Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center.
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ABSTRACT:
SUBMITTER: Lohmueller LC
PROVIDER: S-EPMC6176112 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
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Project description:Existing risk assessment tools for patient selection for left ventricular assist devices (LVADs) such as the Destination Therapy Risk Score and HeartMate II Risk Score (HMRS) have limited predictive ability. This study aims to overcome the limitations of traditional statistical methods by performing the first application of Bayesian analysis to the comprehensive Interagency Registry for Mechanically Assisted Circulatory Support dataset and comparing it to HMRS. We retrospectively analyzed 8,050 continuous flow LVAD patients and 226 preimplant variables. We then derived Bayesian models for mortality at each of five time end-points postimplant (30 days, 90 days, 6 month, 1 year, and 2 years), achieving accuracies of 95%, 90%, 90%, 83%, and 78%, Kappa values of 0.43, 0.37, 0.37, 0.45, and 0.43, and area under the receiver operator characteristic (ROC) of 91%, 82%, 82%, 80%, and 81%, respectively. This was in comparison to the HMRS with an ROC of 57% and 60% at 90 days and 1 year, respectively. Preimplant interventions, such as dialysis, ECMO, and ventilators were major contributing risk markers. Bayesian models have the ability to reliably represent the complex causal relations of multiple variables on clinical outcomes. Their potential to develop a reliable risk stratification tool for use in clinical decision making on LVAD patients encourages further investigation.
| S-EPMC4414734 | biostudies-literature
Project description:Cardiac resynchronization therapy (CRT) reduces morbidity and mortality among individuals with dyssynchronous systolic heart failure (HF). However, patient outcomes vary, with some at higher risk than others for HF progression and death.To develop a risk prediction score incorporating variables associated with mortality, left ventricular assist device (LVAD) implant, or heart transplant in recipients of a primary prevention cardiac resynchronization therapy-defibrillator (CRT-D).We followed 305 CRT-D patients from the Prospective Observational Study of Implantable Cardioverter-Defibrillators for the composite outcome of all-cause mortality, LVAD implant, or heart transplant soon after device implantation. Serum biomarkers and electrocardiographic and clinical variables were collected at implant. Multivariable analysis using the Cox proportional hazards model with stepwise selection method was used to fit the final model.Among 305 patients, 53 experienced the composite endpoint. In multivariable analysis, 5 independent predictors ("HF-CRT") were identified: high-sensitivity C-reactive protein >9.42 ng/L (HR = 2.5 [1.4, 4.5]), New York Heart Association functional class III/IV (HR = 2.3 [1.2, 4.5]), creatinine >1.2 mg/dL (HR = 2.7 [1.4, 5.1]), red blood cell count <4.3 × 10(6)/?L (HR = 2.4 [1.3, 4.7]), and cardiac troponin T >28 ng/L (HR = 2.7 [1.4, 5.2]). One point was attributed to each predictor and 3 score categories were identified. Patients with scores 0-1, 2-3, and 4-5 had a 3-year cumulative event-free survival of 96.8%, 79.7%, and 35.2%, respectively (log-rank, P < .001).A simple score combining clinical and readily available biomarker data can risk-stratify CRT patients for HF progression and death. These findings may help identify patients who are in need of closer monitoring or early application of more aggressive circulatory support.
