One-Year Mortality in Older Patients with Cancer: Development and External Validation of an MNA-Based Prognostic Score

Purpose

The MNA (Mini Nutritional Assessment) is known as a prognosis factor in older population. We analyzed the prognostic value for one-year mortality of MNA items in older patients with cancer treated with chemotherapy as the basis of a simplified prognostic score.

Methods

The prospective derivation cohort included 606 patients older than 70 years with an indication of chemotherapy for cancers. The endpoint to predict was one-year mortality. The 18 items of the Full MNA, age, gender, weight loss, cancer origin, TNM, performance status and lymphocyte count were considered to construct the prognostic model. MNA items were analyzed with a backward step-by-step multivariate logistic regression and other items were added in a forward step-by-step regression. External validation was performed on an independent cohort of 229 patients.

Results

At one year 266 deaths had occurred. Decreased dietary intake (p = 0.0002), decreased protein-rich food intake (p = 0.025), 3 or more prescribed drugs (p = 0.023), calf circumference <31cm (p = 0.0002), tumor origin (p<0.0001), metastatic status (p = 0.0007) and lymphocyte count <1500/mm³ (0.029) were found to be associated with 1-year mortality in the final model and were used to construct a prognostic score. The area under curve (AUC) of the score was 0.793, which was higher than the Full MNA AUC (0.706). The AUC of the score in validation cohort (229 subjects, 137 deaths) was 0.698.

Conclusion

Key predictors of one-year mortality included cancer cachexia clinical features, comorbidities, the origin and the advanced status of the tumor. The prognostic value of this model combining a subset of MNA items and cancer related items was better than the full MNA, thus providing a simple score to predict 1-year mortality in older patients with an indication of chemotherapy.     

Identification of a metabolic gene panel to predict the prognosis of myelodysplastic syndrome

Myelodysplastic syndrome (MDS) is clonal disease featured by ineffective haematopoiesis and potential progression into acute myeloid leukaemia (AML). At present, the risk stratification and prognosis of MDS need to be further optimized. A prognostic model was constructed by the least absolute shrinkage and selection operator (LASSO) regression analysis for MDS patients based on the identified metabolic gene panel in training cohort, followed by external validation in an independent cohort. The patients with lower risk had better prognosis than patients with higher risk. The constructed model was verified as an independent prognostic factor for MDS patients with hazard ratios of 3.721 (1.814-7.630) and 2.047 (1.013-4.138) in the training cohort and validation cohort, respectively. The AUC of 3-year overall survival was 0.846 and 0.743 in the training cohort and validation cohort, respectively. The high-risk score was significantly related to other clinical prognostic characteristics, including higher bone marrow blast cells and lower absolute neutrophil count. Moreover, gene set enrichment analyses (GSEA) showed several significantly enriched pathways, with potential indication of the pathogenesis. In this study, we identified a novel stable metabolic panel, which might not only reveal the dysregulated metabolic microenvironment, but can be used to predict the prognosis of MDS.     

Recalibration and Validation of the Charlson Comorbidity Index in Korean Incident Hemodialysis Patients

