Background/purpose: While many children with renal tumors require long term venous access (VA) for adjuvant chemotherapy, certainly not all do. This study develops and tests a VA decision tree (DT) to direct the placement of VA in patients with renal tumors.

Methods: Utilizing data readily available at surgery a VADT was developed. The VADT was tested retrospectively by 2 independent reviewers on a historic cohort. The ability of the VADT to appropriately select which patients would benefit from VA placement was tested.

Results: 160 patients underwent renal tumor surgery between 2005 and 2018. 70 (43.8%) patients met study criteria with median age of 45.1 months (range 1.1-224); 73% required VA. Using the VADT, VA placement was "needed" in 67.1% of patients and "deferred" in 32.9%. Interrater reliability was very high (kappa = 0.97, 95% CI 0.91-1, p < 0.001). The sensitivity and specificity of the VADT to correctly decide on VA placement were 0.92 (0.8-0.98) and 1 (0.79-1). Using the VADT, no patient would have undergone unnecessary VA placement. In reality, 4.3% of patients had an unnecessary VA placed which required a subsequent removal.

Conclusions: These preliminary data support the continued study of this VADT to guide intraoperative decisions regarding VA placement in patients with renal tumors.

Level Of Evidence: III - Study of diagnostic test.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jpedsurg.2019.04.034DOI Listing

Publication Analysis

Top Keywords

decision tree
8
long term
8
term venous
8
venous access
8
renal tumors
8
patients
5
tree guide
4
guide long
4
placement
4
access placement
4

Similar Publications

Prediction of body weight (BW) using biometric measurements is an important tool especially for animal welfare and automatic phenotyping tools that needs mathematical models. In this study, it was aimed to predict the BW using body length (BL), chest girth (CG) and width of the waist (WW) for rabbits of the maternal form of Hyla NG. The standard rabbit-raising practices were applied for the animals.

View Article and Find Full Text PDF

Machine Learning Algorithm-Based Prediction of Diabetes Among Female Population Using PIMA Dataset.

Healthcare (Basel)

December 2024

Department of Computer Science, School of Arts, Humanities and Social Sciences, University of Roehampton, London SW15 5PH, UK.

: Diabetes is a metabolic disorder characterized by increased blood sugar levels. Early detection of diabetes could help individuals to manage and delay the progression of this disorder effectively. Machine learning (ML) methods are important in forecasting the progression and diagnosis of different medical problems with better accuracy.

View Article and Find Full Text PDF

Construction of prediction model of early glottic cancer based on machine learning.

Acta Otolaryngol

January 2025

Department of Otorhinolaryngology Head and Neck Surgery, Tianjin First Central Hospital, Tianjin, China.

Background: The early diagnosis of glottic laryngeal cancer is the key to successful treatment, and machine learning (ML) combined with narrow-band imaging (NBI) laryngoscopy provides a new idea for the early diagnosis of glottic laryngeal cancer.

Objective: To explore the clinical applicability of the diagnosis of early glottic cancer based on ML combined with NBI.

Material And Methods: A retrospective study was conducted on 200 patients diagnosed with laryngeal mass, and the general clinical characteristics and pathological results of the patients were collected.

View Article and Find Full Text PDF

Background: Urinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method.

View Article and Find Full Text PDF

Background: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research.

Methods: Data from pediatric patients undergoing ECMO were collected from the Chinese Society of Extracorporeal Life Support (CSECLS) registry database and local hospitals.

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!