Search

Your search keyword '"Singde V."' showing total 5 results
Start Over Author "Singde V."
5 results on '"Singde V."'

1. Prediction of outcomes of extremely low gestational age newborns in Australia and New Zealand

Author

Yeo, KT, Safi, N, Wang, YA, Le Marsney, R, Schindler, T, Bolisetty, S, Haslam, R, Lui, K, Marshall, P, Schmidt, P, Craven, P, De Waal, K, Simmer, K, Gill, A, Pillow, J, Stack, J, Cooke, L, Casalaz, D, Holberton, J, Barfield, C, Downe, L, Singde, V, Stewart, M, Berry, A, Carmo, KB, Hunt, R, Kilburn, C, De Paoli, T, Paradisis, M, Rieger, I, Lutz, T, Reid, S, Cartwright, D, Koorts, P, Kuschel, C, Doyle, L, Numa, A, Carlisle, H, Badawi, N, Koh, G, Resnick, S, Luig, M, Andersen, C, Lyn, A, Darlow, B, Broadbent, R, Mildenhall, L, Buksh, M, Bourchier, D, Carpenter, L, Richardson, V, Chambers, G, Buckmaster, A, Rajadurai, VS, and Bajuk, B

Abstract

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. Objective To determine the accuracy of the National Institute of Child Health and Human Development (NICHD) calculator in predicting death and neurodevelopmental impairment in Australian and New Zealand infants. Design Population-based cohort study. setting Australia and New Zealand. Patients Preterm infants 22–25 completed weeks gestation. Interventions Comparison of NICHD calculator predicted rates of death and death or neurodevelopmental impairment, with actual rates recorded in the Australian and New Zealand Neonatal Network cohort. Main outcome measures Infant death and death or neurodevelopmental impairment rates. results A total of 714 infants were included in the study. Of these infants, 100 (14.0%) were

Published
2017

2. Prediction of outcomes of extremely low gestational age newborns in Australia and New Zealand.

Author

Yeo K.T., Wang Y.A., Le Marsney R., Schindler T., Bolisetty S., Haslam R., Lui K., Marshall P., Schmidt P., Craven P., De Waal K., Simmer K., Gill A., Pillow J., Stack J., Cooke L., Casalaz D., Holberton J., Barfield C., Downe L., Singde V., Stewart M., Berry A., Carmo K.B., Darlow B., Broadbent R., Mildenhall L., Buksh M., Bourchier D., Carpenter L., Richardson V., Chambers G., Buckmaster A., Rajadurai V.S., Bajuk B., Safi N., Hunt R., Kilburn C., De Paoli T., Paradisis M., Rieger I., Lutz T., Reid S., Cartwright D., Koorts P., Kuschel C., Doyle L., Numa A., Carlisle H., Badawi N., Koh G., Resnick S., Luig M., Andersen C., Lyn A., Yeo K.T., Wang Y.A., Le Marsney R., Schindler T., Bolisetty S., Haslam R., Lui K., Marshall P., Schmidt P., Craven P., De Waal K., Simmer K., Gill A., Pillow J., Stack J., Cooke L., Casalaz D., Holberton J., Barfield C., Downe L., Singde V., Stewart M., Berry A., Carmo K.B., Darlow B., Broadbent R., Mildenhall L., Buksh M., Bourchier D., Carpenter L., Richardson V., Chambers G., Buckmaster A., Rajadurai V.S., Bajuk B., Safi N., Hunt R., Kilburn C., De Paoli T., Paradisis M., Rieger I., Lutz T., Reid S., Cartwright D., Koorts P., Kuschel C., Doyle L., Numa A., Carlisle H., Badawi N., Koh G., Resnick S., Luig M., Andersen C., and Lyn A.

Abstract

Objective To determine the accuracy of the National Institute of Child Health and Human Development (NICHD) calculator in predicting death and neurodevelopmental impairment in Australian and New Zealand infants. Design Population-based cohort study. setting Australia and New Zealand. Patients Preterm infants 22-25 completed weeks gestation. Interventions Comparison of NICHD calculator predicted rates of death and death or neurodevelopmental impairment, with actual rates recorded in the Australian and New Zealand Neonatal Network cohort. Main outcome measures Infant death and death or neurodevelopmental impairment rates. results A total of 714 infants were included in the study. Of these infants, 100 (14.0%) were <24 weeks, 389 (54.5%) male, 529 (74.1%) were singletons, 42 (5.9%) had intrauterine growth restriction, 563 (78.9%) received antenatal steroids and 625 (87.5 %) were born in a tertiary hospital. There were 288 deaths (40.3%), 75 infants (10.5%) with neurodevelopment impairment and 363 (50.8%) with death or neurodevelopmental impairment. The area under the curve (AUC) for prediction of death and the composite death or neurodevelopmental impairment by the NICHD calculator in our population was 0.65(95% CI 0.61 to 0.69) and 0.65 (95% CI 0.61 to 0.69), respectively. When stratified and compared with gestational age outcomes, the AUC did not change substantially for the outcomes investigated. The calculator was less accurate with outcome predictions at the extreme categories of predicted outcomes-underestimation of outcomes for those predicted to have the lowest risk (<20%) and overestimation for those in the highest risk category (>>80%). conclusion In our recent cohort of extremely preterm infants, the NICHD model does not accurately predict outcomes and is marginally better than gestational age based outcomes.Copyright © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved.

Published
2018

3. Comparing very low birth weight versus very low gestation cohort methods for outcome analysis of high risk preterm infants.

Author

Austin N., Andersen C., Darlow B., Broadbent R., Corban J., Mildenhall L., Battin M., Bourchier D., Richardson V., Haslam R., Rajadurai V.S., Kajetanowicz A., Synnes A., Rouvinez-Bouali N., Piedboeuf B., Bertelle V., Bulleid B., Yee W., Shivananda S., Lee K.-S., Seshia M., Barrington K., Lefebvre F., McMillan D., Andrews W., Kovacs L., Dow K., da Silva O., Riley P., Peliowski A., Aziz K., Cieslak Z., Kalapesi Z., Sankaran K., Faucher D., Alvaro R., Canning R., Ojah C., Monterrosa L., Dunn M., Sorokan T., Harrison A., Nwaesei C., Adie M., Hakansson S., Segerdahl N., Morad T., Moren S., Stenberg A., Simonsson C., Stigsson L., Christensen J.L., Amasn L., Ingemanson F., osterdal L., Ellstrom K.-G., Abrahamsson T., Heimdahl I., Hagg T., Hedlund A., Lund E.E., Westrup B., Sarman I., Jobe A.S., Fredsriksson M., Palm A., Malmstrom B., Lindberg E., Ljungdahl O., Eriksson K., Koller-Smith L.I.M., Shah P., Ye X.Y., Sjors G., Wang Y.A., Chow S.S.W., Darlow B.A., Lee S.K., Hakanson S., Lui K., Marshall P., Craven P., Simmer K., Stack J., Knight D., Watkins A., Ramsden A., Tan K., Bawden K., Downe L., Singde V., Stewart M., Berry A., Hunt R., Kilburn C., Dargaville P., Paradisis M., Evans N., Reid S., Cartwright D., Kuschel C., Doyle L., Numa A., Kecskes Z., Badawi N., Koh G., Resnick S., Tracy M., Tarnow-Mordi W., Austin N., Andersen C., Darlow B., Broadbent R., Corban J., Mildenhall L., Battin M., Bourchier D., Richardson V., Haslam R., Rajadurai V.S., Kajetanowicz A., Synnes A., Rouvinez-Bouali N., Piedboeuf B., Bertelle V., Bulleid B., Yee W., Shivananda S., Lee K.-S., Seshia M., Barrington K., Lefebvre F., McMillan D., Andrews W., Kovacs L., Dow K., da Silva O., Riley P., Peliowski A., Aziz K., Cieslak Z., Kalapesi Z., Sankaran K., Faucher D., Alvaro R., Canning R., Ojah C., Monterrosa L., Dunn M., Sorokan T., Harrison A., Nwaesei C., Adie M., Hakansson S., Segerdahl N., Morad T., Moren S., Stenberg A., Simonsson C., Stigsson L., Christensen J.L., Amasn L., Ingemanson F., osterdal L., Ellstrom K.-G., Abrahamsson T., Heimdahl I., Hagg T., Hedlund A., Lund E.E., Westrup B., Sarman I., Jobe A.S., Fredsriksson M., Palm A., Malmstrom B., Lindberg E., Ljungdahl O., Eriksson K., Koller-Smith L.I.M., Shah P., Ye X.Y., Sjors G., Wang Y.A., Chow S.S.W., Darlow B.A., Lee S.K., Hakanson S., Lui K., Marshall P., Craven P., Simmer K., Stack J., Knight D., Watkins A., Ramsden A., Tan K., Bawden K., Downe L., Singde V., Stewart M., Berry A., Hunt R., Kilburn C., Dargaville P., Paradisis M., Evans N., Reid S., Cartwright D., Kuschel C., Doyle L., Numa A., Kecskes Z., Badawi N., Koh G., Resnick S., Tracy M., and Tarnow-Mordi W.

