Predictor Of 1-year Mortality Research Articles

Machine learning for the prediction of 1-year mortality in patients with sepsis-associated acute kidney injury

IntroductionSepsis-associated acute kidney injury (SA-AKI) is strongly associated with poor prognosis. We aimed to build a machine learning (ML)-based clinical model to predict 1-year mortality in patients with SA-AKI.MethodsSix ML algorithms were included to perform model fitting. Feature selection was based on the feature importance evaluated by the SHapley Additive exPlanations (SHAP) values. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminatory ability of the prediction model. Calibration curve and Brier score were employed to assess the calibrated ability. Our ML-based prediction models were validated both internally and externally.ResultsA total of 12,750 patients with SA-AKI and 55 features were included to build the prediction models. We identified the top 10 predictors including age, ICU stay and GCS score based on the feature importance. Among the six ML algorithms, the CatBoost showed the best prediction performance with an AUROC of 0.813 and Brier score of 0.119. In the external validation set, the predictive value remained favorable (AUROC = 0.784).ConclusionIn this study, we developed and validated a ML-based prediction model based on 10 commonly used clinical features which could accurately and early identify the individuals at high-risk of long-term mortality in patients with SA-AKI.

Validation of the Charlson Comorbidity Index for the prediction of 30-day and 1-year mortality among patients who underwent hip fracture surgery

IntroductionThe aim of our study was to validate the original Charlson Comorbidity Index (1987) (CCI) and adjusted CCI (2011) as a prediction model for 30-day and 1-year mortality after hip fracture surgery. The secondary aim of this study was to verify each variable of the CCI as a factor associated with 30-day and 1-year mortality.MethodsA prospective database of two-level II trauma teaching hospitals in the Netherlands was used. The original CCI from 1987 and the adjusted CCI were calculated based on medical history. To validate the original CCI and the adjusted CCI, the CCI was plotted against the observed 30-day and 1-year mortality, and the area under the curve (AUC) was calculated.ResultsA total of 3523 patients were included in this cohort study. The mean of the original CCI in this cohort was 5.1 (SD ± 2.0) and 4.6 (SD ± 1.9) for the adjusted CCI. The AUCs of the prediction models were 0.674 and 0.696 for 30-day mortality for the original and adjusted CCIs, respectively. The AUCs for 1-year mortality were 0.705 and 0.717 for the original and adjusted CCIs, respectively.ConclusionsA higher original and adjusted CCI is associated with a higher mortality rate. The AUC was relatively low for 30-day and 1-year mortality for both the original and adjusted CCIs compared to other prediction models for hip fracture patients in our cohort. The CCI is not recommended for the prediction of 30-day and 1-year mortality in hip fracture patients.

Left and right ventricular function measured by strain analysis predicts one-year mortality in ventricular tachycardia ablation patients with structural heart disease

Abstract Introduction Radiofrequency catheter ablation has become increasingly important in the management of scar-related ventricular tachycardia (VT). Wide range of clinical and procedural predictors of VT recurrence and mortality are described by previous studies. However, currently known echocardiographic predictors are mainly limited to conventional echo parameters. Speckle tracking echocardiography (STE) is a novel and promising method to evaluate systolic and diastolic function. STE derived parameters, such as global longitudinal strain (GLS), are proven predictors of mortality and arrhythmic events in heart diseases. Aims The aim of the present study was to assess whether STE derived parameters could be considered predictors of VT recurrence and mortality after catheter ablation of ventricular tachycardia in patients with structural heart disease. Methods We measured pre-ablation strain and strain rate parameters with speckle tracking. Left ventricular four-chamber longitudinal strain (LV4CLS), early diastolic strain rate (e’-sr), right ventricular free wall longitudinal strain (RVFWLS) and right ventricular four-chamber longitudinal strain (RV4CLS) were assessed in 73 cases. Patients were followed up for one year. Endpoints were VT recurrence, which was defined as either ICD recording of a sustained VT episode requiring therapy or an episode of sustained VT recorded on 12-lead ECG or CIED, and all-cause mortality. Results Univariate analysis using the Cox proportional hazard model revealed that only lower LVEF (HR: 0.52 [0.31-0.88], p=0.02) was associated with higher VT recurrence in the first year among the echocardiographic parameters. High LVEDD (HR: 3.9 [1.31-11.65], p=0.02), low TAPSE (HR: 0.52 [0.28-0.98], p=0.04), low LV4CLS (HR: 0.4 [0.19-0.83], p=0.01) and low RV4CLS (HR: 0.31 [0.13-0.72], p<0.01) were the predictors of 1-year mortality. Conclusion Strain parameters, especially right ventricular four-chamber longitudinal strain may supplement conventional echocardiographic parameters in the prediction of one-year mortality after catheter ablation in patients with structural heart disease. Further prospective clinical studies are necessary to verify the significance of strain parameters.

#1751 Validation of the ReDO Death Score in a Portuguese cohort

Abstract Background and Aims Mortality within the first year after dialysis start is estimated to be 20-30%, and it is mostly due to cardiovascular disease. Prognostic assessment after starting hemodialysis is challenging. Clark et al. developed the Recovery and Death Outcome risk score, which accurately predicted the likelihood of renal recovery to dialysis independence and of death within 1 year after discharge from in-hospital dialysis initiation. These models can be used at discharge or soon after patients start outpatient dialysis. We aimed to validate the Death Outcome risk score to predict one-year mortality after dialysis start in our population. Method .Retrospective analysis of hospitalized patients who initiated hemodialysis in a tertiary care hospital (Unidade Local de Saúde Santa Maria), from January 1st of 2016 to December 31st of 2019, and were discharged to outpatient dialysis. The risk of death within one year of discharge was calculated according to the ReDO score. We evaluated all-cause mortality within one year of hospital discharge. We classified patients into death outcome risk groups (labeled D1 to D4) according to the ReDO predictive score. Cox regression method was used to determine if the risk score was predictive of mortality within the first year after discharge. The discriminatory ability for the ReDO score to predict mortality was determined using the receiver operating characteristic (ROC) curve. Results 369 patients were included, with a mean age of 71.1 ± 14.3 years. The majority were male (59.9%, n = 221) and 87% were Caucasian (n = 321). The median Charlson score was 7 ± 3. The mortality rate within one year after discharge was 22.2% (n = 82). The one-year survival was significantly lower in patients with the highest probability of death (D4 = 61.3% vs. D3 = 67.6% vs. D2 = 81.3% vs. D4 = 97.3%, p < 0.001). The ReDO Death score accurately predicted the one-year risk of mortality, with an area under the ROC curve of 0.741, [95% CI (0.687–0.794), p < 0.001] (Fig. 1). The optimal REDO Death risk cut-off was >30%, with a hazard ratio of 6.57 [95% CI (3.48–12.2), p < 0.001] for one year risk of death, with a sensitivity of 78.0% and specificity of 60.6%. Conclusion We validated the ReDO score for 1-year mortality prediction after starting hemodialysis during hospitalization in a Portuguese population. This score can be used as a tool to inform goals of care discussion at the time of transition to out-of-hospital care, involving the in-hospital nephrology care team, the patient, and, if applicable, the future care team, as it can enlighten clinical decisions and, in some cases, lead to better end-of-life planning.

Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients

BackgroundMachine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome. MethodsThis study was performed on 13,682 patients at Tehran Heart Center from 2015 to 2021. Patients were split into 70:30 for testing and training. Four ML models were designed: a traditional Logistic Regression (LR) model, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ada Boost models. The importance of features was calculated using the RF feature selector and SHAP based on the XGBoost model. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the prediction on the testing dataset was the main measure of the model's performance. ResultsFrom a total of 9,073 patients with >1-year follow-up, 340 participants died. Higher age and higher rates of comorbidities were observed in these patients. Body mass index and lipid profile demonstrated a U-shaped correlation with the outcome. Among the models, RF had the best discrimination (AUC 0.866), while the highest sensitivity (80.9%) and specificity (88.3%) were for LR and XGBoost models, respectively. All models had AUCs of >0.8. ConclusionML models can predict 1-year mortality after PCI with high performance. A classic LR statistical approach showed comparable results with other ML models. The individual-level assessment of inter-variable correlations provided new insights into the non-linear contribution of risk factors to post-PCI mortality.

1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.

Background and Objectives: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arteries remain at a higher risk of excess morbidity and mortality despite being treated with primary percutaneous coronary intervention (PPCI). Identifying high-risk patients is prudent so that close monitoring and timely interventions can improve outcomes. Materials and Methods: A cohort of 605 STEMI patients [64.2 ± 13.2 years, 432 (71.41%) males] treated with PPCI were recruited. Their arterial pressure (AP) wave recorded throughout the PPCI procedure was analyzed to extract features to predict 1-year mortality. After denoising and extracting features, we developed two distinct feature selection strategies. The first strategy uses linear discriminant analysis (LDA), and the second employs principal component analysis (PCA), with each method selecting the top five features. Then, three machine learning algorithms were employed: LDA, K-nearest neighbor (KNN), and support vector machine (SVM). Results: The performance of these algorithms, measured by the area under the curve (AUC), ranged from 0.73 to 0.77, with accuracy, specificity, and sensitivity ranging between 68% and 73%. Moreover, we extended the analysis by incorporating demographics, risk factors, and catheterization information. This significantly improved the overall accuracy and specificity to more than 76% while maintaining the same level of sensitivity. This resulted in an AUC greater than 0.80 for most models. Conclusions: Machine learning algorithms analyzing hemodynamic traces in STEMI patients identify high-risk patients at risk of mortality.

Prediction of 30-day, 90-day, and 1-year mortality after colorectal cancer surgery using a data-driven approach

PurposeTo develop prediction models for short-term mortality risk assessment following colorectal cancer surgery.MethodsData was harmonized from four Danish observational health databases into the Observational Medical Outcomes Partnership Common Data Model. With a data-driven approach using the Least Absolute Shrinkage and Selection Operator logistic regression on preoperative data, we developed 30-day, 90-day, and 1-year mortality prediction models. We assessed discriminative performance using the area under the receiver operating characteristic and precision-recall curve and calibration using calibration slope, intercept, and calibration-in-the-large. We additionally assessed model performance in subgroups of curative, palliative, elective, and emergency surgery.ResultsA total of 57,521 patients were included in the study population, 51.1% male and with a median age of 72 years. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.88, 0.878, and 0.861 for 30-day, 90-day, and 1-year mortality, respectively, and a calibration-in-the-large of 1.01, 0.99, and 0.99. The overall incidence of mortality were 4.48% for 30-day mortality, 6.64% for 90-day mortality, and 12.8% for 1-year mortality, respectively. Subgroup analysis showed no improvement of discrimination or calibration when separating the cohort into cohorts of elective surgery, emergency surgery, curative surgery, and palliative surgery.ConclusionWe were able to train prediction models for the risk of short-term mortality on a data set of four combined national health databases with good discrimination and calibration. We found that one cohort including all operated patients resulted in better performing models than cohorts based on several subgroups.

Surgical site infection after trochanteric and subtrochanteric fractures: a single centre retrospective analysis

Surgical site infection (SSI) following osteosynthesis of trochanteric or subtrochanteric fractures is a rare but serious complication with incidence rate ranging from 1 to 3%. SSIs are associated with higher mortality and increased length of hospital stay resulting in higher healthcare costs and loss of life quality. In this retrospective analysis all patients with SSI following osteosynthesis of trochanteric or subtrochanteric fractures at the Department of Trauma Surgery were identified. We included all surgical procedures performed from 1992 to 2018, using data from electronic health records and SSI-Trauma-Registry. The aim was to describe epidemiological data, as well as to identify parameters correlating with the occurrence of SSI and mortality. Of 2753 patients, 53 (1.9%) developed SSI. Longer operative time was demonstrated among patients with SSI (P = 0.008). Mortality during the first postoperative year was significantly higher in the SSI group (32.1% vs. 19.1%; P = 0.018), with detection of methicillin-sensitive (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA; HR 4.13, CI 95% 1.24–13.80; P = 0.021) or Enterococcus spp. (HR 5.58, CI 95% 1.67–18.65; P = 0.005) being independent risk factors. Male sex (HR 2.25, 95% CI 1.86–2.73; P < 0.001) and higher mean age (HR 1.05, 95% CI 1.04–1.06; P < 0.001) were found to be predictors for 1-year mortality in non-infected patients. SSI rate was low with 1.9% and longer duration of surgery was associated with infection. Patients with SSI had a higher 1-year mortality, with detection of MSSA, MRSA and enterococci significantly increasing the risk of dying. Male sex and higher age were risk factors for one-year mortality in patients without SSI.

Risk stratification model integrating nutritional and inflammatory factors for predicting 1-year mortality after valvular heart surgery: a retrospective cohort study.

Existing risk-scoring systems for cardiac surgery include only standard preoperative factors, without considering nutritional and inflammatory status or intraoperative factors. The objective of this study was to develop a comprehensive prediction model for mortality incorporating nutritional, inflammatory, and perioperative factors in patients undergoing valvular heart surgery. In this retrospective review of 2,046 patients who underwent valvular heart surgery, Cox and LASSO regression analyses were performed to identify independent prognostic factors of 1-year postoperative mortality among various perioperative factors known to affect prognosis, including objective nutritional and inflammatory indices. A novel nomogram model incorporating selected prognostic factors was developed, and its discrimination ability was evaluated using the C-index. The model was validated in internal and external cohorts. The 1-year mortality rate after valvular heart surgery was 5.1% (105 of 2,046 patients) and was significantly associated with several preoperative objective inflammatory and nutritional indices. Cox and LASSO analyses identified the following five independent prognostic factors for mortality: monocyte-to-lymphocyte ratio (an objective inflammatory index), EuroSCORE II, Controlling Nutritional Status score, cardiopulmonary bypass time, and number of erythrocyte units transfused intraoperatively. Our nomogram model incorporating these five factors had a C-index of 0.834 (95% CI 0.791-0.877), which was higher than that of EuroSCORE II alone (0.744, 95% CI 0.697-0.791) (P<0.001). The nomogram achieved good discrimination ability, with C-indices of 0.836 (95% CI 0.790-0.878) and 0.727 (95% CI 0.651-0.803) in the internal and external validation cohorts, respectively, and showed well-fitted calibration curves. A nomogram model incorporating five inflammatory, nutritional, and perioperative factors, as well as EuroSCORE II, was a better predictor of 1-year mortality after valvular heart surgery than EuroSCORE II alone, with good discrimination and calibration power for predicting mortality in both internal and external validation cohorts.

Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches

Improving 1-Year Mortality Prediction After Pediatric Heart Transplantation Using Hypothetical Donor-Recipient Matches

Prehospital Targeting of 1-Year Mortality in Acute Chest Pain by Cardiac Biomarkers.

