scholarly journals Prediction of Hemorrhagic Transformation After Ischemic Stroke: Development and Validation Study of a Novel Multi-biomarker Model

2021 ◽  
Vol 13 ◽  
Author(s):  
Junfeng Liu ◽  
Yanan Wang ◽  
Yuxi Jin ◽  
Wen Guo ◽  
Quhong Song ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Clinical Decision Making ◽  
External Validation ◽  
Hemorrhagic Transformation ◽  
Chinese Patients ◽  
Circulating Biomarkers ◽  
Derivation Cohort ◽  
Net Reclassification Improvement ◽  
C Statistic ◽  
Multiple Biomarkers

Objectives: We aimed to develop and validate a novel multi-biomarker model for predicting hemorrhagic transformation (HT) risk after acute ischemic stroke (AIS).Methods: We prospectively included patients with AIS admitted within 24 h of stroke from January 1st 2016 to January 31st 2019. A panel of 17 circulating biomarkers was measured and analyzed in this cohort. We assessed the ability of individual circulating biomarkers and the combination of multiple biomarkers to predict any HT, symptomatic HT (sHT) and parenchymal hematoma (PH) after AIS. The strategy of multiple biomarkers in combination was then externally validated in an independent cohort of 288 Chinese patients.Results: A total of 1207 patients with AIS (727 males; mean age, 67.2 ± 13.9 years) were included as a derivation cohort, of whom 179 patients (14.8%) developed HT. The final multi-biomarker model included three biomarkers [platelets, neutrophil-to-lymphocyte ratios (NLR), and high-density lipoprotein (HDL)] from different pathways, showing a good performance for predicting HT in both the derivation cohort (c statistic = 0·64, 95% CI 0·60–0·69), and validation cohort (c statistic = 0·70, 95% CI 0·58–0·82). Adding these three biomarkers simultaneously to the basic model with conventional risk factors improved the ability of HT reclassification [net reclassification improvement (NRI) 65.6%, P < 0.001], PH (NRI 64.7%, P < 0.001), and sHT (NRI 71.3%, P < 0.001).Conclusion: This easily applied multi-biomarker model had a good performance for predicting HT in both the derivation and external validation cohorts. Incorporation of biomarkers into clinical decision making may help to identify patients at high risk of HT after AIS and warrants further consideration.

scholarly journals Development and Validation of a Predictive Model for Spontaneous Hemorrhagic Transformation After Ischemic Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Chenchen Wei ◽  
Junfeng Liu ◽  
Wen Guo ◽  
Yuxi Jin ◽  
Quhong Song ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Characteristic Curve ◽  
External Validation ◽  
Predictive Performance ◽  
Bleeding Risk ◽  
Reperfusion Therapy ◽  
Hemorrhagic Transformation ◽  
Baseline Risk ◽  
Anterior Circulation ◽  
Derivation Cohort

Background: Hemorrhagic transformation (HT) after reperfusion therapy for acute ischemic stroke (AIS) has been well studied; however, there is scarce research focusing on spontaneous HT (sHT). Spontaneous HT is no less important with a relatively high incidence and could be associated with neurological worsening. We aimed to develop and validate a simple and practical model to predict sHT after AIS (SHAIS) and compared the predictive value of the SHAIS score against the models of post-Reperfusion HT for sHT.Methods: Patients with AIS admitted within 24 h of onset were prospectively screened to develop and validate the SHAIS score. The primary outcome was sHT during hospitalization (within 30 days after onset), and the secondary outcomes were symptomatic sHT and parenchymal hematoma (PH). Clinical information, laboratory, and neuroimaging data were screened to construct the SHAIS score. We selected six commonly used scales for predicting HT after reperfusion therapy and compared their predictive ability for sHT with the SHAIS score using Delong's test.Results: The derivation cohort included 539 patients (mean age, 68.1 years; men, 61.4%), of whom 91 (16.9%) patients developed sHT with 25.3% (23/91) being symptomatic sHT and 62.6% (57/91) being PH. Five variables (atrial fibrillation, NIHSS score ≥ 10, hypodensity > 1/3 of middle cerebral artery territory, hyperdense artery sign, and anterior circulation infarction) composed the SHAIS score, which ranged from 0 to 11 points. The area under the receiver-operating characteristic curve (AUC) was 0.86 (95% CI 0.82–0.91, p < 0.001) for the overall sHT, 0.85 (95% CI 0.76–0.92, p < 0.001) for symptomatic sHT, and 0.89 (95% CI 0.85–0.94, p < 0.001) for PH. No evidence of miscalibration of the SHAIS score was found to predict the overall sHT (p = 0.19), symptomatic sHT (p = 0.44), and PH (p = 0.22). The internal (n = 245) and external validation cohorts (n = 200) depicted similar predictive performance compared to the derivation cohort. The SHAIS score had a higher AUC to predict sHT than any of the six pre-Existing models (p < 0.05).Conclusions: The SHAIS score provides an easy-to-use model to predict sHT, which could help providers with decision-making about treatments with high bleeding risk, and to counsel patients and families on the baseline risk of HT, aligning expectations with probable outcomes.

