Disease-free survival after hepatic resection in hepatocellular carcinoma patients: a prediction approach using artificial neural network

Background

A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group.

Methods

The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models.

Conclusions

The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection.     

A two-step screening, measurement of HbA1c in association with FPG, may be useful in predicting diabetes

Backgrounds

We compared the usefulness of fasting plasma glucose (FPG), or hemoglobin A1c (HbA1c), or both in predicting type 2 diabetes.

Methods

This retrospective cohort study investigated 9,322 Japanese adults (4,786 men and 4,536 women), aged 19–69 yrs, free of diabetes at baseline. Usefulness was assessed by predictive values (PV), sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) maximised under the best cut-off point.

Results

During the average 6 years of follow-up, 221 men (4.6%) and 92 women (2%) developed diabetes. The best cut-off points for FPG (i.e., 5.67 mmol/l for men and 5.5 mmol/l for women) gave excellent AUROC, and the highest positive PV (13% for men and 9% for women) in predicting diabetes. In high risk subjects with FPG 6.1–6.9 mmol/l, 119 men (26.8%) and 39 women (28.3%) developed diabetes. Under the best cut-off points of FPG 6.39 mmol/l and A1c 5.8, AUROC and positive PV for FPG slightly decreased indicating FPG became less useful and were statistically indistinguishable from those for HbA1c in men. In fact, HbA1c was the most useful in women: HbA1c of 6.0% gave the highest positive likelihood ratio of 2.74 and larger AUROC than did FPG. Although AUROC for HbA1c was acceptable and indistinguishable from that for the combined use, HbA1c had higher specificity and positive LR than did the combined use.

Conclusions

This study demonstrated that FPG was the most useful to predict diabetes in the general population. However, in subjects with FPG 6.1–6.9 mmol/l, FPG became less useful and diagnostic performance of FPG was indistinguishable from that of HbA1c in men whereas HbA1c was the most useful in women. Thus, a two-step screening, measurement of HbA1c in association with FPG, may be useful in predicting diabetes.     

The role of pre-existing diabetes mellitus on hepatocellular carcinoma occurrence and prognosis: a meta-analysis of prospective cohort studies

Background

The impact of pre-existing diabetes mellitus (DM) on hepatocellular carcinoma (HCC) occurrence and prognosis is complex and unclear. The aim of this meta-analysis is to evaluate the association between pre-existing diabetes mellitus and hepatocellular carcinoma occurrence and prognosis.

Methods

We searched PubMed, Embase and the Cochrane Library from their inception to January, 2011 for prospective epidemiological studies assessing the effect of pre-existing diabetes mellitus on hepatocellular carcinoma occurrence, mortality outcomes, cancer recurrence, and treatment-related complications. Study-specific risk estimates were combined by using fixed effect or random effect models.

Results

The database search generated a total of 28 prospective studies that met the inclusion criteria. Among these studies, 14 reported the risk of HCC incidence and 6 studies reported risk of HCC specific mortality. Six studies provided a total of 8 results for all-cause mortality in HCC patients. Four studies documented HCC recurrence risks and 2 studies reported risks for hepatic decomposition occurrence in HCC patients. Meta-analysis indicated that pre-existing diabetes mellitus (DM) was significantly associated with increased risk of HCC incidence [meta-relative risk (RR) = 1.87, 95% confidence interval (CI): 1.15–2.27] and HCC-specific mortality (meta-RR = 1.88, 95%CI: 1.39–2.55) compared with their non-DM counterparts. HCC patients with pre-existing DM had a 38% increased (95% CI: 1.13–1.48) risk of death from all-causes and 91% increased (95%CI: 1.41–2.57) risk of hepatic decomposition occurrence compared to those without DM. In DM patients, the meta-RR for HCC recurrence-free survival was 1.93(95%CI: 1.12–3.33) compared with non-diabetic patients.

Conclusion

The findings from the current meta-analysis suggest that DM may be both associated with elevated risks of both HCC incidence and mortality. Furthermore, HCC patients with pre-existing diabetes have a poorer prognosis relative to their non-diabetic counterparts.     

