Risk factors for postoperative pulmonary complications in neonates: a retrospective cohort study.

Objective: Postoperative pulmonary complications (PPCs) are an important quality indicator and are associated with significantly increased mortality in infants. The objective of this study was to identify risk factors for PPCs in neonates undergoing non-cardiothoracic surgery. Methods: In this retrospective study, all neonates who underwent non-cardiothoracic surgery in a children's hospital from October 2020 to September 2022 were included for analysis. Demographic data and perioperative variables were obtained. The primary outcome was the occurrence of PPCs. Univariate analysis and multivariable logistic regression analysis were used to investigate the effect of patient-related factors on the occurrence of PPCs. Results: Totally, 867 neonatal surgery patients met the inclusion criteria in this study, among which 35.3% (306/867) patients experienced pulmonary complications within 1 week postoperatively. The PPCs observed in this study were 51 exacerbations of pre-existing pneumonia, 198 new patchy shadows, 123 new pulmonary atelectasis, 10 new pneumothorax, and 6 new pleural effusion. Patients were divided into two groups: PPCs (n=306) and non-PPCs (n=561). The multivariate stepwise logistic regression analysis revealed five independent risk factors for PPCs: corrected gestational age (OR=0.938; 95% CI 0.890 to 0.988), preoperative pneumonia (OR=2.139; 95% CI 1.033 to 4.426), length of surgery (> 60 min) (OR=1.699; 95% CI 1.134 to 2.548), preoperative mechanical ventilation (OR=1.857; 95% CI 1.169 to 2.951), and intraoperative albumin infusion (OR=1.456; 95% CI 1.041 to 2.036) in neonates undergoing non-cardiothoracic surgery. Conclusion: Identifying risk factors for neonatal PPCs will allow for the identification of patients who are at higher risk and intervention for any modifiable risk factors identified.

Development and validation of a risk score for predicting mortality after resection of primary hepatocellular carcinoma.

BACKGROUND: Primary hepatocellular carcinoma (PHCC) has a poor prognosis and high short-term mortality rate, even after resection. Thus, early diagnosis in PHCC cases can help improve quality of life via personalized management strategies. RESULTS: The risk score system (RSS) were classified as low risk (<5 points), medium risk (5-10 points), or high risk (>10 points). The areas under the receiver operating characteristic curves were 0.80 in the training cohort and 0.69 in the validation cohort, which indicated satisfactory prognostic performance. The Hosmer-Lemeshow goodness of fit test (P>0.05) revealed consistent performance in both groups. The concordance index (C-index: 0.663, 95% CI: 0.618-0.708) revealed excellent discrimination and good calibration in the validation cohort. CONCLUSIONS: This simple RSS, which is based on clinical and laboratory data from patients undergoing resection of PHCC, might allow clinicians and medical staff to better manage PHCC. MATERIALS AND METHODS: A total of 672 PHCC cases were retrospectively obtained from the First Affiliated Hospital of Wenzhou Medical University between January 2007 and February 2015. Cox proportional hazard models were used to identify independent predictors of mortality. Kaplan-Meier curves and the log-rank test were used to examine the relationships between the prognostic factors and overall mortality.

Use of a novel index, the A-index, and its associated nomogram to predict overall survival rates after resection of primary hepatocellular carcinoma.

BACKGROUND: Several international staging or scoring systems don't accurately predict overall survival (OS) after resection of primary hepatocellular carcinoma (PHCC). Therefore, we attempted to overcome this limitation by constructing the A-index and its associated nomogram. METHODS: We selected 672 patients who underwent curative resection of PHCC between January 2007 and February 2015 at the first affiliated hospital of the Wenzhou medical university. These subjects were randomly divided into the training (n = 470) and the validation group (n = 202) according to the ratio of 7:3. RESULTS: We prepared the nomogram using eight independent risk factors including the A-index (calculated by 100 × aspartate transaminase /albumin /albumin) in the training cohort. The concordance index (C-index) of the nomogram for both training and validation set was similar in indicating the OS rate. The nomogram showed the strongest predictive power for the 1-year, 3-year, and 5-year OS, with the area under the ROC curve being 0.8182, 0.7892, and 0.7669, respectively. Correction curves showed consistent performance for both groups, stratification of the Kaplan-Meier curve was significant (P < 0.001), and decision curve analysis (DCA) showed the superiority of nomograms considering clinical effects. CONCLUSIONS: The predictive power of the nomogram integrating the A-index for OS was optimal.