Multiple biomarkers covering distinct pathways for predicting outcomes after ischemic stroke

Neurology ◽  
2018 ◽  
Vol 92 (4) ◽  
pp. e295-e304 ◽  
Author(s):  
Chongke Zhong ◽  
Zhengbao Zhu ◽  
Aili Wang ◽  
Tan Xu ◽  
Xiaoqing Bu ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Primary Outcome ◽  
Prognostic Significance ◽  
Complement C3 ◽  
Vascular Events ◽  
Net Reclassification Improvement ◽  
Increased Risk ◽  
Multiple Biomarkers ◽  
Novel Biomarkers ◽  
Integrated Discrimination Improvement

ObjectiveTo study the prognostic significance of multiple novel biomarkers in combination after ischemic stroke.MethodsWe derived data from the China Antihypertensive Trial in Acute Ischemic Stroke, and 12 informative biomarkers were measured. The primary outcome was the combination of death and major disability (modified Rankin Scale score ≥3) at 3 months after ischemic stroke, and secondary outcomes included major disability, death, and vascular events.ResultsIn 3,405 participants, 866 participants (25.4%) experienced major disability or died within 3 months. In multivariable analyses, elevated high-sensitive C-reactive protein, complement C3, matrix metalloproteinase-9, hepatocyte growth factor, and antiphosphatidylserine antibodies were individually associated with the primary outcome. Participants with a larger number of elevated biomarkers had increased risk of all study outcomes. The adjusted odds ratios (95% confidence intervals) of participants with 5 elevated biomarkers were 3.88 (2.05–7.36) for the primary outcome, 2.81 (1.49–5.33) for major disability, 5.67 (1.09–29.52) for death, and 4.00 (1.22–13.14) for vascular events, compared to those with no elevated biomarkers. Simultaneously adding these 5 biomarkers to the basic model with traditional risk factors led to substantial reclassification for the combined outcome (net reclassification improvement 28.5%, p < 0.001; integrated discrimination improvement 2.2%, p < 0.001) and vascular events (net reclassification improvement 37.0%, p = 0.001; integrated discrimination improvement 0.8%, p = 0.001).ConclusionWe observed a clear gradient relationship between the numbers of elevated novel biomarkers and risk of major disability, mortality, and vascular events. Incorporation of a combination of multiple biomarkers observed substantially improved the risk stratification for adverse outcomes in ischemic stroke patients.

scholarly journals The specific ex vivo released cytokine profile is associated with ischemic stroke outcome and improves its prediction

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Elzbieta Klimiec-Moskal ◽  
Marcin Piechota ◽  
Joanna Pera ◽  
Kazimierz Weglarczyk ◽  
Agnieszka Slowik ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Ischemic Stroke ◽  
Ex Vivo ◽  
Stroke Outcome ◽  
Measured Concentration ◽  
Clinical Model ◽  
Poor Outcome ◽  
Net Reclassification Improvement ◽  
C Statistic ◽  
Pro Inflammatory Cytokines

