scholarly journals Prognostic Factors and Diseases-Specific Survival Outcome in Patients with Glioblastoma: A Population-Based Study

2020 ◽  
Author(s):  
Lihua Wu ◽  
Jianbo Song ◽  
Junping Zhang ◽  
Wenhui Yang ◽  
Mengxian Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Tumor Size ◽  
Cox Regression ◽  
Cubic Spline ◽  
Age At Diagnosis ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Risk Effect

Abstract ObjectiveThis study aimed to determine the prognostic factors for disease-specific survival (DSS) of glioblastoma (GBM) and establish a corresponding effective nomogram for clinical prediction.Methods This study was based on Surveillance, Epidemiology, and End Results database between 2004 and 2015. Kaplan-Meier survival analysis was used to evaluate the effect of various prognostic factors on DSS. Lasso regression was used to determine the independent prognostic factors of DSS and multivariate cox regression analysis was performed correspondingly. Additional restricted cubic spline cox regression was used to analyze the trend of the risk effect (hazard ratio) of continuous variables on DSS. Based on the multivariate cox regression model, a nomogram was established to predict DSS. ResultsThe average age at diagnosis of all enrolled patients was 59.8±12.2 years, of which 40.5% were women and 59.5% were men. Lasso regression analysis showed that age at diagnosis, sex, marital status, race, tumor size, primary site, laterality, surgery, radiotherapy and chemotherapy, radiotherapy sequence with surgery, and year of diagnosis were independent prognostic factors for DSS. Multivariate cox regression analysis showed that elderly, males, unmarried status, larger tumors were all risk factors for DSS. Restricted cubic spline cox regression showed that the risk of death from GBM was significantly increased for the elderly, especially older than 75 years. When the tumor was smaller than 75mm, an increasing risk linearly was associated with DSS, but the risk effect remained constant after 75mm. Constructing the nomogram to predict the DSS probability of 1-, 3- and 5-year respectively, and its good predictive performance was proved by the calibration curve.ConclusionThe advanced age was one of the significant risk factors for GBM. How the change of tumor size affected DSS needed further study and discussion. The established nomogram was robust in predicting 1-, 3-, and 5- year DSS.

scholarly journals Incidence, Survival, and Associated Factors Estimation in Osteosarcoma Patients with Lung Metastasis: A Single-center Experience of 11 Years in Tianjin, China

2021 ◽  
Author(s):  
Chao Zhang ◽  
Haixiao Wu ◽  
Guijun Xu ◽  
Wenjuan Ma ◽  
Lisha Qi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Lung Metastasis ◽  
Associated Factors ◽  
Cox Regression ◽  
Cancer Center ◽  
Single Center ◽  
Cox Regression Analysis

Abstract Background: Osteosarcoma is the most common primary malignant bone tumor. The current study was conducted to describe the general condition of patients with primary osteosarcoma in a single cancer center in Tianjin, China and to investigate the associated factors in osteosarcoma patients with lung metastasis. Methods: From February 2009 to October 2020, patients from Tianjin Medical University Cancer Institute and Hospital, China were retrospectively analyzed. The Kaplan–Meier method was used to evaluate the overall survival of osteosarcoma patients. Prognostic factors of patients with osteosarcoma were identified by the Cox proportional hazard regression analysis. Risk factor of lung metastasis in osteosarcoma were investigated by the logistic regression model. Results: A total of 203 patients were involved and 150 patients were successfully followed up for survival status. The 5-year survival rate of osteo-sarcoma patients was 70.0%. Surgery, bone and lung metastasis were the significant prognostic factors in multivariable Cox regression analysis. Twenty-one (10.3%) patients showed lung metastasis at the diagnosis of osteosarcoma and 67 (33%) lung metastases during the later course. T3 stage (OR=11.415, 95%CI 1.362-95.677, P=0.025) and synchronous bone metastasis (OR=6.437, 95%CI 1.69-24.51, P=0.006) were risk factors of synchronous lung metastasis occurrence. Good necrosis (≥90%, OR=0.097, 95%CI 0.028-0.332, P=0.000) and elevated Ki-67 (≥50%, OR=4.529, 95%CI 1.241-16.524, P=0.022) were proved to be significantly associated with metachronous lung metastasis occurrence. Conclusion: The overall survival, prognostic factors and risk factors for lung metastasis in this single center provided insight about osteosarcoma management.

scholarly journals А New Combination of Prognostic Markers in Follicular Lymphoma That Influences the Choice of Therapy

