Incidence and predictors of single drug discontinuation according to the presence of HCV coinfection in HIV patients from the ICONA Foundation Cohort Study

To evaluate incidence rates of and predictors for any antiretroviral (ART) drug discontinuation by HCV infection status in a large Italian cohort of HIV infected patients. All patients enrolled in ICONA who started combination antiretroviral therapy (cART) containing abacavir or tenofovir or emtricitabine or lamivudine plus efavirenz or rilpivirine or atazanavir/r or darunavir/r (DRV/r) or lopinavir/r or dolutegravir or elvitegravir or raltegravir were included. Multivariate Poisson regression models were used to determine factors independently associated with single ART drug discontinuation. Inverse probability weighting method to control for potential informative censoring was applied. Data from 10,637 patients were analyzed and 1,030 (9.7%) were HCV-Ab positive. Overall, there were 15,464 ART discontinuations due to any reason in 82,415.9 person-years of follow-up (PYFU) for an incidence rate (IR) of 18.8 (95% confidence interval [95%CI] 18.5–19.1) per 100 PYFU. No difference in IR of ART discontinuation due to any reason between HCV-infected and -uninfected patients was found. In a multivariable Poisson regression model, HCV-infected participants were at higher risk of darunavir/r discontinuation due to any reason (adjusted incidence rate ratio?=?1.5, 95%CI 1.01–2.22, p value?=?0.045) independently of demographics, HIV-related, ART and life-style factors. Among DRV/r treated patients, we found that HCV-viremic patients had twice the risk of ART discontinuation due to any reason than HCV-aviremic patients. In conclusion, HIV/HCV coinfected patients had a marginal risk increase of DRV/r discontinuation due to any reason compared with those without coinfection.     

A pilot study: dose adaptation of capecitabine using mobile phone toxicity monitoring — supporting patients in their homes

Purpose

Real-time symptom monitoring using a mobile phone is potentially advantageous for patients receiving oral chemotherapy. We therefore conducted a pilot study of patient dose adaptation using mobile phone monitoring of specific symptoms to investigate relative dose intensity of capecitabine, level of toxicity and perceived supportive care.

Methods

Patients with breast or colorectal cancer receiving capecitabine completed a symptom, temperature and dose diary twice a day using a mobile phone application. This information was encrypted and automatically transmitted in real time to a secure server, with moderate levels of toxicity automatically prompting self-care symptom management messages on the screen of the patient’s mobile phone or in severe cases, a call from a specialist nurse to advise on care according to an agreed protocol.

Results

Patients (n?=?26) completed the mobile phone diary on 92.6 % of occasions. Twelve patients had a maximum toxicity grade of 3 (46.2 %). The average dose intensity for all patients as a percentage of standard dose was 90 %. In eight patients, the dose of capecitabine was reduced, and in eight patients, the dose of capecitabine was increased. Patients and healthcare professionals involved felt reassured by the novel monitoring system, in particular, during out of hours.

Conclusion

It is possible to optimise the individual dose of oral chemotherapy safely including dose increase and to manage chemotherapy side effects effectively using real-time mobile phone monitoring of toxicity parameters entered by the patient.     

A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods

BackgroundChronic kidney disease (CKD) is a global health concern that is increasing mainly as the result of increasing incidences of diabetes and hypertension. Furthermore, if left untreated, individuals with CKD may progress to end-stage kidney failure. Identifying individuals with undiagnosed CKD or those who are at an increased risk of developing CKD or progressing to end-stage kidney disease (ESKD) is therefore an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) CKD or end-stage kidney failure in adults.MethodsWe conducted a systematic search of PubMed database to identify studies published up until September 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident CKD or ESKD. We extracted key information that describes aspects of developing a prediction model, including the study design, data quality, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies, and aspects of performance.ResultsEleven studies describing the development of 14 prediction models were included. Eight studies reported the development of 11 models to predict incident CKD or ESKD, whereas 3 studies developed models for prevalent CKD. A total of 97 candidate risk predictors were considered, and 43 different risk predictors featured in the 14 prediction models. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in six studies. Missing data were frequently poorly handled and reported with no mention of missing data in four studies; 4 studies explicitly excluded individuals with missing data, and only 2 studies used multiple imputation to replace missing values.ConclusionWe found that prediction models for chronic kidney were often developed using inappropriate methods and were generally poorly reported. Using poor methods can affect the predictive ability of the models, whereas inadequate reporting hinders an objective evaluation of the potential usefulness of the model.