Abstract Background Inflammation is associated with poor outcome after stroke. A relationship between ex vivo cytokine synthesis and stroke outcome remains unclear. We explored an association between ex vivo cytokine release, circulating interleukin (IL)-6 as a marker of systemic inflammation, and stroke prognosis. We assessed the utility of ex vivo synthesized cytokines for predicting stroke outcome. Methods We collected blood from 248 ischemic stroke patients and stimulated it ex vivo with lipopolysaccharide. We measured concentration of synthesized cytokines (TNFα, IP-10, IL-1β, IL-6, IL-8, IL-10, and IL-12) and plasma IL-6. We assessed functional outcome 3 months after stroke using the modified Rankin Scale. To assess the prognostic ability of cytokines, we applied multivariate logistic regression, cluster analysis, and construction of multimarker score. Results Decreased release of IP-10, TNFα, IL-1β, and IL-12; increased release of IL-10 and IL-8; and higher plasma IL-6 level were associated with poor outcome. Cluster analysis identified three groups of patients with distinct cytokine profiles. The group with the worst outcome demonstrated high synthesis of IL-10, IL-8, IL-1β, and IL-6 and low synthesis of IL-12, IP-10, and TNFα accompanied by high circulating IL-6 level. The group with the best prognosis showed high synthesis of TNFα, IP-10, IL-12, IL-1β, and IL-6; low synthesis of IL-10 and IL-8; and low plasma IL-6. Patients with intermediate outcome had low synthesis of all cytokines accompanied by low circulating IL-6. We constructed a multimarker score composed of ex vivo released IL-12, IL-10, TNFα, and plasma IL-6. Addition of this score to clinical variables led to significant increase in c-statistic (0.81 vs 0.73, p = 0.02) and net reclassification improvement. Conclusion The decreased ex vivo release of pro-inflammatory cytokines and increased release of IL-10 and IL-8 are related to poor outcome after stroke. Cytokine-based multimarker score adds prognostic value to clinical model for predicting stroke outcome.

scholarly journals Prediction model of bleeding after endoscopic submucosal dissection for early gastric cancer: BEST-J score

Gut ◽  
2020 ◽  
pp. gutjnl-2019-319926 ◽  
Author(s):  
Waku Hatta ◽  
Yosuke Tsuji ◽  
Toshiyuki Yoshio ◽  
Naomi Kakushima ◽  
Shu Hoteya ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Early Gastric Cancer ◽  
Prediction Model ◽  
Endoscopic Submucosal Dissection ◽  
Clinical Decision Making ◽  
Validation Cohort ◽  
External Validation ◽  
Derivation Cohort ◽  
Submucosal Dissection

ObjectiveBleeding after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC) is a frequent adverse event after ESD. We aimed to develop and externally validate a clinically useful prediction model (BEST-J score: Bleeding after ESD Trend from Japan) for bleeding after ESD for EGC.DesignThis retrospective study enrolled patients who underwent ESD for EGC. Patients in the derivation cohort (n=8291) were recruited from 25 institutions, and patients in the external validation cohort (n=2029) were recruited from eight institutions in other areas. In the derivation cohort, weighted points were assigned to predictors of bleeding determined in the multivariate logistic regression analysis and a prediction model was established. External validation of the model was conducted to analyse discrimination and calibration.ResultsA prediction model comprised 10 variables (warfarin, direct oral anticoagulant, chronic kidney disease with haemodialysis, P2Y12 receptor antagonist, aspirin, cilostazol, tumour size >30 mm, lower-third in tumour location, presence of multiple tumours and interruption of each kind of antithrombotic agents). The rates of bleeding after ESD at low-risk (0 to 1 points), intermediate-risk (2 points), high-risk (3 to 4 points) and very high-risk (≥5 points) were 2.8%, 6.1%, 11.4% and 29.7%, respectively. In the external validation cohort, the model showed moderately good discrimination, with a c-statistic of 0.70 (95% CI, 0.64 to 0.76), and good calibration (calibration-in-the-large, 0.05; calibration slope, 1.01).ConclusionsIn this nationwide multicentre study, we derived and externally validated a prediction model for bleeding after ESD. This model may be a good clinical decision-making support tool for ESD in patients with EGC.

scholarly journals A Prediction Model for Severe AKI in Critically Ill Adults That Incorporates Clinical and Biomarker Data

2019 ◽  
Vol 14 (4) ◽  
pp. 506-514 ◽  
Author(s):  
Pavan Kumar Bhatraju ◽  
Leila R. Zelnick ◽  
Ronit Katz ◽  
Carmen Mikacenic ◽  
Susanna Kosamo ◽  
...  
Keyword(s):  
Critically Ill ◽  
Critically Ill Patients ◽  
Prediction Models ◽  
Risk Model ◽  
External Validation ◽  
Variable Model ◽  
Internal Validation ◽  
Predictive Values ◽  
Derivation Cohort ◽  
C Statistic