BackgroundWeights assigned to comorbidities to predict mortality may vary based on the type of index disease and advances in the management of comorbidities. We aimed to develop a modified Charlson comorbidity index (CCI) in incident hemodialysis patients (mCCI-IHD), thereby improving risk stratification for mortality.MethodsData on 24,738 Koreans who received their first hemodialysis treatment between 2005 and 2008 were obtained from the Korean Health Insurance dataset. The mCCI-IHD score were calculated by summing up the weights which were assigned to individual comorbidities according to their relative prognostic significance determined by multivariate Cox proportional hazards model. The modified index was validated in an independent nationwide prospective cohort (n=1,100).ResultsThe Cox proportional hazards model revealed that all comorbidities in the CCI except ulcers significantly predicted mortality. Thus, the mCCI-IHD included 14 comorbidities with re-assigned severity weights. In the validation cohort, both the CCI and the mCCI-IHD were correlated with mortality. However, the mCCI-IHD showed modest but significant increases in c statistics compared with the CCI at 6 months and 1 year. The analyses using continuous net reclassification improvement revealed that the mCCI-IHD improved net mortality risk reclassification by 24.6% (95% CI, 2.5-46.7; P=0.03), 26.2% (95% CI, 1.0-51.4; P=0.04) and 42.8% (95% CI, 4.9-80.8; P=0.03) with respect to the CCI at 6 months and 1 and 2 years, respectively.ConclusionsThe mCCI-IHD facilitates better risk stratification for mortality in incident hemodialysis patients compared with the CCI, suggesting that it may be a preferred index for use in clinical practice and the statistical analysis of epidemiological studies.     

The Patient- And Nutrition-Derived Outcome Risk Assessment Score (PANDORA): Development of a Simple Predictive Risk Score for 30-Day In-Hospital Mortality Based on Demographics,Clinical Observation,and Nutrition

ObjectiveTo develop a simple scoring system to predict 30 day in-hospital mortality of in-patients excluding those from intensive care units based on easily obtainable demographic, disease and nutrition related patient data.MethodsScore development with general estimation equation methodology and model selection by P-value thresholding based on a cross-sectional sample of 52 risk indicators with 123 item classes collected with questionnaires and stored in an multilingual online database.SettingWorldwide prospective cross-sectional cohort with 30 day in-hospital mortality from the nutritionDay 2006-2009 and an external validation sample from 2012.ResultsWe included 43894 patients from 2480 units in 32 countries. 1631(3.72%) patients died within 30 days in hospital. The Patient- And Nutrition-Derived Outcome Risk Assessment (PANDORA) score predicts 30-day hospital mortality based on 7 indicators with 31 item classes on a scale from 0 to 75 points. The indicators are age (0 to 17 points), nutrient intake on nutritionDay (0 to 12 points), mobility (0 to 11 points), fluid status (0 to 10 points), BMI (0 to 9 points), cancer (9 points) and main patient group (0 to 7 points). An appropriate model fit has been achieved. The area under the receiver operating characteristic curve for mortality prediction was 0.82 in the development sample and 0.79 in the external validation sample.ConclusionsThe PANDORA score is a simple, robust scoring system for a general population of hospitalised patients to be used for risk stratification and benchmarking.     

A Novel Nomogram for Predicting the Overall Survival in Patients with Unresectable HCC after TACE plus Hepatic Arterial Infusion Chemotherapy

Background and aimTransarterial chemoembolization combined with hepatic arterial infusion chemotherapy (TACE-HAIC) has shown encouraging efficacy in the treatment of unresectable hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict overall survival (OS) of patients with unresectable HCC treated with TACE-HAIC.MethodsA total of 591 patients with unresectable HCC treated with TACE-HAIC between May 2009 and September 2020 were enrolled. These patients were randomly divided into training and validation cohorts. The independent prognostic factors were identified with Cox proportional hazards model. The model's discriminative ability and accuracy were validated using concordance index (C-index), calibration plots, the area under the time-dependent receiver operating characteristic curve (AUC) and decision curve analyses (DCAs).ResultsThe median OS was 15.6 months. A nomogram was established based on these factors, including tumor size, vein invasion, extrahepatic metastasis, tumor number, alpha fetoprotein (AFP), and albumin-bilirubin (ALBI), to predict OS for patients with unresectable HCC treated with TACE-HAIC. The C-index of the nomogram were 0.717 in the training cohort and 0.724 in validation cohort. The calibration plots demonstrated good agreement between the predicted outcomes and the actual observations. The AUC values were better than those of three conventional staging systems. The results of DCA indicated that the nomogram may have clinical usefulness. The patients in the low-risk group had a longer OS than those in intermediate-risk and high-risk groups (P<0.001).ConclusionA prognostic nomogram was developed and validated to assist clinicians in accurately predicting the OS of patients with unresectable HCC after TACE-HAIC.     

