Simple Prognostic Model Research Articles

Validation of a 5-Year Prognostic Model for Parkinson's Disease.

A simple prognostic model was previously developed to predict the probability of recently-diagnosed patients reaching negative outcomes (postural instability, dementia or death) in a 5-year period. To validate this model in an independent cohort and establish utility at later time points. Validation was performed using data collected in an incident cohort at baseline, 2 and 4 years. Predicted negative outcome probabilities were compared to actual 5-year outcomes. The model, based on age, MDS-UPDRS axial score and 60-second animal fluency, predicted poor 5-year outcome when applied at baseline, (area under the curve (AUC) 0.80), 2 years (AUC 0.82) and 4 years (AUC 0.71). Power calculations showed that selecting a subgroup with prognostic score >0.5 reduced the sample size required for a disease-modifying trial. This 5-year prognostic model has good accuracy when employed up to 4 years from diagnosis and may help stratification for disease-modifying trials.

Climatic variability of wave heights in the shelf zone of the North-Eastern part of the Black Sea according field data

Wave parameters and their variability under climate change are important for forecasting in many areas, such as coastal zone management. This study analyzed the variability of key climate indices from August to October over the period 1996–2020. The relationship between these changes and changes in average monthly significant wave heights was assessed based on field measurements taken at a consistent location on the northeastern Black Sea shelf during various years within this period.The analysis revealed that negative values of NAO, AO, and EA, along with positive values (greater than 1) of EA/WR, are associated with increases in wave heights. The study identified the key periods when joint changes in these climate indices are likely to have the greatest impact on average monthly wave heights: 4 and 7 years for August, 17 and 20 years for September, and 4, 13, and 10 years for October. The discussion emphasizes that identifying the primary regional climate indices and studying their relationship with wave parameters in detail could form the foundation for zoning the Black Sea into quasi-homogeneous zones based on climate variability. This approach could also support the development of simple prognostic models for different parts of the Black Sea, taking into account the non-stationary nature of climate change.

Development and validation of novel simple prognostic model for predicting mortality in Korean intensive care units using national insurance claims data.

Intensive care unit (ICU) quality is largely determined by the mortality rate. Therefore, we aimed to develop and validate a novel prognostic model for predicting mortality in Korean ICUs, using national insurance claims data. Data were obtained from the health insurance claims database maintained by the Health Insurance Review and Assessment Service of South Korea. From patients who underwent the third ICU adequacy evaluation, 42,489 cases were enrolled and randomly divided into the derivation and validation cohorts. Using the models derived from the derivation cohort, we analyzed whether they accurately predicted death in the validation cohort. The models were verified using data from one general and two tertiary hospitals. Two severity correction models were created from the derivation cohort data, by applying variables selected through statistical analysis, through clinical consensus, and from performing multiple logistic regression analysis. Model 1 included six categorical variables (age, sex, Charlson comorbidity index, ventilator use, hemodialysis or continuous renal replacement therapy, and vasopressor use). Model 2 additionally included presence/absence of ICU specialists and nursing grades. In external validation, the performance of models 1 and 2 for predicting in-hospital and ICU mortality was not inferior to that of pre-existing scoring systems. The novel and simple models could predict in-hospital and ICU mortality and were not inferior compared to the pre-existing scoring systems.

Validation of the CRAFITY score for predicting prognosis in patients with hepatocellular carcinoma undergoing transarterial chemoembolization combined with systemic therapy

BackgroundThe CRAFITY score can predict survival and radiological response to systemic front-line treatment in patient with hepatocellular carcinoma (HCC). AimsIn this study, we aimed to externally validate the CRAFITY score and compare it with other simple prognostic models in patients with unresectable HCC who underwent transarterial chemoembolization (TACE) combined with systemic therapies. MethodsPatients with unresectable HCC who underwent TACE combined with tyrosine kinase inhibitors (TKIs), with or without immune checkpoint inhibitors (ICIs), were retrospectively analyzed to determine whether the CRAFITY score can predict overall survival (OS) through multivariate analysis and Kaplan‒Meier curves. Prediction of 3- and 6-month objective response rates (ORRs) was validated by the area under the curve (AUC). ResultsAmong 147 patients with unresectable HCC, 113 received TACE ​+ ​TKI and 34 received TACE ​+ ​TKI ​+ ​ICI. The CRAFITY score was identified as an independent predictor for OS (P ​< ​0.001) and showed a good ability to categorize all included patients into low-, moderate-, and high-risk groups (P ​< ​0.001). For predicting the 3- and 6-month ORRs, the CRAFITY score exhibited poor performance, with AUCs of 0.555 and 0.515, respectively. ConclusionThe CRAFITY score is effective in predicting OS but demonstrates limited accuracy in predicting 3- and 6-month ORRs for patients with unresectable HCC undergoing TACE combined with systemic therapies.

