Dynamic nomogram for long-term survival in patients with non-small cell lung cancer after pneumonectomy

Effect of laterality on the postoperative survival of non-small cell lung cancer patients undergoing pneumonectomy

Background

Pneumonectomy is one of the important surgical methods for non-small cell lung cancer (NSCLC). This study evaluated the effects of laterality on the short- and long-term survival of NSCLC patients undergoing pneumonectomy.

Methods

We reviewed the Surveillance, Epidemiology, and End Results database to retrieve the data of patients who underwent pneumonectomy for stage I-III NSCLC from 2004 to 2015. Propensity score matching (PSM) was used to reduce the selection bias. Logistic regression was used to analyze the correlation between laterality and mortality at 3, 6, and 9 months. The Kaplan-Meier curve was used to further assess the effect of laterality on overall survival (OS).

Results

A total of 4,763 patients met the enrollment criteria [right-sided, 1,988 (41.7%); left-sided, 2,775 (58.3%)]. After PSM, 1,911 patients for each side were included in the further analysis. The first 6 months following pneumonectomy was the main period of death, with 32.0% (428/1,336) and 19.9% (250/1,258) of right- and left-sided deaths occurring during this period. The logistic regression analysis showed that right-sided pneumonectomy was an independent risk factor for 3- (P<0.001) and 6-month (P<0.001) mortality. However, laterality had no significant effect on postoperative death at 7-9 months (P=0.82). In the total cohort, right-sided patients had worse OS (P<0.001), but the subgroup survival analysis of patients with a follow-up period >6 months revealed that laterality had no statistically significant effect on OS (P=0.75).

Conclusions

Right-sided pneumonectomy was associated with a higher perioperative mortality risk that lasted about 6 months. After that period, laterality was not observed to have a significant prognostic effect on the OS of patients undergoing pneumonectomy.

Construction and validation of a nomogram model to predict the poor prognosis in patients with pulmonary cryptococcosis

Background

Patients with poor prognosis of pulmonary cryptococcosis (PC) are prone to other complications such as meningeal infection, recurrence or even death. Therefore, this study aims to analyze the influencing factors in the poor prognosis of patients with PC, so as to build a predictive nomograph model of poor prognosis of PC, and verify the predictive performance of the model.

Methods

This retrospective study included 410 patients (78.1%) with improved prognosis of PC and 115 patients (21.9%) with poor prognosis of PC. The 525 patients with PC were randomly divided into the training set and validation set according to the ratio of 7:3. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to screen the demographic information, including clinical characteristics, laboratory test indicators, comorbidity and treatment methods of patients, and other independent factors that affect the prognosis of PC. These factors were included in the multivariable logistic regression model to build a predictive nomograph. The receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to verify the accuracy and application value of the model.

Results

It was finally confirmed that psychological symptoms, cytotoxic drugs, white blood cell count, hematocrit, platelet count, CRP, PCT, albumin, and CD4/CD8 were independent predictors of poor prognosis of PC patients. The area under the curve (AUC) of the predictive model for poor prognosis in the training set and validation set were 0.851 (95% CI: 0.818-0.881) and 0.949, respectively. At the same time, calibration curve and DCA results confirmed the excellent performance of the nomogram in predicting poor prognosis of PC.

Conclusion

The nomograph model for predicting the poor prognosis of PC constructed in this study has good prediction ability, which is helpful for improving the prognosis of PC and further optimizing the clinical management strategy.

Dynamic nomogram for predicting acute kidney injury in patients with acute ischemic stroke: A retrospective study

Background

This study sought to develop and validate a dynamic nomogram chart to assess the risk of acute kidney injury (AKI) in patients with acute ischemic stroke (AIS).

Methods

These data were drawn from the Medical Information Mart for Intensive Care III (MIMIC-III) database, which collects 47 clinical indicators of patients after admission to the hospital. The primary outcome indicator was the occurrence of AKI within 48 h of intensive care unit (ICU) admission. Independent risk factors for AKI were screened from the training set using univariate and multifactorial logistic regression analyses. Multiple logistic regression models were developed, and nomograms were plotted and validated in an internal validation set. Based on the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) to estimate the performance of this nomogram.

Results

Nomogram indicators include blood urea nitrogen (BUN), creatinine, red blood cell distribution width (RDW), heart rate (HR), Oxford Acute Severity of Illness Score (OASIS), the history of congestive heart failure (CHF), the use of vancomycin, contrast agent, and mannitol. The predictive model displayed well discrimination with the area under the ROC curve values of 0.8529 and 0.8598 for the training set and the validator, respectively. Calibration curves revealed favorable concordance between the actual and predicted incidence of AKI (p > 0.05). DCA indicates the excellent net clinical benefit of nomogram in predicting AKI.

Conclusion

In summary, we explored the incidence of AKI in patients with AIS during ICU stay and developed a predictive model to help clinical decision-making.