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2. Supplementary material and dataset from: Impaired adipocyte SLC7A10 promotes lipid storage in association with insulin resistance and altered BCAA metabolism

Author

Jersin RÅ, Tallapragada DSP, Skartveit L, Bjune MS, Muniandy M, Lee-Ødegård S, Heinonen S, Alvarez M, Birkeland KI, Drevon CA, Pajukanta P, McCann A, Pietiläinen KH, Claussnitzer M, Mellgren G, Dankel SN

Abstract

The files contain supplementary material to the articleImpaired adipocyte SLC7A10 promotes lipid storage in association with insulin resistance and altered BCAA metabolism by Jersin et al. published in the Journal of ClinicalEndocrinology &Metabolism.

Published
2023
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4. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis

Author

Allotey, J., Whittle, R., Snell, K. I. E., Smuk, M., Townsend, R., von Dadelszen, P., Heazell, A. E. P., Magee, L., Smith, G. C. S., Sandall, J., Thilaganathan, B., Zamora, J., Riley, R. D., Khalil, A., Thangaratinam, S., Coomarasamy, A., Kwong, A., Savitri, A. I., Salvesen, K. A., Bhattacharya, S., Uiterwaal, C. S. P. M., Staff, A. C., Andersen, L. B., Olive, E. L., Redman, C., Sletner, L., Daskalakis, G., Macleod, M., Abdollahain, M., Ramirez, J. A., Masse, J., Audibert, F., Magnus, P. M., Jenum, A. K., Baschat, A., Ohkuchi, A., Mcauliffe, F. M., West, J., Askie, L. M., Mone, F., Farrar, D., Zimmerman, P. A., Smits, L. J. M., Riddell, C., Kingdom, J. C., van de Post, J., Illanes, S. E., Holzman, C., van Kuijk, S. M. J., Carbillon, L., Villa, P. M., Eskild, A., Chappell, L., Prefumo, F., Velauthar, L., Seed, P., van Oostwaard, M., Verlohren, S., Poston, L., Ferrazzi, E., Vinter, C. A., Nagata, C., Brown, M., Vollebregt, K. C., Takeda, S., Langenveld, J., Widmer, M., Saito, S., Haavaldsen, C., Carroli, G., Olsen, J., Wolf, H., Zavaleta, N., Eisensee, I., Vergani, P., Lumbiganon, P., Makrides, M., Facchinetti, F., Sequeira, E., Gibson, R., Ferrazzani, S., Frusca, T., Norman, J. E., Figueiro, E. A., Lapaire, O., Laivuori, H., Lykke, J. A., Conde-Agudelo, A., Galindo, A., Mbah, A., Betran, A. P., Herraiz, I., Trogstad, L., Smith, G. G. S., Steegers, E. A. P., Salim, R., Huang, T., Adank, A., Zhang, J., Meschino, W. S., Browne, J. L., Allen, R. E., Costa, F. D. S., Klipstein-Grobusch Browne, K., Crowther, C. A., Jorgensen, J. S., Forest, J. -C., Rumbold, A. R., Mol, B. W., Giguere, Y., Kenny, L. C., Ganzevoort, W., Odibo, A. O., Myers, J., Yeo, S. A., Goffinet, F., Mccowan, L., Pajkrt, E., Teede, H. J., Haddad, B. G., Dekker, G., Kleinrouweler, E. C., Lecarpentier, E., Roberts, C. T., Groen, H., Skrastad, R. B., Heinonen, S., Eero, K., Anggraini, D., Souka, A., Cecatti, J. G., Monterio, I., Pillalis, A., Souza, R., Hawkins, L. A., Gabbay-Benziv, R., Crovetto, F., Figuera, F., Jorgensen, L., Dodds, J., Patel, M., Aviram, A., Papageorghiou, A., Khan, K., Clinicum, HUS Gynecology and Obstetrics, Department of Obstetrics and Gynecology, HUS Children and Adolescents, Lastentautien yksikkö, Children's Hospital, Allotey, J, Whittle, R, Snell, K, Smuk, M, Townsend, R, von Dadelszen, P, Heazell, A, Magee, L, Smith, G, Sandall, J, Thilaganathan, B, Zamora, J, Riley, R, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, A, Salvesen, K, Bhattacharya, S, Uiterwaal, C, Staff, A, Andersen, L, Olive, E, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramirez, J, Masse, J, Audibert, F, Magnus, P, Jenum, A, Baschat, A, Ohkuchi, A, Mcauliffe, F, West, J, Askie, L, Mone, F, Farrar, D, Zimmerman, P, Smits, L, Riddell, C, Kingdom, J, van de Post, J, Illanes, S, Holzman, C, van Kuijk, S, Carbillon, L, Villa, P, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, van Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, C, Nagata, C, Brown, M, Vollebregt, K, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, J, Figueiro, E, Lapaire, O, Laivuori, H, Lykke, J, Conde-Agudelo, A, Galindo, A, Mbah, A, Betran, A, Herraiz, I, Trogstad, L, Steegers, E, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, W, Browne, J, Allen, R, Costa, F, Klipstein-Grobusch Browne, K, Crowther, C, Jorgensen, J, Forest, J, Rumbold, A, Mol, B, Giguere, Y, Kenny, L, Ganzevoort, W, Odibo, A, Myers, J, Yeo, S, Goffinet, F, Mccowan, L, Pajkrt, E, Teede, H, Haddad, B, Dekker, G, Kleinrouweler, E, Lecarpentier, E, Roberts, C, Groen, H, Skrastad, R, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, J, Monterio, I, Pillalis, A, Souza, R, Hawkins, L, Gabbay-Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, Khan, K, Tampere University, Obstetrics and Gynaecology, APH - Quality of Care, Amsterdam Reproduction & Development (AR&D), APH - Personalized Medicine, APH - Digital Health, and Obstetrics and gynaecology