Acute coronary syndrome in patients with prior coronary artery bypass surgery: observations from a 20-year registry in a middle-eastern country

Objectives

Clinical characteristics and trends in the outcome of acute coronary syndrome (ACS) in patients with prior coronary artery bypass graft surgery (CABG) are unclear. The aim of this study was to evaluate clinical characteristics, in-hospital treatment, and outcomes in patients presented with ACS with or without a history of prior CABG over 2 decades.

Methods

Data were derived from hospital-based study for collected data from 1991 through 2010 of patients hospitalized with ACS in Doha, Qatar. Data were analyzed according to their history of prior CABG. Baseline clinical characteristics, in-hospital treatment, and outcome were compared.

Results

A total 16,750 consecutive patients with ACS were studied, of which 693 (4.1%) had prior CABG. Patients with prior CABG were older (mean 60.5±11 vs. 53±12 years; P = 0.001), more likely to be females and have more cardiovascular risk factors than the non-CABG group. Prior CABG patients had larger infarct size, were less likely to receive reperfusion therapy, early invasive therapy and more likely to receive evidence-based therapies when compared to non-CABG patients. In-hospital mortality and stroke rates were comparable between the 2 groups. Over 2 decades, there was reduction in the in-hospital mortality rates and stroke rates in both groups (CABG, death; 13.2% to 4%, stroke; 1.9% to 0.0%, non-CABG, death; 10% to 3.2%, stroke 1.0% to 0.1%; all, p = 0.001).

Conclusion

Significant reduction in-hospital morbidity and mortality among ACS patients with prior CABG over a 20-year period.     

Development and Validation of a Clinical Scoring System for Predicting Risk of HCC in Asymptomatic Individuals Seropositive for Anti-HCV Antibodies

Background

The development of a risk assessment tool for long-term hepatocellular carcinoma risk would be helpful in identifying high-risk patients and providing information of clinical consultation.

Methods

The model derivation and validation cohorts consisted of 975 and 572 anti-HCV seropositives, respectively. The model included age, alanine aminotransferase (ALT), the ratio of aspirate aminotransferase to ALT, serum HCV RNA levels and cirrhosis status and HCV genotype. Two risk prediction models were developed: one was for all-anti-HCV seropositives, and the other was for anti-HCV seropositives with detectable HCV RNA. The Cox''s proportional hazards models were utilized to estimate regression coefficients of HCC risk predictors to derive risk scores. The cumulative HCC risks in the validation cohort were estimated by Kaplan-Meier methods. The area under receiver operating curve (AUROC) was used to evaluate the performance of the risk models.

Results

All predictors were significantly associated with HCC. The summary risk scores of two models derived from the derivation cohort had predictability of HCC risk in the validation cohort. The summary risk score of the two risk prediction models clearly divided the validation cohort into three groups (p<0.001). The AUROC for predicting 5-year HCC risk in the validation cohort was satisfactory for the two models, with 0.73 and 0.70, respectively.

Conclusion

Scoring systems for predicting HCC risk of HCV-infected patients had good validity and discrimination capability, which may triage patients for alternative management strategies.     

Predicting hospital-acquired infections by scoring system with simple parameters

Background

Hospital-acquired infections (HAI) are associated with increased attributable morbidity, mortality, prolonged hospitalization, and economic costs. A simple, reliable prediction model for HAI has great clinical relevance. The objective of this study is to develop a scoring system to predict HAI that was derived from Logistic Regression (LR) and validated by Artificial Neural Networks (ANN) simultaneously.

Methodology/Principal Findings

A total of 476 patients from all the 806 HAI inpatients were included for the study between 2004 and 2005. A sample of 1,376 non-HAI inpatients was randomly drawn from all the admitted patients in the same period of time as the control group. External validation of 2,500 patients was abstracted from another academic teaching center. Sixteen variables were extracted from the Electronic Health Records (EHR) and fed into ANN and LR models. With stepwise selection, the following seven variables were identified by LR models as statistically significant: Foley catheterization, central venous catheterization, arterial line, nasogastric tube, hemodialysis, stress ulcer prophylaxes and systemic glucocorticosteroids. Both ANN and LR models displayed excellent discrimination (area under the receiver operating characteristic curve [AUC]: 0.964 versus 0.969, p = 0.507) to identify infection in internal validation. During external validation, high AUC was obtained from both models (AUC: 0.850 versus 0.870, p = 0.447). The scoring system also performed extremely well in the internal (AUC: 0.965) and external (AUC: 0.871) validations.

