External Validation of the ASSURE Model for Predicting Oncological Outcomes After Resection of High-risk Renal Cell Carcinoma (RESCUE Study: UroCCR 88)
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ABSTRACT: Patient summary
The recent ASSURE model evaluates survival after surgery for nonmetastatic kidney cancer. We found no clear improvement in patient classification when we compared ASSURE with older models, so use of this model for patients with nonmetastatic kidney cancer still needs to be clarified.
SUBMITTER: Khene Z
PROVIDER: S-EPMC8502703 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:This study aimed to externally validate and upgrade the recent difficulty scoring system (DSS) proposed by Halls et al. to predict intraoperative complications (IOC) during laparoscopic liver resection (LLR). METHODS:The DSS was validated in a cohort of 128 consecutive patients undergoing pure LLRs between 2008 and 2019 at a single tertiary referral center. The validated DSS includes four difficulty levels based on five risk factors (neoadjuvant chemotherapy, previous open liver resection, lesion type, lesion size and classification of resection). As established by the validated DSS, IOC was defined as excessive blood loss (>?775?mL), conversion to an open approach and unintentional damage to surrounding structures. Additionally, intra- and postoperative outcomes were compared according to the difficulty levels with usual statistic methods. The same five risk factors were used for validation done by linear and advanced nonlinear (artificial neural network) models. The study was supported by mathematical computations to obtain a mean risk curve predicting the probability of IOC for every difficulty score. RESULTS:The difficulty level of LLR was rated as low, moderate, high and extremely high in 36 (28.1%), 63 (49.2%), 27 (21.1%) and 2 (1.6%) patients, respectively. IOC was present in 23 (17.9%) patients. Blood loss of >775?mL occurred in 8 (6.2%) patients. Conversion to open approach was required in 18 (14.0%) patients. No patients suffered from unintentional damage to surrounding structures. Rates of IOC (0, 9.5, 55.5 and 100%) increased gradually with statistically significant value among difficulty levels (P?<?0.001). The relations between the difficulty level, need for transfusion, operative time, hepatic pedicle clamping, and major postoperative morbidity were statistically significant (P?<?0.05). Linear and nonlinear validation models showed a strong correlation (correlation coefficients 0.914 and 0.948, respectively) with the validated DSS. The Weibull cumulative distribution function was used for predicting the mean risk probability curve of IOC. CONCLUSION:This external validation proved this DSS based on patient's, tumor and surgical factors enables us to estimate the risk of intra- and postoperative complications. A surgeon should be aware of an increased risk of complications before starting with more complex procedures.
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Project description:BackgroundDifferentiating benign from malignant renal tumors is important for patient management, and it may be improved by quantitative CT features analysis including radiomic.PurposeThis study aimed to compare performances of machine learning models using bio-clinical, conventional radiologic and 3D-radiomic features for the differentiation of benign and malignant solid renal tumors using pre-operative multiphasic contrast-enhanced CT examinations.Materials and methodsA unicentric retrospective analysis of prospectively acquired data from a national kidney cancer database was conducted between January 2016 and December 2020. Histologic findings were obtained by robotic-assisted partial nephrectomy. Lesion images were semi-automatically segmented, allowing for a 3D-radiomic features extraction in the nephrographic phase. Conventional radiologic parameters such as shape, content and enhancement were combined in the analysis. Biological and clinical features were obtained from the national database. Eight machine learning (ML) models were trained and validated using a ten-fold cross-validation. Predictive performances were evaluated comparing sensitivity, specificity, accuracy and AUC.ResultsA total of 122 patients with 132 renal lesions, including 111 renal cell carcinomas (RCCs) (111/132, 84%) and 21 benign tumors (21/132, 16%), were evaluated (58 +/- 14 years, men 74%). Unilaterality (100/111, 90% vs. 13/21, 62%; p = 0.02), necrosis (81/111, 73% vs. 8/21, 38%; p = 0.02), lower values of tumor/cortex ratio at portal time (0.61 vs. 0.74, p = 0.01) and higher variation of tumor/cortex ratio between arterial and portal times (0.22 vs. 0.05, p = 0.008) were associated with malignancy. A total of 35 radiomics features were selected, and "intensity mean value" was associated with RCCs in multivariate analysis (OR = 0.99). After ten-fold cross-validation, a C5.0Tree model was retained for its predictive performances, yielding a sensitivity of 95%, specificity of 42%, accuracy of 87% and AUC of 0.74.ConclusionOur machine learning-based model combining clinical, radiologic and radiomics features from multiphasic contrast-enhanced CT scans may help differentiate benign from malignant solid renal tumors.
