Objective: Investigate cost effectiveness of first trimester preeclampsia screening using the Fetal Medicine Foundation (FMF) algorithm in comparison to standard care. Design: Retrospective observational study Setting: London tertiary hospital Population: 5957 pregnancies screened for preeclampsia using the National Institute for Health and Care Excellence (NICE) method. Methods: Differences in pregnancy outcomes between those who developed preeclampsia, term preeclampsia and preterm preeclampsia were compared by the Kruskal-Wallis and Chi-square tests. The FMF algorithm was applied retrospectively to the cohort. A decision analytic model was used to estimate costs and outcomes for pregnancies screened using NICE and those screened using the FMF algorithm. The decision point probabilities were calculated using the included cohort. Main outcome measures: Incremental healthcare costs and QALY gained per pregnancy screened. Results: Of 5957 pregnancies, 12.8% and 15.9% were screen positive for the development of preeclampsia using the NICE and FMF methods, respectively. Of those screen positive by NICE recommendations, aspirin was not prescribed in 25%. Across the three groups: pregnancies without preeclampsia, term preeclampsia and preterm preeclampsia, respectively there was a statistically significant trend in rates of emergency caesarean (21%, 43%, 71.4%; p=<0.001), admission to neonatal intensive care unit (NICU) (5.9%, 9.4%, 41%; p=<0.001) and length of stay in NICU. Use of the FMF algorithm was associated with 7 fewer cases of preterm preeclampsia, cost saving of £9.06 and a QALY gain of 0.00006/pregnancy screened. Conclusions: In our cohort, using a conservative approach, application of the FMF algorithm achieved clinical benefit and an economic cost saving.

Purpose:There have been concerted efforts towards cataloging rare and deleterious variants in different world population using high throughput genotyping and sequencing based methods. The Indian populations are underrepresented or its information w.r.t. clinically relevant variants are sparse in public datasets. The aim of this study was to estimate the burden of monogenic disease causing variants in Indian populations. Towards this, we have assessed the frequency profile of monogenic phenotype associated ClinVar variants. Methods: The study utilized genotype dataset (global-screening-array, Illumina) from 2795 individuals (multiple in-house genomics cohorts) representing diverse ethnic and geographically distinct Indian populations. Results: Of the analyzed variants from GSA, ~12% were found to be informative and were either not known earlier or underrepresented in public databases in terms of their frequencies. These variants were linked to disorders, viz. Inborn-errors of Metabolism, Monogenic-diabetes, hereditary cancers and various other hereditary conditions. We have also shown that our study cohort is genetically better representatives of Indian populations than its representation in1000 genome project (South-Asians). Conclusion: We have created a database, ClinIndb [(http://clinindb.igib.res.in) and (https://databases.lovd.nl/shared/variants?search_owned_by_=%3D%22Mohamed%20Faruq%22)], to help clinicians and researchers in diagnosis, counseling and development of appropriate genetic screening tools relevant to the Indian populations and Indians living abroad.