Background and objectivesCritically ill patients with worsening AKI are at high risk for poor outcomes. Predicting which patients will experience progression of AKI remains elusive. We sought to develop and validate a risk model for predicting severe AKI within 72 hours after intensive care unit admission.Design, setting, participants, & measurementsWe applied least absolute shrinkage and selection operator regression methodology to two prospectively enrolled, critically ill cohorts of patients who met criteria for the systemic inflammatory response syndrome, enrolled within 24–48 hours after hospital admission. The risk models were derived and internally validated in 1075 patients and externally validated in 262 patients. Demographics and laboratory and plasma biomarkers of inflammation or endothelial dysfunction were used in the prediction models. Severe AKI was defined as Kidney Disease Improving Global Outcomes (KDIGO) stage 2 or 3.ResultsSevere AKI developed in 62 (8%) patients in the derivation, 26 (8%) patients in the internal validation, and 15 (6%) patients in the external validation cohorts. In the derivation cohort, a three-variable model (age, cirrhosis, and soluble TNF receptor-1 concentrations [ACT]) had a c-statistic of 0.95 (95% confidence interval [95% CI], 0.91 to 0.97). The ACT model performed well in the internal (c-statistic, 0.90; 95% CI, 0.82 to 0.96) and external (c-statistic, 0.93; 95% CI, 0.89 to 0.97) validation cohorts. The ACT model had moderate positive predictive values (0.50–0.95) and high negative predictive values (0.94–0.95) for severe AKI in all three cohorts.ConclusionsACT is a simple, robust model that could be applied to improve risk prognostication and better target clinical trial enrollment in critically ill patients with AKI.

Abstract TP397: Validation of a Pneumonia Score in Acute Stroke

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Sara K Schepp ◽  
Kyra J Becker ◽  
W.T. Longstreth ◽  
David L Tirschwell
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Medical Records ◽  
Prospective Cohort ◽  
The Other ◽  
Discharge Diagnosis ◽  
Derivation Cohort ◽  
C Statistic ◽  
Diagnosis Codes ◽  
Time Of Admission

INTRODUCTION: Accurate prediction of pneumonia (PNA) risk after stroke would 1) allow clinicians to target interventions to patients at highest risk, and 2) help researchers to determine the efficacy of those interventions. We previously derived a PNA risk score based on data available at the time of admission, purposefully leaving out information on swallowing function, which may not be available at time of admission. Items for the 11-point score and point values were: age > 75 (2), male (1), National Institutes of Health Stroke Scale score > 10 (2), mechanical ventilation (4), coronary artery disease (1), chronic obstructive pulmonary disease (1). In a retrospective single-hospital cohort of 1,924 patients with acute ischemic stroke and intracranial hemorrhage (ICH), we used medical records and discharge diagnosis codes to derive the score: C-statistic = .79 (95% CI, .76 - .81). In the current study, we tested whether the score could accurately predict PNA in two other cohorts. METHODS: The one cohort (n=398 with acute ischemic stroke or ICH) was obtained retrospectively and presented to various hospitals within the same city during a time period prior to the derivation cohort. Data on predictor variables and the outcome of PNA were obtained from medical records and discharge diagnosis codes. The other cohort (n=89 with acute ischemic stroke) was a subset of the derivation cohort. Data were collected prospectively, and the diagnosis of PNA was ascertained using rigorous criteria that included clinical, radiographic, and culture data. RESULTS: Within the retrospective cohort, PNA was diagnosed in 46 (12%), and the score achieved a C-statistic of .71 (95% CI, .66 -.75). Within the prospective cohort, pneumonia was diagnosed in 9 (10%), and the C-statistic for the score was .88 (95% CI, .79 -.94). CONCLUSION: The predictive value of the PNA score was validated in two additional cohorts, one with data collected retrospectively and the other, prospectively. The score performed best within the prospective cohort, but sample size was relatively small and the 89 patients were a subset of the derivation cohort. Further refinement and validation of the score is planned.

Abstract 17185: Modest Performance of Heart Failure Clinical Prediction Models: A Systematic External Validation Study

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Jenica N Upshaw ◽  
Jason Nelson ◽  
Benjamin Wessler ◽  
Benjamin Koethe ◽  
Christine Lundquist ◽  
...  
Keyword(s):  
Heart Failure ◽  
Prediction Models ◽  
External Validation ◽  
Significant Heterogeneity ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Derivation Cohort ◽  
C Statistic ◽  
Improved Model ◽  
Relative Change

