Estimation Of Prognosis In Patients Research Articles

Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP grading

Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66–6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05–14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.

Peripheral Neutrophil to Lymphocyte Ratio (NLR), a cogent clinical adjunct for Ki-67 in breast cancer

BackgroundClinical utility of Ki-67 immunohistochemistry (IHC) in breast cancer (BC) is mainly limited to decide for the use of chemotherapy and estimate prognosis in patients with either Ki-67 index < 5% or > 30%; however, lacunae still exists pertaining to its analytical validity. Neutrophilia is common in cancer with accompanying lymphocytopenia. Neutrophil to lymphocyte ratio (NLR) captures the intricate balance between pro-tumor neutrophilia and anti-tumor lymphocyte immunity. This study aimed to correlate cellular proliferation in breast cancer with NLR.MethodsAn observational study was carried out including 73 cases of BC; pre-treatment NLR and Ki-67 grading were performed. NLR < 3 was considered low, while ≥ 3 was high. The Ki-67 expression was graded as low ≤ 5%, intermediate 6–29%, or high ≥ 30%. Various clinico-pathological variables were studied, and the association of categorical variables was analyzed using Pearson’s chi-square test, and a p-value of < 0.05 was taken as significant.ResultsKi-67 correlated significantly with modified Scarff-Bloom-Richardson (SBR) grade (p < 0.01), and tumor-node-metastasis (TNM) stage (p < 0.001). Correlation of NLR was not significant with SBR grade (p > 0.05) and molecular subtype (p > 0.05); however, NLR was found to be significantly correlated with TNM stage (p < 0.001) and Ki-67 (p < 0.001).ConclusionNLR is fast emerging as a personalized theranostic marker in breast cancer. Instead of determining a generalized cut-off value, individual baseline NLR and its dynamics with disease progression will help manage patients better, obviating some of the drawbacks associated with Ki-67.

AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images

The survival analysis on histological whole-slide images (WSIs) is one of the most important means to estimate patient prognosis. Although many weakly-supervised deep learning models have been developed for gigapixel WSIs, their potential is generally restricted by classical survival analysis rules and fully-supervised learning requirements. As a result, these models provide patients only with a completely-certain point estimation of time-to-event, and they could only learn from the labeled WSI data currently at a small scale. To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework. This framework is based on adversarial time-to-event modeling, and integrates the multiple instance learning (MIL) that is much necessary for WSI representation learning. It is a plug-and-play one, so that most existing MIL-based end-to-end methods can be easily upgraded by applying this framework, gaining the improved abilities of survival distribution estimation and semi-supervised learning. Our extensive experiments show that AdvMIL not only could often bring performance improvement to mainstream WSI survival analysis methods at a relatively low computational cost, but also enables these methods to effectively utilize unlabeled data via semi-supervised learning. Moreover, it is observed that AdvMIL could help improving the robustness of models against patch occlusion and two representative image noises. The proposed AdvMIL framework could promote the research of survival analysis in computational pathology with its novel adversarial MIL paradigm.

Survival prediction nomogram for patients with vertebral bone metastases treated with palliative radiotherapy.

In the treatment of vertebral bone metastases, estimating patient prognosis is important to select the optimal treatment strategy. The purpose of this study was to identify prognostic factors for vertebral bone metastases treated with palliative radiotherapy and to establish a nomogram for predicting patient survival. We analyzed patients who underwent palliative radiotherapy for vertebral bone metastasis between January 2010 and December 2020 at a single institution. Exclusion criteria were as follows: (1) primary bone malignancy, (2) stereotactic body radiotherapy, (3) concurrent radiotherapy to sites other than the vertebral bone, (4) radiotherapy to other sites within 12 weeks before or after the current radiotherapy, and (5) lack of more than half of blood test data before radiotherapy. A total of 487 patients met the inclusion criteria. Clinical and hematologic data were collected from the patient record system. Patients were divided into training and test groups in a 7:3 ratio. Multivariate Cox regression analysis in the training cohort revealed six significant factors, including a history of chemotherapy, primary site (breast cancer, prostate cancer, or hematologic malignancy), use of analgesics, neutrophil-lymphocyte ratio, serum albumin, and lactate dehydrogenase. A prognostic nomogram was developed and validated in the test cohort. The area under the curve (AUC) values in predicting survival at 6, 24, and 60 months were 0.83, 0.88, and 0.88 in the training cohort and 0.85, 0.81, and 0.79 in the test cohort, respectively. This nomogram may help to select the treatment strategy for vertebral bone metastases.

Predicting 1-Year Mortality in Outpatients With Heart Failure With Reduced Left Ventricular Ejection Fraction: Do Empiric Models Outperform Physician Intuitive Estimates? A Multicenter Cohort Study.

Many studies have demonstrated that physicians often err in estimating patient prognosis. No studies have directly compared physician to model predictive performance in heart failure (HF). We aimed to compare the accuracy of physician versus model predictions of 1-year mortality. This multicenter prospective cohort study on 11 HF clinics in 5 provinces in Canada included consecutive consented outpatients with HF with reduced left ventricular ejection fraction (<40%). By collecting clinical data, we calculated predicted 1-year mortality using the Seattle HF Model (SHFM), the Meta-Analysis Global Group in Chronic HF score, and the HF Meta-Score. HF cardiologists and family doctors, blinded to model predictions, estimated patient 1-year mortality. During 1-year follow-up, we recorded the composite end point of mortality, urgent ventricular assist device implant, or heart transplant. We compared physicians and model discrimination (C statistic), calibration (observed versus predicted event rate), and risk reclassification. The study included 1643 patients with ambulatory HF with a mean age of 65 years, 24% female, and mean left ventricular ejection fraction of 28%. Over 1-year follow-up, 9% had an event. The SHFM had the best discrimination (SHFM C statistic 0.76; HF Meta-Score 0.73; Meta-Analysis Global Group in Chronic Heart Failure 0.70) and calibration. Physicians' discrimination differed little (0.75 for HF cardiologists and 0.73 for family doctors) but both physician groups substantially overestimated risk by >10% in both low- and high-risk patients (poor calibration). In risk reclassification analysis, among patients without events, the SHFM better classified 51% in comparison to HF cardiologists and 43% in comparison to family doctors. In patients with events, the SHFM erroneously assigned lower risk to 44% in comparison to HF cardiologists and 34% in comparison to family doctors. Family doctors and HF cardiologists showed adequate risk discrimination, with however substantial overestimation of absolute risk. Predictive models showed higher accuracy. Incorporating models in family and HF cardiology practices may improve patient care and resource use in HF with reduced left ventricular ejection fraction. URL: https://www. gov; Unique identifier: NCT04009798.

ProgCAE: a deep learning-based method that integrates multi-omics data to predict cancer subtypes.

Determining cancer subtypes and estimating patient prognosis are crucial for cancer research. The massive amount of multi-omics data generated by high-throughput sequencing technology is an important resource for cancer prognosis. Deep learning methods can integrate such data to accurately identify more cancer subtypes. We propose a prognostic model based on a convolutional autoencoder (ProgCAE) that can predict cancer subtypes associated with survival using multi-omics data. We demonstrated that ProgCAE predicted subtypes of 12 cancer types with significant survival differences and outperformed traditional statistical methods for predicting the survival of most patients with cancer. Supervised classifiers can be constructed based on subtypes predicted by robust ProgCAE.

STUDY OF SERUM AMYLASE LEVELS IN ORGANOPHOSPHATE POISONING

Organophosphate (OP) insecticides are the most probable common cause of acute poisonings in developing countries. OP intoxication often presents as medical emergencies, and its related morbidity and mortality have not decreased despite major advances in critical care.This study aims to determine the impact of serum amylase level for estimation of prognosis in patients with acute OP poisoning. Method: This prospective observational hospital-based study was performed on 100 patients with acute OP poisoning. Demographics data, clinical profile, serum amylase levels, complications,hospital stay and outcome were determined.Data were analyzed in the form of a frequency distributions using SPSS 26.0 version software.Result: Most patients were young adults with mean age of 29yrs ± 11yrs with slightly male predominance. Mean serum amylase level was significantly higher among patients with morbidity and mortality. Conclusion: Among patients with OP poisoning,higher serum amylase than normal was associated with severe clinical course and increased risk for mortality.Determination of serum amylase can be an effective marker of the prognosis and clinical course among OP poisoning patients.