The identification and appropriate management of patients at risk of suffering from acute chest pain (ACP) in prehospital care are not straightforward. This task could benefit, as occurs in emergency departments (EDs), from cardiac enzyme assessment. The aim of the present work was to derive and validate a scoring system based on troponin T (cTnT), N-terminal pro B-type natriuretic peptide (NT-proBNP), and D-dimer to predict 1-year mortality in patients with ACP. This was a prospective, multicenter, ambulance-based cohort study of adult patients with a prehospital ACP diagnosis who were evacuated by ambulance to the ED between October 2019 and July 2021. The primary outcome was 365-day cumulative mortality. A total of 496 patients fulfilled the inclusion criteria. The mortality rate was 12.1% (60 patients). The scores derived from cTnT, NT-proBNP, and D-dimer presented an AUC of 0.802 (95% CI: 0718-0.886) for 365-day mortality. This AUC was superior to that of each individual cardiac enzyme. Our study provides promising evidence for the predictive value of a risk score based on cTnT, NT-proBNP, and D-dimer for the prediction of 1-year mortality in patients with ACP. The implementation of this score has the potential to benefit emergency medical service care and facilitate the on-scene decision-making process.

Predictors of 1-year mortality after gastrectomy for gastric cancer.

One-year mortality is important for referrals to specialist palliative care or advance care planning (ACP). This helps optimize comfort for those who cannot be cured or have a lower life expectancy. Few studies have investigated the risk factors for 1-year mortality after gastrectomy for gastric cancer (GC). A total of 1415 patients with gastric cancer (stages I-IV) who underwent gastrectomy between 2005 and 2020 were included. The patients were randomly assigned to the investigation group (n=850) and validation group (n=565) in a 3:2 ratio. In the investigation group, significant independent prognostic factors for predicting 1-year survival were identified. A scoring system for predicting 1-year mortality was developed which was validated in the validation group. Multivariate analysis revealed that the following seven variables were significant independent factors for 1-year survival: age ≧78, preoperative comorbidity, total gastrectomy, postoperative complication (Clavien-Dindo classification CD≧3a), stage III and IV, and R2 resection. While developing a 1-year mortality score (OMS), an age ≧78 was scored 2, preoperative comorbidity, total gastrectomy, and postoperative complication (CD≧3a) were scored 1, and stage III, IV, and R2-resection were scored 2, 3, and 3, respectively. OMS 3 had a sensitivity of 91% and a specificity of 66% for predicting death within 1year. In the validation group, OMS 5 had a sensitivity of 55% and a specificity of 93% for predicting death within 1year. OMS may provide important information and help surgeons select the timing of ACP in patients with GC.

Acute muscle wasting rate assessment and long-term mortality in critically ill trauma.

To evaluate the relationship between acute muscle wasting rate and long-term mortality in critically ill trauma. A single-center, retrospective study was conducted in critically ill trauma. Patients with Computed Tomography scans including the L3 vertebra within 24 hours and at 1 week after trauma were recruited. Acute muscle wasting rate was defined as the mean percent variation per day of skeletal muscle index in the first week after trauma. Multivariate logistic regression analysis and receiver operating characteristic curve analysis were performed to determine whether acute muscle wasting rate could help predict hospital malnutrition and 1-year mortality. Skeletal muscle index was 49.3±10.7 cm2/m2 at baseline and decreased to 45.1±9.6 cm2/m2 (p<0.001) at 1 week and 39.8±10.8cm2/m2 (p<0.001) at 1 month after trauma. A sustained decrease of skeletal muscle index was observed from baseline up to 6 months (33.7±8.4cm2/m2, p<0.001) post trauma, and lasted for 1 year (37.7±5.6cm2/m2, p=0.004). Logistic regression analysis showed that acute muscle wasting rate was an independent risk factor for hospital malnutrition and 1-year mortality. Every 1% absolute increase of acute muscle wasting rate was associated with 1.82-fold higher odds of 1-year mortality in critically ill trauma. The area under curve of acute muscle wasting rate was 0.813 for hospital malnutrition prediction and 0.715 for 1-year mortality prediction. Acute muscle wasting rate was independently associated with higher 1-year mortality and hospital malnutrition in critically ill trauma.

Predictors of 2-Year Mortality in Patients Receiving Long-Term Oxygen Therapy: A Prospective Observational Study.

Background: Patients receiving long-term oxygen therapy are in a state of progressive respiratory dysfunction and have high mortality. However, the predictors of mortality in these patients have not yet been established. Objectives: This prospective observational study aimed to identify the predictors of two-year mortality in patients receiving long-term oxygen therapy. Design, Setting/Subjects: This two-year prospective observational study included 96 patients who received long-term oxygen therapy in the outpatient department of the National Hospital Organization Nishiniigata Chuo Hospital in Japan. Measurements: The updated Charlson Comorbidity Index, body mass index, handgrip strength, modified British Medical Research Council scale (mMRC), Barthel Index (BI), and Montreal Cognitive Assessment (MoCA) were collected in 2019 as a baseline. Outcome was defined as mortality due to chronic respiratory disease during the two-year follow-up period, and predictors were estimated using age- and sex-adjusted Cox proportional hazards model. Results: The 83 patients that were followed up, 30 (36%) died. The Cox proportional hazards model estimated handgrip strength (adjusted hazard ratio [HR]: 0.89; 95% confidence interval [CI]: 0.84-0.94; p < 0.01; Wald: 14.38.), mMRC (adjusted HR: 1.96; 95% CI: 1.36-2.83; p < 0.01; Wald: 13.16.), BI (adjusted HR: 0.95; 95% CI: 0.93-0.98; p < 0.01; Wald: 17.07.), and MoCA (adjusted HR: 2.17; 95% CI: 1.31-3.59; p < 0.01; Wald: 9.06) as predictors. Conclusions: This study indicated that handgrip strength, dyspnea, activities of daily living, and cognitive function were predictors of two-year mortality in patients receiving long-term oxygen therapy.

Abstract 13800: Comparison of Predictive Value of Risk Scores for Mortality in Asian Patients With Heart Failure With Preserved Ejection Fraction

Background: Several risk scores have been developed to predict mortality and morbidity in heart failure (HF), including heart failure with preserved ejection fraction (HFpEF). However, these scores have primarily been derived from data obtained from Western patients. Research Questions: It remains unclear whether these risk factors are applicable to Asian patients, whose pathophysiology of HFpEF may exhibit ethnic differences compared to Western populations. Aims: To compare the predictive value of widely used risk scores for mortality in Japanese patients with HFpEF. Methods: We assessed the predictive value of three commonly utilized risk scores: MAGGIC, BCN-BioHF, and 3C-HF risk scores, which are applicable to patients with HFpEF. A total of 1,095 patients registered in the PURSUIT-HFpEF, a multicenter registry comprising individuals hospitalized with decompensated HFpEF, were enrolled (mean age 81±9 years, 54.8% female). The MAGGIC risk score and the predicted 1-year mortality rate by the web-based BCN-BioHF and 3C-HF risk calculators were obtained for all patients. Patients were followed for a median duration of 428 days (interquartile range: 208-794 days) to observe all-cause mortality. The predictive value of the risk scores was compared using the area under the curve (AUC) of time-dependent ROC curves for 1-year mortality. Results: Complete risk scores and predicted 1-year mortality rates were available for 1,082 (98.8%) patients. The AUC for 1-year mortality prediction was 0.73 (95% CI 0.68, 0.78) for the MAGGIC score, 0.65 (0.59, 0.71) for BCN-BioHF, and 0.71 (0.64, 0.76) for 3C-HF. The MAGGIC score exhibited a significantly higher AUC than BCN-BioHF (p=0.003), while no significant differences were observed between the MAGGIC score and 3C-HF, or between BCN-BioHF and 3C-HF. Conclusion: This study demonstrates that the MAGGIC, BCN-BioHF, and 3C-HF risk scores provide good predictive value for 1-year mortality in Japanese patients with HFpEF. The superior performance of the MAGGIC score suggests that considering the ethnic differences in HFpEF pathophysiology is crucial for accurate risk prediction in Asian populations.

