Abstract 126: Derivation and Validation of a Proposed Long Length of Stay (≥ 7 days) Score in Patients Hospitalized for Acute Ischemic Stroke

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Syed F Ali ◽  
Nabeel Chauhan ◽  
Nicolas Bianchi ◽  
Aneesh B Singhal ◽  
Lee H Schwamm
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Length Of Stay ◽  
Carotid Stenosis ◽  
External Validation ◽  
Predictive Score ◽  
Internal Validation ◽  
Altered Level ◽  
Derivation Cohort ◽  
Previous Stroke

Introduction: Long length of stay (LLOS) is one of the main factors in the determination of high cost in hospitalized stroke patients. Our aim was to prospectively predict patients more likely to have a long length of stay using our hospitals Get with the guidelines (GWTG) ischemic stroke registry. Methods: We selected 5,400 patients from our database of which 3,400 (~70%) were used for the derivation cohort and 2,000 (~30%) were used for internal validation. For external validation, 730 patients were included from the University of Arkansas. Long length of stay was defined ≥ 7 days. A predictive score was developed using stepwise logistic regression, and its performance assessed using ROC curve analysis. Result: Patients with LLOS in the derivation cohort were more likely to female, self-pay, more often have diabetes mellitus, atrial fibrillation, heart failure, previous stroke and carotid stenosis, and more often presented with weakness. They were more likely to have received IV or IA thrombolysis and early antithrombotics, and had higher rates of pneumonia (18.8% vs. 2.6%) and UTI (16.7% vs. 5.3%). Independent predictors of LLOS were Medicare/Medicaid insurance, self pay, history of atrial fibrillation, CAD, previous stroke, carotid stenosis, higher NIHSS and altered level of consciousness at presentation. The LLOS score (Table 2) performed well on ROC analysis (Derivation cohort AUC=0.72, Internal validation AUC=0.73 and External validation AUC=0.77). Conclusion: Many factors play a role in determining the length of stay for AIS patients. Our study provides a scoring system that may help physicians predict which patients are more likely to have a prolonged hospital stay.

Abstract T P50: ASTRAL-R score Predicts absence of Recanalization after Intravenous Thrombolysis in Acute Ischemic Stroke

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Peter Vanacker ◽  
Dimitris Lambrou ◽  
Mirjam Heldner ◽  
David Seiffge ◽  
Hubertus Mueller ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
External Validation ◽  
Visual Fields ◽  
Arterial Occlusion ◽  
Intravenous Thrombolysis ◽  
Altered Level ◽  
Derivation Cohort ◽  
Level Of Consciousness ◽  
High Prediction

Background: Intravenous thrombolysis (IVT) is the best proven recanalization treatment in acute ischemic stroke (AIS), but may be insufficient or of little value in certain patients. By predicting the probability of absence of recanalization after IVT, the decision for more aggressive revascularization treatment can be individualized with the goal to improve clinical outcome. Aim: To derive and internally validate a predictive scoring system for absence of recanalization with IVT, using readily available variables in the prehospital and emergency room phase. Methods: Data from prospective thrombolysis registries of four academic stroke centers were examined. Patients with arterial occlusion on acute imaging and repeat arterial assessment at 24hours were selected. Based on a logistic regression analysis, an integer-based score for each covariate of the fitted multivariate model was generated. The overall score was calculated as the sum of the weighted scores. In a patient with an ASTRAL-R score > 3, the likelihood of absence of recanalization was > 50%. The area under the receiver-operator curve was 0.65 in the derivation cohort. Results: In 534 thrombolyzed AIS patients, five variables were identified as independent predictors of absence of recanalization: Acute glucose >7mmol/L (A), significant extracranial vessel STenosis (ST), decreased Range of visual fields (R), proximal Arterial occlusion (A) and altered Level of consciousness (L). An altered level of consciousness was weighted 2 and all other variables 1 point based on β-coefficients. In a patient with an ASTRAL-R score > 3, the likelihood of absence of recanalization was >50%. The score was highly predictive (OR 0.65, 95%CI 0.55-0.76) in the derivation cohort. Conclusions: A simple 5-item ASTRAL-R score shows high prediction for absence of recanalization at 24hours in thrombolyzed AIS patients. If confirmed by external validation, planning for more aggressive revascularization strategies may facilitate through this tool.