Prognostic model for unresectable hepatocellular carcinoma treated with dual PD-1 and angiogenesis blockade therapy

Background and aimsDual programmed death 1 (PD-1) and angiogenesis blockade therapy is a frontline treatment for hepatocellular carcinoma (HCC). An accepted model for survival prediction and risk stratification in individual...

PIVKA-II combined with tumor burden score to predict long-term outcomes of AFP-negative hepatocellular carcinoma patients after liver resection.

This study aimed to establish a simple prognostic scoring model based on tumor burden score (TBS) and PIVKA-II to predict long-term outcomes of α-fetoprotein (AFP)-negative hepatocellular carcinoma (HCC) patients. 511 patients were divided into the training cohort (n = 305) and the validation cohort (n = 206) at a ratio of 6:4. Receiver operating characteristic curves (ROC) were established to identify cutoff values of TBS and PIVKA-II. Kaplan-Meier curves were used to analyze survival outcomes. The multivariable Cox regression was used to identify variables independently associated with survival outcomes. The predictive performance of the TBS-PIVKA II score (TPS) model was compared with Barcelona clinic liver cancer (BCLC) stage and American Joint Committee on Cancer (AJCC TNM) stage. The present study established the TPS model using a simple scoring system (0, 1 for low/high TBS [cutoff value: 4.1]; 0, 1 for low/high PIVKA-II [cutoff value: 239 mAU/mL]). The TPS scoring model was divided into three levels according to the summation of TBS score and PIVKA-II score: TPS 0, TPS 1, and TPS 2. The TPS scoring model was able to stratify OS (training: p < 0.001, validation: p < 0.001) and early recurrence (training: p < 0.001; validation: p = 0.001) in the training cohort and the validation cohort. The TPS score was independently associated with OS (TPS 1 vs. 0, HR: 2.28, 95% CI: 1.01-5.17; TPS 2 vs. 0, HR: 4.21, 95% CI: 2.01-8.84) and early recurrence (TPS 1 vs. 0, HR: 3.50, 95% CI: 1.71-7.16; TPS 2 vs. 0, HR: 3.79, 95% CI: 1.86-7.75) in the training cohort. The TPS scoring model outperformed BCLC stage and AJCC TNM stage in predicting OS and early recurrence in the training cohort and the validation cohort. But the TPS scoring model was unable to stratify the late recurrence in the training cohort (p = 0.872) and the validation cohort (p = 0.458). The TPS model outperformed the BCLC stage and AJCC TNM stage in predicting OS and early recurrence of AFP-negative HCC patients after liver resection, which might better assist surgeons in screening AFP-negative HCC patients who may benefit from liver resection.

Diffuse large B-cell lymphoma: strokes to the epidemiological portrait. A review

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous orphan lymphoproliferative disease with an aggressive course, which accounts for 30–40% of all non-Hodgkin lymphomas. Approximately 3,000 new cases of DLBCL are diagnosed annually in Russia. The trend towards a steady increase in DLBCL incidence worldwide and the rapidly changing treatment landscape with the introduction of innovative options require the updating of epidemiological data. Despite a deeper understanding of the lymphomagenesis and molecular heterogeneity of DLBCL, the R-CHOP regimen remains the first-line standard of care for the vast majority (74%) of patients in Russia and worldwide. The effectiveness of this approach varies greatly and depends on several clinical, biological, and genetic factors. Currently, the most effective and simple prognostic model is the International Prognostic Index (IPI), according to which approximately 20% of patients are at intermediate/high risk of early progression (IPI3-5) and require therapy modification. After the first-line standard immunochemotherapy, 60–70% of DLBCL patients achieve long-term remission with the potential for cure. However, the occurrence of relapse or refractory disease in the remaining 40% of patients is associated with an abysmal prognosis with a median overall survival of about 6 months, which tends to decrease with each subsequent line, warranting an urgent need for new innovative treatments for patients with relapsed DLBCL.