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Project description:BackgroundAccurate preoperative risk assessment is necessary for informed decision making for patients and surgeons. Several preoperative risk calculators are available but few have been examined in the general thoracic surgical patient population. The Surgical Risk Preoperative Assessment System (SURPAS), a risk-assessment tool applicable to a wide spectrum of surgical procedures, was developed to predict the risks of common adverse postoperative outcomes using a parsimonious set of preoperative input variables. We sought to externally validate the performance of SURPAS for postoperative complications in patients undergoing pulmonary resection.MethodsBetween January 2016 and December 2018, 2514 patients underwent pulmonary resection at our center. Using data from our institution's prospectively maintained database, we calculated the predicted risks of 12 categories of postoperative outcomes using the latest version of SURPAS. Performance of SURPAS against observed patient outcomes was assessed by discrimination (concordance index) and calibration (calibration curves).ResultsThe discrimination ability of SURPAS was moderate across all outcomes (concordance indices, 0.640 to 0.788). Calibration curves indicated good calibration for all outcomes except infectious and cardiac complications, discharge to a location other than home, and mortality (all overestimated by SURPAS).ConclusionsSURPAS demonstrates outcomes for pulmonary resections with reasonable predictive ability. Discretion should be applied when assessing risk for postoperative infectious and cardiac complications, discharge to a location other than home, and mortality. Although the parsimonious nature of SURPAS is one of its strengths, its performance might be improved by including additional factors known to influence outcomes after pulmonary resection, such as sex and pulmonary function.
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Project description:ObjectiveTo develop and validate a nomogram for predicting renal dysfunction in patients with simple renal cysts (SRCs).MethodsWe performed a multivariable logistic regression analysis of an in-hospital retrospective cohort of patients with SRCs in the Urology Department of the First Affiliated Hospital of Anhui Medical University. For prognostic model development, 386 patients with SRCs were enrolled from January 2016 to December 2018. External validation was performed in 46 patients with SRCs from January 2019 to April 2019. The primary outcome was renal dysfunction.ResultsPatients were divided into normal or abnormal estimated glomerular filtration rate groups (293 vs. 93) based on the cut-off value of 90 mL/minute/1.73 m2. Logistical regression analysis determined that age, haemoglobin, globulin, and creatinine might be associated with renal dysfunction, and a novel nomogram was established. Calibration curves showed that the true prediction rate was 77.42%, and decision curve analysis revealed that the nomogram was more effective with threshold probabilities ranging from 0.1 to 0.8. The area under the curves were 0.829, 0.752, and 0.888 in the overall training, internal, and external validation cohorts, respectively.ConclusionsWe established a nomogram to predict the probability of developing renal dysfunction in patients with SRCs.
| S-EPMC8949791 | biostudies-literature
Project description:BackgroundSeveral prognostic models for outcomes after chronic subdural hematoma (CSDH) treatment have been published in recent years. However, these models are not sufficiently validated for use in daily clinical practice. We aimed to assess the performance of existing prediction models for outcomes in patients diagnosed with CSDH.MethodsWe systematically searched relevant literature databases up to February 2021 to identify prognostic models for outcome prediction in patients diagnosed with CSDH. For the external validation of prognostic models, we used a retrospective database, containing data of 2384 patients from three Dutch regions. Prognostic models were included if they predicted either mortality, hematoma recurrence, functional outcome, or quality of life. Models were excluded when predictors were absent in our database or available for < 150 patients in our database. We assessed calibration, and discrimination (quantified by the concordance index C) of the included prognostic models in our retrospective database.ResultsWe identified 1680 original publications of which 1656 were excluded based on title or abstract, mostly because they did not concern CSDH or did not define a prognostic model. Out of 18 identified models, three could be externally validated in our retrospective database: a model for 30-day mortality in 1656 patients, a model for 2 months, and another for 3-month hematoma recurrence both in 1733 patients. The models overestimated the proportion of patients with these outcomes by 11% (15% predicted vs. 4% observed), 1% (10% vs. 9%), and 2% (11% vs. 9%), respectively. Their discriminative ability was poor to modest (C of 0.70 [0.63-0.77]; 0.46 [0.35-0.56]; 0.59 [0.51-0.66], respectively).ConclusionsNone of the examined models showed good predictive performance for outcomes after CSDH treatment in our dataset. This study confirms the difficulty in predicting outcomes after CSDH and emphasizes the heterogeneity of CSDH patients. The importance of developing high-quality models by using unified predictors and relevant outcome measures and appropriate modeling strategies is warranted.