Mortality prediction by acute kidney injury biomarkers in comparison with serum creatinine

Abstract

Objectives: To investigate the performance of acute kidney injury (AKI) biomarkers for mortality prediction.

Materials and methods: Cutoff values of urinary L-type fatty acid-binding protein (L-FABP) and N-acetyl-β-d-glucosaminidase (NAG) for AKI diagnosis in ICU were determined in the derivation cohort. The performance of these AKI biomarkers for mortality prediction was evaluated in the validation cohort with stratification of serum-creatinine based AKI diagnosis.

Results: Mortality in the AKI patients diagnosed by serum creatinine was increased remarkably when urinary L-FABP and NAG were positive.

Conclusions: These AKI biomarkers can specifically detect high-risk patients among creatinine-base diagnosed AKI.     

Prognostic Significance of the pN Classification Supplemented by Vascular Invasion for Esophageal Squamous Cell Carcinoma

Background

The biological behavior and clinical outcome of esophageal squamous cell carcinoma (ESCC) are difficult to predict.

Methodology/Principal Findings

We investigate the prognostic impact of vascular invasion to establish a risk stratification model to predict recurrence and overall survival. We retrospectively evaluated the vascular invasion of 433 patients with ESCC treated with surgery between 2000 and 2007 at a single academic center. Those patients were assigned to a testing cohort and a validation cohort by random number generated in computer. The presence of vascular invasion was observed in 113 of 216 (52.3%) and 96 of 217 (44.2%) of ESCC in the training and validation cohorts, respectively. Further correlation analysis demonstrated that vascular invasion in ESCC was significantly correlated with more advanced pN classification and stage in both cohorts (P<0.05). Additionally, presence of vascular invasion in ESCC patients was associated closely with poor overall and recurrence-free survival as evidenced by univariate and multivariate analysis in both cohorts (P<0.05). In the subset of ESCC patients without lymph node metastasis, vascular invasion was evaluated as a prognostic predictor as well (P<0.05). More importantly, the combined prognostic model with pN classification supplemented by vascular invasion can significantly stratify the risk (low, intermediate and high) for overall survival and recurrence-free survival in both cohorts (P<0.05). The C-index to the combined model showed improved predictive ability when compared to the pN classification (0.785 vs 0.739 and 0.689 vs 0.650 for the training and validation cohorts, respectively; P<0.05).

Conclusions/Significance

The examination of vascular invasion could be used as an additional effective instrument in identifying those ESCC patients at increased risk of tumor progression. The proposed new prognostic model with the pN classification supplemented by vascular invasion might improve the ability to discriminate ESCC patients’ outcome.     

Prognostic significance of gamma‐glutamyl transpeptidase to albumin ratio in patients with intrahepatic cholangiocarcinoma after hepatectomy

Inflammation has been reported to play an important role in tumour progression and prognosis. In this study, we evaluated the prognostic significance of γ‐glutamyl transpeptidase (GGT) to albumin ratio (GAR) in patients with intrahepatic cholangiocarcinoma (ICC) after hepatectomy. We retrospectively analysed 650 ICC patients underwent hepatectomy at three Chinese medical centres between January 2009 and September 2017. Patients were classified into derivation cohort (n = 509) and validation cohort (n = 141). Receiver operating characteristic (ROC) curve was used to determine the optimal cut‐off value for GAR. Survival curve and cox regression analysis were applied to assess the prognostic power of GAR. The prognostic accuracy of GAR was compared with other variables by ROC curve. The optimal cut‐off value for GAR was 1.3655. Preoperative high GAR was closely related to tumour number, lymph node invasion and GGT. The survival curve of derivation and validation cohorts showed that patients in the high GAR group had significantly shorter overall survival (OS) and disease‐free survival (DFS) than patients in the low GAR group. Multivariate analysis in the derivation cohort confirmed that GAR was an independent prognostic factor for survival outcomes. Moreover, the ROC curve revealed that GAR had better predictive accuracy than other variables. High GAR predicted poor OS and DFS in ICC patients after hepatectomy. GAR may be a novel, simple and effective prognostic marker for ICC patients.     