A Simple Prediction Model of Outcomes after Allogeneic Hematopoietic Stem Cell Transplant (HCT) in Myelodysplastic Syndromes Using HCT-Comorbidity Index, Cytogenetic Risk, and Platelet Count

Introduction: Allogeneic hematopoietic stem cell transplant (HCT) is currently the only potentially curative option for patients (pts) with myelodysplastic syndromes (MDS). However, many pts relapse after HCT. While the International Prognostic Scoring System-Molecular (IPSS-M) is useful, next generation sequencing is not always available particularly in resource limited settings. Hence, we aimed to develop a prediction model utilizing readily available pt and HCT characteristics of post-HCT outcomes in pts with MDS treated with hypomethylating agent (HMA) therapy pre-HCT. Methods: Pts with MDS treated with HMA (azacitidine or decitabine) who received an HCT at Dana-Farber Cancer Institute from 01/2014-12/2020 were included. The primary outcome was progression free survival (PFS) post-HCT, defined as time from transplant to disease relapse or progression. Univariable analysis (UVA) and multivariable analysis (MVA) were used to identify predictors of PFS and build a model. Variables were pt age, sex, donor type (matched related donor (MRD) vs. matched unrelated donor (MUD) vs. 7/8 MUD vs. haploidentical), HCT-comorbidity index (HCT-CI), CMV status of donor/recipient, MDS subtype, graft versus host disease prophylaxis (GVHD ppx), HMA type, number of HMA cycles, conditioning intensity (myeloablative vs. reduced intensity), cell source (bone marrow vs. peripheral blood), response to HMA assessed by IWG 2023 criteria ( Zeidan Blood 2023), IPSS-Revised (IPSS-R) pre-HCT, and cytogenetic risk (very good, good, intermediate, poor, very poor) at diagnosis and pre-HCT. Results: A total of 148 pts with MDS received HMA and underwent subsequent HCT. Median age was 64 years (range 26-79) and 61.5% were men. Pts were treated with 7 days azacitidine (54.1%), 5 days decitabine (39.2%) and other schedules (6.7%) and received a median of 4 HMA cycles (range 1-20). Prior to HCT, IWG 2023 responses were: CR (15.5%), CRbi (14.9%), CRuni (19.6%), CRh (0.7%), PR (1.4%), HI (9.5%), and no response (38.5%). Most donors were MUD (60.8%) or MRD (18.2%), and most pts received reduced intensity conditioning (74.3%). GVHD ppx was tacrolimus (tac)/methotrexate (MTX) (54.7%), tac/MTX/sirolimus (19.6%), cyclophosphamide/mycophenolate mofetil/tac (15.5%), and other (10.1%). Median HCT-CI was 2 (range 0-13); 48.6% had HCT-CI ≥3. Median follow-up time among ongoing survivors was 48.3 months (range 5.5-101.3). The only statistically significant variables associated with post-HCT PFS in both UVA and MVA (with Cox MVA results shared here) were HCT-CI (≥4 vs. 0-3: HR 1.94, 95% CI 1.18-3.19, p=0.009), platelet count pre-HCT (platelets &amp;lt;40 vs. &amp;gt;70: HR 2.46, 95% CI 1.33-4.54, p=0.004; platelets 40-70 vs. &amp;gt;70: HR 1.84, 95% CI 1.02-3.32, p=0.044) and cytogenetic risk pre-HCT (very poor vs. other: HR 2.52, 95% CI 1.43-4.44, p=0.001). HCT-CI impacted non-relapse mortality (NRM) and not relapse whereas very poor cytogenetics impacted relapse and not NRM. A model predicting post-HCT outcomes was built with the following risk scores: HCT-CI: 0-3 (0), ≥4 (1.5); platelet count pre-HCT: &amp;gt;70 (0), 40-70 (1), &amp;lt;40 (2); cytogenetic risk pre-HCT: not very poor (0), very poor (2). Based on the sum risk score, patients were defined as low (0-1), intermediate (int) (1.5-2) or high (≥2.5) risk. The risk classification was significantly associated with post-HCT outcomes. Four-year post-HCT PFS was 68% (95% CI 56-78%), 36% (95% CI 22-51%) and 15% (95% CI 5-31%) for low, int and high risk, respectively (p&amp;lt;0.00001) ( Figure A). Accordingly, 4-year cumulative incidence of relapse was 23% (95% CI 14-33%) for low, 54% for int (95% CI 38-68%) and 62% for high (95%CI 39-78%) risk scores (p=0.00005). Four-year overall survival was 70% (95% CI 58-80%), 45% (95% CI 29-59%), and 19% (95% CI 7-36%) (p&amp;lt;0.00001), for the same respective risk groups ( Figure B). In contrast, NRM did not differ between risk categories (low: 9%, int: 10%, high: 23%; p=0.1). The predictability of this simple risk model (C-index: 0.72) was found to be superior to IPSS-R pre-HCT (C-index: 0.61) and IWG 2023 response pre-HCT (C-index: 0.617). Conclusion: We developed a simplified prognostic model of post-HCT outcomes for pts with MDS treated with HMA based on HCT-CI, platelet count, and cytogenetic risk post-HMA therapy and pre-HCT. We expect that this model will be particularly useful in resource-limited settings without easy access to next generation sequencing.

