Scoring System For Prediction Research Articles

New predictors of microvascular invasion for small hepatocellular carcinoma ≤ 3cm.

Microvascular invasion (MVI) is a risk factor for postoperative recurrence of hepatocellular carcinoma (HCC), even in early-stage HCC. In small HCC ≤ 3cm, treatment options include anatomical resection or non-anatomical resection, and MVI has a major effect on treatment decisions. We aimed to identify the predictors of MVI in small HCC ≤ 3cm. We retrospectively studied 129 patients with very early or early-stage HCC ≤ 3cm who had undergone 18F-fluorodeoxyglucose positron emission tomography/computed tomography and subsequent hepatic resection from January 2016 to August 2023. These patients were divided into the derivation cohort (n = 86) and validation cohort (n = 43). We examined the risk factors for MVI using logistic regression analysis, and established a predictive scoring system in the derivation cohort. We evaluated the accuracy of our scoring system in the validation cohort. In the derivation cohort, a Lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3), prothrombin induced by vitamin K deficiency or antagonist-II (PIVKA-II), and metabolic tumor volume (MTV) were independent predictors of MVI. We established the scoring system using these three factors. In the validation test, there were no MVI-positive cases with a score of 0 and 1, and all cases were MVI-positive with a score of 4. Moreover, with a score ≥ 2, the sensitivity, specificity, and accuracy of our scoring system were 100%, 71.4%, and 81.4%, respectively. Our scoring system can accurately predict MVI in small HCC ≤ 3cm, and could contribute to establishing an appropriate treatment strategy.

Assessing mortality risk in Type 2 Diabetes patients with prolonged ASCVD risk factors: the inclusive Poh-Ai predictive scoring system with CAC Score integration

PurposeTo enhance the predictive risk model for all-cause mortality in individuals with Type 2 Diabetes (T2DM) and prolonged Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Despite the utility of the Coronary Artery Calcium (CAC) score in assessing cardiovascular risk, its capacity to predict all-cause mortality remains limited.MethodsA retrospective cohort study included 1929 asymptomatic T2DM patients with ASCVD risk factors, aged 40–80. Variables encompassed demographic attributes, clinical parameters, CAC scores, comorbidities, and medication usage. Factors predicting all-cause mortality were selected to create a predictive scoring system. By using stepwise selection in a multivariate Cox proportional hazards model, we divided the patients into three risk groups.ResultsIn our analysis of all-cause mortality in T2DM patients with extended ASCVD risk factors over 5 years, we identified significant risk factors, their adjusted hazard ratios (aHR), and scores: e.g., CAC score > 1000 (aHR: 1.57, score: 2), CAC score 401–1000 (aHR: 2.05, score: 2), and more. These factors strongly predict all-cause mortality, with varying risk groups (e.g., very low-risk: 2.0%, very high-risk: 24.0%). Significant differences in 5-year overall survival rates were observed among these groups (log-rank test < 0.001).ConclusionThe Poh-Ai Predictive Scoring System excels in forecasting mortality and cardiovascular events in individuals with Type 2 Diabetes Mellitus and extended ASCVD risk factors.

Development and validation of a predictive model for choledocholithiasis.

Gallstone-related conditions affect a significant portion of the population, with varying prevalence among different ethnic groups. Complications such as pancreatitis and cholangitis are associated with the presence of common bile duct (CBD) stones. Existing guidelines for diagnosing choledocholithiasis lack precision, leading to excessive use of invasive procedures like endoscopic retrograde cholangiopancreatography (ERCP). A prospective study was conducted at Hospital Central "Dr. Ignacio Morones Prieto," involving 374 patients in the development cohort and 154 patients in the validation cohort. Patients meeting inclusion criteria underwent biochemical testing and ultrasonography. A predictive scoring system was developed using logistic regression and validated in an independent cohort. Clinical and laboratory variables were collected, and model performance was assessed using receiver-operator characteristic (ROC) curves. The predictive model incorporated variables such as age, pancreatitis, cholangitis, bilirubin levels, and CBD stone presence on ultrasound. The model demonstrated an area under the ROC curve (AUC) of 93.81% in the validation dataset. By adjusting the threshold defining high-risk probability to 40%, the model improved specificity and sensitivity compared to existing guidelines. Notably, the model reclassified patients, leading to a more accurate risk assessment. The developed algorithm accurately predicts choledocholithiasis non-invasively in patients with symptomatic gallstones. This tool has the potential to reduce reliance on costly or invasive procedures like magnetic resonance cholangiopancreatography and ERCP, offering a more efficient and cost-effective approach to patient management. The user-friendly calculator developed in this study could streamline diagnostic procedures, particularly in resource-limited healthcare settings, ultimately improving patient care.