Malignant Solitary Fibrous Tumours of the Pleura Are Not All the Same: Analysis of Long-Term Outcomes and Evaluation of Risk Stratification Models in a Large Single-Centre Series.

Introduction: Malignant solitary fibrous tumours of the pleura (mSFTP) are extremely rare diseases (<5% of all pleural neoplasms) with unpredictable behaviour. Surgery remains the standard of care for these tumours; however, estimating patient prognosis and planning follow-up remain challenging. Several risk stratification models have been proposed, but a classification with diagnostic and prognostic potential has not been well standardised yet. The aim of this study was to analyse the clinicopathological data of mSFTP to investigate their prognostic features and to compare the performance of three risk stratification models proposed in the literature. Methods: Observational retrospective cohort study on all proven cases of mSFTP surgically resected with radical intent between 2000 and 2019 in a single centre. Demographic, surgical and pathological data were examined. All patients were risk-stratified by using three prediction models: modified Demicco, De Perrot and Tapias. Overall survival (OS) and disease-free survival (DFS) were analysed. Results: There were 21 men and 13 women (median age, 67 years, range, 23-83 years). Twenty-one patients (62%) were symptomatic. The median follow-up was 111 months (range, 6-258 months). The 5-year OS and DFS were 81.2% and 77.4%, respectively. Nine patients (26.5%) experimented recurrences. At univariate analysis, the presence of necrosis (p = 0.019), nuclear atypia (p = 0.006), dimension greater than 11.5 cm (median value of our cohort) (p = 0.037) and relapse/disease progression (p = 0.001) were independent prognostic factor of worse OS. The administration of adjuvant treatment was a protective independent factor for survival (p = 0.001). Radicality of resection (p = 0.005); tumour dimension (p = 0.013), presence of necrosis (p = 0.041) and nuclear atypia (p = 0.007) and pleural pattern (p = 0.011) were independent prognostic factors of worse DFS. Analysing the three risk stratification models, the Tapias score was revealed as the best index to predict both OS (p = 0.002) and DFS (p = 0.047) in patients with mSFTP. Conclusions: Using the risk stratification model proposed by Tapias, patients with the highest risk of recurrence could be identified at the time of surgery to establish a more frequent imaging surveillance and longer follow-up. The role of adjuvant treatment in mSFTP therapy has not been established yet, but further analysis on patients with a high risk of recurrence, stratified according to risk models, along with biomolecular panels may tailor future post-surgical therapies.

A tumor immune microenvironment-related integrated signature can predict the pathological response and prognosis of esophageal squamous cell carcinoma following neoadjuvant chemoradiotherapy: A multicenter study in China

BackgroundCurrently, there are insufficient indicators for the reliable assessment of treatment response following neoadjuvant chemoradiotherapy (nCRT) in patients with esophageal squamous cell carcinoma (ESCC). Considering the essential role of protein-coding and non-coding RNAs in gene regulation and cellular processes, we systematically explored the molecular features and clinical significance of mRNA and lncRNA in 249 pretreatment biopsies from four hospitals in three districts with a high incidence of ESCC patients in China. MethodsDuring the discovery phrase, 13 differentially expressed genes were identified and validated between samples with a complete pathological response (pCR) and those with an incomplete pathological response (<pCR). Subsequently, we constructed a predictive mRNA and lncRNA signature (SERPINE1, LINC00592, and PRKAG2-AS1) using Fisher's linear discriminant analysis (FLDA) with stepwise variant-selection, followed by validation of its predictive ability in both internal and external cohorts. ResultsOur signature showed great value in predicting the response to nCRT among ESCC samples and acted as an independent predictive indicator, in addition to demonstrating great potential in estimating patient prognosis. Interestingly, we found that patients with a high signature score had lower PD-L1 and IDO1 expression levels but higher CD8+ T cells infiltration, suggesting that PD-L1 and IDO1 are negatively correlated with a high signature score and further associated with pCR and a better prognosis. ConclusionThe present study identified a promising three-gene-based predictive signature that has powerful clinical implications for the identification of pCR and a good prognosis among patients with ESCC. Further immune-related exploration may provide an opportunity for future therapeutic combination.