Abstract

Background: Compared to very low gestational age (<32 weeks, VLGA) cohorts, very low birth weight (<1500 g; VLBW) cohorts are more prone to selection bias toward small-for-gestational age (SGA) infants, which may impact upon the validity of data for benchmarking purposes. Method(s): Data from all VLGA or VLBW infants admitted in the 3 Networks between 2008 and 2011 were used. Two-thirds of each network cohort was randomly selected to develop prediction models for mortality and composite adverse outcome (CAO: mortality or cerebral injuries, chronic lung disease, severe retinopathy or necrotizing enterocolitis) and the remaining for internal validation. Areas under the ROC curves (AUC) of the models were compared. Result(s): VLBW cohort (24,335 infants) had twice more SGA infants (20.4% vs. 9.3%) than the VLGA cohort (29,180 infants) and had a higher rate of CAO (36.5% vs. 32.6%). The two models had equal prediction power for mortality and CAO (AUC 0.83), and similarly for all other cross-cohort validations (AUC 0.81-0.85). Neither model performed well for the extremes of birth weight for gestation (<1500 g and >=32 weeks, AUC 0.50-0.65; >=1500 g and <32 weeks, AUC 0.60-0.62). Conclusion(s): There was no difference in prediction power for adverse outcome between cohorting VLGA or VLBW despite substantial bias in SGA population. Either cohorting practises are suitable for international benchmarking.Copyright © 2017 The Author(s).

Published
2017

4. Comparing very low birth weight versus very low gestation cohort methods for outcome analysis of high risk preterm infants

Author

Koller-Smith, LIM, Shah, PS, Ye, XY, Sjörs, G, Wang, YA, Chow, SSW, Darlow, BA, Lee, SK, Håkanson, S, Lui, K, Marshall, P, Craven, P, Simmer, K, Stack, J, Knight, D, Watkins, A, Ramsden, A, Tan, K, Bawden, K, Downe, L, Singde, V, Stewart, M, Berry, A, Hunt, R, Kilburn, C, Dargaville, P, Paradisis, M, Evans, N, Reid, S, Cartwright, D, Kuschel, C, Doyle, L, Numa, A, Kecskes, Z, Badawi, N, Koh, G, Resnick, S, Tracy, M, Tarnow-Mordi, W, Andersen, C, Austin, N, Darlow, B, Broadbent, R, Corban, J, Mildenhall, L, Battin, M, Bourchier, D, Richardson, V, Haslam, R, Rajadurai, VS, Kajetanowicz, A, Synnes, A, Rouvinez-Bouali, N, Piedboeuf, B, Bertelle, V, Bulleid, B, Yee, W, Shivananda, S, Lee, KS, Seshia, M, Barrington, K, Lefebvre, F, McMillan, D, Andrews, W, Kovacs, L, Dow, K, da Silva, O, Riley, P, Peliowski, A, Aziz, K, Cieslak, Z, Kalapesi, Z, Sankaran, K, Faucher, D, Alvaro, R, Canning, R, Ojah, C, Monterrosa, L, Dunn, M, Sorokan, T, Harrison, A, Nwaesei, C, Adie, M, Håkansson, S, Segerdahl, N, Morad, T, Morén, S, Stenberg, Å, Simonsson, C, Stigsson, L, Christensen, JL, Åmasn, L, Ingemanson, F, österdal, L, Ellström, KG, Abrahamsson, T, Heimdahl, I, Hägg, T, Hedlund, A, Lund, EE, Koller-Smith, LIM, Shah, PS, Ye, XY, Sjörs, G, Wang, YA, Chow, SSW, Darlow, BA, Lee, SK, Håkanson, S, Lui, K, Marshall, P, Craven, P, Simmer, K, Stack, J, Knight, D, Watkins, A, Ramsden, A, Tan, K, Bawden, K, Downe, L, Singde, V, Stewart, M, Berry, A, Hunt, R, Kilburn, C, Dargaville, P, Paradisis, M, Evans, N, Reid, S, Cartwright, D, Kuschel, C, Doyle, L, Numa, A, Kecskes, Z, Badawi, N, Koh, G, Resnick, S, Tracy, M, Tarnow-Mordi, W, Andersen, C, Austin, N, Darlow, B, Broadbent, R, Corban, J, Mildenhall, L, Battin, M, Bourchier, D, Richardson, V, Haslam, R, Rajadurai, VS, Kajetanowicz, A, Synnes, A, Rouvinez-Bouali, N, Piedboeuf, B, Bertelle, V, Bulleid, B, Yee, W, Shivananda, S, Lee, KS, Seshia, M, Barrington, K, Lefebvre, F, McMillan, D, Andrews, W, Kovacs, L, Dow, K, da Silva, O, Riley, P, Peliowski, A, Aziz, K, Cieslak, Z, Kalapesi, Z, Sankaran, K, Faucher, D, Alvaro, R, Canning, R, Ojah, C, Monterrosa, L, Dunn, M, Sorokan, T, Harrison, A, Nwaesei, C, Adie, M, Håkansson, S, Segerdahl, N, Morad, T, Morén, S, Stenberg, Å, Simonsson, C, Stigsson, L, Christensen, JL, Åmasn, L, Ingemanson, F, österdal, L, Ellström, KG, Abrahamsson, T, Heimdahl, I, Hägg, T, Hedlund, A, and Lund, EE