Subjects
Calibration (statistics), Perinatal Death, Overfitting, Cohort Studies, Fetal Development, 0302 clinical medicine, Discriminative model, 3123 Gynaecology and paediatrics, Models, Pregnancy, GROWTH RESTRICTION, Statistics, Medicine, Prenatal, 030212 general & internal medicine, Ultrasonography, RISK, 030219 obstetrics & reproductive medicine, PRETERM, Radiological and Ultrasound Technology, LOW-DOSE ASPIRIN, DIAGNOSIS TRIPOD, Obstetrics and Gynecology, General Medicine, Statistical, Stillbirth, Prognosis, Pregnancy Complication, external validation, individual participant data, intrauterine death, prediction model, stillbirth, Female, Humans, Infant, Newborn, Models, Statistical, Pregnancy Complications, Regression Analysis, Risk Assessment, Ultrasonography, Prenatal, 3. Good health, PREECLAMPSIA, Meta-analysis, Human, Cohort study, Prognosi, MEDLINE, Regression Analysi, WEEKS GESTATION, 03 medical and health sciences, VELOCIMETRY, Radiology, Nuclear Medicine and imaging, RECURRENCE, business.industry, Infant, Newborn, R1, HYPERTENSIVE DISORDERS, Reproductive Medicine, Sample size determination, Cohort Studie, RG, business, RA, Predictive modelling
Abstract

Objective Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. Conclusions The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Published
2022

