PErspective and current status of Radiotherapy Service in IRan (PERSIR)-1 study: assessment of current external beam radiotherapy facilities, staff and techniques compared to the international guidelines

BACKGROUND AND PURPOSE

Radiotherapy (RT) is an essential treatment modality against cancer and becoming even more in demand due to the anticipated increase in cancer incidence. Due to the rapid development of RT technologies amid financial challenges, we aimed to assess the available RT facilities and the issues with achieving health equity based on current equipment compared to the previous reports from Iran.

MATERIALS AND METHODS

A survey arranged by the Iran Cancer Institute's Radiation Oncology Research Center (RORC) was sent to all of the country's radiotherapy centers in 2022. Four components were retrieved: the reimbursement type, equipment, human resources, and patient load. To calculate the radiotherapy utilization rate (RUR), the Lancet Commission was used. The findings were compared with the previous national data.

RESULTS

Seventy-six active radiotherapy centers with 123 Linear accelerators (LINACs) were identified. The centers have been directed in three ways. 10 (20 LINACs), 36 (50 LINACs), and 30 centers (53 LINACs) were charity-, private-, and public-based, respectively. Four provinces had no centers. There was no active intraoperative radiotherapy machine despite its availability in 4 centers. One orthovoltage X-ray machine was active and 14 brachytherapy devices were treating patients. There were 344, 252, and 419 active radiation oncologists, medical physicists, and radiation therapy technologists, respectively. The ratio of LINAC and radiation oncologists to one million populations was 1.68 and 4.10, respectively. Since 2017, 35±5 radiation oncology residents have been trained each year.

CONCLUSION

There has been a notable growth in RT facilities since the previous reports and Iran's situation is currently acceptable among LMICs. However, there is an urgent need to improve the distribution of the RT infrastructure and provide more facilities that can deliver advanced techniques.

Differential privacy preserved federated learning for prognostic modeling in COVID-19 patients using large multi-institutional chest CT dataset

BACKGROUND

Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID-19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi-institutional cohort of patients with COVID-19 using a DL-based model.

PURPOSE

This study aimed to evaluate the performance of deep privacy-preserving federated learning (DPFL) in predicting COVID-19 outcomes using chest CT images.

METHODS

After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold-out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold-out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences.

RESULTS

The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79-0.85) and (95% CI: 0.77-0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models (p-value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501.

CONCLUSION

The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi-institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.

State-of-the-Art Molecular Oncology in UK

Molecular oncology is a rapidly evolving field that focuses on the genetic and molecular basis of cancer [...].

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

BACKGROUND

We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients.

METHODS

Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported.

RESULTS

In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance.

CONCLUSION

Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.

COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images

We present a deep learning (DL)-based automated whole lung and COVID-19 pneumonia infectious lesions (COLI-Net) detection and segmentation from chest computed tomography (CT) images. This multicenter/multiscanner study involved 2368 (347'259 2D slices) and 190 (17 341 2D slices) volumetric CT exams along with their corresponding manual segmentation of lungs and lesions, respectively. All images were cropped, resized, and the intensity values clipped and normalized. A residual network with non-square Dice loss function built upon TensorFlow was employed. The accuracy of lung and COVID-19 lesions segmentation was evaluated on an external reverse transcription-polymerase chain reaction positive COVID-19 dataset (7'333 2D slices) collected at five different centers. To evaluate the segmentation performance, we calculated different quantitative metrics, including radiomic features. The mean Dice coefficients were 0.98 ± 0.011 (95% CI, 0.98-0.99) and 0.91 ± 0.038 (95% CI, 0.90-0.91) for lung and lesions segmentation, respectively. The mean relative Hounsfield unit differences were 0.03 ± 0.84% (95% CI, -0.12 to 0.18) and -0.18 ± 3.4% (95% CI, -0.8 to 0.44) for the lung and lesions, respectively. The relative volume difference for lung and lesions were 0.38 ± 1.2% (95% CI, 0.16-0.59) and 0.81 ± 6.6% (95% CI, -0.39 to 2), respectively. Most radiomic features had a mean relative error less than 5% with the highest mean relative error achieved for the lung for the range first-order feature (-6.95%) and least axis length shape feature (8.68%) for lesions. We developed an automated DL-guided three-dimensional whole lung and infected regions segmentation in COVID-19 patients to provide fast, consistent, robust, and human error immune framework for lung and pneumonia lesion detection and quantification.

Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging

BACKGROUND

Despite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients.

RESULTS

A significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior-posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and - 1.5 ± 4.1 mm in superior and inferior directions, respectively. In contrast, the error on external scout views was - 0.7 ± 4.08 and 0.01 ± 14.97 mm for superior and inferior directions, respectively.The ED reduction achieved by automated scan range selection was 21% in the test group. The evaluation of a large multi-centric chest CT dataset revealed unnecessary ED of more than 2 mSv per scan and 67% increase in the thyroid absorbed dose.

CONCLUSION

The proposed DL-based solution outperformed previous automatic methods with acceptable accuracy, even in complicated and challenging cases. The generizability of the model was demonstrated by fine-tuning the model on AP scout views and achieving acceptable results. The method can reduce the unoptimized dose to patients by exclunding unnecessary organs from field of view.

Treatment response prediction using MRI-based pre-, post-, and delta-radiomic features and machine learning algorithms in colorectal cancer

OBJECTIVES

We evaluate the feasibility of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for locally advanced rectal cancer (LARC) patients treated by neoadjuvant chemoradiation therapy (nCRT).

MATERIALS AND METHODS

This retrospective study included 53 LARC patients divided into a training set (Center#1, n = 36) and external validation set (Center#2, n = 17). T2-weighted (T2W) MRI was acquired for all patients, 2 weeks before and 4 weeks after nCRT. Ninety-six radiomic features, including intensity, morphological and second- and high-order texture features were extracted from segmented 3D volumes from T2W MRI. All features were harmonized using ComBat algorithm. Max-Relevance-Min-Redundancy (MRMR) algorithm was used as feature selector and k-nearest neighbors (KNN), Naïve Bayes (NB), Random forests (RF), and eXtreme Gradient Boosting (XGB) algorithms were used as classifiers. The evaluation was performed using the area under the receiver operator characteristic (ROC) curve (AUC), sensitivity, specificity and accuracy.

RESULTS

In univariate analysis, the highest AUC in pre-, post-, and delta-radiomic features were 0.78, 0.70, and 0.71, for GLCM_IMC1, shape (surface area and volume) and GLSZM_GLNU features, respectively. In multivariate analysis, RF and KNN achieved the highest AUC (0.85 ± 0.04 and 0.81 ± 0.14, respectively) among pre- and post-treatment features. The highest AUC was achieved for the delta-radiomic-based RF model (0.96 ± 0.01) followed by NB (0.96 ± 0.04). Overall. Delta-radiomics model, outperformed both pre- and post-treatment features (P-value <0.05).

CONCLUSION

Multivariate analysis of delta-radiomic T2W MRI features using machine learning algorithms could potentially be used for response prediction in LARC patients undergoing nCRT. We also observed that multivariate analysis of delta-radiomic features using RF classifiers can be used as powerful biomarkers for response prediction in LARC.

Randomized, Double-blind Pilot Study of Nanocurcumin in Bladder Cancer Patients Receiving Induction Chemotherapy

PURPOSE

To evaluate the feasibility and potential efficacy of nanocurcumin supplementation in patients with localized muscle-invasive bladder cancer (MIBC) undergoing induction chemotherapy.

MATERIALS AND METHODS

In this double-blind, placebo-controlled trial, 26 MIBC patients were randomized to receive either nanocurcumin (180 mg/day) or placebo during the course of chemotherapy. All patients were followed up to four weeks after the end of treatment to assess the complete clinical response to the chemotherapy as primary endpoint. Secondary endpoints were the comparisons of chemotherapy-induced nephrotoxicity, hematologic nadirs, and toxicities between the two groups. Hematologic nadirs and toxicities were assessed during the treatment.

RESULTS

Nanocurcumin was well tolerated. The complete clinical response rates were 30.8 and 50% in the placebo and nanocurcumin groups, respectively. Although nanocurcumin was shown to be superior to placebo with respect to complete clinical response rates as the primary endpoint, there was no significant difference between the groups (p = 0.417). No significant difference was also found between the two groups with regard to grade 3/4 renal and hematologic toxicities as well as hematologic nadirs.

