Key Clinical Variables Research Articles

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop and test an individualized machine learning model to assess risk factors and predict in-hospital mortality in in these patients. From January 2012 to December 2021, this study collected data on elderly patients with heart failure and hypertension from the Chongqing Medical University Medical Data Platform. Least absolute shrinkage and the selection operator was used for recognizing key clinical variables. The optimal predictive model was chosen among eight machine learning algorithms on the basis of area under curve. SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations was employed to interpret the outcome of the predictive model. This study ultimately comprised 4647 elderly individuals with hypertension and heart failure. The Random Forest model was chosen with the highest area under curve for 0.850 (95% CI 0.789-0.897), high accuracy for 0.738, recall 0.837, specificity 0.734 and brier score 0.178. According to SHapley Additive exPlanations results, the most related factors for in-hospital mortality in elderly patients with heart failure and hypertension were urea, length of stay, neutrophils, albumin and high-density lipoprotein cholesterol. This study developed eight machine learning models to predict in-hospital mortality in elderly patients with hypertension as well as heart failure. Compared to other algorithms, the Random Forest model performed significantly better. Our study successfully predicted in-hospital mortality and identified the factors most associated with in-hospital mortality.

Effectiveness of the Maudsley Model of Anorexia Nervosa Treatment for Adults: A systematic review.

Maudsley Model of Anorexia nervosa (AN) Treatment for Adults (MANTRA) is recommended by NICE for the treatment of adults with AN. However, despite this fact, the approach remains relatively understudied. The aim of this study was to systematically update the research evidence regarding the use of the MANTRA in the treatment of Eating Disorders (ED). The databases used were Web of Science, Scopus, and PsycInfo, including studies up to 31 May 2023. PRISMA guidelines were followed, and Cochrane tools were used to assess the risk of bias. The search focused on identifying published articles that discussed the usefulness of MANTRA as a component of treatment for ED, following PICO criteria. Nine studies spanning the period from 2011 to 2023 were included. Findings suggested that MANTRA was effective in improving body mass index (BMI), eating symptomatology and emotional state. There were generally no significant differences compared to other treatment conditions. Limitations to interpreting this systematic review include the methodological quality of included studies and the elevated risk of bias. This review was the first to examine the effectiveness of MANTRA. The results indicate that MANTRA has shown effectiveness similar to other treatments for adults AN patients in addressing key clinical variables. It has been used in different populations (adolescents, males, inpatients) and formats (group, online) However, more research is needed to determine its effectiveness.

Coronary microvascular dysfunction as assessed by cardiac MRI and its association with aerobic exercise capacity in dilated cardiomyopathy

Abstract Background Dilated cardiomyopathy (DCM) has a poor prognosis. Aerobic exercise capacity (peak VO2) is an independent predictor of mortality but the central mechanisms contributing to exercise intolerance in DCM are unknown. Purpose To characterise myocardial perfusion reserve (MPR) and determine if cardiovascular magnetic resonance (CMR) measures of structure, function and microvascular function are associated with aerobic exercise capacity in DCM. Methods Single centre, prospective, case-control comparison of adults with DCM and matched controls. Adenosine-stress perfusion CMR to assess cardiac structure, function and quantitative perfusion, and cardiopulmonary exercise testing (CPET) to determine peak VO2, was performed. Controls were propensity matched based on age, sex, body mass index and diabetes status, and between group comparison was adjusted for other key clinical characteristics (CMR: ethnicity and systolic blood pressure; CPET: ethnicity, systolic blood pressure, smoking history and lung disease). Comparison of perfusion in segments with and without late gadolinium enhancement was performed using mixed effects linear regression taking into account individual patient segmental perfusion. Pre-specified multivariable linear regression, including key clinical and cardiac variables, was undertaken in patients with DCM to identify independent associations with percentage predicted peak VO2, which incorporates age, sex, height and weight. Results Sixty-six patients with DCM (median age 61 years, 71% male) were matched to 66 controls (median age 59 years, 71% male). The DCM group had greater left ventricular (LV) end-diastolic and end-systolic volumes, lower systolic and diastolic function, and had more focal and diffuse fibrosis compared to controls (Table 1). There was lower rest (0.58±0.14 versus 0.65±0.17 mL/min/g; P=0.033) and stress (1.53±0.49 versus 2.01±0.60 mL/g/min; P<0.001) myocardial blood flow, and lower MPR (2.69±0.84 versus 3.15±0.84 mL/g/min; P=0.002) in the DCM group. In DCM patients, mixed effects linear regression demonstrated lower rest (adjusted mean 0.54(95% CI 0.50-0.57) versus 0.58(0.55-0.62) mL/g/min; P<0.001) and stress MBF (1.38(1.25-1.51) versus 1.55(1.43-1.67) mL/g/min; P<0.001) in segments with LGE compared to segments without LGE, but there was no difference in MPR (2.71(2.47-2.96) versus 2.76(2.55-2.97) mL/g/min; P=0.496). DCM patients had markedly lower peak VO2 (19.8±5.5 versus 25.2±7.3 mL/kg/min; P<0.001). Multivariable linear regression demonstrated that LV ejection fraction, extracellular volume fraction and MPR, but not LV mass, were independently associated with percentage predicted peak VO2 in DCM (R squared=0.531, P<0.001; Table 2). Conclusions DCM patients have lower rest and stress myocardial blood flow and lower MPR compared to controls. In DCM, MPR, LV ejection fraction and fibrosis are independently associated with aerobic exercise capacity.

