Prevalence, characteristics, evaluation, and management of carotid body tumors: Systematic analysis based on available evidence.

OBJECTIVE: Although carotid body tumors (CBTs) are rare, they attract particular attention because of their propensity for malignant transformation and the high surgical risk. Because data are scarce and as it is difficult to achieve a large sample size, no study has yet comprehensively analyzed the characteristics, management, or operative complications of CBTs. Therefore, we collected and analyzed all currently available information on CBTs and used the pooled data to derive quantitative information on disease characteristics and management. METHODS: We systematically searched PubMed, Embase, the Cochrane Library, and the Web of Science up to December 1, 2022, for studies that investigated the characteristics and management of CBTs. The primary objective was to identify the prevalence of the various characteristics and the incidence of complications. The secondary objective was to compare patients who underwent preoperative embolization (PE) and those who did not (non-PE), as well as to compare patients with different Shamblin grades and those with and without succinate dehydrogenase (SDH) mutations in terms of CBT characteristics and complications. Two reviewers selected studies for inclusion and independently extracted data. All statistical analyses were performed using the standard statistical procedures of Review Manager 5.2 and Stata 12.0. RESULTS: A total of 155 studies with 9291 patients and 9862 tumors were identified. The pooled results indicated that the median age of patients with CBT was 45.72 years, and 65% were female. The proportion of patients with bilateral lesions was 13%. In addition, 16% of patients had relevant family histories, and the proportion of those with SDH gene mutations was 36%. Sixteen percent of patients experienced multiple paragangliomas, and 12% of CBTs had catecholamine function. The incidence of cranial nerve injury (CNI) was 27%, and 14% of patients suffered from permanent CNI. The incidence rates of operative mortality and stroke were both 1%, and 4% of patients developed transient ischemic attacks. Of all CBTs, 6% were malignant or associated with metastases or recurrences. The most common metastatic locations were the lymph nodes (3%) and bone (3%), followed by the lungs (2%). Compared with non-PE, PE reduced the estimated blood loss (standardized mean difference, -0.95; 95% confidence interval [CI], -1.70 to -0.20) and the operation time (standardized mean difference, -0.56; 95% CI, -1.03 to -0.09), but it increased the incidence of stroke (odds ratio, 2.44; 95% CI, 1.04-5.73). Higher Shamblin grade tumors were associated with more operative complications. Patients who were SDH gene mutation-positive were more likely to have a relevant family history and had more symptoms. CONCLUSIONS: CBT was most common in middle-aged females, and early surgical resection was feasible; there was a low incidence of serious operative complications. Routine PE is not recommended because this may increase the incidence of stroke, although PE somewhat reduced the estimated blood loss and operation time. Higher Shamblin grade tumors increased the incidence of operative complications. Patients who were SDH gene mutation-positive had the most relevant family histories and symptoms.

Current situation and cost-effectiveness of 18F-FDG PET/CT for the diagnosis of fever of unknown origin and inflammation of unknown origin: A single-center, large-sample study from China.

PURPOSE: 18F-FDG PET/CT has an important role in the evaluation of fever of unknown origin (FUO) and inflammation of unknown origin (IUO). Our study was to investigate the current status of the inclusion of 18F-FDG PET/CT within FUO/ IUO diagnostic work-up and evaluate the cost-effectiveness of it in China. METHODS: A total of 741 FUO/IUO patients admitted to our hospital from January 2012 to December 2019 were retrospectively reviewed. The clinical characteristic, medical expenses to reach diagnosis and the proportion of definite etiological diagnosis achieved upon hospital discharge were compared between patients examined by 18F-FDG PET/CT (18F-FDG PET/CT group) and patients not examined by 18F-FDG PET/CT (non-18F-FDG PET/CT group). RESULTS: The mean age, proportion of critically-ill patients, proportion of rheumatologic diseases, the number of examinations and hospitalisation days to reach diagnosis in the 18F-FDG PET/CT group were significantly higher than those in the non-18F-FDG PET/CT group. The mean medical costs of 18F-FDG PET/CT group were significantly higher than those of non-18F-FDG PET/CT group, whereas the proportion of definite etiological diagnosis achieved upon hospital discharge of 18F-FDG PET/CT group was significantly higher than that of non-18F-FDG PET/CT group. The mean hospitalisation days and mean medical costs before diagnosis were significantly lower in patients who undertook 18F-FDG PET/CT ≤ 7 days after hospital admission than those in patients who undertook 18F-FDG PET/CT > 7 days after hospital admission. CONCLUSIONS: 18F-FDG PET/CT is mostly used in critically-ill and hard-to-diagnose FUO/IUO patients currently in China, which may conceal its cost-effective advantage. While the early use of 18F-FDG PET/CT according to patient characteristics and etiological clues could help to reduce hospitalization stay, limit medical costs, thus producing its diagnostic effect to the great extent.