Exploration of DNA methylation markers for diagnosis and prognosis of patients with endometrial cancer

The accurate diagnosis of endometrial cancer (EC) holds great promise for improving its treatment choice and prognosis prediction. This work aimed to identify diagnostic biomarkers for differentiating EC tumors from tumors in other tissues, as well as prognostic signatures for predicting survival in EC patients. We identified 48 tissue-specific markers using a cohort of genome-wide methylation data from three common gynecological tumors and their corresponding normal tissues. A diagnostic classifier was constructed based on these 48 CpG markers that could predict cancerous versus normal tissue with an overall correct rate of 98.3% in the entire repository. Fifteen CpG markers associated with the overall survival (OS) and development of EC were also identified based on the methylation patterns of the EC samples. A prognostic model that aggregated these prognostic CpG markers was established and shown to have a higher discriminative ability to distinguish EC patients with an elevated risk of mortality than the FIGO staging system and several other clinical prognostic variables. This study presents the utility of DNA methylation in identifying biomarkers for the diagnosis and prognosis of EC and will help improve our understanding of the underlying mechanisms involved in the development of EC.     

Prognostic Fifteen-Gene Signature for Early Stage Pancreatic Ductal Adenocarcinoma

The outcomes of patients treated with surgery for early stage pancreatic ductal adenocarcinoma (PDAC) are variable with median survival ranging from 6 months to more than 5 years. This challenge underscores an unmet need for developing personalized medicine strategies to refine the current treatment decision-making process. To derive a prognostic gene signature for patients with early stage PDAC, a PDAC cohort from Moffitt Cancer Center (n = 63) was used with overall survival (OS) as the primary endpoint. This was further evaluated using an independent microarray cohort dataset (Stratford et al: n = 102). Technical validation was performed by NanoString platform. A prognostic 15-gene signature was developed and showed a statistically significant association with OS in the Moffitt cohort (hazard ratio [HR] = 3.26; p<0.001) and Stratford et al cohort (HR = 2.07; p = 0.02), and was independent of other prognostic variables. Moreover, integration of the signature with the TNM staging system improved risk prediction (p<0.01 in both cohorts). In addition, NanoString validation showed that the signature was robust with a high degree of reproducibility and the association with OS remained significant in the two cohorts. The gene signature could be a potential prognostic tool to allow risk-adapted stratification of PDAC patients into personalized treatment protocols; possibly improving the currently poor clinical outcomes of these patients.     

Validation of a Dutch risk score predicting poor outcome in adults with bacterial meningitis in Vietnam and Malawi

We have previously developed and validated a prognostic model to predict the risk for unfavorable outcome in Dutch adults with bacterial meningitis. The aim of the current study was to validate this model in adults with bacterial meningitis from two developing countries, Vietnam and Malawi. Demographic and clinical characteristics of Vietnamese (n = 426), Malawian patients (n = 465) differed substantially from those of Dutch patients (n = 696). The Dutch model underestimated the risk of poor outcome in both Malawi and Vietnam. The discrimination of the original model (c-statistic [c] 0.84; 95% confidence interval 0.81 to 0.86) fell considerably when re-estimated in the Vietnam cohort (c = 0.70) or in the Malawian cohort (c = 0.68). Our validation study shows that new prognostic models have to be developed for these countries in a sufficiently large series of unselected patients.     

