaoxing People's Hospital, Shaoxing, China.
  • Zhong Y; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Ma M; Department of Anesthesiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China.
  • Hu Y; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Huang K; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Chen C; Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Xia H; Department of Anesthesiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing Medical University, Nanjing, China.
  • Yan L; Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Si Y; Department of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
  • Zou J; Research and Development Department, Nanjing Xiaheng Network System Co.,Ltd, Nanjing, China.
BMC Geriatr ; 24(1): 472, 2024 May 30.
Article en En | MEDLINE | ID: mdl-38816811
ABSTRACT

BACKGROUND:

This study aims to implement a validated prediction model and application medium for postoperative pneumonia (POP) in elderly patients with hip fractures in order to facilitate individualized intervention by clinicians.

METHODS:

Employing clinical data from elderly patients with hip fractures, we derived and externally validated machine learning models for predicting POP. Model derivation utilized a registry from Nanjing First Hospital, and external validation was performed using data from patients at the Fourth Affiliated Hospital of Nanjing Medical University. The derivation cohort was divided into the training set and the testing set. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used for feature screening. We compared the performance of models to select the optimized model and introduced SHapley Additive exPlanations (SHAP) to interpret the model.

RESULTS:

The derivation and validation cohorts comprised 498 and 124 patients, with 14.3% and 10.5% POP rates, respectively. Among these models, Categorical boosting (Catboost) demonstrated superior discrimination ability. AUROC was 0.895 (95%CI 0.841-0.949) and 0.835 (95%CI 0.740-0.930) on the training and testing sets, respectively. At external validation, the AUROC amounted to 0.894 (95% CI 0.821-0.966). The SHAP method showed that CRP, the modified five-item frailty index (mFI-5), and ASA body status were among the top three important predicators of POP.

CONCLUSION:

Our model's good early prediction ability, combined with the implementation of a network risk calculator based on the Catboost model, was anticipated to effectively distinguish high-risk POP groups, facilitating timely intervention.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neumonía / Complicaciones Posoperatorias / Aprendizaje Automático / Fracturas de Cadera Límite: Aged / Aged80 / Female / Humans / Male Idioma: En Revista: BMC Geriatr Asunto de la revista: GERIATRIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neumonía / Complicaciones Posoperatorias / Aprendizaje Automático / Fracturas de Cadera Límite: Aged / Aged80 / Female / Humans / Male Idioma: En Revista: BMC Geriatr Asunto de la revista: GERIATRIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido