Dataset Information

Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study.

ABSTRACT: BACKGROUND:Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pre-surgical mortality is scarce. OBJECTIVES:Our goal is to generate a predictive model calculator adapted to the regional reality focused on individual mortality prediction among patients with congenital heart disease undergoing cardiac surgery. METHODS:Two thousand two hundred forty CHD consecutive patients' data from InCor's heart surgery program was used to develop and validate the preoperative risk-of-death prediction model of congenital patients undergoing heart surgery. There were six artificial intelligence models most cited in medical references used in this study: Multilayer Perceptron (MLP), Random Forest (RF), Extra Trees (ET), Stochastic Gradient Boosting (SGB), Ada Boost Classification (ABC) and Bag Decision Trees (BDT). RESULTS:The top performing areas under the curve were achieved using Random Forest (0.902). Most influential predictors included previous admission to ICU, diagnostic group, patient's height, hypoplastic left heart syndrome, body mass, arterial oxygen saturation, and pulmonary atresia. These combined predictor variables represent 67.8% of importance for the risk of mortality in the Random Forest algorithm. CONCLUSIONS:The representativeness of "hospital death" is greater in patients up to 66 cm in height and body mass index below 13.0 for InCor's patients. The proportion of "hospital death" declines with the increased arterial oxygen saturation index. Patients with prior hospitalization before surgery had higher "hospital death" rates than who did not required such intervention. The diagnoses groups having the higher fatal outcomes probability are aligned with the international literature. A web application is presented where researchers and providers can calculate predicted mortality based on the CgntSCORE on any web browser or smartphone.

SUBMITTER: Chang Junior J

PROVIDER: S-EPMC7473591 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study.

Chang Junior João J Binuesa Fábio F Caneo Luiz Fernando LF Turquetto Aida Luiza Ribeiro ALR Arita Elisandra Cristina Trevisan Calvo ECTC Barbosa Aline Cristina AC Fernandes Alfredo Manoel da Silva AMDS Trindade Evelinda Marramon EM Jatene Fábio Biscegli FB Dossou Paul-Eric PE Jatene Marcelo Biscegli MB

PloS one 20200904 9

<h4>Background</h4>Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pre-surgical mortality is scarce.<h4>Objectives</h4>Our goal is to generate a predictive model calculator adapted to the regional reality focused on individual mortality prediction among patients with congenital heart disease undergoing cardiac ...[more]

PMID: 32886688

Similar Datasets

Project description:AimWe explored the association between preoperative anthropometry and biochemistry, and postoperative outcomes in infants with CHD after cardiac surgery, as infants with congenital heart disease (CHD) often have feeding difficulties and malnutrition.MethodologyThis was a retrospective review of infants (≤ 1-year-old) who underwent congenital heart surgery. Preoperative anthropometryin terms of preoperative weight-for-age z-score (WAZ), length-for-age z-score (LAZ), as well as preoperative serum albumin and hemoglobin concentrations, were evaluated against 6-month mortality, and morbidity outcomes including postoperative complications, vasoactive inotrope score, duration of mechanical ventilation, length of stay in the pediatric intensive care unit and in hospital, using the logistic regression or median regression models accounting for infant-level clustering.ResultsOne hundred and ninety-nine operations were performed in 167 infants. Mean gestational age at birth was 38.0 (SD 2.2) weeks (range 26 to 41 weeks). Thirty (18.0%) infants were born preterm (<37 weeks). The commonest acyanotic and cyanotic lesions were ventricular septal defect (26.3%, 44/167), and tetralogy of Fallot (13.8%, 23/167), respectively. Mean age at cardiac surgery was 94 (SD 95) days. Feeding difficulties, including increased work of breathing during feeding, diaphoresis, choking or coughing during feeding, and inability to complete feeds, was present in 54.3% (108/199) of infants prior to surgery, of which 21.6% (43/199) required tube feeding. The mean preoperative WAZ was-1.31 (SD 1.79). Logistic regression models showed that low preoperative WAZ was associated with increased risk of postoperative complications (odds ratio 1.82; p = 0.02), and 6-month mortality (odds ratio 2.38; p = 0.008) following CHD surgery. There was no meaningful association between the other preoperative variables and other outcomes.ConclusionMore than 50% of infants with CHD undergoing cardiac surgery within the first year of life have feeding difficulties, of which 22% require to be tube-fed. Low preoperative WAZ is associated with increased postoperative complications and 6-month mortality.