Conclusions

We developed a scoring system to predict HAI with simple parameters validated with ANN and LR models. Armed with this scoring system, infectious disease specialists can more efficiently identify patients at high risk for HAI during hospitalization. Further, using parameters either by observation of medical devices used or data obtained from EHR also provided good prediction outcome that can be utilized in different clinical settings.     

Recognition of morphometric vertebral fractures by artificial neural networks: analysis from GISMO Lombardia Database

Background

It is known that bone mineral density (BMD) predicts the fracture''s risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0.

Methodology

We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively.

Conclusions

ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF.     

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

Background

This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches.

Methods and Materials

We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared.

Results

Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses.

Conclusion

The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset.     

Nurses and Physicians in a Medical Admission Unit Can Accurately Predict Mortality of Acutely Admitted Patients: A Prospective Cohort Study

Background

There exist several risk stratification systems for predicting mortality of emergency patients. However, some are complex in clinical use and others have been developed using suboptimal methodology. The objective was to evaluate the capability of the staff at a medical admission unit (MAU) to use clinical intuition to predict in-hospital mortality of acutely admitted patients.

Methods

This is an observational prospective cohort study of adult patients (15 years or older) admitted to a MAU at a regional teaching hospital. The nursing staff and physicians predicted in-hospital mortality upon the patients'' arrival. We calculated discriminatory power as the area under the receiver-operating-characteristic curve (AUROC) and accuracy of prediction (calibration) by Hosmer-Lemeshow goodness-of-fit test.

Results

We had a total of 2,848 admissions (2,463 patients). 89 (3.1%) died while admitted. The nursing staff assessed 2,404 admissions and predicted mortality in 1,820 (63.9%). AUROC was 0.823 (95% CI: 0.762–0.884) and calibration poor. Physicians assessed 738 admissions and predicted mortality in 734 (25.8% of all admissions). AUROC was 0.761 (95% CI: 0.657–0.864) and calibration poor. AUROC and calibration increased with experience. When nursing staff and physicians were in agreement (±5%), discriminatory power was very high, 0.898 (95% CI: 0.773–1.000), and calibration almost perfect. Combining an objective risk prediction score with staff predictions added very little.

Conclusions

Using only clinical intuition, staff in a medical admission unit has a good ability to identify patients at increased risk of dying while admitted. When nursing staff and physicians agreed on their prediction, discriminatory power and calibration were excellent.     

A comparison of administrative and physiologic predictive models in determining risk adjusted mortality rates in critically ill patients

Background

Hospitals are increasingly compared based on clinical outcomes adjusted for severity of illness. Multiple methods exist to adjust for differences between patients. The challenge for consumers of this information, both the public and healthcare providers, is interpreting differences in risk adjustment models particularly when models differ in their use of administrative and physiologic data. We set to examine how administrative and physiologic models compare to each when applied to critically ill patients.

Methods

We prospectively abstracted variables for a physiologic and administrative model of mortality from two intensive care units in the United States. Predicted mortality was compared through the Pearsons Product coefficient and Bland-Altman analysis. A subgroup of patients admitted directly from the emergency department was analyzed to remove potential confounding changes in condition prior to ICU admission.

Results

We included 556 patients from two academic medical centers in this analysis. The administrative model and physiologic models predicted mortalities for the combined cohort were 15.3% (95% CI 13.7%, 16.8%) and 24.6% (95% CI 22.7%, 26.5%) (t-test p-value<0.001). The r² for these models was 0.297. The Bland-Atlman plot suggests that at low predicted mortality there was good agreement; however, as mortality increased the models diverged. Similar results were found when analyzing a subgroup of patients admitted directly from the emergency department. When comparing the two hospitals, there was a statistical difference when using the administrative model but not the physiologic model. Unexplained mortality, defined as those patients who died who had a predicted mortality less than 10%, was a rare event by either model.

Conclusions

In conclusion, while it has been shown that administrative models provide estimates of mortality that are similar to physiologic models in non-critically ill patients with pneumonia, our results suggest this finding can not be applied globally to patients admitted to intensive care units. As patients and providers increasingly use publicly reported information in making health care decisions and referrals, it is critical that the provided information be understood. Our results suggest that severity of illness may influence the mortality index in administrative models. We suggest that when interpreting “report cards” or metrics, health care providers determine how the risk adjustment was made and compares to other risk adjustment models.     

