Prognostic Accuracy Research Articles

Social determinants of health and outcome disparities in spine tumor surgery. Part 1: An analysis of 6.6 million nationwide admissions.

Earlier research has demonstrated that social determinants of health (SDoH) impact neurosurgical access and outcomes, but these trends are less characterized for spine tumors relative to intracranial tumors. The authors aimed to elucidate the association between SDoH and outcomes for a nationwide cohort of spine tumor surgery admissions. The authors identified all admissions with a spine tumor diagnosis in the National Inpatient Sample (NIS) from 2002 to 2019. Four SDoH were analyzed: race and ethnicity, insurance, household income, and safety-net hospital (SNH) treatment. Hospitals in the top quartile of safety-net burden (in terms of percentage of patients receiving Medicaid or uninsured) were categorized as SNHs. Multivariable regression queried the association between 22 variables and 5 perioperative outcomes: mortality, discharge disposition, complications, length of stay (LOS), and hospitalization costs. Interaction term analysis with hospitalization year was used to assess longitudinal changes in outcome disparities. Finally, the authors constructed random forest machine learning models to assess the impact of SDoH variables on prognostic accuracy and to quantify the relative importance of predictors for disposition. Of 6,593,392 total admissions with spine tumors, 219,380 (3.3%) underwent surgery. Non-White race (OR 0.80-0.91, p < 0.001) and nonprivate insurance (OR 0.76-0.83, p < 0.001) were associated with lower odds of receiving surgery. Among surgical admissions, presenting severity, including of myelopathy and plegia, was elevated among non-White, nonprivate insurance, and low-income admissions (all p < 0.001). Black race (OR 0.70, p < 0.001), Medicare (OR 0.70, p < 0.001), Medicaid (OR 0.90, p < 0.001), and lower income (OR 0.88-0.93, all p < 0.001) were associated with decreased odds of favorable discharge disposition. Increased LOS and costs were observed among non-White (+6%-10% in LOS and +5%-9% in costs, both p < 0.001) and Medicaid (+16% in LOS and +6% in costs, both p < 0.001) admissions. SNH treatment was also associated with higher mortality (OR 1.49, p < 0.001) and complication (OR 1.20, p < 0.001) rates. From 2002 to 2019, disposition improved annually for Medicaid patients (OR 1.03 per year, p = 0.022) but worsened for Black patients (OR 0.98 per year, p = 0.046). Random forest models identified household income as the most important predictor of discharge disposition. For spine tumor admissions, SDoH predicted surgical intervention, presenting severity, and perioperative outcomes. Over 2 decades, disparities improved for Medicaid patients but worsened for Black patients. Finally, SDoH significantly improve prognostic accuracy for outcomes after spine tumor surgery. Further study toward ameliorating patient disparities for this population is warranted.

Prognostic Equations and Accuracy of a Total Score of Functional Independence Measure at Discharge for Different Diseases in a Convalescent Rehabilitation Ward.

Prognosisand goal setting from admission in the convalescent rehabilitation ward, supported by a multidisciplinary team, enhance rehabilitation and discharge support. Predicting functional independence measure (FIM) outcomes can further optimize these processes. This study aimed to develop prognostic equations for the motor FIMat discharge for stroke, hip fracture (HF), vertebral compression fractures (VCFs), and total knee arthroplasty (TKA), which are common diseases in patients admitted to convalescent rehabilitation wards, using multiple regression analysis, and to clarify the difference in the accuracy of the predicted motor FIM according to the disease. This study included 965 patients admitted to our hospital. The objective variable consists of the motor FIM at discharge, and the explanatory variables were age, sex, days from onset to admission, total admission motor FIM, and total admission cognitive FIM. A stepwise multiple regression analysis was performed. The analysis of the difference in the accuracy of predicted motor FIM by disease used the absolute value of the residuals. The total motor FIM and cognitive FIM at admission were extracted for all four diseases included in this study. The absolute value of the residuals appeared to be more accurate for TKA, HF, stroke, and VCF in that order. Although differences in the accuracy of the prediction equation were observed by disease, this prediction equation can be used as an approach to review the details of rehabilitation and discharge and can be tailored to each case.