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 4520-4520
Author(s):  
Ekaterina S. Nesterova ◽  
Nataliya A. Severina ◽  
Bella V. Biderman ◽  
Andrey B. Sudarikov ◽  
Tatiana N. Obukhova ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Follicular Lymphoma ◽  
Tumor Size ◽  
Cox Regression ◽  
Proliferation Index ◽  
Ki 67 ◽  
Cox Regression Analysis ◽  
Risk Of Death ◽  
Probability Of Death

Abstract Background: Follicular lymphoma (FL) is characterized by clinical and morphological heterogeneity. It is based on the pathogenetic mechanisms of the development of tumor cells. The identification and assessment of risk factors associated with the course of the disease and treatment outcome in FL is an important task, as it allows to evaluate and predict the effectiveness of therapy. Objective: Identify and estimate risk factors for overall survival (OS) and progression free survival (PFS) in FL. Patients and Methods: The prospective exploratory study conducted at National Research Center for Hematology (Moscow) from 01/2017 to 04/2021 included patients (pts)(in total, 80) with FL. Morpho-immunohistochemical, cytogenetic and molecular studies were performed on biopsies of lymph nodes taken before the start of therapy. The mutational status of exon 16 and intron polymorphism rs_2072407 of the EZH2 gene were investigated by Sanger sequencing. 18q21/BCL-2 rearrangements were determined by conventional cytogenetic analysis and/or FISH study. The results obtained in a blind study were compared with the effect of the therapy. Results: Of the 80 pts 34 were male: Me (median) age 50 years (range 30-72) and 46 were female: Me 56 (range 21-81). The median follow-up (FU) time was 53 months. As a result of the study in the multivariate Cox regression model (likelihood-ratio test, p=0.01) of significant factors, selected in the previously univariate analysis, the following statistically significant (Wald test) risk factors for OS and PFS (the events: progression, relapse, or death) were obtained: • BCL-2 gene rearrangements (no vs yes) • EZH2 gene genotypes (AA/AG vs GG) • proliferation index Ki-67 (>35%) • morphological grade (3А vs 1/2) • tumor size (>6 cm /bulky/) (Tab. 1, Fig. 1) The BCL-2 rearrangements were found in 45 from 80 pts (56%; 95 % CI 45-66). The probability of BCL-2 rearrangements is estimated to be about 0.5 (50%). According to the results of Cox-regression analysis (by OS) in the absence of BCL-2 rearrangements, the risk of death in FL was generally significantly (p = 0.01) higher than in the group with its presence: HR = 4.3 (95 % CI 1.5-13.0) (Fig. 2) Mutations in the 16th exon of the EZH2 gene (mutEZH2) were found in 10/80 (13%) pts. Analysis of EZH2 gene mutations with BCL-2 rearrangements revealed that in the mutEZH2 group with the presence of BCL-2 rearrangements, the number of deaths associated with progression is significantly less than in the control initial groups (mutEZH2 with BCL-2 rearrangements - 0/6, mutEZH2 without BCL-2 rearrangements - 2/4, wEZH2 with BCL-2 rearrangements - 3/39 (8%), wEZH2 without BCL-2 rearrangements - 11/31 (35%)) . The prognostic significance of EZH2 genotypes in lymphomas was studied for the first time in this study. The frequencies of rs_2072407 genotypes were: AA - 24% (19), AG - 42% (34), and GG - 34% (27). AA and AG genotypes of the EZH2 gene in pts with FL were associated with an increased risk of death (compared to the GG genotype) : HR = 2.9 (95% CI: 1.2-10.6), p = 0.01 (Fig. 3). The GG variant in most cases was associated with wEZH2 (26/27 (96%)) with BCL-2 rearrangements (16/26 (62%)) and a favorable prognosis (26/27 (96%)) (p = 0.01). Index of proliferative activity Ki-67> 35% (n = 40) and Ki-67 ≤ 35% (n = 40) were equally common in the study group. With a Ki-67> 35%, the probability of death is 2.9 (95% CI 1.1-9.7) times higher. The frequency distribution of morphological grade was as follows: grade 3A - 53% (n = 43) and grade 1-2 - 47% (n = 37). At grade 3A, the probability of death is 2.5 (95% CI 1.1-7.8) times higher. The number of pts with tumor size >6 cm (bulky) and ≤ 6 cm in the sample is approximately the same (41 and 39, respectively), the presence of bulky increased the mortality risk by 2.1 (95% CI 1.0-6.5) times. A short time from the manifestation of the disease to appeal to medical care is a predictor of poor prognosis, but this result we received earlier on a large sample of pts was not significant on a smaller sample. Conclusions: As a result of the multivariable Cox regression analysis, we identified and confirmed the previously obtained factors (bulky, grade 3A, Ki-67 > 35%, short medical history), and discovered new biogenetic factors (BCL-2 rearrangements and the GG rs2072407 genotype of the EZH2 gene). The model based on these independent risk factors improves the accuracy of predicting adverse events and allows to use more personalized treatment options for patients with FL. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

scholarly journals Prognostic factors in patients with thyroid carcinoma: a competing-risks analysis

