Development and Validation of a Multivariable Predictive Model for Mortality of COVID-19 Patients Demanding High Oxygen Flow at Admission to ICU: AIDA Score

Introduction. Risk stratification is an important aspect of COVID-19 management, especially in patients admitted to ICU as it can provide more useful consumption of health resources, as well as prioritize critical care services in situations of overwhelming number of patients. Materials and Methods. A multivariable predictive model for mortality was developed using data solely from a derivation cohort of 160 COVID-19 patients with moderate to severe ARDS admitted to ICU. The regression coefficients from the final multivariate model of the derivation study were used to assign points for the risk model, consisted of all significant variables from the multivariate analysis and age as a known risk factor for COVID-19 patient mortality. The newly developed AIDA score was arrived at by assigning 5 points for serum albumin and 1 point for IL-6, D dimer, and age. The score was further validated on a cohort of 304 patients admitted to ICU due to the severe form of COVID-19. Results. The study population included 160 COVID-19 patients admitted to ICU in the derivation and 304 in the validation cohort. The mean patient age was 66.7 years (range, 20–93 years), with 68.1% men and 31.9% women. Most patients (76.8%) had comorbidities with hypertension (67.7%), diabetes (31.7), and coronary artery disease (19.3) as the most frequent. A total of 316 patients (68.3%) were treated with mechanical ventilation. Ninety-six (60.0%) in the derivation cohort and 221 (72.7%) patients in the validation cohort had a lethal outcome. The population was divided into the following risk categories for mortality based on the risk model score: low risk (score 0–1) and at-risk ( score > 1 ). In addition, patients were considered at high risk with a risk score > 2 . By applying the risk model to the validation cohort ( n = 304 ), the positive predictive value was 78.8% (95% CI 75.5% to 81.8%); the negative predictive value was 46.6% (95% CI 37.3% to 56.2%); the sensitivity was 82.4% (95% CI 76.7% to 87.1%), and the specificity was 41.0% (95% CI 30.3% to 52.3%). The C statistic was 0.863 (95% CI 0.805-0.921) and 0.665 (95% CI 0.598-0.732) in the derivation and validation cohorts, respectively, indicating a high discriminative value of the proposed score. Conclusion. In the present study, AIDA score showed a valuable significance in estimating the mortality risk in patients with the severe form of COVID-19 disease at admission to ICU. Further external validation on a larger group of patients is needed to provide more insights into the utility of this score in everyday practice.

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Ultrasonography predicts the results of labial salivary gland biopsy in patients with suspected Sjögren’s syndrome: a matrix risk model

Therapeutic Advances in Musculoskeletal Disease ◽

10.1177/1759720x211010592 ◽

2021 ◽

Vol 13 ◽

pp. 1759720X2110105

Author(s):

Ying-Qian Mo ◽

Shao-Yun Hao ◽

Qian-Hua Li ◽

Jin-Jian Liang ◽

Yi Luo ◽

...

Keyword(s):

Sjögren’S Syndrome ◽

Predictive Value ◽

Risk Model ◽

Validation Cohort ◽

Sjögren's Syndrome ◽

Sjogren’S Syndrome ◽

Sjogren's Syndrome ◽

Derivation Cohort ◽

Salivary Gland Biopsy ◽

Sjögren‘S Syndrome

Objective: Although a positive result of labial salivary gland biopsy (LSGB) is critical for the diagnosis of Sjögren’s syndrome, rheumatologists prefer assessing the non-invasive objective items and hope to learn the predicted probability of positive LSGB before referring patients with suspected Sjögren’s syndrome to receive biopsy. This study aimed to explore the predictive value of combined B-mode ultrasonography (US) and shear-wave elastography (SWE) examination on LSGB results. Methods: A derivation cohort and later a validation cohort of patients with suspected Sjögren’s syndrome were recruited. All participants received clinical assessments, B-mode US and SWE examination on bilateral parotid and submandibular glands before LSGB. Positive LSGB was defined by a focus score ⩾1 per 4 mm2 of glandular tissue. Results: In the derivation cohort of 91 participants, either the total US scores or the total SWE values of four glands significantly distinguished patients with positive LSGB from those with negative results (area under the curve (AUC) = 0.956, 0.825, both p < 0.001). The positive predictive value (PPV) was 100% in patients with total US scores ⩾9 or with total SWE values ⩾33 kPa. The negative predictive value (NPV) was 100% in patients with total US scores <5, but 68% in patients with total SWE values <27 kPa. A matrix risk model was derived based on the combination of total US scores and total SWE values. Patients can be stratified into high, moderate, and low risk of positive LSGB. In the validation cohort of 52 participants, the PPV was 94% in the high-risk subpopulation and the NPV was 93% in the low-risk subpopulation. Conclusion: A novel matrix risk model based on the combined B-mode US and SWE examination can help rheumatologists to make a shared decision with suspected Sjögren’s syndrome patients on whether the invasive procedure of LSGB should be performed.