The role of nutritional assessment for predicting radiotherapy-induced adverse events in patients with gastric cancer.

OBJECTIVE: The aim of this study was to investigate the role of nutritional factors in predicting radiotherapy-associated toxicities for gastric cancer patients. METHODS: A total of 285 gastric cancer patients who underwent radiotherapy in our hospital (Fudan University Shanghai Cancer Center) between 2010 and 2017 were included in this retrospective study. Nutritional status assessment included body weight loss (BWL), body mass index (BMI), serum albumin, nutrition risk screening 2002(NRS-2002), patient-generated subjective global assessment(PG-SGA) and nutritional risk index (NRI). RESULTS: Of all patients, 19.6% were underweight (BMI<18.5 kg/m2), 25.6% were hypoalbuminemia (<35 g l-1) and 48.8% lost ≥10% of body weight in the 6-month interval before radiotherapy(BWL). Meanwhile, 73.3%, 78.6 and 47.2% of the patients were diagnosed as malnutrition based on NRS-2002, PG-SGA and NRI, respectively. Hematological adverse events were present in 91.2% (≥Grade 1) and 20.4% (≥Grade 3) of the patients. Non-hematological adverse events occurred in 89.8% (≥Grade1) and 14.4% (≥Grade 3) of the patients. Multivariate analyses indicated that only hypoalbuminemia(<35 g l-1) was independent predictor for Grade 3/4 hematological and non-hematological adverse events. Meanwhile, higher BWL(≥10%) was also independent predictor for Grade 3/4 non-hematological adverse events. NRS-2002, PG-SGA and NRI score were not associated with treatment-induced adverse events. CONCLUSION: BWL and serum albumin are useful factors for predicting severe adverse events in gastric cancer patients who undergo radiotherapy. ADVANCES IN KNOWLEDGE: The use of nutritional factors in predicting severe adverse events enables implementation of individualized treatment strategies for early and intensive nutritional interventions in high-risk patients.

Utility of ctDNA in predicting response to neoadjuvant chemoradiotherapy and prognosis assessment in locally advanced rectal cancer: A prospective cohort study.

BACKGROUND: For locally advanced rectal cancer (LARC) patients who receive neoadjuvant chemoradiotherapy (nCRT), there are no reliable indicators to accurately predict pathological complete response (pCR) before surgery. For patients with clinical complete response (cCR), a "Watch and Wait" (W&W) approach can be adopted to improve quality of life. However, W&W approach may increase the recurrence risk in patients who are judged to be cCR but have minimal residual disease (MRD). Magnetic resonance imaging (MRI) is a major tool to evaluate response to nCRT; however, its ability to predict pCR needs to be improved. In this prospective cohort study, we explored the value of circulating tumor DNA (ctDNA) in combination with MRI in the prediction of pCR before surgery and investigated the utility of ctDNA in risk stratification and prognostic prediction for patients undergoing nCRT and total mesorectal excision (TME). METHODS AND FINDINGS: We recruited 119 Chinese LARC patients (cT3-4/N0-2/M0; median age of 57; 85 males) who were treated with nCRT plus TME at Fudan University Shanghai Cancer Center (China) from February 7, 2016 to October 31, 2017. Plasma samples at baseline, during nCRT, and after surgery were collected. A total of 531 plasma samples were collected and subjected to deep targeted panel sequencing of 422 cancer-related genes. The association among ctDNA status, treatment response, and prognosis was analyzed. The performance of ctDNA alone, MRI alone, and combining ctDNA with MRI was evaluated for their ability to predict pCR/non-pCR. Ranging from complete tumor regression (pathological tumor regression grade 0; pTRG0) to poor regression (pTRG3), the ctDNA clearance rate during nCRT showed a significant decreasing trend (95.7%, 77.8%, 71.1%, and 66.7% in pTRG 0, 1, 2, and 3 groups, respectively, P = 0.008), while the detection rate of acquired mutations in ctDNA showed an increasing trend (3.8%, 8.3%, 19.2%, and 23.1% in pTRG 0, 1, 2, and 3 groups, respectively, P = 0.02). Univariable logistic regression showed that ctDNA clearance was associated with a low probability of non-pCR (odds ratio = 0.11, 95% confidence interval [95% CI] = 0.01 to 0.6, P = 0.04). A risk score predictive model, which incorporated both ctDNA (i.e., features of baseline ctDNA, ctDNA clearance, and acquired mutation status) and MRI tumor regression grade (mrTRG), was developed and demonstrated improved performance in predicting pCR/non-pCR (area under the curve [AUC] = 0.886, 95% CI = 0.810 to 0.962) compared with models derived from only ctDNA (AUC = 0.818, 95% CI = 0.725 to 0.912) or only mrTRG (AUC = 0.729, 95% CI = 0.641 to 0.816). The detection of potential colorectal cancer (CRC) driver genes in ctDNA after nCRT indicated a significantly worse recurrence-free survival (RFS) (hazard ratio [HR] = 9.29, 95% CI = 3.74 to 23.10, P < 0.001). Patients with detectable driver mutations and positive high-risk feature (HR_feature) after surgery had the highest recurrence risk (HR = 90.29, 95% CI = 17.01 to 479.26, P < 0.001). Limitations include relatively small sample size, lack of independent external validation, no serial ctDNA testing after surgery, and a relatively short follow-up period. CONCLUSIONS: The model combining ctDNA and MRI improved the predictive performance compared with the models derived from individual information, and combining ctDNA with HR_feature can stratify patients with a high risk of recurrence. Therefore, ctDNA can supplement MRI to better predict nCRT response, and it could potentially help patient selection for nonoperative management and guide the treatment strategy for those with different recurrence risks.

