Timing of elective surgery and risk assessment after SARS-CoV-2 infection: 2023 update

Guidance for the timing of surgery following SARS-CoV-2 infection needed reassessment given widespread vaccination, less virulent variants, contemporary evidence and a need to increase access to safe surgery. We, therefore, updated previous recommendations to assist policymakers, administrative staff, clinicians and, most importantly, patients. Patients who develop symptoms of SARS-CoV-2 infection within 7 weeks of planned surgery, including on the day of surgery, should be screened for SARS-CoV-2. Elective surgery should not usually be undertaken within 2 weeks of diagnosis of SARS-CoV-2 infection. For patients who have recovered from SARS-CoV-2 infection and who are low risk or having low-risk surgery, most elective surgery can proceed 2 weeks following a SARS-CoV-2 positive test. For patients who are not low risk or having anything other than low-risk surgery between 2 and 7 weeks following infection, an individual risk assessment must be performed. This should consider: patient factors (age; comorbid and functional status); infection factors (severity; ongoing symptoms; vaccination); and surgical factors (clinical priority; risk of disease progression; grade of surgery). This assessment should include the use of an objective and validated risk prediction tool and shared decision-making, taking into account the patient's own attitude to risk. In most circumstances, surgery should proceed unless risk assessment indicates that the risk of proceeding exceeds the risk of delay. There is currently no evidence to support delaying surgery beyond 7 weeks for patients who have fully recovered from or have had mild SARS-CoV-2 infection

Abnormalities of semen parameters among male partners of infertile couples in a sub-urban tertiary hospital in Nigeria

Background: Infertility is a common reason for gynecological clinic visits in Nigeria. Men contribute significantly to the cause of infertility; however male factor infertility remain underreported compared to commonly reported female factor infertility. This study aimed to evaluate the abnormalities of semen parameters among male partners of infertile couples in Irrua Specialist Teaching Hospital in Edo State, Nigeria. Methods: A prospective cross-sectional study of 285 semen samples of male partners of infertile couples was conducted at Irrua specialist teaching hospital. The laboratory staff carried out analysis of the semen samples using set parameters outlined by the World Health Organization laboratory manual for the examination and processing of human semen. Socio-demographic data of the participants was also taken in a proforma. The data obtained was then analyzed with the Statistical Package for Social Sciences. Results: The age range of participants in this study was 20 to 65 years, with a mean age of 33.38 years. A total of 80.7% of the cases reviewed had one or more abnormal semen parameters. In 45.6%, 51.6%, and 47.0% of the analyzed samples, sperm cell count, morphology and total motility were below the WHO reference level, respectively. The severe forms of abnormal semen analysis findings detected in this study were asthenozoospermia (47%), oligozoospermia (15.8%), azoospermia (45.6%), and oligoasthenoteratozoospermia (15.5%). There was also statistically significant finding of rising cases of oligospermia with increasing age among the participants Conclusions: Male factor infertility although grossly under reported, contributes significantly to the burden of infertility in our setting. There is a need to raise awareness so that men are properly evaluated and treated

Timing of elective surgery and risk assessment after SARS-CoV-2 infection: an update: A multidisciplinary consensus statement on behalf of the Association of Anaesthetists, Centre for Perioperative Care, Federation of Surgical Specialty Associations, Royal College of Anaesthetists, Royal College of Surgeons of England

The impact of vaccination and new SARS-CoV-2 variants on peri-operative outcomes is unclear. We aimed to update previously published consensus recommendations on timing of elective surgery after SARS-CoV-2 infection to assist policymakers, administrative staff, clinicians and patients. The guidance remains that patients should avoid elective surgery within 7 weeks of infection, unless the benefits of doing so exceed the risk of waiting. We recommend individualised multidisciplinary risk assessment for patients requiring elective surgery within 7 weeks of SARS-CoV-2 infection. This should include baseline mortality risk calculation and assessment of risk modifiers (patient factors; SARS-CoV-2 infection; surgical factors). Asymptomatic SARS-CoV-2 infection with previous variants increased peri-operative mortality risk three-fold throughout the 6 weeks after infection, and assumptions that asymptomatic or mildly symptomatic omicron SARS-CoV-2 infection does not add risk are currently unfounded. Patients with persistent symptoms and those with moderate-to-severe COVID-19 may require a longer delay than 7 weeks. Elective surgery should not take place within 10 days of diagnosis of SARS-CoV-2 infection, predominantly because the patient may be infectious, which is a risk to surgical pathways, staff and other patients. We now emphasise that timing of surgery should include the assessment of baseline and increased risk, optimising vaccination and functional status, and shared decision-making. While these recommendations focus on the omicron variant and current evidence, the principles may also be of relevance to future variants. As further data emerge, these recommendations may be revised