A system of predictive scores to evaluate the risk of multilevel noncontiguous spinal fractures in patients with traumatic cervical spine injury.

To develop a predictive scoring system to identify traumatic cervical spine injury patients at a high risk of having multilevel noncontiguous spinal fractures. This 12-year retrospective observational cohort study included 588 traumatic cervical spine-injured patients. Patients were categorized into two groups: patients with multilevel noncontiguous spinal fractures and patients without this remote injury. Potential risk factors were examined using multivariable analysis to derive a predictive risk score from independent predictors. Results are presented as odds ratio with a 95% confidence interval (95% CI). The accuracy of the calculated predicted score was demonstrated by the area under the receiver operating characteristic curve (AuROC). The incidence of noncontiguous fracture among the patients was 17% (100 of 588). The independent risk factors associated with multilevel noncontiguous spinal fractures were motor weakness, intracranial injury, intrathoracic injury, and intraabdominal injury. The AuROC of the prediction score was 0.74 (95% CI 0.69, 0.80). The patients were classified into three groups, low-risk group (score< 1), moderate-risk group (score 1-2.5), and high-risk group (score≥ 3), based on the predicted risk of multilevel noncontiguous spinal fractures. This tool can potentially help preventing the missed diagnosis of cervical spine injuries with multilevel noncontiguous spinal fractures. CT scans or MRI of the entire spine to investigate remote multilevel noncontiguous spinal fractures may have a role in cervical spine-injured patients who have at least one of the independent risk factors and are strongly suggested for patients with scores in the high-risk group.

Predicting Helicobacter pylori infection from endoscopic features.

Helicobacter pylori infection, prevalent in more than half of the global population, is associated with various gastrointestinal diseases, including peptic ulcers and gastric cancer. The effectiveness of early diagnosis and treatment in preventing gastric cancer highlights the need for improved diagnostic methods. This study aimed to develop a simple scoring system based on endoscopic findings to predict H. pylori infection. A retrospective analysis was conducted on 1,007 patients who underwent upper gastrointestinal endoscopy at Asan Medical Center from January 2019 to December 2021. Exclusion criteria included prior H. pylori treatment, gastric surgery, or gastric malignancies. Diagnostic techniques included rapid urease and 13C-urea breath tests, H. pylori culture, and assessment of endoscopic features following the Kyoto gastritis classification. A new scoring system based on endoscopic findings including regular arrangement of collecting venules (RAC), nodularity, and diffuse or spotty redness was developed for predicting H. pylori infection, utilizing logistic regression analysis in the development set. The scoring system demonstrated high predictive accuracy for H. pylori infection in the validation set. Scores of 2 and 3 were associated with 96% and 99% infection risk, respectively. Additionally, there was a higher prevalence of diffuse redness and sticky mucus in cases where the initial H. pylori eradication treatment failed. Our scoring system showed potential for improving diagnostic accuracy in H. pylori infection. H. pylori testing should be considered upon spotty redness, diffuse redness, nodularity, and RAC absence on endoscopic findings as determined by the predictive scoring system.

Risk prediction of advanced colorectal neoplasia among diabetic patients: A derivation and validation study.

