Validation of a modified obstetric comorbidity index for prediction of postpartum adverse events including fetal morbidity - a retrospective cohort study from Qatar.

BACKGROUND: The Obstetric Comorbidity Index (OBCMI) is an internationally validated scoring system for maternal risk factors intended to reliably predict the occurrence of severe maternal morbidity (SMM). This retrospective cohort study applied the OBCMI to pregnant women in Qatar to validate its performance in predicting SMM and cumulative fetal morbidity. METHODS: Data from 1000 women who delivered in July 2021 in a large tertiary centre was extracted from medical records. The OBCMI index included maternal demographics, pre-existing comorbidities, and various current pregnancy risk factors such as hypertension, including preeclampsia, intrauterine fetal death, prolonged rupture of membranes and unbooked pregnancies. SMM was based on the ACOG consensus definition, and the cumulative fetal morbidity (CFM) included fetal distress in labour, low APGAR and umbilical artery (UA) pH, admission to neonatal intensive care (NICU), and hypoxic-ischemic encephalopathy (HIE). A c-statistic or area under curve (AUC) was calculated to determine the ability of OBCMI to predict SMM and CFM. RESULTS: The median OBCMI score for the cohort was 1 (interquartile range- 0 to 2); 50% of women scored 0, while 85% (n = 842) had a score ranging from 0 to 2. Ten women (1%) scored ≥ 7; the highest score was 10. The incidence of SMM was 13%. According to the modified scoring system, the mean OBCMI score in those who developed SMM was 2.18 (± 2.20) compared to a mean of 1.04 (± 1.40) in those who did not (median 1, IQR:1-3 versus median 0, IQR: 0-2; p < 0.001). The incidence of CFM was 11.3%. The incidence of low APGAR score, HIE and NICU admission was nearly 1 in 1000. Around 5% of the babies had fetal distress in labour and low UA pH. For every 1 unit increase in OBCMI score, the odds of SMM increased by 44% (OR 1.44 95% CI 1.30-1.59; p < 0.001; AUC 0.66), and CFM increased by 28% (OR 1.28 95% CI 1.15-1.42; p < 0.001; AUC 0.61). A cut-off score of 4 had a high specificity (> 90%); 1 in 4 and 1 in 6 women with OBCMI score ≥ 4 developed SMM and CFM, respectively. CONCLUSION: The OBCMI performed moderately well in predicting SMM in pregnant women of Qatar and can be effectively used as a risk assessment tool to red-flag high-risk cases so that appropriate and timely multidisciplinary care can be initiated to reduce SMM and maternal mortality. The index is also helpful in predicting fetal morbidity; however, further prospective studies are required to validate OBCMI for CFM.

Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG.

This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML. In particular, we present two methods that differ in the order of the analysis steps. After a gene selection based on fold-change value thresholding, we applied statistical tests to select differentially expressed genes. Therefore, we applied two different methods on the set of differentially expressed genes. With the first method (Method 1), we implemented GSEA, followed by the identification of transcription factors. With the second method (Method 2), we first selected the transcription factors from the set of differentially expressed genes and implemented GSEA on this set. Method 1 is a standard method commonly used in this type of analysis, while Method 2 is unconventional and is motivated by the intention to identify transcription factors more specifically involved in biological processes relevant to the CML condition. Both methods have been equipped in ontological knowledge mining and word cloud analysis, as elements of novelty in our analytical procedure. Data analysis identified RARG and CD36 as a potential PTPRG up-regulated genes, suggesting a possible induction of cell differentiation toward an erithromyeloid phenotype. The prediction was confirmed at the mRNA and protein level, further validating the approach and identifying a new molecular mechanism of tumor suppression governed by PTPRG in a CML context.