A sample model established by S-index predicting overall survival after curative resection of primary hepatocellular carcinoma.

PURPOSE: Prognostic prediction after curative resection of primary hepatocellular carcinoma (PHCC) remains an arduous task. The S-index calculated from γ-glutamyl transpeptidase, albumin, and platelets is reported to predict the severity of liver fibrosis. We constructed a nomogram for predicting the survival probability of PHCC based on a new indicator, the S-index, combined with other routine clinical parameters. PATIENTS AND METHODS: We selected 490 patients with PHCC postradical surgery at the First Affiliated Hospital of Wenzhou Medical University between January 2007 and January 2014. The subjects were randomly allocated into the training cohort and the validation cohort in the ratio 7:3 by the digital method. Important variables screened by univariate analysis were included in multivariate analysis to obtain independent risk factors for predicting the prognosis of PHCC. The construction of the nomogram was based on Cox proportional hazard regression models. The concordance index (C-index) was used in the nomogram for evaluating the model performance for prognosis. We drew time-dependent receiver operating characteristic curves to compare our model with other staging systems. RESULTS: The nomogram based on six independent risk factors after multivariate analyses had good predictive power after radical surgery of PHCC. In the training and validation groups, the C-index of the nomogram was highly consistent for evaluating survival from PHCC. Compared with the traditional scoring system, the areas under time-dependent receiver operating characteristic curves were 0.7382, 0.7293, and 0.7520 for 1-, 3-, and 5-year overall survival, respectively. In summary, the nomogram showed excellent results in terms of prognosis of PHCC. CONCLUSION: Based on the S-index and the other clinical indicators, we developed a precise nomogram that predicts the survival probability of patients with PHCC after radical surgery. This tool can provide effective information for surgeons and patients.

A nomogram integrating hepatic reserve and tumor characteristics for hepatocellular carcinoma following curative liver resection.

BACKGROUND: Because of the mutual influence of liver dysfunction and malignancy, overall survival (OS) is a composite clinical endpoint in hepatocellular carcinoma (HCC). We developed a nomogram integrating albumin-bilirubin (ALBI) grade, a new index of hepatic reserve, and tumor characteristics of HCC for predicting OS following curative liver resection. METHODS: The nomogram was built to estimate the probabilities of 1, 3, and 5-y OS based on training cohort of 709 HCC, which was validated in an international independent dataset. The prognostic value of the nomogram was determined by concordance index (C-index), time-dependent receiver operating characteristics (tdROC), and decision curves, comparing with ALBI grade alone, the Cancer of the Liver Italian Program (CLIP), the Barcelona Clinic Liver Cancer (BCLC), and Okuda staging systems. RESULTS: Independent factors derived from multivariable Cox analysis of the training cohort to predict OS were tumor grade, microvascular invasion, tumor size and ALBI grade which were assembled into nomogram. The calibration curves for probability of OS showed optimal agreement between nomogram-prediction and actual observation, which was tested in validation cohort. The C-index, tdROC and decision curves showed the nomogram was superior to CLIP, ALBI grade, BCLC and Okuda. The patients could also be stratified into low, intermediate risk, and high risk of the mortality by the normogram in both development and validation cohorts. CONCLUSIONS: The nomogram integrating hepatic reserve and tumor characteristics provided a highly accurate estimation of OS in patients with HCC after curative liver resection, contributing to assess patient prognosis.