A Prediction Model for Postoperative Pulmonary Complication in Pulmonary Function-Impaired Patients Following Lung Resection

Mao, Xiaowei; Zhang, Wei; Ni, Yiqian; Niu, Yiming; Jiang, Liyan

doi:10.2147/jmdh.s327285

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…One reason is that ppoFEV1% is a synthetic parameter adjusted by height, age, sex, and the extent of surgical resections. Hence, it was also included in the Brunelli, Eurolung model, and European Society Objective Scores (8,9,13,31,32). A lower ppoFEV1% reflects the reduction of lung volume and decreased lung function.…”

Section: Discussionmentioning

confidence: 99%

“…However, these models did not perform satisfactory discrimination among the Chinese population due to patient characteristics discrepancies, with the values of the area under the curve (AUC) less than 0.7 (12). Predictive studies based on Chinese populations have mainly focused on a certain type of complication, a subgroup of patients, or a particular predictor (13)(14)(15)(16)(17)(18). For example, Li et al developed two prediction models for pneumonia and arrhythmia, but they did not pay attention to prolonged air leak, atelectasis, and other complications (16).…”

Section: Introductionmentioning

confidence: 99%

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Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: A machine learning-based study

et al. 2022

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BackgroundApproximately 20% of patients with lung cancer would experience postoperative cardiopulmonary complications after anatomic lung resection. Current prediction models for postoperative complications were not suitable for Chinese patients. This study aimed to develop and validate novel prediction models based on machine learning algorithms in a Chinese population.MethodsPatients with lung cancer receiving anatomic lung resection and no neoadjuvant therapies from September 1, 2018 to August 31, 2019 were enrolled. The dataset was split into two cohorts at a 7:3 ratio. The logistic regression, random forest, and extreme gradient boosting were applied to construct models in the derivation cohort with 5-fold cross validation. The validation cohort accessed the model performance. The area under the curves measured the model discrimination, while the Spiegelhalter z test evaluated the model calibration.ResultsA total of 1085 patients were included, and 760 were assigned to the derivation cohort. 8.4% and 8.0% of patients experienced postoperative cardiopulmonary complications in the two cohorts. All baseline characteristics were balanced. The values of the area under the curve were 0.728, 0.721, and 0.767 for the logistic, random forest and extreme gradient boosting models, respectively. No significant differences existed among them. They all showed good calibration (p > 0.05). The logistic model consisted of male, arrhythmia, cerebrovascular disease, the percentage of predicted postoperative forced expiratory volume in one second, and the ratio of forced expiratory volume in one second to forced vital capacity. The last two variables, the percentage of forced vital capacity and age ranked in the top five important variables for novel machine learning models. A nomogram was plotted for the logistic model.ConclusionThree models were developed and validated for predicting postoperative cardiopulmonary complications among Chinese patients with lung cancer. They all exerted good discrimination and calibration. The percentage of predicted postoperative forced expiratory volume in one second and the ratio of forced expiratory volume in one second to forced vital capacity might be the most important variables. Further validation in different scenarios is still warranted.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: A machine learning-based study

et al. 2022

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“…Послеоперационные легочные осложнения являются основной причиной периоперационной заболеваемости и смертности, возникающей у 14-40 % пациентов после резекции легкого [6]. Американский колледж врачей-пульмонологов (ACCP) рекомендует тщательную предоперационную физиологическую оценку легких и сердца для отбора пациентов на хирургическое лечение [7].…”

Section: выводы с помощью разработанных прогностических моделей можно...unclassified

Predictive Models for Assessing Risks of Postoperative Cardiac and Respiratory Complications in Patients With Lung Cancer