Nine-gene signature and nomogram for predicting survival in patients with head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinomas (HNSCCs) are derived from the mucosal linings of the upper aerodigestive tract, salivary glands, thyroid, oropharynx, larynx, and hypopharynx. The present study aimed to identify the novel genes and pathways underlying HNSCC. Despite the advances in HNSCC research, diagnosis, and treatment, its incidence continues to rise, and the mortality of advanced HNSCC is expected to increase by 50%. Therefore, there is an urgent need for effective biomarkers to predict HNSCC patients’ prognosis and provide guidance to the personalized treatment. Methods: Both HNSCC clinical and gene expression data were abstracted from The Cancer Genome Atlas (TCGA) database. Intersecting analysis was adopted between the gene expression matrix of HNSCC patients from TCGA database to extract TME-related genes. Differential gene expression analysis between HNSCC tissue samples and normal tissue samples was performed by R software. Then, HNSCC patients were categorized into clusters 1 and 2 via NMF. Next, TME-related prognosis genes (p < 0.05) were analyzed by univariate Cox regression analysis, LASSO Cox regression analysis, and multivariate Cox regression analysis. Finally, nine genes were selected to construct a prognostic risk model and a prognostic gene signature. We also established a nomogram using relevant clinical parameters and a risk score. The Kaplan–Meier curve, survival analysis, time-dependent receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and the concordance index (C-index) were carried out to assess the accuracy of the prognostic risk model and nomogram. Potential molecular mechanisms were revealed by gene set enrichment analysis (GSEA). Additionally, gene correlation analysis and immune cell correlation analysis were conducted for further enriching our results. Results: A novel HNSCC prognostic model was established based on the nine genes (GTSE1, LRRN4CL, CRYAB, SHOX2, ASNS, KRT23, ANGPT2, HOXA9, and CARD11). The value of area under the ROC curves (AUCs) (0.769, 0.841, and 0.816) in TCGA whole set showed that the model effectively predicted the 1-, 3-, and 5-year overall survival (OS). Results of the Cox regression assessment confirmed the nine-gene signature as a reliable independent prognostic factor in HNSCC patients. The prognostic nomogram developed using multivariate Cox regression analysis showed a superior C-index over other clinical signatures. Also, the calibration curve had a high level of concordance between estimated OS and the observed OS. This showed that its clinical net can precisely estimate the one-, three-, and five-year OS in HNSCC patients. The gene set enrichment analysis (GSEA) to some extent revealed the immune- and tumor-linked cascades. Conclusion: In conclusion, the TME-related nine-gene signature and nomogram can effectively improve the estimation of prognosis in patients with HNSCC.

Sarcopenia defined by skeletal muscle mass index at the third lumbar vertebra is a prognostic factor for extensive-stage small cell lung cancer patients: a retrospective study.