9. Treatment of Multisystem Inflammatory Syndrome in Children

Author

McArdle A. J., Vito O., Patel H., Seaby E. G., Shah P., Wilson C., Broderick C., Nijman R., Tremoulet A. H., Munblit D., Ulloa-Gutierrez R., Carter M. J., De T., Hoggart C., Whittaker E., Herberg J. A., Kaforou M., Cunnington A. J., Levin M., Vazquez J. A., Carmona R., Perez L., Rubinos M., Veliz N., Yori S., Haerynck F., Hoste L., Leal I. A., Da Silva A. R. A., Silva A. E. A., Barchik A., Barreiro S. T. A., Cochrane N., Teixeira C. H., Arauj J. M., Ossa R. A. P. -D. L., Vieira C. S., Dimitrova A., Ganeva M., Stefanov S., Telcharova-Mihaylovska A., Biggs C. M., Scuccimarri R., Withington D., Raul B. B., Ampuero C., Aravena J., Casanova D., Cruces P., Diaz F., Garcia-Salum T., Godoy L., Medina R. A., Galaz G. V., Avila-Aguero M. L., Brenes-Chacon H., Ivankovich-Escoto G., Yock-Corrales A., Badib A., Badreldin K., Elkhashab Y., Heshmat H., Heinonen S., Angoulvant F., Belot A., Ouldali N., Beske F., Heep A., Masjosthusmann K., Reiter K., Heuvel I. V. D., Both U. V., Agrafiotou A., Antachopoulos C., Eleftheriou I., Farmaki E., Fotis L., Kafetzis D., Lampidi S., Liakopoulou T., Maritsi D., Michailidou E., Milioudi M., Mparmpounaki I., Papadimitriou E., Papaevangelou V., Roilides E., Tsiatsiou O., Tsolas G., Tsolia M., Vantsi P., Pineda L. Y. B., Aguilar K. L. B., Quintero E. M. C., Ip P., Kwan M. Y. W., Kwok J., Lau Y. L., To K., Wong J. S. C., David M., Farkas D., Kalcakosz S., Szekeres K., Zsigmond B., Aslam N., Andreozzi L., Bianco F., Bucciarelli V., Buonsenso D., Cimaz R., D'Argenio P., Dellepiane R. M., Fabi M., Mastrolia M. V., Mauro A., Mazza A., Romani L., Simonini G., Tipo V., Valentini P., Verdoni L., Reel B., Pace D., Torpiano P., Flores M. F., Dominguez M. G., Vargas A. L. G., Hernandez L. L., Figueroa R. P. M., Gaxiola G. P., Valadez J., Klevberg S., Knudsen P. K., Maseide P. H., Carrera J. M., Castano E. G., Timana C. A. D., Leon T. D., Estripeaut D., Levy J., Norero X., Record J., Rojas-Bonilla M., Iramain R., Hernandez R., Huaman G., Munaico M., Peralta C., Seminario D., Yarleque E. H. Z., Gadzinska J., Mandziuk J., Okarska-Napierala M., Alacheva Z. A., Alexeeva E., Ananin P. V., Antsupova M., Bakradze M. D., Bobkova P., Borzakova S., Chashchina I. L., Fisenko A. P., Gautier M. S., Glazyrina A., Kondrikova E., Korobyants E., Korsunskiy A. A., Kovygina K., Krasnaya E., Kurbanova S., Kurdup M. K., Mamutova A. V., Mazankova L., Mitushin I. L., Nargizyan A., Orlova Y. O., Osmanov I. M., Polyakova A. S., Romanova O., Samitova E., Sologub A., Spiridonova E., Tepaev R. F., Tkacheva A. A., Yusupova V., Zholobova E., Grasa C. D., Segura N. L., Martinon-Torres F., Melendo S., Echevarria A. M., Guzman J. M. M., Argueta J. R. P., Rivero-Calle I., Riviere J., Rodriguez-Gonzalez M., Rojo P., Manubens J. S., Soler-Palacin P., Soriano-Arandes A., Tagarro A., Villaverde S., Altman M., Brodin P., Horne A., Palmblad K., Brotschi B., Sauteur P. M., Schmid J. P., Prader S., Relly C., Schlapbach L. J., Seiler M., Truck J., Wutz D., Ketharanathan N., Vermont C., Ozkan E. A., Erdeniz E. H., Borisova G., Boychenko L., Diudenko N., Kasiyan O., Katerynych K., Melnyk K., Miagka N., Teslenko M., Trykosh M., Volokha A., Akomolafe T., Al-Abadi E., Alders N., Avram P., Bamford A., Bank M., Roy R. B., Beattie T., Boleti O., Broad J., Carrol E. D., Chandran A., Cooper H., Davies P., Emonts M., Evans C., Fidler K., Foster C., Gong C., Gongrun B., Gonzalez C., Grandjean L., Grant K., Hacohen Y., Hall J., Hassell J., Hesketh C., Hewlett J., Hnieno A., Holt-Davis H., Hossain A., Hudson L. D., Johnson M., Johnson S., Jyothish D., Kampmann B., Kavirayani A., Kelly D., Kucera F., Langer D., Lillie J., Longbottom K., Lyall H., MacKdermott N., Maltby S., McLelland T., McMahon A. -M., Miller D., Morrison Z., Mosha K., Muller J., Myttaraki E., Nadel S., Osaghae D., Osman F., Ostrzewska A., Panthula M., Papachatzi E., Papadopoulou C., Penner J., Polandi S., Prendergast A. J., Ramnarayan P., Rhys-Evans S., Riordan A., Rodrigues C. M. C., Romaine S., Seddon J., Shingadia D., Srivastava A., Struik S., Taylor A., Tran S., Tudor-Williams G., Van Der Velden F., Ventilacion L., Wellman P. A., Yanney M. P., Yeung S., Badheka A., Badran S., Bailey D. M., Burch A. K., Burns J. C., Cichon C., Cirks B., Dallman M. D., Delany D. R., Fairchok M., Friedman S., Geracht J., Langs-Barlow A., Mann K., Padhye A., Quade A., Ramirez K. A., Rockett J., Sayed I. A., Shahin A. A., Umaru S., Widener R., Angela M. H., Kandawasvika G., McArdle A.J., Vito O., Patel H., Seaby E.G., Shah P., Wilson C., Broderick C., Nijman R., Tremoulet A.H., Munblit D., Ulloa-Gutierrez R., Carter M.J., De T., Hoggart C., Whittaker E., Herberg J.A., Kaforou M., Cunnington A.J., Levin M., Vazquez J.A., Carmona R., Perez L., Rubinos M., Veliz N., Yori S., Haerynck F., Hoste L., Leal I.A., Da Silva A.R.A., Silva A.E.A., Barchik A., Barreiro S.T.A., Cochrane N., Teixeira C.H., Arauj J.M., Ossa R.A.P.-D.L., Vieira C.S., Dimitrova A., Ganeva M., Stefanov S., Telcharova-Mihaylovska A., Biggs C.M., Scuccimarri R., Withington D., Raul B.B., Ampuero C., Aravena J., Casanova D., Cruces P., Diaz F., Garcia-Salum T., Godoy L., Medina R.A., Galaz G.V., Avila-Aguero M.L., Brenes-Chacon H., Ivankovich-Escoto G., Yock-Corrales A., Badib A., Badreldin K., Elkhashab Y., Heshmat H., Heinonen S., Angoulvant F., Belot A., Ouldali N., Beske F., Heep A., Masjosthusmann K., Reiter K., Heuvel I.V.D., Both U.V., Agrafiotou A., Antachopoulos C., Eleftheriou I., Farmaki E., Fotis L., Kafetzis D., Lampidi S., Liakopoulou T., Maritsi D., Michailidou E., Milioudi M., Mparmpounaki I., Papadimitriou E., Papaevangelou V., Roilides E., Tsiatsiou O., Tsolas G., Tsolia M., Vantsi P., Pineda L.Y.B., Aguilar K.L.B., Quintero E.M.C., Ip P., Kwan M.Y.W., Kwok J., Lau Y.L., To K., Wong J.S.C., David M., Farkas D., Kalcakosz S., Szekeres K., Zsigmond B., Aslam N., Andreozzi L., Bianco F., Bucciarelli V., Buonsenso D., Cimaz R., D'Argenio P., Dellepiane R.M., Fabi M., Mastrolia M.V., Mauro A., Mazza A., Romani L., Simonini G., Tipo V., Valentini P., Verdoni L., Reel B., Pace D., Torpiano P., Flores M.F., Dominguez M.G., Vargas A.L.G., Hernandez L.L., Figueroa R.P.M., Gaxiola G.P., Valadez J., Klevberg S., Knudsen P.K., Maseide P.H., Carrera J.M., Castano E.G., Timana C.A.D., Leon T.D., Estripeaut D., Levy J., Norero X., Record J., Rojas-Bonilla M., Iramain R., Hernandez R., Huaman G., Munaico M., Peralta C., Seminario D., Yarleque E.H.Z., Gadzinska J., Mandziuk J., Okarska-Napierala M., Alacheva Z.A., Alexeeva E., Ananin P.V., Antsupova M., Bakradze M.D., Bobkova P., Borzakova S., Chashchina I.L., Fisenko A.P., Gautier M.S., Glazyrina A., Kondrikova E., Korobyants E., Korsunskiy A.A., Kovygina K., Krasnaya E., Kurbanova S., Kurdup M.K., Mamutova A.V., Mazankova L., Mitushin I.L., Nargizyan A., Orlova Y.O., Osmanov I.M., Polyakova A.S., Romanova O., Samitova E., Sologub A., Spiridonova E., Tepaev R.F., Tkacheva A.A., Yusupova V., Zholobova E., Grasa C.D., Segura N.L., Martinon-Torres F., Melendo S., Echevarria A.M., Guzman J.M.M., Argueta J.R.P., Rivero-Calle I., Riviere J., Rodriguez-Gonzalez M., Rojo P., Manubens J.S., Soler-Palacin P., Soriano-Arandes A., Tagarro A., Villaverde S., Altman M., Brodin P., Horne A., Palmblad K., Brotschi B., Sauteur P.M., Schmid J.P., Prader S., Relly C., Schlapbach L.J., Seiler M., Truck J., Wutz D., Ketharanathan N., Vermont C., Ozkan E.A., Erdeniz E.H., Borisova G., Boychenko L., Diudenko N., Kasiyan O., Katerynych K., Melnyk K., Miagka N., Teslenko M., Trykosh M., Volokha A., Akomolafe T., Al-Abadi E., Alders N., Avram P., Bamford A., Bank M., Roy R.B., Beattie T., Boleti O., Broad J., Carrol E.D., Chandran A., Cooper H., Davies P., Emonts M., Evans C., Fidler K., Foster C., Gong C., Gongrun B., Gonzalez C., Grandjean L., Grant K., Hacohen Y., Hall J., Hassell J., Hesketh C., Hewlett J., Hnieno A., Holt-Davis H., Hossain A., Hudson L.D., Johnson M., Johnson S., Jyothish D., Kampmann B., Kavirayani A., Kelly D., Kucera F., Langer D., Lillie J., Longbottom K., Lyall H., MacKdermott N., Maltby S., McLelland T., McMahon A.-M., Miller D., Morrison Z., Mosha K., Muller J., Myttaraki E., Nadel S., Osaghae D., Osman F., Ostrzewska A., Panthula M., Papachatzi E., Papadopoulou C., Penner J., Polandi S., Prendergast A.J., Ramnarayan P., Rhys-Evans S., Riordan A., Rodrigues C.M.C., Romaine S., Seddon J., Shingadia D., Srivastava A., Struik S., Taylor A., Tran S., Tudor-Williams G., Van Der Velden F., Ventilacion L., Wellman P.A., Yanney M.P., Yeung S., Badheka A., Badran S., Bailey D.M., Burch A.K., Burns J.C., Cichon C., Cirks B., Dallman M.D., Delany D.R., Fairchok M., Friedman S., Geracht J., Langs-Barlow A., Mann K., Padhye A., Quade A., Ramirez K.A., Rockett J., Sayed I.A., Shahin A.A., Umaru S., Widener R., Angela M.H., Kandawasvika G., Pediatric Surgery, Pediatrics, University of Zurich, National Institute of Health and Medical Research, Wellcome Trust, Medical Research Foundation, Shah, Priyen [0000-0001-9164-8862], Ulloa-Gutierrez, Rolando [0000-0002-9157-9227], Herberg, Jethro A [0000-0001-6941-6491], Cunnington, Aubrey J [0000-0002-1305-3529], Levin, Michael [0000-0003-2767-6919], and Apollo - University of Cambridge Repository

