Incidence and risk factors for periprosthetic joint infection: A common data model analysis.

OBJECTIVES: The aim of this study was to determine the incidence of periprosthetic joint infection (PJI) following primary total joint arthroplasty (TJA) and to investigate risk factors in a large cohort utilizing common data model (CDM). PATIENTS AND METHODS: The entire cohort of primary and revision hip or knee TJA between January 2003 and December 2017 was retrospectively analyzed utilizing the CDM database. We detected patients who had revision TJA as a consequence of PJI. We determined the incidence of PJI and examined risk factors, including demographic features, comorbidities, prior corticosteroid usage, and preoperative laboratory values. RESULTS: There were 34 revision TJAs as a consequence of PJI (hip, 16; knee, 18) among 12,320 primary TJAs (hip, 4,758; knee 7,562), representing 0.27% incidence of PJI (hip, 0.33%; knee 0.23%). Of the patients, 15 were males and 19 were females. The mean age at the time of primary TJA was 59.8±17.5 (range, 31 to 85) years in hip PJI patients and 71.4±7.2 (range, 56 to 80) years in knee PJI patients. Hypertension and urinary tract infection were both associated with PJI following primary hip TJA. Age between 70 and 79 years, male sex, urinary tract infection, anemia, and prior corticosteroid usage were all associated with PJI following primary knee TJA. CONCLUSION: This study indicates the viability of employing CDM to undertake research on PJI and serves as a reference for future CDM-based risk factor analysis. Preoperative screening and mitigating identified risk factors can aid in the reduction of PJI following TJA.

Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds.

Importance: Breathing sounds during sleep are an important characteristic feature of obstructive sleep apnea (OSA) and have been regarded as a potential biomarker. Breathing sounds during sleep can be easily recorded using a microphone, which is found in most smartphone devices. Therefore, it may be easy to implement an evaluation tool for prescreening purposes. Objective: To evaluate OSA prediction models using smartphone-recorded sounds and identify optimal settings with regard to noise processing and sound feature selection. Design, Setting, and Participants: A cross-sectional study was performed among patients who visited the sleep center of Seoul National University Bundang Hospital for snoring or sleep apnea from August 2015 to August 2019. Audio recordings during sleep were performed using a smartphone during routine, full-night, in-laboratory polysomnography. Using a random forest algorithm, binary classifications were separately conducted for 3 different threshold criteria according to an apnea hypopnea index (AHI) threshold of 5, 15, or 30 events/h. Four regression models were created according to noise reduction and feature selection from the input sound to predict actual AHI: (1) noise reduction without feature selection, (2) noise reduction with feature selection, (3) neither noise reduction nor feature selection, and (4) feature selection without noise reduction. Clinical and polysomnographic parameters that may have been associated with errors were assessed. Data were analyzed from September 2019 to September 2020. Main Outcomes and Measures: Accuracy of OSA prediction models. Results: A total of 423 patients (mean [SD] age, 48.1 [12.8] years; 356 [84.1%] male) were analyzed. Data were split into training (n = 256 [60.5%]) and test data sets (n = 167 [39.5%]). Accuracies were 88.2%, 82.3%, and 81.7%, and the areas under curve were 0.90, 0.89, and 0.90 for an AHI threshold of 5, 15, and 30 events/h, respectively. In the regression analysis, using recorded sounds that had not been denoised and had only selected attributes resulted in the highest correlation coefficient (r = 0.78; 95% CI, 0.69-0.88). The AHI (ß = 0.33; 95% CI, 0.24-0.42) and sleep efficiency (ß = -0.20; 95% CI, -0.35 to -0.05) were found to be associated with estimation error. Conclusions and Relevance: In this cross-sectional study, recorded sleep breathing sounds using a smartphone were used to create reasonably accurate OSA prediction models. Future research should focus on real-life recordings using various smartphone devices.

Overview of the Cancer Genetics and Pathway Curation tasks of BioNLP Shared Task 2013.