2020 ◽  
Author(s):  
Junhu Wang ◽  
Lisong Heng ◽  
Jie Yang ◽  
Feng Tian ◽  
Xiaojun Liang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Thyroid Cancer ◽  
Prognostic Factors ◽  
Thyroid Carcinoma ◽  
Competing Risks ◽  
Tumor Size ◽  
Hazard Function ◽  
Cox Regression ◽  
Regional Lymph Nodes ◽  
Cox Regression Analysis

Abstract Background: Cox proportional-hazards models are widely used to describe survival trends and identify prognostic factors for thyroid carcinoma, but the prognostic model is not accurate enough. This study therefore used a competing-risks model to identify the significant prognostic factors for different types of thyroid carcinoma.Methods: We identified 38,444 eligible patients in the SEER (Surveillance, Epidemiology, and End Result) database. The potential prognostic factors for thyroid carcinoma were analyzed by Cox regression analysis, cause-specific hazard function (CS) analysis, and subdistribution hazard function (SD).Results: Cox regression analysis, CS analysis and SD analysis found identifying age, being unmarried, no regional lymph nodes examined, AJCC-6 II, III, IV vs I , having follicular, medullary, anaplastic vs Papillary carcinomas, no surgery, no radioiodine, liver metastasis, and lung metastasis as the significant risk factors for thyroid carcinoma, while being female was protective factor. However, the results from the three multivariate models for being black, tumor size >1 cm, and brain metastasis were inconsistent.Conclusion: In addition to finding that age, pathological type, tumor size, AJCC-6 stage, surgery status, radioiodine status, metastasis as common factors affected the prognosis, we also found that women, being unmarried and had their regional lymph nodes examined can improve the prognosis of thyroid cancer. The discovery of these factors will provide evidences for the prevention and treatment of thyroid cancer.

scholarly journals Clinical Characteristics and Prognostic Risk Factors of Patients With Proven Invasive Pulmonary Aspergillosis: A Single-Institution Retrospective Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xiang Tong ◽  
Tao Liu ◽  
Kexin Jiang ◽  
Dongguang Wang ◽  
Sitong Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Pleural Effusion ◽  
Cox Regression ◽  
Invasive Pulmonary Aspergillosis ◽  
Imaging Features ◽  
Pulmonary Aspergillosis ◽  
Cox Regression Analysis ◽  
Survivor Group

Background: The mortality and burden of medical costs associated with invasive pulmonary aspergillosis (IPA) is very high. Currently, the clinical features and prognostic factors of patients with proven IPA are not very clear, especially in the Chinese population. In this retrospective analysis, we aimed to identify the clinical features and prognostic factors of patients with proven IPA.Methods: The diagnostic criteria for proven IPA were based on the international consensus of the EORTC/MSG. Data of patients with proven IPA at the West China Hospital of Sichuan University between January 2012 and December 2018 were collected. The optimal cut-off value of continuous variables was determined by Receiver Operating Characteristic curve and maximum Youden's index. Finally, using the Cox regression analysis to identify correlations between the clinical parameters associated with morbidity.Results: A total of 117 patients with proven IPA were included in the study, and 32 (27.4%) patients died during the follow-up period. Compared with the survivor group, elderly, patients with comorbidities, and patients undergoing chemotherapy and the level of inflammatory biomarkers [erythrocyte sedimentation rate, platelet count, interleukin-6, C-reactive protein (CRP)] in the non-survivor group were higher, while the albumin level was lower (P = 0.018). The imaging features were consolidation, nodules, cavities, pleural effusion, ground-glass shadows, and halo signs in order. Overall, 41.0% patients had mixed imaging features. The results suggested the most appropriate cut-off value of age and CRP were 60 years and 14.1 mg/L, respectively. The multivariate Cox regression analysis suggested that advanced age (>60 years) [hazard ratio (HR): 10.7, confidence interval (CI): 2.5–44.9, P < 0.001), undergoing chemotherapy (HR: 9.5, CI: 2.7–32.9, P < 0.001), presence of pleural effusion (HR: 5.74, CI: 1.6–20.8, P = 0.008), and increased CRP levels (>14.1 mg/L) (HR: 6.3, CI: 1.2–34.3, P = 0.033) were risk factors for all-cause mortality in patients with proven aspergillosis.Conclusions: This study showed that the prognosis of proven IPA is poor, and the age >60 years, undergoing chemotherapy, pleural effusion on CT image, and CRP levels >14.1 mg/L may be as risk factors for mortality in patients with proven IPA. large samples and real-world studies are needed to confirm these results in the future.