| S-EPMC4656051 | biostudies-literature
Project description:BackgroundValve replacement surgeries holds risks of morbidity and mortality.Materials and methodsThe study cohort included 346 patients who underwent different types of valve surgery, excluding redo and Bentall operations. All operations were performed through a median sternotomy using cardiopulmonary bypass.ResultsMean patient age was 51.6 ± 16.1 years, and 51% were male. Approximately 21% had diabetes, and 44.6% were hypertensive. Aortic valve replacement (AVR) was performed in 125 patients (37%), mitral valve replacement (MVR) in 95 (28%), combined AVR and MVR in 42 (13%), AVR plus coronary artery bypass grafting (CABG) in 19 (6%), and MVR plus CABG in 32 (10%). Operative mortality was 5.8% (n = 20). In the bivariate-level analysis, older age, operation type, hypertension, emergency surgery, use of a biological valve in the aortic or mitral position, pump time greater than 120 min, and aortic clamp time greater than 60 min were significant predictors of 30-day mortality. Use of medications stratified by duration (less than or more than a month) was also shown to be a predictor of mortality. Use of angiotensin-converting enzyme inhibitors, digoxin, beta-blockers, statins, and loop diuretics was associated with mortality. Older age, emergency/salvage surgery, use of beta-blockers for less than 1 month preoperatively, and use of a biological valve in the aortic position were significant and independent predictors of 30-day mortality.ConclusionAge, emergency valve surgery, use of a biological valve, use of beta-blockers for less than 1 month before surgery, type of surgery, EF<35%, pump time, and cross clamp time were all found to be independent predictors of mortality in patients undergoing valve surgery. Further prospective multicenter studies may be needed to provide a comprehensive assessment of mortality in patients undergoing valve surgery in Jordan.
| S-EPMC7851328 | biostudies-literature
Project description:ObjectivesWe investigated the impact of anemia based on admission hemoglobin (Hb) level as a prognostic risk factor for severe outcomes in hospitalized patients with coronavirus disease 2019 (COVID-19).MethodsA single-center, retrospective cohort study was conducted from a random sample of 733 adult patients (age ≥ 18 years) obtained from a total of 4356 laboratory confirmed SARS-CoV-2 cases who presented to the Emergency Department of Montefiore Medical Center between March-June 2020. The primary outcome was a composite endpoint of in-hospital severe outcomes of COVID-19. A secondary outcome was in-hospital all-cause mortality.ResultsAmong the 733 patients included in our final analysis, 438 patients (59.8%) presented with anemia. 105 patients (14.3%) had mild, and 333 patients (45.5%) had moderate-severe anemia. Overall, 437 patients (59.6%) had a composite endpoint of severe outcomes. On-admission anemia was an independent risk factor for all-cause mortality, (Odds Ratio 1.52, 95% CI [1.01-2.30], p = 0.046) but not for composite severe outcomes. However, moderate-severe anemia (Hb < 11 g/dL) on admission was independently associated with both severe outcomes (OR1.53, 95% CI [1.05-2.23], p = 0.028) and mortality (OR 1.67, 95% CI [1.09-2.56], p = 0.019) during hospitalization.ConclusionAnemia on admission was independently associated with increased odds of all-cause mortality in patients hospitalized with COVID-19. Furthermore, moderate-severe anemia (Hb <11 g/dL) was an independent risk factor for severe COVID-19 outcomes. Moving forward, COVID-19 patient management and risk stratification may benefit from addressing anemia on admission.
| S-EPMC8007204 | biostudies-literature
Project description:Background Prior studies have shown that women have worse 3-month survival after receiving a left ventricular assist device compared with men. Currently used prognostic scores, including the Heartmate II Risk Score, do not account for the increased residual risk in women. We used the IMACS (International Society for Heart and Lung Transplantation Mechanically Assisted Circulatory Support) registry to create and validate a sex-specific risk score for early mortality in left ventricular assist device recipients. Methods and Results Adult patients with a continuous-flow LVAD from the IMACS registry were randomly divided into a derivation cohort (DC; n=9113; 21% female) and a validation cohort (VC; n=6074; 21% female). The IMACS Risk Score was developed in the DC to predict 3-month mortality, from preoperative candidate predictors selected using the Akaike information criterion, or significant sex × variable interaction. In the DC, age, cardiogenic shock at implantation, body mass index, blood urea nitrogen, bilirubin, hemoglobin, albumin, platelet count, left ventricular end-diastolic diameter, tricuspid regurgitation, dialysis, and major infection before implantation were retained as significant predictors of 3-month mortality. There was significant ischemic heart failure × sex and platelet count × sex interaction. For each quartile increase in IMACS risk score, men (odds ratio [OR], 1.86; 95% CI, 1.74-2.00; P<0.0001), and women (OR, 1.93; 95% CI, 1.47-2.59; P<0.0001) had higher odds of 3-month mortality. The IMACS risk score represented a significant improvement over Heartmate II Risk Score (IMACS risk score area under the receiver operating characteristic curve: men: DC, 0.71; 95% CI, 0.69-0.73; VC, 0.69; 95% CI, 0.66-0.72; women: DC, 0.73; 95% CI, 0.70-0.77; VC, 0.71 [95% CI, 0.66-0.76; P<0.01 for improvement in receiver operating characteristic) and provided excellent risk calibration in both sexes. Removal of sex-specific interaction terms resulted in significant loss of model fit. Conclusions A sex-specific risk score provides excellent risk prediction in LVAD recipients.
