Risk Stratification Model Research Articles

Development of a predictive model for risk stratification of acute kidney injury in patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy

Acute kidney injury (AKI) following hyperthermic intraperitoneal chemotherapy (HIPEC) is common. Identifying patients at risk could have implications for surgical and anesthetic management. We aimed to develop a predictive model that could predict AKI based on patients’ preoperative characteristics and intraperitoneal chemotherapy regimen. We retrospectively gathered data of adult patients undergoing HIPEC at our health system between November 2013 and April 2022. Next, we developed a model predicting postoperative AKI using multivariable logistic regression and calculated the performance of the model (area under the receiver operating characteristics curve [AUC]) via tenfold cross-validation. A total of 412 patients were included, of which 36 (8.7%) developed postoperative AKI. Based on our multivariable logistic regression model, multiple preoperative and intraoperative characteristics were associated with AKI. We included the total intraoperative cisplatin dose, body mass index, male sex, and preoperative hemoglobin level in the final model. The mean area under the receiver operating characteristics curve value was 0.82 (95% confidence interval 0.71–0.93). Our risk model predicted AKI with high accuracy in patients undergoing HIPEC in our institution. The external validity of our model should now be tested in independent and prospective patient cohorts.

Molecular Pathology of Pancreatic Cystic Lesions with a Focus on Malignant Progression.

The malignant progression of pancreatic cystic lesions (PCLs) remains understudied with a knowledge gap, yet its exploration is pivotal for effectively stratifying patient risk and detecting cancer at its earliest stages. Within this review, we delve into the latest discoveries on the molecular level, revealing insights into the IPMN molecular landscape and revised progression model, associated histologic subtypes, and the role of inflammation in the pathogenesis and malignant progression of IPMN. Low-grade PCLs, particularly IPMNs, can develop into high-grade lesions or invasive carcinoma, underscoring the need for long-term surveillance of these lesions if they are not resected. Although KRAS and GNAS remain the primary oncogenic drivers of neoplastic development in IPMNs, additional genes that are important in tumorigenesis have been recently identified by whole exome sequencing. A more complete understanding of the genes involved in the molecular progression of IPMN is critical for effective monitoring to minimize the risk of malignant progression. Complicating these strategies, IPMNs are also frequently multifocal and multiclonal, as demonstrated by comparative molecular analysis. Algorithms for preoperative cyst sampling and improved radiomic techniques are emerging to model this spatial and temporal genetic heterogeneity better. Here, we review the molecular pathology of PCLs, focusing on changes associated with malignant progression. Developing models of molecular risk stratification in PCLs which can complement radiologic and clinical features, facilitate the early detection of pancreatic cancer, and enable the development of more personalized surveillance and management strategies are summarized.

Developing Novel Genomic Risk Stratification Models in Soft Tissue and Uterine Leiomyosarcoma.

Leiomyosarcomas (LMS) are clinically and molecularly heterogeneous tumors. Despite recent large-scale genomic studies, current LMS risk stratification is not informed by molecular alterations. We propose a clinically applicable genomic risk stratification model. We performed comprehensive genomic profiling in a cohort of 195 soft tissue LMS (STLMS), 151 primary at presentation, and a control group of 238 uterine LMS (ULMS), 177 primary at presentation, with at least 1-year follow-up. In STLMS, French Federation of Cancer Centers (FNCLCC) grade but not tumor size predicted progression-free survival (PFS) or disease-specific survival (DSS). In contrast, in ULMS, tumor size, mitotic rate, and necrosis were associated with inferior PFS and DSS. In STLMS, a 3-tier genomic risk stratification performed well for DSS: high risk: co-occurrence of RB1 mutation and chr12q deletion (del12q)/ATRX mutation; intermediate risk: presence of RB1 mutation, ATRX mutation, or del12q; low risk: lack of any of these three alterations. The ability of RB1 and ATRX alterations to stratify STLMS was validated in an external AACR GENIE cohort. In ULMS, a 3-tier genomic risk stratification was significant for both PFS and DSS: high risk: concurrent TP53 mutation and chr20q amplification/ATRX mutations; intermediate risk: presence of TP53 mutation, ATRX mutation, or amp20q; low risk: lack of any of these three alterations. Longitudinal sequencing showed that most molecular alterations were early clonal events that persisted during disease progression. Compared with traditional clinicopathologic models, genomic risk stratification demonstrates superior prediction of clinical outcome in STLMS and is comparable in ULMS.

Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study

BackgroundThe Diabetic Retinopathy Extended Screening Study (DRESS) aims to develop and validate a new DR/diabetic macular edema (DME) risk stratification model in patients with Type 2 diabetes (DM) to identify low-risk groups who can be safely assigned to biennial or triennial screening intervals. We describe the study methodology, participants’ baseline characteristics, and preliminary DR progression rates at the first annual follow-up.MethodsDRESS is a 3-year ongoing longitudinal study of patients with T2DM and no or mild non-proliferative DR (NPDR, non-referable) who underwent teleophthalmic screening under the Singapore integrated Diabetic Retinopathy Programme (SiDRP) at four SingHealth Polyclinics. Patients with referable DR/DME (> mild NPDR) or ungradable fundus images were excluded. Sociodemographic, lifestyle, medical and clinical information was obtained from medical records and interviewer-administered questionnaires at baseline. These data are extracted from medical records at 12, 24 and 36 months post-enrollment. Baseline descriptive characteristics stratified by DR severity at baseline and rates of progression to referable DR at 12-month follow-up were calculated.ResultsOf 5,840 eligible patients, 78.3% (n = 4,570, median [interquartile range [IQR] age 61.0 [55–67] years; 54.7% male; 68.0% Chinese) completed the baseline assessment. At baseline, 97.4% and 2.6% had none and mild NPDR (worse eye), respectively. Most participants had hypertension (79.2%) and dyslipidemia (92.8%); and almost half were obese (43.4%, BMI ≥ 27.5 kg/m2). Participants without DR (vs mild DR) reported shorter DM duration, and had lower haemoglobin A1c, triglycerides and urine albumin/creatinine ratio (all p < 0.05). To date, we have extracted 41.8% (n = 1909) of the 12-month follow-up data. Of these, 99.7% (n = 1,904) did not progress to referable DR. Those who progressed to referable DR status (0.3%) had no DR at baseline.ConclusionsIn our prospective study of patients with T2DM and non-referable DR attending polyclinics, we found extremely low annual DR progression rates. These preliminary results suggest that extending screening intervals beyond 12 months may be viable and safe for most participants, although our 3-year follow up data are needed to substantiate this claim and develop the risk stratification model to identify low-risk patients with T2DM who can be assigned biennial or triennial screening intervals.

Thrombosis in Acute Promyelocytic Leukemia: The Current Understanding.

Despite enormous improvement in the management of patients with acute promyelocytic leukemia (APL), the distinctive coagulopathy observed at presentation in affected patients is often life-threatening. While hemorrhagic manifestations are well known and described in this setting, APL-related thromboses are underappreciated. Data regarding this complication are scarce showing variable incidence. Furthermore, risk factors for thrombosis are inconsistent and unreliable; so, differentiation of increased risk of hemorrhage from an increased thrombotic risk is quite difficult in the absence of adequate predictive scores. Besides, prophylactic use of anticoagulants and recombinant thrombomodulin are a matter of ongoing debate. Also, due to the common feature of thrombocytopenia and other hemorrhagic risks, patients with APL are excluded from trials analyzing anticoagulant prophylaxis in cancers; so, data from prospective trials are lacking. A detailed analysis of thrombotic risks in APL with the development of a reliable risk stratification model is needed to further improve the care of APL patients.

[Artículo traducido] Modelo de estratificación de riesgo de atención farmacéutica para pacientes oncológicos con neoplasias sólidas o hematológicas