BackgroundSmall cell lung cancer (SCLC) is one of the most aggressive types of lung cancer and reliable indicators are needed for improved patient management. The evaluation of skeletal muscle index of the third lumbar vertebra (L3MI) based on computed tomography (CT) is used to estimate patient prognosis in multiple cancers. However, its function in extensive-stage SCLC remains controversial. Considering that the maintenance of muscle mass may affect the survival of cancer patients. Herein, a retrospective study was conducted to investigate whether sarcopenia defined by skeletal muscle mass index at the third lumbar vertebra is a prognostic factor in extensive-stage SCLC cancer patients.MethodsThis retrospective analysis included extensive-stage SCLC patients diagnosed at the Sun Yat-sen University Cancer Center from January 2009 to March 2017 with platinum-based chemotherapy. Clinical data were collated for further examination, and CT or positron emission tomography (PET)/CT datasets were analyzed for body mass index (BMI) and L3MI. Follow-up data were collected by contacting patients or their families. Overall survival (OS) was defined as the interval between the date of treatment started and the date of death or censoring. The Kaplan-Meier product limit method and log-rank tests were used to assess differences in OS between the high L3MI and low L3MI groups. Cox regression analysis was used to identify independent factors of OS.ResultsFor the 139 extensive-stage SCLC patients, the median follow-up time was 26.1 months (range, 0.4 to 79.4 months). The median OS was 9.5 months. There were no differences in age, inflammatory factors, nor progression after first-line treatment between the high L3MI and low L3MI groups. Kaplan-Meier analysis showed that the OS of the high L3MI group was significantly longer than that of the low L3MI group (14.045 vs. 9.985 months; P=0.007), and multivariate analysis identified high L3MI to be an independent prognostic factor for predicting longer OS in extensive-stage SCLC patients [hazard ratio (HR), 0.623; 95% confidence interval (CI), 0.405–0.960; P=0.032].ConclusionsSarcopenia defined by L3MI is a prognostic factor for extensive-stage SCLC patients and early intervention of muscle mass maintaining may achieve better cancer management.

Management considerations of Hodgkin lymphoma for patients with Fontan physiology.

While treatment protocols for Hodgkin lymphoma (HL) are well established, there is no literature available to guide therapy or estimate prognosis for patients with Fontan physiology who develop HL. The physiology of a Fontan procedure can result in the inability to tolerate chemotherapy toxicities, supportive care, and infection. We present a series of three patients with Fontan physiology who were treated for HL and discuss their clinical course and treatment.

Sarcopenia Predicts Cancer Mortality in Male but Not in Female Patients Undergoing Surgery for Cholangiocellular Carcinoma.

Simple SummaryCholangiocellular adenocarcinoma is the second most common primary liver tumor. If resectable, this is the therapeutic method of choice. Unfortunately, there are insufficient prognostic markers to determine which patients benefit most from surgery and which do not. The general condition of the patient, especially their muscle mass and quality, has become more and more the focus of recent research as a possible marker for therapeutic outcome. In our study we investigated how the preoperative muscle mass of patients with cholangiocellular adenocarcinoma affects postoperative overall survival. We showed that patients with a higher preoperative muscle mass had a significantly longer survival than patients with reduced muscle mass. Furthermore, we were able to show that this is particularly relevant in men and that in women preoperative muscle mass plays a significantly less important role in postoperative survival than in men.Introduction: Surgery represents the only curative treatment option for patients with cholangiocarcinoma. However, complete tumor resection requires extensive surgery in many patients, and it is still debated which patients represent the ideal candidates for such therapy in terms of overall survival. Sarcopenia has been associated with an adverse outcome for various malignancies, but its role in the context of patients undergoing tumor resection for cholangiocellular adenocarcinoma (CCA) is only poorly understood. Here, we evaluated the role of sarcopenia in the outcome of CCA patients undergoing radical tumor resection. Methods: Pre-operative CT scans were used to assess the skeletal muscle index (L3SMI) as well as the psoas muscle index (L3PMI) in n = 76 patients receiving curative intended surgery for CCA. L3SMI and L3PMI were correlated with clinical and laboratory markers. Results: Patients with a skeletal muscle index or psoas muscle index above an established ideal cut-off (54.26 and 1.685 cm2/m2) showed a significantly better overall survival in Kaplan–Meier Curve analyses (L3SMI: 1814 days (95% CI: 520–3108) vs. 467 days (95% CI: 225–709) days; log rank X2(1) = 7.18, p = 0.007; L3PMI: 608 days (95% CI: 297–919) vs. 87 days (95% CI: 33–141), log rank X2(1) = 18.71; p < 0.001). Notably, these findings, especially for L3PMI, were confirmed in univariate (L3SMI: HR 0.962 (0.936–0.989); p = 0.006; L3PMI: HR 0.529 (0.366–0.766); p ≤ 0.001) and multivariate Cox regression analyses. Further analyses revealed that the prognostic value of both L3SMI and L3PMI was restricted to male patients, while in female patients survival was independent of the individual muscle mass. Conclusion: Measurement of muscle mass from preoperative CT scans represents an easily obtainable tool to estimate patient prognosis following curative surgery. The prognostic value was restricted to male patients, while in female patients these parameters did not reflect the patient outcome.