Subjects
Inotrope, Male, medicine.medical_treatment, 2700 General Medicine, 030204 cardiovascular system & hematology, Antibodies, Viral, Medical and Health Sciences, Cohort Studies, 0302 clinical medicine, Glucocorticoid, hemic and lymphatic diseases, Medicine and Health Sciences, 030212 general & internal medicine, Viral, Child, 11 Medical and Health Sciences, OUTCOMES, Respiration, Immunoglobulins, Intravenous, General Medicine, Systemic Inflammatory Response Syndrome, 3. Good health, Hospitalization, Treatment Outcome, Child, Preschool, Combination, Artificial, Regression Analysis, Drug Therapy, Combination, Female, Original Article, Intravenous, Life Sciences & Biomedicine, Cohort study, Human, medicine.medical_specialty, BATS Consortium, Adolescent, Immunoglobulins, 610 Medicine & health, Regression Analysi, Antibodies, Immunomodulation, 03 medical and health sciences, Medicine, General & Internal, Pharmacotherapy, Drug Therapy, Clinical Research, Internal medicine, General & Internal Medicine, medicine, MANAGEMENT, Confidence Intervals, Humans, Preschool, Propensity Score, Glucocorticoids, Mechanical ventilation, Science & Technology, business.industry, SARS-CoV-2, Inflammatory and immune system, COVID-19, Odds ratio, medicine.disease, Respiration, Artificial, Confidence interval, KAWASAKI-LIKE DISEASE, COVID-19 Drug Treatment, Systemic inflammatory response syndrome, 10036 Medical Clinic, Immunoglobulins, Intravenou, Propensity score matching, Cohort Studie, business, ACUTE RESPIRATORY SYNDROME, Confidence Interval, TOXIC-SHOCK-SYNDROME
Abstract

BackgroundEvidence is urgently needed to support treatment decisions for children with multisystem inflammatory syndrome (MIS-C) associated with severe acute respiratory syndrome coronavirus 2.MethodsWe performed an international observational cohort study of clinical and outcome data regarding suspected MIS-C that had been uploaded by physicians onto a Web-based database. We used inverse-probability weighting and generalized linear models to evaluate intravenous immune globulin (IVIG) as a reference, as compared with IVIG plus glucocorticoids and glucocorticoids alone. There were two primary outcomes: the first was a composite of inotropic support or mechanical ventilation by day 2 or later or death; the second was a reduction in disease severity on an ordinal scale by day 2. Secondary outcomes included treatment escalation and the time until a reduction in organ failure and inflammation.ResultsData were available regarding the course of treatment for 614 children from 32 countries from June 2020 through February 2021; 490 met the World Health Organization criteria for MIS-C. Of the 614 children with suspected MIS-C, 246 received primary treatment with IVIG alone, 208 with IVIG plus glucocorticoids, and 99 with glucocorticoids alone; 22 children received other treatment combinations, including biologic agents, and 39 received no immunomodulatory therapy. Receipt of inotropic or ventilatory support or death occurred in 56 patients who received IVIG plus glucocorticoids (adjusted odds ratio for the comparison with IVIG alone, 0.77; 95% confidence interval [CI], 0.33 to 1.82) and in 17 patients who received glucocorticoids alone (adjusted odds ratio, 0.54; 95% CI, 0.22 to 1.33). The adjusted odds ratios for a reduction in disease severity were similar in the two groups, as compared with IVIG alone (0.90 for IVIG plus glucocorticoids and 0.93 for glucocorticoids alone). The time until a reduction in disease severity was similar in the three groups.ConclusionsWe found no evidence that recovery from MIS-C differed after primary treatment with IVIG alone, IVIG plus glucocorticoids, or glucocorticoids alone, although significant differences may emerge as more data accrue. (Funded by the European Union's Horizon 2020 Program and others; BATS ISRCTN number, ISRCTN69546370.).

Published
2021

15. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications ( <scp>IPPIC</scp> ) Network database: individual participant data meta‐analysis

Author

Allotey, J, Whittle, R, Snell, KIE, Smuk, M, Townsend, R, Dadelszen, P, Heazell, AEP, Magee, L, Smith, GCS, Sandall, J, Thilaganathan, B, Zamora, J, Riley, RD, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, AI, Salvesen, KÅ, Bhattacharya, S, Uiterwaal, CSPM, Staff, AC, Andersen, LB, Olive, EL, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramírez, JA, Massé, J, Audibert, F, Magnus, PM, Jenum, AK, Baschat, A, Ohkuchi, A, McAuliffe, FM, West, J, Askie, LM, Mone, F, Farrar, D, Zimmerman, PA, Smits, LJM, Riddell, C, Kingdom, JC, Post, J, Illanes, SE, Holzman, C, Kuijk, SMJ, Carbillon, L, Villa, PM, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, CA, Nagata, C, Brown, M, Vollebregt, KC, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, JE, Figueiró‐Filho, EA, Lapaire, O, Laivuori, H, Lykke, JA, Conde‐Agudelo, A, Galindo, A, Mbah, A, Betran, AP, Herraiz, I, Trogstad, L, Smith, GGS, Steegers, EAP, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, WS, Browne, JL, Allen, RE, Costa, F Da Silva, Klipstein‐Grobusch, K, Crowther, CA, Jørgensen, JS, Forest, J‐C, Rumbold, AR, Mol, BW, Giguère, Y, Kenny, LC, Ganzevoort, W, Odibo, AO, Myers, J, Yeo, SA, Goffinet, F, McCowan, L, Pajkrt, E, Teede, HJ, Haddad, BG, Dekker, G, Kleinrouweler, EC, LeCarpentier, É, Roberts, CT, Groen, H, Skråstad, RB, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, JG, Monterio, I, Pillalis, A, Souza, R, Hawkins, LA, Gabbay‐Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, and Khan, K