An administrative claims model for profiling hospital 30-day mortality rates for pneumonia patients

Background

Outcome measures for patients hospitalized with pneumonia may complement process measures in characterizing quality of care. We sought to develop and validate a hierarchical regression model using Medicare claims data that produces hospital-level, risk-standardized 30-day mortality rates useful for public reporting for patients hospitalized with pneumonia.

Methodology/Principal Findings

Retrospective study of fee-for-service Medicare beneficiaries age 66 years and older with a principal discharge diagnosis of pneumonia. Candidate risk-adjustment variables included patient demographics, administrative diagnosis codes from the index hospitalization, and all inpatient and outpatient encounters from the year before admission. The model derivation cohort included 224,608 pneumonia cases admitted to 4,664 hospitals in 2000, and validation cohorts included cases from each of years 1998–2003. We compared model-derived state-level standardized mortality estimates with medical record-derived state-level standardized mortality estimates using data from the Medicare National Pneumonia Project on 50,858 patients hospitalized from 1998–2001. The final model included 31 variables and had an area under the Receiver Operating Characteristic curve of 0.72. In each administrative claims validation cohort, model fit was similar to the derivation cohort. The distribution of standardized mortality rates among hospitals ranged from 13.0% to 23.7%, with 25^th, 50^th, and 75^th percentiles of 16.5%, 17.4%, and 18.3%, respectively. Comparing model-derived risk-standardized state mortality rates with medical record-derived estimates, the correlation coefficient was 0.86 (Standard Error = 0.032).

Conclusions/Significance

An administrative claims-based model for profiling hospitals for pneumonia mortality performs consistently over several years and produces hospital estimates close to those using a medical record model.     

Testing the Prognostic Accuracy of the Updated Pediatric Sepsis Biomarker Risk Model

Background

We previously derived and validated a risk model to estimate mortality probability in children with septic shock (PERSEVERE; PEdiatRic SEpsis biomarkEr Risk modEl). PERSEVERE uses five biomarkers and age to estimate mortality probability. After the initial derivation and validation of PERSEVERE, we combined the derivation and validation cohorts (n = 355) and updated PERSEVERE. An important step in the development of updated risk models is to test their accuracy using an independent test cohort.

Objective

To test the prognostic accuracy of the updated version PERSEVERE in an independent test cohort.

Methods

Study subjects were recruited from multiple pediatric intensive care units in the United States. Biomarkers were measured in 182 pediatric subjects with septic shock using serum samples obtained during the first 24 hours of presentation. The accuracy of PERSEVERE 28-day mortality risk estimate was tested using diagnostic test statistics, and the net reclassification improvement (NRI) was used to test whether PERSEVERE adds information to a physiology-based scoring system.

Results

Mortality in the test cohort was 13.2%. Using a risk cut-off of 2.5%, the sensitivity of PERSEVERE for mortality was 83% (95% CI 62–95), specificity was 75% (68–82), positive predictive value was 34% (22–47), and negative predictive value was 97% (91–99). The area under the receiver operating characteristic curve was 0.81 (0.70–0.92). The false positive subjects had a greater degree of organ failure burden and longer intensive care unit length of stay, compared to the true negative subjects. When adding PERSEVERE to a physiology-based scoring system, the net reclassification improvement was 0.91 (0.47–1.35; p<0.001).

Conclusions

The updated version of PERSEVERE estimates mortality probability reliably in a heterogeneous test cohort of children with septic shock and provides information over and above a physiology-based scoring system.     

A Modified MELD Model for Chinese Pre-ACLF and ACLF Patients and It Reveals Poor Prognosis in Pre-ACLF Patients

Background & Aims

Acute-on-chronic liver failure (ACLF) is one of the most deadly, prevalent, and costly diseases in Asia. However, no prognostic model has been developed that is based specifically on data gathered from Asian patients with ACLF. The aim of the present study was to quantify the survival time of ACLF among Asians and to develop a prognostic model to estimate the probability of death related to ACLF.

