A Powerful Nomogram Based on the Novel D-Index to Predict Prognosis After Surgical Resection of Hepatocellular Carcinoma

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

OBJECTIVE

The 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.

METHODS

The 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.

RESULTS

Ultimately, 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.

CONCLUSIONS

This dynamic prediction model can determine the PONV risk in patients undergoing gynecologic laparoscopic surgery.

Development and validation of a nomogram for survival prediction in hepatocellular carcinoma after partial hepatectomy

BACKGROUND

The prognosis for hepatocellular carcinoma (HCC) is complex due to its high level of heterogeneity, even after radical resection. This study was designed to develop and validate a prognostic nomogram for predicting the postoperative prognosis for HCC patients following partial hepatectomy.

PATIENTS AND METHODS

We extracted data on HCC patients and randomly divided them into two groups (primary and validation cohorts), using the Surveillance, Epidemiology and End Results (SEER) database. We developed the prediction model based on the data of the primary cohort and prognostic factors were evaluated using univariate and multivariate Cox regression analysis. A nomogram was constructed for predicting the 1-, 3-, and 5-year survival probability of HCC patients after surgery based on the results of the multivariate Cox regression analysis. The performance of the nomogram was evaluated in terms of its discrimination and calibration. To validated the model, discrimination and calibration were also evaluated in the validation cohort. Decision curve analysis (DCA) was performed to assess the clinical utility of the nomogram.

RESULTS

A total of 890 patients who underwent partial hepatectomy for HCC were included in the study. The primary cohort enrolled 628 patients with a median follow-up time of 39 months, the 1-, 3-, and 5-year survival rate were 95.4%, 52.7% and 25.8% during follow-up. Multivariate Cox regression analysis showed that differentiation, tumor size, AFP and fibrosis were independently association with the prognosis of HCC patients after partial hepatectomy. The nomogram showed a moderate discrimination ith a C-index of 0.705 (95% CI 0.669 to 0.742), and good calibration. Similar discrimination with a C-index of 0.681 (95% CI 0.625 to 0.737), and calibration were also observed in the validation cohort. Decision curve analysis showed that the nomogram could be useful to predicting the prognosis in HCC patients following partial hepatectomy.

CONCLUSIONS

The proposed nomogram is highly predictive and has moderate calibration and discrimination, potentially contributing to the process of managing HCC patients after partial hepatectomy in an individualized way.

Development and Validation of a Risk Nomogram Model for Perioperative Respiratory Adverse Events in Children Undergoing Airway Surgery: An Observational Prospective Cohort Study

Purpose

The aim of this study was to explore the associated risk factors of perioperative respiratory adverse events (PRAEs) in children undergoing airway surgery and establish and validate a nomogram prediction model for PRAEs.

Patients and Methods

This study involved 709 children undergoing airway surgery between November 2020 and July 2021, aged ≤18 years in the affiliated hospital of Xuzhou Medical University. They were divided into training (70%; n = 496) and validation (30%; n = 213) cohorts. The least absolute shrinkage and selection operator (LASSO) was used to develop a risk nomogram model. Concordance index values, calibration plot, decision curve analysis, and the area under the curve (AUC) were examined.

Results

PRAEs were found in 226 of 496 patients (45.6%) and 88 of 213 patients (41.3%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PRAEs were age, obesity, degree of upper respiratory tract infection, premedication, and passive smoking. The risk nomogram model showed good discrimination power, and the AUC generated to predict survival in the training cohort was 0.760 (95% confidence interval, 0.695–0.875). In the validation cohort, the AUC of survival predictions was 0.802 (95% confidence interval, 0.797–0.895). Calibration plots and decision curve analysis showed good model performance in both datasets. The sensitivity and specificity of the risk nomogram model were calculated, and the result showed the sensitivity of 69.5% and 64.8% and specificity of 73.3% and 81.6% for the training and validation cohorts, respectively.

Conclusion

The present study showed the proposed nomogram achieved an optimal prediction of PRAEs in patients undergoing airway surgery, which can provide a certain reference value for predicting the high-risk population of perioperative respiratory adverse events and can lead to reasonable preventive and treatment measures.

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

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.