Clinical prediction rule

Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources...

BACKGROUND: Antimicrobial stewardship focuses on identifying patients who require ESBL-targeted therapy. Rule-in tools have been extensively researched in areas of low endemicity; however, such tools are inadequate for areas with high rates of ESBL, as almost all patients will be selected. AIM: To develop a machine learning-based rule-out tool suitable for areas with high levels of resistance. METHODS: We used gradient boosted decision trees to train and validate a risk prediction model on data from 17,913 (45% ESBL) patients with Escherichia coli and Klebsiella pneumoniae in urine cultures...

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences...

This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare...

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches...

In a multi-hospital cohort study of 3392 patients, positive urinalysis parameters had poor positive predictive value for diagnosing urinary tract infection (UTI). Combined urinalysis parameters (pyuria or nitrite) performed better than pyuria alone for ruling out UTI. However, performance of all urinalysis parameters was poor in older women.

Diabetes is an enduring metabolic condition identified by heightened blood sugar levels stemming from insufficient production of insulin or ineffective utilization of insulin within the body. India is commonly labeled as the "diabetes capital of the world" owing to the widespread prevalence of this condition. To the best of the authors' last knowledge updated on September 2021, approximately 77 million adults in India were reported to be affected by diabetes, reported by the International Diabetes Federation...

INTRODUCTION AND OBJECTIVES: Ruling out pulmonary embolism (PE) through a combination of clinical assessment and D-dimer level can potentially avoid excessive use of computed tomography pulmonary angiography (CTPA). We aimed to compare the diagnostic accuracy of the standard approach based on the Wells and Geneva scores combined with a standard D-dimer cut-off (500 ng/ml), with three alternative strategies (age-adjusted and the YEARS and PEGeD algorithms) in patients admitted to the emergency department (ED) with suspected PE...

Artificial intelligence (AI) in healthcare is the ability of a computer to perform tasks typically associated with clinical care (e.g. medical decision-making and documentation). AI will soon be integrated into an increasing number of healthcare applications, including elements of emergency department (ED) care. Here, we describe the basics of AI, various categories of its functions (including machine learning and natural language processing) and review emerging and potential future use-cases for emergency care...

BACKGROUND: The PediBIRN-7 clinical prediction rule incorporates the (positive or negative) predictive contributions of completed abuse evaluations to estimate abusive head trauma (AHT) probability after abuse evaluation. Applying definitional criteria as proxies for AHT and non-AHT ground truth, it performed with sensitivity 0.73 (95 % CI: 0.66-0.79), specificity 0.87 (95 % CI: 0.82-0.90), and ROC-AUC 0.88 (95 % CI: 0.85-0.92) in its derivation study. OBJECTIVE: To validate the PediBIRN-7's AHT prediction performance in a novel, equivalent, patient population...

STUDY OBJECTIVE: Computed tomography pulmonary angiogram (CTPA) is overused during pulmonary embolism (PE) testing in the emergency department (ED), whereas prediction rules and D-dimer are underused. We report the adherence, clinical benefit, and safety of a D-dimer-only strategy to guide need for PE imaging in the ED. METHODS: This was a prospective multicenter implementation study in 2 EDs with historical and external controls. Patients with suspected PE underwent D-dimer testing and imaging (CTPA or ventilation-perfusion scan) when D-dimer levels were 500 ng/mL or more...

Pulmonary embolism (PE) is a common disease, which can present with a variety of symptoms. Optimal use of diagnostics is challenging given the tight and delicate balance between underdiagnosis and over-testing or overdiagnosis. Diagnostic delay occurs in a substantial part of patients, and seems more common in those with known cardiopulmonary disease or non-specific signs and symptoms. At the other end of the spectrum, the amount of diagnostic imaging increases. Increased use of diagnostic imaging in general leads to more harmful exposures and might result in overtreatment, as may be the case in subsegmental PE...

Background Sivelestat is a potent and specific neutrophil elastase inhibitor. It is clinically used in treating lung injury and respiratory distress syndrome. This engaged us to undertake the present study in which sivelestat was studied as an anti-inflammatory and anti-viral agent. Methodology The docking study of sivelestat on matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), chikungunya virus nonstructural protein-2 (CVnsP2) protease, and influenza A (H1N9) virus neuraminidase was assessed using the Chemistry at Harvard Macromolecular Mechanics (CHARMM) Dock (CDOCK) method...

INTRODUCTION: This analysis evaluated the relative performance of vedolizumab and anti-tumor necrosis factor alpha (anti-TNFα) agents in subpopulations of biologic therapy-naive patients with Crohn's disease (CD) and assessed whether patients in whom vedolizumab would have a larger treatment effect vs anti-TNFα agents could be identified. METHODS: Data were from EVOLVE, a real-world, multicountry, retrospective cohort study of patients with inflammatory bowel disease who initiated first-line biologic treatment with vedolizumab (n = 195) or anti-TNFα agents (n = 245)...

BACKGROUND: Childhood Sjögren's disease is a rare, underdiagnosed, and poorly-understood condition. By integrating machine learning models on a paediatric cohort in the USA, we aimed to develop a novel system (the Florida Scoring System) for stratifying symptomatic paediatric patients with suspected Sjögren's disease. METHODS: This cross-sectional study was done in symptomatic patients who visited the Department of Pediatric Rheumatology at the University of Florida, FL, USA...

Islet autoantibodies predict type 1 diabetes (T1D) but can be transient in murine and human T1D and are not thought to be directly pathogenic. Rather, these autoantibodies signal B cell activity as antigen-presenting cells (APCs) that present islet autoantigen to diabetogenic T cells to promote T1D pathogenesis. Disrupting B cell APC function prevents T1D in mouse models and has shown promise in clinical trials. Autoantigen-specific B cells thus hold potential as sophisticated T1D biomarkers and therapeutic targets...

BACKGROUND: Mortality due to immune-mediated thrombotic thrombocytopenic purpura (iTTP) remains significant. Predicting mortality risk may potentially help individualize treatment. The French Thrombotic Microangiopathy (TMA) Reference Score has not been externally validated in the United States. Recent advances in machine learning technology can help analyze large numbers of variables with complex interactions for the development of prediction models. OBJECTIVES: To validate the French TMA Reference Score in the United States Thrombotic Microangiopathy (USTMA) iTTP database and subsequently develop a novel mortality prediction tool, the USTMA TTP Mortality Index...

PURPOSE/OBJECTIVE: To investigate the occurrence of behavioral problems 7 years after severe pediatric traumatic brain injury (TBI), and their evolution from 3 months to 7 years postinjury. METHOD/DESIGN: Thirty-four participants, 38% girls, M ( SD ) age at injury 7.6 (4.7) years, age at assessment 15 (4.6) years, underwent comprehensive assessments 7 years after severe TBI from March 2014 to March 2016 and were matched to a control group by age, gender, and parental education...

PURPOSE: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume...

BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for the clinical changes and events that occur after the baseline visit, which can modify risk of bleeding. However, it is difficult to develop predictive models from the routine follow-up clinical interviews which are irregular sequences of multivariate time series data...