ObjectiveWe aimed to develop of a risk stratification model for the pharmaceutical care (PC) of patients with solid or hematologic neoplasms who required antineoplastic agents or supportive treatments. MethodThe risk stratification model was collaboratively developed by oncology pharmacists from the Spanish Society of Hospital Pharmacy (SEFH). It underwent refinement through three workshops and a pilot study. Variables were defined, grouped into four dimensions, and assigned relative weights. The pilot study collected and analyzed data from participating centers to determine priority levels and evaluate variable contributions. The study followed the Kaiser Permanente pyramid model, categorizing patients into three priority levels: Priority 1 (intensive PC, 90th percentile), Priority 2 (60th-90th percentiles), and Priority 3 (60th percentile). Cut-off points were determined based on this stratification. Participating centers recorded variables in an Excel sheet, calculating mean weight scores for each priority level and the total risk score. ResultsThe participants agreed to complete a questionnaire that comprised 22 variables grouped into 4 dimensions: demographic (maximum score =11); social and health variables and cognitive and functional status (maximum = 19); clinical and health services utilization (maximum = 25); and treatment-related (maximum = 41). From the results of applying the model to the 199 patients enrolled, the cutoff points for categorization were 28 or more points for priority 1, 16 to 27 points for priority 2 and less than 16 for priority 3; more than 80% of the total score was based on the dimensions of ‘clinical and health services utilization’ and ‘treatment-related’. Interventions based on the pharmaceutical care model were recommended for patients with solid or hematological neoplasms, according to their prioritization level. ConclusionThis stratification model enables the identification of cancer patients requiring a higher level of pharmaceutical care and facilitates the adjustment of care capacity. Validation of the model in a representative population is necessary to establish its effectiveness.

A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals

The development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient’s condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.

Clinical outcomes and prognostic factors after HCV clearance with DAA in HIV/HCV-coinfected patients with advanced fibrosis/cirrhosis.

We assessed long-term clinical outcomes and prognostic factors for liver disease progression after sustained viral response with direct-acting antivirals in patients coinfected with HIV/HCV with advanced fibrosis or cirrhosis. A total of 1300 patients who achieved sustained viral response with direct-acting antivirals from 2014 to 2017 in Spain were included: 1145 with compensated advanced chronic liver disease (384 advanced fibrosis and 761 compensated cirrhosis) and 155 with decompensated cirrhosis. The median follow-up was 40.9 months. Overall, 85 deaths occurred, 61 due to non-liver non-AIDS-related causes that were the leading cause of death across all stages of liver disease. The incidence (95% CI) of decompensation per 100 person-years (py) was 0 in patients with advanced fibrosis, 1.01 (0.68-1.51) in patients with compensated cirrhosis, and 8.35 (6.05-11.53) in patients with decompensated cirrhosis. The incidence (95% CI) of HCC per 100 py was 0.34 (0.13-0.91) in patients with advanced fibrosis, 0.73 (0.45-1.18) in patients with compensated cirrhosis, and 1.92 (1.00-3.70) per 100 py in patients with decompensated cirrhosis. Prognostic factors for decompensation in patients with compensated advanced chronic liver disease included serum albumin, liver stiffness measurement (LSM), and fibrosis 4. In this population, LSM and LSM-based posttreatment risk stratification models showed their predictive ability for decompensation and HCC. Non-liver non-AIDS-related events were the leading causes of morbidity and mortality after direct-acting antiviral cure among coinfected patients with advanced fibrosis/cirrhosis. Among those with compensated advanced chronic liver disease, baseline LSM and posttreatment LSM-based models helped to assess decompensation and HCC risk.

Relationship between ultra-short heart rate variability and short-term mortality in hospitalized COVID-19 patients

ObjectiveTo assess the association between ultra-short heart rate variability (US-HRV) and short-term mortality in patients with COVID-19 and develop prognostic prediction models to identify high-risk patients as early as possible. MethodsA retrospective cohort study was performed on 488 patients diagnosed with COVID-19 and hospitalized in the First Affiliated Hospital of Fujian Medical University from December 2022 to January 2023. 10-s electrocardiogram (ECG) data were available for these patients. The US-HRV parameters including standard deviation of all normal-to-normal R-R intervals (SDNN) and root mean square of successive differences between normal-to-normal R-R intervals (rMSSD) were calculated using Nalong ECG software. The endpoint was short-term mortality, including in-hospital mortality or mortality within 1 week after discharge. ResultsOf the 488 patients, 76 (15.6%) died. The SDNN and rMSSD in the death group were significantly lower than those in the survival group (P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) for SDNN and rMSSD to predict mortality was 0.761 and 0.715, respectively. The combined use of SDNN and rMSSD had an AUC of 0.774. The mortality rate in the group with SDNN ≤7.5 ms was higher than that of SDNN >7.5 ms group (P < 0.05). With the decrease of SDNN, the mortality of patients showed an upward trend, and the mortality of patients with SDNN ≤2 ms was the highest (66.7%). Multivariate logistic regression analysis identified SDNN as an independent predictor of prognosis (odds ratio (OR) = 5.791, 95% confidential interval (CI) 1.615–20.765, P = 0.007). The AUC of Model 1 (simple model) was 0.866 (95% CI 0.826–0.905). The AUC of Model 2 (comprehensive model) was 0.914 (95% CI 0.881–0.947). ConclusionSDNN was associated with short-term mortality and provided the additional discriminatory power of the risk stratification model for hospitalized COVID-19 patients.