Abstract

Objective: Stillbirth is a potentially preventable complication of pregnancy. Identifying women at risk can guide decisions on closer surveillance or timing of birth to prevent fetal death.Prognostic models have been developed to predict the risk of stillbirth, but none have yet been externally validated. We externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods: We searched Medline, EMBASE, DH-DATA and AMED databases from inception to December 2020 to identify stillbirth prediction models. We included studies that developed or updated prediction models for stillbirth for use at any time during pregnancy. IPD from cohorts within the International Prediction of Pregnancy Complication (IPPIC) Network were used to externally validate the identified prediction models whose individual variables were available in the IPD. We assessed the risk of bias of the models and IPD using PROBAST, and reported discriminative performance using the C-statistic, and calibration performance using calibration plots, calibration slopeand calibration-in-the-large. We estimated performance measures separately in each study, and then summarised across studies using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results: We identified 17 studies reporting the development of 40 prognostic models for stillbirth. None of the models were previously externally validated, and only a fifth (20%, 8/40) reported the full model equation. We were able to validate three of these models using the IPD from 19 cohort studies (491,201 pregnant women) within the IPPIC Network database. Based on evaluating their development studies, all three models had an overall high risk of bias according to PROBAST. In our IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65; summary calibration slopes of 0.40to 0.88, and generally with observed risks predictions that were too extreme compared to observed risks; and little to no clinical utility as assessed by net benefit. However, there remained uncertainty in performance for some models due to small available sample sizes. Conclusion: The three validated models generally showed poor and uncertain predictive performancein new data, with limited evidence to support their clinical application. Findings suggest methodological shortcomings in their development including overfitting of models. Further research is needed to further validate these and other models, identify stronger prognostic factors, and to develop more robust prediction models

Published
2021

17. Treatment of Multisystem Inflammatory Syndrome in Children

Author

McArdle, A.J. Vito, O. Patel, H. Seaby, E.G. Shah, P. Wilson, C. Broderick, C. Nijman, R. Tremoulet, A.H. Munblit, D. Ulloa-Gutierrez, R. Carter, M.J. De, T. Hoggart, C. Whittaker, E. Herberg, J.A. Kaforou, M. Cunnington, A.J. Levin, M. Vazquez, J.A. Carmona, R. Perez, L. Rubinos, M. Veliz, N. Yori, S. Haerynck, F. Hoste, L. Leal, I.A. Da Silva, A.R.A. Silva, A.E.A. Barchik, A. Barreiro, S.T.A. Cochrane, N. Teixeira, C.H. Arauj, J.M. Ossa, R.A.P.-D.L. Vieira, C.S. Dimitrova, A. Ganeva, M. Stefanov, S. Telcharova-Mihaylovska, A. Biggs, C.M. Scuccimarri, R. Withington, D. Raul, B.B. Ampuero, C. Aravena, J. Casanova, D. Cruces, P. Diaz, F. Garcia-Salum, T. Godoy, L. Medina, R.A. Galaz, G.V. Avila-Aguero, M.L. Brenes-Chacon, H. Ivankovich-Escoto, G. Yock-Corrales, A. Badib, A. Badreldin, K. Elkhashab, Y. Heshmat, H. Heinonen, S. Angoulvant, F. Belot, A. Ouldali, N. Beske, F. Heep, A. Masjosthusmann, K. Reiter, K. Heuvel, I.V.D. Both, U.V. Agrafiotou, A. Antachopoulos, C. Eleftheriou, I. Farmaki, E. Fotis, L. Kafetzis, D. Lampidi, S. Liakopoulou, T. Maritsi, D. Michailidou, E. Milioudi, M. Mparmpounaki, I. Papadimitriou, E. Papaevangelou, V. Roilides, E. Tsiatsiou, O. Tsolas, G. Tsolia, M. Vantsi, P. Pineda, L.Y.B. Aguilar, K.L.B. Quintero, E.M.C. Ip, P. Kwan, M.Y.W. Kwok, J. Lau, Y.L. To, K. Wong, J.S.C. David, M. Farkas, D. Kalcakosz, S. Szekeres, K. Zsigmond, B. Aslam, N. Andreozzi, L. Bianco, F. Bucciarelli, V. Buonsenso, D. Cimaz, R. D'Argenio, P. Dellepiane, R.M. Fabi, M. Mastrolia, M.V. Mauro, A. Mazza, A. Romani, L. Simonini, G. Tipo, V. Valentini, P. Verdoni, L. Reel, B. Pace, D. Torpiano, P. Flores, M.F. Domínguez, M.G. Vargas, A.L.G. Hernandez, L.L. Figueroa, R.P.M. Gaxiola, G.P. Valadez, J. Klevberg, S. Knudsen, P.K. Maseide, P.H. Carrera, J.M. Castano, E.G. Timana, C.A.D. Leon, T.D. Estripeaut, D. Levy, J. Norero, X. Record, J. Rojas-Bonilla, M. Iramain, R. Hernandez, R. Huaman, G. Munaico, M. Peralta, C. Seminario, D. Yarleque, E.H.Z. Gadzinska, J. Mandziuk, J. Okarska-Napierała, M. Alacheva, Z.A. Alexeeva, E. Ananin, P.V. Antsupova, M. Bakradze, M.D. Bobkova, P. Borzakova, S. Chashchina, I.L. Fisenko, A.P. Gautier, M.S. Glazyrina, A. Kondrikova, E. Korobyants, E. Korsunskiy, A.A. Kovygina, K. Krasnaya, E. Kurbanova, S. Kurdup, M.K. Mamutova, A.V. Mazankova, L. Mitushin, I.L. Nargizyan, A. Orlova, Y.O. Osmanov, I.M. Polyakova, A.S. Romanova, O. Samitova, E. Sologub, A. Spiridonova, E. Tepaev, R.F. Tkacheva, A.A. Yusupova, V. Zholobova, E. Grasa, C.D. Segura, N.L. Martinon-Torres, F. Melendo, S. Echevarria, A.M. Guzman, J.M.M. Argueta, J.R.P. Rivero-Calle, I. Riviere, J. Rodriguez-Gonzalez, M. Rojo, P. Manubens, J.S. Soler-Palacin, P. Soriano-Arandes, A. Tagarro, A. Villaverde, S. Altman, M. Brodin, P. Horne, A. Palmblad, K. Brotschi, B. Sauteur, P.M. Schmid, J.P. Prader, S. Relly, C. Schlapbach, L.J. Seiler, M. Truck, J. Wutz, D. Ketharanathan, N. Vermont, C. Ozkan, E.A. Erdeniz, E.H. Borisova, G. Boychenko, L. Diudenko, N. Kasiyan, O. Katerynych, K. Melnyk, K. Miagka, N. Teslenko, M. Trykosh, M. Volokha, A. Akomolafe, T. Al-Abadi, E. Alders, N. Avram, P. Bamford, A. Bank, M. Roy, R.B. Beattie, T. Boleti, O. Broad, J. Carrol, E.D. Chandran, A. Cooper, H. Davies, P. Emonts, M. Evans, C. Fidler, K. Foster, C. Gong, C. Gongrun, B. Gonzalez, C. Grandjean, L. Grant, K. Hacohen, Y. Hall, J. Hassell, J. Hesketh, C. Hewlett, J. Hnieno, A. Holt-Davis, H. Hossain, A. Hudson, L.D. Johnson, M. Johnson, S. Jyothish, D. Kampmann, B. Kavirayani, A. Kelly, D. Kucera, F. Langer, D. Lillie, J. Longbottom, K. Lyall, H. MacKdermott, N. Maltby, S. McLelland, T. McMahon, A.-M. Miller, D. Morrison, Z. Mosha, K. Muller, J. Myttaraki, E. Nadel, S. Osaghae, D. Osman, F. Ostrzewska, A. Panthula, M. Papachatzi, E. Papadopoulou, C. Penner, J. Polandi, S. Prendergast, A.J. Ramnarayan, P. Rhys-Evans, S. Riordan, A. Rodrigues, C.M.C. Romaine, S. Seddon, J. Shingadia, D. Srivastava, A. Struik, S. Taylor, A. Taylor, A. Taylor, A. Tran, S. Tudor-Williams, G. Van Der Velden, F. Ventilacion, L. Wellman, P.A. Yanney, M.P. Yeung, S. Badheka, A. Badran, S. Bailey, D.M. Burch, A.K. Burns, J.C. Cichon, C. Cirks, B. Dallman, M.D. Delany, D.R. Fairchok, M. Friedman, S. Geracht, J. Langs-Barlow, A. Mann, K. Padhye, A. Quade, A. Ramirez, K.A. Rockett, J. Sayed, I.A. Shahin, A.A. Umaru, S. Widener, R. Angela, M.H. Kandawasvika, G. BATS Consortium