Clinical Characteristics of Anti-Synthetase Syndrome: Analysis from the CLASS project.

Anti-synthetase syndrome (ASSD) is a rare systemic autoimmune rheumatic disease (SARD) with significant heterogeneity and no shared classification criteria. We aimed to identify clinical and serological features associated with ASSD that may be suitable for inclusion in the data-driven classification criteria for ASSD. We utilized a large, international, multi-center "Classification Criteria for Anti-synthetase Syndrome" (CLASS) project database, which includes both ASSD patients and controls with mimicking conditions, namely SARDs and/or interstitial lung disease (ILD). The local diagnoses of ASSD and controls were confirmed by project team members. We employed univariable logistic regression and multivariable Ridge regression to evaluate clinical and serological features associated with an ASSD diagnosis in a randomly selected subset of the cohort. Our analysis included 948 ASSD cases and 1077 controls. Joint, muscle, lung, skin, and cardiac involvement were more prevalent in ASSD than in controls. Specific variables associated with ASSD included arthritis, diffuse myalgia, muscle weakness, muscle enzyme elevation, ILD, mechanic's hands, secondary pulmonary hypertension due to ILD, Raynaud phenomenon, and unexplained fever. In terms of serological variables, Jo-1 and non-Jo-1 anti-synthetase autoantibodies, antinuclear antibodies with cytoplasmic pattern, and anti-Ro52 autoantibodies were associated with ASSD. In contrast, isolated arthralgia, dysphagia, electromyography/MRI/muscle biopsy findings suggestive of myopathy, inflammatory rashes, myocarditis, and pulmonary arterial hypertension did not differentiate between ASSD and controls or were inversely associated with ASSD. We identified key clinical and serological variables associated with ASSD, which will help clinicians and offer insights into the development of data-driven classification criteria for ASSD.

Automated Extraction of Stroke Severity From Unstructured Electronic Health Records Using Natural Language Processing.

Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitations of electronic health record-based research include challenges in abstracting key clinical variables, including stroke severity, along with missing data. We developed a natural language processing model that reads electronic health record notes to directly extract the National Institutes of Health Stroke Scale score when documented and predict the score from clinical documentation when missing. The study included notes from patients with acute stroke (aged ≥18 years) admitted to Massachusetts General Hospital (2015-2022). The Massachusetts General Hospital data were divided into training/holdout test (70%/30%) sets. We developed a 2-stage model to predict the admission National Institutes of Health Stroke Scale, obtained from the GWTG (Get With The Guidelines) stroke registry. We trained a model with the least absolute shrinkage and selection operator. For test notes with documented National Institutes of Health Stroke Scale, scores were extracted using regular expressions (stage 1); when not documented, least absolute shrinkage and selection operator was used for prediction (stage 2). The 2-stage model was tested on the holdout test set and validated in the Medical Information Mart for Intensive Care (2001-2012) version 1.4, using root mean squared error and Spearman correlation. We included 4163 patients (Massachusetts General Hospital, 3876; Medical Information Mart for Intensive Care, 287); average age, 69 (SD, 15) years; 53% men, and 72% White individuals. The model achieved a root mean squared error of 2.89 (95% CI, 2.62-3.19) and Spearman correlation of 0.92 (95% CI, 0.91-0.93) in the Massachusetts General Hospital test set, and 2.20 (95% CI, 1.69-2.66) and 0.96 (95% CI, 0.94-0.97) in the MIMIC validation set, respectively. The automatic natural language processing-based model can enable large-scale stroke severity phenotyping from the electronic health record and support real-world quality improvement and comparative effectiveness studies in stroke.

Association Between Coronary Microvascular Dysfunction and Exercise Capacity in Dilated Cardiomyopathy.

Aerobic exercise capacity is an independent predictor of mortality in dilated cardiomyopathy (DCM), but the central mechanisms contributing to exercise intolerance in DCM are unknown. Characterize coronary microvascular function in DCM and determine if cardiovascular magnetic resonance (CMR) measures are associated with aerobic exercise capacity. Prospective case-control comparison of adults with DCM and matched controls. Adenosine-stress perfusion CMR to assess cardiac structure, function and automated inline myocardial blood flow quantification, and cardiopulmonary exercise testing (CPET) to determine peak VO2, were performed. Pre-specified multivariable linear regression, including key clinical and cardiac variables, was undertaken to identify independent associations with peak VO2. Sixty-six patients with DCM (mean age 61 years, 71% male) were propensity-matched to 66 controls (mean age 59 years, 71% male) based on age, sex, body mass index and diabetes. DCM patients had markedly lower peak VO2 (19.8±5.5 versus 25.2±7.3mL/kg/min; P<0.001). The DCM group had greater left ventricular (LV) volumes, lower systolic function, and had more fibrosis compared to controls. In the DCM group, there was similar rest but lower stress myocardial blood flow (1.53±0.49 versus 2.01±0.60mL/g/min; P<0.001) and lower MPR (2.69±0.84 versus 3.15±0.84; P=0.002). Multivariable linear regression demonstrated that LV ejection fraction, extracellular volume fraction and MPR, were independently associated with percentage predicted peak VO2 in DCM (R2=0.531, P<0.001). In comparison to controls, DCM patients have lower stress myocardial blood flow and MPR. In DCM, MPR, LV ejection fraction and fibrosis are independently associated with aerobic exercise capacity.

