Predicting the risk of pT3a stage in cT1 clear cell renal cell carcinoma

Nocera, Luigi; Stolzenbach, Lara Franziska; Ruvolo, Claudia Collà; Wenzel, Mike; Tian, Zhe; Rosiello, Giuseppe; Bravi, Carlo Andrea; Candela, Luigi; Basile, Giuseppe; Larcher, Alessandro; Shariat, Shahrokh F.; Bertini, Roberto; Capitanio, Umberto; Salonia, Andrea; Montorsi, Francesco; Briganti, Alberto; Karakiewicz, Pierre I.

doi:10.1016/j.ejso.2020.10.040

Cited by 13 publications

(22 citation statements)

References 11 publications

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“…Besides, the model developed by Nocera et al. ( 37 ) also appeared to outperform our model (0.81 vs. 0.71). Discrepancy in performance of predictive models between our and the former two studies may be attributed to the fact that there were more participants enrolled in our series, and the rate of upstaging in our patient cohort was significantly lower.…”

Section: Discussionsupporting

confidence: 49%

“…Discrepancy in performance of predictive models between our and the former two studies may be attributed to the fact that there were more participants enrolled in our series, and the rate of upstaging in our patient cohort was significantly lower. However, it should be noted that the performance of predictive model derived from data in the subpopulation (AUC = 0.86) was not inferior to that of Carlos et al (36) or Nocera et al (37). Considering the small sample size in the subpopulation, the true predictive value of serum lipid parameters should be further, externally validated in large datasets.…”

Section: Discussionmentioning

confidence: 81%

“…( 36 ) or Nocera et al. ( 37 ). Considering the small sample size in the subpopulation, the true predictive value of serum lipid parameters should be further, externally validated in large datasets.…”

Section: Discussionmentioning

confidence: 99%

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Added Value of Systemic Inflammation Markers in Predicting Clinical Stage T1 Renal Cell Carcinoma Pathologically Upstaged to T3a

et al. 2021

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ObjectivesWe aimed to determine preoperative risk factors associated with pathologic T3a (pT3a) upstaging of clinical T1 (cT1) renal cell carcinomas (RCCs) and develop a novel model capable of accurately identifying those patients at high risk of harboring occult pT3a characteristics.MethodsA retrospective analysis of 1324 cT1 RCC patients who underwent partial nephrectomy (PN) or radical nephrectomy (RN) was performed. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Univariable and multivariable logistic regression analyses were performed to identify predictors associated with cT1 to pT3a upstaging and subsequently, those significant risk factors were used to construct models. We used the area under the curve (AUC) to determine the model with the highest discrimination power. Decision curve analyses (DCAs) were applied to evaluate clinical net benefit associated with using the predictive models.ResultsThe rates of upstaging were 6.1% (n = 81), 5.8% (n = 54) and 6.8% (n = 27) in the total population, training cohort and validation cohort, respectively. Tumor size, clinical T stage, R.E.N.A.L. (radius, exophytic/endophytic properties, nearness of tumor to collecting system or sinus, anterior/posterior) nephrometry score, lymphocyte to monocyte ratio (LMR), prognostic nutrition index (PNI) and albumin to globulin ratio (AGR) were significantly associated with pT3a upstaging. The model that consisted of R.E.N.A.L. score, LMR, AGR and PNI achieved the highest AUC of 0.70 in the validation cohort and yielded the highest net benefit. In the subpopulation with complete serum lipid profile, the inclusion of low-density lipoprotein cholesterol (LDL-C) and Castelli risk index-I (CRI-I) significantly improved the discrimination of model (AUC = 0.86).ConclusionsOur finding highlights the importance of systemic inflammation response markers and serum lipid parameters in predicting pT3a upstaging. Our model had relatively good discrimination in predicting occult pT3a disease among patients with cT1 renal lesions, and the use of the model may be greatly beneficial to urologists in risk stratification and management decisions.

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Section: Discussionsupporting

confidence: 49%

Section: Discussionmentioning

confidence: 81%

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Added Value of Systemic Inflammation Markers in Predicting Clinical Stage T1 Renal Cell Carcinoma Pathologically Upstaged to T3a

et al. 2021

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“…In the subgroup analyses focusing on patients that had also been evaluated at 1-month follow-up, an additional logistic regression model was fitted, relying on the same covariates as the original nomogram (sex, obesity, COPD, PaO 2 /FiO 2 at hospital admission and NIV administration) in addition to RR and mMRC for dyspnea score at 1 month (Figure 3). Overall, 220 patients assessed after a median time of 34 (25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) days after hospital discharge were included. Of these, 77 (35%) had reduced respiratory function at 6 months.…”

Section: Resultsmentioning

confidence: 99%

“…Statistical significance of differences in the resulting AUC values obtained within the same cohort, for, respectively, the original vs. the extended nomogram, was tested according to DeLong et al methodology ( 24 ). For both the original and extended nomograms, 95% confidence interval (CI) of AUC was obtained through 2000-resample bootstrap-based internal validation, which simulated the application of our model to 2000 newly created patient cohorts derived from random resampling of the original population ( 25 , 26 ). All statistical tests were performed using the R statistical package v.4.0.0 (R Project for Statistical Computing, www.r-project.org ).…”

Section: Methodsmentioning

confidence: 99%

A Nomogram-Based Model to Predict Respiratory Dysfunction at 6 Months in Non-Critical COVID-19 Survivors

et al. 2022

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ObjectiveTo assess the prevalence of respiratory sequelae of Coronavirus disease 2019 (COVID-19) survivors at 6 months after hospital discharge and develop a model to identify at-risk patients.Patients and MethodsIn this prospective cohort study, hospitalized, non-critical COVID-19 patients evaluated at 6-month follow-up between 26 August, 2020 and 16 December, 2020 were included. Primary outcome was respiratory dysfunction at 6 months, defined as at least one among tachypnea at rest, percent predicted 6-min walking distance at 6-min walking test (6MWT) ≤ 70%, pre-post 6MWT difference in Borg score ≥ 1 or a difference between pre- and post-6MWT oxygen saturation ≥ 5%. A nomogram-based multivariable logistic regression model was built to predict primary outcome. Validation relied on 2000-resample bootstrap. The model was compared to one based uniquely on degree of hypoxemia at admission.ResultsOverall, 316 patients were included, of whom 118 (37.3%) showed respiratory dysfunction at 6 months. The nomogram relied on sex, obesity, chronic obstructive pulmonary disease, degree of hypoxemia at admission, and non-invasive ventilation. It was 73.0% (95% confidence interval 67.3–78.4%) accurate in predicting primary outcome and exhibited minimal departure from ideal prediction. Compared to the model including only hypoxemia at admission, the nomogram showed higher accuracy (73.0 vs 59.1%, P < 0.001) and greater net-benefit in decision curve analyses. When the model included also respiratory data at 1 month, it yielded better accuracy (78.2 vs. 73.2%) and more favorable net-benefit than the original model.ConclusionThe newly developed nomograms accurately identify patients at risk of persistent respiratory dysfunction and may help inform clinical priorities.

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