CONCLUSION

These preliminary data indicate the feasibility of nanocurcumin supplementation as a complementary therapy in MIBC patients and support further larger studies. Moreover, a substantial translational insight to fill the gap between the experiment and clinical practice in the field is provided.

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

OBJECTIVES

The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients.

METHODS

In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE).

RESULTS

The radiation dose in terms of CT dose index (CTDIvol) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8.

CONCLUSIONS

The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction.

KEY POINTS

• Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning-based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations.

Review on the Oncology Practice in the Midst of COVID-19 Crisis: The Challenges and Solutions

As of late 2019, the outbreak of novel coronavirus disease (COVID-19) –that started in China– has rapidly afflicted all over the world. The COVID-19 pandemic has challenged health-care facilities to provide optimal care. In this context, cancer care requires special attention because of its peculiar status by including patients who are commonly immunocompromised and treatments that are often highly toxic. In this review article, we have classified the main impacts of the COVID-19 pandemic on oncology practices –followed by their solutions– into ten categories, including impacts on (1) health care providers, (2) medical equipment, (3) access to medications, (4) treatment approaches, (5) patients’ referral, (6) patients’ accommodation, (7) patients’ psychological health, (8) cancer research, (9) tumor board meetings, and (10) economic income of cancer centers. The effective identification and management of all these challenges will improve the standards of cancer care over the viral pandemic and can be a practical paradigm for possible future crises.

Predictors of Pathological Complete Response to Neoadjuvant Chemotherapy in Iranian Breast Cancer Patients

Background:

Achievement of pathologic complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy (NAC) is associated with both overall survival and disease-free survival. The aim of present study was to identify clinical and pathological factors associated with achieving pCR in Iranian breast cancer patients receiving NAC.

Methods:

A retrospective review of all breast cancer patients treated with neoadjuvant chemotherapy between April 2012 and September 2016 at our institution was performed; 207 cases were evaluable for analysis. pCR was defined as having no residual invasive tumor in the breast surgical specimen removed following neoadjuvant therapy.

Results:

In univariate analysis, factors associated with pCR were age less than 35 years (p = 0.03), absence of Lymphovascular invasion (LVI) (p = 0.002) and negative hormone receptor status (p = 0.003). Hormone receptor status (P = 0.01; OR, 2.45; CI, 1.20 - 4.99) and LVI (P = 0.001; OR, 0.22; CI, 0.10 - 0.46) remained predictive variables in multivariate analysis after correction for the other variables.

Conclusions:

In conclusion, the results of this study suggests that presence of Lymphovascular invasion and positive hormone receptor status are associated with poorer response to neoadjuvant chemotherapy in breast cancer patients.

Frequency and Predictors of Axillary Lymph Node Metastases in Iranian Women with Early Breast Cancer

Background:

Axillary lymph node metastasis is the most important predictive factor for recurrence risk and survival in patients with invasive breast carcinoma. The aim of this study was to determine factors associated with metastatic involvement of axillary lymph nodes in Iranian women with early breast cancer.

Methods:

This article reports a retrospective study of 774 patients with T1-T2 breast cancer who underwent resection of the primary tumor and axillary staging by SLNB and/or ALND between 2005 and 2015 at our institution.

Results:

Of the 774 patients included in this study, 35.5% (275 cases) had axillary lymph node involvement at the time of diagnosis. Factors associated with nodal involvement in univariate analyses were tumor size, lymphovascular invasion (LVI), tumor grade, ER/PR status and HER2 expression. All factors identified with univariate analyses were entered into a multivariate logistic regression model and tumor size (OR= 3.01, CI 2.01–4.49, P <0.001), ER/PR positivity (OR = 1.74, CI 1.1.16–2.62, P = 0.007) and presence of LVI (OR = 3.3.8, CI 2.31–4.95, P <0.001) remained as independent predictors of axillary lymph node involvement.

Conclusions:

In conclusion, the results of this study suggests that positive hormonal receptor status, LVI and tumor size are predictive factors for ALNM in Iranian women with early breast cancer.