Disability Related to the Upper Extremities in Early Rheumatoid Arthritis-Long-Term Course and Disease Variable Impact: A Cohort Study.

To investigate the course of disability related to the upper extremities (UEs) in early rheumatoid arthritis (RA), and to assess correlations between such disability and clinical variables, including grip force. In an inception cohort of patients with early RA (diagnosed 1995-2005, N = 222, follow-up 10 yrs), disability of the UEs was assessed using a subscore of the Health Assessment Questionnaire-Disability Index (HAQ-DI-UE), and average grip force of the dominant hand was measured. Changes between consecutive follow-up visits in the HAQ-DI-UE subscore, and correlations at each visit with key clinical variables, were assessed. The relation between joint involvement and HAQ-DI-UE was examined using multivariate linear regression analysis. The HAQ-DI-UE decreased significantly from inclusion to the 6-month follow-up (mean change -0.26, 95% CI -0.18 to -0.34), and increased significantly after 2 years. There were fairly strong correlations for HAQ-DI-UE with grip force (r = -0.50 to -0.62), patient global assessment (r = 0.53 to 0.64), and patient-reported pain (r = 0.54 to 0.60) at all timepoints through 5 years, but only moderate to weak correlations with swollen joints, C-reactive protein, and erythrocyte sedimentation rate. At inclusion, wrist synovitis and tender proximal interphalangeal joints both had an independent effect on HAQ-DI-UE, whereas tenderness of the shoulder and the wrist had a greater importance at 6 months. Disability related to the UEs decreased significantly during the first 6 months, and increased again after 2 years. The correlations with clinical variables underline the major effect of pain and impaired hand function in early RA.

The impact of stress testing to guide PCI in patients with chronic coronary disease

BackgroundGuidelines and international appropriate use criteria increasingly endorse non-invasive stress testing to evaluate patients with suspected chronic coronary disease (CCD). We sought to review the real-world utilisation of non-invasive stress testing and investigate whether their use prior to PCI associates with outcomes in patients with CCD. MethodsConsecutive patients from a multicentre registry who underwent PCI for CCD between 2006 and 2018 were included. Clinical characteristics and outcomes were stratified according to whether stress testing was performed prior to PCI (stress vs no-stress groups). The primary outcome was 3-year all-cause mortality. ResultsAmong the 8251 patients included, 4970 (60.2 %) underwent pre-PCI stress testing and this proportion increased over time (p-for-trend<0.001). The stress group had a lower prevalence of prior revascularization, myocardial infarction, or heart failure, and a lower incidence of triple vessel disease, in stent re-stenosis, and ACC/AHA class B2/C lesions (all p < 0.001). When comparing post-procedural outcomes, the stress group had lower rates of arrhythmia (1.5 % vs 2.6 %, p = 0.001), new heart failure (0.2 % vs 0.8 %, p = 0.001), renal impairment, and a shorter length of stay (1.6 vs 2.1 days, p < 0.001). Mortality at 3-years was lower in those undergoing PCI following stress testing (5.8 % vs 8.8 %, p < 0.001). After adjusting for key clinical variables, stress guided revascularization was associated with a significantly lower risk of 3-year mortality (adjusted Hazard Ratio 0.77, 95 % CI 0.64–0.92). ConclusionsIn patients with CCD, PCI guided by non-invasive stress testing is increasingly utilized and associated with improved survival. Further studies are necessary to investigate whether this results from differences in patient characteristics, optimized patient selection, or refined choice of target vessel.