Risk Stratification Tools to Aid Decisions on Adjuvant Chemotherapy Usage in Resected Soft Tissue Sarcomas: A Ten-Year Review of an Irish Sarcoma Center Experience.

Soft tissue sarcoma (STS) is comprised of approximately 80 subtypes, with an incidence of 4 - 5 per 100,000 annually in Europe. The National Comprehensive Cancer Network (NCCN) guidelines recommend consideration of neoadjuvant/adjuvant chemotherapy in tumors at high risk of recurrence based on the American Joint Committee on Cancer (AJCC) staging. Alternatively, the Sarculator is a risk prediction tool that has identified a threshold of risk, above which chemotherapy may provide an overall survival (OS) benefit. Using this nomogram, patients with a 10-year predicted OS < 60% are classified as high risk and should be considered for chemotherapy. The aim of this study was to assess the prognostic accuracy of these two risk prediction methods in an Irish population. All newly diagnosed patients with resected STS discussed in the STS tumor board in Cork University Hospital between January 2012 and December 2021 were identified. Clinicopathological data were collected. Risk assessment using AJCC and Sarculator nomogram was performed on all patients with an extremity/trunk sarcoma. The OS was calculated including Kaplan-Meier method for time to event analysis. In total, 200 STS patients were reviewed, of whom 134 had truncal or extremity tumors. Sarculator score was calculated for 60 of these (well differentiated liposarcomas, desmoid tumors and dermatofibrosarcoma protuberans were excluded). Using the Sarculator nomogram to calculate 10-year predicted OS, 19 patients were categorized as high risk and 41 were categorized as low risk. Using AJCC staging, 25 patients were categorized as high risk and 35 as low risk. The 5-year OS rate in the Sarculator high-risk group was 60.2%, compared with 87.1% in the low-risk group (P = 0.009). The 5-year OS rate in the AJCC high-risk group was 67.6%, compared with 86.3% in the low-risk group (P = 0.083). Our cohort is representative of the broad histological subtypes expected. In our population, Sarculator score results correlate with international outcomes and higher scores were associated with increased mortality. The Sarculator was more predictive of clinical outcome than AJCC staging, and its use would lower the proportion of patients being considered for adjuvant chemotherapy thereby sparing toxicity, which is important in the setting of uncertain clinical benefit.

Clinical utility of diffusion MRI-derived measures of cortical microstructure in a real-world memory clinic setting.

To investigate cortical microstructural measures from diffusion MRI as "neurodegeneration" markers that could improve prognostic accuracy in mild cognitive impairment (MCI). The prognostic power of Amyloid/Tau/Neurodegeneration (ATN) biomarkers to predict progression from MCI to AD or non-AD dementia was investigated. Ninety patients underwent clinical evaluation (follow-up interval 32 ± 18 months), lumbar puncture, and MRI. Participants were grouped by clinical stage and cerebrospinal fluid Amyloid and Tau status. T1-structural and diffusion MRI scans were analyzed to calculate diffusion metrics related to cortical columnar structure (AngleR, ParlPD, PerpPD+), cortical mean diffusivity, and fractional anisotropy. Statistical tests were corrected for multiple comparisons. Prognostic power was assessed using receiver operating characteristic (ROC) analysis and related indices. A progressive increase of whole-brain cortical diffusion values was observed along the AD continuum, with all A+ groups showing significantly higher AngleR than A-T-. Investigating clinical progression to dementia, the AT biomarkers together showed good positive predictive value (with 90.91% of MCI A+T+ converting to dementia) but poor negative predictive value (with 40% of MCI A-T- progressing to a mix of AD and non-AD dementias). Adding whole-brain AngleR as an N marker, produced good differentiation between stable and converting MCI A-T- patients (0.8 area under ROC curve) and substantially improved negative predictive value (+21.25%). Results support the clinical utility of cortical microstructure to aid prognosis, especially in A-T- patients. Further work will investigate other complexities of the real-world clinical setting, including A-T+ groups. Diffusion MRI measures of neurodegeneration may complement fluid AT markers to support clinical decision-making.

Artificial intelligence and nonoperating room anesthesia.