A model for predicting poor survival in patients with cirrhosis undergoing portosystemic shunt embolization.

Portosystemic shunt embolization (PSSE) is a promising treatment for hepatic encephalopathy (HEP) and gastric varix (GV) in cirrhotic patients with a spontaneous portosystemic shunt. However, PSSE may worsen portal hypertension causing hepatorenal syndrome, liver failure, and mortality. This study aimed to develop and validate a prognostic model that helps identify patients with a risk of poor short-term survival after PSSE. We included 188 patients who underwent PSSE for recurrent HEP or GV at a tertiary center in Korea. To develop a prediction model for 6-month survival after PSSE, Cox proportional-hazard model was used. The developed model was validated in a separate cohort of 184 patients from two other tertiary centers. In multivariable analysis, the 1-year overall survival after PSSE was significantly associated with baseline levels of serum albumin, total bilirubin, and international normalized ratio (INR). We therefore developed the albumin-bilirubin-INR (ABI) score by assigning 1 point each for albumin <3.0g/dL, total bilirubin ≥1.5mg/dL, and INR≥1.5. Time-dependent areas under the curve of the ABI score for predicting 3-month and 6-month survival were 0.85 and 0.85 in the development cohort and 0.83 and 0.78 in the validation cohort, indicating good discrimination performance. The ABI score showed a better discrimination and calibration performance than the model for end-stage liver disease and the Child-Pugh scores, especially in high-risk patients. The ABI score is a simple prognostic model that helps decide whether to proceed with PSSE for the prevention of HEP or GV bleeding in patients with spontaneous portosystemic shunt.

Application of different prognostic scores in liver transplantation decision-making for acute-on-chronic liver failure

Objective: To compare the impact of different prognostic scores in patients with acute-on-chronic liver failure (ACLF) in order to provide treatment guidance for liver transplantation. Methods: The information on inpatients with ACLF admitted at Beijing You'an Hospital Affiliated to Capital Medical University and the First Affiliated Hospital of Zhejiang University School of Medicine from January 2015 to October 2022 was collected retrospectively. ACLF patients were divided into liver transplantation and non-liver transplantation groups, and the two groups prognostic conditions were followed-up. Propensity score matching was carried out between the two groups on the basis of liver disease (non-cirrhosis, compensated cirrhosis, and decompensated cirrhosis), the model for end-stage liver disease incorporating serum sodium (MELD-Na), and ACLF classification as matching factors. The prognostic condition of the two groups after matching was compared. The difference in 1-year survival rate between the two groups was analyzed under different ACLF grades and MELD-Na scores. The independent sample t-test or rank sum test was used for inter-group comparison, and the χ (2) test was used for the comparison of count data between groups. Results: In total, 865 ACLF inpatients were collected over the study period. Of these, 291 had liver transplantation and 574 did not. The overall survival rates at 28, 90, and 360 days were 78%, 66%, and 62%, respectively. There were 270 cases of matched ACLF post-liver transplantation and 270 cases without ACLF, in accordance with a ratio of 1:1. At 28, 90, and 360 days, patients with non-liver transplantation had significantly lower survival rates (68%, 53%, and 49%) than patients with liver transplantation (87%, 87%, and 78%, respectively; P < 0.001). Patients were classified into four groups according to the ACLF classification criteria. Kaplan-Meier survival analysis showed that the survival rates of liver transplantation and non-liver transplantation patients in ACLF grade 0 were 77.2% and 69.4%, respectively, with no statistically significant difference (P = 0.168). The survival rate with an ACLF 1-3 grade was significantly higher in liver transplantation patients than that of non-liver transplantation patients (P < 0.05). Patients with ACLF grades 1, 2, and 3 had higher 1-year survival rates compared to non-liver transplant patients by 50.6%, 43.6%, and 61.7%, respectively. Patients were divided into four groups according to the MELD-Na score. Among the patients with a MELD-Na score of < 25, the 1-year survival rates for liver transplantation and non-liver transplantation were 78.2% and 74.0%, respectively, and the difference was not statistically significant (P = 0.149). However, among patients with MELD-Na scores of 25-30, 30-35, and≥35, the survival rate was significantly higher in liver transplantation than that of non-liver transplantation, and the 1-year survival rate increased by 36.4%, 54.9%, and 62.5%, respectively (P < 0.001). Further analysis of the prognosis of patients with different ACLF grades and MELD-Na scores showed that ACLF grades 0 or 1 and MELD-Na score of < 30 had no statistically significant difference in the 1-year survival rate between liver transplantation and non-liver transplantation (P > 0.05), but in patients with MELD-Na score≥30, the 1-year survival rate of liver transplantation was higher than that of non-liver transplantation patients (P < 0.05). In the ACLF grade 0 and MELD-Na score of≥30 group, the 1-year survival rates of liver transplantation and non-liver transplantation patients were 77.8% and 25.0% respectively (P < 0.05); while in the ACLF grade 1 and MELD-Na score of≥30 group, the 1-year survival rates of liver transplantation and non-liver transplantation patients were 100% and 20.0%, respectively (P < 0.01). Among patients with ACLF grade 2, the 1-year survival rate with MELD-Na score of < 25 in patients with liver transplantation was 73.9% and 61.6%, respectively, and the difference was not statistically significant (P > 0.05); while in the liver transplantation patients group with MELD-Na score of ≥25, the 1-year survival rate was 79.5%, 80.8%, and 75%, respectively, which was significantly higher than that of non-liver transplantation patients (36.6%, 27.6%, 15.0%) (P < 0.001). Among patients with ACLF grade 3, regardless of the MELD-Na score, the 1-year survival rate was significantly higher in liver transplantation patients than that of non-liver transplantation patients (P < 0.01). Additionally, among patients with non-liver transplantation with an ACLF grade 0~1 and a MELD-Na score of < 30 at admission, 99.4% survived 1 year and still had an ACLF grade 0-1 at discharge, while 70% of deaths progressed to ACLF grade 2-3. Conclusion: Both the MELD-Na score and the EASL-CLIF C ACLF classification are capable of guiding liver transplantation; however, no single model possesses a consistent and precise prediction ability. Therefore, the combined application of the two models is necessary for comprehensive and dynamic evaluation, but the clinical application is relatively complex. A simplified prognostic model and a risk assessment model will be required in the future to improve patient prognosis as well as the effectiveness and efficiency of liver transplantation.

