A novel approach to distinguish complicated and non-complicated acute cholecystitis: Decision tree method.

It is difficult to differentiate between non-complicated acute cholecystitis (NCAC) and complicated acute cholecystitis (CAC) preoperatively, which are two separate pathologies with different management. The aim of this study was to create an algorithm that distinguishes between CAC and NCAC using the decision tree method, which includes simple examinations. In this retrospective study, the patients were divided into 2 groups: CAC (149 patients) and NCAC (885 patients). Parameters such as patient demographic data, American Society of Anesthesiologists (ASA) score, Tokyo grade, comorbidity findings, white blood cell (WBC) count, neutrophil/lymphocyte ratio, C-reactive protein (CRP) level, albumin level, CRP/albumin ratio (CAR), and gallbladder wall thickness (GBWT) were evaluated. In this algorithm, the CRP value became a very important parameter in the distinction between NCAC and CAC. Age was an important predictive factor in patients with CRP levels >57 mg/L, and the critical value for age was 42. After the age factor, the important parameters in the decision tree were WBC and GBWT. In patients with a CRP value of ≤57 mg/L, GBWT is decisive and the critical value is 4.85 mm. Age, neutrophil/lymphocyte ratio, and WBC count were among the other important factors after GBWT. Sex, ASA score, Tokyo grade, comorbidity, CAR, and albumin value did not have an effect on the distinction between NCAC and CAC. In statistical analysis, significant differences were found groups in terms of gender (34.8% vs 51.7% male), ASA score (P < .001), Tokyo grade (P < .001), comorbidity (P < .001), albumin (4 vs 3.4 g/dL), and CAR (2.4 vs 38.4). By means of this algorithm, which includes low-cost examinations, NCAC and CAC distinction can be made easily and quickly within limited possibilities. Preoperative prediction of pathologies that are difficult to manage, such as CAC, can minimize patient morbidity and mortality.

Predictors of rapidly progressive glomerulonephritis in acute poststreptococcal glomerulonephritis.

BACKGROUND: Acute post-streptococcal glomerulonephritis (APSGN) is an immune-mediated inflammatory respsonse in the kidneys caused by nephritogenic strains of group A ß-hemolytic streptococcus (GAS). The present study aimed to present a large patient cohort of APSGN patients to determine the factors that can be used for predicting the prognosis and progression to rapidly progressive glomerulonephritis (RPGN). METHODS: The study included 153 children with APSGN that were seen between January 2010 and January 2022. Inclusion criteria were age 1-18 years and follow-up of ≥ 1 years. Patients with a diagnosis that could not be clearly proven clinically or via biopsy and with prior clinical or histological evidence of underlying kidney disease or chronic kidney disease (CKD) were excluded from the study. RESULTS: Mean age was 7.36 ± 2.92 years, and 30.7% of the group was female. Among the 153 patients, 19 (12.4%) progressed to RPGN. The complement factor 3 and albumin levels were significantly low in the patients who had RPGN (P = 0.019). Inflammatory parameters, such as C-reactive protein (CRP), platelet-to-lymphocyte ratio, CRP/albumin ratio, and the erythrocyte sedimentation rate level at presentation were significantly higher in the patients with RPGN (P < 0.05). Additionally, there was a significant correlation between nephrotic range proteinuria and the course of RPGN (P = 0.024). CONCLUSIONS: We suggest the possibility that RPGN can be predicted in APSGN with clinical and laboratory findings. A higher resolution version of the Graphical abstract is available as Supplementary information.

A new approach to the management of acute appendicitis: Decision tree method.

BACKGROUND: It is important to distinguish between complicated acute appendicitis (CAA) and noncomplicated acute appendicitis (NCAA) because the treatment methods are different. We aimed to create an algorithm that determines the severity of acute appendicitis (AA) without the need for imaging methods, using the decision tree method. METHODS: The patients were analyzed retrospectively and divided into two groups as CAA and NCAA. Age, gender, Alvarado scores, white blood cell values (WBC), neutrophil/lymphocyte ratios (NLR), C-reactive protein value (CRP), albumin value and CRP/Albumin ratios of the patients were recorded. RESULTS: In the algorithm we created, the most important parameter in the distinction between CAA and NCAA is CRP. NLR is predictive in patients with a CRP value of ≤107.565 mg/L, and the critical value is NLR 2.165. In patients with a CRP value of >107.565 mg/L, albumin is the determinant and the critical value is 2.85 g/dL. Age, gender, alvarado score and CRP/albumin ratio have no significance in distinguishing between CAA and NCAA. In the statistical analysis, there were significant differences between NCAA and CAA groups in terms of age (39.56 years vs 13,675 years), gender (48.1% male vs 71.4% male), WBC (13,891.10/mL vs 11,614.76/mL), CRP (27 mg/L vs 127 mg/L), albumin (3 g/dL vs 3 g/dL) and CRP/albumin (9.50 vs. 41). CONCLUSION: Thanks to the algorithm we created, CAA and NCAA distinction can be made quickly. In addition, by avoiding unnecessary surgical procedures in NCAA cases, patients' quality of life can be increased and morbidity rates can be minimized.