Shear wave elastography-based deep learning model for prognosis of patients with acutely decompensated cirrhosis.

This study aimed to develop and validate a deep learning model based on two-dimensional (2D) shear wave elastography (SWE) for predicting prognosis in patients with acutely decompensated cirrhosis. We prospectively enrolled 288 acutely decompensated cirrhosis patients with a minimum 1-year follow-up, divided into a training cohort (202 patients, 1010 2D SWE images) and a test cohort (86 patients, 430 2D SWE images). Using transfer learning by Resnet-50 to analyze 2D SWE images, a SWE-based deep learning signature (DLswe) was developed for 1-year mortality prediction. A combined nomogram was established by incorporating deep learning SWE information and laboratory data through a multivariate Cox regression analysis. The performance of the nomogram was evaluated with respect to predictive discrimination, calibration, and clinical usefulness in the training and test cohorts. The C-index for DLswe was 0.748 (95% CI 0.666-0.829) and 0.744 (95% CI 0.623-0.864) in the training and test cohorts, respectively. The combined nomogram significantly improved the C-index, accuracy, sensitivity, and specificity of DLswe to 0.823 (95% CI 0.763-0.883), 86%, 75%, and 89% in the training cohort, and 0.808 (95% CI 0.707-0.909), 83%, 74%, and 85% in the test cohort (both p < 0.05). Calibration curves demonstrated good calibration of the combined nomogram. Decision curve analysis indicated that the nomogram was clinically valuable. The 2D SWE-based deep learning model holds promise as a noninvasive tool to capture valuable prognostic information, thereby improving outcome prediction in patients with acutely decompensated cirrhosis.

Machine learning-based mortality prediction in hip fracture patients using biomarkers.

The purpose of this retrospective study was to assess whether mortality following a hip fracture can be predicted by a machine learning model trained on basic blood and lab test data as well as basic demographic data. Additionally, the purpose was to identify the key variables most associated with 1-, 5-, and 10-year mortality and investigate their clinical significance. Input data included 3751 hip fracture patient records sourced from the Medical Information Mart for Intensive Care IV database, which provided records from in-hospital database systems at the Beth Israel Deaconess Medical Center. The 1-year mortality rate for all patients studied was 21% and for those aged 80+ was 29%. We assessed 10 different machine learning classification models, finding LightGBM to have the strongest 1-year mortality prediction performance, with accuracy of 81%, AUC of 0.79, sensitivity of 0.34, and specificity of 0.98 on the test set. The strongest-weighted features of the 1-year model included age, glucose, red blood cell distribution width, mean corpuscular hemoglobin concentration, white blood cells, urea nitrogen, prothrombin time, platelet count, calcium levels, and partial thromboplastin time. Most of these were also in the top 10 features of the LightGBM 5- and 10-year prediction models trained. Testing for these high-ranking biomarkers in new hip fracture patients can aid clinicians in assessing the likelihood of poor outcomes for hip fracture patients, and additional research can use these biomarkers to develop a mortality risk score.

Mortality Prediction in Patients With or Without Heart Failure Using a Machine Learning Model