Clinical Presentation and Outcomes of Kawasaki Disease in Children from Latin America: A Multicenter Observational Study from the REKAMLATINA Network

To describe the clinical presentation, management, and outcomes of Kawasaki disease (KD) in Latin America and to evaluate early prognostic indicators of coronary artery aneurysm (CAA). An observational KD registry-based study was conducted in 64 participating pediatric centers across 19 Latin American countries retrospectively between January 1, 2009, and December 31, 2013, and prospectively from June 1, 2014, to May 31, 2017. Demographic and initial clinical and laboratory data were collected. Logistic regression incorporating clinical factors and maximum coronary artery z-score at initial presentation (between 10days before and 5days after intravenous immunoglobulin [IVIG]) was used to develop a prognostic model for CAA during follow-up (>5days after IVIG). Of 1853 patients with KD, delayed admission (>10days after fever onset) occurred in 16%, 25% had incomplete KD, and 11% were resistant to IVIG. Among 671 subjects with reported coronary artery z-score during follow-up (median: 79days; IQR: 36, 186), 21% had CAA, including 4% with giant aneurysms. A simple prognostic model utilizing only a maximum coronary artery z-score ≥2.5 at initial presentation was optimal to predict CAA during follow-up (area under the curve: 0.84; 95% CI: 0.80, 0.88). From our Latin American population, coronary artery z-score ≥2.5 at initial presentation was the most important prognostic factor preceding CAA during follow-up. These results highlight the importanceof early echocardiography during the initial presentation of KD.

A simple mortality prediction model for sepsis patients in intensive care.

Sepsis is common in the intensive care unit (ICU). Two of the ICU's most widely used mortality prediction models are the Simplified Acute Physiology Score 3 (SAPS-3) and the Sequential Organ Failure Assessment (SOFA) score. We aimed to assess the mortality prediction performance of SAPS-3 and SOFA upon ICU admission for sepsis and find a simpler mortality prediction model for these patients to be used in clinical practice and when conducting studies. A retrospective study of adult patients fulfilling the Sepsis-3 criteria admitted to four general ICUs was performed. A simple prognostic model was created using backward stepwise multivariate logistic regression. The area under the curve (AUC) of SAPS-3, SOFA and the simple model was assessed. One thousand nine hundred eighty four admissions were included. A simple six-parameter model consisting of age, immunosuppression, Glasgow Coma Scale, body temperature, C-reactive protein and bilirubin had an AUC of 0.72 (95% confidence interval (CI) 0.69-0.75) for 30-day mortality, which was non-inferior to SAPS-3 (AUC 0.75, 95% CI 0.72-0.77) (p = 0.071). SOFA had an AUC of 0.67 (95% CI 0.64-0.70) and was inferior to SAPS-3 (p < 0.001) and our simple model (p = 0.0019). SAPS-3 has a lower prognostic value in sepsis than in the general ICU population. SOFA performs less well than SAPS-3. Our simple six-parameter model predicts mortality just as well as SAPS-3 upon ICU admission for sepsis, allowing the design of simple studies and performance monitoring.

