Biomedical subjects

Syi Su

Publications and source records attributed to Syi Su.

6 recordsLinked to original sources

PubMed2007

Survival, mortality, and complications in patients with beta-thalassemia major in northern Taiwan.

BACKGROUND: Advances in treatment have improved the prognosis in beta-thalassemia major. We present the survival and complications pattern of those patients in northern Taiwan born after 1970. PROCEDURE: One-hundred and sixty patients with beta-thalassemia major born after 1970 were collected. The Kaplan-Meier method and log-rank test were used to estimate and compare survival. Cox regression models were used to examine the associations of bone marrow transplantation (BMT), time of BMT procedure, and time of complications with survival. RESULTS: Better survival was observed for patients born after 1980 (P = 0.0121). Heart disease, BMT-related deaths, and infections were the main causes of death. Among the living patients over age 15, hypogonadotropic hypogonadism, HCV infection, diabetes, heart failure, and arrhythmia were the common complications. No patients under age 15 had complications. CONCLUSIONS: Survival for patients with beta-thalassemia major has improved significantly in Taiwan. More time is required to demonstrate whether these modalities added to the treatment of these patients will impact favorably on their outcome. Our success with BMT is improving and we are now in a position to offer this curative alternative.

Adolescent↗

PubMed2006

Development and psychometric properties of the dialysis module of the WHOQOL-BREF Taiwan version.

BACKGROUND: Quality of life (QOL) is now considered to be an important part of the assessment of dialysis patients. The aim of this study was to develop and assess the reliability, validity and sensitivity of the dialysis module of the World Health Organization Quality of Life - Brief (WHOQOL-BREF) Taiwan version [WHOQOL-BREF(TW)] in patients undergoing regular hemodialysis (HD). METHODS: QOL survey was administered to 283 regular HD patients in metropolitan Taipei. The instruments used included: (1) the proposed module - composed of the core part, the WHOQOL-BREF(TW), and the six specific items; (2) the symptom/problem (S/P) scale - composed of 12 items specific for dialysis patients; (3) the utility measure, which was performed with standard gamble (SG) methods; and (4) the rating scale (RS). RESULTS: Based on the six criteria of validity, reliability and variance of the items, four HD-specific items were selected. Reliability study showed that Cronbach's alphas, composite reliability, and test-retest reliability (intraclass correlation at an average retest interval of 4-8 weeks) of the four domains of physical, psychological, social relationship and environment, ranged from 0.74-0.82, 0.79-0.84 and 0.61-0.79, respectively. Validity study showed that all the correlations between an item and its corresponding domain were highly significant (r>0.4, p<0.01) and larger than the correlations between the item and other domains. SG and psychometric measures showed relatively low correlations (0.12-0.26). The module showed the same construct as the WHOQOL-BREF(TW) under confirmatory factor analysis, whereas the exploratory factor analysis showed mild variation. Convergent and discriminant validity were good. Global QOL, physical, psychological and environment domains had some sensitivity to differentiate the severity of the condition of patients receiving HD. Clinical validity was demonstrated in global QOL, physical and psychological domains to have significant correlations with S/P scores. CONCLUSION: Besides broader coverage than the core WHOQOL-BREF(TW), the dialysis module of the WHOQOL-BREF(TW) is a valid, reliable and sensitive QOL instrument for the assessment of HD patients in Taiwan.

Adult↗

PubMed2005

Quality of life and its determinants of hemodialysis patients in Taiwan measured with WHOQOL-BREF(TW).

BACKGROUND: In 1991, the World Health Organization (WHO) initiated a cross-cultural project to develop a quality-of-life (QOL) questionnaire (WHOQOL); soon after this, the clinically applicable short form was developed and named WHOQOL-BREF, followed by a Taiwanese version (WHOQOL-BREF[TW]). METHODS: We first administered the WHOQOL-BREF(TW) and symptom/problem scale to 376 patients with end-stage renal disease on regular hemodialysis therapy in Taiwan. Analysis with multiple stepwise regressions was conducted to study determinants of QOL domains and items. RESULTS: The WHOQOL-BREF(TW) was reliable and valid from various validation studies. The 4 domains (physical, psychological, social relations, and environment) and global items (overall quality of life and general health) of the WHOQOL-BREF(TW) each differentiated symptoms/problems of hemodialysis patients from age-, sex-, and education-matched healthy referents. The 4 domains, except for environment and global items of the WHOQOL-BREF(TW), each differentiated erythropoietin dosage from age-, sex-, and education-matched healthy referents. After adjusting for age, sex, marriage, and education, the prominent associated factors of various QOL domains and items were age, area (Taipei or Keelung), hemoglobin level, normalized protein catabolic rate, and symptom/problem scale. CONCLUSION: The WHOQOL-BREF(TW) is reliable and valid for long-term study of hemodialysis patients, and hemodialysis had negative impacts on QOL, especially in patients with more severe disease with greater symptom/problem scores, lower hemoglobin levels, and lower normalized protein catabolic rates.