Next-to-Leading order approximation of polarized valon and parton distributions

Polarized parton distributions and structure functions of the nucleon are analyzed in the improved valon model. The valon representation provides a model to represent hadrons in terms of quarks, providing a unified description of bound state and scattering properties of hadrons. Polarized valon distributions are seen to play an important role in describing the spin dependence of parton distributions in the leading order (LO) and next-to-leading order (NLO) approximations. In the polarized case, a convolution integral is derived in the framework of the valon model. The Polarized valon distribution in a proton and the polarized parton distributions inside the valon are necessary to obtain the polarized parton distributions in a proton. Bernstein polynomial averages are used to extract the unknown parameters of the polarized valon distributions by fitting to the available experimental data. The predictions for the NLO calculations of the polarized parton distributions and proton structure functions are compared with the LO approximation. It is shown that the results of the calculations for the proton structure function, $xg_1^p$, and its first moment, $\Gamma_{1}^p$, are in good agreement with the experimental data for a range of values of $Q^{2}$. Finally the spin contribution of the valons to the proton is calculated.Comment: 22 pages, 7 figures. Published in Journal of High Energy Physics (JHEP

SARS-CoV-2 infection, COVID-19 and timing of elective surgery: A multidisciplinary consensus statement on behalf of the Association of Anaesthetists, the Centre for Peri-operative Care, the Federation of Surgical Specialty Associations, the Royal College of Anaesthetists and the Royal College of Surgeons of England

The scale of the COVID-19 pandemic means that a significant number of patients who have previously been infected with SARS-CoV-2 will require surgery. Given the potential for multisystem involvement, timing of surgery needs to be carefully considered to plan for safe surgery. This consensus statement uses evidence from a systematic review and expert opinion to highlight key principles in the timing of surgery. Shared decision-making regarding timing of surgery after SARS-CoV-2 infection must account for severity of the initial infection; ongoing symptoms of COVID-19; comorbid and functional status; clinical priority and risk of disease progression; and complexity of surgery. For the protection of staff, other patients and the public, planned surgery should not be considered during the period that a patient may be infectious. Precautions should be undertaken to prevent pre- and peri-operative infection, especially in higher risk patients. Elective surgery should not be scheduled within 7 weeks of a diagnosis of SARS-CoV-2 infection unless the risks of deferring surgery outweigh the risk of postoperative morbidity or mortality associated with COVID-19. SARS-CoV-2 causes either transient or asymptomatic disease for most patients, who require no additional precautions beyond a 7-week delay, but those who have persistent symptoms or have been hospitalised require special attention. Patients with persistent symptoms of COVID-19 are at increased risk of postoperative morbidity and mortality even after 7 weeks. The time before surgery should be used for functional assessment, prehabilitation and multidisciplinary optimisation. Vaccination several weeks before surgery will reduce risk to patients and might lessen the risk of nosocomial SARS-CoV-2 infection of other patients and staff. National vaccine committees should consider whether such patients can be prioritised for vaccination. As further data emerge, these recommendations may need to be revised, but the principles presented should be considered to ensure safety of patients, the public and staff

Timing of elective surgery and risk assessment after SARS‐CoV ‐2 infection:an update: A multidisciplinary consensus statement on behalf of the Association of Anaesthetists, Centre for Perioperative Care, Federation of Surgical Specialty Associations, Royal College of Anaesthetists, Royal College of Surgeons of England