Subjects
hemic and lymphatic diseases
Abstract

BACKGROUND Evidence is urgently needed to support treatment decisions for children with multisystem inflammatory syndrome (MIS-C) associated with severe acute respiratory syndrome coronavirus 2. METHODS We performed an international observational cohort study of clinical and outcome data regarding suspected MIS-C that had been uploaded by physicians onto a Web-based database. We used inverse-probability weighting and generalized linear models to evaluate intravenous immune globulin (IVIG) as a reference, as compared with IVIG plus glucocorticoids and glucocorticoids alone. There were two primary outcomes: the first was a composite of inotropic support or mechanical ventilation by day 2 or later or death; the second was a reduction in disease severity on an ordinal scale by day 2. Secondary outcomes included treatment escalation and the time until a reduction in organ failure and inflammation. RESULTS Data were available regarding the course of treatment for 614 children from 32 countries from June 2020 through February 2021; 490 met the World Health Organization criteria for MIS-C. Of the 614 children with suspected MIS-C, 246 received primary treatment with IVIG alone, 208 with IVIG plus glucocorticoids, and 99 with glucocorticoids alone; 22 children received other treatment combinations, including biologic agents, and 39 received no immunomodulatory therapy. Receipt of inotropic or ventilatory support or death occurred in 56 patients who received IVIG plus glucocorticoids (adjusted odds ratio for the comparison with IVIG alone, 0.77; 95% confidence interval [CI], 0.33 to 1.82) and in 17 patients who received glucocorticoids alone (adjusted odds ratio, 0.54; 95% CI, 0.22 to 1.33). The adjusted odds ratios for a reduction in disease severity were similar in the two groups, as compared with IVIG alone (0.90 for IVIG plus glucocorticoids and 0.93 for glucocorticoids alone). The time until a reduction in disease severity was similar in the three groups. CONCLUSIONS We found no evidence that recovery from MIS-C differed after primary treatment with IVIG alone, IVIG plus glucocorticoids, or glucocorticoids alone, although significant differences may emerge as more data accrue. Copyright © 2021 Massachusetts Medical Society.

Published
2021

18. A non-targeted LC–MS metabolic profiling of pregnancy:longitudinal evidence from healthy and pre-eclamptic pregnancies

Author

Jääskeläinen, T. (Tiina), Kärkkäinen, O. (Olli), Jokkala, J. (Jenna), Klåvus, A. (Anton), Heinonen, S. (Seppo), Auriola, S. (Seppo), Lehtonen, M. (Marko), T. F. (The FINNPEC Core Investigator Group), Hanhineva, K. (Kati), and Laivuori, H. (Hannele)

Subjects
Pregnancy, Metabolomics, Preeclampsia, LC–MS, humanities
Abstract

Introduction: Maternal metabolism changes substantially during pregnancy. However, few studies have used metabolomics technologies to characterize changes across gestation. Objectives and methods: We applied liquid chromatography–mass spectrometry (LC–MS) based non-targeted metabolomics to determine whether the metabolic profile of serum differs throughout the pregnancy between pre-eclamptic and healthy women in the FINNPEC (Finnish Genetics of Preeclampsia Consortium) Study. Serum samples were available from early and late pregnancy. Results: Progression of pregnancy had large-scale effects to the serum metabolite profile. Altogether 50 identified metabolites increased and 49 metabolites decreased when samples of early pregnancy were compared to samples of late pregnancy. The metabolic signatures of pregnancy were largely shared in pre-eclamptic and healthy women, only urea, monoacylglyceride 18:1 and glycerophosphocholine were identified to be increased in the pre-eclamptic women when compared to healthy controls. Conclusions: Our study highlights the need of large-scale longitudinal metabolomic studies in non-complicated pregnancies before more detailed understanding of metabolism in adverse outcomes could be provided. Our findings are one of the first steps for a broader metabolic understanding of the physiological changes caused by pregnancy per se. Collaborators for The FINNPEC Core Investigator Group Eero Kajantie (Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland; Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; PEDEGO Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland). Juha Kere (Department of Biosciences and Nutrition, and Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden; Molecular Neurology Research Program, University of Helsinki, Helsinki, Finland; Folkhälsan Institute of Genetics, Helsinki, Finland), Katja Kivinen (Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland), Anneli Pouta (Department of Government Services, National Institute for Health and Welfare, Helsinki, Finland).