Provider Care Team Segregation and Operative Mortality Following Coronary Artery Bypass Grafting.

BACKGROUND: Studies have shown that Black patients die more frequently following coronary artery bypass grafting than their White counterparts for reasons not fully explained by disease severity or comorbidity. To examine whether provider care team segregation within hospitals contributes to this inequity, we analyzed national Medicare data. METHODS: Using national Medicare data, we identified beneficiaries who underwent coronary artery bypass grafting at hospitals where this procedure was performed on at least 10 Black and 10 White patients between 2008 and 2014 (n=12 646). After determining the providers who participated in their perioperative care, we examined the extent to which Black and White patients were cared for by unique networks of provider care teams within the same hospital. We then evaluated whether a lack of overlap in composition of the provider care teams treating Black versus White patients (ie, high segregation) was associated with higher 90-day operative mortality among Black patients. RESULTS: The median level of provider care team segregation was high (0.89) but varied across hospitals (interquartile range, 0.85-0.90). On multivariable analysis, after controlling for patient-, hospital-, and community-level differences, mortality rates for White patients were comparable at hospitals with high and low levels of provider care segregation (5.4% [95% CI, 4.7%-6.1%] versus 5.8% [95% CI, 4.7%-7.0%], respectively; P=0.601), while Black patients treated at high-segregation hospitals had significantly higher mortality than those treated at low-segregation hospitals (8.3% [95% CI, 5.4%-12.4%] versus 3.3% [95% CI, 2.0%-5.4%], respectively; P=0.017). The difference in mortality rates for Black and White patients treated at low-segregation hospitals was nonsignificant (-2.5%; P=0.098). CONCLUSIONS: Black patients who undergo coronary artery bypass grafting at a hospital with a higher level of provider care team segregation die more frequently after surgery than Black patients treated at a hospital with a lower level.

Evaluating national trends in outcomes after implementation of a star rating system: Results from dialysis facility compare.