The impact of vaccination and new SARS‐CoV‐2 variants on peri‐operative outcomes is unclear. We aimed to update previously published consensus recommendations on timing of elective surgery after SARS‐CoV‐2 infection to assist policymakers, administrative staff, clinicians and patients. The guidance remains that patients should avoid elective surgery within 7 weeks of infection, unless the benefits of doing so exceed the risk of waiting. We recommend individualised multidisciplinary risk assessment for patients requiring elective surgery within 7 weeks of SARS‐CoV‐2 infection. This should include baseline mortality risk calculation and assessment of risk modifiers (patient factors; SARS‐CoV‐2 infection; surgical factors). Asymptomatic SARS‐CoV‐2 infection with previous variants increased peri‐operative mortality risk three‐fold throughout the 6 weeks after infection, and assumptions that asymptomatic or mildly symptomatic omicron SARS‐CoV‐2 infection does not add risk are currently unfounded. Patients with persistent symptoms and those with moderate‐to‐severe COVID‐19 may require a longer delay than 7 weeks. Elective surgery should not take place within 10 days of diagnosis of SARS‐CoV‐2 infection, predominantly because the patient may be infectious, which is a risk to surgical pathways, staff and other patients. We now emphasise that timing of surgery should include the assessment of baseline and increased risk, optimising vaccination and functional status, and shared decision‐making. While these recommendations focus on the omicron variant and current evidence, the principles may also be of relevance to future variants. As further data emerge, these recommendations may be revised

Current status of acute hepatopancreatic necrosis disease (AHPND) of farmed shrimp in Malaysia

A report about a disease problem in cultured whiteleg shrimp (Penaeus vannamei) was first received by the National Fish Health Research Center (NaFisH) in 2011 from Perak State showing signs of white feces and slow death leading to serious mortality rate. Later, in September of the same year, the Malaysian Shrimp Farmers Association (MSFA) reported to Department of Fisheries (DOF) severe mortalities in almost all of the whiteleg shrimp farms throughout Peninsular Malaysia. Sampling of shrimps for disease diagnosis was then conducted by NaFisH. The bacteriological and histopathological examinations revealed respectively the isolation of V. parahemolyticus and massive sloughing of hepatopancreatic epithelial cells. The disease was subsequently identified as acute hepatopancreatic necrosis disease (AHPND). From our 3-year study, the annual prevalence rates of AHPND were 50%, 26% and 73% in 2011, 2012 and 2013, respectively. At present, AHPND still persists in Malaysia but at a lower prevalence. The risk factors associated with the disease were studied, however, varied environmental and management data analyzed were inconclusive to relate any one parameter directly to the disease. To help ensure the early detection of AHPND, an experimental observation study on `gut scorecard was carried out and this was confirmed by PCR and histopathology. Validation of this technique has yet to be carried out to ensure its reliability. We also examined the potential use of some commercial products such as probiotics and disinfectants available in the market but unfortunately results showed that they were not effective in controlling AHPND. Control measures applied by the farmers such as the use of probiotics were also verified but data generated likewise appeared to be inconclusive. On the contrary, our preliminary study on the antibacterial property of the plant extracts, i.e. betel and lemongrass, incorporated in the feed showed some prophylactic and chemotherapeutic potential against AHPND. However, comprehensive in vitro and in vivo trials are still currently being undertaken to elucidate its efficacy and practical applications. To ensure the shrimp industry s sustainability in Malaysia, results of our ongoing and future studies aimed at preventing and controlling unwarranted outbreaks of AHPND and other emerging transboundary diseases of penaeid shrimps will be continually disseminated to shrimp farmers and pertinent stakeholders

Next-to-Leading Order Constituent Quark Structure and Hadronic Structure Functions