Methods

We conducted a retrospective observational cohort study to analyze clinical data from 857 patients with ACLF/pre-ACLF who did not undergo liver transplantation. Kaplan–Meier and Cox proportional hazards regression model were used to estimate survival rates and survival affected factors. The area under the receiver operating characteristic curve (auROC) was used to evaluate the performance of the models for predicting early mortality.

Results

The mortality rates among patients with pre-ACLF at 12 weeks and 24 weeks after diagnosis were 30.5% and 33.2%, respectively. The mortality rates among patients with early-stage ACLF at 12 weeks and 24 weeks after diagnosis were 33.9% and 37.1%, respectively. The difference in survival between pre-ACLF patients and patients in the early stage of ACLF was not statistically significant. The prognostic model identified 5 independent factors significantly associated with survival among patients with ACLF and pre-ACLF: the model for end-stage liver disease (MELD) score; age, hepatic encephalopathy; triglyceride level and platelet count.

Conclusion

The findings of the present study suggest that the Chinese diagnostic criteria of ACLF might be broadened, thus enabling implementation of a novel model to predict ACLF-related death after comprehensive medical treatment.     

A simple mortality risk prediction score for viper envenoming in India (VENOMS): A model development and validation study

BackgroundSnakebite is a neglected problem with a high mortality in India. There are no simple clinical prognostic tools which can predict mortality in viper envenomings. We aimed to develop and validate a mortality-risk prediction score for patients of viper envenoming from Southern India.MethodsWe used clinical predictors from a prospective cohort of 248 patients with syndromic diagnosis of viper envenoming and had a positive 20-minute whole blood clotting test (WBCT 20) from a tertiary-care hospital in Puducherry, India. We applied multivariable logistic regression with backward elimination approach. External validation of this score was done among 140 patients from the same centre and its performance was assessed with concordance statistic and calibration plots.FindingsThe final model termed VENOMS from the term “Viper ENvenOming Mortality Score included 7 admission clinical parameters (recorded in the first 48 hours after bite): presence of overt bleeding manifestations, presence of capillary leak syndrome, haemoglobin <10 g/dL, bite to antivenom administration time > 6.5 h, systolic blood pressure < 100 mm Hg, urine output <20 mL/h in 24 h and female gender. The lowest possible VENOMS score of 0 predicted an in-hospital mortality risk of 0.06% while highest score of 12 predicted a mortality of 99.1%. The model had a concordance statistic of 0·86 (95% CI 0·79–0·94) in the validation cohort. Calibration plots indicated good agreement of predicted and observed outcomes.ConclusionsThe VENOMS score is a good predictor of the mortality in viper envenoming in southern India where Russell’s viper envenoming burden is high. The score may have potential applications in triaging patients and guiding management after further validation.     

Preoperative prediction of microvascular invasion in non-metastatic hepatocellular carcinoma based on nomogram analysis

PurposeThe presence of microvascular invasion (MVI) is an unfavorable prognostic factor for hepatocellular carcinoma (HCC). This study aimed to construct a nomogram-based preoperative prediction model of MVI, thereby assisting to preoperatively select proper surgical procedures.MethodsA total of 714 non-metastatic HCC patients undergoing radical hepatectomy were retrospectively selected from Zhongshan Hospital between 2010 and 2018, followed by random assignment into training (N = 520) and validation cohorts (N = 194). Nomogram-based prediction model for MVI risk was constructed by incorporating independent risk factors of MVI presence identified from multivariate backward logistic regression analysis in the training cohort. The performance of nomogram was evaluated by calibration curve and ROC curve. Finally, decision curve analysis (DCA) was used to determine the clinical utility of the nomogram.ResultsIn total, 503 (70.4%) patients presented MVI. Multivariate analysis in the training cohort revealed that age (OR: 0.98), alpha-fetoprotein (≥400 ng/mL) (OR: 2.34), tumor size (>5 cm) (OR: 3.15), cirrhosis (OR: 2.03) and γ-glutamyl transpeptidase (OR: 1.61) were significantly associated with MVI presence. The incorporation of five risk factors into a nomogram-based preoperative estimation of MVI risk demonstrated satisfactory discriminative capacity, with C-index of 0.702 and 0.690 in training and validation cohorts, respectively. Calibration curve showed good agreement between actual and predicted MVI risks. Finally, DCA revealed the clinical utility of the nomogram.ConclusionThe nomogram showed a satisfactory discriminative capacity of MVI risk in HCC patients, and could be used to preoperatively estimate MVI risk, thereby establishing more rational therapeutic strategies.     