Supplementing systematic review findings with healthcare system data: pilot projects from the Agency for Healthcare Research and Quality Evidence-based Practice Center program

ObjectivesThe US Agency for Healthcare Research and Quality, through the Evidence-based Practice Center (EPC) Program, aims to provide health system decision makers with the highest-quality evidence to inform clinical decisions. However, limitations in the literature may lead to inconclusive findings in EPC systematic reviews (SRs). The EPC Program conducted pilot projects to understand the feasibility, benefits, and challenges of utilizing health system data to augment SR findings to support confidence in healthcare decision-making based on real-world experiences. Study Design and SettingThree contractors (each an EPC located at a different health system) selected a recently completed SR conducted by their center and identified an evidence gap that electronic health record (EHR) data might address. All pilot project topics addressed clinical questions as opposed to care delivery, care organization, or care disparities topics that are common in EPC reports. Topic areas addressed by each EPC included infantile epilepsy, migraine, and hip fracture. EPCs also tracked additional resources needed to conduct supplemental analyses. The workgroup met monthly in 2022-2023 to discuss challenges and lessons learned from the pilot projects. ResultsTwo supplemental data analyses filled an evidence gap identified in the SRs (raised certainty of evidence, improved applicability) and the third filled a health system knowledge gap. Project challenges fell under three themes: regulatory and logistical issues, data collection and analysis, and interpretation and presentation of findings. Limited ability to capture key clinical variables given inconsistent or missing data within the EHR was a major limitation. The workgroup found that conducting supplemental data analysis alongside an SR was feasible but adds considerable time and resources to the review process (estimated total hours to complete pilot projects ranged from 283 to 595 across EPCs), and that the increased effort and resources added limited incremental value. ConclusionSupplementing existing SRs with analyses of EHR data is resource intensive and requires specialized skillsets throughout the process. While using EHR data for research has immense potential to generate real-world evidence and fill knowledge gaps, these data may not yet be ready for routine use alongside SRs.

Cost burden following esophagectomy: A single centre observational study.

Cost analyses of patients undergoing esophagectomy is valuable for identifying modifiable expenditure drivers to target and curtail costs while improving the quality of care. We aimed to define the cost-complication relationship after esophagectomy and delineate the incremental contributions to costs. To assess the relationship between the hospital costs and potential cost drivers post esophagectomy and investigate the relationship between the cost-driving variables (predicting variables) and hospital costs (dependent variable). In this retrospective single center study, the severity of complications was graded using the Clavien-Dindo (CD) classification system. Key esophagectomy complications were categorized and defined according to consensus guidelines. Raw costing data included the in-hospital costs of the index admission and any unplanned admission within 30 postoperative days. We used correlation analysis to assess the relationship between key clinical variables and hospital costs (in United States dollars) to identify cost drivers. A mediation model was used to investigate the relationship between these variables and hospital costs. A total of 110 patients underwent primary esophageal resection. The median admission cost was $47822.7 (interquartile range: 35670.2-68214.0). The total effects on costs were $13593.9 (95%CI: 10187.1-17000.8, P < 0.001) for each increase in CD severity grade, $4781 (95%CI: 3772.7-5789.3, P < 0.001) for each increase in the number of complications, and $42552.2 (95%CI: 8309-76795.4, P = 0.015) if a key esophagectomy complication developed. Key esophagectomy complications drove the costs directly by $11415.7 (95%CI: 992.5-21838.9, P = 0.032). The severity and number of complications, and the development of key esophagectomy complications significantly contributed to total hospital costs. Continuous institutional initiatives and strategies are needed to enhance patient outcomes and minimize costs.

#2031 Exploring the utility of serum proteomics for outcome prediction of autosomal polycystic kidney disease (ADPKD)

Abstract Background and Aims ADPKD is an inherited disorder characterized by the development of cysts in both kidneys, leading to renal enlargement and a gradual decline in renal function. It is the leading cause of end stage renal disease (ESRD) with an estimated prevalence of 1:1000. The estimated glomerular filtration rate (eGFR) is the calculated estimation of kidney function by using different equations. The eGFR equation used in this study is Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI, 2009) equation, which includes serum creatinine level, age, and gender. eGFR is also frequently used for assessing overall health of ADPKD patients, classifying the disease stages, and determining appropriate therapeutic interventions. As in ADPKD, clinical symptoms manifest typically in later disease stages, early diagnosis is crucial to slow the disease progression and improve patients’ life quality. This study has two main objectives: (1) the identification of molecular and clinical parameters associated with the annual eGFR decline and (2) the development of a predictive model for future eGFR in patients with ADPKD using serum proteomics. This research aims to advance more effective strategies for the misdiagnosis, stratification, and treatment of ADPKD by combining proteomic and clinical parameters. Method Utilizing retrospective eGFR values (n = 7187) of patients with ADPKD (n = 1291) recruited from the AD(H)PKD patient cohort, the annual decline in eGFR (slope) was calculated. A minimum of three eGFR values and the absence of tolvaptan use (a medication that inhibits the mechanisms underlying eGFR decline) were inclusion criteria. In order to determine the slope, robust linear regression was applied. Prior to the analyses, patients whose slopes were clinically extreme (−10 &amp; &amp;gt;5 mL/min/1.73 m2 per year) were excluded. Total of 2469 eGFR values from 219 ADPKD patients were included in this study. To derive a feature set (FS) suitable for slope prediction, the least absolute shrinkage and selection operator (LASSO) was applied independently to two subsets of the serum proteome (SP) data: (i) SP alone and (ii) SP in conjunction with key clinical variables: age, gender, eGFR, and MAYO class (SPMC). Linear regression (LR) models were constructed using the acquired FSs individually (SP and SPMC), and cross-validation (CV) was incorporated to prevent overfitting. Following that, prediction models were generated and compared in terms of their ability to predict slopes and were visualized for an overview. Results Using feature selection, four features associated with slope were identified in both SP and SPMC data. Three of those features were proteomic-based and common to both feature sets (SC and SPMC). LR models exhibited stability in R2s in test sets of CV, indicating no sign of overfitting. Therefore, the LR slope prediction models were built with SP and SPMC feature sets by using all available data. The adjusted R2 of SP and SCMP LR models were 0.274 (R2 = 0.287, F(4,213) = 21.48, p = 6.74 × 10−15) and 0.301 (R2 = 0.317, F(5,208) = 19.31, p = 8.94 × 10−16). This step was followed by the slope prediction of the same patients (internal validation) to examine models’ prediction capacity by comparing the predicted to observed slopes. According to comparisons, our LR models predict closer to actual slope if slope is greater than −5 and lower than 0 mL/min/1.73 m2 per year. Most of the observed slopes were in between the specified thresholds, which explains why the model is working well. However, the deviations between predicted and the observed slopes increases as the slopes exceed this range. Conclusion Identified biomarkers could be used for how the disease will progress, however, fine-tuning of the models might be necessary. Even though there were patients with positive and extremely negative eGFR slopes, our model accurately predicts slopes in the previously specified range. These extreme cases might also be responsible for models’ reduced accuracy outside of that range. One possible solution could be to use weighted lasso to reduce the effect of “extreme” slopes on the models.