Project description:Early diagnosis is the most important determinant of oral and oropharyngeal squamous cell carcinoma (OPSCC) outcomes, yet most of these cancers are detected late, when outcomes are poor. Typically, nonspecialists such as dentists screen for oral cancer risk, and then they refer high-risk patients to specialists for biopsy-based diagnosis. Because the clinical appearance of oral mucosal lesions is not an adequate indicator of their diagnosis, status, or risk level, this initial triage process is inaccurate, with poor sensitivity and specificity. The objective of this study is to provide an overview of emerging optical imaging modalities and novel artificial intelligence-based approaches, as well as to evaluate their individual and combined utility and implications for improving oral cancer detection and outcomes. The principles of image-based approaches to detecting oral cancer are placed within the context of clinical needs and parameters. A brief overview of artificial intelligence approaches and algorithms is presented, and studies that use these 2 approaches singly and together are cited and evaluated. In recent years, a range of novel imaging modalities has been investigated for their applicability to improving oral cancer outcomes, yet none of them have found widespread adoption or significantly affected clinical practice or outcomes. Artificial intelligence approaches are beginning to have considerable impact in improving diagnostic accuracy in some fields of medicine, but to date, only limited studies apply to oral cancer. These studies demonstrate that artificial intelligence approaches combined with imaging can have considerable impact on oral cancer outcomes, with applications ranging from low-cost screening with smartphone-based probes to algorithm-guided detection of oral lesion heterogeneity and margins using optical coherence tomography. Combined imaging and artificial intelligence approaches can improve oral cancer outcomes through improved detection and diagnosis.

Project description:BackgroundEstablishing rapport and empathy between patients and their health care provider is important but challenging in the context of a busy and crowded emergency department (ED).ObjectiveWe explore the hypotheses that rapport building, documentation, and time efficiency might be improved in the ED by providing patients a digital tool that uses Bayesian reasoning-based techniques to gather relevant symptoms and history for handover to clinicians.MethodsA 2-phase pilot evaluation was carried out in the ED of a German tertiary referral and major trauma hospital that treats an average of 120 patients daily. Phase 1 observations guided iterative improvement of the digital tool, which was then further evaluated in phase 2. All patients who were willing and able to provide consent were invited to participate, excluding those with severe injury or illness requiring immediate treatment, with traumatic injury, incapable of completing a health assessment, and aged <18 years. Over an 18-day period with 1699 patients presenting to the ED, 815 (47.96%) were eligible based on triage level. With available recruitment staff, 135 were approached, of whom 81 (60%) were included in the study. In a mixed methods evaluation, patients entered information into the tool, accessed by clinicians through a dashboard. All users completed evaluation Likert-scale questionnaires rating the tool's performance. The feasibility of a larger trial was evaluated through rates of recruitment and questionnaire completion.ResultsRespondents strongly endorsed the tool for facilitating conversation (61/81, 75% of patients, 57/78, 73% of physician ratings, and 10/10, 100% of nurse ratings). Most nurses judged the tool as potentially time saving, whereas most physicians only agreed for a subset of medical specialties (eg, surgery). Patients reported high usability and understood the tool's questions. The tool was recommended by most patients (63/81, 78%), in 53% (41/77) of physician ratings, and in 76% (61/80) of nurse ratings. Questionnaire completion rates were 100% (81/81) by patients and 96% (78/81 enrolled patients) by physicians.ConclusionsThis pilot confirmed that a larger study in the setting would be feasible. The tool has clear potential to improve patient-health care provider interaction and could also contribute to ED efficiency savings. Future research and development will extend the range of patients for whom the history-taking tool has clinical utility.Trial registrationGerman Clinical Trials Register DRKS00024115; https://drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00024115.

Dataset Information

Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study.

Publications

Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets