Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.
Hong Nyun Kim, Dong Heon Yang, Bo Eun Park, Yoon Jung Park, Hyeon Jeong Kim, Se Yong Jang, Myung Hwan Bae, Jang Hoon Lee, Hun Sik Park, Yongkeun Cho, Shung Chull Chae
Yeungnam Univ J Med. 2021;38(4):337-343. Published online July 8, 2021
Background Chromogranin A (CgA) levels have been reported to predict mortality in patients with heart failure. However, information on the prognostic value and clinical availability of CgA is limited. We compared the prognostic value of CgA to that of previously proven natriuretic peptide biomarkers in patients with acute heart failure.
Methods We retrospectively evaluated 272 patients (mean age, 68.5±15.6 years; 62.9% male) who underwent CgA test in the acute stage of heart failure hospitalization between June 2017 and June 2018. The median follow-up period was 348 days. Prognosis was assessed using the composite events of 1-year death and heart failure hospitalization.
Results In-hospital mortality rate during index admission was 7.0% (n=19). During the 1-year follow-up, a composite event rate was observed in 12.1% (n=33) of the patients. The areas under the receiver-operating characteristic curves for predicting 1-year adverse events were 0.737 and 0.697 for N-terminal pro-B-type natriuretic peptide (NT-proBNP) and CgA, respectively. During follow-up, patients with high CgA levels (>158 pmol/L) had worse outcomes than those with low CgA levels (≤158 pmol/L) (85.2% vs. 58.6%, p<0.001). When stratifying the patients into four subgroups based on CgA and NT-proBNP levels, patients with high NT-proBNP and high CgA had the worst outcome. CgA had an incremental prognostic value when added to the combination of NT-proBNP and clinically relevant risk factors.
Conclusion The prognostic power of CgA was comparable to that of NT-proBNP in patients with acute heart failure. The combination of CgA and NT-proBNP can improve prognosis prediction in these patients.
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Background The diagnosis and prediction of prognosis are important in patients with sepsis, and presepsin is helpful. In this study, we aimed to examine the usefulness of presepsin in predicting the prognosis of sepsis in Korea.
Methods Patients diagnosed with sepsis according to the sepsis-3 criteria were recruited into the study and classified into surviving and non-surviving groups based on in-hospital mortality. A total of 153 patients (33 and 121 patients with sepsis and septic shock, respectively) were included from July 2019 to August 2020.
Results Among the 153 patients with sepsis, 91 and 62 were in the survivor and non-survivor groups, respectively. Presepsin (p=0.004) and lactate (p=0.003) levels and the sequential organ failure assessment (SOFA) scores (p<0.001) were higher in the non-survivor group. Receiver operating characteristic curve analysis revealed poor performances of presepsin and lactate in predicting the prognosis of sepsis (presepsin: area under the curve [AUC]=0.656, p=0.001; lactate: AUC=0.646, p=0.003). The SOFA score showed the best performance, with the highest AUC value (AUC=0.751, p<0.001). The prognostic cutoff point for presepsin was 1,176 pg/mL. Presepsin levels of >1,176 pg/mL (odds ratio [OR], 3.352; p<0.001), lactate levels (OR, 1.203; p=0.003), and SOFA score (OR, 1.249; p<0.001) were risk factors for in-hospital mortality.
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Background We aimed to analyze the effectiveness of albumin to globulin ratio (AGR) in predicting postoperative febrile urinary tract infection (fUTI) after ureteroscopic lithotripsy (URS) and retrograde intrarenal surgery (RIRS).
Methods From January 2013 to May 2018, 332 patients underwent URS and RIRS. The rate of postoperative fUTI and risk factors for postoperative fUTI were analyzed using logistic regression. Patients were divided into postoperative fUTI and non-postoperative fUTI (non-fUTI) groups. AGR with other demographic and perioperative data were compared between the two groups to predict the development of fUTI after URS.
Results Of the 332 patients, postoperative fUTI occurred in 41 (12.3%). Preoperative pyuria, microscopic hematuria, diabetes mellitus, hypoalbuminemia, and hyperglobulinemia were more prevalent in the fUTI group. Patients in the fUTI group had larger stone size, lower preoperative AGR, longer operation time, and longer preoperative antibiotic coverage period. In a multivariable logistic analysis, preoperative pyuria, AGR, and stone size were independently correlated with postoperative fUTI (p<0.001, p=0.008, and p=0.041, respectively). Receiver operating curve analysis showed that the cutoff value of AGR that could predict a high risk of fUTI after URS was 1.437 (sensitivity, 77.3%; specificity, 76.9%), while the cutoff value of stone size was 8.5 mm (sensitivity, 55.3%; specificity, 44.7%).
Conclusion This study demonstrated that preoperative pyuria, AGR, and stone size can serve as prognostic factors for predicting fUTI after URS.
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