The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient selection, perioperative care, and anesthesia delivery. This review examines AI's growing impact on NORA and how it can optimize our clinical practice in the near future. AI has already improved various aspects of anesthesia, including preoperative assessment, intraoperative management, and postoperative care. Studies highlight AI's role in patient risk stratification, real-time decision support, and predictive modeling for patient outcomes. Notably, AI applications can be used to target patients at risk of complications, alert clinicians to the upcoming occurrence of an intraoperative adverse event such as hypotension or hypoxemia, or predict their tolerance of anesthesia after the procedure. Despite these advances, challenges persist, including ethical considerations, algorithmic bias, data security, and the need for transparent decision-making processes within AI systems. The findings underscore the substantial benefits of AI in NORA, which include improved safety, efficiency, and personalized care. AI's predictive capabilities in assessing hypoxemia risk and other perioperative events, have demonstrated potential to exceed human prognostic accuracy. The implications of these findings advocate for a careful yet progressive adoption of AI in clinical practice, encouraging the development of robust ethical guidelines, continual professional training, and comprehensive data management strategies. Furthermore, AI's role in anesthesia underscores the need for multidisciplinary research to address the limitations and fully leverage AI's capabilities for patient-centered anesthesia care.

Outcomes Measures in Subarachnoid Hemorrhage Research.

Despite advancements in acute management, morbidity rates for subarachnoid hemorrhage (SAH) remain high. Therefore, it is imperative to utilize standardized outcome scales in SAH research for evaluating new therapies effectively. This review offers a comprehensive overview of prevalent scales and clinical outcomes used in SAH assessment, accompanied by recommendations for their application and prognostic accuracy. Standardized terminology and diagnostic criteria should be employed when reporting pathophysiological outcomes such as symptomatic vasospasm and delayed cerebral ischemia. Furthermore, integrating clinical severity scales like the World Federation of Neurosurgical Societies scale and modified Fisher score into clinical trials is advised to evaluate their prognostic significance, despite their limited correlation with outcomes. The modified Rankin score is widely used for assessing functional outcomes, while the Glasgow outcome scale-extended version is suitable for broader social and behavioral evaluations. Avoiding score dichotomization is crucial to retain valuable information. Cognitive and behavioral outcomes, though frequently affected in patients with favorable neurological outcomes, are often overlooked during follow-up outpatient visits, despite their significant impact on quality of life. Comprehensive neuropsychological evaluations conducted by trained professionals are recommended for characterizing cognitive function, with the Montreal Cognitive Assessment serving as a viable screening tool. Additionally, integrating psychological inventories like the Beck Depression and Anxiety Inventory, along with quality-of-life scales such as the Stroke-Specific Quality of Life Scale, can effectively assess behavioral and quality of life outcomes in SAH studies.

Growth differentiation factor-15 predicts all-cause death and major adverse cardiovascular events in patients with coronary heart disease: a prospective cohort study.

The prognostic value of growth differentiation factor-15 (GDF-15) in predicting long-term adverse outcomes in coronary heart disease (CHD) patients remains limited. Our study examines the association between GDF-15 and adverse outcomes over an extended period in CHD patients and firstly assesses the incremental prognostic effect of incorporating GDF-15 into the Framingham risk score (FRS)-based model. This single-center prospective cohort study included 3,321 patients with CHD categorized into 2,479 acute coronary syndrome (ACS) (74.6%) and 842 non-ACS (25.4%) groups. The median age was 61.0 years (range: 53.0-70.0), and 917 (27.6%) were females. Mortality and major adverse cardiovascular events (MACEs) included cardiovascular mortality, myocardial infarction (MI), stroke, and heart failure (HF) (inclusive of HF episodes requiring outpatient treatment and/or hospital admission). Cox regression models assessed the associations between GDF-15 and the incidence of all-cause mortality and MACEs. Patients were stratified into three groups based on GDF-15 levels: the first tertile group (< 1,370 ng/L), the second tertile group (1,370-2,556 ng/L), and the third tertile group (> 2,556 ng/L). The C-index, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA) were used to assess incremental value. Over a median 9.4-year follow-up, 759 patients (22.9%) died, and 1,291 (38.9%) experienced MACEs. The multivariate Cox model indicated that GDF-15 was significantly associated with all-cause mortality (per ln unit increase, HR = 1.49, 95% CI: 1.36-1.64) and MACEs (per ln unit increase, HR = 1.29, 95% CI: 1.20-1.38). These associations persisted when GDF-15 was analyzed as an ordinal variable (p for trend < 0.05). Subgroup analysis of ACS and non-ACS for the components of MACEs separately showed a significant association between GDF-15 and both cardiovascular mortality and HF, but no association was observed between GDF-15 and MI /stroke in both ACS and non-ACS patients. The addition of GDF-15 to the FRS-based model enhanced the discrimination for both all-cause mortality (∆ C-index = 0.009, 95% CI: 0.005-0.014; IDI = 0.030, 95% CI: 0.015-0.047; continuous NRI = 0.631, 95% CI: 0.569-0.652) and MACEs (∆ C-index = 0.009, 95% CI: 0.006-0.012; IDI = 0.026, 95% CI: 0.009-0.042; continuous NRI = 0.593, 95% CI: 0.478-0.682). DCA suggested that incorporating GDF-15 into the FRS-based model demonstrated higher net benefits compared to FRS-based models alone (All-cause mortality: FRS-based model: area under the curve of DCA (AUDC) = 0.0903, FRS-based model + GDF-15: AUDC = 0.0908; MACEs: FRS-based model: AUDC = 0.1806, FRS-based model + GDF-15: AUDC = 0.1833). GDF-15 significantly associates with the long-term prognosis of all-cause mortality and MACEs in CHD patients and significantly improves the prognostic accuracy of the FRS-based model for both outcomes.

