Prognostic value of preoperative high-sensitivity modified Glasgow prognostic score in advanced colon cancer: a retrospective observational study

Background Several studies have demonstrated that the preoperative Glasgow prognostic score (GPS) and modified GPS (mGPS) reflected the prognosis in patients undergoing curative surgery for colorectal cancer. However, there are no reports on long-term prognosis prediction using high-sensitivity mGPS (HS-GPS) in colorectal cancer. Therefore, this study aimed to calculate the prognostic value of preoperative HS-GPS in patients with colon cancer. Methods A cohort of 595 patients with advanced resectable colon cancer managed at our institution was analysed retrospectively. HS-GPS, GPS, and mGPS were evaluated for their ability to predict prognosis based on overall survival (OS) and recurrence-free survival (RFS). Results In the univariate analysis, HS-GPS was able to predict the prognosis with significant differences in OS but was not superior in assessing RFS. In the multivariate analysis of the HS-GPS model, age, pT, pN, and HS-GPS of 2 compared to HS-GPS of 0 (2 vs 0; hazard ratio [HR], 2.638; 95% confidence interval [CI], 1.046–6.650; P = 0.04) were identified as independent prognostic predictors of OS. In the multivariate analysis of the GPS model, GPS 2 vs 0 (HR, 1.444; 95% CI, 1.018–2.048; P = 0.04) and GPS 2 vs 1 (HR, 2.933; 95% CI, 1.209–7.144; P = 0.017), and in that of the mGPS model, mGPS 2 vs 0 (HR, 1.51; 95% CI, 1.066–2.140; P = 0.02) were independent prognostic predictors of OS. In each classification, GPS outperformed HS-GPS in predicting OS with a significant difference in the area under the receiver operating characteristic curve. In the multivariate analysis of the GPS model, GPS 2 vs 0 (HR, 1.537; 95% CI, 1.190–1.987; P = 0.002), and in that of the mGPS model, pN, CEA were independent prognostic predictors of RFS. Conclusion HS-GPS is useful for predicting the prognosis of resectable advanced colon cancer. However, GPS may be more useful than HS-GPS as a prognostic model for advanced colon cancer.

Survival Outcomes and Prognostic Predictors in Patients With Malignant Struma Ovarii

Background: Malignant struma ovarii (MSO) is an extremely rare ovarian malignant tumor and there is limited data on the survival outcomes and prognostic predictors of MSO. The objectives of this study were to investigate the disease-free survival (DFS), overall survival (OS), and disease-specific survival (DSS) rates of patients with MSO, and also evaluate the prognostic factors in this population.Methods: A retrospective study was conducted and 194 cases of MSO were selected. DFS was assessed by the logistic regression, OS by the Kaplan–Meier method, and DSS was evaluated by the Cox regression.Results: The median age of these patients was 46.0 years; 142 cases (73.2%) were confined to the ovary and 52 cases (26.8%) had extraovarian metastasis at the initial diagnosis of MSO. During the follow-up, 75.3% of these patients showed no evidence of disease and 18.0% were alive with disease. Only 13 deaths occurred, with 10 attributed to MSO. The 5, 10, and 15-year OS rates were 91.4, 87.7, and 83.5%, respectively. The 5, 10, and 15-year DSS rates were 93.8, 90.0, and 85.7%, respectively. Logistic regression revealed that International Federation of Gynecology and Obstetrics (FIGO) stage IV was the only risk factor for DFS [p < 0.001; odds ratio (OR) 7.328; 95% CI 3.103–16.885, FIGO stage IV vs. stage I; p = 0.021; OR 4.750, 95% CI 1.264–17.856, FIGO stage IV vs. stage II-III]. The multivariate Cox regression analysis showed that poor differentiation was the only risk factor for both OS (p = 0.005, OR 6.406; 95% CI 1.730–23.717) and DSS (p = 0.001, OR 9.664; 95% CI 2.409–38.760), while age ≥45 years was the prognostic predictor for OS (p = 0.038, OR 4.959; 95% CI 1.093–22.508).Conclusion: Survival outcomes were excellent in patients with MSO, irrespective of the treatment strategy, FIGO stage IV, age ≥45 years, and poor differentiation of tumors were the independent risk factors.