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Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram

European Heart Journal - Digital Health ◽

10.1093/ehjdh/ztab081 ◽

2021 ◽

Author(s):

Matthias Unterhuber ◽

Karl-Philipp Rommel ◽

Karl-Patrik Kresoja ◽

Julia Lurz ◽

Jelena Kornej ◽

...

Keyword(s):

Heart Failure ◽

At Risk ◽

Deep Learning ◽

Ejection Fraction ◽

Predictive Value ◽

Validation Cohort ◽

External Validation ◽

Preserved Ejection Fraction ◽

Derivation Cohort ◽

Patients At Risk

Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a rapidly growing global health problem. To date, diagnosis of HFpEF is based on clinical, invasive and laboratory examinations. Electrocardiographic findings may vary, and there are no known typical ECG features for HFpEF. Methods This study included two patient cohorts. In the derivation cohort, we included n = 1884 patients who presented with exertional dyspnea or equivalent and preserved ejection fraction (≥50%) and clinical suspicion for coronary artery disease. The ECGs were divided in segments, yielding a total of 77.558 samples. We trained a convolutional neural network (CNN) to classify HFpEF and control patients according to ESC criteria. An external group of 203 volunteers in a prospective heart failure screening program served as validation cohort of the CNN. Results The external validation of the CNN yielded an AUC of 0.80 (95% CI 0.74–0.86) for detection of HFpEF according to ESC criteria, with a sensitivity of 0.99 (CI 0.98–0.99) and a specificity of 0.60 (95% CI 0.56–0.64), with a positive predictive value of 0.68 (95%CI 0.64–0.72) and a negative predictive value of 0.98 (95% CI 0.95–0.99). Conclusion In this study, we report the first deep learning-enabled CNN for identifying patients with HFpEF according to ESC criteria including NT-proBNP measurements in the diagnostic algorithm among patients at risk. The suitability of the CNN was validated on an external validation cohort of patients at risk for developing heart failure, showing a convincing screening performance.

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Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: a multicohort stud

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2019-000381 ◽

2020 ◽

Vol 8 (1) ◽

pp. e000381

Author(s):

Xue Bai ◽

De-Hua Wu ◽

Si-Cong Ma ◽

Jian Wang ◽

Xin-Ran Tang ◽

...

Keyword(s):

Predictive Value ◽

Risk Model ◽

Validation Cohort ◽

External Validation ◽

Progression Free Survival ◽

Predictive Capacity ◽

Driver Genes ◽

Independent Predictive Factor ◽

Training Cohort ◽

Genomic Mutation

BackgroundGenetic variations of some driver genes in non-small cell lung cancer (NSCLC) had shown potential impact on immune microenvironment and associated with response or resistance to programmed cell death protein 1 (PD-1) blockade immunotherapy. We therefore undertook an exploratory analysis to develop a genomic mutation signature (GMS) and predict the response to anti-PD-(L)1 therapy.MethodsIn this multicohort analysis, 316 patients with non-squamous NSCLC treated with anti-PD-(L)1 from three independent cohorts were included in our study. Tumor samples from the patients were molecularly profiled by MSK-IMPACT or whole exome sequencing. We developed a risk model named GMS based on the MSK training cohort (n=123). The predictive model was first validated in the separate internal MSK cohort (n=82) and then validated in an external cohort containing 111 patients from previously published clinical trials.ResultsA GMS risk model consisting of eight genes (TP53, KRAS, STK11, EGFR, PTPRD, KMT2C, SMAD4, and HGF) was generated to classify patients into high and low GMS groups in the training cohort. Patients with high GMS in the training cohort had longer progression-free survival (hazard ratio (HR) 0.41, 0.28–0.61, p<0.0001) and overall survival (HR 0.53, 0.32–0.89, p=0.0275) compared with low GMS. We noted equivalent findings in the internal validation cohort and in the external validation cohort. The GMS was demonstrated as an independent predictive factor for anti-PD-(L)1 therapy comparing with tumor mutational burden. Meanwhile, GMS showed undifferentiated predictive value in patients with different clinicopathological features. Notably, both GMS and PD-L1 were independent predictors and demonstrated poorly correlated; inclusion of PD-L1 with GMS further improved the predictive capacity for PD-1 blockade immunotherapy.ConclusionsOur study highlights the potential predictive value of GMS for immunotherapeutic benefit in non-squamous NSCLC. Besides, the combination of GMS and PD-L1 may serve as an optimal partner in guiding treatment decisions for anti-PD-(L)1 based therapy.