Epidemiology of Neuroendocrine Tumors in an Iranian Population

Neuroendocrine tumors (NETs) are a rare and heterogeneous group of malignancies most commonly found in the gastrointestinal system. In this study, we examined the epidemiology of NETs in an Iranian population. The incident NET cases diagnosed between January 1, 2009 and December 31, 2014 were collected from databases of three hospitals in Tehran (Shoada-e-Tajrish Hospital, Imam Hossein Hospital and Pars Hospital).  A total of 291 cases with NET diagnosis were identified. The most common NET location was gastrointestinal (71.4%), followed by Bronchopulmonary (7.2%) and Genitourinary (7.2%). The total number of identified NETs in our study increased from 25 cases in 2009 to 66 cases in 2014. In conclusion, our data suggests that the incidence of NETs is increasing slowly. Thus, etiologic studies for NETs are needed to help plan future preventive strategies. The authors declare no conflicts of interests.

The Efficacy of Licorice Root (Glycyrrhiza glabra) and Yarrow (Achillea millefolium) in Preventing Radiation Dermatitis in Patients with Breast Cancer: A Randomized, Double-Blinded, Placebo-Controlled Clinical Trial

Background: Radiation dermatitis is one of the most common side effects of radiotherapy for breast cancer, affecting approximately 85 percent of patients. The aim of this study is to assess the effect of Licorice root (Glycyrrhizin glabra) and Yarrow (Achillea millefolium) on preventing radiotherapy-induced dermatitis in breast cancer patients.Methods: Seventy-five patients with breast cancer who had undergone mastectomy and were planned to receive radiotherapy (RT) were enrolled in this randomized, double-blind, placebo-controlled study. The extract of Achillea millefolium and Glycyrrhizin glabra root were incorporated into a vanishing cream base. Patients were randomly divided into three groups and received Glycyrrhizin glabra cream, placebo or Achillea millefolium cream for five weeks during RT. The rate and grade of radiation dermatitis were recorded at baseline, at the end of third week and at the end of treatment using a modified Radiation Therapy Oncology Group (RTOG) grading tool.Results: At the end of the third week, the group receiving Achillea millefolium cream showed milder skin complications than other groups. At the end of treatment, rate of skin complications in groups receiving herbal drugs was lower than placebo group but it was not statistically significant. Conclusions: In conclusion, the results of this study did not present a significant difference between Glycyrrhiza glabra, Achillea millefolium and placebo on preventing radiation dermatitis.

Length of second-order chordae as a predictor of systolic anterior motion of the mitral valve

OBJECTIVES

The aim of the present study was to ascertain whether the length of anterior mitral leaflet second-order chordae (SOC) could be considered as a predictor of the incidence of post-repair systolic anterior motion (SAM) and left ventricular outflow tract obstruction (LVOTO) in patients with myxomatous mitral valve disease.

METHODS

With the implementation of preoperative transoesophageal echocardiography (TEE), the length of anterior mitral leaflet SOC, anterior leaflet (AL) and posterior leaflet (PL) as well as the distance from the coaptation point to the septum (C-S distance) before and after mitral valve repair (MVR) surgery were measured in 190 patients, comprising 12 who developed SAM and 178 who did not.

RESULTS

The results revealed that, in patients who developed SAM, SOC were significantly higher (2.76 ± 0.15 vs 1.83 ± 0.32 mm, P < 0.001) and the C-S distance was significantly lower (2.18 ± 0.36 vs 2.91 ± 0.36 mm, P < 0.001) in comparison to the obtained results for those who did not develop SAM. SOC and the C-S distance were independent risk factors of developing SAM and had the largest area under the receiver operating characteristic (ROC) curve (P < 0.001). With application of a cut-off ROC curve analysis, the cut-offs selected for the two variables of C-S distance and SOC were 2.5 and 2.6, respectively. Sensitivity and specificity of SAM development were 100% [95% confidence interval (CI): 73.5-100] and 87.1% (95% CI: 81.0-91.4) for SOC ≥2.6 and 83.3% (95% CI: 51.6-97.9) and 73.6% (95% CI: 66.4-79.9) for the C-S distance ≤2.5.