Introduction: Most heart failure (HF) clinical prediction models (CPMs] have not been independently externally validated. We sought to test the performance of HF models in a diverse population using a systematic approach. Methods: A systematic review identified CPMs predicting outcomes for patients with HF. Individual patient data from 5 large publicaly available clinical trials enrolling patients with chronic HF were matched to published CPMs based on similarity in populations and available outcome and predictor variables in the clinical trial databases. CPM performance was evaluated for discrimination (c-statistic, % relative change in c-statistic) and calibration (Harrell’s E and E 90 , the mean and the 90% quantile of the error distribution from the smoothed loess observed value) for the original and recalibrated models. Results: Out of 135 HF CPMs reviewed, we identified 45 CPM-trial pairs including 13 unique CPMs. The outcome was mortality for all of the models with a trial match. During external validations, median c-statistic was 0.595 (IQR 0.563 to 0.630) with a median relative decrease in the c-statistic of -57 % (IQR, -49% to -71%) compared to the c-statistic reported in the derivation cohort. Overall, the median Harrell’s E was 0.09 (IQR, 0.04 to 0.135) and E 90 was 0.11 (IQR, 0.07 to 0.21). Recalibration of the intercept and slope led to substantially improved calibration with median change in Harrell’s E of -35% [IQR 0 to -75%] for the intercept and -56% [IQR -17% to -75%] for the intercept and slope. Refitting model covariates improved the median c-statistic by 38% to 0.629 [IQR 0.613 to 0.649]. Conclusion: For HF CPMs, independent external validations demonstrate that CPMs perform significantly worse than originally presented; however with significant heterogeneity. Recalibration of the intercept and slope improved model calibration. These results underscore the need to carefully consider the derivation cohort characteristics when using published CPMs.

Abstract 164: Outcome Prediction After Endovascular Reperfusion Therapy For Acute Ischemic Stroke: The SNARL Score

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Shyam Prabhakaran ◽  
Kevin N Sheth ◽  
John B Terry ◽  
Raul G Nogueira ◽  
Anat Horev ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction ◽  
Validation Cohort ◽  
Reperfusion Therapy ◽  
Anterior Circulation ◽  
Derivation Cohort ◽  
Good Outcome ◽  
C Statistic ◽  
Pre Treatment

Background: Tools to predict outcome after endovascular reperfusion therapy (ERT) for acute ischemic stroke (AIS) have previously included only pre-treatment variables. We sought to derive and validate an outcome prediction score based on readily available pre-treatment and treatment factors. Methods: The derivation cohort consisted of 516 patients with anterior circulation AIS from 9 centers from September 2009-July 2011. The validation cohort consisted of 110 patients with anterior circulation AIS from the Penumbra Pivotal Trial. Multivariable logistic regression identified predictors of good outcome, defined as a modified Rankin Score (mRS) of < 2, in the derivation sample; model beta coefficients were used to assign point scores. Discrimination was tested using C-statistics. We then validated the score in the Penumbra cohort and performed calibration (predicted versus observed good outcome) in both cohorts. Results: Good outcome at 3 months was noted in 189 (36.8%) patients in the derivation cohort. The independent predictors of good outcome were A ge (2 pts: <60; 1 pt: 60-79; 0 pts: >79), N IHSS score (4 pts: 0-10; 2 pts: 11-20; 0 pts: > 20), L ocation of clot (2 pts: M2; 1 pt: M1; 0 pts: ICA), R ecanalization (5 pts: TICI 2 or 3), and S ymptomatic hemorrhage (2 pts: none, HT1-2, or PH1; 0 pts: PH2). The outcome (SNARL) score demonstrated good discrimination in the derivation cohort (C-statistic 0.78, 95% CI 0.72-0.78) and validation cohort (C-statistic 0.74, 95% CI 0.64-0.84). There was excellent calibration in each cohort (Figure). Conclusions: The SNARL score is a validated tool to determine the probability of functional recovery among AIS treated with endovascular reperfusion strategies. Unlike previous scores that did not include treatment factors such as successful recanalization or hemorrhagic complications, our score can be applied to patients after treatment and may provide guidance to physicians, patients, and families about expected functional outcome.