Colorectal cancer (CRC) is the third most common cancer in the world. This study devises and validates a clinical scoring system for risk prediction of advanced colorectal neoplasia (ACN) to guide colonoscopy evaluation among diabetic patients. We identified 55 964 diabetic patients who received colonoscopies from a large database in a Chinese population (2008-2018). We recruited a derivation cohort based on random sampling. The risk factors of CRC evaluated by univariate analysis were examined for ACN, defined as advanced adenoma, CRC, or any combination thereof using binary logistic regression analysis. We used the adjusted odds ratios (aORs) for independent risk factors to devise a risk score, ranging from 0 to 6: 0-4 "average risk" (AR) and 5-6 "high risk" (HR). The other subjects acted as an independent validation cohort. The prevalence of ACN in both the derivation and validation cohorts was 2.0%. Using the scoring system constructed, 78.5% and 21.5% of patients in the validation cohort were classified as AR and HR, respectively. The prevalence of ACN in the AR and HR groups was 1.5% and 4.1%, respectively. Individuals in the HR group had a 2.78-fold increased prevalence of ACN than the AR group. The concordance (c-) statistics was 0.70, implying a good discriminatory capability of the risk score to stratify high-risk individuals who should consider colonoscopy. The clinical risk scoring system based on age, gender, smoking, presence of hypertension, and use of aspirin is useful for ACN risk prediction among diabetic patients.

Risk factors and prediction score for new-onset diabetes mellitus after liver transplantation.

New-onset diabetes mellitus is a frequent and severe complication arising after liver transplantation (LT). We aimed to identify the risk factors for new-onset diabetes mellitus after liver transplantation (NODALT) and to develop a risk prediction score system for relevant risks. We collected and analyzed data from all recipients who underwent liver transplantation at the First Affiliated Hospital of Xi'an Jiaotong University. The OR derived from a multiple logistic regression predicting the presence of NODALT was used to calculate the risk prediction score. The performance of the risk prediction score was externally validated in patients who were from the CLTR (China Liver Transplant Registry) database. A total of 468 patients met the outlined criteria and finished the follow-up. Overall, NODALT was diagnosed in 115 (24.6%) patients. Age, preoperative impaired fasting glucose (IFG), postoperative fasting plasma glucose (FPG), and the length of hospital stay were significantly associated with the presence of NODALT. The risk prediction score includes age, preoperative IFG, postoperative FPG, and the length of hospital stay. The risk prediction score of the area under the receiver operating curve was 0.785 (95% CI: 0.724-0.846) in the experimental population and 0.782 (95% CI: 0.708-0.856) in the validation population. Age at the time of transplantation, preoperative IFG, postoperative FPG, and length of hospital stay were independent predictive factors of NODALT. The use of a simple risk prediction score can identify the patients who have the highest risk of NODALT and interventions may start early.

Comprehensive analyses of the cancer-associated fibroblast subtypes and their score system for prediction of outcomes and immunosuppressive microenvironment in prostate cancer

BackgroundCancer-associated fibroblasts (CAFs) drive cancer progression and treatment failure on one hand, while their tumor-restraining functions are also observed on the other. Recent single cell RNA sequencing (scRNA-seq) analyses demonstrates heterogeneity of CAFs and defines molecular subtypes of CAFs, which help explain their different functions. However, it remains unclear whether these CAF subtypes have the same or different biological/clinical implications in prostate cancer (PCa) or other malignancies.MethodsPCa cells were incubated with supernatant from normal fibroblasts and CAFs to assess their effects on cell behaviors. Sequencing, genomic, and clinical data were collected from TCGA, MSKCC, CPGEA and GEO databases. CAF molecular subtypes and total CAF scores were constructed and grouped into low and high groups based on CAF-specific gene expression. Progression free interval (PFI), clinicopathological features, telomere length, immune cell infiltration, drug treatment and somatic mutations were compared among CAF molecular subtypes and low/high score groups.ResultsThe PCa CAF-derived supernatant promoted PCa cell proliferation and invasion. Based on differentially expressed genes identified by scRNA-seq analyses, we classified CAFs into 6 molecular subtypes in PCa tumors, and each subtype was then categorized into score-high and low groups according to the subtype-specific gene expression level. Such score models in 6 CAF subtypes all predicted PFI. Telomeres were significantly shorter in high-score tumors. The total CAF score from 6 CAF subtypes was also associated with PFI in PCa patients inversely, which was consistent with results from cellular experiments. Immunosuppressive microenvironment occurred more frequently in tumors with a high CAF score, which was characterized by increased CTLA4 expression and indicated better responses to CTLA4 inhibitors. Moreover, this model can also serve as a useful PFI predictor in pan-cancers.ConclusionBy combining scRNA-seq and bulk RNA-seq data analyses, we develop a CAF subtype score system as a prognostic factor for PCa and other cancer types. This model system also helps distinguish different immune-suppressive mechanisms in PCa, suggesting its implications in predicting response to immunotherapy. Thus, the present findings should contribute to personalized PCa intervention.