The emerging potential of quantitative MRI biomarkers for the early prediction of brain metastasis response after stereotactic radiosurgery: a scoping review.

At present, the simple prognostic models based on clinical information for predicting the treatment outcomes of brain metastases (BMs) are subjective and delayed. Thus, we performed this systematic review of multiple studies to assess the potential of quantitative magnetic resonance imaging (MRI) biomarkers for the early prediction of treatment outcomes of brain metastases with stereotactic radiosurgery (SRS). We systematically searched the PubMed, Embase, Cochrane, Web of Science, and Clinical Trials.gov databases for articles published between February 1, 1991, and April 11, 2022, with no language restrictions. We included studies involving patients with BMs receiving SRS; the included patients were required to have definite pathology of a primary tumor and complete imaging data (pre- and post-SRS). We excluded the articles that included patients who had undergone previous surgery and those that did not include regular follow-up or corresponding MRI scans. We identified 2,162 studies, of which 26 were included in our analysis, involving a total of 1,362 participants. All 26 studies explored the relevant MRI parameters to predict the prognosis of patients with BMs who received SRS. The outcomes were generalized according to the relationships between the anatomical/morphological, microstructural, vascular, and metabolic changes and SRS. Generally, with traditional MRI, there are several quantitative prognostic models based on preradiosurgical radiomics that predict the outcome of SRS treatment in local BM control. With the implementation of advanced MRI, the relative apparent diffusion coefficient (ADC), perfusion fraction (f), relative cerebral blood volume (rCBV), relative regional cerebral blood flow (rrCBF), interstitial fluid pressure (IFP), quadratic of time-dependent leakage (Ktrans 2), extracellular extravascular volume (ve), choline/creatine (Cho/Cr), nuclear Overhauser effect (NOE) peak, and intraextracellular water exchange rate constant (kIE ) were confirmed to be indicative of the therapeutic effect of SRS for BMs. Quantitative MRI biomarkers extracted from traditional or advanced MRI at different time points, which can represent the anatomical/morphological, microstructural, vascular, and metabolic changes, respectively, have been proposed as promising markers for the early prediction of SRS response in those with BMs. There are some limitations in this review, including the risk of selection bias, the limited number of study objects, the incomparability of the total data, and the subjectivity of the review process.

External validation of a simplified prognostic model for survival in patients with extrinsic malignant ureteral obstruction treated with tandem ureteral stents – a retrospective cohort study

Introduction Prognostic models of survival can identify patients with extrinsic malignant ureteral obstruction who will benefit from long-term drainage as offered by tandem ureteral stents. The study aims to validate a simplified prognostic model published by Cordeiro et al. and to identify additional prognostic predictors in a cohort of patients drained solely with tandem ureteral stents. Methods Medical records of consecutive patients who underwent drainage of malignant ureteral obstruction with tandem ureteral stents between 2007 and 2020 were reviewed retrospectively; patients with benign ureteral obstruction were excluded. Risk factors for survival included were: [1] the number of malignancy-related events (categorized as ≥4 and <4) and [2] the Eastern Cooperative Oncology Group Index (categorized as ≥2 and <2)]. Patients with ≥1 risk factor were grouped as intermediate-unfavorable risk and those without risk factors as favorable risk. The Kaplan–Meier and log-rank tests were used for survival analysis. Univariable and multivariable Cox regression analyses were used to identify predictors of outcome. Results The study cohort consisted of 65 patients; the median age was 60 years (IQR 51–72). The median follow-up time from diagnosis of hydronephrosis was 51 months (IQR 38–64). Estimated probabilities of survival at 1 month, 6 months 1 year, and 2 years were 100%, 87%, 75% and 57%, respectively in the favorable risk group (n = 40), and in the intermediate-unfavorable risk group (n = 25), 96%, 72%, 52%, and 20%, respectively, (p = .003). On multivariable analysis, the presence of ≥4 malignancy-related events (HR = 2.04, 95% CI [1.07–3.86], p = .03) and lung metastasis (HR = 2.37, 95% CI [1.0–5.6], p = .05) were associated with shorter survival. Conclusions Our findings validate the prognostic model published by Cordeiro et al. The model can be applied when counseling patients being considered for drainage with tandem ureteral stents.