Clinical relevance of circulating activin A and follistatin in small cell lung cancer

ObjectivesCirculating levels of activin A (ActA) and follistatin (FST) have been investigated in various disorders including malignancies. However, to date, their diagnostic and prognostic relevance is largely unknown in small cell lung cancer (SCLC). Our aim was to evaluate circulating ActA and FST levels as potential biomarkers in this devastating disease. MethodsSeventy-nine Caucasian SCLC patients and 67 age- and sex-matched healthy volunteers were included in this study. Circulating ActA and FST concentrations were measured by ELISA and correlated with clinicopathological parameters and long-term outcomes. ResultsPlasma ActA and FST concentrations were significantly elevated in SCLC patients when compared to healthy volunteers (p < 0.0001). Furthermore, extensive-stage SCLC patients had significantly higher circulating ActA levels than those with limited-stage disease (p = 0.0179). Circulating FST concentration was not associated with disease stage (p = 0.6859). Notably, patients with high (≥548.8 pg/ml) plasma ActA concentration exhibited significantly worse median overall survival (OS) compared to those with low (<548.8 pg/ml) ActA levels (p = 0.0009). Moreover, Cox regression analysis adjusted for clinicopathological parameters revealed that high ActA concentration is an independent predictor of shorter OS (HR: 1.932; p = 0.023). No significant differences in OS have been observed with regards to plasma FST levels (p = 0.1218). ConclusionBlood ActA levels are elevated and correlate with disease stage in SCLC patients. Measurement of circulating ActA levels might help in the estimation of prognosis in patients with SCLC.

Serum amylase as a prognostic marker of organophosphate poisoning

:Background:Organophosphate (OP) insecticides are important compounds as the most probable common cause of acute poisonings in developing countries. OP intoxication often presents as medical emergencies, and its related morbidity and mortality have not decreased despite major advances in critical care. This study aims to determine the impact of serum amylase level for estimation of prognosis in patients with acute OP poisoning. Methods:This observational case-control study was done during two years on 332 consecutive patients with acute OP poisoning. Clinical and demographic data, serum amylase level on early admission time, morbidity, and outcome were determined. Data were analyzed in the form of a frequency distribution table by using SPSS 11.0 version software. Results:The mean age of patients with acute OP poisoning was 28.9 ± 23.95 with slightly female dominance. All patients were intoxicated via the gastrointestinal route. The mean amylase level of patients with deterioration of mental status, tachycardia, ICU admission, and death was significantly higher. Conclusions:Among patients with OP poisoning, higher serum amylase than normal was associated with severe clinical course and increased risk for mortality. Determination of serum amylase can be effective in the quick prediction of the outcome.

Abstract P577: An Association Between CHADS-VASc Score Morbidities, Coronary Artery Disease Severity, and Post-Elective Percutaneous Coronary Intervention Stroke and Death

