External validation and comparison of current scoring systems in retrograde intrarenal surgery: Multi‐institutional study with 949 patients

2021 ◽  
Author(s):  
Ibrahim Halil Bozkurt ◽  
Ahmet Nihat Karakoyunlu ◽  
Omer Koras ◽  
Serdar Celik ◽  
Ertugrul Sefik ◽  
...  
Keyword(s):  
External Validation ◽  
Scoring Systems ◽  
Retrograde Intrarenal Surgery

scholarly journals External validation of the R.I.R.S. scoring system to predict stone-free rate after retrograde intrarenal surgery

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Cong Wang ◽  
ShouTong Wang ◽  
Xuemei Wang ◽  
Jun Lu
Keyword(s):  
Scoring System ◽  
Kidney Stone ◽  
Medical Center ◽  
External Validation ◽  
Retrograde Intrarenal Surgery ◽  
Stone Burden ◽  
Stone Free Rate ◽  
Stone Density ◽  
Free Rate ◽  
Free Status

Abstract Background The R.I.R.S. scoring system is defined as a novel and straightforward scoring system that uses the main parameters (kidney stone density, inferior pole stones, stone burden, and renal infundibular length) to identify most appropriate patients for retrograde intrarenal surgery (RIRS). We strived to evaluate the accuracy of the R.I.R.S. scoring system in predicting the stone-free rate (SFR) after RIRS. Methods In our medical center, we retrospectively analyzed charts of patients who had, between September 2018 and December 2019, been treated by RIRS for kidney stones. A total of 147 patients were enrolled in the study. Parameters were measured for each of the four specified variables. Results Stone-free status was achieved in 105 patients (71.43%), and 42 patients had one or more residual fragments (28.57%). Differences in stone characteristics, including renal infundibulopelvic angle, renal infundibular length, lower pole stone, kidney stone density, and stone burden were statistically significant in patients whether RIRS achieved stone-free status or not (P < 0.001, P: 0.005, P < 0.001, P < 0.001, P: 0.003, respectively). R.I.R.S. scores were significantly lower in patients treated successfully with RIRS than patients in which RIRS failed (P < 0.001). Binary logistic regression analyses revealed that R.I.R.S. scores were independent factors affecting RIRS success (P = 0.033). The area under the curve of the R.I.R.S. scoring system was 0.737. Conclusions Our study retrospectively validates that the R.I.R.S. scoring system is associated with SFR after RIRS in the treatment of renal stones, and can predict accurately.

scholarly journals Development of a Sex-Specific Risk Scoring System for Predicting Cognitive Normal to Mild Cognitive Impairment (SRSS-CNMCI)

2021 ◽  
Author(s):  
Wen Luo ◽  
Hao Wen ◽  
Shuqi Ge ◽  
Chunzhi Tang ◽  
Xiufeng Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
High Risk ◽  
Scoring System ◽  
External Validation ◽  
Scoring Systems ◽  
Cutoff Point ◽  
Risk Scoring ◽  
Risk Scoring System

Abstract Objective: We aim to develop a sex-specific risk scoring system for predicting cognitive normal (CN) to mild cognitive impairment (MCI), abbreviated SRSS-CNMCI, to provide a reliable tool for the prevention of MCI.Methods: Participants aged 61-90 years old with a baseline diagnosis of CN and an endpoint diagnosis of MCI were screened from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database with at least one follow-up. Multivariable Cox proportional hazards models were used to identify risk factors associated with conversion from CN to MCI and to build risk scoring systems for male and female groups. Receiver operating characteristic (ROC) curve analysis was applied to determine the risk probability cutoff point corresponding to the optimal prediction effect. We ran an external validation of the discrimination and calibration based on the Harvard Aging Brain Study (HABS) database.Results: A total of 471 participants, including 240 women (51%) and 231 men (49%), aged 61 to 90 years, were included in the study cohort for subsequent primary analysis. The final multivariable models and the risk scoring systems for females and males included age, APOE ε4, Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). The scoring systems for females and males revealed C statistics of 0.902 (95% CI 0.840-0.963) and 0.911 (95% CI 0.863-0.959), respectively, as measures of discrimination. The cutoff point of high and low risk was 33% in females, and more than 33% was considered high risk, while more than 9% was considered high risk for males. The external validation effect of the scoring systems was good: C statistic 0.950 for the females and C statistic 0.965 for the males. Conclusions: Our parsimonious model accurately predicts conversion from CN to MCI with four risk factors and can be used as a predictive tool for the prevention of MCI.

Comparison of scoring systems for predicting stone-free status and complications after retrograde ıntrarenal surgery

2020 ◽  
Author(s):  
Ridvan Ozbek ◽  
Cagri Senocak ◽  
Hakan Bahadir Haberal ◽  
Erman Damar ◽  
Fahri Erkan Sadioglu ◽  
...  
Keyword(s):  
Scoring Systems ◽  
Retrograde Intrarenal Surgery ◽  
Free Status

External validation of severity scoring systems for acute renal failure using a multinational database

2005 ◽  
Vol 33 (9) ◽  
pp. 1961-1967 ◽  
Author(s):  
Shigehiko Uchino ◽  
Rinaldo Bellomo ◽  
Hiroshi Morimatsu ◽  
Stanislao Morgera ◽  
Miet Schetz ◽  
...  
Keyword(s):  
Renal Failure ◽  
Acute Renal Failure ◽  
External Validation ◽  
Scoring Systems ◽  
Severity Scoring

External Validation of the Two Newly Proposed Criteria for Assessing Coagulopathy in Sepsis