Impact of atrial fibrillation on inpatient cost for ischemic stroke in the USA

2018 ◽  
Vol 14 (2) ◽  
pp. 159-166 ◽  
Author(s):  
Kumar Mukherjee ◽  
Khalid M Kamal
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Length Of Stay ◽  
Hospital Discharge ◽  
Significant Risk ◽  
Primary Diagnosis ◽  
Inpatient Cost ◽  
Targeted Interventions ◽  
Hospital Discharge Records ◽  
Hospital Characteristics

Background Atrial fibrillation is a significant risk factor for ischemic stroke and increases cost of treatment. Aims To estimate the incremental inpatient cost and length of stay due to atrial fibrillation among adults hospitalized with a primary diagnosis of ischemic stroke after controlling for sociodemographic, clinical, and hospital characteristics in a nationally representative discharge record of US population. Methods Hospital discharge records with a primary diagnosis of ischemic stroke were identified from the National Inpatient Sample data for the years 2010–2013. Generalized linear model with log link and least-square means were utilized to estimate the incremental inpatient cost and length of stay in ischemic stroke due to atrial fibrillation after controlling for sociodemographic, clinical, and hospital characteristics. Results Among 434,544 hospital discharge records with a primary diagnosis of ischemic stroke, 90,190 (20.76%) discharge records had a secondary diagnosis of atrial fibrillation. The average inpatient cost for all discharge records with a primary diagnosis of ischemic stroke was (mean = $13,072, median = $9270.87) significantly (p < 0.0001) higher compared to all discharge records without ischemic stroke (mean = $12,543.07, median = $7517.13). The mean length of stay for all records was 4.55 days (95% CI = 4.53–4.56). Among those identified with ischemic stroke, adjusted mean inpatient cost was higher by $2829 (95% CI = $2708–$2949) and mean length of stay was greater by 0.85 (95% CI = 0.81–0.89) for those with atrial fibrillation compared to those without. Conclusions The presence of atrial fibrillation was associated with increased inpatient cost and length of stay among patients diagnosed with ischemic stroke. Increased inpatient cost and length of stay call for a more comprehensive patient care approach including targeted interventions among adults diagnosed with ischemic stroke and atrial fibrillation, which could potentially reduce the overall cost in this population.

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.

scholarly journals Association Between High-Sensitivity Cardiac Troponin and Risk of Stroke in 96 702 Individuals

Stroke ◽  
2020 ◽  
Vol 51 (4) ◽  
pp. 1085-1093 ◽  
Author(s):  
Leonie H.A. Broersen ◽  
Helena Stengl ◽  
Christian H. Nolte ◽  
Dirk Westermann ◽  
Matthias Endres ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
General Population ◽  
Cardiac Troponin ◽  
Meta Analysis ◽  
High Sensitivity ◽  
Hazard Ratio ◽  
Risk Of Bias ◽  
Adjusted Hazard Ratio ◽  
Previous Stroke

Background and Purpose— Our study aim was to estimate risk of incident stroke based on levels of hs-cTn (high-sensitivity cardiac troponin), a specific biomarker indicating myocardial injury, in the general population, patients with atrial fibrillation, and patients with previous stroke. Methods— Embase, PubMed, and Web of Science were searched until March 14, 2019 to identify relevant articles. Randomized controlled trials and cohort studies assessing the risk of incident stroke based on hs-cTn were eligible. Pooled adjusted hazard ratios including 95% CI were calculated using a random-effects model due to study heterogeneity per population, coding of hs-cTn (categorical/continuous data), per hs-cTn subunit (T or I), for low risk of bias, and for all-cause and ischemic stroke separately. Results— We included 17 articles with 96 702 participants. In studies conducted in the general population (n=12; 77 780 participants), the pooled adjusted hazard ratio for incident stroke was 1.25 (CI, 1.10–1.40) for high versus low hs-cTn (as defined by included studies) during an average follow-up of 1 to 20 years (median 10). When categorical data were used, this was increased to 1.58 (CI, 1.26–1.90). The results were robust when accounting for stroke classification (all-cause stroke/ischemic stroke), hs-cTn subunit, risk of bias, and coding of hs-cTn. In patients with atrial fibrillation (4 studies; 18 725 participants), the pooled adjusted hazard ratio for incident stroke was 1.95 (CI, 1.29–2.62) for high versus low hs-cTn. Due to lack of data (one study, 197 participants), no meta-analysis could be performed in patients with previous stroke. Conclusions— This meta-analysis suggests that hs-cTn can be regarded as a risk marker for incident stroke, with different effect size in different subgroups. More research about the association between hs-cTn and incident stroke in high-risk populations is needed, especially in patients with history of ischemic stroke.

Abstract 25: Paroxysmal Atrial Fibrillation is Common in Patients With Defined Etiology for Stroke: Prolonged Monitoring of Cardiac Rhythm for Detection of Atrial Fibrillation After a Cerebral Ischemic Event (PEAACE) Study

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Esseddeeg M Ghrooda ◽  
Peter Dobrowolski ◽  
Ghazala Basir ◽  
Ibrahim Yaseen ◽  
Nazim khan ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ischemic Stroke ◽  
Cardiac Rhythm ◽  
Previous History ◽  
Cryptogenic Stroke ◽  
Predictive Score ◽  
Stroke Patients ◽  
Similar Frequency ◽  
Stroke Team ◽  
Rhythm Monitoring

Introduction: Atrial fibrillation (AF) related cardioembolic stroke accounts for over 20% of ischemic stroke. Recent reports using prolonged cardiac rhythm monitoring (PCRM) in cryptogenic stroke reveal paroxysmal AF (PAF) in an additional 20% of patients. We report our findings with PCRM in patients with and without cryptogenic stroke patients in whom an initial 24-h Holter was negative. Methods: Patients admitted to the stroke service with no previous history of AF and no AF on Holter monitoring were enrolled for 3 weeks of PCRM. We used a PAF predictive score to determine the risk of the arrhythmia. All studies were interpreted by the stroke team prior to final review by the cardiologist. Results: Between Sept 2012 and June 2013, 96 patients were evaluated. Over all PAF was diagnoses in 37.5 % of patients. PAF was diagnosed in 32% of patients with cryptogenic stroke and 36 % of patients where an additional etiology may account for the stroke diagnosis. The AF prediction score was not useful in the recognition of patients that were more likely to be at risk for AF. 96 of 98 recordings were correctly identified by the stroke team prior to final diagnosis by the cardiologist. Interpretation: PAF is more common in stroke patients than was previously suspected. It occurs with similar frequency in patients with and without cryptogenic stroke. Our data strongly supports the need for prolonged cardiac rhythm monitoring in all stroke patients to diagnose this important preventable cause of ischemic stroke.

Abstract P819: Individualized Patients’ Risk of Post Rt-pa ICH in Acute Ischemic Stroke by Using a Simple Predictive Model

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Waitayaporn Pengtong ◽  
Mananchaya Kongmuangpak ◽  
Niraya Noitamyae ◽  
Nongnapas Hiranshayangoon ◽  
Pimchanok Sriprayoon ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Confidence Interval ◽  
Intracerebral Hemorrhage ◽  
Acute Ischemic Stroke ◽  
Predictive Model ◽  
External Validation ◽  
Predictive Score ◽  
Individual Risk ◽  
Point Score ◽  
Independent Variables

Background and Purpose: A simple and reliable prediction of post intravenous recombination tissue plasminogen activator (rt-PA) intracerebral hemorrhage (ICH) is useful for stroke team to inform individual risk and improve hospitalization care. We aim to develop and validate a simple predictive score model to assess the 24-hour risk of ICH post rt-PA. Methods: This retrospective study included 739 acute ischemic stroke patients who received intravenous rt-PA between October 2005 and June 2020 at Siriraj Hospital, Bangkok, Thailand. Multiple logistic regression was used to evaluate the value of independent variables associated with any ICH and symptomatic intracerebral hemorrhage (SICH) measure by the European Cooperative Acute Stroke Study II (ECASS II) definition. Adjusted ORs of independent variables were converted to point score, summated of point score provide a probability of ICH and SICH. The predictive scores were internal validated in 95 patients and performance tested with an area under a receiver operating characteristic curve (AUC-ROC). Results: From a total of 739 patients, 19.2% had an ICH in which 5.9% was SICH. The predictive model of ICH included hyperdense middle cerebral artery sign (HDMCA) (3 points), initial blood glucose > 180mg/dl (3 points), coronary artery disease (CAD) (2 points), international normalized ratio (INR) > 1.0 (2 points) and 10 year increase of age ( 1 point per 10 year increase; age ≤ 20 year = 1). The performance of the predictive model showed AUC-ROC was 0.72 (95% confidence interval, 0.60 - 0.82) in the derivative cohort and AUC-ROC was 0.74 (95% confidence interval, 0.66 - 0.85) in the validation cohort. Conclusions: This simple predictive model provides a better prediction of a 24-hour risk of ICH post rt-PA in AIS among Thai patients. An individualized ICH risk post rt-PA can assist frontline physicians and relatives in rt-PA decision making process. Further external validation and prospectively confirmed in a larger cohort is recommend.

scholarly journals A new deep learning algorithm of 12-lead electrocardiogram for identifying atrial fibrillation during sinus rhythm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yong-Soo Baek ◽  
Sang-Chul Lee ◽  
Wonik Choi ◽  
Dae-Hyeok Kim
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Sinus Rhythm ◽  
Learning Algorithm ◽  
Normal Sinus Rhythm ◽  
Characteristic Curve ◽  
External Validation ◽  
Digital Data ◽  
Internal Validation ◽  
Optimal Interval

AbstractAtrial fibrillation (AF) is the most prevalent arrhythmia and is associated with increased morbidity and mortality. Its early detection is challenging because of the low detection yield of conventional methods. We aimed to develop a deep learning-based algorithm to identify AF during normal sinus rhythm (NSR) using 12-lead electrocardiogram (ECG) findings. We developed a new deep neural network to detect subtle differences in paroxysmal AF (PAF) during NSR using digital data from standard 12-lead ECGs. Raw digital data of 2,412 12-lead ECGs were analyzed. The artificial intelligence (AI) model showed that the optimal interval to detect subtle changes in PAF was within 0.24 s before the QRS complex in the 12-lead ECG. We allocated the enrolled ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. Regarding AF identification, the AI-based algorithm showed the following values in the internal and external validation datasets: area under the receiver operating characteristic curve, 0.79 and 0.75; recall, 82% and 77%; specificity, 78% and 72%; F1 score, 75% and 74%; and overall accuracy, 72.8% and 71.2%, respectively. The deep learning-based algorithm using 12-lead ECG demonstrated high accuracy for detecting AF during NSR.

scholarly journals Knowledge of stroke risk factors among the patients with previous stroke or transient ischemic attack in a tertiary care hospital of Bangladesh

2020 ◽  
Vol 11 (1) ◽  
pp. 22-29
Author(s):  
Md Rashedul Islam ◽  
Tanbin Rahman ◽  
Rafi Nazrul Islam ◽  
Mohammad Sakhawat Hossen Khan ◽  
Mofizul Islam ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Carotid Stenosis ◽  
Analysis Of Variance ◽  
Stroke Risk ◽  
Categorical Variables ◽  
Care Hospital ◽  
Stroke Risk Factors ◽  
Previous Stroke ◽  
History Of

