Factors predictive of cystoid macular oedema following endothelial keratoplasty: a single-centre review of 2233 cases

AimsTo describe the incidence of postoperative cystoid macular oedema (CMO) after endothelial keratoplasty (EK) and to identify its contributory risk factors.Methods2233 patients undergoing EK at Ospedali Privati Forlì ‘Villa Igea’, between January 2005 to October 2018 for Descemet stripping automated endothelial keratoplasty (DSAEK) and June 2014 to August 2018 for Descemet membrane endothelial keratoplasty (DMEK) with a minimum follow-up of 18 months were evaluated. Univariate and multivariate analyses were conducted to identify and quantify contributory risk factors. Receiver operating characteristic (ROC) curve analysis were performed to determine ideal cut-off points of continuous variables.ResultsCMO was identified in 2.82% (n=63) of the cases. CMO occurred in 2.36% of DSAEK eyes and in 5.56% of DMEK eyes (p=0.001). Average onset of CMO was 4.27±6.63 months (range: 1–34 months) postoperatively. Compared with those who did not develop CMO, a higher proportion of patients in the CMO group had diabetes (24.2% vs 9.8%, p<0.001) (OR=3.16, 95% CI: 1.72 to 5.81, p<0.001), a higher proportion of patients who underwent DMEK rather than DSAEK (28.6% vs 14.1%, p=0.001) (OR=2.42, 95% CI: 1.35 to 4.33, p=0.003) and were older (70.5±10.0 vs 67.1±14.3 years, p=0.01). Using the cut-off of 67 years as identified by ROC curve analysis, subjects aged >67 years (OR=2.35, 95% CI: 1.30 to 4.26, p=0.005) were more likely to develop CMO. There were no other significant differences between the groups.ConclusionsOlder age (>67 years), diabetes mellitus and DMEK have been identified as independent risk factors for postoperative CMO following EK. Close observation is necessary during the first postoperative year after EK, particularly in patients with risk factors.

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Risk Stratification in COVID-19 Pneumonia – Determining the Role of Lung Ultrasound

Ultraschall in der Medizin - European Journal of Ultrasound ◽

10.1055/a-1344-4715 ◽

2021 ◽

Author(s):

Valeria Tombini ◽

Mirko Di Capua ◽

Nicolò Capsoni ◽

Andrea Lazzati ◽

Marta Bergamaschi ◽

...

Keyword(s):

Risk Factors ◽

Roc Curve ◽

Primary Outcome ◽

Clinical Laboratory ◽

Curve Analysis ◽

Secondary Outcome ◽

Roc Curve Analysis ◽

Additional Tool ◽

Factors Associated ◽

Independent Risk Factors

AbstractLUS patterns of COVID-19 pneumonia have been described and shown to be characteristic. The aim of the study was to predict the prognosis of patients with COVID-19 pneumonia, using a score based on LUS findings. Materials and Methods An observational, retrospective study was conducted on patients admitted to Niguarda hospital with a diagnosis of COVID-19 pneumonia during the period of a month, from March 2nd to April 3rd 2020. Demographics, clinical, laboratory, and radiological findings were collected. LUS was performed in all patients. The chest was divided into 12 areas. The LUS report was drafted using a score from 0 to 3 with 0 corresponding to A pattern, 1 corresponding to well separated vertical artifacts (B lines), 2 corresponding to white lung and small consolidations, 3 corresponding to wide consolidations. The total score results from the sum of the scores for each area. The primary outcome was endotracheal intubation, no active further management, or death. The secondary outcome was discharge from the emergency room (ER). Results 255 patients were enrolled. 93.7 % had a positive LUS. ETI was performed in 43 patients, and 24 received a DNI order. The general mortality rate was 15.7 %. Male sex (OR 3.04, p = 0.014), cardiovascular disease and hypertension (OR 2.75, p = 0.006), P/F (OR 0.99, p < 0.001) and an LUS score > 20 (OR 2.52, p = 0.046) were independent risk factors associated with the primary outcome. Receiver operating characteristic (ROC) curve analysis for an LUS score > 20 was performed with an AUC of 0.837. Independent risk factors associated with the secondary outcome were age (OR 0.96, p = 0.073), BMI (OR 0.87, p = 0,13), P/F (OR 1.03, p < 0.001), and LUS score < 10 (OR 20.9, p = 0.006). ROC curve analysis was performed using an LUS score < 10 with an AUC 0.967. Conclusion The extent of lung abnormalities evaluated by LUS score is a predictor of a worse outcome, ETI, or death. Moreover, the LUS score could be an additional tool for the safe discharge of patient from the ER.

