ABSTRACT
Objective: To analyze the etiology, diagnosis, and treatment of unexplained conductive hearing loss (UCHL) with intact tympanic membrane. Methods: A systematic review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 642 articles were retrieved from databases such as PubMed, Embase, Web of Science, and Cochrane. Fifty-four research articles and 21 case reports were screened out according to the inclusion and exclusion criteria for analysis of the etiology of UCHL. Seven research articles with UCHL who underwent exploratory tympanotomy were selected for data extraction and analysis of clinical characteristics. Results: UCHL is a common manifestation of various diseases, including congenital ossicular anomalies (COA), otosclerosis (OTS), congenital middle ear cholesteatoma (CMEC), oval window atresia, superior semicircular-canal dehiscence, congenital stapedial footplate fixation, middle ear osteoma or adenoma, congenital ossification of stapedial tendon, and so on. A total of 522 patients were included in the 7 articles; among whom OTS showed a tendency to increase with age. The main symptoms were hearing loss, followed by tinnitus, dizziness, ear fullness, ear pain, facial paralysis. A total of 87.5% to 93.0% patients with COA manifested as nonprogressive deafness that occurred since childhood, with tinnitus incidence of 15.6% to 30.2%, and 86.4% to 96.4% patients with OTS presented with progressive hearing loss, with tinnitus incidence of 60.1% to 90.9%. The diagnosis positive rate of high-resolution computed tomography (HRCT) was 33.8% to 87.1%, and CMEC was higher than that of COA (83.3%-100% vs 28.6%-64%). All the articles reported good hearing recovery. The most common surgical complications included taste abnormalities, tinnitus, and dizziness. Conclusion: UCHL presents with similar clinical manifestations and poses challenges in preoperative diagnosis. Exploratory tympanotomy is the primary method for diagnosis and treatment, with good prognosis after removing the lesion and reconstructing hearing during the operation. Children can also safely undergo the surgery.
ABSTRACT
BACKGROUND: Studies have shown that more than half of patients with heart failure (HF) with acute kidney injury (AKI) have newonset AKI, and renal function evaluation markers such as estimated glomerular filtration rate are usually not repeatedly tested during the hospitalization. As an independent risk factor, delayed AKI recognition has been shown to be associated with the adverse events of patients with HF, such as chronic kidney disease and death. OBJECTIVE: The aim of this study is to develop and assess of an unsupervised machine learning model that identifies patients with HF and normal renal function but who are susceptible to de novo AKI. METHODS: We analyzed an electronic health record data set that included 5075 patients admitted for HF with normal renal function, from which 2 phenogroups were categorized using an unsupervised machine learning algorithm called K-means clustering. We then determined whether the inferred phenogroup index had the potential to be an essential risk indicator by conducting survival analysis, AKI prediction, and the hazard ratio test. RESULTS: The AKI incidence rate in the generated phenogroup 2 was significantly higher than that in phenogroup 1 (group 1: 106/2823, 3.75%; group 2: 259/2252, 11.50%; P<.001). The survival rate of phenogroup 2 was consistently lower than that of phenogroup 1 (P<.005). According to logistic regression, the univariate model using the phenogroup index achieved promising performance in AKI prediction (sensitivity 0.710). The generated phenogroup index was also significant in serving as a risk indicator for AKI (hazard ratio 3.20, 95% CI 2.55-4.01). Consistent results were yielded by applying the proposed model on an external validation data set extracted from Medical Information Mart for Intensive Care (MIMIC) III pertaining to 1006 patients with HF and normal renal function. CONCLUSIONS: According to a machine learning analysis on electronic health record data, patients with HF who had normal renal function were clustered into separate phenogroups associated with different risk levels of de novo AKI. Our investigation suggests that using machine learning can facilitate patient phengrouping and stratification in clinical settings where the identification of high-risk patients has been challenging.
ABSTRACT
Survival analysis, aimed at investigating the relationships between covariates and event time, has exhibited profound effects on health service management. Longitudinal data with sequential patterns, such as electronic health records (EHRs), contain a large volume of patient treatment trajectories, and therefore, provide great potential for survival analysis. However, most existing studies address the survival analysis problem in a static manner, that is, they only utilize a fraction of longitudinal data, ignore the correlations between multiple visits, and usually may not be able to capture the latent representations of patient treatment trajectories. This inevitably deteriorates the performance of the survival analysis. To address this challenge, we propose an end-to-end contrastive-based model CD-Surv to better understand the patient treatment trajectories and dynamically predict the survival probability of a target patient. Specifically, two data augmentation strategies, namely, mask generation and shuffle generation, are adopted to augment the real treatment trajectories documented in the EHR. Based on this, the hidden representations of the real trajectories can be improved by utilizing contrastive learning between augmented and real trajectories. We evaluated our proposed CD-Surv on two real-world datasets, and the experimental results indicated that our proposed model could outperform state-of-the-art baselines on various evaluation metrics.
