Key performance indicators score (KPIs-score) based on clinical and laboratorial parameters can establish benchmarks for internal quality control in an ART program.

José G Franco Jr,Laura D Vagnini,Adriana Renzi,João Batista A Oliveira,Bruna Petersen,Ricardo L.R Baruffi,M.C Mattila,Felipe Dieamant,Juliana Ricci,Vanessa A Comar,Ana L Mauri,Claudia G Petersen

doi:10.5935/1518-0557.20170016

Abstract

ObjectiveKPIs have been employed for internal quality control (IQC) in ART. However, clinical KPIs (C-KPIs) such as age, AMH and number of oocytes collected are never added to laboratory KPIs (L-KPIs), such as fertilization rate and morphological quality of the embryos for analysis, even though the final endpoint is the evaluation of clinical pregnancy rates. This paper analyzed if a KPIs-score strategy with clinical and laboratorial parameters could be used to establish benchmarks for IQC in ART cycles.MethodsIn this prospective cohort study, 280 patients (36.4±4.3years) underwent ART. The total KPIs-score was obtained by the analysis of age, AMH (AMH Gen II ELISA/pre-mixing modified, Beckman Coulter Inc.), number of metaphase-II oocytes, fertilization rates and morphological quality of the embryonic lot.ResultsThe total KPIs-score (C-KPIs+L-KPIs) was correlated with the presence or absence of clinical pregnancy. The relationship between the C-KPIs and L-KPIs scores was analyzed to establish quality standards, to increase the performance of clinical and laboratorial processes in ART. The logistic regression model (LRM), with respect to pregnancy and total KPIs-score (280 patients/102 clinical pregnancies), yielded an odds ratio of 1.24 (95%CI = 1.16-1.32). There was also a significant difference (p<0.0001) with respect to the total KPIs-score mean value between the group of patients with clinical pregnancies (total KPIs-score=20.4±3.7) and the group without clinical pregnancies (total KPIs-score=15.9±5). Clinical pregnancy probabilities (CPP) can be obtained using the LRM (prediction key) with the total KPIs-score as a predictor variable. The mean C-KPIs and L-KPIs scores obtained in the pregnancy group were 11.9±2.9 and 8.5±1.7, respectively. Routinely, in all cases where the C-KPIs score was ≥9, after the procedure, the L-KPIs score obtained was ≤6, a revision of the laboratory procedure was performed to assess quality standards.ConclusionThis total KPIs-score could set up benchmarks for clinical pregnancy. Moreover, IQC can use C-KPIs and L-KPIs scores to detect problems in the clinical-laboratorial interface.

Highlights

Systems to monitor clinical and laboratorial performance have gained much importance in medical practice (Leandro et al, 2005; Vermeulen et al, 2008; Salinas et al, 2010)
In no time, clinical key performance indicator (KPI) (C-KPIs) such as age, AMH and number of oocytes collected are added to laboratory KPIs (L-KPIs) for analysis, even though the final endpoint is the evaluation of clinical pregnancy rates
C-KPIs and L-KPIs benchmarks On the other hand, the mean C-KPIs score and L-KPIs scores obtained in the pregnancy group were 11.9±2.9 and 8.5±1.7, respectively

Summary

Introduction

Systems to monitor clinical and laboratorial performance have gained much importance in medical practice (Leandro et al, 2005; Vermeulen et al, 2008; Salinas et al, 2010). Whether in a biomedical or non-biomedical field, can be subject to inherent deviations from the optimum or from established limits. These deviations may lead to defective end-products or, in the medical field, defective patient care. Monitoring, which is a process able to identify deviations, and being able to act should such deviation exceed certain limits, plays an important role in avoiding adverse consequences and maintaining optimal performance

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