P5661Validation of CHA2DS2-VA score (excluding female sex) in non-valvular atrial fibrillation patients: a nationwide population-based study

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
S Y Choi ◽  
M H Kim ◽  
K M Lee ◽  
J K Kim ◽  
J Y Woo ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Population Based ◽  
Female Sex ◽  
Net Reclassification Improvement ◽  
C Statistic ◽  
Significant Difference ◽  
Risk Patients ◽  
Adjusted Incidence Rate ◽  
Adjusted Incidence

Abstract Background Sex category (Sc, ie, female sex) confers 1 point on CHA2DS2-VASc score. So, no woman with atrial fibrillation (AF) can have a CHA2DS2-VASc score of 0. This study aimed to compare CHA2DS2-VA (excluding female sex) and CHA2DS2-VASc score in Korean AF patients. Methods Using the Korean National Health Insurance Service database, we analyzed the risk of ischemic stroke in non-valvular AF patients between 2013 and 2017. The predictive value of the CHA2DS2-VA and CHA2DS2-VASc scores for ischemic stroke was evaluated by c-statistic difference and net reclassification improvement (NRI). The propensity score matching method was used to balance covariates across male and female AF patients. Results A total of 182,133 patients with AF (49.2% women) were included to this study. The adjusted incidence rate (IR) of ischemic stroke was not significantly different between males and females (0.89%/y and 0.90%/y, respectively, p=0.411) in low-risk patients without risk factor. Also, no sex difference was found in high-risk patients with above 2 risk factors for ischemic stroke (4.46%/y for male and 4.49%/y for male, p=0.498). In c-statistic analysis for ischemic stroke, there was no significant difference between the CHA2DS2-VA and CHA2DS2-VASc scores (AUC 0.662 vs. 0.664, z=1.572, p=0.116). When compared with CHA2DS2-VASc score, CHA2DS2-VA score was not significantly inferior in net reclassification improvement (NRI 0.031, 95% CI 0.002–0.037, p=0.118) for ischemic stroke. C-statistics Conclusions In Korean AF patients, the CHA2DS2-VA score excluding female sex is a useful risk scoring system for ischemic stroke.

Clinical models to predict response and survival in metastatic melanoma (MM) patients (pts) treated with anti-PD-1 alone (PD1) or combined with ipilimumab (IPI+PD1).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 9542-9542
Author(s):  
Ines Esteves Domingues Pires Da Silva ◽  
Tasnia Ahmed ◽  
Serigne Lo ◽  
Rajat Rai ◽  
Jessica Louise Smith ◽  
...  
Keyword(s):  
Predictive Models ◽  
Clinical Decision Making ◽  
External Validation ◽  
Prognostic Index ◽  
Primary Melanoma ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Monocyte Count ◽  
C Statistic ◽  
Mutational Status

9542 Background: Currently there are no robust biomarkers to predict immunotherapy response in MM. Specific clinical and molecular variables have been proposed, but in most cases, these factors have been studied individually. We sought to build a predictive model for response rate (RR), progression-free survival (PFS) and overall survival (OS), by including clinical data available at the point of treatment selection for MM pts treated with PD1 or IPI+PD1. Methods: 786 MM pts were included in 4 cohorts; 447 pts treated with PD1 (discovery, n = 343; validation, n = 104) and 339 pts treated with IPI+PD1 (discovery, n = 229; validation, n = 110). Demographics, disease characteristics and baseline blood parameters were examined. Predictive models were selected using multivariate Cox proportional hazard model, logistic regression and LASSO. ROC curve analyses were performed for each model and validation was measured by discrimination índex (c-statistic). Results: Predictive models for RR and PFS in PD1 pts (AUC = 0.69 and AUC = 0.71, respectively) included mutational status (HR for PFS: BRAF 1; NRAS 0.68; WT 0.57; P = 0.002), primary melanoma site (HR for PFS: occult 1; head and neck 0.67, others 1.04; P = 0.052), elevated LDH (HR for PFS: 1.77, P < 0.0001) and monocyte count > median (HR for PFS: 1.56, P = 0.003). Predictive models for RR and PFS in IPI+PD1 treated pts (AUC = 0.71 and AUC = 0.73, respectively) included AJCC stage M1C/M1D (HR for PFS: 2.12, P = 0.009), elevated LDH (HR for PFS: 2.65, P < 0.0001), liver mets (HR for PFS: 1.63, P = 0.038) and basophil count > median (HR for PFS: 0.50, P = 0.003). ECOG ≥ 1, elevated LDH and brain mets associated with worse OS and were included in predictive models for OS in PD1 (AUC = 0.74) and IPI+PD1 (AUC = 0.85). These models showed consistency with internal and external validation (c-statistic: < 10% difference between the original model and validations for all outcomes). Conclusions: A combination of routinely collected clinical factors are highly predictive of outcome in MM pts treated with PD1 and IPI+PD1. A prognostic index will be presented for each treatment. Such tools may be practical, cheap and valuable for clinical decision making.

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