Development and validation of a clinicopathological-based nomogram to predict seeding risk after percutaneous thermal ablation of primary liver carcinoma

Development and validation of a nomogram to predict intracranial haemorrhage in neonates

BACKGROUND

The aim of this study was to establish and validate a Susceptibility-weighted imaging (SWI)-based predictive model for neonatal intracranial haemorrhage (ICH).

METHODS

A total of 1190 neonates suspected of ICH after cranial ultrasound screening in a tertiary hospital were retrospectively enrolled. The neonates were randomly divided into a training cohort and a internal validation cohort by a ratio of 7:3. Univariate analysis was used to analyze the correlation between risk factors and ICH, and the prediction model of neonatal ICH was established by multivariate logistic regression based on minimum Akaike information criterion (AIC). The nomogram was externally validated in another tertiary hospital of 91 neonates. The performance of the nomogram was evaluated in terms of discrimination by the area under the curve (AUC), calibration by the calibration curve and clinical net benefit by the decision curve analysis (DCA).

RESULTS

Univariate analysis and min AIC-based multivariate logistic regression screened the following variables to establish a predictive model for neonatal ICH: Platelet count (PLT), gestational diabetes, mode of delivery, amniotic fluid contamination, 1-min Apgar score. The AUC was 0.715, 0.711, and 0.700 for the training cohort, internal validation cohort, and external validation cohort, respectively. The calibration curve showed a good correlation between the nomogram prediction and actual observation for ICH. DCA showed the nomogram was clinically useful.

CONCLUSION

We developed and validated an easy-to-use nomogram to predict ICH for neonates. This model could support individualized risk assessment and healthcare.

A Nomogram Model to Predict Deep Vein Thrombosis Risk After Surgery in Patients with Hip Fractures

Aims

This study aimed to establish a nomogram model for predicting the probability of postoperative deep vein thrombosis (DVT) risk in patients with hip fractures.

Methods

504 patients were randomly assigned to the training set and validation set, and then divided into a DVT group and a non-DVT group. The study analysed the risk factors for DVT using univariate and multivariate analyses. Based on these parameters, a nomogram model was constructed and validated. The predicting performance of nomogram was evaluated by discrimination, calibration, and clinical usefulness.

Results

The predictors contained in the nomogram model included age, surgical approach, 1-day postoperative D-dimer value and admission ultrasound diagnosis of the lower limb vein. Furthermore, the area under the ROC curve (AUC) for the specific DVT risk-stratification nomogram model (0.815; 95% CI 0.746-0.884) was significantly higher than the current model (Caprini) (0.659; 95% CI 0.572-0.746, P < 0.05). According to the calibration plots, the prediction and actual observation were in good agreement. In the range of threshold probabilities of 0.2-0.8, the predictive performance of the model on DVT risk could be maximized.

Conclusions

The current predictive model could serve as a reliable tool to quantify the possibility of postoperative DVT in hip fractures patients.

Factors associated with increased risk of peritoneal seeding after radiofrequency ablation for hepatocellular carcinoma

PURPOSE

To evaluate the incidence, risk factors, and prognosis associated with peritoneal seeding after percutaneous radiofrequency ablation (RFA) for HCC, focusing on viable tumors after previous locoregional treatment, including TACE and RFA.

METHODS

Exactly 290 patients (mean age, 67.9 years ± 9.74; 223 men) with 383 HCCs (mean size, 15.9 mm ± 5.49) who underwent RFA between June 2012 and December 2019 were included in this retrospective study. Among them, 158 had history of previous treatment (mean number, 1.3 ± 1.8) with 109 viable HCCs. Cumulative seeding after RFA was estimated using the Kaplan-Meier method. Independent factors affecting seeding were investigated using multivariable Cox proportional hazards regression analysis.

RESULTS

Median follow-up was 1175 days (range: 28-4116). Seeding incidence was 4.1 (12/290) and 4.7% (17/383) per patient and tumor, respectively. The median time interval between RFA and detection of seeding was 785 days (range: 81-1961). Independent risk factors for seeding included subcapsular tumor location (hazard ratio [HR] 4.2; 95% confidence interval [CI] 1.4, 13.0; p = 0.012) and RFA for viable HCC after previous locoregional treatment (HR 4.5; 95% CI 1.7, 12.3; p = 0.003). Subgroup analysis for viable tumors, revealed no significant difference in cumulative seeding rates between the TACE and RFA groups (p = 0.078). Cumulative overall survival rates differed significantly between patients with and without seeding metastases (p < 0.001).