CONCLUSIONS

The two variables of the second-order chordae and the distance from the coaptation point to the septum were associated with an increased risk of the post-repair systolic anterior motion after mitral valve repair.

Effect of acute lithium administration on penile erection: involvement of nitric oxide system

BACKGROUND

Lithium has been the treatment of choice for bipolar disorder (BD) for many years. Although erectile dysfunction is a known adverse effect of this drug, the mechanism of action by which lithium affects erectile function is still unknown.

OBJECTIVE

The aim was to investigate the possible involvement of nitric oxide (NO) in modulatory effect of lithium on penile erection (PE). We further evaluated the possible role of Sildenafil in treatment of lithium-induced erectile dysfunction.

MATERIALS AND METHODS

Erectile function was determined using rat model of apomorphine-induced erections. For evaluating the effect of lithium on penile erection, rats received intraperitoneal injection of graded doses of lithium chloride 30 mins before subcutaneous injection of apomorphine. To determine the possible role of NO pathway, sub-effective dose of N (G)-nitro-L-arginine methyl ester (L-NAME), a nitric oxide synthase (NOS) inhibitor, was administered 15 min before administration of sub-effective dose of lithium chloride. In other separate experimental groups, sub- effective dose of the nitric oxide precursor, L-arginine, or Sildenafil was injected into the animals 15 min before administration of a potent dose of lithium. 30 min after administration of lithium chloride, animals were assessed in apomorphine test. Serum lithium levels were measured 30 min after administration of effective dose of lithium.

RESULTS

Lithium at 50 and 100 mg/kg significantly decreased number of PE (p<0.001), whereas at lower doses (5, 10 and 30 mg/kg) had no effect on apomorphine induced PE. The serum Li+ level of rats receiving 50 mg/kg lithium was 1±0.15 mmol/L which is in therapeutic range of lithium. The inhibitory effect of Lithium was blocked by administration of sub-effective dose of nitric oxide precursor L-arginine (100 mg/kg) (p<0.001) and sildenafil (3.5 mg/kg) (p<0.001) whereas pretreatment with a low and sub-effective dose of L-NAME (10mg/kg) potentiated sub-effective dose of lithium, (p<0.001).

CONCLUSION

These results suggest acute treatments with lithium cause erectile dysfunction in an in-vivo rat model. Furthermore it seems that the NO pathway might play role in erectile dysfunction associated with lithium treatment. Findings also suggest that Sildenafil may be effective in treatment of lithium-associated erectile dysfunction.

Efficacy of Prolonged Treatment With Pegylated Interferon (Peg-IFN) and Ribavirin in Thalassemic Patients With Hepatitis C Who Relapsed After Previous Peg-IFN-Based Therapy

BACKGROUND

Most thalassemic patients with chronic hepatitis C virus (HCV) infection do not respond to therapy with pegylated interferon (Peg-IFN) plus ribavirin (RBV) due to hepatic siderosis and RBV dose reduction caused by RBV-induced anemia.

OBJECTIVES

In the present study, we recruited HCV genotype 1-infected thalassemic patients who had relapsed after a 48-week treatment with Peg-IFN plus RBV in order to evaluate the efficacy of a 72-week regimen of Peg-IFN plus RBV.

PATIENTS AND METHODS

In this retrospective study, 23 thalassemic patients with HCV genotype 1 infection who had prior relapse after treatment with Peg-IFN and RBV for 48 weeks were consecutively enrolled in this study for evaluation of the efficacy of a 72-week treatment regimen.

RESULTS

For the 21 included cases, mean age was 29.7 years; 81% were men and 28.6% had cirrhosis. At the end of the treatment, nine (42.9%) patients had an undetectable level of HCV RNA in their sera. However, six months after treatment completion four of these patients relapsed and a sustained virological response (SVR) was found in five (23.8%) patients. Undetectable HCV RNA level at week 4 (P = 0.03) and undetectable HCV RNA level at week 12 (P < 0.01) were found to be predictors of SVR. There was an average 47.9% increase in blood transfusion during therapy and treatment was discontinued for 12 (57.1%) patients prematurely.