Genetic alterations in chronic lymphocytic leukemia and plasma cell neoplasms- a practical guide to WHO HAEM5.

The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) provides a revised classification of lymphoid malignancies including chronic lymphocytic leukemia (CLL) and plasma cell myeloma/multiple myeloma (PCM/MM). For both diseases the descriptions of precursor states such as monoclonal B-cell lymphocytosis and monoclonal gammopathy of uncertain significance (MGUS) have been updated including a better risk stratification model. New insights on mutational landscapes and branching evolutionary pattern were embedded as diagnostic and prognostic factors, accompanied by a revised structure for the chapter of plasma cell neoplasms. Thus, the WHO-HAEM5 leads to practical improvements of biological and clinical relevance for pathologists, clinicians, geneticists and scientists in the field of lymphoid malignancies. The present review gives an overview on the landscape of genetic alterations in CLL and plasma cell neoplasms with a focus on their impact on classification and treatment.

Abstract B047: Development and validation of an improved risk stratification model for ovarian cancer

Abstract B047: Development and validation of an improved risk stratification model for ovarian cancer

Sendaway capillary NT-proBNP in pulmonary hypertension

BackgroundN-terminal pro-B-type natriuretic peptide (NT-proBNP) is a biomarker of cardiac ventricular wall stress that is incorporated into pulmonary hypertension (PH) risk stratification models. Sendaway sampling may enable patients to perform...

Precise diagnosis and risk stratification of prostate cancer by comprehensive serum metabolic fingerprints: a prediction model study.

Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. The authors introduced a novel approach based on mass spectrometry for precise diagnosis and stratification of PCa to facilitate clinical decision-making. Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validation. Subsequent metabolomic analysis was carried out using Autoflex MALDI-TOF, and the mass spectra were introduced into various algorithms to construct different models. Serum metabolic fingerprints were successfully obtained from 181 patients with PCa and 186 patients with non-PCa. The diagnostic model based on the eight signals demonstrated a remarkable area under curve of 100% and was validated in the external cohort with the area under curve of 87.3%. Fifteen signals were selected for enrichment analysis, revealing the potential metabolic pathways that facilitate tumorigenesis. Furthermore, the stage prediction model with an overall accuracy of 85.9% precisely classified subjects with localized disease and those with metastasis. The risk stratification model, with an overall accuracy of 89.6%, precisely classified the subjects as low-risk and high-risk. Our study facilitated the timely diagnosis and risk stratification of PCa and provided new insights into the underlying mechanisms of metabolic alterations in PCa.

Epidemiological features and a survival nomogram for primary lymphoma of the male genital tract.

Primary lymphoma of the male genital tract (PLMGT) is rare, and data on its epidemiology and prognosis are lacking. Our study aimed to estimate the incidence and develop a predictive nomogram for PLMGT. We pooled the incidence and survival data of PLMGT over the last 20 years from the Surveillance, Epidemiology, and End Results (SEER) database. Incidence rates were calculated by year of diagnosis, age, race, and histology. Independent prognostic factors selected by Cox regression analysis were used to develop a nomogram for predicting overall survival (OS). Our study enrolled 1312 patients with PLMGT. The overall incidence rate of PLMGT was 0.437/1,000,000 during 2000-2019. OS was associated with age, marital status, histological subtype, Ann Arbor stage, and therapeutic strategy, which were used to construct nomograms to predict 1-, 3-, and 5-year OS rates. Receiver operating characteristic curves, calibration plots, and decision curve analysis showed good performance of the nomogram. Based on the total score of each patient from the nomogram, the patients were clustered into three risk groups, and the risk stratification model was more successful in predicting clinical outcomes than the traditional Ann Arbor staging system. The incidence rate of PLMGT has remained relatively stable over the past two decades. For the OS of patients with PLMGT, we established a novel predictive nomogram involving all independent risk factors obtained from the SEER database and developed a corresponding risk classification system that showed better predictive performance than the Ann Arbor staging system.