scholarly journals Identification of Prognostic RBPs in Osteosarcoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110049
Author(s):  
Bei Li ◽  
Long Fang ◽  
Baolong Wang ◽  
Zengkun Yang ◽  
Tingbao Zhao
Keyword(s):  
Regression Analysis ◽  
Prognostic Factors ◽  
Differential Expression ◽  
Ribosome Biogenesis ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Malignant Tumors ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

scholarly journals A Prognostic Model for Patients With Gastric Signet Ring Cell Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110279
Author(s):  
Qinping Guo ◽  
Yinquan Wang ◽  
Jie An ◽  
Siben Wang ◽  
Xiushan Dong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Cox Regression ◽  
Signet Ring Cell Carcinoma ◽  
Signet Ring Cell ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Signet Ring ◽  
Ring Cell ◽  
Independent Risk Factors

Background: The aim of our study was to develop a nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with gastric signet ring cell carcinoma (GSRC). Methods: GSRC patients from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly assigned to the training and validation sets. Multivariate Cox regression analyses screened for OS and CSS independent risk factors and nomograms were constructed. Results: A total of 7,149 eligible GSRC patients were identified, including 4,766 in the training set and 2,383 in the validation set. Multivariate Cox regression analysis showed that gender, marital status, race, AJCC stage, TNM stage, surgery and chemotherapy were independent risk factors for both OS and CSS. Based on the results of the multivariate Cox regression analysis, prognostic nomograms were constructed for OS and CSS. In the training set, the C-index was 0.754 (95% CI = 0.746-0.762) for the OS nomogram and 0.762 (95% CI: 0.753-0.771) for the CSS nomogram. In the internal validation, the C-index for the OS nomogram was 0.758 (95% CI: 0.746-0.770), while the C-index for the CSS nomogram was 0.762 (95% CI: 0.749-0.775). Compared with TNM stage and SEER stage, the nomogram had better predictive ability. In addition, the calibration curves also showed good consistency between the predicted and actual 3-year and 5-year OS and CSS. Conclusion: The nomogram can effectively predict OS and CSS in patients with GSRC, which may help clinicians to personalize prognostic assessments and clinical decisions.

scholarly journals Prognostic Significance of PD-L1 and mTOR Expression in Oral Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Nattinee Charoen ◽  
Kitti Jantharapattana ◽  
Paramee Thongsuksai
Keyword(s):  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cox Regression ◽  
Prognostic Significance ◽  
P Value ◽  
Cox Regression Analysis

Objective: Programmed cell death ligand 1 (PD-L1) and mammalian target of rapamycin (mTOR) are key players in host immune evasion and oncogenic activation, respectively. Evidence of the prognostic role in oral squamous cell carcinoma (OSCC) is conflicting. This study examined the associations of PD-L1 and mTOR expression with 5-year overall survival in OSCC patients. Material and Methods: The expressions of PD-L1 and mTOR proteins were immunohistochemically evaluated on tissue microarrays of 191 patients with OSCC who were treated by surgery at Songklanagarind Hospital, Thailand from 2008 to 2011. Cox regression analysis was used to determine independent prognostic factors. Results: PD-L1 expression was observed in 14.1% of cases while mTOR expression was present in 74.3% of cases. Females were more likely to have tumors with PD-L1 (p-value=0.007) and mTOR expressions (p-value=0.003) than males. In addition, lower clinical stage and well differentiated tumor are more likely to have mTOR expression (p-value= 0.038 and p-value<0.001, respectively). Cox regression analysis showed that age, tumor stage, nodal stage, combined surgical treatment with radiation or chemoradiation therapy, surgical margin status, PD-L1 expression and mTOR expression are independent prognostic factors. High PD-L1 expression (hazard ratio (HR) 3.14, 95% confidence interval (CI), 1.26–7.79) and high mTOR expression (HR 1.69, 95% CI, 1.00–2.84) are strong predictors of poor outcome. Conclusion: A proportion of OSCC expressed PD-L1 and mTOR proteins. Expression of PD-L1 and mTOR proteins are strong prognostic factors of OSCC.