Published
2021

25. Genetic predisposition to hypertension is associated with preeclampsia in European and Central Asian women

Author

Steinthorsdottir, V. (Valgerdur), McGinnis, R. (Ralph), Williams, N. O. (Nicholas O.), Stefansdottir, L. (Lilja), Thorleifsson, G. (Gudmar), Shooter, S. (Scott), Fadista, J. (Joao), Sigurdsson, J. K. (Jon K.), Auro, K. M. (Kirsi M.), Berezina, G. (Galina), Borges, M.-C. (Maria-Carolina), Bumpstead, S. (Suzannah), Bybjerg-Grauholm, J. (Jonas), Colgiu, I. (Irina), Dolby, V. A. (Vivien A.), Dudbridge, F. (Frank), Engel, S. M. (Stephanie M.), Franklin, C. S. (Christopher S.), Frigge, M. L. (Michael L.), Frisbaek, Y. (Yr), Geirsson, R. T. (Reynir T.), Geller, F. (Frank), Gretarsdottir, S. (Solveig), Gudbjartsson, D. F. (Daniel F.), Harmon, Q. (Quaker), Hougaard, D. M. (David Michael), Hegay, T. (Tatyana), Helgadottir, A. (Anna), Hjartardottir, S. (Sigrun), Jaeaeskelaeinen, T. (Tiina), Johannsdottir, H. (Hrefna), Jonsdottir, I. (Ingileif), Juliusdottir, T. (Thorhildur), Kalsheker, N. (Noor), Kasimov, A. (Abdumadjit), Kemp, J. P. (John P.), Kivinen, K. (Katja), Klungsoyr, K. (Kari), Lee, W. K. (Wai K.), Melbye, M. (Mads), Miedzybrodska, Z. (Zosia), Moffett, A. (Ashley), Najmutdinova, D. (Dilbar), Nishanova, F. (Firuza), Olafsdottir, T. (Thorunn), Perola, M. (Markus), Pipkin, F. B. (Fiona Broughton), Poston, L. (Lucilla), Prescott, G. (Gordon), Saevarsdottir, S. (Saedis), Salimbayeva, D. (Damilya), Scaife, P. J. (Paula Juliet), Skotte, L. (Line), Staines-Urias, E. (Eleonora), Stefansson, O. A. (Olafur A.), Sorensen, K. M. (Karina Meden), Thomsen, L. C. (Liv Cecilie Vestrheim), Tragante, V. (Vinicius), Trogstad, L. (Lill), Simpson, N. A. (Nigel A. B.), Laivuori, H. (Hannele), Morgan, L. (Linda), Aripova, T. (Tamara), Casas, J. P. (Juan P.), Dominiczak, A. F. (Anna F.), Walker, J. J. (James J.), Thorsteinsdottir, U. (Unnur), Iversen, A.-C. (Ann-Charlotte), Feenstra, B. (Bjarke), Lawlor, D. A. (Deborah A.), Boyd, H. A. (Heather Allison), Magnus, P. (Per), Zakhidova, N. (Nodira), Svyatova, G. (Gulnara), Stefansson, K. (Kari), Heinonen, S. (Seppo), Kajantie, E. (Eero), Kere, J. (Juha), Pouta, A. (Anneli), Macphail, S. (Sheila), Kilby, M. (Mark), Habiba, M. (Marwan), Williamson, C. (Catherine), O'Shaughnessy, K. (Kevin), O'Brien, S. (Shaughn), Cameron, A. (Alan), Redman, C. W. (Christopher W. G.), Farrall, M. (Martin), and Caulfield, M. (Mark)

Subjects
embryonic structures, reproductive and urinary physiology, female genital diseases and pregnancy complications
Abstract

Preeclampsia is a serious complication of pregnancy, affecting both maternal and fetal health. In genome-wide association meta-analysis of European and Central Asian mothers, we identify sequence variants that associate with preeclampsia in the maternal genome at ZNF831/20q13 and FTO/16q12. These are previously established variants for blood pressure (BP) and the FTO variant has also been associated with body mass index (BMI). Further analysis of BP variants establishes that variants at MECOM/3q26, FGF5/4q21 and SH2B3/12q24 also associate with preeclampsia through the maternal genome. We further show that a polygenic risk score for hypertension associates with preeclampsia. However, comparison with gestational hypertension indicates that additional factors modify the risk of preeclampsia. Studies to identify maternal variants associated with preeclampsia have been limited by sample size. Here, the authors meta-analyze eight GWAS of 9,515 preeclamptic women, identifying five variants associated with preeclampsia and showing that genetic predisposition to hypertension is a major risk factor for preeclampsia.

Published
2020

26. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: An individual participant data meta-analysis

Author

Allotey, J. Snell, K.I.E. Smuk, M. Hooper, R. Chan, C.L. Ahmed, A. Chappell, L.C. von Dadelszen, P. Dodds, J. Green, M. Kenny, L. Khalil, A. Khan, K.S. Mol, B.W. Myers, J. Poston, L. Thilaganathan, B. Staff, A.C. Smith, G.C.S. Ganzevoort, W. Laivuori, H. Odibo, A.O. Ramírez, J.A. Kingdom, J. Daskalakis, G. Farrar, D. Baschat, A.A. Seed, P.T. Prefumo, F. da Silva Costa, F. Groen, H. Audibert, F. Massé, J. Skråstad, R.B. Salvesen, K.A. Haavaldsen, C. Nagata, C. Rumbold, A.R. Heinonen, S. Askie, L.M. Smits, L.J.M. Vinter, C.A. Magnus, P.M. Eero, K. Villa, P.M. Jenum, A.K. Andersen, L.B. Norman, J.E. Ohkuchi, A. Eskild, A. Bhattacharya, S. McAuliffe, F.M. Galindo, A. Herraiz, I. Carbillon, L. Klipstein-Grobusch, K. Yeo, S. Teede, H.J. Browne, J.L. Moons, K.G.M. Riley, R.D. Thangaratinam, S. The IPPIC Collaborative Network

Abstract

Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. Objectives: To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. Design: This was an individual participant data meta-analysis of cohort studies. Setting: Source data from secondary and tertiary care. Predictors: We identified predictors from systematic reviews, and prioritised for importance in an international survey. Primary outcomes: Early-onset (delivery at < 34 weeks’ gestation), late-onset (delivery at ≥ 34 weeks’ gestation) and any-onset pre-eclampsia. Analysis: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration.We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I2 and 2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. Results: The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. Limitations: Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. Conclusion: For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. © 2020, NIHR Journals Library. All rights reserved.