Background: The CHADS-VASc score is a clinical prediction tool used to assess stroke risk in patients with atrial fibrillation or flutter. An association between CHADS-VASc score morbidities and outcomes of elective percutaneous coronary intervention (PCI) has not been well studied. Methods: This cohort study utilized 2015 NY State Angioplasty Registry data and included 33,568 consecutive patients. Patients with prior CABG, shock, or acute coronary syndrome (ACS) within 24-hours of PCI placement were excluded. CHADS-VASc score was calculated by adding 1 point each for positive history of congestive heart failure (CHF), diabetes mellitus (DM), vascular disease, age 65-75 years old, female sex, and 2 points each for age &gt;75 years old and history of stroke. As the Registry does not record history of hypertension, the calculated score used 0 for hypertension in all patients. Results: In the studied cohort, 68.4% were males, mean age 65.5 +/- 11.5 years old. Increased burden of CHADS-VASc morbidities was associated with more advanced CAD, including 3 vessel and left-main (LM) CAD. Of patients with CHADS-VASc score of 0, 16.3% had 3 vessel disease and 0.7% had LM disease, score of 3 had 20% 3 vessel disease and 2.3% LM disease, and score of 6 and/or 7 had 31.2% 3 vessel disease and 5.7% LM disease (p&lt;0.0001). Increased burden of CHADS-VASc morbidities was also associated with increasing incidence of post-elective PCI stroke and mortality. Stroke was observed in 0.03% of cases with score of 0, 0.3% of cases with score of 3, and 1.4% of cases with score of 6 and/or 7 (p&lt;0.0001). Mortality rate was 0.05% in patients with score of 0, 0.7% with score of 3, and 1.4% in patients with score of 6 and/or 7 (p&lt;0.0001). Conclusions: This pilot study indicates that increased burden of CHADS-VASc morbidities is associated with more advanced CAD and increased incidence of post-elective PCI stroke and mortality. These data suggest that the simple CHADS-VASc calculation may be useful in identifying patients at greater risk of post-elective PCI complications, aid in estimating patient prognosis, and provide a reference point for clinicians contemplating PCI in stable CAD patients.

Serum amylase as a prognostic marker of organophosphate poisoning.

Organophosphate (OP) insecticides are important compounds as the most probable common cause of acute poisonings in developing countries. OP intoxication often presents as medical emergencies, and its related morbidity and mortality have not decreased despite major advances in critical care. This study aims to determine the impact of serum amylase level for estimation of prognosis in patients with acute OP poisoning. This observational case-control study was done during two years on 332 consecutive patients with acute OP poisoning. Clinical and demographic data, serum amylase level on early admission time, morbidity, and outcome were determined. Data were analyzed in the form of a frequency distribution table by using SPSS 11.0 version software. The mean age of patients with acute OP poisoning was 28.9 ± 23.95 with slightly female dominance. All patients were intoxicated via the gastrointestinal route. The mean amylase level of patients with deterioration of mental status, tachycardia, ICU admission, and death was significantly higher. Among patients with OP poisoning, higher serum amylase than normal was associated with severe clinical course and increased risk for mortality. Determination of serum amylase can be effective in the quick prediction of the outcome.

18F-FDG PET/CT Habitat Radiomics Predicts Outcome of Patients with Cervical Cancer Treated with Chemoradiotherapy.

To determine if quantitative features extracted from pretherapy fluorine 18 fluorodeoxyglucose (18F-FDG) PET/CT estimate prognosis in patients with locally advanced cervical cancer treated with chemoradiotherapy. In this retrospective study, PET/CT images and outcomes were curated from 154 patients with locally advanced cervical cancer, who underwent chemoradiotherapy from two institutions between March 2008 and June 2016, separated into independent training (n = 78; mean age, 51 years ± 13 [standard deviation]) and testing (n = 76; mean age, 50 years ± 10) cohorts. Radiomic features were extracted from PET, CT, and habitat (subregions with different metabolic characteristics) images that were derived by fusing PET and CT images. Parsimonious sets of these features were identified by the least absolute shrinkage and selection operator analysis and used to generate predictive radiomics signatures for progression-free survival (PFS) and overall survival (OS) estimation. Prognostic validation of the radiomic signatures as independent prognostic markers was performed using multivariable Cox regression, which was expressed as nomograms, together with other clinical risk factors. The radiomics nomograms constructed with T stage, lymph node status, and radiomics signatures resulted in significantly better performance for the estimation of PFS (Harrell concordance index [C-index], 0.85 for training and 0.82 for test) and OS (C-index, 0.86 for training and 0.82 for test) compared with International Federation of Gynecology and Obstetrics staging system (C-index for PFS, 0.70 for training [P = .001] and 0.70 for test [P = .002]; C-index for OS, 0.73 for training [P < .001] and 0.70 for test [P < .001]), respectively. Prognostic models were generated and validated from quantitative analysis of 18F-FDG PET/CT habitat images and clinical data, and may have the potential to identify the patients who need more aggressive treatment in clinical practice, pending further validation with larger prospective cohorts.Supplemental material is available for this article.© RSNA, 2020.