Lung Immune Therapy Evaluation (LITE) Risk, a Novel Prognostic Model for Patients With Advanced Non-Small Cell Lung Cancer Treated With Immune Checkpoint Blockade

Introduction/BackgroundImmune checkpoint inhibitors (ICI) have revolutionized non-small cell lung cancer (NSCLC). We aimed to identify baseline characteristics, that are prognostic factors for overall survival (OS) in patients with NSCLC treated with ICI monotherapy, in order to derive the Lung Immune Therapy Evaluation (LITE) risk, a prognostic model. Materials and MethodsMulti-center observational cohort study of patients with advanced NSCLC that received ≥1 dose of ICI monotherapy. The training set (n=342) consisted of patients with NSCLC who received first line ICI. The test set (n=153) used for external validation was a discrete cohort of patients who received second line ICI. 20 candidate prognostic factors were examined. Penalized Cox regression was used for variable selection. Multiple imputation was used to address missingness. ResultsThree baseline characteristics populated the final model: ECOG (0, 1 or ≥2), lactate dehydrogenase>upper limit of normal, and derived neutrophil to lymphocyte ratio ≥3. Patients were parsed into 3 risk groups; favorable (n=146, risk score 0-1), intermediate (n=101, risk score 2) and poor (n=95, risk score ≥3). The c-statistic of the training cohort was 0.702 and 0.694 after bootstrapping. The test cohort c-statistic was 0.664.The median OS for favorable, intermediate and poor LITE risk were; 28.3 months, 9.1 months and 2.1 months respectively. Improving LITE risk group was associated with improved OS, intermediate vs favorable HR 2.08 (95%CI 1.46-2.97, P < .001); poor vs favorable HR 5.21 (95%CI 3.69-7.34, P < .001). ConclusionA simple prognostic model, utilizing accessible clinical data, can discriminate survival outcomes in patients with advanced NSCLC.

Simplified Prognostic Model for Predicting the First 15 Days Mortality in Alcoholic Acute On Chronic Liver Failure (ACLF) Patients

Simplified Prognostic Model for Predicting the First 15 Days Mortality in Alcoholic Acute On Chronic Liver Failure (ACLF) Patients

Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) is currently one of the most life-threatening diseases worldwide. However, the factors, genes, and processes involved in the mechanisms of HCC initiation, development, and metastasis remain to be identified.Methods: WNT signalling pathways may play important roles in cancer initiation and progression. Thus, it would be informative to construct a WNT signature-based gene model for the prognosis of HCC and the prediction of therapeutic efficacy. We curated genomic profiles for HCC from The Cancer Genome Atlas (TCGA) and divided them into training and internal validation datasets. We also used samples from GSE14520 and HCCDB18 as validation datasets and clustered them by ConsensusClusterPlus analysis. We applied WebGestaltR to the WNT score-associated differentially expressed genes (DEGs) and conducted a signalling pathway enrichment analysis. We assessed the tumour immune microenvironment with ESTIMATE, Microenvironment Cell Populations (MCP)-counter, single-sample gene set enrichment analysis (ssGSEA), and tumour immune dysfunction and exclusion (TIDE).Results: We performed a least absolute shrinkage and selection operator (LASSO) regression analysis to identify the prognosis-related hub genes, identified the risk and protective factor genes associated with HCC, classified them into two clusters, and found that Cluster 2 had a significantly better prognosis than Cluster 1. Moreover, the latter had advanced clinical features compared with the former. Uridine-cytosine kinase 1 (UCK1), myristoylated alanine-rich C-kinase substrate-like protein 1 (MARCKSL1), P-antigen family member 1 (PAGE1), and killer cell lectin-like receptor B1 (KLRB1) were detected and used to construct a simplified prognostic model for HCC. The high risk score subgroup showed a poorer prognosis than the low risk score subgroup, and the model assessed HCC prognosis consistently and effectively.Conclusions: The WNT score-related gene-based model designed and evaluated herein had strong prognostic and predictive ability for HCC and could, therefore, facilitate decision-making in the prognosis and therapeutic efficacy assessment of HCC.