Abstract

© 2017 The Author(s). Background: Compared to very low gestational age (<32 weeks, VLGA) cohorts, very low birth weight (<1500 g; VLBW) cohorts are more prone to selection bias toward small-for-gestational age (SGA) infants, which may impact upon the validity of data for benchmarking purposes. Method: Data from all VLGA or VLBW infants admitted in the 3 Networks between 2008 and 2011 were used. Two-thirds of each network cohort was randomly selected to develop prediction models for mortality and composite adverse outcome (CAO: mortality or cerebral injuries, chronic lung disease, severe retinopathy or necrotizing enterocolitis) and the remaining for internal validation. Areas under the ROC curves (AUC) of the models were compared. Results: VLBW cohort (24,335 infants) had twice more SGA infants (20.4% vs. 9.3%) than the VLGA cohort (29,180 infants) and had a higher rate of CAO (36.5% vs. 32.6%). The two models had equal prediction power for mortality and CAO (AUC 0.83), and similarly for all other cross-cohort validations (AUC 0.81-0.85). Neither model performed well for the extremes of birth weight for gestation (<1500 g and ≥32 weeks, AUC 0.50-0.65; ≥1500 g and <32 weeks, AUC 0.60-0.62). Conclusion: There was no difference in prediction power for adverse outcome between cohorting VLGA or VLBW despite substantial bias in SGA population. Either cohorting practises are suitable for international benchmarking.

Published
2017

5. Comparing very low birth weight versus very low gestation cohort methods for outcome analysis of high risk preterm infants

Author

Louise Koller-Smith, Shah, Ps, Ye, Xy, Sjörs, G., Wang, Ya, Chow, Ssw, Darlow, Ba, Lee, Sk, Håkanson, S., Lui, K., Marshall, P., Craven, P., Simmer, K., Stack, J., Knight, D., Watkins, A., Ramsden, A., Tan, K., Bawden, K., Downe, L., Singde, V., Stewart, M., Berry, A., Hunt, R., Kilburn, C., Dargaville, P., Paradisis, M., Evans, N., Reid, S., Cartwright, D., Kuschel, C., Doyle, L., Numa, A., Kecskes, Z., Badawi, N., Koh, G., Resnick, S., Tracy, M., Tarnow-Mordi, W., Andersen, C., Austin, N., Darlow, B., Broadbent, R., Corban, J., Mildenhall, L., Battin, M., Bourchier, D., Richardson, V., Haslam, R., Rajadurai, Vs, Kajetanowicz, A., Synnes, A., Rouvinez-Bouali, N., Piedboeuf, B., Bertelle, V., Bulleid, B., Yee, W., Shivananda, S., Lee, Ks, Seshia, M., Barrington, K., Lefebvre, F., Mcmillan, D., Andrews, W., Kovacs, L., Dow, K., Da Silva, O., Riley, P., Peliowski, A., Aziz, K., Cieslak, Z., Kalapesi, Z., Sankaran, K., Faucher, D., Alvaro, R., Canning, R., Ojah, C., Monterrosa, L., Dunn, M., Sorokan, T., Harrison, A., Nwaesei, C., Adie, M., Håkansson, S., Segerdahl, N., Morad, T., Morén, S., Stenberg, Å, Simonsson, C., Stigsson, L., Christensen, Jl, Åmasn, L., Ingemanson, F., Österdal, L., Ellström, Kg, Abrahamsson, T., Heimdahl, I., Hägg, T., Hedlund, A., and Lund, Ee

Subjects
Male, Canada, Gestational Age, Infant, Premature, Diseases, Pediatrics, Decision Support Techniques, Risk Factors, Infant Mortality, Humans, Infant, Very Low Birth Weight, Hospital Mortality, Selection Bias, Retrospective Studies, Sweden, Models, Statistical, Infant, Newborn, Australia, Infant, Prognosis, Benchmarking, ROC Curve, Area Under Curve, Infant, Extremely Premature, Infant, Small for Gestational Age, Intensive Care, Neonatal, Female, Infant, Premature, New Zealand
Abstract

© 2017 The Author(s). Background: Compared to very low gestational age (

Catalog

Books, media, physical & digital resources
See catalog results