Using Highly Detailed Administrative Data to Predict Pneumonia Mortality

Background

Mortality prediction models generally require clinical data or are derived from information coded at discharge, limiting adjustment for presenting severity of illness in observational studies using administrative data.

Objectives

To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days.

Research Design

After dividing the dataset into derivation and validation sets, we created a hierarchical generalized linear mortality model that included patient demographics, comorbidities, medications, therapies, and diagnostic tests administered in the first 2 hospital days. We then applied the model to the validation set.

Subjects

Patients aged ≥18 years admitted with pneumonia between July 2007 and June 2010 to 347 hospitals in Premier, Inc.’s Perspective database.

Measures

In hospital mortality.

Results

The derivation cohort included 200,870 patients and the validation cohort had 50,037. Mortality was 7.2%. In the multivariable model, 3 demographic factors, 25 comorbidities, 41 medications, 7 diagnostic tests, and 9 treatments were associated with mortality. Factors that were most strongly associated with mortality included receipt of vasopressors, non-invasive ventilation, and bicarbonate. The model had a c-statistic of 0.85 in both cohorts. In the validation cohort, deciles of predicted risk ranged from 0.3% to 34.3% with observed risk over the same deciles from 0.1% to 33.7%.

Conclusions

A mortality model based on detailed administrative data available in the first 2 hospital days had good discrimination and calibration. The model compares favorably to clinically based prediction models and may be useful in observational studies when clinical data are not available.     

Prediction of Week 4 Virological Response in Hepatitis C for Making Decision on Triple Therapy: The Optim Study

Background

Virological response to peginterferon + ribavirin (P+R) at week 4 can predict sustained virological response (SVR). While patients with rapid virological response (RVR) do not require triple therapy, patients with a decline <1log₁₀ IU/ml HCVRNA (D1L) should have treatment discontinued due to low SVR rate.

Aim

To develop a tool to predict first 4 weeks’ viral response in patients with hepatitis C genotype 1&4 treated with P+R.

Methods

In this prospective and multicenter study, HCV mono-infected (n=538) and HCV/HIV co-infected (n=186) patients were included. To develop and validate a prognostic tool to detect RVR and D1L, we segregated the patients as an estimation cohort (to construct the model) and a validation cohort (to validate the model).

Results

D1L was reached in 509 (80.2%) and RVR in 148 (22.5%) patients. Multivariate analyses demonstrated that HIV co-infection, Forns’ index, LVL, IL28B-CC and Genotype-1 were independently related to RVR as well as D1L. Diagnostic accuracy (AUROC) for D1L was: 0.81 (95%CI: 0.76 — 0.86) in the estimation cohort and 0.71 (95%CI: 0.62 — 0.79) in the validation cohort; RVR prediction: AUROC 0.83 (95%CI: 0.78 — 0.88) in the estimation cohort and 0.82 (95%CI: 0.76 — 0.88) in the validation cohort. Cost-analysis of standard 48-week treatment indicated a saving of 30.3% if the prognostic tool is implemented.

Conclusions

The combination of genetic (IL28B polymorphism) and viral genotype together with viral load, HIV co-infection and fibrosis stage defined a tool able to predict RVR and D1L at week 4. Using this tool would be a cost-saving strategy compared to universal triple therapy for hepatitis C.