An improved prognostic model for predicting the mortality of critically ill patients: a retrospective cohort study

A simple prognostic model is needed for ICU patients. This study aimed to construct a modified prognostic model using easy-to-use indexes for prediction of the 28-day mortality of critically ill patients. Clinical information of ICU patients included in the Medical Information Mart for Intensive Care III (MIMIC-III) database were collected. After identifying independent risk factors for 28-day mortality, an improved mortality prediction model (mionl-MEWS) was constructed with multivariate logistic regression. We evaluated the predictive performance of mionl-MEWS using area under the receiver operating characteristic curve (AUROC), internal validation and fivefold cross validation. A nomogram was used for rapid calculation of predicted risks. A total of 51,121 patients were included with 34,081 patients in the development cohort and 17,040 patients in the validation cohort (17,040 patients). Six predictors, including Modified Early Warning Score, neutrophil-to-lymphocyte ratio, lactate, international normalized ratio, osmolarity level and metastatic cancer were integrated to construct the mionl-MEWS model with AUROC of 0.717 and 0.908 for the development and validation cohorts respectively. The mionl-MEWS model showed good validation capacities with clinical utility. The developed mionl-MEWS model yielded good predictive value for prediction of 28-day mortality in critically ill patients for assisting decision-making in ICU patients.

Prognostic model of immune checkpoint inhibitors combined with anti-angiogenic agents in unresectable hepatocellular carcinoma.

The combination of immune checkpoint inhibitors (ICIs) and anti-angiogenic agents has shown promising efficacy in unresectable hepatocellular carcinoma (HCC), but until now no clinical prognostic models or predictive biomarkers have been established. From 2016 to 2021, a total of 258 HCCs treated with ICIs and tyrosine kinase inhibitors (TKIs) were retrospectively enrolled, as the study cohort. Patients' baseline data was extracted by least absolute and shrinkage selection operator (LASSO) and Cox regression. Finally, a prognostic model in the form of nomogram was developed. Model performance was assessed in terms of discrimination, calibration, and clinical utility. A 5-fold cross-validation was used to evaluate the internal repeatability of the model. In addition, the patient cohort was divided into three subgroups according to nomogram scores. Their survivals were estimated by Kaplan-Meier methods and the differences were analyzed using log-rank tests. Seven clinical parameters were selected: Eastern Cooperative Oncology Group performance status (ECOG PS), combination of transarterial chemoembolization (TACE), extrahepatic metastasis (EHM), platelet to lymphocyte ratio (PLR), alanine aminotransferase (ALT), alpha-fetoprotein (AFP), and Child-Pugh score. The model had an area under the curve (AUC) of 0.777 at 1 year and 0.772 at 2 years. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) showed that the discrimination, consistency and applicability of the model were good. In addition, cross-validation validated the discrimination of the model, and the C index value of the model is 0.7405. The median overall survival (OS) of the high-, medium- and low-risk subgroups was 7.58, 17.50 and 53.17 months, respectively, with a significant difference between the groups (P < 0.0001). We developed a comprehensive and simple prognostic model for the combination of ICIs plus TKIs. And it may predict the efficacy of the combination regimen for unresectable HCC.

Prediction of Mortality in Coronary Artery Disease: Role of Machine Learning and Maximal Exercise Capacity

ObjectiveTo develop a prediction model for survival of patients with coronary artery disease (CAD) using health conditions beyond cardiovascular risk factors, including maximal exercise capacity, through the application of machine learning (ML) techniques. MethodsAnalysis of data from a retrospective cohort linking clinical, administrative, and vital status databases from 1995 to 2016 was performed. Inclusion criteria were age 18 years or older, diagnosis of CAD, referral to a cardiac rehabilitation program, and available baseline exercise test results. Primary outcome was death from any cause. Feature selection was performed using supervised and unsupervised ML techniques. The final prognostic model used the survival tree (ST) algorithm. ResultsFrom the cohort of 13,362 patients (60±11 years; 2400 [18%] women), 1577 died during a median follow-up of 8 years (interquartile range, 4 to 13 years), with an estimated survival of 67% up to 21 years. Feature selection revealed age and peak metabolic equivalents (METs) as the features with the greatest importance for mortality prediction. Using these 2 features, the ST generated a long-term prediction with a C-index of 0.729 by splitting patients in 8 clusters with different survival probabilities (P<.001). The ST root node was split by peak METs of 6.15 or less or more than 6.15, and each patient’s subgroup was further split by age or other peak METs cut points. ConclusionApplying ML techniques, age and maximal exercise capacity accurately predict mortality in patients with CAD and outperform variables commonly used for decision-making in clinical practice. A novel and simple prognostic model was established, and maximal exercise capacity was further suggested to be one of the most powerful predictors of mortality in CAD.