• 한국융합학회논문지
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• 제13권1호
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• pp.399-408
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• 2022

본 연구에서는 65세 이상 고령자를 대상으로 대사증후군 진단을 위한 지질비율 지표의 유용성을 비교하고자 하였다. 2018년 1월부터 2020년 12월까지 서울지역 종합병원에서 건강검진을 받은 65세 이상 1,464명을 대상으로 하였다. 혈액검사를 통해 혈장 동맥경화 지수를 포함한 지질비율 지표를 측정하였다. 지질비율 지표의 사분위수에 따른 대사증후군 유병률은 로지스틱 회귀분석으로 확인하였다. 또한 수신자 조작 특성(receiver operating characteristic, ROC) 곡선으로 지질비율 지표의 대사증후군 예측능력과 절단값을 추정하였다. 동맥경화 지수와 허리둘레의 상관성이 남녀 모두에서 가장 높았으며(r=0.278, p<0.001 vs r=0.252, p<0.001), 지질비율 지표는 1사분위수와 비교하여 4사분위수에서 대사증후군 발병률이 높았다. 혈장 동맥경화 지수는 다른 지질비율 지표보다 ROC 곡선 아래의 면적(area under the ROC curve; AUC)값이 남녀 각각 0.826(95% CI=0.799-0.850), 0.852(95% CI=0.820-0.881)로 가장 높게 나타났으며, 최적 절단값은 남녀 모두 0.44였다(p<0.001). 따라서 지질비율 지표 중 혈장 동맥경화 지수는 65세 이상 고령자의 대사증후군 진단에 가장 유용한 지표로 나타났다.

• Journal of Preventive Medicine and Public Health
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• 제32권3호
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• pp.361-373
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• 1999

ROC곡선의 AUC는 측전도구의 기준 타당도를 나타내는 가장 일반화된 지표다. 본 연구는 ROC분석법을 이용하여 현행의 근로자건강진단에서 업무관련성 근골격계 질환의 고위험군을 변별하는 표준 설문지를 개발하고자 하였다. 컴퓨터를 이용하는 선박 설계업 종사자 89명, 전화번호 안내원 113명, 일반 직업 여성 79명, 주부 89명 등 총 370명의 일차 연구대상군에 대한 재활의 학과 전문의의 최종 진단결과를 기준으로 1996년에 개발된 '근로자의 신체 증상에 관한 설문지'의 응답결과를 비교하였다. 근골격계 질환과의 관련성이 높은 문항조합을 선정하고 문항별 가중치를 산출하기 위해 로짓회귀분석, 상관분석 등을 실시하였으며, 문항조합 및 가중치 산출방법이 서로 다른 4가지 설문모형에 따른 AUC를 비교 하였다. 또한, 국내 모 자동차조립공장 근로자 225명의 설문결과와 산업의학 전문의의 진단결과 자료를 이용하여 4가지 설문모형의 AUC 재현도를 확인하였다. 분석 결과, 통계적으로 유의 한 차이는 없었으나 문항수를 줄여도 문항별 응답수준별 가중치를 부여하면 AUC가 일관되게 증가함을 확인하였다. 증상문항 4개와 신체부위문항 7개를 통합한 11개 문항에 가중치를 부여하는 모형이 변별력, 재현도, 편의성 측면에서 우수한 것으로 나타나, 이를 기준으로 새로운 업무관련성 근골격계 질환 설문지를 설계할 수 있었다. 문항수가 적으면서도 타당도는 높은 설문지를 개발하고, 상대적인 비교평가에 쓰일 수 있는 정량적 가중치를 제시한 것이 본 연구의 주요성과라 할 수 있다. 본 연구는 전문의 사이의 진단기준 차이를 고려하지 못한 점, 다양한 인구집단에 적용할만한 절대적인 참고치를 제시하지 못한 점 등에서 한계가 있다. 그러나, '측정 도구의 정량적 타당도 검증을 통한 질병 감시용 도구 개발'이라는 본 연구의 기본 취지 및 접근방법은 향후 조직적인 질병 예방활동에 활용될 여지가 있을 것이다.