Prognostic Nomogram for Liver Metastatic Colon Cancer Based on Histological Type, Tumor Differentiation, and Tumor Deposit: A TRIPOD Compliant Large-Scale Survival Study

ObjectiveA proportional hazard model was applied to develop a large-scale prognostic model and nomogram incorporating clinicopathological characteristics, histological type, tumor differentiation grade, and tumor deposit count to provide clinicians and patients diagnosed with colon cancer liver metastases (CLM) a more comprehensive and practical outcome measure.MethodsUsing the Transparent Reporting of multivariable prediction models for individual Prognosis or Diagnosis (TRIPOD) guidelines, this study identified 14,697 patients diagnosed with CLM from 1975 to 2017 in the Surveillance, Epidemiology, and End Results (SEER) 21 registry database. Patients were divided into a modeling group (n=9800), an internal validation group (n=4897) using computerized randomization. An independent external validation cohort (n=60) was obtained. Univariable and multivariate Cox analyses were performed to identify prognostic predictors for overall survival (OS). Subsequently, the nomogram was constructed, and the verification was undertaken by receiver operating curves (AUC) and calibration curves.ResultsHistological type, tumor differentiation grade, and tumor deposit count were independent prognostic predictors for CLM. The nomogram consisted of age, sex, primary site, T category, N category, metastasis of bone, brain or lung, surgery, and chemotherapy. The model achieved excellent prediction power on both internal (mean AUC=0.811) and external validation (mean AUC=0.727), respectively, which were significantly higher than the American Joint Committee on Cancer (AJCC) TNM system.ConclusionThis study proposes a prognostic nomogram for predicting 1- and 2-year survival based on histopathological and population-based data of CLM patients developed using TRIPOD guidelines. Compared with the TNM stage, our nomogram has better consistency and calibration for predicting the OS of CLM patients.

The Role of Urinary Biomarkers as Diagnostic and Prognostic Predictors of Acute Kidney Injury Associated With Vancomycin

Introduction: The incidence of acute kidney injury (AKI) related to vancomycin is variable, and several risk factors related to the treatment and patients may explain the nephrotoxicity. The role of urinary biomarkers in AKI related to vancomycin is unknown.Objective: The aim of this study was to evaluate the role of urinary IL-18, KIM-1, NGAL, TIMP-2, and IGFBP7 as diagnostic and prognostic predictors of AKI related to vancomycin.Methods: A prospective cohort study of patients receiving vancomycin and admitted to wards of a public university hospital from July 2019 to May 2020 was performed. We excluded patients that had AKI before starting vancomycin, hemodynamic instability, inability to collect urine, and chronic kidney disease stage 5.Results: Ninety-four patients were included, and the prevalence of AKI was 24.5%, while the general mortality was 8.7%. AKI occurred 11 ± 2 days after the first vancomycin dose. The most frequent KDIGO stage was 1 (61%). There was no difference between patients who developed and did not develop AKI due to gender, length of hospital stay, dose, and time of vancomycin use. Logistic regression identified age (OR 6.6, CI 1.16–38.22, p = 0.03), plasmatic vancomycin concentrations between 96 and 144 h (OR 1.18, CI 1.04-1.40, p = 0.04), and urinary NGAL levels between 96 and 144 h (OR 1.123, CI 1.096–1.290, p = 0.03) as predictors of AKI. The time of vancomycin use (OR 4.61, CI 1.11–22.02, p = 0.03), higher plasmatic vancomycin concentrations between 192 and 240 h (OR 1.02, CI 0.98–1.06, p = 0.26), and higher cell cycle arrest urinary biomarkers TIMP-2 multiplied by IGFBP-7 between 144 and 192 h (OR 1.33, CI 1.10–1.62, p = 0.02; OR 1.19, CI 1.09–1.39, p = 0.04, respectively) were identified as prognostic factors for non-recovery of kidney function at discharge.Conclusion: AKI related to vancomycin was frequent in patients hospitalized in wards. Age, plasmatic vancomycin concentrations, and NGAL between 96 and 144 h were identified as predictors of AKI related to vancomycin use. Plasmatic vancomycin concentrations and urinary NGAL were predictors of AKI diagnosis within the next 5 days. The urinary biomarkers of cell cycle arrest TIMP-2 and IGFBP-7 and the duration of vancomycin use were associated with non-recovery of kidney function at hospital discharge moment.