Assessing outcomes in traumatic brain injury: Helsinki score versus Glasgow coma scale.

The precision of assessment and prognosis in traumatic brain injury (TBI) is paramount for effective triage and informed therapeutic strategies. While the Glasgow Coma Scale (GCS) remains the cornerstone for TBI evaluation, it overlooks critical primary imaging findings. The Helsinki Score (HS), a novel tool designed to incorporate radiological data, offers a promising approach to predicting TBI outcomes. This study aims to evaluate the prognostic efficacy of HS in comparison to GCS across a substantial TBI patient cohort. This retrospective study encompassed TBI patients treated at our institution between 2008 and 2019, specifically those with an admission GCS of 14 or lower. We assessed both the initial GCS and the HS derived from primary CT scans. Key outcome metrics included the Glasgow Outcome Scale (GOS) and mortality rates at hospital discharge and at 6 and 12-month intervals post-discharge. Predictive performances of GCS and HS were analyzed through Receiver Operating Characteristic (ROC) curves and Kendall tau-b correlation coefficients against each outcome. The study included 544 patients, with an average age of 62.2 ± 21.5 years, median initial GCS of 14, and a median HS of 3. The mortality rate at discharge stood at 8.6%, with a median GOS of 4. Both GCS and HS demonstrated significant correlations with mortality and GOS outcomes (p < 0.05). Notably, HS showed a markedly superior correlation with mortality (τb = 0.36) compared to GCS (τb = -0.11) and with GOS outcomes (τb = -0.40 for HS vs. τb = 0.33 for GCS). ROC analyses affirmed HS's enhanced predictive accuracy over GCS for both mortality (AUC of 0.79 for HS vs. 0.62 for GCS) and overall outcomes (AUC of 0.77 for HS vs. 0.71 for GCS). The findings validate the HS in a large German cohort and suggest that radiological assessments alone, as exemplified by HS, can surpass the traditional GCS in predicting TBI outcomes. However, the HS, despite its efficacy, lacks the integration of clinical evaluation, a vital component in TBI management. This underscores the necessity for a holistic approach that amalgamates both radiological and clinical insights for a more comprehensive and accurate prognostication in TBI care.

15-Gene Expression Profile and PRAME as Integrated Prognostic Test for Uveal Melanoma: First Report of Collaborative Ocular Oncology Group Study No. 2 (COOG2.1).