We calculate the partonic structure of a constituent quark in the Next-to-Leading Order framework. The structure of any hadron can be obtained thereafter using a convolution method. Such a procedure is used to generate the structure function of proton and pion in NLO, neglecting certain corrections to $\Lambda_{QCD}$. It is shown that while the constituent quark structure is generated purely perturbatively and accounts for the most part of the hadronic structure, there is a few percent contributions coming from the nonperturbative sector in the hadronic structure. This contribution plays the key role in explaining the SU(2) symmetry breaking of the nucleon sea and the observed violation of Gottfried sum rule. These effects are calculated. We obtained an Excellent agreement with the experimental data in a wide range of $x=[10^{-6}, 1]$ and $Q^{2}=[0.5, 5000]$ $GeV^{2}$ for the proton structure function. We have also calculated Pion structure and compared it with the existing data. Again, the model calculations agree rather well with the data from experiment.Comment: 32 pages,10 figures, Accepted to publish in Phys. Rev.

Look ahead to improve QoE in DASH streaming

[EN] When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.This work is supported by the PAID-10-18 Program of the Universitat Politecnica de Valencia (Ayudas para contratos de acceso al sistema espanol de Ciencia, Tecnologia e Innovacion, en estructuras de investigacion de la Universitat Politecnica de Valencia) and by the Project 20180810 from the Universitat Politecnica de Valencia ("Tecnologias de distribucion y procesado de informacion multimedia y QoE").Belda Ortega, R.; De Fez Lava, I.; Arce Vila, P.; Guerri Cebollada, JC. (2020). Look ahead to improve QoE in DASH streaming. Multimedia Tools and Applications. 79(33-34):25143-25170. https://doi.org/10.1007/s11042-020-09214-9S25143251707933-34Akhshabi S, Narayanaswamy S, Begen AC, Dovrolis C (2012) An experimental evaluation of rate-adaptive video players over HTTP. Signal process. Image Commun 27(4):271–287. https://doi.org/10.1016/j.image.2011.10.003Android Developers webpage, ExoPlayer. Available online at: https://developer.android.com/guide/topics/media/exoplayer.html . Accessed: Jun. (2019)Bampis CG, Li Z, Bovik AC (2018) SpatioTemporal feature integration and model fusion for full reference video quality assessment. IEEE Trans on Circuits and Syst for Video Tech 29:2256–2270. https://doi.org/10.1109/TCSVT.2018.2868262Barman N, Martini MG (2019) QoE modeling for HTTP adaptive video streaming - a survey and open challenges. IEEE Access 7:30831–30859. https://doi.org/10.1109/ACCESS.2019.2901778Belda R (2013) Algoritmo de adaptación DASH: Look Ahead. Master Thesis. Universitat Politècnica de València. http://hdl.handle.net/10251/33359 .Belda R, de Fez I, Arce P, Guerri J C (2018) Look ahead: a DASH adaptation algorithm. Proc. of the IEEE Int. Symp. On broadband multimed. Syst. And broadcast., Valencia, Spain: article no. 158. https://doi.org/10.1109/BMSB.2018.8436718 .Blender Foundation webpage. Available online at: https://www.blender.org/foundation . Accessed: Jun. (2019).Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20-3:273–297. https://doi.org/10.1023/A:1022627411411DASH Industry forum webpage. Available online at: http://dashif.org . Accessed: Jun. (2019)Ghadiyaram D, Pan J, Bovik AC (2019) A subjective and objective study of stalling events in mobile streaming videos. IEEE Trans on Circuits and Syst for Video Technol 29(1):183–197. https://doi.org/10.1109/TCSVT.2017.2768542Ghent University. 4G/LTE bandwidth logs. Available online at: http://users.ugent.be/~jvdrhoof/dataset-4g . Accessed: Jun. (2019).Github webpage. A DASH segment size aware rate adaptation model for DASH. Available online at: https://github.com/pari685/AStream . Accessed: Jun. (2019)GitHub website. Dashgen, Multimedia Communications Group. Available online at: https://github.com/comm-iteam/dashgen . Accessed: Jun. (2019).van der Hooft J, Petrangeli S, Wauters T, Huysegems R, Alface PR, Bostoen T, De Turck F (2016) HTTP/2-based adaptive streaming of HEVC video over 4G/LTE networks. IEEE Commun Lett 20(1):2177–2180. https://doi.org/10.1109/LCOMM.2016.2601087Huang TY, Johari R, McKeown N, Trunnell M, Watson M (2014) A buffer-based approach to rate adaptation: evidence from a large video streaming service. Proc. of the 2014 ACM Conf. On SIGCOMM, Chicago, IL, USA: 187-198. https://doi.org/10.1145/2619239.2626296Institute of Telecommunications and Multimedia Applications website. Look Ahead Demo. Available online at: https://lookahead.iteam.upv.es . Accessed: Jun. (2019)ISO/IEC 23009–1:2014 (2014) Dynamic adaptive streaming over HTTP (DASH) - Part 1: media presentation description and segment formats.Juluri P, Tamarapalli V, Medhi D (2015) SARA: segment aware rate adaptation algorithm for dynamic adaptive streaming over HTTP. Proc. of the IEEE Int. Conf. On Commun. Workshop (ICCW), London, UK: 1765-1770. https://doi.org/10.1109/ICCW.2015.7247436 .Juluri P, Tamarapalli V, Medhi D (2016) QoE management in DASH systems using the segment aware rate adaptation algorithm. Proc. of the IEEE/IFIP Netw. Oper. And Manag. Symp. (NOMS), Istanbul, Turkey: 129-136. https://doi.org/10.1109/NOMS.2016.7502805 .Kua J, Armitage G, Branch P (2017) A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Commun Surv & Tutor 19(3):1842–1866. https://doi.org/10.1109/COMST.2017.2685630Lee S, Youn K, Chung K (2015) Adaptive video quality control scheme to improve QoE of MPEG DASH. Proc. of IEEE Int. Conf. On Consum. Electron. (ICCE), Las Vegas, NV, USA: 126-127. https://doi.org/10.1109/ICCE.2015.7066348 .Li S, Zhang F, Ma L, Ngan K (2011) Image quality assessment by separately evaluating detail losses and additive impairments. IEEE Trans. on Multimed. 13-5:935–949. https://doi.org/10.1109/TMM.2011.2152382Liu C, Bouazizi I, Gabbouj M (2011) Rate adaptation for adaptive HTTP streaming. Proc. of the second annual ACM Conf. On multimed. Syst. (MMSys), San Jose, CA, USA: 169-174. https://doi.org/10.1145/1943552.1943575 .Medium webpage (2016) Toward a practical perceptual video quality metric. Available online at: https://medium.com/netflix-techblog/toward-a-practical-perceptual-video-quality-metric-653f208b9652 . Accessed: Jun. 2019.Mobile Video Service Performance Study (2015) HUAWEI white paper. Available online at: http://www.ctiforum.com/uploadfile/2015/0701/20150701091255294.pdf .Mok RKP, Luo X, Chan EWW, Chang RKC (2012) QDASH: a QoE-aware DASH system. Proc. of multim. Syst. Conf. (MMSys), Chapel Hill, NC, USA: 11-22. https://doi.org/10.1145/2155555.2155558Moldovan C, Hagn K, Sieber C, Kellerer W, Hoßfeld T (2017) Keep calm and don’t switch: about the relationship between switches and quality in HAS. Proc. of the Int. Teletraffic Congr. (ITC), Genoa, Italy: pp. 1-6. https://doi.org/10.23919/ITC.2017.8065802Müller C, Lederer S, Timmerer C (2012) An evaluation of dynamic adaptive streaming over HTTP in vehicular environments. Proc. of the 4th workshop on mob. Video (MoVid), Chapel Hill, NC, USA: 37-42. https://doi.org/10.1145/2151677.2151686Nguyen T, Vu T, Nguyen DV, Ngoc NP, and Thang TC (2015) QoE optimization for adaptive streaming with multiple VBR videos. Proc. of the Int. Conf. On comp., Manag. And Telecommun. (ComManTel), DaNang, Vietnam: 189-193. https://doi.org/10.1109/ComManTel.2015.7394285 .Qin Y, H. Shuai, Pattipati K R, Qian F, Sen S, Wang B, Yue C (2018) ABR Streaming of VBR-encoded videos: characterization, challenges, and solutions. Proc. of ACM CoNext 2018, Heraklion, Greece: 366–378. https://doi.org/10.1145/3281411.3281439 .Samain J, Carofiglio G, Muscariello L, Papalini M, Sardara M, Tortelli M, Rossi D (2017) Dynamic adaptive video streaming: towards a systematic comparison of ICN and TCP/IP. IEEE Trans on Multimed 19(10):2166–2181. https://doi.org/10.1109/TMM.2017.2733340Sheikh H, Bovik A (2006) Image information and visual quality. IEEE Trans on Image Process 15(2):430–444. https://doi.org/10.1109/TIP.2005.859378Shuai Y, Herfet T (2016). A buffer dynamic stabilizer for low-latency adaptive video streaming. Proc. of the Int. Conf. on Consum. Electron., Berlin: 1–5. https://doi.org/10.1109/ICCE-Berlin.2016.7684742 .Tavakoli S, Egger S, Seufert M, Schatz R, Brunnström K, García N (2016) Perceptual quality of HTTP adaptive streaming strategies: cross-experimental analysis of multi-laboratory and crowdsourced subjective studies. 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Published: May (2018)Yu L, Tillo T, Xiao J (2017) QoE-driven dynamic adaptive video streaming strategy with future information. IEEE Trans on Broadcast 63-3:523–534. https://doi.org/10.1109/TBC.2017.2687698Zhao S, Li Z, Medhi D, Lai P, Liu S (2017) Study of user QoE improvement for dynamic adaptive streaming over HTTP (MPEG-DASH). Proc. of the Int. Conf. On Comput., network. And Commun. (ICNC): multimed. Comput. And Commun., Santa Clara, CA, USA: 566-570. https://doi.org/10.1109/ICCNC.2017.7876191 .Zhou Y, Duan Y, Sun J, Guo Z (2014) Towards a simple and smooth rate adaption for VBR video in DASH. Proc. of the IEEE Vis. Commun. and Image Process. Conf, Valletta, pp 9–12. https://doi.org/10.1109/VCIP.2014.7051491Zhou C, Lin C-W, Guo Z (2016) mDASH: a Markov decision-based rate adaptation approach for dynamic HTTP streaming. IEEE Trans. on Multimed 18(4):738–751. https://doi.org/10.1109/TMM.2016.252265