| S-EPMC8174331 | biostudies-literature
Project description:Background The outbreak of coronavirus disease 2019 (COVID-19) has posed a huge threat to human health. However, little is known regarding the risk factors associated with COVID-19 severity. We aimed to explore early-stage disease risk factors associated with eventual disease severity. Methods This study enrolled 486 hospitalized, non-intensive care unit (ICU)-admitted adult patients with COVID-19 (age ≥ 18 years) treated at Wuhan Jinyintan Hospital, who were divided into three groups according to disease severity. The demographic, clinical, and laboratory data at admission and clinical outcomes were compared among severity groups, and the risk factors for disease severity were identified by multiple regression analysis. Results Of 486 patients with COVID-19, 405 (83.33%) were discharged, 33 (6.71%) died outside of the ICU, and 48 (7.20%) were still being treated in the ICU by the time the study period ended. Significant differences in age, lymphocyte counts, and the levels of procalcitonin, aspartate aminotransferase, and D-dimer (P < 0.001 for all) among the three groups. Further analysis showed that older age, decreased lymphocyte counts, and increased procalcitonin, aspartate aminotransferase, and D-dimer levels were significantly associated with disease progression. Conclusion Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may impair the immune system, the blood coagulation system, and hepatic and cardiac function. Some clinical characteristics and laboratory findings can help identify patients with a high risk of disease severity, which can be significant for appropriate resource allocation during the COVID-19 pandemic.
| S-EPMC7997849 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) and renal replacement therapy (RRT) are common after heart transplantation (HT). The need for RRT has been reported to be one of the most important predictors of a poor prognosis after HT. Therefore, it is important to early identify risk factors of RRT after HT. However, in the heart transplantation setting, the risk factors are less well studied, and some of the conclusions are controversial. This study aimed to identify the clinical predictors of RRT after HT.MethodsThis single-center, retrospective study from January 2010 to June 2021 analyzed risk factors (pre-, intra-, and postoperative characteristics) of 163 patients who underwent HT. The endpoint of the study was RRT within 7 days of HT. Risk factors were analyzed by multivariable logistic regression models.ResultsFifty-five (33.74%) recipients required RRT within 7 days of HT. Factors independently associated with RRT after HT were as follows: a baseline estimated glomerular filtration rate (eGFR) <60 mL/min per 1.73 m2 [odds ratio (OR) =3.123; 95% confidence interval (CI): 1.183-8.244; P=0.022], a dose of intraoperative methylprednisolone >10 mg/kg (OR =3.197; 95% CI: 1.290-7.923; P=0.012), the use of mechanical circulatory support (MCS) during surgery (OR =4.903; 95% CI: 1.628-14.766; P=0.005), a cardiopulmonary bypass (CPB) time ≥5 hours (OR =3.929; 95% CI: 1.222-12.634; P=0.022), and postoperative serum total bilirubin (TBIL) ≥60 umol/L (OR =5.105; 95% CI: 1.868-13.952; P=0.001). Protective factors were higher postoperative serum albumin (OR =0.907; 95% CI: 0.837-0.983; P=0.017) and higher postoperative left ventricular ejection fraction (LVEF) (OR =0.908; 95% CI: 0.838-0.985; P=0.020).ConclusionsA low preoperative eGFR, a high intraoperative dose of methylprednisolone, a long CPB time, the use of mechanical circulatory support, and a high postoperative TBIL were risk factors for RRT after HT. While a high postoperative serum albumin level and a high left ventricular ejection fraction were protective factors. Understanding these risk factors may help us identify high-risk patients and intervene early.
| S-EPMC8987878 | biostudies-literature
Project description:The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.
| S-EPMC9255527 | biostudies-literature
Project description:BackgroundAs lung transplantation (LTX) is a valuable treatment procedure for end-stage pulmonary disease, delayed referral to a transplant center should be avoided. We aimed to conduct a single-center analysis of the survival time after listing for LTX and waitlist mortality in each disease category in a Japanese population.MethodsWe included patients listed for LTX at Tohoku University Hospital from January 2007 to December 2020 who were followed up until March 2021. Pulmonary disease was categorized into the Obstructive, Vascular, Suppurative, Fibrosis, and Allogeneic groups. Risk factors for waitlist mortality were assessed using a Cox proportional hazards model. The Kaplan-Meier method was used to model time to death.ResultsWe included 269 LTX candidates. Of those, 100, 72, and 97 patients were transplanted, waiting, and dead, respectively. The median time to LTX and time to death were 796 days (interquartile range [IQR] 579-1056) and 323 days (IQR 129-528), respectively. The Fibrosis group showed the highest mortality (50.9%; p < .001), followed by the Allogeneic (35.0%), Suppurative (33.3%), Vascular (32.1%), and Obstructive (13.1%) groups. The Fibrosis group showed a remarkable risk for waitlist mortality (hazard ratio 3.32, 95% CI 2.11-4.85).ConclusionsIn Japan, the waiting time is extremely long and candidates with Fibrosis have high mortality. There is a need to document outcomes based on the underlying disease for listed LTX candidates to help determine the optimal timing for listing patients based on the estimated local waiting time.
| S-EPMC8630869 | biostudies-literature
Project description:AimTo evaluate the Implant Disease Risk Assessment (IDRA) tool for the prediction of peri-implantitis in treated periodontitis patients with implant-supported fixed dental prostheses (FDPs) after at least 5 years of function.Material and methodsFrom the patient pool of implant patients enrolled in a regular supportive periodontal therapy programme (SPT) for at least 5 years, 239 patients were screened. Eighty patients met the inclusion criteria and underwent evaluation through the criteria of the IDRA tool. Areas under the curve (AUCs) for receiver operating characteristic (ROC) curves including 95% confidence intervals were estimated.ResultsSeventy-nine patients (43 males and 36 females, 8 smokers), aged on average 59.0 years (range: 40-79 years) at baseline (i.e. FDP delivery) were analysed. The calculated IDRA-risk was in 34 patients (42.5%) a moderate risk, while 45 patients (56.3%) were considered at high IDRA-risk. One patient categorized at low IDRA-risk was excluded from the analysis. The AUC was 0.613 (95% CI: 0.464-0.762) if the IDRA-risk was associated with prevalence of peri-implantitis at the most recent follow-up. Peri-implantitis was diagnosed in 4 patients (12%) at moderate and in 12 patients (27%) at high IDRA-risk, respectively. The calculated odds ratio for developing peri-implantitis in patients with high IDRA-risk compared with patients with moderate IDRA-risk was 2.727 with no statistically significant difference between the two groups (95% CI: 0.793-9.376).ConclusionWithin the limitations of the present retrospective study, the IDRA algorithm might represent a promising tool to assess patients at moderate or high risk of developing peri-implantitis.
| S-EPMC9290928 | biostudies-literature