• Child Health Nursing Research
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• 제19권1호
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• pp.12-20
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• 2013

목적 본 연구는 영유아의 발달지연의심에 영향 미치는 요인을 파악하여 영유아의 성장발달 지연 예방 프로그램 개발에 기초자료를 제공하고자 시도하였다. 방법 연구대상은 B시 16개구 보건소를 방문한 0-6세까지의 영유아와 그의 어머니로 총 133명이었고, 영유아의 발달검사를 위해서는 한국형 덴버 II 도구를 사용하였으며, 자료분석은 SPSS 19.0을 이용하여 ${\chi}^2$-test와 Fisher's exact test, 다중 로지스틱 회귀분석을 실시하였다. 결과 신체계측 결과 체중 3백분위 미만은 7.5%였고, 현재체중 백분위가 출생시 체중백분위보다 성장곡선 두 칸 이상 감소한 경우는 8.4%였다. 한국형 덴버 II 결과에 의한 발달지연의심 비율은 9.8%였다. 영유아의 발달지연의심 예측요인은 체중백분위 변화, 경제상태, 어머니가 지각한 아동의 발달상태였다. 즉, 출생시 체중백분위가 '감소'하는 경우가 '유지'되는 경우보다 발달지연의심이 나타날 확률이 6.69배 높았고, 경제상태가 '하'인 경우가 '중'인 경우보다 발달지연의심이 나타날 확률이 6.26배 높았으며, 어머니가 지각한 아기의 발달상태가 '의심'인 경우가 '정상'인 경우보다 발달지연의심 비율이 4.99배 높게 나타났다. 결론 영유아들의 건강한 성장발달을 위해서는 출생 시부터 주기적으로 아동의 성장발달상태를 관리하는 정부차원의 시스템 구축이 필요하고, 특히 저소득층 및 1세 이하의 영아들을 위한 보다 짧은 주기의 성장발달평가 프로그램이 필요하다.

• 대한교통학회지
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• 제30권2호
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• pp.43-52
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• 2012

본 연구에서는 첨단안전장치의 운전지원장치중 하나인 차선이탈경고장치(Lane Departure Warning System; LDWS)의 이용자 만족도 분석에 초점을 맞추어 연구를 수행하였다. 본 연구에서는 국내 실제 화물자동차 이용자를 대상으로 차선이탈경고장치를 보급하여 사용후 차선이탈경고장치의 사용만족도 및 교통사고예방효과등을 설문조사를 수행하였다. 설문분석을 통해 차선이탈경고장치의 효과를 이용자 중심 측면에서 분석하였다. 대부분 장거리 운전자가 응답대상이 되었으며, 사고발생의 경우 장시간 운전으로 인해 졸음운전등에 위험이 있는 것으로 나타났다. 교차분석 결과, 사용만족도는 평균주행거리, 경고제공시기, 차로이탈검지정확성, 날씨에 따른 검지정확성, 곡선도로주행시 검지정확성, 경고제공방식만족도와 관련성이 높은 것으로 도출되었다. 또한, 교통사고 예방효과는 경고제공시기, 차로이탈 검지정확성, 날씨에 따른 검지정확성, 속도에 따른 검지정확성, 곡선도로주행시 검지정확성이 관련성이 높은 것으로 나타났다. 이항 로지스틱 회귀분석결과 사용만족도는 곡선도로에서의 경고정보시스템 정확성이 이용만족도에 가장 큰 영향을 미치는 것으로 분석되었다. 본 연구결과는 추후 LDWS와 같은 첨단장비를 장착한 차량들의 확대 보급시 교통안전 효과분석을 위한 기초자료로 활용 가능할 것이다. 또한 차로이탈경고장치의 연구 및 보완시 도출된 변수에 초점을 맞춘다면 장치의 효과를 극대화 할 수 있을 것으로 판단된다. 아울러 LDWS기능 및 성능 개선을 위한 평가방법 개발에도 연구결과가 효과적으로 적용될 수 있을 것으로 기대된다.

• 한국방사선학회논문지
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• 제10권3호
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• pp.187-193
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• 2016

본 연구는 건강검진을 목적으로 내원하여 경동맥초음파를 실시한 140명을 대상으로 경동맥의 내중막 두께(IMT; intima media thickness)변화 및 죽상경화반(Plaque)의 유무와 혈액학적 검사와의 상호연관성을 알아보고자 시행되었다. 경동맥초음파상 IMT두께가 1 mm 이상을 비정상으로 간주하고 죽상경화반의 유무를 평가하였으며, 혈청검사를 통하여 지질학적 수치 및 공복혈당수치를 분류하여 상관관계를 알아보았다. 그 결과 공복혈당수치가 죽상경화반을 일으키는 유일한 독립적인 예측인자로 분석되었고(p=0.033), ROC 곡선분석에서 cut off value는 126 mg/dL(민감도 56.25%, 특이도 68.33%)로 결정되었다. 또한 로지스틱 회귀분석에서 위험율(Odds ratio)은 1.01배로 나타났다. 따라서 향후 다수의 대상자로 장기적인 전향적연구가 필요할 것으로 사료되며, 혈액검사수치를 고려하여 심뇌혈관질환의 효과적인 일차예방 역할과 혈관의 추적관찰을 위한 경동맥초음파가 적극적으로 권고 된다.