Comparison of nine trauma scoring systems in prediction of inhospital outcomes of pediatric trauma patients: a multicenter study

Hereby, we aimed to comprehensively compare different scoring systems for pediatric trauma and their ability to predict in-hospital mortality and intensive care unit (ICU) admission. The current registry-based multicenter study encompassed a comprehensive dataset of 6709 pediatric trauma patients aged ≤ 18 years from July 2016 to September 2023. To ascertain the predictive efficacy of the scoring systems, the area under the receiver operating characteristic curve (AUC) was calculated. A total of 720 individuals (10.7%) required admission to the ICU. The mortality rate was 1.1% (n = 72). The most predictive scoring system for in-hospital mortality was the adjusted trauma and injury severity score (aTRISS) (AUC = 0.982), followed by trauma and injury severity score (TRISS) (AUC = 0.980), new trauma and injury severity score (NTRISS) (AUC = 0.972), Glasgow coma scale (GCS) (AUC = 0.9546), revised trauma score (RTS) (AUC = 0.944), pre-hospital index (PHI) (AUC = 0.936), injury severity score (ISS) (AUC = 0.901), new injury severity score (NISS) (AUC = 0.900), and abbreviated injury scale (AIS) (AUC = 0.734). Given the predictive performance of the scoring systems for ICU admission, NTRISS had the highest predictive performance (AUC = 0.837), followed by aTRISS (AUC = 0.836), TRISS (AUC = 0.823), ISS (AUC = 0.807), NISS (AUC = 0.805), GCS (AUC = 0.735), RTS (AUC = 0.698), PHI (AUC = 0.662), and AIS (AUC = 0.651). In the present study, we concluded the superiority of the TRISS and its two derived counterparts, aTRISS and NTRISS, compared to other scoring systems, to efficiently discerning individuals who possess a heightened susceptibility to unfavorable consequences. The significance of these findings underscores the necessity of incorporating these metrics into the realm of clinical practice.

SOFA and APACHE II scoring systems for predicting outcome of neurological patients admitted in a tertiary hospital intensive care unit.

The field of neurology encompasses the study and treatment of disorders that affect the nervous system, and patients with neurological conditions often require specialized care, particularly in the ICU. Predictive scoring systems are measures of disease severity used to predict patient outcomes. The aim of this study was to compare the discriminative power of commonly used scoring systems, namely the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) in the ICU of a tertiary care hospital. This retrospective study included patients with neurological disorders in the ICUs of Tribhuvan University Teaching Hospital from 1 January 2022 to 31 December 2022. A total of 153 patients were included. The mean age of the patients was 54.76 ± 17.32 years with higher male predominance (60.78%). Ischaemic stroke was the most common neurological disorder. There were 58 patients (37.9%) who required mechanical ventilation and all-cause mortality was 20.9%. The mean SOFA score was significantly higher (P=0.002) in survivors, whereas the mean APACHE II did not show a significant difference (P=0.238). Receiver operating characteristic (ROC) analysis showed the area of curve (AUC) of SOFA score was 0.765 and of APACHE II was 0.722. SOFA score had comparatively higher discriminative power than APACHE II. Assessment of the performance of scoring systems in a specific ICU setting improves the sensitivity and applicability of the model to these settings.

A scoring system to predict the technical difficulty of endoscopic resection for cardial submucosal tumors.