Background: Patients of stroke or transient ischaemic attacks (TIA) are at risk of further stroke. Our objective was to study patients admitted with stroke/TIA regarding their knowledge about risk factors for having anew event of stroke/TIA, possible associations between patient characteristics and patients’ knowledge about risk factors, and patients’ knowledge about their preventive treatment for stroke/TIA. Methods: A questionnaire was used for 200 patients with stroke/TIA diagnoses. We asked 13 questions about diseases/conditions and lifestyle factors known to be risk factors and four questions regarding other diseases/ conditions (“distractors”). Additional questions concerned with the patients’ social and functional status and their drug use were asked. Categorical variables were analyzed using chi square test, while one-way analysis of variance and univariate analysis of variance were used for continuous variables. Logistic regression was employed to describe risk. A p value of, p < 0.05 was considered statistically significant. Results: The risk factors that were most often identified by the patients were Diabetes(75.9%), hypertension(83.3%), previous stroke or TIA(81.5%), smoking (85.2%), regular exercise(75.9%), older age(83.3%), overweight (75.9%) and patients with ischemic heart disease (70.4%). Atrial fibrillation and carotid stenosis were identified by less than 50% of the patients. 44.5% of the patients could identify 10 or more stroke/TIA risk factors. We observed that higher age, having a diagnosis of cerebral infarction/TIA, patients residing in urban area, high income group, businessman/retired service holder, family history of cardiovascular disease, past history of stroke / TIA were related to better knowledge of stroke/TIA risk factors. Anticoagulants and antiplatelets are important drugs for stroke/TIA prevention but only 20(9.3%) of the patients who reported anticoagulants and 76(35.2%) of the patients taking these drugs marked them as intended for prevention. Conclusion: Knowledge about diabetes, hypertension and smoking as risk factors was good, and patients who suffered from atrial fibrillation or carotid stenosis seemed to be less informed about these conditions as risk factors. The knowledge level was low regarding the use of anticoagulants and antiplatelets for stroke/TIA prevention. Better patient educational strategies for stroke/TIA patients should be developed. Furthermore, individuals with less knowledge should be given special consideration when developing strategies and programmes thus improving awareness of stroke risk factors. Birdem Med J 2021; 11(1): 22-29

scholarly journals Identification and validation of objective triggers for initiation of resuscitation management of acutely ill non-trauma patients: the INITIATE IRON MAN study

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Alexandros Rovas ◽  
Efe Paracikoglu ◽  
Mark Michael ◽  
André Gries ◽  
Janina Dziegielewski ◽  
...  
Keyword(s):  
External Validation ◽  
University Hospital ◽  
Trigger Factor ◽  
Early Warning Score ◽  
Trauma Patients ◽  
Internal Validation ◽  
Resuscitation Room ◽  
Derivation Cohort ◽  
Management Protocol ◽  
Qsofa Score

Abstract Background While there are clear national resuscitation room admission guidelines for major trauma patients, there are no comparable alarm criteria for critically ill nontrauma (CINT) patients in the emergency department (ED). The aim of this study was to define and validate specific trigger factor cut-offs for identification of CINT patients in need of a structured resuscitation management protocol. Methods All CINT patients at a German university hospital ED for whom structured resuscitation management would have been deemed desirable were prospectively enrolled over a 6-week period (derivation cohort, n = 108). The performance of different thresholds and/or combinations of trigger factors immediately available during triage were compared with the National Early Warning Score (NEWS) and Quick Sequential Organ Failure Assessment (qSOFA) score. Identified combinations were then tested in a retrospective sample of consecutive nontrauma patients presenting at the ED during a 4-week period (n = 996), and two large external datasets of CINT patients treated in two German university hospital EDs (validation cohorts 1 [n = 357] and 2 [n = 187]). Results The any-of-the-following trigger factor iteration with the best performance in the derivation cohort included: systolic blood pressure < 90 mmHg, oxygen saturation < 90%, and Glasgow Coma Scale score < 15 points. This set of triggers identified > 80% of patients in the derivation cohort and performed better than NEWS and qSOFA scores in the internal validation cohort (sensitivity = 98.5%, specificity = 98.6%). When applied to the external validation cohorts, need for advanced resuscitation measures and hospital mortality (6.7 vs. 28.6%, p < 0.0001 and 2.7 vs. 20.0%, p < 0.012) were significantly lower in trigger factor-negative patients. Conclusion Our simple, any-of-the-following decision rule can serve as an objective trigger for initiating resuscitation room management of CINT patients in the ED.