OBJECTIVE: To examine which factors are driving improvement in the Dialysis Facility Compare (DFC) star ratings and to test whether nonclinical facility characteristics are associated with observed longitudinal changes in the star ratings. DATA SOURCES: Data were collected from eligible patients in over 6,000 Medicare-certified dialysis facilities from three annual star rating and individual measure updates, publicly released on DFC in October 2015, October 2016, and April 2018. STUDY DESIGN: Changes in the star rating and individual quality measures were investigated across three public data releases. Year-to-year changes in the star ratings were linked to facility characteristics, adjusting for baseline differences in quality measure performance. DATA COLLECTION: Data from publicly reported quality measures, including standardized mortality, hospitalization, and transfusion ratios, dialysis adequacy, type of vascular access for dialysis, and management of mineral and bone disease, were extracted from annual DFC data releases. PRINCIPAL FINDINGS: The proportion of four- and five-star facilities increased from 30.0% to 53.4% between October 2015 and April 2018. Quality improvement was driven by the domain of care containing the dialysis adequacy and hypercalcemia measures. Additionally, independently owned facilities and facilities belonging to smaller dialysis organizations had significantly lower odds of year-to-year improvement than facilities belonging to either of the two large dialysis organizations (Odds Ratio [OR]: 0.736, 95% Confidence Interval [CI]: 0.631-0.856 and OR: 0.797, 95% CI: 0.723-0.879, respectively). CONCLUSIONS: The percentage of four- and five-star facilities has increased markedly over a three-year time period. These changes were driven by improvement in the specific quality measures that may be most directly under the control of the dialysis facility.

Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis.

Importance: Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains. Objective: To examine whether deep learning recurrent neural network (RNN) models that use raw longitudinal data extracted directly from electronic health records outperform conventional regression models in predicting the risk of developing hepatocellular carcinoma (HCC). Design, Setting, and Participants: This prognostic study included 48 151 patients with hepatitis C virus (HCV)-related cirrhosis in the national Veterans Health Administration who had at least 3 years of follow-up after the diagnosis of cirrhosis. Patients were identified by having at least 1 positive HCV RNA test between January 1, 2000, to January 1, 2016, and were followed up from the diagnosis of cirrhosis to January 1, 2019, for the development of incident HCC. A total of 3 models predicting HCC during a 3-year period were developed and compared, as follows: (1) logistic regression (LR) with cross-sectional inputs (cross-sectional LR); (2) LR with longitudinal inputs (longitudinal LR); and (3) RNN with longitudinal inputs. Data analysis was conducted from April 2018 to August 2020. Exposures: Development of HCC. Main Outcomes and Measures: Area under the receiver operating characteristic curve, area under the precision-recall curve, and Brier score. Results: During a mean (SD) follow-up of 11.6 (5.0) years, 10 741 of 48 151 patients (22.3%) developed HCC (annual incidence, 3.1%), and a total of 52 983 samples (51 948 [98.0%] from men) were collected. Patients who developed HCC within 3 years were older than patients who did not (mean [SD] age, 58.2 [6.6] years vs 56.9 [6.9] years). RNN models had superior mean (SD) area under the receiver operating characteristic curve (0.759 [0.009]) and mean (SD) Brier score (0.136 [0.003]) than cross-sectional LR (0.689 [0.009] and 0.149 [0.003], respectively) and longitudinal LR (0.682 [0.007] and 0.150 [0.003], respectively) models. Using the RNN model, the samples with the mean (SD) highest 51% (1.5%) of HCC risk, in which 80% of all HCCs occurred, or the mean (SD) highest 66% (1.2%) of HCC risk, in which 90% of all HCCs occurred, could potentially be targeted. Among samples from patients who achieved sustained virologic response, the performance of the RNN models was even better (mean [SD] area under receiver operating characteristic curve, 0.806 [0.025]; mean [SD] Brier score, 0.117 [0.007]). Conclusions and Relevance: In this study, deep learning RNN models outperformed conventional LR models, suggesting that RNN models could be used to identify patients with HCV-related cirrhosis with a high risk of developing HCC for risk-based HCC outreach and surveillance strategies.

Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models.

In life cycle assessment, characterization factors are used to convert the amount of the chemicals and other pollutants generated in a product's life cycle to the standard unit of an impact category, such as ecotoxicity. However, as a widely used impact assessment method, USEtox (version 2.11) only has ecotoxicity characterization factors for a small portion of chemicals due to the lack of laboratory experiment data. Here we develop machine learning models to estimate ecotoxicity hazardous concentrations 50% (HC50) in USEtox to calculate characterization factors for chemicals based on their physical-chemical properties in EPA's CompTox Chemical Dashborad and the classification of their mode of action. The model is validated by ten randomly selected test sets that are not used for training. The results show that the random forest model has the best predictive performance. The average root mean squared error of the estimated HC50 on the test sets is 0.761. The average coefficient of determination (R2) on the test set is 0.630, meaning 63% of the variability of HC50 in USEtox can be explained by the predicted HC50 from the random forest model. Our model outperforms a traditional quantitative structure-activity relationship (QSAR) model (ECOSAR) and linear regression models. We also provide estimates of missing ecotoxicity characterization factors for 552 chemicals in USEtox using the validated random forest model.

Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines.

BACKGROUND AND AIMS: Big data analytics leverage patterns in data to harvest valuable information, but are rarely implemented in clinical care. Optimising thiopurine therapy for inflammatory bowel disease [IBD] has proved difficult. Current methods using 6-thioguanine nucleotide [6-TGN] metabolites have failed in randomized controlled trials [RCTs], and have not been used to predict objective remission [OR]. Our aims were to: 1) develop machine learning algorithms [MLA] using laboratory values and age to identify patients in objective remission on thiopurines; and 2) determine whether achieving algorithm-predicted objective remission resulted in fewer clinical events per year. METHODS: Objective remission was defined as the absence of objective evidence of intestinal inflammation. MLAs were developed to predict three outcomes: objective remission, non-adherence, and preferential shunting to 6-methylmercaptopurine [6-MMP]. The performance of the algorithms was evaluated using the area under the receiver operating characteristic curve [AuROC]. Clinical event rates of new steroid prescriptions, hospitalisations, and abdominal surgeries were measured. RESULTS: Retrospective review was performed on medical records of 1080 IBD patients on thiopurines. The AuROC for algorithm-predicted remission in the validation set was 0.79 vs 0.49 for 6-TGN. The mean number of clinical events per year in patients with sustained algorithm-predicted remission [APR] was 1.08 vs 3.95 in those that did not have sustained APR [p < 1 x 10-5]. Reductions in the individual endpoints of steroid prescriptions/year [-1.63, p < 1 x 10-5], hospitalisations/year [-1.05, p < 1 x 10-5], and surgeries/year [-0.19, p = 0.065] were seen with algorithm-predicted remission. CONCLUSIONS: A machine learning algorithm was able to identify IBD patients on thiopurines with algorithm-predicted objective remission, a state associated with significant clinical benefits, including decreased steroid prescriptions, hospitalisations, and surgeries.

A sparse Ising model with covariates.

There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a dataset on genomic instability collected from tumor samples of several types, we propose a sparse covariate dependent Ising model to study both the conditional dependency within the binary data and its relationship with the additional covariates. This results in subject-specific Ising models, where the subject's covariates influence the strength of association between the genes. As in all exploratory data analysis, interpretability of results is important, and we use ℓ1 penalties to induce sparsity in the fitted graphs and in the number of selected covariates. Two algorithms to fit the model are proposed and compared on a set of simulated data, and asymptotic results are established. The results on the tumor dataset and their biological significance are discussed in detail.

[Cost-effectiveness of schistosomiasis comprehensive control in Lushan County from 2007 to 2012].

OBJECTIVE: To evaluate the cost-effectiveness of comprehensive control strategy which mainly controls the schistosomiasis infection source in Lushan County from 2007 to 2012. METHODS: The data of the schistosomiasis endemic, Oncomelania hupensis snail status, control technology, and funding were collected and analyzed statistically in the endemic villages of Lushan County from 2007 to 2012. RESULTS: The schistosome infection rate of the residents decreased from 0.04% in 2007 to 0 in 2012. The numbers of advanced schistosomiasis patients decreased from 128 in 2007 to 121 in 2012. No acute schistosomiasis patients were found. The average density of living snails decreased from 0.06 snial/0.1 m2 to 0.05 snail/0.1 m2, with the decreased rate of 16.67%, and no schistosome infected snails were found. However, the snail area increased from 15.84 hm2 in 2007 to 52.12 hm2 in 2012, with the rise rate of 229.04%. The year per capita was 200.48 yuan, the cost of the infection rate decreasing by 1% was 949,900 yuan, and the cost of living snail density decreasing by 1% was 5,698,200 yuan. CONCLUSION: The cost-effectiveness of comprehensive control strategy which mainly controls the schistosomiasis infection source is relatively good in Lushan County from 2007 to 2012.

Algorithms outperform metabolite tests in predicting response of patients with inflammatory bowel disease to thiopurines.