#2155 Evaluating the health-related quality of life of patients undergoing haemodialysis: highlights from a multicentric nationwide study

Abstract Background and Aims Haemodialysis (HD) represents the most common renal replacement therapy for end-stage renal disease worldwide. Despite recent advances in HD the health-related quality of life (HRQoL) should be monitored in conjunction with clinical variables to optimize a comprehensive treatment. Method From June to July 2022, all patients from 47 clinics were included. Among other key relevant clinical variables, HRQoL was assessed by using the Kidney Disease Quality of Life Short Form (KDQOL-SF™ 1.3).It`s generic part is summarised in the physical component summary (PCS) and mental component summary (MCS). The specific part evaluates 11 domains: symptom/problem list; effects of kidney disease on daily life; burden of kidney disease; work status; cognitive function; quality of social interactions; sexual function; sleep; social support; dialysis staff encouragement; and patient satisfaction. All scores range from 0 (worst) to 100 (best). To ensure comparability of results, mean ± SD values are outlined, even though non-parametric tests were used to test associations. Results A total of 3,595 patients (67.3% male) completed the KDQOL (out of 4259 HD patients treated during study period). Most of them (67.18%) were over 60yo of age, and 65.67% had a severe comorbidity index score. In addition, 50.54% had a native AV fistula and only 3.92% were incident HD. PCS scores reported were higher in over 75yo (47.68 ± 10.63) rather than normative population (40.16 ± 11.62), indicating an overall good physical function. However, MCS values (33.17 ± 9.36) were indicative of substantial impact. Gender differences were noted across almost all disease-specific and generic domains (all p &amp;lt; 0.05). Severe comorbidity scores were associated with lower scores in certain renal specific domains and MCS. Finally, incident HD patients reported lower satisfaction and poorer perceived overall health (p-values = 0.001). Conclusion Although PCS scores reported were reasonable, a remarkable impact on mental health was found. Moreover, a differential impact of patient's HRQoL was captured based on gender and other variables highlighting the need of individualised evaluation and management of HD patients.

Prospective derivation and validation of a NECROtizing Soft tissue InfectionS (NECROSIS) score: An EAST multicenter trial.

Although several risk indices have been developed to aid in the diagnosis of NSTIs, these instruments suffer from varying levels of reproducibility and failure to incorporate key clinical variables in model development. The objective of this study was to derive and validate a clinical risk index score - NECROSIS - for identifying NSTIs in emergency general surgery (EGS) patients being evaluated for severe skin and soft tissue infections. We performed a prospective study across 16 sites in the US of adult EGS patients with suspected NSTIs over a 30-month period. Variables analyzed included demographics, admission vitals and labs, physical exam, radiographic, and operative findings. The main outcome measure was the presence of NSTI diagnosed clinically at the time of surgery. Multivariate analysis was performed to identify independent predictors for the presence of NSTI using the Hosmer-Lemeshow test and the Akaike information criteria. Of 362 patients, 297 (82%) were diagnosed with a NSTI. Overall mortality was 12.3%. Multivariate analysis identified 3 independent predictors for NSTI: systolic blood pressure ≤ 120 mmHg, violaceous skin, and WBC ≥15 (x103/uL). Multivariate modelling demonstrated Hosmer-Lemeshow goodness of fit (p = 0.9) with a c-statistic for the prediction curve of 0.75. Test characteristics of the NECROSIS score were similar between the derivation and validation cohorts. NECROSIS is a simple and potentially useful clinical index score for identifying at-risk EGS patients with NSTIs. Future validation studies are warranted. Diagnostic Tests or Criteria, Level III.