Assessment of risk factors and glycosylated haemoglobin in early pregnancy as predictors of diabetes in pregnancy

Background: The aim of the study was to determine the performance of history of risk factors and universal HBA1c testing as screening tools for diabetes mellitus in the first trimester of pregnancy using OGTT as a gold standard. Methods: A prospective cross-sectional study conducted between 8 and 13±6 weeks in 305 consecutive pregnancies in the antenatal clinics of the University of Port Harcourt Teaching (UPTH) and Rivers State University Teaching Hospital (RSUTH) between January and August 2020. Each woman had oral glucose tolerance test (OGTT), and glycosylated haemoglobin (HBA1c) levels estimation. Multivariate logistic regression analysis was carried out with history of risk factors and HBA1c level as independent variables and OGTT as the dependent variable for the assessment of their predictive performances. Results: The prevalence of DM was 28.85%, 2.62% and 31.48% for GDM, pre-gestational and for both respectively. Family history of DM was associated with high specificity (91.4%) and negative predictive value (NPV) of 68.7% but low sensitivity (9.4%) and positive predictive value (PPV) (33.3%). The receiver operator characteristic curve for HBA1c revealed a significant area under the curve value: 0.653 (CI: 058-0.72), p<0.01. The optimal cut-off for HBA1c from Youden index was 5.25%. HBA1c levels had high specificity (88.5%) and NPV (75.2%) with low sensitivity (36.5%) and PPV (59.3%). Multivariate logistic regression analysis showed HbA1c as the only independent predictor of GDM (p=0.0001). Conclusions: The high prevalence of diabetes (31.48%), underscores the need for universal screening in early pregnancy. The high NPV and specificity of the risk factors for GDM and HBA1c levels better predict pregnancies that are not likely to develop GDM, but they are not suitable for diagnosis because of the low sensitivity and PPV