Toneev,

Komarov,

Midlenko

et al. 2024

Ulyanovsk Medico-biological Journal

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The aim of the study is to identify risk factors for the development of postoperative cardiac and respiratory complications in patients after lobectomy and to develop nomograms to predict such complications. Materials and Methods. From January 2020 to August 2023, 411 lobectomies were performed in patients with non-small cell lung cancer in the surgical department of thoracic oncology, Regional Clinical Oncology Dispensary (Ulyanovsk). The present study included 162 patients according to the designed enrolment criteria. The authors identified statistically significant risk factors for the development of severe cardiac and respiratory complications after lobectomy with systematic lymph node dissection. Results. Due to insufficient data cardinality, artificial data with a similar distribution were synthesized using the MICE method (multivariate imputation by chained equations). The sample is divided into training (n=1258) and validation (n=342) datasets at a ratio 80 to 20. Final testing of the initial data was carried out (n=162). Logistic regression for respiratory complications: respiratory complications = 23.4232 – 0.7427 × stair climbing test – 0.0259 × shuttle walking test. Based on these data, a nomogram was constructed to predict the development of respiratory complications. Error matrix characteristics: accuracy – 0.95; sensitivity – 0.86; specificity – 0.97; positive predictive value – 0.86; negative predictive value – 0.97. Logistic regression for cardiac complications: cardiac complications = 11.1147 – 0.1247 × stair climbing test – 0.1651 × ejection fraction + 0.1568 × BMI + 1.133 × hypertension. Based on these data, a nomogram was constructed to predict cardiac complications. Error matrix characteristics: accuracy – 0.91; sensitive- ty – 0.63; specificity – 0.98; positive predictive value – 0.9; negative predictive value – 0.92. Conclusions. The developed prognostic models allow us to determine the risk group among patients who are planning a lobectomy for lung cancer at the preoperative stage. It also allows us to ensure high-quality prevention for these complications in patients at high risk.

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“…capacity (FEV1/FVC), forced expiratory volume in one second as a percentage of predicted value (ppo FEV1), the ratio of diffusion lung capacity for CO as a percentage of predicted value (ppo DLCO) and peak expiratory flow (PEF). 14 The classification of lung function indices was made with reference to Clinical Pulmonary Function and in relation to the purpose of the study. 15 The FEV1/FVC ratio can indicate the presence of an airflow restriction, and less than 70% was abnormal.…”

Section: Part 2: Perceived Social Support Scale (Psss)mentioning

confidence: 99%

Preoperative Physical Dysfunction Characteristics and Influence Factors Among Elderly Patients with Early Lung Cancer: A Latent Class Analysis

Zhang,

Yang,

Shen

et al. 2024

JMDH

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To identify latent classes of preoperative physical dysfunction in elderly patients with early lung cancer. To analyze the differences in demographic characteristics between different classes. Methods: We invited elderly patients with early lung cancer who were scheduled for surgery at Shanghai Elderly Characteristic Hospital to participate in the study using a convenience sampling method. We took latent class analysis to divide elderly patients with early lung cancer into latent classes based on preoperative physical dysfunction features. Furthermore, we used single-factor analysis and multinomial logistic regression to investigate the influence variables of each latent class. Results: The characteristics of preoperative physical dysfunction in elderly patients with early lung cancer can be divided into "Anxiety/depression emotion-poor sleep group" "Frailty of physical function group" "Pulmonary hypofunction-low activity tolerance group". The distribution of age, chronic disease history, COPD history, smoking history and perceived social support level of elderly patients with early lung cancer in different potential categories were not the same, and the differences were statistically significant (P<0.05). The elderly lung cancer patients with chronic disease history and age ≥75 years were more likely to be classified as "frailty of physical function group". The elderly lung cancer patients with COPD and smoking history were more likely to be classified into "pulmonary hypofunction-low activity tolerance group". Elderly lung cancer patients with moderate or low degree of perceived social support were more prone to be grouped into "anxiety/depression emotion-poor sleep group". Conclusion:The variety of preoperative physical dysfunction seen in elderly patients with early lung cancer can be categorized into three latent classes. Medical professionals should create strategies for intervention for multiple patient populations with the goal of further enhancing their general state of life.

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A Prediction Model for Postoperative Pulmonary Complication in Pulmonary Function-Impaired Patients Following Lung Resection

Cited by 5 publications

References 38 publications

Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: A machine learning-based study

Prediction of postoperative cardiopulmonary complications after lung resection in a Chinese population: A machine learning-based study

Predictive Models for Assessing Risks of Postoperative Cardiac and Respiratory Complications in Patients With Lung Cancer

Preoperative Physical Dysfunction Characteristics and Influence Factors Among Elderly Patients with Early Lung Cancer: A Latent Class Analysis

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