CONCLUSION

Peritoneal seeding after RFA is a rare, delayed complication. Subcapsular-located and viable HCC after previous locoregional treatment are potential risk factors for seeding. Seeding metastases could affect the prognosis of patients who cannot receive local therapy.

Microwave ablation versus laparoscopic resection as first-line therapy for solitary 3-5-cm HCC

BACKGROUND AND AIMS

The study objective was to compare the effectiveness of microwave ablation (MWA) and laparoscopic liver resection (LLR) on solitary 3-5-cm HCC over time.

APPROACH AND RESULTS

From 2008 to 2019, 1289 patients from 12 hospitals were enrolled in this retrospective study. Diagnosis of all lesions were based on histopathology. Propensity score matching was used to balance all baseline variables between the two groups in 2008-2019 (n = 335 in each group) and 2014-2019 (n = 257 in each group) cohorts, respectively. For cohort 2008-2019, during a median follow-up of 35.8 months, there were no differences in overall survival (OS) between MWA and LLR (HR: 0.88, 95% CI 0.65-1.19, p = 0.420), and MWA was inferior to LLR regarding disease-free survival (DFS) (HR 1.36, 95% CI 1.05-1.75, p = 0.017). For cohort 2014-2019, there was comparable OS (HR 0.85, 95% CI 0.56-1.30, p = 0.460) and approached statistical significance for DFS (HR 1.33, 95% CI 0.98-1.82, p = 0.071) between MWA and LLR. Subgroup analyses showed comparable OS in 3.1-4.0-cm HCCs (HR 0.88, 95% CI 0.53-1.47, p = 0.630) and 4.1-5.0-cm HCCs (HR 0.77, 95% CI 0.37-1.60, p = 0.483) between two modalities. For both cohorts, MWA shared comparable major complications (both p > 0.05), shorter hospitalization, and lower cost to LLR (all p < 0.001).

CONCLUSIONS

MWA might be a first-line alternative to LLR for solitary 3-5-cm HCC in selected patients with technical advances, especially for patients unsuitable for LLR.

Development and validation of a clinicopathological-based nomogram to predict seeding risk after percutaneous thermal ablation of primary liver carcinoma

Objectives

To develop a clinicopathological‐based nomogram to improve the prediction of the seeding risk of after percutaneous thermal ablation (PTA) in primary liver carcinoma (PLC).

Methods

A total of 2030 patients with PLC who underwent PTA were included between April 2009 and December 2018. The patients were grouped into a training dataset (n = 1024) and an external validation dataset (n = 1006). Baseline characteristics were collected to identify the risk factors of seeding after PTA. The multivariate Cox proportional hazards model based on the risk factors was used to develop the nomogram, which was used for assessment for its predictive accuracy using mainly the Harrell's C‐index and receiver operating characteristic curve (AUC).

Results

The median follow‐up time was 30.3 months (range, 3.2‐115.7 months). The seeding risk was 0.89% per tumor and 1.5% per patient in the training set. The nomogram was developed based on tumor size, subcapsular, α‐fetoprotein (AFP), and international normalized ratio (INR). The 1‐, 2‐, and 3‐year cumulative seeding rates were 0.1%, 0.7% and 1.2% in the low‐risk group, and 1.7%, 6.3% and 6.3% in the high‐risk group, respectively, showing significant statistical difference (P < .001). The nomogram had good calibration and discriminatory abilities in the training set, with C‐indexes of 0.722 (95% confidence interval [CI]: 0.661, 0.883) and AUC of 0.850 (95% CI: 0.767, 0.934). External validation with 1000 bootstrapped sample sets showed a good C‐index of 0.706 (95% CI: 0.546, 0.866) and AUC of 0.736 (95% CI: 0. 646, 0.827).

Conclusions

The clinicopathological‐based nomogram could be used to quantify the probability of seeding risk after PTA in PLC.