Endoscopic resection has been successfully used for the removal of digestive submucosal tumors (SMTs). However, the cardia has been considered a challenging location for endoscopic resection due to its narrow lumen and sharp angle. The objective of this study was to establish a clinical scoring model to grade the technical difficulty of endoscopic resection for cardial SMTs. A total of 246 patients who suffered cardial SMTs and received endoscopic resection were included in this retrospective study. All of them were randomized into the training cohort (n=123) or internal validation cohort (n=123). Potential predictors were analyzed using univariate analysis. Then, covariates with P<0.05 were selected for the multivariate logistic regression model. The β coefficients from the logistic regression model were used to create a scoring system for technical difficulty prediction by rounding the score to the nearest integer of the absolute β coefficient value. The clinical score consisted of the following factors: male gender (2 points), extraluminal growth (3 points), and maximum diameter ≥3cm (3 points). The scoring model demonstrated good discriminatory power, with an area under the receiver operating characteristic curve of 0.860 and a 95% confidence interval of 0.763-0.958. The model also showed a good goodness of fit in the Hosmer-Lemeshow test (P=0.979). In the training cohort, the probability of encountering technical difficulty in the easy (score=0), intermediate (score=1-3), difficult (score=4-6), and very difficult (score >6) categories was 0, 6.8%, 33.3%, and 100.0%, respectively; similarly, in the validation cohort, it was 0, 5.6%, 22.2%, and 50.0%, respectively. This scoring system could serve as a valuable tool for clinicians in predicting the technical difficulty of endoscopic resection for cardial SMTs.

329 Same Day Discharge After Craniotomy for Brain Tumor Resection: Enhancing Patient Selection Through a Prognostic Scoring System

INTRODUCTION: Neurosurgery, among other surgical fields, is in the midst of a shift in patient management with enhanced recovery and same day discharge protocols slowly becoming more popular and feasible. While such protocols reduce risk of nosocomial complications and improve patient satisfaction, appropriate patient selection remains an area of debate. METHODS: Three arms of analysis were carried out. First, clinical data of SDD patients was prospectively collected between August 2021 and August 2022. In parallel, a retrospective analysis of patients who qualified for SDD but were excluded at surgeon clinical discretion over the same period was performed. Third, a comparative analysis of the pilot and follow-up studies was done from which a clinical scoring system for patient selection was derived. RESULTS: Over the length of the study, 31 of 334 patients were selected for SDD while 59 qualified by previously defined criteria but were not selected at the surgeon’s discretion. There was no difference in outcomes between the two groups nor any post-operative complications among the SDD group within 30 days of surgery. Preoperative clinical characteristics found to be significantly different between the two cohorts (Left sided lesion, extra-axial pathology, prior treatment of brain tumor, and tumor volume &lt; 11.75 cm3) were included in a predictive scoring system for successful SDD. The system was found to significantly predict high or low likelihood for successful SDD when tested on the mixedprospective cohort. CONCLUSIONS: This study provides a straightforward clinical scoring system for appropriate selection of candidates for SDD after craniotomy for brain tumor resection. The application of this clinical tool aims to aid clinicians in appropriate admission course selection and builds on the growing literature surrounding same-day and outpatient cranial neurosurgery.

Predictive Scoring System for Spontaneous Closure of Infant Ventricular Septal Defect: The P-VSD Score.

Ventricular septal defect (VSD) is a common congenital heart disease. However, consensus on the utility of echocardiography in predicting spontaneous closure (SC) of VSD remains lacking. This study aimed to identify and validate significant predictors of SC through a predictive scoring system. This retrospective study included medical records of 712 echocardiography instances performed on 304 patients diagnosed with VSD from 2016 to 2020 in their first year of life. A novel scoring system for predicting the SC of VSD was developed and validated using another dataset from different hospitals. Of the 304 patients, 215 (70.7%) had perimembranous (PM) VSDs and 89 had muscular (29.3%) VSDs. The median follow-up periods were 36.2 (interquartile range [IQR], 13-59) months and 13.7 9 (IQR, 5-37.4) days for PM and muscular VSDs, respectively. The overall SC rate during follow-up was 29.3%. Pulmonary hypertension (HTN), concomitant left ventricle (LV)-right atrium (RA) shunt, VSD size to aortic valve (AV) annulus size ratio, and left ventricular end-diastolic dimension (LVEDD) z-score were significant risk factors affecting SC of VSD. The "P-VSD" score, a new scoring system, demonstrated an area under the curve for predictability of 0.769. Pulmonary HTN, concomitant LV-RA shunt, LVEDD z-score, and VSD size-to-AV annulus size ratio at diagnosis were significantly associated with non-SC VSD after infancy. The P-VSD score can predict the SC of VSD in clinical settings and simplify the identification and appropriate management of high-risk patients.

Scoring systems for prediction of malaria and dengue fever in non-endemic areas among travellers arriving from tropical and subtropical areas

BackgroundFever is a common symptom among travellers returning from tropical/subtropical areas to Europe, and promptly distinguishing severe illnesses from self-limiting febrile syndromes is important but can be challenging due to...

Reverse shock index multiplied by simplified motor score as a predictor of clinical outcomes for patients with COVID-19

BackgroundThe reverse shock index (rSI) combined with the Simplified Motor Score (sMS), that is, the rSI-sMS, is a novel and efficient prehospital triage scoring system for patients with COVID-19. In this study, we evaluated the predictive accuracy of the rSI-sMS for general ward and intensive care unit (ICU) admission among patients with COVID-19 and compared it with that of other measures, including the shock index (SI), modified SI (mSI), rSI combined with the Glasgow Coma Scale (rSI-GCS), and rSI combined with the GCS motor subscale (rSI-GCSM).MethodsAll patients who visited the emergency department of Taipei Tzu Chi Hospital between January 2021 and June 2022 were included in this retrospective cohort. A diagnosis of COVID-19 was confirmed through a SARS-CoV-2 reverse-transcription polymerase chain reaction test or SARS-CoV-2 rapid test with oropharyngeal or nasopharyngeal swabs and was double confirmed by checking International Classification of Diseases, Tenth Revision, Clinical Modification codes in electronic medical records. In-hospital mortality was regarded as the primary outcome, and sepsis, general ward or ICU admission, endotracheal intubation, and total hospital length of stay (LOS) were regarded as secondary outcomes. Multivariate logistic regression was used to determine the relationship between the scoring systems and the three major outcomes of patients with COVID-19, including. The discriminant ability of the predictive scoring systems was investigated using the area under the receiver operating characteristic curve, and the most favorable cutoff value of the rSI-sMS for each major outcome was determined using Youden’s index.ResultsAfter 74,183 patients younger than 20 years (n = 11,572) and without COVID-19 (n = 62,611) were excluded, 9,282 patients with COVID-19 (median age: 45 years, interquartile range: 33–60 years, 46.1% men) were identified as eligible for inclusion in the study. The rate of in-hospital mortality was determined to be 0.75%. The rSI-sMS scores were significantly lower in the patient groups with sepsis, hyperlactatemia, admission to a general ward, admission to the ICU, total length of stay ≥ 14 days, and mortality. Compared with the SI, mSI, and rSI-GCSM, the rSI-sMS exhibited a significantly higher accuracy for predicting general ward admission, ICU admission, and mortality but a similar accuracy to that of the rSI-GCS. The optimal cutoff values of the rSI-sMS for predicting general ward admission, ICU admission, and mortality were calculated to be 3.17, 3.45, and 3.15, respectively, with a predictive accuracy of 86.83%, 81.94%%, and 90.96%, respectively.ConclusionsCompared with the SI, mSI, and rSI-GCSM, the rSI-sMS has a higher predictive accuracy for general ward admission, ICU admission, and mortality among patients with COVID-19.

