The increased survival of patients with breast cancer has given rise to other problems associated with the complications of chemotherapy. One major complication is premature ovarian failure, an especially harmful outcome for women of reproductive age. This study was performed to evaluate the efficacy of GnRH agonist (GnRHa) treatment on protecting ovarian function in young breast cancer patients (30.59+/-5.1 yr) receiving chemotherapy after surgery. Twenty-two women were enrolled and given subcutaneous injections of leuprolide acetate (3.75 mg) every 4 weeks during chemotherapy. Follow-up laboratory tests (luteinizing hormone [LH], follicle stimulating hormone [FSH], and estradiol) were performed 1, 3, and 6 months after chemotherapy. Menstruation patterns and clinical symptoms were followed up for a mean duration of 35.6+/-1.7 months. FSH and LH levels were normal in all patients 6 months after completing chemotherapy (8.0+/-5.3, 4.4+/-2.7 mIU/mL, respectively). During follow-up, none of the patients complained of menopausal symptoms and 81.8% experienced recovery of menstruation. This report is the first trial of GnRHa as a treatment modality to protect ovarian function during adjuvant chemotherapy in young Korean breast cancer patients.
Adult ; Antineoplastic Agents/adverse effects/therapeutic use ; Antineoplastic Agents, Hormonal/therapeutic use ; Breast Neoplasms/diagnosis/*drug therapy/surgery ; Combined Modality Therapy ; Cyclophosphamide/adverse effects/therapeutic use ; Doxorubicin/adverse effects/therapeutic use ; Female ; Follicle Stimulating Hormone/analysis ; Gonadotropin-Releasing Hormone/*agonists ; Humans ; Leuprolide/administration & dosage ; Luteinizing Hormone/analysis ; Menstruation ; Ovarian Function Tests ; Primary Ovarian Insufficiency/etiology/*prevention & control ; Republic of Korea ; Tamoxifen/therapeutic use ; Time Factors

OBJECTIVES: We evaluated the effects of adding intravenous pamidronate to ongoing menopausal hormone therapy (MHT) on bone mineral density (BMD) in postmenopausal Korean women with low BMD.METHODS: This retrospective cohort study included 74 postmenopausal women who received MHT for at least 1 year and had a BMD T-score of less than −2.0. Maintaining the same MHT regimen, these women were divided into two groups: oral placebo group (n = 44) and a pamidronate group of patients with gastrointestinal discomfort (n = 30) who received 15–30 mg pamidronate intravenously every 3–12 months. BMD was reviewed at 12-month follow-up. Bone resorption markers in both groups, urinary deoxypyridinoline levels in the placebo group, and serum N-telopeptide of type I collagen in the pamidronate group were assessed at 6 and 12 months.RESULTS: At baseline, the body mass index (BMI), duration of previous MHT, and femur neck (FN) BMD differed between the groups. Within-group analysis revealed that BMD of the lumbar spine (LS) and total hip (TH) significantly increased in the placebo group, whereas those of the LS, FN, and TH increased in the pamidronate group. The increase in BMD of LS was significantly greater in the pamidronate group, after adjusting for BMI and duration of previous MHT (mean change: 3.7% vs. 6.2%; P < 0.001). There were no changes in bone resorption markers in either group.CONCLUSIONS: Adding intravenous pamidronate to ongoing MHT for 12 months might increase LS BMD in postmenopausal Korean women with low BMD.
Body Mass Index ; Bone Density ; Bone Resorption ; Cohort Studies ; Collagen Type I ; Female ; Femur Neck ; Follow-Up Studies ; Hip ; Hormone Replacement Therapy ; Humans ; Osteoporosis ; Postmenopause ; Retrospective Studies ; Spine