CONCLUSIONS

The present study suggests that thalassemic patients with chronic hepatitis C genotype 1 infection who did not achieve SVR after a course of therapy with Peg-IFN and RBV may benefit from being retreated with a 72-week regimen.

Predictors of blood transfusion in patients undergoing coronary artery bypass grafting surgery

OBJECTIVES

The aim of this retrospective study is to identify intraoperative patient's characteristics predicting the need for blood transfusion during CABG in our local cardiac surgical service.

METHODS

This study included 1835 consecutive patients, 1311 males and 524 females with mean age 58.8±9.9 years, undergoing coronary artery bypass grafting. Risk factors detected by univariate study were entered in a multivariate logistic regression model of the relationship between preoperative variables and blood transfusion.

RESULTS

Blood transfusion was used in 435 patients (29.9%). Univariate analysis identified hemoglobin, smoking, hypertension, sex, diabetes, BMI and use of cardiopulmonary bypass (CPB) as significant predictors. Multivariate analysis revealed hemoglobin (OR: 0.8; CI: 0.74-0.86; P<0.001), CPB use (OR: 12.2; CI: 8.2-18.1; P<0.001) and female gender (OR: 2.29; CI:1.72-3.04; P<0.001) as independent risk factors for blood transfusion.

CONCLUSIONS

The predictors of RBC transfusion after isolated CABG were performing CPB, preoperative hemoglobin and female gender. These factors can be used as a clinical tool to preserve blood bank resources without increasing patient's risk.

The viable mitral annular dynamics and left ventricular function after mitral valve repair by biological rings

OBJECTIVE

Considering the importance of annular dynamics in the valvular and ventricular function, we sought to evaluate the effects of treated pericardial annuloplasty rings on mitral annular dynamics and left-ventricular (LV) function after mitral valve repair. The results were compared with the mitral annular dynamics and LV function in patients with rigid and flexible rings and also in those without any heart problems.

MATERIALS AND METHODS

One hundred and thirty-six consecutive patients with a myxomatous mitral valve and severe regurgitation were prospectively enrolled in this observational cohort study. The patients underwent comparable surgical mitral valve reconstruction; of these 100 received autologous pericardium rings (Group I), 20 were given flexible prosthetic rings (Group II), and 16 received rigid rings (Group III). Other repair modalities were also performed, depending on the involved segments. The patients were compared with 100 normal subjects in whom an evaluation of the coronary artery was not indicative of valvular or myocardial abnormalities (Group IV). At follow-up, LV systolic indices were assessed via two-dimensional echocardiography at rest and during dobutamine stress echocardiography. Mitral annular motion was examined through mitral annulus systolic excursion (MASE). Peak transmitral flow velocities (TMFV) and mitral valve area (MVA) were also evaluated by means of continuous-wave Doppler.

RESULTS

A postoperative echocardiographic study showed significant mitral regurgitation (>=2+) in one patient in Group I, one patient in Group II, and none in Group III. None of the patients died. There was a noteworthy increase in TMFV with stress in all the groups, the increase being more considerable in the prosthetic ring groups (Group I from 1.10 ± 0.08 to 1.36 ± 0.13 m/s, Group II from 1.30 ± 0.11 to 1.59 ± 0.19 m/s, Group III from 1.33 ± 0.09 to 1.69 ± 0.21 m/s, and Group IV from 1.08 ± 0.08 to 1.21 ± 0.12 m/s). Recruitment of LVEF reserve during stress was observed in the pericardial ring and normal groups (Group I from 54.6±6.2 to 64.6±7.3%, P<0.005; and Group IV from 55.3 ± 5.7 to 66 ± 6.2%, P<0.05), but no significant changes were detected in the prosthetic ring groups (Group II from 50.4 ± 5 to 55.0 ± 5.1, and Group III from 51.1 ± 6.6 to 53.8 ± 4.7). There was a significant MASE increase in both of the studied longitudinal segments at rest and during stress in Groups I and IV compared with the prosthetic ring groups. There was no calcification of the pericardial rings.

CONCLUSIONS

The use of treated autologous pericardium rings for mitral valve annuloplasty yields excellent mitral annular dynamics, preserves LV function during stress conditions, and leaves no echocardiographic signs of degeneration.