Value of original and modified pathological scoring systems for prognostic prediction in paraffin-embedded donor kidney core biopsy

Background No study has validated, compared and adapted scoring systems for prognosis prediction based on donor kidney core biopsy (CB), with less glomeruli than wedge biopsy. Methods A total of 185 donor kidney CB specimens were reviewed using seven scoring systems. The association between the total score, item scores, score-based grading, and allograft prognosis was investigated. In specimens with less than ten glomeruli (88/185, 47.6%), scoring systems were modified by adjusting weights of the item scores. Results The Maryland aggregate pathology index (MAPI) score-based grading and periglomerular fibrosis (PGF) associated with delayed graft function (DGF) (Grade: OR = 1.59, p < 0.001; PGF: OR = 1.06, p = 0.006). Total score, score-based grading and chronic lesion score in scoring systems associated with one-year and 3-year eGFR after transplantation. Total-score-based models had similar predictive capacities for eGFR in all scoring systems, except MAPI and Ugarte. Score of glomerulosclerosis (GS), interstitial fibrosis (IF), tubular atrophy (TA), and arteriolar hyalinosis (AH) had good eGFR predictive capacities. In specimens with less than ten glomeruli, modified scoring systems had better eGFR predictive capacities than original scoring systems. Conclusions Scoring systems could predict allograft prognosis in paraffin-embedded CB with ten more glomeruli. A simple and pragmatic scoring system should include GS, IF, TA and AH, with weights assigned based on predictive capacity for prognosis. Replacing GS scores with tubulointerstitial scores could significantly improve the predictive capacity of eGFR. The conclusion should be further validated in frozen section.

A Novel Scoring System Predicting Red Blood Cell Transfusion Requirements in Patients Undergoing Invasive Spine Surgery.

Background: Access to blood products is crucial for patient safety during the perioperative course. However, reduced donations and seasonally occurring blood shortages pose a significant challenge to the healthcare system, with surgeries being postponed. The German Blood Transfusion act requires that RBC packages become assigned to an individual patient, resulting in a significant reduction in the available blood products, further aggravating shortages. We aimed to develop a scoring system predicting transfusion probability in patients undergoing spine surgery to reduce assignment and, thus, increase the availability of blood products. Methods: The medical records of 252 patients who underwent spine surgery were evaluated and 18 potential predictors for RBC transfusion were tested to construct a logistic-regression-based predictive scoring system for blood transfusion in patients undergoing spine surgery. Results: The variables found to be the most important included the type of surgery, vertebral body replacement, number of stages, and pre-operative Hb concentration, indicating that surgical specification and the extent of the surgical procedure were more influential than the pre-existing patient condition and medication. Conclusions: Our model showed a good discrimination ability with an average AUC [min, max] of 0.87 [0.6, 0.97] and internal validation with a similar AUC of 0.84 [0.66, 0.97]. In summary, we developed a scoring system to forecast patients' perioperative transfusion needs when undergoing spine surgery using pre-operative predictors, potentially reducing the need for RBC allocation and, thus, resulting in an increased availability of this valuable resource.

Predictive score for identifying intrahepatic cholangiocarcinoma patients without lymph node metastasis: a basis for omitting lymph node dissection

BackgroundThis study aimed to develop a predictive score for intrahepatic cholangiocarcinoma (ICC) in patients without lymph node metastasis (LNM) using preoperative factors. MethodsA retrospective analysis of 113 ICC patients who underwent liver resection with systemic lymph node dissection between 2002 and 2021 was conducted. A multivariate logistic regression analysis was used as a predictive scoring system for node-negative patients based on the β coefficients of preoperatively available factors. ResultsLNM was observed in 36 patients (31.9%). Four factors were associated with LNM: suspicion of LNM on MDCT (odds ratio [OR] 13.40, p < 0.001), low-vascularity tumor (OR 6.28, p = 0.005), CA19-9 ≥500 U/mL (OR 5.90, p = 0.010), and tumor location in the left lobe (OR 3.67, p = 0.057). The predictive scoring system was created using these factors (assigning 3 points for suspected LNM on MDCT, 2 points for CA19-9 ≥500 U/mL, 2 points for low vascularity tumor, and 1 point for tumor location in the left lobe). A score cutoff value of 4 resulted in 0.861 sensitivity and a negative predictive value of 0.922 for detecting LNM. Notably, no patients with peripheral tumors and a score of ≤3 had LNM. ConclusionThe developed scoring system may effectively help identify ICC patients without LNM.