Validated and accurate prognostic testing is critical for precision medicine in uveal melanoma (UM). Our aims were to (1) prospectively validate an integrated prognostic classifier combining a 15-gene expression profile (15-GEP) and PRAME RNA expression and (2) identify clinical variables that enhance the prognostic accuracy of the 15-GEP/PRAME classifier. This study included 1,577 patients with UM of the choroid and/or ciliary body who were enrolled in the Collaborative Ocular Oncology Group Study Number 2 (COOG2) and prospectively monitored across 26 North American centers. Test results for 15-GEP (class 1 or class 2) and PRAME expression status (negative or positive) were available for all patients. The primary end point was metastasis-free survival (MFS). 15-GEP was class 1 in 1,082 (68.6%) and class 2 in 495 (31.4%) patients. PRAME status was negative in 1,106 (70.1%) and positive in 471 (29.9%) patients. Five-year MFS was 95.6% (95% CI, 93.9 to 97.4) for class 1/PRAME(-), 80.6% (95% CI, 73.9 to 87.9) for class 1/PRAME(+), 58.3% (95% CI, 51.1 to 66.4) for class 2/PRAME(-), and 44.8% (95% CI, 37.9 to 52.8) for class 2/PRAME(+). By multivariable Cox proportional hazards analysis, 15-GEP was the most important independent predictor of MFS (hazard ratio [HR], 5.95 [95% CI, 4.43 to 7.99]; P < .001), followed by PRAME status (HR, 1.82 [95% CI, 1.42 to 2.33]; P < .001). The only clinical variable demonstrating additional prognostic value was tumor diameter. In the largest prospective multicenter prognostic biomarker study performed to date in UM to our knowledge, the COOG2 study validated the superior prognostic accuracy of the integrated 15-GEP/PRAME classifier over 15-GEP alone and clinical prognostic variables. Tumor diameter was found to be the only clinical variable to provide additional prognostic information. This prognostic classifier provides an advanced resource for risk-adjusted metastatic surveillance and adjuvant trial stratification in patients with UM.

Prognostic Performance of the Maximal Systolic Acceleration at the Hallux in Healing of Foot Ulcers: A Retrospective Cohort Study

The accurate prediction of foot ulcer healing remains a major challenge in clinical practice. To date, no reliable bedside tests are available. The primary aim of this study was to determine the prognostic performance of the maximal systolic acceleration (ACCmax) to predict ulcer healing. Secondary objectives comprised the investigation of the prognostic accuracy in patients prone to medial arterial calcification and to assess the potential risk of amputation. A single-center retrospective cohort study was conducted. Patients aged ≥18 years who presented with a new-onset ulcer (i.e. Fontaine IV and neuropathic ulcers) on the foot and underwent an ACCmax measurement at the hallux were included. Ulcer healing was defined as an intact skin with epithelialization after 3 or 12months of follow-up. Prognostic performance was calculated by using sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR). In total, 136 patients with 143 wounds were included. Almost half of the patients were diagnosed with diabetes mellitus (47%), and wound infection was present in 42% of cases. After 3months of follow-up, an NPV of 97.9%, PLR of 3.25, and NLR of 0.19 were found when applying an ACCmax threshold of 0.5m/s2. When looking at 12months, these numbers were 85.6%, 2.72, and 0.50, respectively. Subgroup analysis for patients with diabetes mellitus and chronic kidney disease showed comparable results. The risk of amputation increased significantly when a measurement below 1.0m/s2 was present (odd ratio 5.3, P=0.010). ACCmax measurements at the hallux can have additional prognostic value in patients with foot ulcers. An ACCmax below 1.0m/s2 is associated with nonhealing of an ulcer and a higher risk of amputation, while higher ACCmax values are associated with limb salvage. Therefore, ACCmax could be used for grading ischemia in a wound classification system.

Using Multimodal Assessments to Reevaluate Depression Designations for Spine Surgery Candidates.