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Project description:BackgroundThe aim of this study was to validate the 'Predicting Infectious ComplicatioNs in Children with Cancer' (PICNICC) clinical decision rule (CDR) that predicts microbiologically documented infection (MDI) in children with cancer and fever and neutropenia (FN). We also investigated costs associated with current FN management strategies in Australia.MethodsDemographic, episode, outcome and cost data were retrospectively collected on 650 episodes of FN. We assessed the discrimination, calibration, sensitivity and specificity of the PICNICC CDR in our cohort compared with the derivation data set.ResultsUsing the original variable coefficients, the CDR performed poorly. After recalibration the PICNICC CDR had an area under the receiver operating characteristic (AUC-ROC) curve of 0.638 (95% CI 0.590-0.685) and calibration slope of 0.24. The sensitivity, specificity, positive predictive value and negative predictive value of the PICNICC CDR at presentation was 78.4%, 39.8%, 28.6% and 85.7%, respectively. For bacteraemia, the sensitivity improved to 85.2% and AUC-ROC to 0.71. Application at day 2, taking into consideration the proportion of MDI known (43%), further improved the sensitivity to 87.7%. Length of stay is the main contributor to cost of FN treatment, with an average cost per day of AUD 2183 in the low-risk group.ConclusionsFor prediction of any MDI, the PICNICC rule did not perform as well at presentation in our cohort as compared with the derivation study. However, for bacteraemia, the predictive ability was similar to that of the derivation study, highlighting the importance of recalibration using local data. Performance also improved after an overnight period of observation. Implementation of a low-risk pathway, using the PICNICC CDR after a short period of inpatient observation, is likely to be safe and has the potential to reduce health-care expenditure.
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Project description:BACKGROUND:The objective of this study was to derive and validate a prognostic nomogram to predict disease-specific survival (DSS) after a curative intent resection of perihilar cholangiocarcinoma (PHC). PATIENTS AND METHODS:A nomogram was developed from 173 patients treated at Memorial Sloan Kettering Cancer Center (MSKCC), New York, USA. The nomogram was externally validated in 133 patients treated at the Academic Medical Center (AMC), Amsterdam, The Netherlands. Prognostic accuracy was assessed with concordance estimates and calibration, and compared with the American Joint Committee on Cancer (AJCC) staging system. The nomogram will be available as web-based calculator at mskcc.org/nomograms. RESULTS:For all 306 patients, the median overall survival (OS) was 40 months and the median DSS 41 months. Median follow-up for patients alive at last follow-up was 48 months. Lymph node involvement, resection margin status, and tumor differentiation were independent prognostic factors in the derivation cohort (MSKCC). A nomogram with these prognostic factors had a concordance index of 0.73 compared with 0.66 for the AJCC staging system. In the validation cohort (AMC), the concordance index was 0.72, compared with 0.60 for the AJCC staging system. Calibration was good in the derivation cohort; in the validation cohort patients had a better median DSS than predicted by the model. CONCLUSIONS:The proposed nomogram to predict DSS after curative intent resection of PHC had a better prognostic accuracy than the AJCC staging system. Calibration was suboptimal because DSS differed between the two institutions. The nomogram can inform patients and physicians, guide shared decision making for adjuvant therapy, and stratify patients in future randomized, controlled trials.