• 대한영상의학회지
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• 제85권2호
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• pp.394-408
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• 2024

목적 CT 영상 소견을 이용하여 편평세포폐암에서 programmed death ligand 1 (이하 PD-L1)의 발현을 예측하는 모델을 구축해 보고자 하였다. 대상과 방법 PD-L1 발현검사 결과를 포함하고 있는 97명의 편평세포폐암 환자를 포함하였고 종양 치료 전 시행한 CT 영상 소견을 분석하였다. 전체 환자군과 40명의 진행성(≥ stage IIIB) 병기 환자군에 대하여 PD-L1 발현 예측을 위한 다중 로지스틱 회귀 분석 모델 구축을 시행하였다. 각각의 환자군에 대하여 곡선 아래 면적(areas under the receiver operating characteristic curves; 이하 AUCs)을 분석하여 예측력을 평가하였다. 결과 전체 환자군에서 '전체 유의인자 모델'(종양병기, 종양크기, 흉막결절, 폐전이)의 AUC 값은 0.652이며, '선택 유의인자 모델'(흉막결절)은 0.556이었다. 진행성 병기 환자군에서 '선택 유의인자 모델'(종양크기, 흉막결절, 폐소수전이, 간질성폐렴의 부재)의 AUC 값은 0.897이었다. 이러한 인자들 중 흉막결절과 폐소수전이는 높은 오즈비를 보였다(각각, 8.78과 16.35). 결론 본 연구에서의 모델은 편평세포폐암의 PD-L1 발현예측의 가능성을 보여주었으며 흉막결절과 폐소수전이는 PD-L1 발현을 예측하는데 중요한 CT 예측인자였다.

• Journal of Yeungnam Medical Science
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• 제38권4호
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• pp.318-325
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• 2021

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 (32 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) score (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 higher than 1,176 pg/mL (odds ratio [OR], 3.352; p<0.001), higher lactate levels (OR, 1.203; p=0.003), and higher SOFA score (OR, 1.249; p<0.001) were risk factors for in-hospital mortality. Conclusion: Presepsin levels were higher in non-survivors than in survivors. Thus, presepsin may be a valuable biomarker in predicting the prognosis of sepsis.

• Clinical and Experimental Reproductive Medicine
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• 제49권3호
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• pp.202-209
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• 2022

Objective: The aim of this study was to assess the correlation of oocyte number with serum anti-Müllerian hormone (AMH) levels measured by two automated methods (Access or Elecsys) in fresh stimulated in vitro fertilization (IVF) cycles. Methods: In this retrospective study at a university hospital, data were collected from 243 fresh stimulated IVF cycles performed from August 2016 to December 2020. The serum AMH level was measured by Access in 120 cycles and by Elecsys in 123 cycles. The cut-off of serum AMH for prediction of poor responders (three or fewer oocytes) or high responders (15 or more oocytes) was calculated by the receiver operating characteristic curve analysis. Results: For the two automated methods, the following equations were derived: total oocyte number=2.378+1.418×(Access-AMH) (r=0.645, p<0.001) and total oocyte number=2.417+2.163×(Elecsys-AMH) (r=0.686, p<0.001). The following combined equation could be derived: (Access-AMH)=0.028+1.525×(Elecsys-AMH). To predict poor responders, the cut-off of Access-AMH was 1.215 ng/mL (area under the curve [AUC], 0.807; 95% confidence interval [CI], 0.730-0.884; p<0.001), and the cut-off of Elecsys-AMH was 1.095 ng/mL (AUC, 0.848; 95% CI, 0.773-0.923; p<0.001). To predict high responders, the cut-off of Access-AMH was 3.450 ng/mL (AUC, 0.922; 95% CI, 0.862-0.981; p<0.001), and the cut-off of Elecsys-AMH was 2.500 ng/mL (AUC, 0.884; 95% CI, 0.778-0.991; p<0.001). Conclusion: Both automated methods for serum AMH measurement showed a good correlation with oocyte number and good performance for predicting poor and high responders in fresh stimulated IVF cycles. The Access method usually yielded higher measured serum AMH levels than the Elecsys method.

• Asian Pacific Journal of Cancer Prevention
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• 제16권2호
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• pp.707-712
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• 2015