We retrospectively reviewed the sex distribution, age at oepration, chief complaints, peroperative and postoperative refractive errors, and corrected visual acuity in 160 previously operated patients to evaluate the clinical manifestations, reractive error, frequency of amblyopia, age at operation, and the postoperative factors affected by the operation of epiblepharon and congenital entropion itself that influence visual acuity and refractive error. The average age at operation was 7.9 years. The chief complaint was ocular discomfort, followed by visual disturbance, photophobia, eyelid rubbing and epiphora. One hundred thirty-three wyws(41.6%) whose preoperative corrected visual acuity was below 0.6 had a mean visual acuity of 0.7. Ninety-five eyes (29.7%) were preoperative myopes above -1.0D, 77 eyes(24%) were hyperopes above +1.0D, and 163 eyes were astigmatic above -1.0D. Twenty-two out of 76 eyes who were followed up for more than one year had a corrected visual acuity of below 0.6. The difference between the older and less than 7 years groups was significantly not significant. Astigmatic changes were not statistically different at any age group. However, the mean significantly visual acuity at postoperative one year was 0.73, which was significantly different from the preoperative value(P=0.006). In conclusion, concurrent postoperative glasses correction and amblyyopia therapy is indicated because the incidence of refractive errors and amblyopia is higher in epiblepharon and congenital entropion.
Amblyopia ; Entropion* ; Eyeglasses ; Eyelids ; Glass ; Humans ; Incidence ; Lacrimal Apparatus Diseases ; Photophobia ; Refractive Errors ; Retrospective Studies ; Sex Distribution ; Visual Acuity

Changes in contrast sensitivity have been demonstrated in patients with normal Snellen acuity. In an attempt to elucidate more sensitively the visual dysfunction before developement of either overt retinopathy or a reduction in Snellen acuity in patients with retinal disorders, contrast sensitivity test was performed in diabetic patients with normal Snellen acuity and control subjects matched for age and sex. The results were as follows. 1) Throughout all spatial frequencies(1.5 - 3.0 - 6.0 - 12.0 - 18.0 cpd), contrast sensitivity was significantly lower(P-value<0.01) in the diabetic eyes with retinopathy(30.7 - 49.3 - 52.5 - 16.1 - 7.8) than in the normal controls(42.5 - 84.3 - 103.0 - 60.5 - 25.1) or the diabetic eyes without retinopathy(43.1 - 92.2 - 95.8 - 43.4 - 16.4 ). 2) In high spatial frequencies(12.0 - 18.0 cpd) contrast sensitivity in the diabetic eyes without retinopathy group(43.4 - 16.4) was significantly decreased(P-value<0.01) in comparison with the normal controls(60.5 - 25.1). So, contrast sensitivity test is more sensitive test for central visual function than Snellen acuity.
Contrast Sensitivity* ; Diabetic Retinopathy* ; Humans ; Retinaldehyde

BACKGROUND AND OBJECTIVES: The olfactory identification test has been used in clinical assessment of olfactory ability for the following reasons: it is fast, it yields results compatible to a threshold test, and it gives a picture of how well the patient can deal with odors of everyday life. However, items in UPSIT (University of Pennsylvania Smell Identification Test)and CCCRC (Connecticut Chemosensory Clinical Research Center)identification test which are widely used in the world are selected for western people. Accordingly, these items in the tests are not appropriate for Koreans of different cultural background. MATERIALS AND METHODS: For the selection of proper items for the olfactory identification test, 42 natural odors familiar to Koreans were applied to 40 normal subjects and 40 patients with decreased sense of smell without sinonasal diseases. Among 42 items, 16 items with high identifiability and familiarity were chosen according to the results of test-retest in normal subjects. RESULTS: The results of olfactory identification test using 16 selected items showed high correlation with olfactory threshold. CONCLUSION: These 16 items can be used for an olfactory identification test for Koreans.
Humans ; Odors ; Pennsylvania ; Recognition (Psychology) ; Smell

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. Materials and Methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an opensource registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767–0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763–0.772), AdaBoost regressor (0.752; 95% CI, 0.743–0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669–0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001). Conclusion ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.