A semiparametric joint model for terminal trend of quality of life and survival in palliative care research.

Palliative medicine is an interdisciplinary specialty focusing on improving quality of life (QOL) for patients with serious illness and their families. Palliative care programs are available or under development at over 80% of large US hospitals (300+ beds). Palliative care clinical trials present unique analytic challenges relative to evaluating the palliative care treatment efficacy which is to improve patients' diminishing QOL as disease progresses towards end of life (EOL). A unique feature of palliative care clinical trials is that patients will experience decreasing QOL during the trial despite potentially beneficial treatment. Often longitudinal QOL and survival data are highly correlated which, in the face of censoring, makes it challenging to properly analyze and interpret terminal QOL trend. To address these issues, we propose a novel semiparametric statistical approach to jointly model the terminal trend of QOL and survival data. There are two sub-models in our approach: a semiparametric mixed effects model for longitudinal QOL and a Cox model for survival. We use regression splines method to estimate the nonparametric curves and AIC to select knots. We assess the model performance through simulation to establish a novel modeling approach that could be used in future palliative care research trials. Application of our approach in a recently completed palliative care clinical trial is also presented.

Updated emm-typing protocol for Streptococcus pyogenes.

OBJECTIVES: PCR-based typing of the emm gene Streptococcus pyogenes often results in the amplification of multiple bands. This has resulted in the misclassification of strains into types based on non-emm gene sequences. We aimed to improve the specificity of the emm typing PCR reaction using a primer called CDC3, the sequence for which has been previously used to identify emm genes in silico. METHODS: The proposed primer CDC3 was validated in silico from a global database of 1688 GAS genomes and in vitro with 32 isolates. PCR reactions were performed on genomic DNA from each isolate, using the published CDC1 forward primer with the CDC2 reverse primer or the new CDC3 reverse primer. The products were examined by gel electrophoresis, and representative PCR products were sequenced. RESULTS: In 1688 S. pyogenes genomes, the previous CDC2 reverse primer annealed in silico in 1671 emm genes and also in 2109 non emm genes in close proximity, whereas the new CDC3 primer annealed in 1669 emm genes only. The remaining 19 genes without a CDC3 binding site were chimeric emm genes. The PCR pair CDC1+CDC3 produced a single band at appropriate molecular weight in all 32 isolates tested, while the CDC1+CDC2 pair produced more than one band in 13 of 32 isolates (40%). CONCLUSIONS: The new CDC3 primer is more specific for emm genes than the previous CDC2 primer and represents a simple solution to reduce the potential for mistyping S. pyogenes strains.

Validation of an automated colony counting system for group A Streptococcus.

BACKGROUND: The practice of counting bacterial colony forming units on agar plates has long been used as a method to estimate the concentration of live bacteria in culture. However, due to the laborious and potentially error prone nature of this measurement technique, an alternative method is desirable. Recent technologic advancements have facilitated the development of automated colony counting systems, which reduce errors introduced during the manual counting process and recording of information. An additional benefit is the significant reduction in time taken to analyse colony counting data. Whilst automated counting procedures have been validated for a number of microorganisms, the process has not been successful for all bacteria due to the requirement for a relatively high contrast between bacterial colonies and growth medium. The purpose of this study was to validate an automated counting system for use with group A Streptococcus (GAS). METHODS: Twenty-one different GAS strains, representative of major emm-types, were selected for assessment. In order to introduce the required contrast for automated counting, 2,3,5-triphenyl-2H-tetrazolium chloride (TTC) dye was added to Todd-Hewitt broth with yeast extract (THY) agar. Growth on THY agar with TTC was compared with growth on blood agar and THY agar to ensure the dye was not detrimental to bacterial growth. Automated colony counts using a ProtoCOL 3 instrument were compared with manual counting to confirm accuracy over the stages of the growth cycle (latent, mid-log and stationary phases) and in a number of different assays. The average percentage differences between plating and counting methods were analysed using the Bland-Altman method. RESULTS: A percentage difference of ±10 % was determined as the cut-off for a critical difference between plating and counting methods. All strains measured had an average difference of less than 10 % when plated on THY agar with TTC. This consistency was also observed over all phases of the growth cycle and when plated in blood following bactericidal assays. Agreement between these methods suggest the use of an automated colony counting technique for GAS will significantly reduce time spent counting bacteria to enable a more efficient and accurate measurement of bacteria concentration in culture.