BACKGROUND & AIMS: Levels of the thiopurine metabolites 6-thioguanine nucleotide (6-TGN) and 6-methylmercaptopurine commonly are monitored during thiopurine therapy for inflammatory bowel disease despite this test's high cost and poor prediction of clinical response (sensitivity, 62%; specificity, 72%). We investigated whether patterns in common laboratory parameters might be used to identify appropriate immunologic responses to thiopurine and whether they are more accurate than measurements of thiopurine metabolites in identifying patients who respond to therapy. METHODS: We identified 774 patients with inflammatory bowel disease on thiopurine therapy using metabolite and standard laboratory tests over a 24-hour time period. Machine learning algorithms were developed using laboratory values and age in a random training set of 70% of the cases; these algorithms were tested in the remaining 30% of the cases. RESULTS: A random forest algorithm was developed based on laboratory and age data; it differentiated clinical responders from nonresponders in the test set with an area under the receiver operating characteristic (AUROC) curve of 0.856. In contrast, 6-TGN levels differentiated clinical responders from nonresponders with an AUROC of 0.594 (P < .001). Algorithms developed to identify thiopurine nonadherence (AUROC, 0.813) and thiopurine shunters (AUROC, 0.797) were accurate. CONCLUSIONS: Algorithms that use age and laboratory values can differentiate clinical response, nonadherence, and shunting of thiopurine metabolism among patients who take thiopurines. This approach was less costly and more accurate than 6-TGN metabolite measurements in predicting clinical response. If validated, this approach would provide a low-cost, rapid alternative to metabolite measurements for monitoring thiopurine use.

[Analysis on relationship between regional economic development and sewage disposal].

Based on the relationship between district GDP and sewage disposal, the water environment protection effect in 3 cities, Suzhou, Nanjing and Xuzhou, with different economic development degrees in Jiangsu Province was dynamically analyzed. The economy in Suzhou was well developed, where the foreign capital proportion was in a high level. Its GDP per capita was 53,800 yuan in 2005 and the sewage disposal grew linearly when its GDP increased in the study time period. Nanjing was less developed than Suzhou, and the state-owned enterprises in large and medium sizes were in a high percentage. Its GDP per capita was 37,100 yuan in 2005, while the sewage disposal reduced linearly when its GDP increased in the study time period. The economy in Xuzhou is under-developed, where coal-based heavy industry was the most important one. The GDP per capita in this city was 13,200 yuan in 2005 and the sewage disposal fluctuated when its GDP increased in the study time period. According to the relationship between economic development and sewage disposal in different cities, we suggested that the water environment protection in Suzhou should focus on the control of both water pollutant total emission and emission concentration, the major work in Nanjing should focus on adjusting the industrial structure and meanwhile controlling the total emission of water pollutants, while in Xuzhou the water pollutant emission concentration should be firstly controlled.

Efficient computation and model selection for the support vector regression.

In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR). We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.

A multicentre, randomized, controlled trial of oseltamivir in the treatment of influenza in a high-risk Chinese population.

OBJECTIVE: To evaluate the efficacy and safety of oseltamivir treatment in a population at high risk for influenza. RESEARCH DESIGN AND METHODS: This was a randomized, open-label, controlled trial involving Chinese patients with chronic respiratory diseases (chronic bronchitis, obstructive emphysema, bronchial asthma or bronchiectasis) or chronic cardiac disease. Patients showing symptoms of influenza were randomly assigned to receive oral oseltamivir 75 mg twice daily for 5 days (oseltamivir group), or symptomatic treatment (control group) within 48 h after symptom onset. MAIN OUTCOME MEASURES: The main outcome measures were duration and severity of illness in influenza-infected patients. Other outcome measures included incidence of complications, antibiotic use, hospitalization and total medical cost. RESULTS: Of the 118 recruited patients, 56 were identified as influenza-infected through laboratory tests (oseltamivir, N = 27; control, N = 29). Relative to symptomatic treatment, oseltamivir significantly reduced the duration of influenza symptoms by 36.8% (p = 0.0479), and the severity by 43.1% (p = 0.0002). In addition, oseltamivir significantly reduced the duration of fever by 45.2% (p = 0.0051), and the time to return to baseline health status by 5 days (p = 0.0011). The incidence of complications (11% vs. 45%, p = 0.0053) and antibiotic use (37% vs. 69%, p = 0.0167) were also significantly lower in the oseltamivir group compared with the control group. The cost of treating influenza and its complications was comparable between the two groups (p = 0.2462). CONCLUSIONS: Oseltamivir is effective and well tolerated in high-risk patients with chronic respiratory or cardiac diseases. It can reduce the duration and severity of influenza symptoms and decrease the incidence of secondary complications and antibiotic use, without increasing the total medical cost.