Clinical Prediction Rule for Stratifying Risk of Pulmonary Multidrug-Resistant Tuberculosis

Background

Multidrug-resistant tuberculosis (MDR-TB), resistance to at least isoniazid and rifampin, is a worldwide problem.

Objective

To develop a clinical prediction rule to stratify risk for MDR-TB among patients with pulmonary tuberculosis.

Methods

Derivation and internal validation of the rule among adult patients prospectively recruited from 37 health centers (Perú), either a) presenting with a positive acid-fast bacillus smear, or b) had failed therapy or had a relapse within the first 12 months.

Results

Among 964 patients, 82 had MDR-TB (prevalence, 8.5%). Variables included were MDR-TB contact within the family, previous tuberculosis, cavitary radiologic pattern, and abnormal lung exam. The area under the receiver-operating curve (AUROC) was 0.76. Selecting a cut-off score of one or greater resulted in a sensitivity of 72.6%, specificity of 62.8%, likelihood ratio (LR) positive of 1.95, and LR negative of 0.44. Similarly, selecting a cut-off score of two or greater resulted in a sensitivity of 60.8%, specificity of 87.5%, LR positive of 4.85, and LR negative of 0.45. Finally, selecting a cut-off score of three or greater resulted in a sensitivity of 45.1%, specificity of 95.3%, LR positive of 9.56, and LR negative of 0.58.

Conclusion

A simple clinical prediction rule at presentation can stratify risk for MDR-TB. If further validated, the rule could be used for management decisions in resource-limited areas.     

Acute kidney injury biomarkers for patients in a coronary care unit: a prospective cohort study

Background

Renal dysfunction is an established predictor of all-cause mortality in intensive care units. This study analyzed the outcomes of coronary care unit (CCU) patients and evaluated several biomarkers of acute kidney injury (AKI), including neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18) and cystatin C (CysC) on the first day of CCU admission.

Methodology/Principal Findings

Serum and urinary samples collected from 150 patients in the coronary care unit of a tertiary care university hospital between September 2009 and August 2010 were tested for NGAL, IL-18 and CysC. Prospective demographic, clinical and laboratory data were evaluated as predictors of survival in this patient group. The most common cause of CCU admission was acute myocardial infarction (80%). According to Acute Kidney Injury Network criteria, 28.7% (43/150) of CCU patients had AKI of varying severity. Cumulative survival rates at 6-month follow-up following hospital discharge differed significantly (p<0.05) between patients with AKI versus those without AKI. For predicting AKI, serum CysC displayed an excellent areas under the receiver operating characteristic curve (AUROC) (0.895±0.031, p<0.001). The overall 180-day survival rate was 88.7% (133/150). Multiple Cox logistic regression hazard analysis revealed that urinary NGAL, serum IL-18, Acute Physiology, Age and Chronic Health Evaluation II (APACHE II) and sodium on CCU admission day one were independent risk factors for 6-month mortality. In terms of 6-month mortality, urinary NGAL had the best discriminatory power, the best Youden index, and the highest overall correctness of prediction.

Conclusions

Our data showed that serum CysC has the best discriminative power for predicting AKI in CCU patients. However, urinary NGAL and serum IL-18 are associated with short-term mortality in these critically ill patients.     

Craniectomy for malignant cerebral infarction: prevalence and outcomes in US hospitals

Object

Randomized trials have demonstrated the efficacy of craniectomy for the treatment of malignant cerebral edema following ischemic stroke. We sought to determine the prevalence and outcomes related to this by using a national database.

Methods

Patient discharges with ischemic stroke as the primary diagnosis undergoing craniectomy were queried from the US Nationwide Inpatient Sample from 1999 to 2008. A subpopulation of patients was identified that underwent thrombolysis. Two primary end points were examined: in-hospital mortality and discharge to home/routine care. To facilitate interpretations, adjusted prevalence was calculated from the overall prevalence and two age-specific logistic regression models. The predictive margin was then generated using a multivariate logistic regression model to estimate the probability of in-hospital mortality after adjustment for admission type, admission source, length of stay, total hospital charges, chronic comorbidities, and medical complications.