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Analysis of fungal bloodstream infection in intensive care units in the Meizhou region of China: species distribution and resistance and the risk factors for patient mortality

10.21203/rs.2.12729/v1 ◽

2019 ◽

Author(s):

Guang-Wen Xiao ◽

Wan-qing Liao ◽

Yuenong Zhang ◽

Xiaodong Luo ◽

Cailing Zhang ◽

...

Keyword(s):

Risk Factors ◽

Intensive Care ◽

Serum Albumin ◽

Roc Curve ◽

Bloodstream Infections ◽

Mortality Rates ◽

Curve Analysis ◽

Roc Curve Analysis ◽

Candida Spp ◽

Icu Patients

Abstract Background : Fungal bloodstream infections (FBI) among intensive care unit (ICU) patients are increasing. Our objective was to characterize the fungal pathogens that cause bloodstream infections and determine the epidemiology and risk factors for patient mortality among ICU patients in Meizhou, China. Methods Eighty-one ICU patients with FBI during their stays were included in the study conducted from January 2008 to December 2017. Blood cultures were performed and the antimicrobial susceptibility profiles of the resulting isolates were determined. Logistic multiple regression and receiver operating characteristics (ROC) curve analysis were used to assess the risk factors for mortality among the cases. Results The prevalence of FBI in ICU patients was 0.38% (81/21,098) with a mortality rate of 35.8% (29/81). Ninety-eight strains of bloodstream-infecting fungi, mainly Candida spp., were identified from these patients. Candida albicans was most common (42.9%). Two strains of C. parapsilosis were no-sensitive to caspofungin, C. glabrata were less than 80% sensitive to azole drugs.. Logistic multiple regression showed that age, serum albumin, Acute Physiology and Chronic Health Evaluation (APACHE) II score, three or more underlying diseases, and length of stay in ICU were independent risk factors for mortality in FBI. ROC curve analysis showed that APACHE II scores > 19 and serum albumin ≤ 25g/L were the best predictors of mortality. Conclusion Candida spp. predominated with high mortality rates among cases of FBI in ICU. Thus, clinical staff should enhance overall patient monitoring and especially monitor fungal susceptibility to reduce mortality rates.

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Title: Preoperative albumin-to-fibrinogen ratio predicts postoperative pulmonary complications in patients after minimally invasive esophagectomy

10.21203/rs.3.rs-604731/v1 ◽

2021 ◽

Author(s):

Zhang Peng ◽

Zhao Song

Keyword(s):

Risk Factors ◽

Esophageal Cancer ◽

Logistic Regression ◽

Roc Curve ◽

Training Group ◽

Pulmonary Complications ◽

Curve Analysis ◽

Postoperative Pulmonary Complications ◽

Multivariate Logistic Regression ◽

Roc Curve Analysis

Abstract Background Postoperative pulmonary complications (PPCs) are the most common postoperative complications in patients with esophageal cancer. Prediction of PPCs by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin-to-fibrinogen ratio (AFR), prognostic nutritional index (PNI), albumin-to-globulin ratio (AGR), neutrophils-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), and monocyte-to -lymphocyte ratio (MLR) and other preoperative laboratory tests and PPCs in patients after esophagectomy. Methods Retrospective analysis was performed on total 712 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from July 2018 to December 2020. Patients were divided into training (535 patients) and validation (177) groups for comparison of baseline data, perioperative indicators, and laboratory examination data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR. Univariate and multivariate logistic regression analyses were used to assess the risk factors for PPCs in training group. Results 112 (20.9%) in training group and 36 (20.3%) in validation group developed PPCs. The AUC value predicted by AFR using ROC curve analysis was 0.817, sensitivity 76.2% and specificity 78.7% in training group while AUC 0.803, sensitivity 69.4% and specificity 85.8%. Multivariate logistic regression analysis showed that smoking index, American Society of Anesthesiologists (ASA), AFR, and recurrent laryngeal nerve palsy were independent risk factors for PPCs. Conclusion Preoperative AFR can effectively predict the occurrence of PPCs in patients with esophageal cancer

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The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

Emergency Medicine International ◽

10.1155/2021/7711056 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Zhichuang Lian ◽

Yafang Li ◽

Wenyi Wang ◽

Wei Ding ◽

Zongxin Niu ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Analysis ◽

Prediction Model ◽

Logistic Regression Analysis ◽

Roc Curve ◽

Curve Analysis ◽

Severe Illness ◽

Roc Curve Analysis ◽

Underlying Diseases

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

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Risk factors to predict ocular metastasis in older adult patients with gastric cancer:LDL, ApoA1, and CA724

10.21203/rs.3.rs-245016/v1 ◽

2021 ◽

Author(s):

Wenqing Shi ◽

Shinan Wu ◽

Tie Sun ◽

Huiye Shu ◽

Qichen Yang ◽

...

Keyword(s):

Risk Factors ◽

Older Adult ◽

Roc Curve ◽

Demographic Characteristics ◽

Curve Analysis ◽

Adult Patients ◽

Apolipoprotein A1 ◽

Roc Curve Analysis ◽

Significant Difference ◽

Ocular Metastasis

Abstract Background: Gastric cancer (GC) is one of the most common malignancies in the population. Although the incidence of GC has reduced, patient prognosis remains poor. Ocular metastases (OM) from GC are rare, and the occurrence of OM is often indicative of severe disease. The purpose of this study was to explore the risk factors for OM of GC.Methods: A total of 1165 older adult patients with GC were enrolled in this study from June 2003 to May 2019 and divided into OM and non-ocular metastasis (NOM) groups. Chi-square and independent samples t tests were used to determine whether differences in demographic characteristics and serological indicators (SI) between the two groups were significant. In addition, binary logistic regression was used to analyze the value of various SI as risk factors for OM in patients with GC. The statistical threshold was set as P < 0.05. Finally, receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic value of various SI in differentiating the occurrence of OM in patients with GC. Results: The incidence of OM in older adults with GC was 1.1%. Adenocarcinoma was the most common type of GC in both groups, and there was no significant difference in demographic characteristics, including sex and age between the groups. Low-density lipoprotein (LDL), carbohydrate antigen-724 (CA724), and carcinoembryonic antigen levels were significantly higher in the OM group than the NOM group, while those of apolipoprotein A1 (ApoA1) were significantly lower in the OM than the NOM group. Binary logistic analysis showed that LDL, ApoA1, and CA724 were independent risk factors for OM in patients with GC (P < 0.001,P = 0.033, and P = 0.008, respectively). ROC curve analysis generated area under the curve (AUC) values of 0.881, 0.576, and 0.906 for LDL, ApoA1, and CA724, respectively. In addition, combined analysis of LDL, ApoA1, and CA724 generated the highest AUC value of 0.924 (P < 0.001).Conclusion: Among SI, LDL, ApoA1, and CA724 have predictive value for the occurrence of OM in GC, with the three factors combined having the highest value.