Efficient organized colorectal cancer screening in Shenzhen: a microsimulation modelling study

BackgroundColorectal cancer (CRC) is a global health issue with noticeably high incidence and mortality. Microsimulation models offer a time-efficient method to dynamically analyze multiple screening strategies. The study aimed to identify the efficient organized CRC screening strategies for Shenzhen City.MethodsA microsimulation model named CMOST was employed to simulate CRC screening among 1 million people without migration in Shenzhen, with two CRC developing pathways and real-world participation rates. Initial screening included the National Colorectal Polyp Care score (NCPCS), fecal immunochemical test (FIT), and risk-stratification model (RS model), followed by diagnostic colonoscopy for positive results. Several start-ages (40, 45, 50 years), stop-ages (70, 75, 80 years), and screening intervals (annual, biennial, triennial) were assessed for each strategy. The efficiency of CRC screening was assessed by number of colonoscopies versus life-years gained (LYG).ResultsThe screening strategies reduced CRC lifetime incidence by 14–27 cases (30.9–59.0%) and mortality by 7–12 deaths (41.5–71.3%), yielded 83–155 LYG, while requiring 920 to 5901 colonoscopies per 1000 individuals. Out of 81 screening, 23 strategies were estimated efficient. Most of the efficient screening strategies started at age 40 (17 out of 23 strategies) and stopped at age 70 (13 out of 23 strategies). Predominant screening intervals identified were annual for NCPCS, biennial for FIT, and triennial for RS models. The incremental colonoscopies to LYG ratios of efficient screening increased with shorter intervals within the same test category. Compared with no screening, when screening at the same start-to-stop age and interval, the additional colonoscopies per LYG increased progressively for FIT, NCPCS and RS model.ConclusionThis study identifies efficient CRC screening strategies for the average-risk population in Shenzhen. Most efficient screening strategies indeed start at age 40, but the optimal starting age depends on the chosen willingness-to-pay threshold. Within insufficient colonoscopy resources, efficient FIT and NCPCS screening strategies might be CRC initial screening strategies. We acknowledged the age-dependency bias of the results with NCPCS and RS.

Prognostic Evaluation for Hepatocellular Carcinoma with Portal Vein Tumor Thrombus Patients Treated with Transarterial Chemoembolization Plus Molecular Targeted Therapies—Development and Validation of the ABPS Score

Rationale and objectivesTransarterial chemoembolization (TACE) plus molecular targeted therapies has emerged as the main approach for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). A robust model for outcome prediction and risk stratification of recommended TACE plus molecular targeted therapies candidates is lacking. We aimed to develop an easy-to-use tool specifically for these patients. MethodsA retrospective analysis was conducted on 384 patients with HCC and PVTT who underwent TACE plus molecular targeted therapies at 16 different institutions. We developed and validated a new prognostic score which called ABPS score.Additionally, an external validation was performed on data from 200 patients enrolled in a prospective cohort study. ResultsThe ABPS score (ranging from 0 to 3 scores), which involves only Albumin-bilirubin (ALBI, grade 1：0 score；grade 2：1 score), PVTT(I-II type：0 score；III-IV type：1 score), and systemic-immune inflammation index (SII,<550×1012：0 score；≥550×1012：1 score). Patients were categorized into three risk groups based on their ABPS score: ABPS-A, B, and C (scored 0, 1-2, and 3, respectively). The concordance index (C-index) of the ABPS scoring system was calculated to be 0.802, significantly outperforming the HAP score (0.758), 6-12 (0.712), Up to 7 (0.683), and ALBI (0.595) scoring systems (all P< 0.05). These research findings were further validated in the external validation cohorts. ConclusionsThe ABPS score demonstrated a strong association with survival outcomes and radiological response in patients undergoing TACE plus molecular targeted therapy for HCC with PVTT. The ABPS scoring system could serve as a valuable tool to guide treatment selection for these patients.

