Background: Hospital readmission within 30 days is associated with higher risks of complications, death, and increased costs. Accurate statistical models to stratify the risk of 30-day readmission or death after cardiac surgery could help clinical teams focus care on those patients at highest risk. We hypothesized biomarkers could improve prediction for readmission or mortality.

Methods: Levels of ST2, galectin-3, N-terminal pro-brain natriuretic peptide, cystatin C, interleukin-6, and interleukin-10 were measured in samples from 1,046 patients discharged after isolated coronary artery bypass graft surgery from eight medical centers, with external validation in 1,194 patients from five medical centers. Thirty-day readmission or mortality were ascertained using Medicare, state all-payer claims, and the National Death Index. We tested and externally validated the clinical models and the biomarker panels using area under the receiver-operating characteristics (AUROC) statistics.

Results: There were 112 patients (10.7%) who were readmitted or died within 30 days after coronary artery bypass graft surgery. The Society of Thoracic Surgeons augmented clinical model resulted in an AUROC of 0.66 (95% confidence interval: 0.61 to 0.71). The biomarker panel with The Society of Thoracic Surgeons augmented clinical model resulted in an AUROC of 0.74 (bootstrapped 95% confidence interval: 0.69 to 0.79, p < 0.0001). External validation of the model showed limited improvement with the addition of a biomarker panel, with an AUROC of 0.51 (95% confidence interval: 0.45 to 0.56).

Conclusions: Although biomarkers significantly improved prediction of 30-day readmission or mortality in our derivation cohort, the external validation of the biomarker panel was poor. Biomarkers perform poorly, much like other efforts to improve prediction of readmission, suggesting there are many other factors yet to be explored to improve prediction of readmission.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668355PMC
http://dx.doi.org/10.1016/j.athoracsur.2018.06.052DOI Listing

Publication Analysis

Top Keywords

improve prediction
16
prediction readmission
16
readmission mortality
12
external validation
12
95% confidence
12
confidence interval
12
biomarker panel
12
biomarkers improve
8
readmission
8
cardiac surgery
8

Similar Publications

deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities.

J Chem Inf Model

January 2025

School of Information and Artificial Intelligence, Anhui Provincial Engineering Research Center for Beidou Precision Agriculture Information, Key Laboratory of Agricultural Sensors for Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei, Anhui 230036, China.

Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years.

View Article and Find Full Text PDF

Objective: To examine the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) with Sonazoid (Sonazoid-CEUS) for endometrial lesions.

Methods: In this prospective and multicenter study, data were collected from 84 patients with endometrial lesions from 11 hospitals in China. All the patients received a conventional US and Sonazoid-CEUS examination.

View Article and Find Full Text PDF

Background: Falls are among the most prevalent workplace accidents, necessitating thorough screening for susceptibility to falls and customization of individualized fall prevention programs. The aim of this study was to develop and validate a high fall risk prediction model using machine learning (ML) and video-based first three steps in middle-aged workers.

Methods: Train data (n=190, age 54.

View Article and Find Full Text PDF

Objective: Based on our previous research, which demonstrated that elevated plasma endoglin (ENG) levels in lung cancer patients were associated with a better prognosis, increased sensitivity to pemetrexed, and enhanced tumor suppression, this study aims to validate these findings at the cellular level. The focus is on membrane and extracellular ENG and their influence on drug response and tumor cell behavior in non-small cell lung cancer (NSCLC) cells.

Methods: The correlation between ENG expression and pemetrexed-induced cytotoxicity in eight human non-squamous subtype NSCLC cell lines was analyzed.

View Article and Find Full Text PDF

The Impact of Artificial Intelligence and Machine Learning in Organ Retrieval and Transplantation: A Comprehensive Review.

Curr Res Transl Med

January 2025

Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.

This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!