Adult↗

PubMed2003

Modeling an emergency medical services system using computer simulation.

STUDY OBJECTIVES: In the emergency medical services (EMS) system, appropriate prehospital care can substantially decrease casualty mortality and morbidity. This study designed a simulation model, evaluated the existing EMS system, and suggested improvements. METHODS: The study focused on 23 networked EMS hospitals affiliated with 36 emergency response units (subgroups) to perform two-tier rescues (advanced life support [ALS] in addition to basic life support [BLS] services) in Taipei, Taiwan. Using the existing EMS model as a base, this research constructed a computer simulation model and explored several model alternatives to achieve the study's objectives. The virtual models varied with staffing level, number of assigned emergency network hospitals, and various two-tier rescue probabilities. RESULTS: Increasing the staffing to two teams for Hospital 22 lessened the call waiting probability (delay between rescue call and ambulance dispatch) by 50%, even if the dispatch rate of the two-tier rescue increased from the empirical 2% to a simulated 10 and 20%. Changing the two-tier rescue pattern so each EMS subgroup cooperated with two specific, preassigned network hospitals lowered the probability of patients having to wait for rescue dispatch to under 1%. CONCLUSION: The following alternatives provided the greatest combination of effectiveness, quality patient care, and cost-efficiency: (1) because of its unique location, increase Hospital 22's staffing level to two ALS teams. (2) Establish a specific rescue protocol for the two-tier system that preassigns two network hospitals to each of the 36 EMS subgroups along with a prearranged calling sequence. If implemented, this will improve EMS performance, streamline the system, reduce randomness, and enhance efficiency.

Computer Simulation↗

PubMed2003

Managing a mixed-registration-type appointment system in outpatient clinics.

INTRODUCTION: Improving outpatient resource utilization significantly enhances the efficiency of healthcare organizations. Substantial number of walk-in patients (average of 72% in our study) to outpatient services is a universal characteristic of Taiwan's healthcare organizations. Consequently, scheduling becomes extremely complicated and important. Selecting the right scheduling alternative, a healthcare organization can markedly improve operating efficiency of outpatient resources. OBJECTIVE: This research applied simulation methodology to analyze several scheduling solutions and found that setting the appropriate arrival time interval for preregistered patients significantly impacts queuing problems in outpatient services. METHOD: Using established simulation models, the effects of various scheduling policies on patients' throughput time and waiting times were revealed. Under alternative model A, the first 20 numbers are reserved for scheduled patients; after that, only even numbers are offered for scheduled ones. Odd numbers after 20 are left for walk-ins. Under alternative model B, front numbers were assigned to scheduled patients successively. The later numbers were left for walk-ins. Alternative model C assigned scheduled patients with even numbers and walk-ins with odd numbers in sequence. Finally, alternative model D was designed to examine the optimal scheduled time interval by conducting the model with different scheduled time intervals such as 3, 5, 7, 9, and 11 min. RESULT: The alternative sequence (alternative model C-assigning even numbers for scheduled patients and odd numbers for walk-in patients, or vice versa) significantly has the least throughput time (average: 34.9 min vs 55.2, 56.2, and 46.2 min) and waiting times (average: 14.7 min vs 34.9, 35.8, and 25.8 min) for walk-in patients compared with other registration strategies. Scheduling the appointments with flexible time interval (alternative model D) has the least throughput time (average: 24.2 min vs 28.4, 28.2, and 37.2 min) and waiting times (average: 8.0 min vs 12.5, 12.3, and 20.5 min) for scheduled patients compared with other registration strategies. CONCLUSION: The findings of this research could be applied possibly to any outpatient clinic with mixed-registration-type (walk-in and scheduled), particularly which accounts for high percentage of walk-in patients.

Appointments and Schedules↗

PubMed2002

Resource reallocation in an emergency medical service system using computer simulation.

Emergency medical service (EMS) policy makers must seek to achieve maximum effectiveness with finite resources. This research establishes an EMS computer simulation model using eM-Plant software. The simulation model is based on Taipei city's EMS system with input data from prehospital care records from December 2000; it manipulates resource allocation levels and rates of idle errands. Presently, EMS ambulance utilization is about 8.78%. On average, 20.89 minutes are required to transport a patient to the hospital. Computer simulations showed that reducing the number of ambulances to one at each of the 36 response units increases the utilization rate to 15.47% but does not compromise the current service quality level. Thus, ambulance utilization improves, times of patients waiting for pre-hospital care and arrival at hospitals are only slightly affected, and considerable cost savings result. This study provides a research methodology and suggests specific policy directions for resource allocation in EMS. Limiting the number of ambulances to one per response unit reduces costs, increases efficiency, and yet maintains the same operational pattern of medical service.

Ambulances↗