Prediction of significant renal function decline after open, laparoscopic, and robotic partial nephrectomy: External validation of the Martini's nomogram on the RECORD2 project cohort

Comparing robotic and open partial nephrectomy under the prism of surgical precision: a meta-analysis of the average blood loss rate as a novel variable

Exploration of surgical precision in robotic procedures is extensive, yet lacks a unified framework for comparability. This study examines tissue handling precision by comparing the per-minute blood loss rate between robotic and open partial nephrectomy. A literature search from August 2022 to June 2024 identified 43 relevant studies providing data on estimated blood loss and procedure duration. The expected values and standard errors of these variables were used to compute the per-minute blood loss rate (Q). Meta-analytical methods estimated pooled and subgroup-level mean differences, favoring robotic surgery (MDQ = - 1.043 ml/min, CI95% = [- 1.338; - 0.747]). Subgroup analyses by publication year, patient matching, referral center count, and ROBINS-I status consistently supported this advantage. Sensitivity analyses confirmed the above benefit in studies with increased accuracy in reported results (MDQ = - 0.957 ml/min, CI95% = [- 1.269; - 0.646]), low risk of bias involving matched comparisons (MDQ = - 0.563 ml/min, CI95% = [- 0.716; - 0.410]), large sample sizes and increased statistical power (MDQ = - 0.780 ml/min, CI95% = [- 1.134; - 0.425]), and multicenter analyses with patient matching (MDQ = - 0.481 ml/min, CI95% = [- 0.698; - 0.263]). The subsequent analysis of correlation between the original variables suggested a slight reduction in the robotic advantage when the latter were proportionally related. Multilevel meta-regression at both temporal and qualitative scales consistently indicated a comparative benefit of the robotic approach. Potentially, lower per-minute blood loss compared to open surgery suggests that robotic partial nephrectomy demonstrates enhanced precision in tissue handling.

A systematic review of robot-assisted partial nephrectomy outcomes for advanced indications: Large tumors (cT2-T3), solitary kidney, completely endophytic, hilar, recurrent, and multiple renal tumors

Objective

Robot-assisted partial nephrectomy (RAPN) has become widely used for treatment of renal cell carcinoma and it is expanding in the field of complex renal masses. The aim of this systematic review was to analyze outcomes of RAPN for completely endophytic renal masses, large tumors (cT2-T3), renal cell carcinoma in solitary kidney, recurrent tumors, completely endophytic and hilar masses, and simultaneous and multiple tumors.

Methods

A comprehensive search in the PubMed, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials databases was performed in December 2022 for English language papers. The primary endpoint was to evaluate the role of RAPN in the setting of each category of complex renal masses considered. The secondary endpoint was to evaluate the surgical and functional outcomes.

Results

After screening 1250 records, 43 full-text manuscripts were selected, comprising over 8500 patients. Twelve and thirteen studies reported data for endophytic and hilar renal masses, respectively. Five and three studies reported outcomes for cT2-T3 and solitary kidney patients, respectively. Four studies focused on redo-RAPN for recurrent tumors. Two studies investigated simultaneous bilateral renal masses and five reports focused on multiple tumor excision in ipsilateral kidney.

Conclusion

Over the past decade, evidence supporting the use of RAPN for the most challenging nephron-sparing surgery indications has continuously grown. Although limitations remain including study design and lack of detailed long-term functional and oncological outcomes, the adoption of RAPN for the included advanced indications is associated with favorable surgical outcomes with good preservation of renal function without compromising the oncological result. Certainly, a higher likelihood of complication might be expected when facing extremely challenging cases. However, none of these indications should be considered per se an exclusion criterion for performing RAPN. Ultimately, a risk-adapted approach should be employed.

Limited non-linear impact of warm ischemia time on renal functional decline after partial nephrectomy: a propensity score-matched study

OBJECTIVE

To quantificationally illustrate the impact of ischemia time (IT) on renal function decline after partial nephrectomy (PN), especially for patients with compromised baseline renal function (estimated glomerular filtration rate [eGFR] < 90 mL/min/1.73 m²).

METHODS

Patients undergoing PN during 2014-2021 from a prospectively maintained database were reviewed. Propensity score matching (PSM) was employed to balance the possible covariates between patients with or without baseline compromised renal function. Specifically, the relationship of IT with postoperative renal function was illustrated. Two machine learning methods (logistic least absolute shrinkage and selection operator [LASSO] logistic regression and random forest) were applied to quantify the relative impact of each covariables.

RESULTS

The average drop percent of eGFR was -10.9% (- 12.2%, - 9.0%). Multivariable Cox proportional regression and linear regression analyses identified five risk factors for renal function decline, namely RENAL Nephrometry Score (RNS), age, baseline eGFR, diabetes and IT (all p < 0.05). Specifically, the relationship of IT with postoperative functional decline emerged as non-linear, with an increase from 10-30 min and a plateau afterwards among patients with normal function (eGFR ≥ 90 mL/min/1.73 m²), whereas with an increase from 10 to 20 min and a plateau afterwards among patients with compromised function (eGFR < 90 mL/min/1.73 m²). Furthermore, the coefficient's path and random forest analysis revealed that the top two most important features were RNS and age.

CONCLUSION

IT exhibits the secondarily non-linear relationship with postoperative renal function decline. Patients with compromised baseline renal function are less tolerant to ischemia damage. The use of a single cut-off interval of IT in the setting of PN is flawed.

Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with SFTS

BACKGROUND

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with high mortality. Early identification of patients who may advance to critical stages is crucial. This investigation aimed to establish models to predict SFTS before it reaches the critical illness stage.

METHODS

Between January 2016 and September 2022, 278 cases have been included in this study. There were 87 demographic and systemic chosen variables. For selecting the predictive variables from the cohort, the LASSO was utilized, and for identifying independent predictors, multivariate logistic regression was performed. Based on these factors, a nomogram was established for critical illness. Concordance index values, decision curve analysis and the area under the curve (AUC) were also examined.

RESULTS

Multivariate logistic regression demonstrated the most important differentiating factors as;> 65 years old (P < 0.001, OR 3.388, 95 % CI 1.767-6.696), elevated serum PT (P = 0.011, OR 6.641, 95 % CI 1.584-31.934), elevated serum TT (P = 0.005, OR 3.384, 95 % CI 1.503-8.491), and elevated serum bicarbonate (P = 0.014, OR 0.242, 95 % CI 0.070-0.707). The C-index of the nomogram was 0.812 (95 % CI: 0.754-0.869), representing good discrimination. The model also showed excellent calibration. The AUC of the nomogram established based on four factors, as mentioned earlier, was 0.806. Furthermore, the model had the excellent net benefit, as revealed by the decision curve analysis.

CONCLUSION

An accurate risk score system built on manifestations noted in patients with SFTS upon admission to hospital, might be advantageous in managing SFTS.

Estimating Postoperative Renal Function After Surgery for Nonmetastatic Renal Masses: A Systematic Review of Available Prediction Models

CONTEXT

A variety of models predicting postoperative renal function following surgery for nonmetastatic renal tumors have been reported, but their validity and clinical usefulness have not been formally assessed.

OBJECTIVE

To summarize prediction models available for estimation of mid- to long-term (>3 mo) postoperative renal function after partial nephrectomy (PN) or radical nephrectomy (RN) for nonmetastatic renal masses that include only preoperative or modifiable intraoperative variables.

EVIDENCE ACQUISITION

A systematic review of the English-language literature was conducted using the MEDLINE, Embase, and Web of Science databases from January 2000 to March 2022 according to the PRISMA guidelines (PROSPERO ID: CRD42022303492). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool.

EVIDENCE SYNTHESIS

Overall, 21 prediction models from 18 studies were included (nine for PN only; eight for RN only; four for PN or RN). Most studies relied on retrospective patient cohorts and had a high risk of bias and high concern regarding the overall applicability of the proposed model. Patient-, kidney-, surgery-, tumor-, and provider-related factors were included among the predictors in 95%, 86%, 100%, 61%, and 0% of the models, respectively. All but one model included both patient age and preoperative renal function, while only a few took into account patient gender, race, comorbidities, tumor size/complexity, and surgical approach. There was significant heterogeneity in both the model building strategy and the performance metrics reported. Five studies reported external validation of six models, while three assessed their clinical usefulness using decision curve analysis.

CONCLUSIONS

Several models are available for predicting postoperative renal function after kidney cancer surgery. Most of these are not ready for routine clinical practice, while a few have been externally validated and might be of value for patients and clinicians.

PATIENT SUMMARY

We reviewed the tools available for predicting kidney function after partial or total surgical removal of a kidney for nonmetastatic cancer. Most of the models include patient and kidney characteristics such as age, comorbidities, and preoperative kidney function, and a few also include tumor characteristics and intraoperative variables. Some models have been validated by additional research groups and appear promising for improving counseling for patients with nonmetastatic cancer who are candidates for surgery.

Development and validation of a prognostic nomogram for predicting cancer-specific survival in patients with metastatic clear cell renal carcinoma: A study based on SEER database

Objectives

Clear cell renal cell carcinoma (ccRCC) is highly prevalent, prone to metastasis, and has a poor prognosis after metastasis. Therefore, this study aimed to develop a prognostic model to predict the individualized prognosis of patients with metastatic clear cell renal cell carcinoma (mccRCC).

Patients and Methods

Data of 1790 patients with mccRCC, registered from 2010 to 2015, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The included patients were randomly divided into a training set (n = 1253) and a validation set (n = 537) based on the ratio of 7:3. The univariate and multivariate Cox regression analyses were used to identify the important independent prognostic factors. A nomogram was then constructed to predict cancer specific survival (CSS). The performance of the nomogram was internally validated by using the concordance index (C-index), calibration plots, receiver operating characteristic curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). We compared the nomogram with the TNM staging system. Kaplan–Meier survival analysis was applied to validate the application of the risk stratification system.

Results

Diagnostic age, T-stage, N-stage, bone metastases, brain metastases, liver metastases, lung metastases, chemotherapy, radiotherapy, surgery, and histological grade were identified as independent predictors of CSS. The C-index of training and validation sets are 0.707 and 0.650 respectively. In the training set, the AUC of CSS predicted by nomogram in patients with mccRCC at 1-, 3- and 5-years were 0.770, 0.758, and 0.757, respectively. And that in the validation set were 0.717, 0.700, and 0.700 respectively. Calibration plots also showed great prediction accuracy. Compared with the TNM staging system, NRI and IDI results showed that the predictive ability of the nomogram was greatly improved, and DCA showed that patients obtained clinical benefits. The risk stratification system can significantly distinguish the patients with different survival risks.

Conclusion

In this study, we developed and validated a nomogram to predict the CSS rate in patients with mccRCC. It showed consistent reliability and clinical applicability. Nomogram may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.