Development and validation of a predictive scoring system for post-transarterial chemoembolization pain management in liver cancer patients.

Patients experiencing severe postoperative pain often show lower adherence to prescribed treatments, highlighting the clinical need for effective pain prediction and management strategies. This study aims to address this gap by identifying key risk factors associated with post-transarterial chemoembolization (TACE) pain and developing a predictive scoring system. We retrospectively analyzed data from liver cancer patients who underwent their first TACE procedure at our institution between January 2019 and December 2020. Pain levels were assessed using an 11-point numerical rating scale (NRS-11). Patients were randomly assigned to training and validation cohorts. In the training cohort, logistic regression was used to evaluate the correlation between various parameters and post-TACE pain, leading to the development of a risk prediction model. This model's performance was subsequently assessed in the validation cohort. The study included 255 patients. Univariate analysis in the training cohort identified tumor number, size, microsphere volume, and operation time as factors associated with postoperative pain. These factors were included in a multivariate model, which demonstrated areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.71 in the training cohort and 0.74 in the validation cohort for predicting moderate to severe pain. A nomogram was also developed for clinical application, categorizing patients with scores above 72.90 as high risk for moderate to severe pain. Our research successfully developed and validated a novel scoring system capable of predicting moderate to severe pain following initial TACE treatment. However, the study's predictive accuracy, as reflected by AUC values, suggests that further refinement and validation in larger, diverse cohorts are necessary to enhance its clinical utility. This work underscores the importance of predictive tools in improving postoperative pain management and patient outcomes.

Abstract 87: Predictive Scoring System for Pre-Thrombectomy Diagnosis of Intracranial Atherosclerotic Disease LVOS

Introduction: Early identification of intracranial atherosclerotic disease (ICAD) underlying large vessel occlusion strokes (LVOS) may allow for mechanical thrombectomy (MT) optimization. We sought to create a pre-procedural scoring system to predict ICAD. Methods: Retrospective analysis of prospectively collected MT databases from 2 comprehensive stroke centers (derivation and validation) including patients with anterior circulation LVOS. ICAD cases were matched for age / sex (1:1) to non-ICAD controls and stepwise logistic regression utilized for various clinical and radiological (NCCT, CTA and CTP) variables. Calibration slope / intercept as well as the area under curve (auROC) were assessed. Results: Of 2870 MTs within the study period, 174 anterior circulation ICAD (6.6%) were matched with 174 controls (n=348) in the derivation cohort. Multivariable analysis β coefficients lead to a 20 point-scale: absence of AF (5), vascular risk factor burden (1 for each: hypertension, diabetes, smoking, hyperlipidemia), CTA multifocal ICAD (3), absence of cortical infarcts (3), presence of borderzone infarct (3), calcium at the siphon (2). The validation cohort comprised 75 ICAD patients (6.9% of 1359 MTs) and 75 controls. AuROC for the derivation cohort was 0.88(0.84-0.91) while for the validation cohort 0.82(0.73-0.89). Calibration slope and intercept showed a good fit for the development cohort although with overestimated risk for the validation cohort. After intercept adjustment, the overestimation was corrected (intercept 0; 95%CI -0.5-0.5 / slope 0.8 95%CI 0.5-1.1). In the full cohort (n=498), ≥ 11 points showed the best performance for distinguishing ICAD from non-ICAD patients, with a sensitivity 0.71 (0.65-0.78) and specificity 0.82 (0.77-0.87), a positive likelihood ratio of 3.92 (2.92, 5.28) and a negative LR of 0.35 (0.28, 0.44). Scores ≥ 12 showed 90% specificity, although sensitivity of 63% (55%-69%). Conclusion: We developed a predictive scoring system for pre-procedural diagnosis of ICAD LVOS with satisfactory discrimination and calibration based on clinical and non-invasive radiological data.