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Impact of comorbidity burden on mortality in patients with COVID-19 using the Korean health insurance database

Scientific Reports ◽

10.1038/s41598-021-85813-2 ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 2

Author(s):

Soo Ick Cho ◽

Susie Yoon ◽

Ho-Jin Lee

Keyword(s):

Risk Factors ◽

Health Insurance ◽

Roc Curve ◽

Significant Risk ◽

Curve Analysis ◽

Roc Curve Analysis ◽

Comorbidity Burden ◽

Comorbidity Index ◽

The Impact ◽

Age And Sex

AbstractWe aimed to investigate the impact of comorbidity burden on mortality in patients with coronavirus disease (COVID-19). We analyzed the COVID-19 data from the nationwide health insurance claims of South Korea. Data on demographic characteristics, comorbidities, and mortality records of patients with COVID-19 were extracted from the database. The odds ratios of mortality according to comorbidities in these patients with and without adjustment for age and sex were calculated. The predictive value of the original Charlson comorbidity index (CCI) and the age-adjusted CCI (ACCI) for mortality in these patients were investigated using the receiver operating characteristic (ROC) curve analysis. Among 7590 patients, 227 (3.0%) had died. After age and sex adjustment, hypertension, diabetes mellitus, congestive heart failure, dementia, chronic pulmonary disease, liver disease, renal disease, and cancer were significant risk factors for mortality. The ROC curve analysis showed that an ACCI threshold > 3.5 yielded the best cut-off point for predicting mortality (area under the ROC 0.92; 95% confidence interval 0.91–0.94). Our study revealed multiple risk factors for mortality in patients with COVID-19. The high predictive power of the ACCI for mortality in our results can support the importance of old age and comorbidities in the severity of COVID-19.

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The association the patient-reported outcomes after periacetabular osteotomy with radiographic features: a short-term retrospective study

Journal of Orthopaedic Surgery and Research ◽

10.1186/s13018-021-02858-9 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Yinuo Fan ◽

Weifeng Li ◽

Yunlong Wu ◽

Ruoyu Li ◽

Guoju Hong ◽

...

Keyword(s):

Risk Factors ◽

Roc Curve ◽

Patient Reported Outcomes ◽

Periacetabular Osteotomy ◽

Multivariate Logistic Regression Analysis ◽

Curve Analysis ◽

Roc Curve Analysis ◽

Joint Congruency ◽

Patient Reported

Abstract Background Bernese periacetabular osteotomy (PAO) is an effective treatment for patients with developmental dysplasia of the hip (DDH). PAO has been widely used in China, but few follow-up outcomes have been reported in the international community. Moreover, the risk factors affecting patient-reported outcomes have not been discussed in recent studies. In this study, patient-reported outcomes after PAO were reported, and risk factors affecting patient-reported outcomes were analyzed. Methods Patients who underwent PAO for DDH from January 2014 to January 2020 were selected as the study subjects, and 66 hips were included in the analysis after screening (59 patients, with an average follow-up time of 3.01 years). The Harris Hip Score (HHS) and International Hip Outcome Instrument-12 (iHOT-12) were used to assess hip function and patient quality of life. The changes of preoperative and latest follow-up HHSs less than 9 were defined as symptomatic hips, that is, an adverse outcome; otherwise, the score indicates preserved hips. Also, the changes of preoperative and latest follow-up iHOT-12 were defined as symptomatic hips and preserved hips. Multivariate logistic regression analysis was used to predict the risk factors influencing the patient-reported outcomes, and receiver operating characteristic (ROC) curve analysis was performed on the risk factors to determine their sensitivity, specificity and cutoff value. Results Clinical outcome analysis demonstrates marked improvements in patient-reported outcomes. The multivariate logistic regression analysis showed that when the postoperative LCEA was > 38°, adverse outcomes were much more likely. However, a Tönnis angle of − 10° to 0° was a protective factor. In addition, hips with fair or poor joint congruency were more likely to develop negative outcomes. The ROC curve analysis showed that the optimal thresholds for the LCEA and Tönnis angles used to predict outcomes after PAO were 38.2° and − 9°, respectively. Based on the results of the ROC curve analysis, among hips with poor or fair joint congruency preoperatively treated by surgeons who obtained the improper postoperative LCEAs and Tönnis angles, bad patient-reported outcomes will most likely be obtained. Conclusions Our results demonstrate marked improvements in patient-reported outcomes. Among hips with preoperative excellent or good joint congruency treated by experienced surgeons who obtain the proper postoperative LCEA and Tönnis angles, good patient-reported outcomes can be expected.