Angiography‑based quantitative flow ratio for functional assessment of intracranial atherosclerotic disease.

Intracranial atherosclerotic stenosis (ICAS), an important cause of stroke, is associated with a considerable stroke recurrence rate despite optimal medical treatment. Further assessment of the functional significance of ICAS is urgently needed to enable individualised treatment and, thus, improve patient outcomes. We aimed to evaluate the haemodynamic significance of ICAS using the quantitative flow ratio (QFR) technique and to develop a risk stratification model for ICAS patients. Patients with moderate to severe stenosis of the middle cerebral artery, as shown on angiography, were retrospectively enrolled. For haemodynamic assessment, the Murray law-based QFR (μQFR) was performed on eligible patients. Multivariate logistic regression models composed of μQFR and other risk factors were developed and compared for the identification of symptomatic lesions. Based on the superior model, a nomogram was established and validated by calibration. Among 412 eligible patients, symptomatic lesions were found in 313 (76.0%) patients. The μQFR outperformed the degree of stenosis in discriminating culprit lesions (area under the curve [AUC]: 0.726 vs 0.631; DeLong test p-value=0.001), and the model incorporating μQFR and conventional risk factors also performed better than that containing conventional risk factors only (AUC: 0.850 vs 0.827; DeLong test p-value=0.034; continuous net reclassification index=0.620, integrated discrimination improvement=0.057; both p<0.001). The final nomogram showed good calibration (p for Hosmer-Lemeshow test=0.102) and discrimination (C-statistic 0.850, 95% confidence interval: 0.812-0.883). The μQFR was significantly associated with symptomatic ICAS and outperformed the angiographic stenosis severity. The final nomogram effectively discriminated symptomatic lesions and may provide a useful tool for risk stratification in ICAS patients.

Development of a Risk Stratification Model for Pre-liver-Transplant Screening Colonoscopy.

Colonoscopies are routinely obtained before liver transplantation, although their utility is a highly debated topic in the literature. We aimed to determine the risk factors in patients with decompensated cirrhosis (DC) for post-colonoscopy complications (PCC). We performed a single-center retrospective study of patients with DC undergoing colonoscopy as part of their pre-liver-transplant evaluation. The primary composite outcome was defined as a complication occurring within 30 days of the colonoscopy. Complications included acute renal failure, new or worsening ascites or hepatic encephalopathy, gastrointestinal bleeding, or any cardiopulmonary or infectious complication. Logistic regression analysis was utilized to derive a risk score in predicting the primary composite outcome. The strongest predictors of post-colonoscopy complication were MELD-Na ≥21 [aOR 4.0026 ( P =0.0050)] and history of any infection in the 30 days before colonoscopy [aOR 8.4345 ( P =0.0093)]. The area under the receiver operating characteristic curve of the final model was 0.78. The predicted risk of any complication at the lowest quartile was 16.2% to 39.4%, and the observed risk was 30.6% (95% CI: 15.5-45.6%), while the predicted risk at the highest quartile was 71.9% to 97.1%, and the observed risk was 81.3% (95% CI: 67.7-95%). In this cohort of patients with DC undergoing colonoscopy for pre-liver-transplant evaluation, a history of ascites, spontaneous bacterial peritonitis, and MELD-Na were found to be predictive of PCC. This risk score may help to predict PCC in patients with DC undergoing a pre-transplant colonoscopy. External validation is recommended.

Risk stratification models overestimate progression risk in contemporary patients with smoldering multiple myeloma.

Risk stratification models overestimate progression risk in contemporary patients with smoldering multiple myeloma.

Introduction to the novel model of preoperative Multi - Domain Risk Stratification (pMDRS).

Preoperative risk stratification is an important step in surgical procedures. The current scoring systems do not predict accurate overall surgical outcomes in complex comorbid patients. The novel model of preoperative multi-domain risk stratification is described in this article, which categorises patients in to three risk groups, aiming to modify the risk for optimal surgical outcomes.