scholarly journals Development and Validation of a Prognostic Model for Transplant-Associated Thrombotic Microangiopathy Following Allogeneic Hematopoietic Stem Cell Transplantation

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 16-17
Author(s):  
Peng Zhao ◽  
Ye-Jun Wu ◽  
Qing-Yuan Qu ◽  
Shan Chong ◽  
Xiao-Wan Sun ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Model ◽  
Validation Cohort ◽  
Risk Group ◽  
External Validation ◽  
Internal Validation ◽  
Hematopoietic Stem ◽  
Derivation Cohort ◽  
Peking University ◽  
Score Model

Introduction Transplant-associated thrombotic microangiopathy (TA-TMA) is a potentially life-threatening complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT), which can result in multiorgan injury and increased risk for mortality. Renewed interest has emerged in the prognostication of TA-TMA with the development of novel diagnostic and management algorithms. Our previous study reported an adverse outcome in patients with TA-TMA and concomitant acute graft-versus-host disease (Eur J Haematol, 2018). However, information on markers for the early identification of severe cases remains limited. Therefore, this study is concentrated on the development and validation of a prognostic model for TA-TMA, which might facilitate risk stratification and contribute to individualized management. Methods Patients receiving allo-HSCT in Peking University People's Hospital with 1) a diagnosis of microangiopathic hemolytic anemia (MAHA) or 2) evidence of microangiopathy were retrospectively identified from 2010 to 2018. The diagnosis of TA-TMA was reviewed according to the Overall-TMA criteria (Transplantation, 2010). Patients without fulfillment of the diagnostic criteria or complicated with other causes of MAHA were excluded from analysis. Prognostic factors for TA-TMA were determined among patients receiving HSCT between 2010 and 2014 (derivation cohort). Candidate predictors (univariate P &lt; 0.1) were included in the multivariate analysis using a backward stepwise logistic regression model. A risk score model was then established according to the regression coefficient of each independent prognostic factor. The performance of this predictive model was evaluated through internal validation (bootstrap method with 1000 repetitions) and external temporal validation performed on data from those who received HSCT between 2015 and 2018 (validation cohort). Results 5337 patients underwent allo-HSCT at Peking University Institute of Hematology from 2010 to 2018. A total of 1255 patients with a diagnosis of MAHA and/or evidence of microangiopathy were retrospectively identified, among whom 493 patients met the inclusion criteria for this analysis (269 in the derivation cohort and 224 in the validation cohort). The median age at the time of TA-TMA diagnosis was 28 (IQR: 17-41) years. The median duration from the time of transplantation to the diagnosis of TA-TMA was 63 (IQR: 38-121) days. The 6-month overall survival rate was 42.2% (208/493), and the 1-year overall survival rate was 45.0% (222/493). In the derivation cohort, patient age (≥35 years), anemia (hemoglobin &lt;70 g/L), severe thrombocytopenia (platelet count &lt;15,000/μL), elevated lactic dehydrogenase (serum LDH &gt;800 U/L) and elevated total bilirubin (TBIL &gt;1.5*ULN) were identified by multivariate analysis as independent prognostic factors for the 6-month outcome of TA-TMA. A risk score model was constructed according to the regression coefficients (Table 1), and patients were stratified into a low-risk group (0-1 points), an intermediate-risk group (2-4 points) and a high-risk group (5-6 points). The Kaplan-Meier estimations of overall survival separated well between these risk groups (Figure 1). The prognostic model showed significant discriminatory capacity, with a cross-validated c-index of 0.770 (95%CI, 0.714-0.826) in the internal validation and 0.768 (95%CI, 0.707-0.829) in the external validation cohort. The calibration plots also indicated a good correlation between model-predicted and observed probabilities. Conclusions A prognostic model for TA-TMA incorporating several baseline laboratory factors was developed and evaluated, which demonstrated significant predictive capacity through internal and external validation. This predictive model might facilitate prognostication of TA-TMA and contribute to early identification of patients at higher risk for adverse outcomes. Further study may focus on whether these high-risk patients could benefit from early application of specific management. Disclosures No relevant conflicts of interest to declare.

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