Background: To evaluate use of magnetic resonance imaging (MRI) and a logistic model including risk factors for lymph node metastasis for improved diagnosis. Materials and Methods: The subjects were 176 patients with rectal cancer who underwent preoperative MRI. The longest lymph node diameter was measured and a cut-off value for positive lymph node metastasis was established based on a receiver operating characteristic (ROC) curve. A logistic model was constructed based on MRI findings and risk factors for lymph node metastasis extracted from logistic-regression analysis. The diagnostic capabilities of MRI alone and those of the logistic model were compared using the area under the curve (AUC) of the ROC curve. Results: The cut-off value was a diameter of 5.47 mm. Diagnosis using MRI had an accuracy of 65.9%, sensitivity 73.5%, specificity 61.3%, positive predictive value (PPV) 62.9%, and negative predictive value (NPV) 72.2% [AUC: 0.6739 (95%CI: 0.6016-0.7388)]. Age (<59) (p=0.0163), pT (T3+T4) (p=0.0001), and BMI (<23.5) (p=0.0003) were extracted as independent risk factors for lymph node metastasis. Diagnosis using MRI with the logistic model had an accuracy of 75.0%, sensitivity 72.3%, specificity 77.4%, PPV 74.1%, and NPV 75.8% [AUC: 0.7853 (95%CI: 0.7098-0.8454)], showing a significantly improved diagnostic capacity using the logistic model (p=0.0002). Conclusions: A logistic model including risk factors for lymph node metastasis can improve the accuracy of MRI diagnosis of rectal cancer.

• 중환자간호학회지
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• 제8권1호
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• pp.71-79
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• 2015

Purpose: The purpose of this study was to compare the applicability of the Charlson Comorbidity Index (CCI) and Acute Physiology, Age, Chronic Health Evaluation III (APACHE III) to the prediction of the healthcare outcomes of intensive care unit (ICU) patients. Methods: This research was performed with 136 adult patients (age>18 years) who were admitted to the ICU between May and June 2012. Data were measured using the CCI score with a comorbidity index of 19 and the APACHE III score on the standard of the worst result with vital signs and laboratory results. Discrimination was evaluated using receiver operating characteristic (ROC) curves and area under an ROC curve (AUC). Calibration was performed using logistic regression. Results: The overall mortality was 25.7%. The mean CCI and APACHE III scores for survivors were found to be significantly lower than those of non-survivors. The AUC was 0.835 for the APACHE III score and remained high, at 0.688, for the CCI score. The rate of concordance according to the CCI and the APACHE III score was 69.1%. Conclusion: The route of admission, days in ICU, CCI, and APACHE III score are associated with an increased mortality risk in ICU patients.

• Genomics & Informatics
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• 제11권1호
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• pp.46-51
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• 2013

Normal-karyotype acute myeloid leukemia (NK-AML) is a highly malignant and cytogenetically heterogeneous hematologic cancer. We searched for somatic mutations from 10 pairs of tumor and normal cells by using a highly efficient and reliable analysis workflow for whole-exome sequencing data and performed association tests between the NK-AML and somatic mutations. We identified 21 nonsynonymous single nucleotide variants (SNVs) located in a coding region of 18 genes. Among them, the SNVs of three leukemia-related genes (MUC4, CNTNAP2, and GNAS) reported in previous studies were replicated in this study. We conducted stepwise genetic risk score (GRS) models composed of the NK-AML susceptible variants and evaluated the prediction accuracy of each GRS model by computing the area under the receiver operating characteristic curve (AUC). The GRS model that was composed of five SNVs (rs75156964, rs56213454, rs6604516, rs10888338, and rs2443878) showed 100% prediction accuracy, and the combined effect of the three reported genes was validated in the current study (AUC, 0.98; 95% confidence interval, 0.92 to 1.00). Further study with large sample sizes is warranted to validate the combined effect of these somatic point mutations, and the discovery of novel markers may provide an opportunity to develop novel diagnostic and therapeutic targets for NK-AML.

• Genomics & Informatics
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• 제14권4호
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Asian Pacific Journal of Cancer Prevention
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• 제15권11호
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• pp.4467-4474
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• 2014

Background: We aimed to comprehensively review the evidence for using sputum DNA to detect non-small cell lung cancer (NSCLC). Materials and Methods: We searched PubMed, Science Direct, Web of Science, Chinese Biological Medicine (CBM), Chinese National Knowledge Infrastructure (CNKI), Wanfang, Vip Databases and Google Scholar from 2003 to 2013. The meta-analysis was carried out using a random-effect model with sensitivity, specificity, diagnostic odd ratios (DOR), summary receiver operating characteristic curves (ROC curves), area under the curve (AUC), and 95% confidence intervals (CI) as effect measurements. Results: There were twenty-two studies meeting the inclusion criteria for the meta-analysis. Combined sensitivity and specificity were 0.62 (95%CI: 0.59-0.65) and 0.73 (95%CI: 0.70-0.75), respectively. The DOR was 10.3 (95%CI: 5.88-18.1) and the AUC was 0.78. Conclusions: The overall accuracy of the test was currently not strong enough for the detection of NSCLC for clinical application. Dscovery and evaluation of additional biomarkers with improved sensitivity and specificity from studies rated high quality deserve further attention.