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Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

Scientific Reports ◽

10.1038/s41598-020-80474-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bongjin Lee ◽

Kyunghoon Kim ◽

Hyejin Hwang ◽

You Sun Kim ◽

Eun Hee Chung ◽

...

Keyword(s):

Machine Learning ◽

Intensive Care Unit ◽

Intensive Care ◽

Validation Cohort ◽

External Validation ◽

Learning Model ◽

Derivation Cohort ◽

Icu Admission ◽

Early Stages ◽

Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

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Echocardiography for the 10-year prediction of cardiomyopathy in long-term survivors of childhood cancer

European Heart Journal ◽

10.1093/ehjci/ehaa946.3254 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

J.M Leerink ◽

H.J.H Van Der Pal ◽

E.A.M Feijen ◽

P.G Meregalli ◽

M.S Pourier ◽

...

Keyword(s):

Childhood Cancer ◽

Median Time ◽

Cancer Diagnosis ◽

Predictive Value ◽

Validation Cohort ◽

Funding Source ◽

Derivation Cohort ◽

After Cancer ◽

Radiotherapy Dose

Abstract Background Childhood cancer survivors (CCS) treated with anthracyclines and/or chest-directed radiotherapy receive life-long echocardiographic surveillance to detect cardiomyopathy early. Current risk stratification and surveillance frequency recommendations are based on anthracycline- and chest-directed radiotherapy dose. We assessed the added prognostic value of an initial left ventricular ejection fraction (EF) measurement at >5 years after cancer diagnosis. Patients and methods Echocardiographic follow-up was performed in asymptomatic CCS from the Emma Children's Hospital (derivation; n=299; median time after diagnosis, 16.7 years [inter quartile range (IQR) 11.8–23.15]) and from the Radboud University Medical Center (validation; n=218, median time after diagnosis, 17.0 years [IQR 13.0–21.7]) in the Netherlands. CCS with cardiomyopathy at baseline were excluded (n=16). The endpoint was cardiomyopathy, defined as a clinically significant decreased EF (EF<40%). The predictive value of the initial EF at >5 years after cancer diagnosis was analyzed with multivariable Cox regression models in the derivation cohort and the model was validated in the validation cohort. Results The median follow-up after the initial EF was 10.9 years and 8.9 years in the derivation and validation cohort, respectively, with cardiomyopathy developing in 11/299 (3.7%) and 7/218 (3.2%), respectively. Addition of the initial EF on top of anthracycline and chest radiotherapy dose increased the C-index from 0.75 to 0.85 in the derivation cohort and from 0.71 to 0.92 in the validation cohort (p<0.01). The model was well calibrated at 10-year predicted probabilities up to 5%. An initial EF between 40–49% was associated with a hazard ratio of 6.8 (95% CI 1.8–25) for development of cardiomyopathy during follow-up. For those with a predicted 10-year cardiomyopathy probability <3% (76.9% of the derivation cohort and 74.3% of validation cohort) the negative predictive value was >99% in both cohorts. Conclusion The addition of the initial EF >5 years after cancer diagnosis to anthracycline- and chest-directed radiotherapy dose improves the 10-year cardiomyopathy prediction in CCS. Our validated prediction model identifies low-risk survivors in whom the surveillance frequency may be reduced to every 10 years. Calibration in both cohorts Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Dutch Heart Foundation

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A novel severity score to predict inpatient mortality in COVID-19 patients

Scientific Reports ◽

10.1038/s41598-020-73962-9 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 2

Author(s):

David J. Altschul ◽

Santiago R. Unda ◽

Joshua Benton ◽

Rafael de la Garza Ramos ◽

Phillip Cezayirli ◽

...

Keyword(s):

Severity Score ◽

Clinical Decision Making ◽

Risk Model ◽

Validation Cohort ◽

Clinical Decision ◽

International Normalized Ratio ◽

Severe Illness ◽

Roc Curve Analysis ◽

Derivation Cohort ◽

Mortality Incidence

Abstract COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. We propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality. 4711 patients with confirmed SARS-CoV-2 infection were included. We derived a risk model using the first half of the cohort (n = 2355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2356 patients. Mortality incidence was 26.4% in the derivation cohort and 22.4% in the validation cohort. A COVID-19 severity score ranging from 0 to 10, consisting of age, oxygen saturation, mean arterial pressure, blood urea nitrogen, C-Reactive protein, and the international normalized ratio was developed. A ROC curve analysis was performed in the derivation cohort achieved an AUC of 0.824 (95% CI 0.814–0.851) and an AUC of 0.798 (95% CI 0.789–0.818) in the validation cohort. Furthermore, based on the risk categorization the probability of mortality was 11.8%, 39% and 78% for patient with low (0–3), moderate (4–6) and high (7–10) COVID-19 severity score. This developed and validated novel COVID-19 severity score will aid physicians in predicting mortality during surge periods.