A model for acute kidney injury in severe burn patients.

INTRODUCTION: In patients with severe burns, morbidity and mortality are high. One factor related to poor prognosis is acute kidney injury. According to the AKIN criteria, acute kidney injury has 3 stages based on urine output, serum creatinine level, and renal replacement therapy. In this study, we aimed to create a decision tree for estimating risk of acute kidney injury in patients with severe burn injuries. METHODS: We retrospectively evaluated 437 adult patients with ≥20% total burn surface area injury who were treated at the Baskent University Ankara and Konya Burn Centers from January 2000 to March 2020. Patients who had high-voltage burn and previous history of kidney disease were excluded. Patient demographics, medical history, mechanism of injury, presence of inhalation injury, depth of burn, laboratory values, presence of oliguria, need for renal replacement therapy, central venous pressure, and prognosis were evaluated. These data were used in a "decision tree method" to create the Baskent University model to estimate risk of acute kidney injury in severe burn patients. RESULTS: Our model provided an accuracy of 71.09% for risk estimation. Of 172 patients, 78 (45%) had different degrees of acute kidney injury, with 26 of these (15.1%) receiving renal replacement therapy. Our model showed that total burn surface area was the most important factor for estimation of acute kidney injury occurrence. Other important factors included serum creatinine value, burn injury severity score, hemoglobin value, neutrophil-to-lymphocyte ratio, and platelet count. CONCLUSION: The Baskent University model for acute kidney injury may be helpful to determine risk of acute kidney injury in burn patients. This determination would allow appropriate treatment to be given to high-risk patients in the early period, reducing the incidence of acute kidney injury.

Evaluation with Decision Trees of Efficacy and Safety of Semirigid Ureteroscopy in the Treatment of Proximal Ureteral Calculi.

PURPOSE: The decision on the choice of proximal ureteral stone therapy depends on many factors, and sometimes urologists have difficulty in choosing the treatment option. This study is aimed at evaluating the factors affecting the success of semirigid ureterorenoscopy (URS) using the "decision tree" method. MATERIALS AND METHODS: From January 2005 to November 2015, the data of consecutive patients treated for proximal ureteral stone were retrospectively analyzed. A total of 920 patients with proximal ureteral stone treated with semirigid URS were included in the study. All statistically significant attributes were tested using the decision tree method. RESULTS: The model created using decision tree had a sensitivity of 0.993 and an accuracy of 0.857. While URS treatment was successful in 752 patients (81.7%), it was unsuccessful in 168 patients (18.3%). According to the decision tree method, the most important factor affecting the success of URS is whether the stone is impacted to the ureteral wall. The second most important factor affecting treatment was intramural stricture requiring dilatation if the stone is impacted, and the size of the stone if not impacted. CONCLUSIONS: Our study suggests that the impacted stone, intramural stricture requiring dilatation and stone size may have a significant effect on the success rate of semirigid URS for proximal ureteral stone. Further studies with population-based and longitudinal design should be conducted to confirm this finding.

A novel SVM-ID3 hybrid feature selection method to build a disease model for melanoma using integrated genotyping and phenotype data from dbGaP.

The relations between Single Nucleotide Polymorphism (SNP) and complex diseases are likely to be non-linear and require analysis of the high dimensional data. Previous studies in the field mostly focus on genotyping and effects of various phenotypes are not considered. To fill this gap a hybrid feature selection model of support vector machine and decision tree has been designed. The designed method is tested on melanoma. We were able to select phenotypic features such as moles and dysplastic nevi, and SNPs those maps to specific genes such as CAMK1D. The performance results of the proposed hybrid model, on melanoma dataset are 79.07% of sensitivity and 0.81 of area under ROC curve.

A prostate cancer model build by a novel SVM-ID3 hybrid feature selection method using both genotyping and phenotype data from dbGaP.

Through Genome Wide Association Studies (GWAS) many Single Nucleotide Polymorphism (SNP)-complex disease relations can be investigated. The output of GWAS can be high in amount and high dimensional, also relations between SNPs, phenotypes and diseases are most likely to be nonlinear. In order to handle high volume-high dimensional data and to be able to find the nonlinear relations we have utilized data mining approaches and a hybrid feature selection model of support vector machine and decision tree has been designed. The designed model is tested on prostate cancer data and for the first time combined genotype and phenotype information is used to increase the diagnostic performance. We were able to select phenotypic features such as ethnicity and body mass index, and SNPs those map to specific genes such as CRR9, TERT. The performance results of the proposed hybrid model, on prostate cancer dataset, with 90.92% of sensitivity and 0.91 of area under ROC curve, shows the potential of the approach for prediction and early detection of the prostate cancer.