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Project description:BackgroundEuroLung Risk scores were established to predict postoperative morbidity and mortality in patients undergoing anatomic lung resections. We aimed to perform an external validation of the EuroLung scores, which were calculated from data of the European Society of Thoracic Surgeons database, in our video-assisted thoracoscopic surgery cohort.MethodsAll available EuroLung scores were calculated for 718 patients scheduled for anatomic video-assisted thoracoscopic surgery resections between 2009 and 2019. Morbidity and mortality according to the definitions of the EuroLung scores were analyzed in a prospectively maintained database.ResultsOverall observed complication rate was 10.45%. Scores showed weak individual correlation (η = 0.155-0.174). The EuroLung1 app score showed the biggest area under the receiver operative characteristic (ROC) curve with 0.660. Binary logistic regression analysis showed that predicted postoperative forced expiratory volume in 1 s was associated with increased complications in both EuroLung1 and parsimonious EuroLung1 scores. Thirty-day mortality was 0.7% (predicted 1.10-1.40%) and was associated with predicted postoperative forced expiratory volume in 1 s for both EuroLung2 and parsimonious EuroLung2 scores. The EuroLung2 (2016) showed the biggest area under the ROC curve with 0.673. Only a very weak eta correlation between predicted and observed mortality was found for both aggregate EuroLung2, EuroLung2 (2016), EuroLung2 (2019), and parsimonious EuroLung2 (2016) (η = 0.025/0.015/0.011/0.009).ConclusionEuroLung scores help to estimate postoperative morbidity. However, even with the highest aggregate EuroLung scores possible only 50% suffer from postoperative morbidity. Although calibration of the scores was acceptable, discrimination between predicted and observed events was poor. Therefore, individual correlation between predicted and observed events is weak. Therefore, EuroLung scores may be best used to compare institutional quality of care to the European Society of Thoracic Surgeons database but should not be used to preclude patients from surgical treatment.
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Project description:BackgroundInstruments designed to predict the extent of pain and function following knee arthroplasty (KA) recovery has strong potential to guide patients and clinicians in shared decision making. Our purpose was to test the external validity of a recently developed prognostic instrument designed to estimate the probability of nonresponse following KA.MethodsWe used data from the Osteoarthritis Initiative (OAI), a 9-year multisite National Institutes of Health study designed to examine the natural history of knee osteoarthritis in 4796 subjects. A total of 427 subjects underwent KA over the study period. Dowsey et al examined the prognostic role of obesity, general mental health, pain and function, and Kellgren and Lawrence knee osteoarthritis grades. Calibration of the prognostic model was determined using a calibration curve. The c-statistic was used to indicate discrimination of the model.ResultsIn the primary analysis, 63 (19.3%) of 326 subjects in OAI were classified as nonresponders. The calibration curve generated from OAI data indicated poor calibration relative to the recently developed instrument. Discrimination as measured by the c-statistic was 0.76.ConclusionThe external validity of the prognostic instrument was partially supported. While discrimination of the model was very similar to the recently developed instrument, calibration was poor indicating poor agreement between actual vs predicted probabilities of nonresponse. Western Ontario and McMaster Universities Arthritis Index and Kellgren and Lawrence grades show strong potential for use in future prognostic model development. Measurements of general mental health and obesity were not prognostic for nonresponse.
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Project description:Multiple Sclerosis Spasticity Scale (MSSS)-88 has been developed for self-assessment of spasticity symptoms in patients with multiple sclerosis (MS). The objective of this study was to validate MSSS-88 and evaluate the psychometric properties in patients with MS in Serbia.The study comprised 65 MS patients with spasticity. MSSS-88 consists of 88 items grouped in eight sections. Internal consistency of the MSSS-88SR subscales was determined using Cronbach's alpha coefficient. Test/retest reliability with an intra-class correlation coefficient (ICC) for each MSSS-88SR subscale was performed. Clinical validity of MSSS-88SR was determined by correlations with the Numeric Rating Scale (NRS) and the Modified Ashworth Scale (MAS).The range of Cronbach's alpha for all scales and ICC was 0.91-0.96 and 0.84-0.91, respectively. All ICCs were statistically significant (p<0.05). All evaluated subscales of MSSS-88 were significantly correlated with the NRS scale. The highest correlation coefficients were registered between the WL subscale and the EDSS and MAS, while the strongest relationship was observed between the MSS subscale and the NRS.The Serbian translated version of this instrument may be useful as a clinical measure for spasticity and functionality in patients with MS.
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