Results

After excluding 71,996 patients with the diagnosis of intracranial hemorrhage and posterior intracranial circulation occlusion, we identified 4,248,955 adult hospitalizations with ischemic stroke as a primary diagnosis. The estimated rates of hospitalizations in craniectomy per 10,000 hospitalizations with ischemic stroke increased from 3.9 in 1999–2000 to 14.46 in 2007–2008 (p for linear trend<0.001). Patients 60+ years of age had in-hospital mortality of 44% while the 18–59 year old group was found to be 24%(p = 0.14). Outcomes were comparable if recombinant tissue plasminogen activator had been administered.

Conclusions

Craniectomy is being increasingly performed for malignant cerebral edema following large territory cerebral ischemia. We suspect that the increase in the annual incidence of DC for malignant cerebral edema is directly related to the expanding collection of evidence in randomized trials that the operation is efficacious when performed in the correct patient population. In hospital mortality is high for all patients undergoing this procedure.     

Readmission rates of patients discharged against medical advice: a matched cohort study

Objective

We compared the readmission rates and the pattern of readmission among patients discharged against medical advice (AMA) to control patients discharged with approval over a one-year follow-up period.

Methods

A retrospective matched-cohort study of 656 patients(328 were discharged AMA) who were followed for one year after their initial hospitalization at an urban university-affiliated teaching hospital in Vancouver, Canada that serves a population with high prevalence of addiction and psychiatric disorders. Multivariate conditional logistic regression was used to examine the independent association of discharge AMA on 14-day related diagnosis hospital readmission. We fit a multivariate conditional negative binomial regression model to examine the readmission frequency ratio between the AMA and non-AMA group.

Principal Findings

AMA patients were more likely to be homeless (32.3% vs. 11%) and have co-morbid conditions such as psychiatric illnesses, injection drug use, HIV, hepatitis C and previous gastrointestinal bleeding. Patients discharged AMA were more likely to be readmitted: 25.6% vs. 3.4%, p<0.001 by day 14. The AMA group were more likely to be readmitted within 14 days with a related diagnosis than the non-AMA group (Adjusted Odds Ratio 12.0; 95% Confidence Interval [CI]: 3.7–38.9). Patients who left AMA were more likely to be readmitted multiple times at one year compared to the non-AMA group (adjusted frequency ratio 1.6; 95% CI: 1.3–2.0). There was also higher all-cause in-hospital mortality during the 12-month follow-up in the AMA group compared to non-AMA group (6.7% vs. 2.4%, p = 0.01).

Conclusions

Patients discharged AMA were more likely to be homeless and have multiple co-morbid conditions. At one year follow-up, the AMA group had higher readmission rates, were predisposed to multiple readmissions and had a higher in-hospital mortality. Interventions to reduce discharges AMA in high-risk groups need to be developed and tested.     

ABO blood group and the risk of hepatocellular carcinoma: a case-control study in patients with chronic hepatitis B

Background

Studies have observed an association between the ABO blood group and risk of certain malignancies. However, no studies of the association with hepatocellular carcinoma (HCC) risk are available. We conducted this hospital-based case-control study to examine the association with HCC in patients with chronic hepatitis B (CHB).

Methods

From January 2004 to December 2008, a total of 6275 consecutive eligible patients with chronic hepatitis B virus (HBV) infection were recruited. 1105 of them were patients with HBV-related HCC and 5,170 patients were CHB without HCC. Multivariate logistic regression models were used to investigate the association between the ABO blood group and HCC risk.

Results

Compared with subjects with blood type O, the adjusted odds ratio (AOR) for the association of those with blood type A and HCC risk was 1.39 [95% confidence interval (CI), 1.05–1.83] after adjusting for age, sex, type 2 diabetes, cirrhosis, hepatitis B e antigen, and HBV DNA. The associations were only statistically significant [AOR (95%CI) = 1.56(1.14–2.13)] for men, for being hepatitis B e antigen positive [AOR (95%CI) = 4.92(2.83–8.57)], for those with cirrhosis [AOR (95%CI), 1.57(1.12–2.20)], and for those with HBV DNA≤10⁵copies/mL [AOR (95%CI), 1.58(1.04–2.42)]. Stratified analysis by sex indicated that compared with those with blood type O, those with blood type B also had a significantly high risk of HCC among men, whereas, those with blood type AB or B had a low risk of HCC among women.