2018 ◽  
Vol 119 (02) ◽  
pp. 203-212 ◽  
Author(s):  
Kazuma Yamakawa ◽  
Jumpei Yoshimura ◽  
Takashi Ito ◽  
Mineji Hayakawa ◽  
Toshimitsu Hamasaki ◽  
...  
Keyword(s):  
Anticoagulant Therapy ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
External Validation ◽  
Scoring Systems ◽  
Thrombin Time ◽  
Acute Medicine ◽  
Cox Regression Analysis ◽  
Propensity Scoring ◽  
Japanese Cohort

Background Two different criteria for evaluating coagulopathy in sepsis were recently released: sepsis-induced coagulopathy (SIC) and sepsis-associated coagulopathy (SAC). Although both use universal haemostatic markers of platelet count and pro-thrombin time, significance and usefulness of these criteria remain unclear. Objective This article validates and evaluates the significance of SIC and SAC criteria compared with the International Society on Thrombosis and Haemostasis (ISTH) overt disseminated intravascular coagulation (DIC) and Japanese Association for Acute Medicine (JAAM) DIC criteria. Methods Clinical characteristics of patients from a nationwide Japanese cohort were classified by SIC, SAC or DIC status and relations between criteria were examined. We evaluated associations between in-hospital mortality and anticoagulant therapy according to the SIC, SAC or DIC status to clarify the significance of criteria for introducing anticoagulants. Intervention effects were analysed by Cox regression analysis adjusted by propensity scoring. Results Incidences of coagulopathy diagnosed by SIC and JAAM DIC were similar, whereas those of SAC and ISTH overt DIC were about half of the former two (61.4%, 60.8% vs. 45.3%, 29.3%). Severity and mortality of all criteria were almost comparable. For validating initiation of anticoagulation, favourable effects of anticoagulant therapy were observed only in sub-sets with, and not without, coagulopathy diagnosed by all four criteria. Slight non-significant differences between anticoagulant groupings were found in ISTH overt DIC- and SAC-negative populations, suggesting that some patients even ‘without’ these criteria may benefit from anticoagulant therapy. Conclusion Newly developed SIC diagnostic criteria for coagulopathy may be valuable in detecting appropriate candidates for anticoagulant therapy in sepsis and a useful alternative to conventional DIC scoring systems.

scholarly journals Machine Learning Prediction Model for Acute Renal Failure After Acute Aortic Syndrome Surgery

2022 ◽  
Vol 8 ◽  
Author(s):  
Jinzhang Li ◽  
Ming Gong ◽  
Yashutosh Joshi ◽  
Lizhong Sun ◽  
Lianjun Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Failure ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Scoring Systems ◽  
Acute Aortic Syndrome ◽  
Internal Validation ◽  
Medical Centers ◽  
Better Than

BackgroundAcute renal failure (ARF) is the most common major complication following cardiac surgery for acute aortic syndrome (AAS) and worsens the postoperative prognosis. Our aim was to establish a machine learning prediction model for ARF occurrence in AAS patients.MethodsWe included AAS patient data from nine medical centers (n = 1,637) and analyzed the incidence of ARF and the risk factors for postoperative ARF. We used data from six medical centers to compare the performance of four machine learning models and performed internal validation to identify AAS patients who developed postoperative ARF. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to compare the performance of the predictive models. We compared the performance of the optimal machine learning prediction model with that of traditional prediction models. Data from three medical centers were used for external validation.ResultsThe eXtreme Gradient Boosting (XGBoost) algorithm performed best in the internal validation process (AUC = 0.82), which was better than both the logistic regression (LR) prediction model (AUC = 0.77, p &lt; 0.001) and the traditional scoring systems. Upon external validation, the XGBoost prediction model (AUC =0.81) also performed better than both the LR prediction model (AUC = 0.75, p = 0.03) and the traditional scoring systems. We created an online application based on the XGBoost prediction model.ConclusionsWe have developed a machine learning model that has better predictive performance than traditional LR prediction models as well as other existing risk scoring systems for postoperative ARF. This model can be utilized to provide early warnings when high-risk patients are found, enabling clinicians to take prompt measures.

scholarly journals External validation of Tzanakis scoring system in acute appendicitis in a rural hospital

2019 ◽  
Vol 7 (5) ◽  
pp. 1536
Author(s):  
Mohd Riyaz Lattoo ◽  
Shabir Ahmad Mir ◽  
Nayeemul Hassan Ganie ◽  
Shabir Hussain Rather
Keyword(s):  
Retrospective Study ◽  
Acute Appendicitis ◽  
Predictive Value ◽  
Scoring System ◽  
Histopathological Examination ◽  
External Validation ◽  
Scoring Systems ◽  
Common Cause ◽  
Negative Laparotomy ◽  
Negative Appendicectomy

Background: Acute appendicitis is one of the most common cause of acute abdomen surgery. Several scoring systems have been adopted by physicians to aid in the diagnosis and decrease the negative appendicectomy rate. Tzanakis scoring system is one such score. Objective of present study was the validation of this scoring system in our population and compare its accuracy with histopathological examination (HPE).Methods: A retrospective study was carried out at the Department of Surgery at Mohammad Afzal Beigh Memorial Hospital Anantnag India. Tzanakis score was calculated in 288 patients who underwent appendicectomy from September 2016-2018 and HPE results were analysed.Results: 276 patients were eligible for the study. The sensitivity and specificity of Tzanakis score in diagnosing appendicitis was 90.66% and 73.68% respectively. The overall diagnostic accuracy was 86.23% with positive predictive value of 97.89% and negative predictive value of 36.84%.Conclusions: Tzanakis scoring system is an accurate modality in establishing the diagnosis of acute appendicitis and preventing a negative laparotomy.

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