• 정신신체의학
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• 제23권2호
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• pp.114-120
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• 2015

연구목적 결핵환자 중 우울증 고위험 환자와 저위험 환자의 비교연구를 통해 결핵환자의 우울증 위험요인을 밝히고자 했다. 방 법 57명의 결핵환자를 대상으로 벡 우울 검사 2판을 이용하여 우울증상을 평가하였다. 우울증 고위험군과 저위험군으로 나누어 이분형로지스틱회귀분석 및 계산도표를 작성하였다. 결 과 신체비만지수가 낮아질수록 우울증 고위험군에 속할 위험은 높았다. 결핵치료 임의중단력이 있을 경우 우울증 고위험군에 속할 위험은 6배 높았다. 우울증 과거병력이 있는 경우 우울증 고위험군에 속할 위험은 25배 높았다. Original C-index는 0.789였고 bias corrected C-index는 0.754로 상당한 일치를 보였다. 결 론 낮은 신체비만지수, 결핵치료 임의중단력, 우울증 과거병력은 결핵환자의 우울증 위험요인임을 밝혔다. 이는 결핵환자에 대한 정신건강의학과적 개입 및 치료를 위한 근거자료가 될 것이다.

• Journal of Breast Disease
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• 제6권2호
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• pp.52-59
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• 2018

Purpose: This study aimed to determine whether clinicopathological factors are potentially associated with successful breast-conserving surgery (BCS) after neoadjuvant chemotherapy (NAC) and develop a nomogram for predicting successful BCS candidates, focusing on those who are diagnosed with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative tumors during the pre-NAC period. Methods: The training cohort included 239 patients with an HR-positive, HER2-negative tumor (${\geq}3cm$), and all of these patients had received NAC. Patients were excluded if they met any of the following criteria: diffuse, suspicious, malignant microcalcification (extent >4 cm); multicentric or multifocal breast cancer; inflammatory breast cancer; distant metastases at the time of diagnosis; excisional biopsy prior to NAC; and bilateral breast cancer. Multivariate logistic regression analysis was conducted to evaluate the possible predictors of BCS eligibility after NAC, and the regression model was used to develop the predicting nomogram. This nomogram was built using the training cohort (n=239) and was later validated with an independent validation cohort (n=123). Results: Small tumor size (p<0.001) at initial diagnosis, long distance from the nipple (p=0.002), high body mass index (p=0.001), and weak positivity for progesterone receptor (p=0.037) were found to be four independent predictors of an increased probability of BCS after NAC; further, these variables were used as covariates in developing the nomogram. For the training and validation cohorts, the areas under the receiver operating characteristic curve were 0.833 and 0.786, respectively; these values demonstrate the potential predictive power of this nomogram. Conclusion: This study established a new nomogram to predict successful BCS in patients with HR-positive, HER2-negative breast cancer. Given that chemotherapy is an option with unreliable outcomes for this subtype, this nomogram may be used to select patients for NAC followed by successful BCS.

• Journal of Korean Neurosurgical Society
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• 제62권4호
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.

• Journal of Korean Neurosurgical Society
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• 제64권5호
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• pp.776-783
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• 2021