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Risk Factors to Predict Ocular Metastasis in Older Adult Patients With Gastric Cancer:LDL, ApoA1, and CA724

Technology in Cancer Research & Treatment ◽

10.1177/15330338211065876 ◽

2022 ◽

Vol 21 ◽

pp. 153303382110658

Author(s):

Wen-Qing Shi ◽

Shi-Nan Wu ◽

Tie Sun ◽

Hui-Ye Shu ◽

Qi-Chen Yang ◽

...

Keyword(s):

Risk Factors ◽

Roc Curve ◽

Area Under The Curve ◽

Density Lipoprotein ◽

Demographic Characteristics ◽

Curve Analysis ◽

Apolipoprotein A1 ◽

Roc Curve Analysis ◽

Significant Difference ◽

Ocular Metastasis

Objective: The purpose of this study was to explore the risk factors for Ocular metastasis (OM) of Gastric cancer (GC). Methods: This is a retrospective cohort study. A total of 1165 patients with GC were enrolled in this study and divided into OM and non-ocular metastasis (NOM) groups. Chi-square and independent samples t tests were used to determine whether differences in demographic characteristics and serological indicators (SI) between the two groups were significant. In addition, binary logistic regression was used to analyze the value of various SI as risk factors for OM in patients with GC. The statistical threshold was set as P < .05. Finally, receiver operating characteristic (ROC) curve analysis was used to assess the diagnostic value of various SI in differentiating the occurrence of OM in patients with GC. Results: The incidence of OM in older adults with GC was 1.1%. Adenocarcinoma was the most common type of GC in both groups, and there was no significant difference in demographic characteristics between the groups. Low-density lipoprotein (LDL), carbohydrate antigen-724 (CA724), and carcinoembryonic antigen levels were significantly higher in the OM group than the NOM group, while those of apolipoprotein A1 (ApoA1) were significantly lower in the OM than the NOM group. Binary logistic analysis showed that LDL, ApoA1, and CA724 were independent risk factors for OM in patients with GC ( P < .001, P = .033, and P = .008, respectively). ROC curve analysis generated area under the curve (AUC) values of 0.881, 0.576, and 0.906 for LDL, ApoA1, and CA724, respectively. In addition, combined analysis of LDL, ApoA1, and CA724 generated the highest AUC value of 0.924 ( P < .001). Conclusion: Among SI, LDL, ApoA1, and CA724 have predictive value for the occurrence of OM in GC, with the three factors combined having the highest value.

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Preoperative Albumin-to-Fibrinogen Ratio Predicts Postoperative Pneumonia in Patients with Esophageal Squamous Cell Carcinoma: A Retrospective Study

10.21203/rs.3.rs-127793/v1 ◽

2020 ◽

Author(s):

Peng Zhang ◽

Song Zhao

Keyword(s):