Abstract ED03-03: Impact of exercise on breast cancer outcomes

Abstract Background: Exercise may improve breast cancer outcomes; however, current research is limited in its ability to inform clinical exercise trials and clinical oncology practice. The purpose of this presentation is to summarize the current research on exercise and breast cancer outcomes using a framework proposed to improve the clinical utility of this research. The Exercise as Cancer Treatment (EXACT) Framework organizes and characterizes the critical aspects of a clinical oncology setting to allow for a more systematic and clinically informative approach to the study of exercise as a cancer treatment. The EXACT Framework proposes that two key clinical oncology variables—tumor/disease status (i.e., primary tumor present, primary tumor removed, metastatic disease present) and treatment status (i.e., treatment naïve, actively being treated, previously treated)—are critical for understanding the effects of exercise on cancer outcomes, but few studies have addressed these variables. Application of the EXACT Framework to existing studies may improve their interpretation and identify directions for future research. Results: Presently, there are no adequately powered randomized controlled trials that have examined the effects of exercise on breast cancer outcomes in any clinical oncology scenario, although secondary analysis of smaller phase II trials have been reported. Preclinical research using rodent models has generally shown that exercise slows the growth and spread of treatment naïve breast tumors through various mechanisms including immune response, tumor metabolism, apoptosis, and DNA synthesis and repair. More recently, these preclinical studies have enhanced their clinical utility by including an existing cancer treatment in their design and have generally shown that exercise enhances the delivery and/or efficacy of concurrent cancer treatments through mechanisms related to improved tumor vasculature and perfusion. Most human evidence on exercise and breast cancer outcomes has been gleaned from secondary analysis of observational studies involving healthy cohorts or breast cancer survivor cohorts. These studies have generally included early-stage breast cancer patients who received heterogeneous cancer treatments. Physical activity data was typically collected either well before diagnosis and/or well after treatments. Overall, these studies have generally shown that higher prediagnosis and postdiagnosis physical activity are associated with a lower risk of breast cancer recurrence and mortality. Discussion: There is a disconnect between the clinical oncology scenarios addressed by animal and human studies of exercise and breast cancer outcomes. Moreover, most studies do not adequately address treatment combinations and sequencing. Future observational studies of exercise and breast cancer outcomes may improve their clinical utility by: (a) recruiting breast cancer patients with the same tumor/disease status receiving the same or similar first-line treatment protocols, (b) collecting detailed data on all planned and unplanned cancer treatments, (c) assessing exercise in relation to those cancer treatments (i.e., before, during, between, after) rather than in relation to the cancer diagnosis (i.e., various time periods before and after diagnosis), (d) collecting data on cancer-specific outcomes (e.g., disease response, progression, recurrence) in addition to mortality, and (e) conducting subgroup analyses based on cancer treatments received. Conclusions: Preclinical and observational studies may contribute important knowledge regarding the role of exercise as a cancer treatment; however, modifications to study design and analysis are necessary if they are to inform clinical research and practice. The ultimate goal of precision exercise oncology is to provide the right exercise prescription to the right breast cancer patient at the right time postdiagnosis. Citation Format: K. Courneya. Impact of exercise on breast cancer outcomes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr ED03-03.

Development and Evaluation of a Digital App for Patient Self-Management of Opioid Use Disorder: Usability, Acceptability, and Utility Study.

Self-management of opioid use disorder (OUD) is an important component of treatment. Many patients receiving opioid agonist treatment in methadone maintenance treatment settings benefit from counseling treatments to help them improve their recovery skills but have insufficient access to these treatments between clinic appointments. In addition, many addiction medicine clinicians treating patients with OUD in a general medical clinic setting do not have consistent access to counseling referrals for their patients. This can lead to decreases in both treatment retention and overall progress in the patient's recovery from substance misuse. Digital apps may help to bridge this gap by coaching, supporting, and reinforcing behavioral change that is initiated and directed by their psychosocial and medical providers. This study aimed to conduct an acceptability, usability, and utility pilot study of the KIOS app to address these clinical needs. We developed a unique, patient-centered computational software system (KIOS; Biomedical Development Corporation) to assist in managing OUD in an outpatient, methadone maintenance clinic setting. KIOS tracks interacting self-reported symptoms (craving, depressed mood, anxiety, irritability, pain, agitation or restlessness, difficulty sleeping, absenteeism, difficulty with usual activities, and conflicts with others) to determine changes in both the trajectory and severity of symptom patterns over time. KIOS then applies a proprietary algorithm to assess the individual's patterns of symptom interaction in accordance with models previously established by OUD experts. After this analysis, KIOS provides specific behavioral advice addressing the individual's changing trajectory of symptoms to help the person self-manage their symptoms. The KIOS software also provides analytics on the self-reported data that can be used by patients, clinicians, and researchers to track outcomes. In a 4-week acceptability, usability (mean System Usability Scale-Modified score 89.5, SD 9.2, maximum of 10.0), and utility (mean KIOS utility questionnaire score 6.32, SD 0.25, maximum of 7.0) pilot study of 15 methadone-maintained participants with OUD, user experience, usability, and software-generated advice received high and positive assessment scores. The KIOS clinical variables closely correlated with craving self-report measures. Therefore, managing these variables with advice generated by the KIOS software could have an impact on craving and ultimately substance use. KIOS tracks key clinical variables and generates advice specifically relevant to the patient's current and changing clinical state. Patients in this pilot study assigned high positive values to the KIOS user experience, ease of use, and the appropriateness, relevance, and usefulness of the specific behavioral guidance they received to match their evolving experiences. KIOS may therefore be useful to augment in-person treatment of opioid agonist patients and help fill treatment gaps that currently exist in the continuum of care. A National Institute on Drug Abuse-funded randomized controlled trial of KIOS to augment in-person treatment of patients with OUD is currently being conducted.

Revolutionizing Pancreatic Cancer Diagnosis and Prognosis through Machine Learning Technology

Abstract: Pancreatic cancer presents significant challenges in terms of early detection and treatment, resulting in poor patient outcomes. This research article explores the application of random forest, a powerful machine learning algorithm, to enhance predictive modeling in pancreatic cancer research. Leveraging a dataset comprising clinical and molecular features, we trained and evaluated random forest models to predict key outcomes such as tumor progression, treatment response, and overall survival. Our findings demonstrate the effectiveness of random forest in accurately stratifying patients based on their prognosis and treatment outcomes. Furthermore, we identified key biomarkers and clinical variables contributing to predictive accuracy, providing valuable insights into the underlying biological mechanisms of pancreatic cancer progression. The integration of random forest into pancreatic cancer research holds promise for improving patient stratification, guiding treatment decisions, and ultimately, advancing personalized medicine approaches in the management of this challenging disease

Abstract 3646: A spatially resolved single cell proteomic atlas of small bowel adenocarcinoma

Abstract Small bowel adenocarcinoma (SBA) is a rare malignancy associated with poor prognosis. The cellular and proteomic heterogeneity within the tumor immune microenvironment (TIME) of SBA is a likely driver of prognosis, disease progression and response to therapy. There is, however, a major gap in our knowledge of tumor and immune interactions in SBA. Cyclic Immunofluorescence (CycIF), an antibody-based, highly multiplexed imaging technology for spatially resolved single cell level proteomic profiling, is well suited to map the proteomic heterogeneity and organization of TIME in SBA. Here, using CycIF, we have generated a spatially resolved single-cell proteomic atlas of TIME for 136 tumor and normal samples from clinically and genomically annotated SBA patients (N=37). The SBA TIME Atlas covers information on expression levels, spatial position, and cell morphology for &amp;gt; 40 proteomic markers of tumor-intrinsic processes and states, diverse immune cell types, immune checkpoints, and tumor vascularization. In total, we collected proteomics data from &amp;gt; 600,000 cells. Using a hierarchical decision tree of cell type markers, we have generated the cell identity annotations for &amp;gt; 350,000 cells, with positional information. A comprehensive computational analysis of the SBA atlas revealed various spatial and heterogeneity features that may impact the TIME organization in fine detail. Using those features, we have compared the proteomic heterogeneity in tumor vs. normal cell populations, identifying spatial and single-cell correlates of key clinical variables (e.g., patient survival, age, sex, disease stage, metastatic status). We have also performed differential analyses of spatial features involving protein markers and cell types with respect to genomic alteration states and have mapped the spatial enrichment of likely actionable immune check proteins to immune and tumor cells. Such mapping may guide future therapeutic decisions. Overall, we expect that the SBA TIME Atlas will provide a useful resource and inform future translational studies, as well as basic research for better mechanistic understanding and treatment of this disease. Citation Format: Zeynep Dereli, Behnaz Bozorgui, Huamin Wang, John Weinstein, Michael Overman, Anil Korkut. A spatially resolved single cell proteomic atlas of small bowel adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3646.

Abstract 24: Differences in Door to Needle and Door In Door Out Times for Tenecteplase vs Alteplase in Acute Ischemic Stroke: Findings From Get With the Guidelines Stroke

Introduction: Tenecteplase (TNK) has arisen as an alternative to alteplase (ALT) for emergent treatment of acute ischemic stroke. Shorter times to prepare and administer TNK raises the possibility that TNK use leads to faster treatment and transfer times. Hypothesis: We hypothesized that treatment with TNK is associated with shorter door-to-needle (DTN) and door-in-door-out (DIDO) times. Methods: Using the US Get With The Guidelines-Stroke registry, we performed a retrospective, observational cohort study of consecutive patients treated with either TNK or ALT between July 1, 2020 and June 30, 2022. The exposure was treatment with TNK vs ALT. The primary endpoints were DTN and DIDO. We fit generalized linear mixed models to determine the association between TNK (vs ALT) and endpoints after adjustment for key demographic, clinical, and hospital-level variables. A secondary analysis compared changes in DTN among hospitals that switched to TNK in 2021 with at least 10 cases per year pre and post switch. Results: From 2092 sites, 133,228 patients received intravenous thrombolysis. Among the 13,988 (10%) treated with TNK, median age was 70 yrs, median NIHSS 7, 47% female, 21% received endovascular thrombectomy (EVT), and 9% were transferred from the hospital emergency department after receiving lytic. Among 119,240 (90%) treated with ALT, median age was 69 yrs, median NIHSS 7, 48% female; 17% received EVT, and 12% were transferred after lytic. In the primary DTN analysis, time to treatment was shorter with TNK, with mean 47.0 vs 52.7 minutes and DTN ≤60 mins in 77.5% vs 70.7% (TABLE). In the primary DIDO analysis, time to departure was shorter with TNK, 108.3 vs 114.1 minutes. In centers that changed from ALT to TNK during this period DTN times were significantly lower after switching. Conclusions: In this largest study of TNK vs ALT workflow time intervals in ischemic stroke using population-based data, TNK use was associated with more favorable DTN and DIDO times relative to ALT use.

Establishment of a prognostic risk prediction model incorporating disulfidptosis-related lncRNA for patients with prostate cancer

PurposeProstate cancer (PCa) is one of the major tumor diseases that threaten men’s health globally, and biochemical recurrence significantly impacts its prognosis. Disulfidptosis, a recently discovered cell death mechanism triggered by intracellular disulfide accumulation leading to membrane rupture, is a new area of research in the context of PCa. Currently, its impact on PCa remains largely unexplored. This study aims to investigate the correlation between long non-coding RNAs (lncRNAs) associated with disulfidptosis and the prognosis of PCa, seeking potential connections between the two.MethodsTranscriptomic data for a PCa cohort were obtained from the Cancer Genome Atlas database. Disulfidptosis-related lncRNAs (DDRLs) were identified through differential expression and Pearson correlation analysis. DDRLs associated with biochemical recurrence-free survival (BRFS) were precisely identified using univariate Cox and LASSO regression, resulting in the development of a risk score model. Clinical factors linked to BRFS were determined through both univariate and multivariate Cox analyses. A prognostic nomogram combined the risk score with key clinical variables. Model performance was assessed using Receiver Operating Characteristic (ROC) curves, Decision Curve Analysis (DCA), and calibration curves. The functional impact of a critical DDRL was substantiated through assays involving CCK8, invasion, migration, and cell cloning. Additionally, immunohistochemical (IHC) staining for the disulfidptosis-related protein SLC7A11 was conducted.ResultsThe prognostic signature included AC026401.3, SNHG4, SNHG25, and U73166.1 as key components. The derived risk score from these signatures stood as one of the independent prognostic factor for PCa patients, correlating with poorer BRFS in the high-risk group. By combining the risk score with clinical variables, a practical nomogram was created, accurately predicting BRFS of PCa patients. Notably, silencing AC026401.3 significantly hindered PCa cell proliferation, invasion, migration, and colony formation. IHC staining revealed elevated expression of the dithiosulfatide-related protein SLC7A11 in tumor tissue.ConclusionsA novel prognostic signature for PCa DDRLs, possessing commendable predictive power, has been constructed, simultaneously providing potential therapeutic targets associated with disulfidptosis, among which AC026401.3 has been validated in vitro and demonstrated inhibition of PCa tumorigenesis after its silencing.

Negative Symptoms and Their Associations With Other Clinical Variables and Working Memory Across the Schizophrenia Spectrum and Bipolar Disorder.

Negative symptoms (NS) of schizophrenia spectrum disorders (SSD) are also prevalent in bipolar disorder I (BD-I) and show associations with impaired working memory (WM). However, empirical work on their relationship to other clinical factors across SSD and BD-I is sparse. Here, we characterized the associations of NS with key clinical variables and WM capacity across a combined sample of SSD and BD. We included 50 outpatients with SSD and 49 with BD-I and assessed NS domains using SANS global scores for avolition-apathy, anhedonia-asociality, alogia, and blunted affect. We assessed the transdiagnostic relationship between NS and other clinical variables, including positive symptoms, disorganization, depressive symptoms, and antipsychotic medication, using multiple regressions. The strength of these associations was further determined through dominance analyses. Finally, we used multiple regression to assess the relationship between NS domains and WM. To assess the generalizability of transdiagnostic associations, analyses were repeated in each diagnostic group separately. Across SSD and BD-I, disorganization was associated with avolition-apathy and anhedonia-asociality and depressive symptoms additionally predicted anhedonia-asociality. Antipsychotic dose was associated with blunted affect while group differences only predicted alogia. Higher avolition-apathy was related to impaired WM transdiagnostically, partially mediated by the severity of disorganization, whereas only in BD-I higher anhedonia-asociality was associated with better WM capacity. This study demonstrated transdiagnostic associations of both avolition-apathy and anhedonia-asociality with disorganization and identified avolition-apathy as a potential transdiagnostic predictor of WM impairments. Overall, our findings highlight the importance of understanding the relationship between NS domains and other clinical factors with cognitive function across SSD and BD.