BackgroundMost risk prediction models are confined to specific medical conditions, thus limiting their application to general medical populations. ObjectivesThe MARKER-HF (Machine learning Assessment of RisK and EaRly mortality in Heart Failure) risk model was developed in heart failure (HF) patients. We assessed the ability of MARKER-HF to predict 1-year mortality in a large community-based hospital registry database including patients with and without HF. MethodsThis study included 41,749 consecutive patients who underwent echocardiography in a tertiary referral hospital (4,640 patients with and 37,109 without HF). Patients without HF were further subdivided into those with (n = 22,946) and without cardiovascular disease (n = 14,163) and also into cohorts based on recent acute coronary syndrome or history of atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, hypertension, or malignancy. ResultsThe median age of the 41,749 patients was 65 years, and 56.2% were male. The receiver operated area under the curves for MARKER-HF prediction of 1-year mortality of patients with HF was 0.729 (95% CI: 0.706-0.752) and for patients without HF was 0.770 (95% CI: 0.760-0.780). MARKER-HF prediction of mortality was consistent across subgroups with and without cardiovascular disease and in patients diagnosed with acute coronary syndrome, atrial fibrillation, chronic obstructive pulmonary disease, chronic kidney disease, diabetes mellitus, or hypertension. Patients with malignancy demonstrated higher mortality at a given MARKER-HF score than did patients in the other groups. ConclusionsMARKER-HF predicts mortality for patients with HF as well as for patients suffering from a variety of diseases.

Predictors of 5-Year Mortality in Patients Managed With a Magnetically Levitated Left Ventricular Assist Device

BackgroundIn advanced heart failure patients implanted with a fully magnetically levitated HeartMate 3 (HM3, Abbott) left ventricular assist device (LVAD), it is unknown how preimplant factors and postimplant index hospitalization events influence 5-year mortality in those able to be discharged. ObjectivesThe goal was to identify risk predictors of mortality through 5 years among HM3 LVAD recipients conditional on discharge from index hospitalization in the MOMENTUM 3 pivotal trial. MethodsThis analysis evaluated 485 of 515 (94%) patients discharged after implantation of the HM3 LVAD. Preimplant (baseline), implant surgery, and index hospitalization characteristics were analyzed individually, and as multivariable predictors for mortality risk through 5 years. ResultsCumulative 5-year mortality in the cohort (median age: 62 years, 80% male, 65% White, 61% destination therapy due to transplant ineligibility) was 38%. Two preimplant characteristics (elevated blood urea nitrogen and prior coronary artery bypass graft or valve procedure) and 3 postimplant characteristics (hemocompatibility-related adverse events, ventricular arrhythmias, and estimated glomerular filtration rate <60 mL/min/1.73 m2 at discharge) were predictors of 5-year mortality. In 171 of 485 patients (35.3%) without any risk predictors, 5-year mortality was reduced to 22.6% (95% CI: 15.4%-32.7%). Even among those with 1 or more predictors, mortality was <50% at 5 years (45.7% [95% CI: 39.0%-52.8%]). ConclusionsLong-term survival in successfully discharged HM3 LVAD recipients is largely influenced by clinical events experienced during the index surgical hospitalization in tandem with baseline factors, with mortality of <50% at 5 years. In patients without identified predictors of risk, long-term 5-year mortality is low and rivals that achieved with heart transplantation, even though most were implanted with destination therapy intent. (MOMENTUM 3 IDE Clinical Study Protocol, NCT02224755; MOMENTUM 3 Pivotal Cohort Extended Follow-up PAS, NCT03982979)

Systemic inflammation and biological aging in the Health and Retirement Study

Chronic, low-level systemic inflammation associated with aging, or inflammaging, is a risk factor for several chronic diseases and mortality. Using data from the Health and Retirement Study, we generated a continuous latent variable for systemic inflammation from seven measured indicators of inflammation and examined associations with another biomarker of biological aging, DNA methylation age acceleration measured by epigenetic clocks, and 4-year mortality (N = 3,113). We found that greater systemic inflammation was positively associated with DNA methylation age acceleration for 10 of the 13 epigenetic clocks, after adjustment for sociodemographics and chronic disease risk factors. The latent variable for systemic inflammation was associated with 4-year mortality independent of DNA methylation age acceleration and was a better predictor of 4-year mortality than any of the epigenetic clocks examined, as well as mortality risk factors, including obesity and multimorbidity. Inflammaging and DNA methylation age acceleration may represent different biological processes contributing to mortality risk. Leveraging multiple measured inflammation markers to capture inflammaging is important for biology of aging research.