Risk Factors ◽

Esophageal Cancer ◽

Logistic Regression ◽

Roc Curve ◽

Laboratory Tests ◽

Curve Analysis ◽

Postoperative Pneumonia ◽

Function Parameter ◽

Multivariate Logistic Regression ◽

Roc Curve Analysis

Abstract Background: Postoperative pneumonia is the most common postoperative complication in patients with esophageal cancer. Prediction of postoperative pneumonia by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin, fibrinogen, albumin-to-fibrinogen ratio(AFR) , and other preoperative laboratory tests and postoperative pneumonia in patients with esophageal cancer after esophagectomy.Methods: Retrospective analysis was performed on 177 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from December 2018 to December 2019.Postoperative pneumonia was defined according to the revised Uniform Pneumonia Score(rUPS).Patients were divided into pneumonia and non-pneumonia groups for comparison of baseline data, perioperative indicators, and laboratory examination data.(Receiver operating characteristic)ROC curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR and other laboratory tests data. Univariate and multivariate logistic regression analyses were used to assess the risk factors for postoperative pneumoniaResults: Of the 177 patients, 32 (18%) developed postoperative pneumonia. The AUC value predicted by AFR using ROC curve analysis was 0.767, 65.6% sensitivity and 83.4% specificity. Multivariate logistic regression analysis showed that albumin (P=0.013), creatinine (P=0.01), and AFR (P=0.016) were independent risk factors for postoperative pneumonia.Conclusion: Preoperative AFR can effectively predict the occurrence of postoperative pneumonia in patients with esophageal cancer

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Value of serum neuroendocrine markers in evaluation of neuroendocrine prostate cancer: A validation study using metastatic biopsies.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.7_suppl.278 ◽

2019 ◽

Vol 37 (7_suppl) ◽

pp. 278-278 ◽

Cited By ~ 1

Author(s):

Panagiotis J. Vlachostergios ◽

Nicolas Peruzzo ◽

Jones Nauseef ◽

Clara Oromendia ◽

Jyothi Manohar ◽

...

Keyword(s):

Roc Curve ◽

De Novo ◽

Predictive Ability ◽

Curve Analysis ◽

Continuous Variables ◽

Roc Curve Analysis ◽

Non Invasive ◽

Neuroendocrine Prostate Cancer ◽

Median Serum ◽

Genomic Characterization

278 Background: NEPC, de novo or treatment-related in late stage CRPC, is a distinct entity with poor prognosis. Developing non-invasive methods for detection of NEPC is important for clinical practice and trial enrollment. We previously reported on the clinical and genomic characterization of NEPC (Conteduca et al ESMO 2018). A separate study (Aggarwal et al JCO 2018) suggested that low levels of NSE and CgA were associated with a strong NPV for NEPC on biopsy (bx). This study aimed to validate the utility of NSE and CgA in evaluation of NEPC by comparison with met bx. Methods: Our IRB-approved NEPC database was screened for pts who underwent met bx and had concurrent serum NSE, CgA. Clinical data, serum PSA, LDH, ALP, Hb were recorded at time of bx. Comparison of continuous variables between CRPC adeno and NEPC was assessed by nonparametric Kruskal-Wallis test. ROC curve analysis was performed for evaluation of predictive models with serum NE markers. Results: 152 men were identified, median age 71 yrs (49-97). 35 pts had pure/mixed NEPC, while the rest (N=117) had typical adenoca on bx. Half of pts (80/152, 52.6%) received abiraterone or/and enzalutamide. Liver mets were more common in NEPC pts (P=0.001). Median serum NSE (11.2 vs 8.6 ng/mL, P=0.008) and CgA (211 vs 135 ng/mL, P=0.035) were higher in pts with NEPC vs CRPC adeno (Table). Using ROC curve analysis for NSE (normal 3.7-8.9 ng/mL) and CgA (normal 0-95 ng/mL) as independent diagnostic tests, the following cut-offs were identified: NSE 30.1 (Sn: 37%, Sp: 94%, PPV: 34%, NPV: 82%), CgA 170 (Sn: 63%, Sp: 59%, PPV: 23%, NPV: 83%). Conclusions: Our study confirms the potential utility of serum NSE and CgA in excluding a morphological dx of NEPC when below certain thresholds. However, our findings cannot support deferring a met bx in such cases. Larger studies are needed to evaluate for a more robust predictive ability of serum NE markers. [Table: see text]

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