Conclusions

The ABO blood type was associated with the risk of HCC in Chinese patients with CHB. The association was gender-related.     

Catheter related bloodstream infection (CR-BSI) in ICU patients: making the decision to remove or not to remove the central venous catheter

Background

Approximately 150 million central venous catheters (CVC) are used each year in the United States. Catheter-related bloodstream infections (CR-BSI) are one of the most important complications of the central venous catheters (CVCs). Our objective was to compare the in-hospital mortality when the catheter is removed or not removed in patients with CR-BSI.

Methods

We reviewed all episodes of CR-BSI that occurred in our intensive care unit (ICU) from January 2000 to December 2008. The standard method was defined as a patient with a CVC and at least one positive blood culture obtained from a peripheral vein and a positive semi quantitative (>15 CFU) culture of a catheter segment from where the same organism was isolated. The conservative method was defined as a patient with a CVC and at least one positive blood culture obtained from a peripheral vein and one of the following: (1) differential time period of CVC culture versus peripheral culture positivity of more than 2 hours, or (2) simultaneous quantitative blood culture with 5∶1 ratio (CVC versus peripheral).

Results

53 CR-BSI (37 diagnosed by the standard method and 16 by the conservative method) were diagnosed during the study period. There was a no statistically significant difference in the in-hospital mortality for the standard versus the conservative method (57% vs. 75%, p = 0.208) in ICU patients.

Conclusion

In our study there was a no statistically significant difference between the standard and conservative methods in-hospital mortality.     

Molecular refinement of clinical staging in hepatocellular carcinoma patients evaluated for potentially curative therapies

Aim

VEGF and AFP mRNA determinations in the blood are promising prognostic factors for patients with HCC. This study explores their potential prognostic synergy in a cohort of HCC patients evaluated for potentially curative therapies.

Methods

One hundred twenty-four patients with a diagnosis of HCC were prospectively enrolled in the study. Inclusion criteria were: (a) histological diagnosis of HCC and assessment of tumour grade and (b) determination of AFP mRNA status and VEGF levels in the blood before therapy.

Results

At baseline evaluation, 40% of the study group had AFP mRNA in the blood (AFP mRNA positive), and 35% had VEGF23 pg ml⁻¹ (VEGF positive). Surgery was performed in 58 patients (47%), 54 (43%) had tumour ablation, and 12 had chemoembolisation (10%). Median follow-up and survival of the study group were 19 and 26 months (range, 1 to 60), respectively. The association of AFP mRNA and VEGF proved to be prognostically more accurate than their single use in discriminating the risk of death (ROC curve analysis) and survival probability (Cox analysis). In particular, we identified 3 main molecular stages (0,0001): both negative (3-year survival = 63%), one positive (3-year survival = 40%), both positive (3-year survival = 16%). Multivariate analysis identified BCLC staging, surgery, and molecular staging as the most significant survival variables.

Conclusions

The preoperative determination of AFP mRNA status and VEGF may potentially refine the prognostic evaluation of HCC patients and improve the selection process for potentially curative therapies.     

Contribution of early detection and adjuvant treatments to breast cancer mortality reduction in Catalonia, Spain

Background

Reductions in breast cancer (BC) mortality in Western countries have been attributed to the use of screening mammography and adjuvant treatments. The goal of this work was to analyze the contributions of both interventions to the decrease in BC mortality between 1975 and 2008 in Catalonia.

Methodology/Principal Findings

A stochastic model was used to quantify the contribution of each intervention. Age standardized BC mortality rates for calendar years 1975–2008 were estimated in four hypothetical scenarios: 1) Only screening, 2) Only adjuvant treatment, 3) Both interventions, and 4) No intervention. For the 30–69 age group, observed Catalan BC mortality rates per 100,000 women-year rose from 29.4 in 1975 to 38.3 in 1993, and afterwards continuously decreased to 23.2 in 2008. If neither of the two interventions had been used, in 2008 the estimated BC mortality would have been 43.5, which, compared to the observed BC mortality rate, indicates a 46.7% reduction. In 2008 the reduction attributable to screening was 20.4%, to adjuvant treatments was 15.8% and to both interventions 34.1%.

