Development and Internal Validation of a Nomogram Used to Predict Chemotherapy-Induced Neutropenia in Non-Small Cell Lung Cancer Patients: A Retrospective Cohort Study

Zou, W.; Xu, Nengluan

doi:10.2147/cmar.s302722

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…Of the currently available prediction tools, the nomogram is a superior visual tool with the user-friendly display, easily understand, high accuracy and good discrimination characteristics in predicting results. [15][16][17] Here, we generated and validated a novel nomogram to accurately forecast PSD risk in non-cardiac surgery patients. As far as we know, this is the first attempt to predict PSD in noncardiac surgery patients.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study

Yang¹,

Zhang²,

Xu³

et al. 2021

NSS

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Purpose To develop a risk prediction nomogram of postoperative sleep disturbance (PSD) in patients undergoing non-cardiac surgery. Patients and methods Data on 881 consecutive patients who underwent non-cardiac surgery at the Affiliated Hospital of Xuzhou Medical University between June 2020 and April 2021 were prospectively collected. Of these, we randomly divided 881 non-cardiac patients into two groups, training cohort (n = 617) and validation cohort (n = 264) at the ratio of 7:3. Characteristic variables were selected based on the data of training cohort through least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was used to identify the independent risk factors associated with PSD that then were incorporated into the nomogram. The predictive performance of the nomogram was measured by concordance index (C index), receiver operating characteristic (ROC) curve, and calibration with 1000 bootstrap samples to decrease the over-fit bias. Results PSD was found in 443 of 617 patients (71.8%) and 190 of 264 patients (72.0%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PSD were female sex, anxiety, dissatisfaction of ward environment, absence of combined regional nerve block, postoperative nausea and vomiting (PONV), the longer duration stayed in post anesthesia care unit (PACU), the higher dose of midazolam and sufentanil, the higher postoperative numeric rating score for pain (NRS) score. Incorporating these 9 factors, the nomogram achieved good concordance indexes of 0.82 (95% confidence interval [CI], 0.78–0.85) and 0.80 (95% CI, 0.74–0.85) in predicting PSD in the training and validation cohorts, respectively, and obtained well-fitted calibration curves. The sensitivity and specificity (95% CIs) of the nomogram were calculated, resulting in sensitivity of 74.0% (70.0–78.2%) and 75.3% (68.4–81.7%) and specificity of 79.3% (72.5–85.2%) and 70.3% (58.4–80.7%) for the training and validation cohorts, respectively. Patients who had a nomogram score of less than 262 or 262 or greater were considered to have low or high risks of PSD presence, respectively. Conclusion The proposed nomogram achieved an optimal prediction of PSD in patients undergoing non-cardiac surgery. The risks for an individual patient to harbor PSD can be determined by this model, which can lead to a reasonable preventive and treatment measures.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study

Yang¹,

Zhang²,

Xu³

et al. 2021

NSS

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“…The webbased dynamic risk prediction model has the advantages of good discriminative, high accuracy and easy to understand. [27][28][29] The developed web-based dynamic nomogram model incorporated five comprehensive and easily acquired perioperative predictors including BP variation, motion sickness, PCA, NVP history, and pain score. Furthermore, intraoperative BP changes were found for the first time in this study to potentially increase the risk of PONV in patients undergoing gynecologic laparoscopic surgery.…”

Section: Discussionmentioning

confidence: 99%

A web‐based dynamic predictive model for postoperative nausea and vomiting in patient receiving gynecological laparoscopic surgery

Liu,

Fang,

Cheng

et al. 2024

J of Obstet and Gynaecol

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ObjectiveThe aim of this study was to develop a web‐based dynamic prediction model for postoperative nausea and vomiting (PONV) in patients undergoing gynecologic laparoscopic surgery.MethodsThe patients (N = 647) undergoing gynecologic laparoscopic surgery were included in this observational study. The candidate risk‐factors related to PONV were included through literature search. Lasso regression was utilized to screen candidate risk‐factors, and the variables with statistical significance were selected in multivariable logistic model building. The web‐based dynamic Nomogram was used for model exhibition. Accuracy and validity of the experimental model (EM) were evaluated by generating receiver operating characteristic (ROC) curves and calibration curves. Hosmer–Lemeshow test was used to evaluate the goodness of fit of the model. Decision curve analysis (DCA) was used to evaluate the clinical practicability of the risk prediction model.ResultsUltimately, a total of five predictors including patient‐controlled analgesia (odds ratio [OR], 4.78; 95% confidence interval [CI], 1.98–12.44), motion sickness (OR, 4.80; 95% CI, 2.71–8.65), variation of blood pressure (OR, 4.30; 95% CI, 2.41–7.91), pregnancy vomiting history (OR, 2.21; 95% CI, 1.44–3.43), and pain response (OR, 1.64; 95% CI, 1.48–1.83) were selected in model building. Assessment of the model indicates the discriminating power of EM was adequate (ROC‐areas under the curve, 93.0%; 95% CI, 90.7%–95.3%). EM showed better accuracy and goodness of fit based on the results of the calibration curve. The DCA curve of EM showed favorable clinical benefits.ConclusionsThis dynamic prediction model can determine the PONV risk in patients undergoing gynecologic laparoscopic surgery.

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“…Firstly, the nomogram could transform predictive model into a single estimate of probability according to patient's characteristics, making model simple to understand (26). Secondly, the scoring system has high precision and good stability characteristics in predicting results (27,28). Therefore, we used nomogram to build the visual model to help clinicians to stratify patients and develop individual clinical treatment strategies according to patient's conditions.…”

Section: Discussionmentioning

confidence: 99%

Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study

Jin

Liu²,

Qiao³

et al. 2023

Front. Oncol.

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BackgroundThe prediction model of postoperative pneumonia (POP) after lung cancer surgery is still scarce.MethodsRetrospective analysis of patients with lung cancer who underwent surgery at The Fourth Hospital of Hebei Medical University from September 2019 to March 2020 was performed. All patients were randomly divided into two groups, training cohort and validation cohort at the ratio of 7:3. The nomogram was formulated based on the results of multivariable logistic regression analysis and clinically important factors associated with POP. Concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow goodness-of-fit test and decision curve analysis (DCA) were used to evaluate the predictive performance of the nomogram.ResultsA total of 1252 patients with lung cancer was enrolled, including 877 cases in the training cohort and 375 cases in the validation cohort. POP was found in 201 of 877 patients (22.9%) and 89 of 375 patients (23.7%) in the training and validation cohorts, respectively. The model consisted of six variables, including smoking, diabetes mellitus, history of preoperative chemotherapy, thoracotomy, ASA grade and surgery time. The C-index from AUC was 0.717 (95%CI:0.677-0.758) in the training cohort and 0.726 (95%CI:0.661-0.790) in the validation cohort. The calibration curves showed the model had good agreement. The result of DCA showed that the model had good clinical benefits.ConclusionThis proposed nomogram could predict the risk of POP in patients with lung cancer surgery in advance, which can help clinician make reasonable preventive and treatment measures.

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Development and Internal Validation of a Nomogram Used to Predict Chemotherapy-Induced Neutropenia in Non-Small Cell Lung Cancer Patients: A Retrospective Cohort Study

Cited by 6 publications

References 24 publications

Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study

Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study

A web‐based dynamic predictive model for postoperative nausea and vomiting in patient receiving gynecological laparoscopic surgery

Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study

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