BACKGROUND: Since their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies. RESULTS: Six groups participated in the CG task and two groups in the PC task, together applying a wide range of extraction approaches including both established state-of-the-art systems and newly introduced extraction methods. The best-performing systems achieved F-scores of 55% on the CG task and 53% on the PC task, demonstrating a level of performance comparable to the best results achieved in similar previously proposed tasks. CONCLUSIONS: The results indicate that existing event extraction technology can generalize to meet the novel challenges represented by the CG and PC task settings, suggesting that extraction methods are capable of supporting the construction of knowledge bases on the molecular mechanisms of cancer and the curation of biomolecular pathway models. The CG and PC tasks continue as open challenges for all interested parties, with data, tools and resources available from the shared task homepage.

Association between the markers of metabolic acid load and higher all-cause and cardiovascular mortality in a general population with preserved renal function.

Although metabolic acid load has been associated with many well-known risk factors for mortality, its clinical implications are not yet clear. To evaluate the association between biomarkers of metabolic acid load, such as serum bicarbonate, serum anion gap and urine pH and mortality, we analyzed the health records of 31,590 adults who underwent a health screening between January 2001 and December 2010 and had an estimated glomerular filtration rate ⩾60 ml min(-1) per 1.73 m2. Urine pH was measured by a dipstick test performed on fast morning urine sample and categorized as acidic (urine pH ⩽5.5), neutral and alkaline (urine pH ⩾8.0). Using the Cox proportional hazard model, the adjusted hazard ratio (aHR) of all-cause mortality of the lowest quartile of serum bicarbonate was 1.460 (95% confidence interval (CI) 1.068-1.995) compared with the highest quartile, after a median follow-up of 93 months. The aHRs of cardiovascular and cancer mortality of the lowest quartile of serum bicarbonate were 2.647 (95% CI 1.148-6.103) and 1.604 (95% CI 1.024-2.513), respectively, compared with the highest quartile. Acidic and neutral urine pH were significantly associated with a higher all-cause mortality (aHR 2.550, 95% CI 1.316-4.935; aHR 2.376 95% CI 1.254-4.501, respectively), compared with an alkaline urine pH. In conclusion, higher metabolic acid load was associated with an increased all-cause and cardiovascular mortality in a healthy population. The association between metabolic acid load and mortality and the causality of the relationship need to be confirmed.

Biological features of core networks that result from a high-fat diet in hepatic and pulmonary tissues in mammary tumour-bearing, obesity-resistant mice.

We previously demonstrated that the chronic consumption of a high-fat diet (HFD) promotes lung and liver metastases of 4T1 mammary carcinoma cells in obesity-resistant BALB/c mice. To examine early transcriptional responses to tumour progression in the liver and lungs of HFD-fed mice, 4-week-old female BALB/c mice were divided into four groups: sham-injected, control diet (CD)-fed; sham-injected, HFD-fed (SH); 4T1 cell-injected, CD-fed (TC); 4T1 cell-injected, HFD-fed (TH). Following 16 weeks of either a CD or HFD, 4T1 cells were injected into the mammary fat pads of mice in the TC and TH groups and all mice were continuously fed identical diets. At 14 d post-injection, RNA was isolated from hepatic and pulmonary tissues for microarray analysis of mRNA expression. Functional annotation and core network analyses were conducted for the TH/SH Unique gene set. Inflammation in hepatic tissues and cell mitosis in pulmonary tissues were the most significant biological functions in the TH/SH Unique gene set. The biological core networks of the hepatic TH/SH Unique gene set were characterised as those genes involved in the activation of acute inflammatory responses (Orm1, Lbp, Hp and Cfb), disordered lipid metabolism and deregulated cell cycle progression. Networks of the pulmonary Unique gene set displayed the deregulation of cell cycle progression (Cdc20, Cdk1 and Bub1b). These HFD-influenced alterations may have led to favourable conditions for the formation of both pro-inflammatory and pro-mitotic microenvironments in the target organs that promote immune cell infiltration and differentiation, as well as the infiltration and proliferation of metastatic tumour cells.