Patients undergoing surgery for oligometastatic oesophageal cancer survive for more than 2 years: bootstrapping systematic review data

Oesophageal cancer oligometastasis is a state of limited systemic disease characterized by ˂5 metastases. Without surgery average survival is 4-12 months. We sought to estimate patient prognosis following the surgical resection of oligometastatic disease from oesophageal cancer. Eligible studies were identified through systematic search of PubMed and the Cochrane Library (end-of-search date: 20 November 2019). We estimated cumulative 1-, 3- and 5-year, as well as overall survival using bootstrap methodology with 1000000 repetitions per outcome. We investigated six studies involving 420 patients who underwent metastasectomy for oligometastasis from oesophageal cancer. Adenocarcinoma [77.3%; 95% confidence interval (CI) 62.8-87.3] was the most prevalent histological type followed by squamous cell carcinoma (22.7%; 95% CI 12.7-37.2). Metastatic lesions were typically synchronous (91.5%; 95% CI 87.5-94.1). Overall, 73.5% (95% CI 67.5-78.6) of the patients underwent resection of the primary and metastatic tumours synchronously. Neoadjuvant chemoradiotherapy was utilized in the majority of the patients (66.7%; 95% CI 49.5-80.3) followed by neoadjuvant chemotherapy (33.3%; 95% CI 19.6-50.5). The mean overall survival was 24.5 months (95% CI 14.4-34.6). One-year survival was 88.3% (95% CI 85.6-90.8). Three-year survival and 5-year survival were 36.3% (95% CI 15.3-7.3) and 23.8% (95% CI 12.0-35.7), respectively. Patients undergoing surgical resection of oesophageal oligometastasis survive for more than 24 months. Therefore, loco-regional control of oligometastatic disease appears to improve survival by at least 100%.

Improving IMDC criteria in patients with metastatic renal cell carcinoma through the addition of initial metastatic site in bone, brain, and liver.

754 Background: IMDC criteria are largely adopted to estimate patient prognosis. In a retrospective study we assessed that the addition of first site of metastases in brain bone and liver (bbl) metastases as variable improves prognostic stratification of patients (pts) with metastatic renal cell carcinoma (mRCC). We performed a validation study in an external cohort of patients with mRCC. Methods: All consecutive mRCC pts treated at a single institute and included in the local IGReCC database. All pts received at least one line of targeted therapy or ICIs. Primary outcome was OS from first line start. Univariate and multivariate analysis (cox regression model) were performed. Bootstrap was also carried out for validation. Results: 1073 mRCC pts were analysed (808 with known IMDC score). Of 13 initial variables considered for prognostic model, the presence of bbl as first site/s of metastases plus the 6 variables included in IMDC resulted as statistically significant factors associated with OS (table). By including this new parameter we developed a 7-variables based score (IMDC-7). Patients with 0, 1-2 and≥3 positive variable/s were considered as good, intermediate and poor risk patients respectively. 56 (22%) pts moved from good (IMDC) to intermediate (IMDC-7) and 66 (15.4%) pts moved from intermediate (IMDC) to poor (IMDC-7) group (table). Final bias corrected concordance c-index was 0.69 and 0.71 in overall cohort and in pts treated with ICI (n=241). Conclusions: The addition of bbl primary metastases to the others IMDC variables improves prognostic predictive power of the model.[Table: see text]