Objective : The purpose of this study was to identify risk factors for distal adding on (AO) or distal junctional kyphosis (DJK) in adolescent idiopathic scoliosis (AIS) treated by posterior spinal fusion (PSF) to L3 with a minimum 2-year follow-up. Methods : AIS patients undergoing PSF to L3 by two senior surgeons from 2000-2010 were analyzed. Distal AO and DJK were deemed poor radiographic results and defined as >3 cm of deviation from L3 to the center sacral vertical line (CSVL), or >10° angle at L3-4 on the posterior anterior- or lateral X-ray at ultimate follow-up. New stable vertebra (SV) and neutral vertebra (NV) scores were defined for this study. The total stability (TS) score was the sum of the SV and NV scores. Results : Ten of 76 patients (13.1%) were included in the poor radiographic outcome group. The other 66 patients were included in the good radiographic outcome group. Lower Risser grade, more SV-3 (CSVL doesn't touch the lowest instrumented vertebra [LIV]) on standing and side bending films, lesser NV and TS score, rigid L3-4 disc, more rotation and deviation of L3 were identified risk factors for AO or DJK. Age, number of fused vertebrae, curve correction, preoperative coronal/sagittal L3-4 disc angle did not differ significantly between the two groups. Multiple logistic regression results indicated that preoperative Risser grade 0, 1 (odds ratio [OR], 1.8), SV-3 at L3 in standing and side benders (OR, 2.1 and 2.8, respectively), TS score -5, -6 at L3 (OR, 4.4), rigid disc at L3-4 (OR, 3.1), LIV rotation >15° (OR, 2.9), and LIV deviation >2 cm from CSVL (OR, 2.2) were independent predictive factors. Although there was significant improvement of the of Scoliosis Research Society-22 average scores only in the good radiographic outcome group, there was no significant difference in the scores between the groups. Conclusion : The prevalence of AO or DJK at ultimate follow-up for AIS with LIV at L3 was 13.1%. To prevent AO or DJK following fusion to L3, we recommend that the CSVL touch L3 in both standing and side bending, TS score is -4 or less, the L3/4 disc is flexible, L3 is neutral (<15°) and ≤2 cm from the midline and the patient is ≥ Risser 2.

• Journal of Gastric Cancer
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• 제21권4호
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• pp.368-378
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• 2021

Purpose: When patients with early gastric cancer (EGC) undergo non-curative endoscopic submucosal dissection requiring gastrectomy (NC-ESD-RG), additional medical resources and expenses are required for surgery. To reduce this burden, predictive model for NC-ESD-RG is required. Materials and Methods: Data from 2,997 patients undergoing ESD for 3,127 forceps biopsy-proven differentiated-type EGCs (2,345 and 782 in training and validation sets, respectively) were reviewed. Using the training set, the logistic stepwise regression analysis determined the independent predictors of NC-ESD-RG (NC-ESD other than cases with lateral resection margin involvement or piecemeal resection as the only non-curative factor). Using these predictors, a risk-scoring system for predicting NC-ESD-RG was developed. Performance of the predictive model was examined internally with the validation set. Results: Rate of NC-ESD-RG was 17.3%. Independent pre-ESD predictors for NC-ESD-RG included moderately differentiated or papillary EGC, large tumor size, proximal tumor location, lesion at greater curvature, elevated or depressed morphology, and presence of ulcers. A risk-score was assigned to each predictor of NC-ESD-RG. The area under the receiver operating characteristic curve for predicting NC-ESD-RG was 0.672 in both training and validation sets. A risk-score of 5 points was the optimal cut-off value for predicting NC-ESD-RG, and the overall accuracy was 72.7%. As the total risk score increased, the predicted risk for NC-ESD-RG increased from 3.8% to 72.6%. Conclusions: We developed and validated a risk-scoring system for predicting NC-ESD-RG based on pre-ESD variables. Our risk-scoring system can facilitate informed consent and decision-making for preoperative treatment selection between ESD and surgery in patients with EGC.

• Korean Journal of Radiology
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• 제22권6호
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• pp.994-1004
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• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

• Korean Journal of Radiology
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• 제21권4호
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• pp.413-421
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• 2020

Objective: A widely applicable, non-invasive screening method for non-alcoholic fatty liver disease (NAFLD) is needed. We aimed to develop and validate an index combining computed tomography (CT) and routine clinical data for screening for NAFLD in a large cohort of adults with pathologically proven NAFLD. Materials and Methods: This retrospective study included 2218 living liver donors who had undergone liver biopsy and CT within a span of 3 days. Donors were randomized 2:1 into development and test cohorts. CTL-S was measured by subtracting splenic attenuation from hepatic attenuation on non-enhanced CT. Multivariable logistic regression analysis of the development cohort was utilized to develop a clinical-CT index predicting pathologically proven NAFLD. The diagnostic performance was evaluated by analyzing the areas under the receiver operating characteristic curve (AUC). The cutoffs for the clinical-CT index were determined for 90% sensitivity and 90% specificity in the development cohort, and their diagnostic performance was evaluated in the test cohort. Results: The clinical-CT index included CTL-S, body mass index, and aspartate transaminase and triglyceride concentrations. In the test cohort, the clinical-CT index (AUC, 0.81) outperformed CTL-S (0.74; p < 0.001) and clinical indices (0.73-0.75; p < 0.001) in diagnosing NAFLD. A cutoff of ≥ 46 had a sensitivity of 89% and a specificity of 41%, whereas a cutoff of ≥ 56.5 had a sensitivity of 57% and a specificity of 89%. Conclusion: The clinical-CT index is more accurate than CTL-S and clinical indices alone for the diagnosis of NAFLD and may be clinically useful in screening for NAFLD.