Depression is common in spine surgery candidates and may influence postoperative outcomes. Ecological momentary assessments (EMAs) can overcome limitations of existing depression screening methods (e.g., recall bias, inaccuracy of historical diagnoses) by longitudinally monitoring depression symptoms in daily life. In this study, we compared EMA-based depression assessment with retrospective self-report (a 9-item Patient Health Questionnaire [PHQ-9]) and chart-based depression diagnosis in lumbar spine surgery candidates. We further examined the associations of each depression assessment method with surgical outcomes. Adult patients undergoing lumbar spine surgery (n = 122) completed EMAs quantifying depressive symptoms up to 5 times daily for 3 weeks preoperatively. Correlations (rank-biserial or Spearman) among EMA means, a chart-based depression history, and 1-time preoperative depression surveys (PHQ-9 and Psychache Scale) were analyzed. Confirmatory factor analysis was used to categorize PHQ-9 questions as somatic or non-somatic; subscores were compared with a propensity score-matched general population cohort. The associations of each screening modality with 6-month surgical outcomes (pain, disability, physical function, pain interference) were analyzed with multivariable regression. The association between EMA Depression scores and a depression history was weak (rrb = 0.34 [95% confidence interval (CI), 0.14 to 0.52]). Moderate correlations with EMA-measured depression symptoms were observed for the PHQ-9 (rs = 0.51 [95% CI, 0.37 to 0.63]) and the Psychache Scale (rs = 0.68 [95% CI, 0.57 to 0.76]). Compared with the matched general population cohort, spine surgery candidates endorsed similar non-somatic symptoms but significantly greater somatic symptoms on the PHQ-9. EMA Depression scores had a stronger association with 6-month surgical outcomes than the other depression screening modalities did. A history of depression in the medical record is not a reliable indication of preoperative depression symptom severity. Cross-sectional depression assessments such as PHQ-9 have stronger associations with daily depression symptoms but may conflate somatic depression symptoms with spine-related disability. As an alternative to these methods, mobile health technology and EMAs provide an opportunity to collect real-time, longitudinal data on depression symptom severity, potentially improving prognostic accuracy. Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

The Prognostic Test Accuracy of the Short and Standard Forms of the Montreal Cognitive Assessment.

Cognitive impairment is a critical concern in stroke care, and international guidelines recommend early cognitive screening. The aim of this study was to determine the prognostic accuracy of both the short and standard forms of the Montreal Cognitive Assessment (MoCA) in predicting long-term cognitive recovery following a stroke. For this study, we used data from the Efficacy of Fluoxetine - a Randomized Controlled Trial in Stroke (EFFECTS) study, which encompassed stroke patients from 35 Swedish centers over the period from 2014 to 2019. Cognitive assessments were initially conducted at 2-15 days post-stroke, with follow-up data gathered at 6 months. We used the MoCA for objective cognitive evaluation. For assessing subjective cognitive impairment, we used the memory and thinking domain of the Stroke Impact Scale. For psychometric evaluation of the short Swedish version of MoCA (s-MoCA-SWE), we used cross tables and binary logistic regression. The study included 1,141 patients (62.2% men; median [interquartile range; IQR] age, 72.3 [13.2] years; median [IQR] stroke severity, 3.0 [3.0]). At baseline, the prevalence of cognitive impairment was 71.7% according to the s-MoCA-SWE (≤12) and 67.0% according to the MoCA (≤25). The s-MoCA-SWE demonstrated a sensitivity of 92.3% for correctly identifying patients with objective cognitive impairment and 81.5% for identifying those with subjective impairments at 6 months. Although the s-MoCA-SWE had higher sensitivity, the MoCA had a more balanced sensitivity and specificity in detecting both subjective and objective cognitive impairments. In both crude and multivariable models, the s-MoCA-SWE was more strongly associated than the MoCA with cognitive impairment at 6 months. Both the short and standard versions of the MoCA appear to be effective in identifying individuals likely to experience persistent cognitive issues following a stroke. Considering the limited time available in an acute stroke unit, the short-form version may be more practical. Nevertheless, further prospective studies are required to validate these findings.

Heterogeneous Pathological Changes in Liver Lobes During Liver Disease: A Perspective Review.

Liver diseases have a global prevalence of 25%, accounting for 4% of all deaths worldwide, and are associated with a 36% increased risk of fatal and nonfatal cardiovascular events. Metabolic dysfunction-associated steatotic liver disease constitutes the liver expression of metabolic syndrome and represents the primary type of liver disease. Microscopical analysis of biopsies, which allows the evaluation of a small portion of tissue with inferences made to the entire organ, is considered the gold standard for determining the presence of liver diseases. However, potential sampling errors in liver biopsies are conceivable because the obtained tissue represents only a tiny fraction of the entire liver mass and may not accurately reflect the true pathological state. Studies have demonstrated the existence of sampling errors in liver biopsies, particularly concerning the severity of inflammation, degree of fibrosis, and the presence of cirrhosis. Also, clinical studies have shown that histopathological abnormalities are better detected in humans when liver samples are collected from both the right and the left lobes. However, a gap exists in clinical investigation to clarify the role of differences between these lobes in improving the diagnostic and prognostic for liver diseases. Building upon the heterogeneous nature of pathological alterations observed in liver lobes, this perspective review provided recommendations to enhance the precision of diagnosis and prognostic accuracy of liver diseases.

Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS.

Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis. Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset. N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA. Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.

Unveiling Bladder Cancer Prognostic Insights by Integrating Patient-Matched Sample and CpG Methylation Analysis.

Bladder cancer prognosis remains a pressing clinical challenge, necessitating the identification of novel biomarkers for precise survival prediction and improved quality of life outcomes. This study proposes a comprehensive strategy to uncover key prognostic biomarkers in bladder cancer using DNA methylation analysis and extreme survival pattern observations in matched pairs of cancer and adjacent normal cells. Unlike traditional approaches that overlook cancer heterogeneity by analyzing entire samples, our methodology leverages patient-matched samples to account for this variability. Specifically, DNA methylation profiles from adjacent normal bladder tissue and bladder cancer tissue collected from the same individuals were analyzed to pinpoint critical methylation changes specific to cancer cells while mitigating confounding effects from individual genetic differences. Utilizing differential threshold settings for methylation levels within cancer-associated pathways enabled the identification of biomarkers that significantly impact patient survival. Our analysis identified distinct survival patterns associated with specific CpG sites, underscoring these sites' pivotal roles in bladder cancer outcomes. By hypothesizing and testing the influence of methylation levels on survival, we pinpointed CpG biomarkers that profoundly affect the prognosis. Notably, CpG markers, such as cg16269144 (PRKCZ), cg16624272 (PTK2), cg11304234, and cg26534425 (IL18), exhibited critical methylation thresholds that correlate with patient mortality. This study emphasizes the importance of tailored approaches to enhancing prognostic accuracy and refining therapeutic strategies for bladder cancer patients. The identified biomarkers pave the way for personalized prognostication and targeted interventions, promising advancements in bladder cancer management and patient care.

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response. Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified. This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients. Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Myelodysplastic syndrome risk assessment using priority linked correlated feature set using ResNet50

Myelodysplastic syndrome (MDS) is more common as people get older, and the best way to treat these conditions is to catch them early. Acute myeloid leukemia (AML) is a more aggressive form of MDS; about 30 % of cases advance to this disease. The key to intervening before MDS develops into AML is early identification. If doctors can diagnose MDS early, they can start treatment sooner, which could slow the disease’s progression and enhance patients’ quality of life. But because it is so similar to other conditions, MDS is frequently misdiagnosed. Early detection of MDS can let medical experts to provide better treatment, which may slow the disease’s progression and boost survival rates. Assessment of Bone Marrow (BM) histopathology is used to diagnose MDS and myeloproliferative neoplasm (MPN) by identifying dysplastic cellular shape, cellularity, and blast excess. Deep Learning (DL) algorithms can account for the genome’s numerous complicated interconnections, allowing for the creation of more accurate prognostication models. The ResNet50 network consists of 50 layers: 48 convolutional layers, 1 MaxPool layer, and 1 average pool layer. This research presents a Priority Linked Correlated Feature Set using ResNet50 (PLCFS-ResNet50) for risk assessment of MDS. The proposed model uses the feature subset for training the Resnet50 model and for risk assessment so that proper diagnosis can be done in advance. The proposed model achieves 98.6 % accuracy in feature set generation and 98.7 % accuracy in risk assessment of MDS. The proposed model when contrasted with the traditional model exhibits better prediction levels.

The Role of Speckle Tracking Echocardiography in the Evaluation of Advanced-Heart-Failure Patients.

Health care is currently showing a fall in heart failure (HF) incidence and prevalence, particularly in developed countries, but with only a subset receiving appropriate therapy to protect the heart against maladaptive processes such as fibrosis and hypertrophy. Appropriate markers of advanced HF remain unidentified, which would help in choosing the most suitable therapy and avoid major compliance problems. Speckle tracking echocardiography (STE) is a good choice, being a non-invasive imaging technique which is able to assess cardiac deformation in a variety of conditions. Several multicenter studies and meta-analyses have demonstrated the clinical application and accuracy of STE in early and late stages of HF, as well as its association with both left ventricular (LV) filling pressures and myocardial oxygen consumption. Furthermore, STE assists in assessing right ventricular free-wall longitudinal strain (RVFWLS), which is a solid predictor of right ventricle failure (RVF) following LV assist device (LVAD) implantation. However, STE is known for its limitations; despite these, it has been shown to explain symptoms and signs and also to be an accurate prognosticator. The aim of this review is to examine the advantages of STE in the early evaluation of myocardial dysfunction and its correlation with right heart catheterization (RHC) parameters, which should have significant clinical relevance in the management of HF patients.

Verification of the Prediction Accuracy of a Biomarker-Based Prognostic for Veno-Occlusive Disease/Sinusoidal Obstruction Syndrome (VOD/SOS) After Hematopoietic Cell Transplantation (HCT)

Veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is a potentially life-threatening complication of hematopoietic cell transplantation (HCT), especially in severe cases. Patient outcomes are improved with prompt treatment; however, diagnosing this disease is challenging as many clinical symptoms of VOD/SOS overlap with other post-HCT complications. A biomarker-based prognostic test for the assessment of VOD/SOS risk, termed "VODCheck" was developed in a previous study but has not yet been validated. This study aimed to validate the accuracy of VODCheck as a prognostic test for VOD/SOS in an independent cohort of patients. VODCheck incorporates hyaluronan (HA), angiopoietin-2 (ANG-2), and thrombomodulin (TM) based on their association with VOD/SOS, their analytical characteristics, and their ability to complement each other in a multivariate prognostic model. To validate VODCheck we measured plasma biomarker levels from a subset of patients enrolled in the control arm of a phase 3 study that tested VOD/SOS prophylaxis. We used a hierarchical design with prespecified primary (day 7), secondary A (day 15), and secondary B (day 1) hypotheses to verify the prognostic accuracy of VODCheck post-HCT. The cases of VOD/SOS (n = 22) were age-matched ∼1:3 with controls (n = 65). VODCheck was prognostic of VOD/SOS risk at all 3 time points with an area under the curve (AUC) of .815 (P < .001) for day 7, .836 (P < .001) for day 15, and .706 (P = .002) for day 1 post-HCT. A multivariate analysis confirmed the prognostic accuracy of VODCheck after adjustment for confounders such as age, VOD/SOS risk status, primary disease, and ozogamicin treatment. VODCheck was validated as an independent predictor of risk for VOD/SOS within 15 days post-HCT and appears to provide clinicians with actionable information to improve patient care.

Novel MAGIC composite scores using both clinical symptoms and biomarkers best predict treatment outcomes of acute GVHD

AbstractAcute graft-versus-host disease (GVHD) grading systems that use only clinical symptoms at treatment initiation such as the Minnesota risk identify standard and high-risk categories but lack a low-risk category suitable to minimize immunosuppressive strategies. We developed a new grading system that includes a low-risk stratum based on clinical symptoms alone and determined whether the incorporation of biomarkers would improve the model’s prognostic accuracy. We randomly divided 1863 patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) who were treated for GVHD into training and validation cohorts. Patients in the training cohort were divided into 14 groups based on similarity of clinical symptoms and similar nonrelapse mortality (NRM); we used a classification and regression tree (CART) algorithm to create three Manhattan risk groups that produced a significantly higher area under the receiver operating characteristic curve (AUC) for 6-month NRM than the Minnesota risk classification (0.69 vs 0.64, P = .009) in the validation cohort. We integrated serum GVHD biomarker scores with Manhattan risk using patients with available serum samples and again used a CART algorithm to establish 3 MAGIC composite scores that significantly improved prediction of NRM compared to Manhattan risk (AUC, 0.76 vs 0.70, P = .010). Each increase in MAGIC composite score also corresponded to a significant decrease in day 28 treatment response (80% vs 63% vs 30%, P < .001). We conclude that the MAGIC composite score more accurately predicts response to therapy and long-term outcomes than systems based on clinical symptoms alone and may help guide clinical decisions and trial design.