Conclusions/Significance

Screening and adjuvant treatments similarly contributed to reducing BC mortality in Catalonia. Mathematical models have been useful to assess the impact of interventions addressed to reduce BC mortality that occurred over nearly the same periods.     

Performance of Three Prognostic Models in Patients with Cancer in Need of Intensive Care in a Medical Center in China

Objective

The aim of this study was to evaluate the performance of Acute Physiology and Chronic Health Evaluation II (APACHE II), Simplified Acute Physiology Score 3 (SAPS 3), and Acute Physiology and Chronic Health Evaluation IV (APACHE IV) in patients with cancer admitted to intensive care unit (ICU) in a single medical center in China.

Materials and Methods

This is a retrospective observational cohort study including nine hundred and eighty one consecutive patients over a 2-year period.

Results

The hospital mortality rate was 4.5%. When all 981 patients were evaluated, the area under the receiver operating characteristic curve (AUROC, 95% Confidential Intervals) of the three models in predicting hospital mortality were 0.948 (0.914–0.982), 0.863 (0.804–0.923), and 0.873 (0.813–0.934) for SAPS 3, APACHE II and APACHE IV respectively. The p values of Hosmer-Lemeshow statistics for the models were 0.759, 0.900 and 0.878 for SAPS 3, APACHE II and APACHE IV respectively. However, SAPS 3 and APACHE IV underestimated the in-hospital mortality with standardized mortality ratio (SMR) of 1.5 and 1.17 respectively, while APACHE II overestimated the in-hospital mortality with SMR of 0.72. Further analysis showed that discrimination power was better with SAPS 3 than with APACHE II and APACHE IV whether for emergency surgical and medical patients (AUROC of 0.912 vs 0.866 and 0.857) or for scheduled surgical patients (AUROC of 0.945 vs 0.834 and 0.851). Calibration was good for all models (all p > 0.05) whether for scheduled surgical patients or emergency surgical and medical patients. However, in terms of SMR, SAPS 3 was both accurate in predicting the in-hospital mortality for emergency surgical and medical patients and for scheduled surgical patients, while APACHE IV and APACHE II were not.

Conclusion

In this cohort, we found that APACHE II, APACHE IV and SAPS 3 models had good discrimination and calibration ability in predicting in-hospital mortality of critically ill patients with cancer in need of intensive care. Of these three severity scores, SAPS 3 was superior to APACHE II and APACHE IV, whether in terms of discrimination and calibration power, or standardized mortality ratios.     

Analysing the large decline in coronary heart disease mortality in the Icelandic population aged 25-74 between the years 1981 and 2006

Background

Coronary heart disease (CHD) mortality rates have been decreasing in Iceland since the 1980s. We examined how much of the decrease between 1981 and 2006 could be attributed to medical and surgical treatments and how much to changes in cardiovascular risk factors.

Methodology

The previously validated IMPACT CHD mortality model was applied to the Icelandic population. The data sources were official statistics, national quality registers, published trials and meta-analyses, clinical audits and a series of national population surveys.

Principal Findings

Between 1981 and 2006, CHD mortality rates in Iceland decreased by 80% in men and women aged 25 to 74 years, which resulted in 295 fewer deaths in 2006 than if the 1981 rates had persisted. Incidence of myocardial infarction (MI) decreased by 66% and resulted in some 500 fewer incident MI cases per year, which is a major determinant of possible deaths from MI. Based on the IMPACT model approximately 73% (lower and upper bound estimates: 54%–93%) of the mortality decrease was attributable to risk factor reductions: cholesterol 32%; smoking 22%; systolic blood pressure 22%, and physical inactivity 5% with adverse trends for diabetes (−5%), and obesity (−4%). Approximately 25% (lower and upper bound estimates: 8%–40%) of the mortality decrease was attributable to treatments in individuals: secondary prevention 8%; heart failure treatments 6%; acute coronary syndrome treatments 5%; revascularisation 3%; hypertension treatments 2%, and statins 0.5%.

Conclusions

Almost three quarters of the large CHD mortality decrease in Iceland between 1981 and 2006 was attributable to reductions in major cardiovascular risk factors in the population. These findings emphasize the value of a comprehensive prevention strategy that promotes tobacco control and a healthier diet to reduce incidence of MI and highlights the potential importance of effective, evidence based medical treatments.