Published
2020

35. Analysis of high-frequency ambient seismic noise recorded during XSodEx experiment in Finland

Author

Afonin, N. (Nikita), Kozlovskaya, E. (Elena), Buske, S. (Stefan), Heinonen, S. (Suvi), and Karjalainen, J. (Jari)

Abstract

Development of passive seismic methods is an important task for the solution of many practical problems where the study of shallow structure of subsurface is necessary (mineral exploration, microseismic zonic, groundwater study, etc.). Nowadays there are many techniques for estimating Empirical Greens Functions (EGF) from highfrequency seismic noise generated by industrial objects, transports or other human activity (Afonin et. al., 2016; Cheng et. al., 2015; Le Feuvre et. al., 2015; Nakata et. al., 2011; Shirzad et. al., 2014, etc.). Nevertheless, there is the necessity of using high-frequency ambient noise on quiet areas, for example, greenfield exploration tasks in remote territories. In this case, extraction of EGFs is difficult because of inhomogeneous distribution of sources and strong attenuation of high-frequency noise wavefield. That is why the study of high-frequency ambient noise behaviour in quiet areas is an important task for the development of passive seismic methods. For this purpose, we analyze ambient seismic noise recorded during XSodEx experiment by 24 3-component and 54 1-component DSU-SA MEMS seismic sensors with the autonomous data acquisition units produced by Sercel Ltd. The sensors were installed along about 1 km long line with intersensor distances of 7–15 m. The profile recorded continuous passive seismic data since 21.08.2017 to 23.08.2017 with the sampling rate of 500 sps. The data were processed by several steps including single station data analysis, prefiltering and cross-correlation of night-time records between all possible station pairs. The cross-correlation functions were used for EGFs estimation. The analysis of apparent velocities of cross-correlation functions and EGFs shows that there were several noise sources with frequencies of 1–20 Hz along the profile. In some cases, we extracted symmetric EGF from short time records (several hours), which indicates homogeneous azimuthal distribution of noise sources. Nevertheless, wavefields, generated by these sources distributed to about several dozen meters. In our paper, we concentrate mainly on details of our data processing routine and its influence on the quality of EGF extraction results.

Published
2019

47. Fetal microsatellite in the heme oxygenase 1 promoter is associated with severe and early-onset preeclampsia

Author

Kaartokallio, T. (Tea), Utge, S. (Siddheshwar), Klemetti, M. M. (Miira M.), Paananen, J. (Jussi), Pulkki, K. (Kari), Romppanen, J. (Jarkko), Tikkanen, I. (Ilkka), Heinonen, S. (Seppo), Kajantie, E. (Eero), Kere, J. (Juha), Kivinen, K. (Katja), Pouta, A. (Anneli), Lakkisto, P. (Päivi), and Laivuori, H. (Hannele)

Subjects
preeclampsia, placenta, embryonic structures, pregnancy, serum, heme oxygenase 1
Abstract

Preeclampsia is a vascular pregnancy disorder that often involves impaired placental development. HO-1 (heme oxygenase 1, encoded by HMOX1) is a stress response enzyme crucial for endothelial and placental function. Long version of the guanine–thymine (GTn) microsatellite in the HMOX1 promoter decreases HO-1 expression, and the long maternal repeat is associated with late-onset preeclampsia. Our aim was to study whether the length of fetal repeat is associated with mother’s preeclampsia, whether the length of fetal and maternal repeats affect HO-1 levels in placenta and maternal serum, and whether HO-1 levels are altered in preeclampsia. We genotyped the repeat in the cord blood of 609 preeclamptic and 745 nonpreeclamptic neonates. HO-1 levels were measured in 36 placental samples, and in the first (222 cases/243 controls) and third (176 cases/53 controls) pregnancy trimester serum samples using enzyme-linked immunosorbent assay. The long fetal GTn repeat was associated with preeclampsia and its severe and early-onset subtypes. Interaction analysis suggested the maternal and fetal effects to be independent. Placental or serum HO-1 levels were not altered in preeclamptics, possibly reflecting heterogeneity of preeclampsia. Carriers of the long fetal and maternal repeats had lower placental and serum HO-1 levels, respectively, providing functional evidence for the association. We conclude that the long fetal GTn repeat may increase mother’s risk for especially severe and early-onset preeclampsia. The fetal and maternal risk alleles likely predispose to different disease subtypes.

Published
2018

49. Testing methods for mobile game development a case study on user feedback in different development phases

Author

Annanperä, E. (Elina), Yli-Kantola, J. (Jani), Sauvola, T. (Tanja), Liukkunen, K. (Kari), Heinonen, S. (Samuli), and Siira, E. (Erkki)

Subjects
development process, ComputingMilieux_PERSONALCOMPUTING, mobile games, users, game testing, methods
Abstract

Mobile games are relatively new industry, and their relevance on the game markets are rising. At the same time, it is very competitive market, as new games are coming out by hundreds every day. The challenges for the new game development come from the creative process and the human factors on how to make the game appealing. In this research, we study the use of test groups as part of new mobile game and concept development process in the early phases. They were used to complement the testing automation at the development phases when use of analytics was not yet possible. Six separate feedback collection sessions were arranged using different methods. The paper presents the main learnings of each used method, the perceived usefulness of the methods for the entire development process. We also contribute to the growing knowledge for mobile game development, focusing on methods for the testing with test user approaches. We conclude that testing with user groups can help clarify the mobile game design to create a more appealing game, but choosing the right groups and right moments in the game development phase can be difficult, as can be the interpretation of the feedback for decision making.

Published
2018

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