scholarly journals Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

2020 ◽  
Author(s):  
Tao Fan ◽  
Bo Hao ◽  
Shuo Yang ◽  
Bo Shen ◽  
Zhixin Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Disease Onset ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Calibration Curves ◽  
Independent Risk Factors ◽  
Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (<i>P</i>&lt;.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; <i>P</i>&lt;.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; <i>P</i>=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; <i>P</i>=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, <i>P</i>&lt;.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; <i>P</i>&lt;.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; <i>P</i>=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

scholarly journals Role of Disease Modifying Treatment in the Risk of Alloimmunization in Transfused Patients with Myelodysplastic Syndromes: A Population-Based Study

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 4253-4253
Author(s):  
Hanne Rozema ◽  
Robby Kibbelaar ◽  
Nic Veeger ◽  
Mels Hoogendoorn ◽  
Eric van Roon
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Cox Regression ◽  
Population Based ◽  
Incidence Rates ◽  
Blood Products ◽  
Cox Regression Analysis ◽  
In The Beginning ◽  
Observation Period

The majority of patients with myelodysplastic syndromes (MDS) require regular red blood cell (RBC) transfusions. Alloimmunization (AI) against blood products is an adverse event, causing time-consuming RBC compatibility testing. The reported incidence of AI in MDS patients varies greatly. Even though different studies on AI in MDS patients have been performed, there are still knowledge gaps. Current literature has not yet fully identified the risk factors and dynamics of AI in individual patients, nor has the influence of disease modifying treatment (DMT) been explored. Therefore, we performed this study to evaluate the effect of DMT on AI. An observational, population-based study, using the HemoBase registry, was performed including all newly diagnosed MDS patients between 2005 and 2017 in Friesland, a province of the Netherlands. All available information about treatment and transfusions, including transfusion dates, types, and treatment regimens, was collected from the electronic health records and laboratory systems. Follow-up occurred through March 2019. For our patient cohort, blood products were matched for AB0 and RhD, and transfused per the 'type and screen' policy (i.e. electronic matching of blood group phenotype between patient and donor). After a positive antibody screening, antibody identification and Rh/K phenotyping was performed and subsequent blood products were (cross)matched accordingly. The observation period was counted from first transfusion until last transfusion or first AI event. Univariate analyses and cumulative frequency distributions were performed to study possible risk factors and dynamics of AI. DMT was defined as hypomethylating agents, lenalidomide, chemotherapy and monoclonal antibodies. The effect of DMT as a temporary risk period on the risk of AI was estimated with incidence rates, relative risks (RR) and hazard ratios (HR) using a cox regression analysis. Follow-up was limited to 24 months for the cox regression analysis to avoid possible bias by survival differences. Statistical analyses were performed using IBM SPSS 24 and SAS 9.4. Out of 292 MDS patients, 236 patients received transfusions and were included in this study, covering 463 years of follow-up. AI occurred in 24 patients (10%). AI occurred mostly in the beginning of the observation period: Eighteen patients (75%) were alloimmunized after receiving 20 units of RBCs, whereas 22 patients (92%) showed AI after 45 units of RBCs (Figure 1). We found no significant risk factors for AI in MDS patients at baseline. DMT was given to 67 patients (28%) during the observation period. Patients on DMT received more RBC transfusions than patients that did not receive DMT (median of 33 (range: 3-154) and 11 (range: 0-322) RBC units respectively, p<0,001). Four AI events (6%) occurred in patients on DMT and 20 AI events (12%) occurred in patients not on DMT. Cox regression analysis of the first 24 months of follow-up showed an HR of 0.30 (95% CI: 0.07-1.31; p=0.11). The incidence rates per 100 person-years were 3.19 and 5.92 respectively. The corresponding RR was 0.54 (95% CI: 0.16-1.48; p=0.26). Based on our results, we conclude that the incidence of AI in an unselected, real world MDS population receiving RBC transfusions is 10% and predominantly occurred in the beginning of follow-up. Risk factors for AI at baseline could not be identified. Our data showed that patients on DMT received significantly more RBC transfusions but were less susceptible to AI. Therefore, extensive matching of blood products may not be necessary for patients on DMT. Larger studies are needed to confirm the protective effect of DMT on AI. Disclosures Rozema: Celgene: Other: Financial support for visiting MDS Foundation conference.

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