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

Gut ◽

10.1136/gutjnl-2019-319926 ◽

2020 ◽

pp. gutjnl-2019-319926 ◽

Cited By ~ 1

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.

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Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-020-05111-3 ◽

2020 ◽

Author(s):

Constantinos Zamboglou ◽

Alisa S. Bettermann ◽

Christian Gratzke ◽

Michael Mix ◽

Juri Ruf ◽

...

Keyword(s):

Prostate Cancer ◽

Validation Cohort ◽

External Validation ◽

Image Interpretation ◽

Receiver Operating Curve ◽

Training Cohort ◽

Primary Prostate Cancer ◽

Psma Pet ◽

Number Of Patients ◽

Clinically Significant

Abstract Introduction Primary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions. Methodology This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent 68Ga-PSMA-11 PET/CT and histology sections were obtained after surgery. PCa lesions missed by visual PET image interpretation were counted and their International Society of Urological Pathology score (ISUP) was obtained. Finally, 154 RFs were derived from the PET images and the discriminative power to differentiate between prostates with or without visually undetectable lesions was assessed and areas under the receiver-operating curve (ROC-AUC) as well as sensitivities/specificities were calculated. Results In the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1–6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8. Conclusion Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.

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Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records

Journal of Clinical Medicine ◽

10.3390/jcm10071473 ◽

2021 ◽

Vol 10 (7) ◽

pp. 1473

Author(s):

Ru Wang ◽

Zhuqi Miao ◽

Tieming Liu ◽

Mei Liu ◽

Kristine Grdinovac ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Early Detection ◽

Electronic Health Records ◽

Predictive Model ◽

Validation Cohort ◽

Risk Index ◽

Diabetic Patients ◽

Health Records ◽

Derivation Cohort ◽

Electronic Health

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94,127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

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Machine learning identifies clinical parameters to predict mortality in patients undergoing transcatheter mitral valve repair

European Heart Journal ◽

10.1093/eurheartj/ehab724.2221 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

E Zweck ◽

M Spieker ◽

P Horn ◽

C Iliadis ◽

C Metze ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Mitral Valve ◽

Risk Stratification ◽

Mitral Valve Repair ◽

Validation Cohort ◽

External Validation ◽

Valve Repair ◽

Derivation Cohort ◽

Transcatheter Mitral Valve Repair

Abstract Background Transcatheter Mitral Valve Repair (TMVR) with MitraClip is an important treatment option for patients with severe mitral regurgitation. The lack of appropriate, validated and specific means to risk stratify TMVR patients complicates the evaluation of prognostic benefits of TMVR in clinical trials and practice. Purpose We aimed to develop an optimized risk stratification model for TMVR patients using machine learning (ML). Methods We included a total of 1009 TMVR patients from three large university hospitals, of which one (n=317) served as an external validation cohort. The primary endpoint was all-cause 1-year mortality, which was known in 95% of patients. Model performance was assessed using receiver operating characteristics. In the derivation cohort, different ML algorithms, including random forest, logistic regression, support vectors machines, k nearest neighbors, multilayer perceptron, and extreme gradient boosting (XGBoost) were tested using 5-fold cross-validation in the derivation cohort. The final model (Transcatheter MITral Valve Repair MortALIty PredicTion SYstem; MITRALITY) was tested in the validation cohort with respect to existing clinical scores. Results XGBoost was selected as the final algorithm for the MITRALITY Score, using only six baseline clinical features for prediction (in order of predictive importance): blood urea nitrogen, hemoglobin, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), mean arterial pressure, body mass index, and creatinine. In the external validation cohort, the MITRALITY Score's area under the curve (AUC) was 0.783, outperforming existing scores which yielded AUCs of 0.721 and 0.657 at best. 1-year mortality in the MITRALITY Score quartiles across the total cohort was 0.8%, 1.3%, 10.5%, and 54.6%, respectively. Odds of mortality in MITRALITY Score quartile 4 as compared to quartile 1 were 143.02 [34.75; 588.57]. Survival analyses showed that the differences in outcomes between the MITRALITY Score quartiles remained even over a timeframe of 3 years post intervention (log rank: p<0.005). With each increase by 1% in the MITRALITY score, the respective proportional hazard ratio for 3-year survival was 1.06 [1.05, 1.07] (Cox regression, p<0.05). Conclusion The MITRALITY Score is a novel, internally and externally validated ML-based tool for risk stratification of patients prior to TMVR. These findings may potentially allow for more precise design of future clinical trials, may enable novel treatment strategies tailored to populations of specific risk and thereby serve future daily clinical practice. FUNDunding Acknowledgement Type of funding sources: None. Summary Figure

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